From 23224bf5e0abad557ceeb605986a10b16e6f6ca9 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Mon, 13 Oct 2025 10:00:19 +0000 Subject: [PATCH 1/3] Initial plan From a21475723e2a2729279dc94c196c3b81b2989440 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Mon, 13 Oct 2025 10:08:03 +0000 Subject: [PATCH 2/3] Add purpose-map.md and feature-surface.json reports Co-authored-by: DeepExtrema <175066046+DeepExtrema@users.noreply.github.com> --- reports/feature-surface.json | 621 +++++++++++++++++++++++++++++++++++ reports/purpose-map.md | 156 +++++++++ 2 files changed, 777 insertions(+) create mode 100644 reports/feature-surface.json create mode 100644 reports/purpose-map.md diff --git a/reports/feature-surface.json b/reports/feature-surface.json new file mode 100644 index 0000000..c635a8b --- /dev/null +++ b/reports/feature-surface.json @@ -0,0 +1,621 @@ +{ + "repository": { + "name": "Sherlock-Multiagent-Data-Scientist", + "owner": "DeepExtrema", + "version": "2.1.0", + "purpose": "End-to-end, orchestrator-driven data science platform enabling EDA, data quality validation, feature engineering, and model training through microservices agents with real-time observability", + "architecture": "microservices", + "license": { + "type": "hybrid", + "server": "BUSL-1.1", + "clients": "Apache-2.0" + } + }, + "features": [ + { + "id": "dataset-upload-load", + "name": "Upload & Load Dataset", + "description": "Upload CSV/Parquet datasets and load into memory for analysis", + "primary_files": [ + "mcp-server/master_orchestrator_api.py", + "mcp-server/eda_agent.py", + "mcp-server/data_sources/" + ], + "endpoints": [ + { + "method": "POST", + "path": "/datasets/upload", + "service": "master_orchestrator", + "port": 8000 + }, + { + "method": "POST", + "path": "/load_data", + "service": "eda_agent", + "port": 8001 + } + ], + "entry_commands": [ + "curl -X POST http://localhost:8000/datasets/upload -F \"file=@data.csv\" -F \"name=my_dataset\"", + "python start_master_orchestrator.py" + ], + "data_dependencies": [ + "local_filesystem", + "mongodb_optional" + ], + "risk_level": "medium", + "risk_notes": "File upload validation needed; supports CSV, Parquet formats" + }, + { + "id": "exploratory-data-analysis", + "name": "Exploratory Data Analysis (EDA)", + "description": "Statistical summaries, missing data analysis, correlation matrices, outlier detection, and publication-ready visualizations", + "primary_files": [ + "mcp-server/eda_agent.py", + "mcp-server/server.py", + "docs/USER_GUIDE.md" + ], + "endpoints": [ + { + "method": "GET", + "path": "/health", + "service": "eda_agent", + "port": 8001 + }, + { + "method": "POST", + "path": "/basic_info", + "service": "eda_agent", + "port": 8001 + }, + { + "method": "POST", + "path": "/statistical_summary", + "service": "eda_agent", + "port": 8001 + }, + { + "method": "POST", + "path": "/missing_data_analysis", + "service": "eda_agent", + "port": 8001 + }, + { + "method": "POST", + "path": "/detect_outliers", + "service": "eda_agent", + "port": 8001 + }, + { + "method": "POST", + "path": "/create_visualization", + "service": "eda_agent", + "port": 8001 + } + ], + "entry_commands": [ + "python start_eda_service.py", + "curl http://localhost:8001/health" + ], + "data_dependencies": [ + "redis_caching", + "dataset_in_memory" + ], + "risk_level": "medium", + "risk_notes": "Large datasets may cause memory issues; 10k row sampling used for correlations" + }, + { + "id": "data-quality-validation", + "name": "Data Quality Validation", + "description": "Schema consistency checks, missing value analysis, distribution validation, drift detection, and comprehensive quality reports", + "primary_files": [ + "mcp-server/refinery_agent.py", + "mcp-server/REFINERY_AGENT_GUIDE.md", + "mcp-server/dq/" + ], + "endpoints": [ + { + "method": "POST", + "path": "/execute", + "service": "refinery_agent", + "port": 8005 + }, + { + "method": "GET", + "path": "/health", + "service": "refinery_agent", + "port": 8005 + }, + { + "method": "GET", + "path": "/metrics", + "service": "refinery_agent", + "port": 8005 + } + ], + "actions": [ + "check_schema_consistency", + "check_missing_values", + "check_distributions", + "check_duplicates", + "check_leakage", + "check_drift", + "comprehensive_quality_report" + ], + "entry_commands": [ + "python -m uvicorn refinery_agent:app --port 8005" + ], + "data_dependencies": [ + "evidently_library", + "dataset_files" + ], + "risk_level": "low", + "risk_notes": "Read-only operations; comprehensive validation framework" + }, + { + "id": "feature-engineering", + "name": "Feature Engineering Pipeline", + "description": "Imputation, scaling, encoding, datetime features, text vectorization, feature interactions, and selection", + "primary_files": [ + "mcp-server/refinery_agent.py", + "mcp-server/fe/" + ], + "endpoints": [ + { + "method": "POST", + "path": "/execute", + "service": "refinery_agent", + "port": 8005 + } + ], + "actions": [ + "assign_feature_roles", + "basic_impute_missing_values", + "basic_scale_numeric_features", + "basic_encode_categorical_features", + "basic_generate_datetime_features", + "basic_vectorise_text_features", + "basic_generate_interactions", + "basic_select_features", + "save_fe_pipeline", + "execute_feature_pipeline", + "advanced_impute_missing_values", + "advanced_encode_categorical_features", + "advanced_feature_selection" + ], + "entry_commands": [ + "curl -X POST http://localhost:8005/execute -H \"Content-Type: application/json\" -d '{\"action\": \"basic_impute_missing_values\", \"params\": {\"data_path\": \"data.csv\"}}'" + ], + "data_dependencies": [ + "scikit_learn_pipelines", + "feature_metadata" + ], + "risk_level": "medium", + "risk_notes": "Transformation logic; mode validation prevents accidental transforms" + }, + { + "id": "model-training-evaluation", + "name": "Model Training & Evaluation", + "description": "Class imbalance analysis, train/validation/test splits, cross-validation, baseline models, experiment tracking with MLflow", + "primary_files": [ + "mcp-server/ml_agent.py", + "mcp-server/ML_WORKFLOW_GUIDE.md", + "mcp-server/mlruns/" + ], + "endpoints": [ + { + "method": "GET", + "path": "/health", + "service": "ml_agent", + "port": 8002 + }, + { + "method": "POST", + "path": "/class_imbalance", + "service": "ml_agent", + "port": 8002 + }, + { + "method": "POST", + "path": "/train_validation_test", + "service": "ml_agent", + "port": 8002 + }, + { + "method": "POST", + "path": "/baseline_sanity", + "service": "ml_agent", + "port": 8002 + }, + { + "method": "POST", + "path": "/experiment_tracking", + "service": "ml_agent", + "port": 8002 + } + ], + "entry_commands": [ + "python -m uvicorn ml_agent:app --port 8002" + ], + "data_dependencies": [ + "mlflow_backend", + "training_datasets", + "scikit_learn" + ], + "risk_level": "high", + "risk_notes": "Model persistence, hyperparameter tuning; GPU/CPU resource management required" + }, + { + "id": "workflow-orchestration", + "name": "Workflow Orchestration", + "description": "Task coordination, dependency management, retry logic, deadlock monitoring, graceful cancellation, artifact management", + "primary_files": [ + "mcp-server/master_orchestrator_api.py", + "mcp-server/orchestrator/workflow_manager.py", + "mcp-server/orchestrator/agent_registry.py", + "mcp-server/orchestrator/sla_monitor.py" + ], + "endpoints": [ + { + "method": "GET", + "path": "/health", + "service": "master_orchestrator", + "port": 8000 + }, + { + "method": "POST", + "path": "/workflows/start", + "service": "master_orchestrator", + "port": 8000 + }, + { + "method": "GET", + "path": "/runs/{run_id}/status", + "service": "master_orchestrator", + "port": 8000 + }, + { + "method": "GET", + "path": "/runs/{run_id}/artifacts", + "service": "master_orchestrator", + "port": 8000 + }, + { + "method": "DELETE", + "path": "/runs/{run_id}", + "service": "master_orchestrator", + "port": 8000 + } + ], + "entry_commands": [ + "python start_master_orchestrator.py", + "curl http://localhost:8000/docs" + ], + "data_dependencies": [ + "mongodb_runs", + "redis_locks", + "kafka_events" + ], + "risk_level": "medium", + "risk_notes": "Deadlock monitoring, graceful cancellation; task dependencies managed" + }, + { + "id": "realtime-observability", + "name": "Real-time Observability Dashboard", + "description": "React-based dashboard with live event streaming, workflow status, agent health monitoring, and performance charts", + "primary_files": [ + "dashboard-ui/src/", + "dashboard-ui/package.json" + ], + "endpoints": [ + { + "method": "WebSocket", + "path": "/ws/events", + "service": "master_orchestrator", + "port": 8000 + } + ], + "entry_commands": [ + "cd dashboard-ui && npm run dev", + "open http://localhost:3000" + ], + "data_dependencies": [ + "fastapi_websocket", + "kafka_event_stream", + "recent_runs_api" + ], + "risk_level": "medium", + "risk_notes": "WebSocket connection stability; event streaming from Kafka" + } + ], + "runnable_surfaces": { + "services": [ + { + "name": "master_orchestrator", + "type": "fastapi_service", + "entrypoint": "mcp-server/start_master_orchestrator.py", + "alternative_entrypoint": "python master_orchestrator_api.py", + "port": 8000, + "health_endpoint": "/health", + "api_docs": "/docs", + "description": "Workflow coordination, task dispatch, artifact management", + "startup_command": "python start_master_orchestrator.py" + }, + { + "name": "eda_agent", + "type": "fastapi_service", + "entrypoint": "mcp-server/start_eda_service.py", + "alternative_entrypoint": "python eda_agent.py", + "port": 8001, + "health_endpoint": "/health", + "api_docs": "/docs", + "description": "Data loading, statistical analysis, visualization, outlier detection", + "startup_command": "python start_eda_service.py" + }, + { + "name": "refinery_agent", + "type": "fastapi_service", + "entrypoint": "python -m uvicorn refinery_agent:app --port 8005", + "port": 8005, + "health_endpoint": "/health", + "api_docs": "/docs", + "description": "Data quality validation, feature engineering, drift detection", + "startup_command": "python -m uvicorn refinery_agent:app --port 8005" + }, + { + "name": "ml_agent", + "type": "fastapi_service", + "entrypoint": "python -m uvicorn ml_agent:app --port 8002", + "port": 8002, + "health_endpoint": "/health", + "api_docs": "/docs", + "description": "Model training, cross-validation, baseline checks, experiment tracking", + "startup_command": "python -m uvicorn ml_agent:app --port 8002" + }, + { + "name": "mcp_server", + "type": "mcp_protocol", + "entrypoint": "mcp-server/server.py", + "alternative_entrypoint": "python launch_server.py", + "port": null, + "health_endpoint": null, + "description": "Claude Desktop integration for conversational data analysis", + "startup_command": "python launch_server.py" + }, + { + "name": "dashboard_ui", + "type": "react_spa", + "entrypoint": "dashboard-ui/", + "port": 3000, + "health_endpoint": null, + "description": "React-based real-time monitoring interface", + "startup_command": "cd dashboard-ui && npm run dev" + } + ], + "infrastructure": [ + { + "name": "mongodb", + "type": "database", + "startup_command": "docker-compose up -d mongodb", + "required_by": [ + "master_orchestrator" + ], + "purpose": "Run persistence, task history" + }, + { + "name": "redis", + "type": "cache", + "startup_command": "docker-compose up -d redis", + "required_by": [ + "all_agents" + ], + "purpose": "Caching, distributed locks, translation queue" + }, + { + "name": "kafka", + "type": "message_queue", + "startup_command": "docker-compose up -d kafka", + "required_by": [ + "master_orchestrator", + "dashboard" + ], + "purpose": "Event streaming, task routing" + }, + { + "name": "nginx", + "type": "reverse_proxy", + "startup_command": "docker-compose up -d nginx", + "required_by": [ + "production_deployment" + ], + "purpose": "Load balancing, reverse proxy", + "optional": true + } + ], + "docker_deployment": [ + { + "name": "full_stack", + "compose_file": "mcp-server/docker-compose.yml", + "startup_command": "cd mcp-server && docker-compose up -d", + "description": "Full multi-service deployment with infrastructure" + }, + { + "name": "local_development", + "compose_file": "mcp-server/docker-compose.local.yml", + "startup_command": "cd mcp-server && docker-compose -f docker-compose.local.yml up -d", + "description": "Local development variant" + }, + { + "name": "production", + "script": "mcp-server/production_deployment.py", + "startup_command": "python production_deployment.py", + "description": "Production-ready deployment with monitoring" + } + ], + "scripts": [ + { + "name": "install_orchestrator", + "path": "mcp-server/install_orchestrator.py", + "purpose": "Setup dependencies, create directories, verify infrastructure", + "command": "python install_orchestrator.py" + }, + { + "name": "production_deployment", + "path": "mcp-server/production_deployment.py", + "purpose": "Production-ready deployment with monitoring", + "command": "python production_deployment.py" + }, + { + "name": "connectivity_tester", + "path": "mcp-server/connectivity_tester.py", + "purpose": "Test inter-service connectivity", + "command": "python connectivity_tester.py" + }, + { + "name": "verify_setup", + "path": "mcp-server/verify_setup.py", + "purpose": "Verify Python dependencies and configuration", + "command": "python verify_setup.py" + }, + { + "name": "bug_hunter", + "path": "mcp-server/bug_hunter.py", + "purpose": "Diagnostic tool for troubleshooting", + "command": "python bug_hunter.py" + } + ] + }, + "data_flow": { + "entry_point": "master_orchestrator:8000", + "event_bus": "kafka", + "storage": [ + { + "type": "mongodb", + "database": "deepline", + "collections": [ + "workflow_runs", + "task_status", + "artifacts_metadata" + ] + }, + { + "type": "redis", + "purpose": [ + "caching", + "distributed_locks", + "translation_queue" + ], + "queue_name": "translation:q" + }, + { + "type": "mlflow", + "path": "mcp-server/mlruns/", + "purpose": [ + "experiment_tracking", + "model_registry" + ] + }, + { + "type": "filesystem", + "paths": [ + { + "path": "artifacts/", + "purpose": "workflow_artifacts_reports_visualizations" + }, + { + "path": "snapshots/", + "purpose": "data_versioning" + }, + { + "path": "mcp-server/data_sources/", + "purpose": "uploaded_datasets" + } + ] + } + ] + }, + "configuration": { + "primary": "mcp-server/config.yaml", + "environment": ".env", + "claude_integration": "claude_desktop_config.json", + "key_settings": { + "max_concurrent_workflows": 1, + "max_rows_processed": 100000, + "correlation_sample_size": 10000, + "deadlock_check_interval_s": 60, + "task_timeout_s": 600, + "workflow_timeout_s": 3600 + } + }, + "risk_summary": { + "high": { + "count": 1, + "features": [ + "model-training-evaluation" + ], + "mitigation": "Resource quotas, GPU/CPU agent routing" + }, + "medium": { + "count": 5, + "features": [ + "dataset-upload-load", + "exploratory-data-analysis", + "feature-engineering", + "workflow-orchestration", + "realtime-observability" + ], + "mitigation": "Health checks, deadlock monitoring, graceful cancellation, SLA monitoring" + }, + "low": { + "count": 1, + "features": [ + "data-quality-validation" + ], + "mitigation": "Read-only operations, comprehensive validation" + } + }, + "assumptions": { + "confidence_level": "high", + "uncertainty_percentage": 15, + "list": [ + "Primary use case: Data scientists performing end-to-end analysis workflows via API/UI", + "Deployment target: Single-node or small-cluster deployment with Docker Compose", + "Data scale: Datasets up to 100k rows (configurable)", + "Concurrency: 1 workflow at a time (configurable)", + "Authentication: Development mode - production deployment includes API key support" + ] + }, + "uncertainties": [ + { + "item": "Missing Mission Definition Module", + "details": "Mentioned in COMPREHENSIVE_SYSTEM_AUDIT_REPORT.md but no implementation found", + "impact": "low" + }, + { + "item": "Data Governance Implementation", + "details": "Configuration exists but enforcement unclear", + "impact": "medium" + }, + { + "item": "Kafka/MongoDB fallback behavior", + "details": "System continues with reduced functionality per CONNECTIVITY_TEST_REPORT.md", + "impact": "low" + }, + { + "item": "Multi-tenant support", + "details": "Not implemented; roadmap item per README.md", + "impact": "medium" + }, + { + "item": "Hybrid API translation", + "details": "LLM integration references Claude/Llama2 but implementation incomplete", + "impact": "medium" + } + ], + "metadata": { + "generated_date": "2025-10-13", + "generator": "A0 Purpose & Surface Mapper", + "repository_version": "2.1.0", + "last_updated": "January 2024" + } +} diff --git a/reports/purpose-map.md b/reports/purpose-map.md new file mode 100644 index 0000000..a67e811 --- /dev/null +++ b/reports/purpose-map.md @@ -0,0 +1,156 @@ +# Purpose & Surface Map: Sherlock Multiagent Data Scientist + +## MVP Purpose + +**Sherlock is an end-to-end, orchestrator-driven data science platform that enables users to perform exploratory data analysis, data quality validation, feature engineering, and model training through microservices agents coordinated by a master orchestrator with real-time observability.** + +--- + +## Feature & User Journey Mapping + +| Journey | Primary Files | Entry Command | Data Dependencies | Risk | +|---------|--------------|---------------|-------------------|------| +| **1. Upload & Load Dataset** | `mcp-server/master_orchestrator_api.py` (POST /datasets/upload)
`mcp-server/eda_agent.py` (POST /load_data)
`mcp-server/data_sources/` | `curl -X POST http://localhost:8000/datasets/upload -F "file=@data.csv" -F "name=my_dataset"`
OR
`python start_master_orchestrator.py` (then API call) | MongoDB (optional), Local filesystem | **M** - File upload validation needed; supports CSV, Parquet | +| **2. Exploratory Data Analysis (EDA)** | `mcp-server/eda_agent.py` (/basic_info, /statistical_summary, /missing_data_analysis, /detect_outliers)
`mcp-server/server.py` (MCP tools)
`docs/USER_GUIDE.md` | `python start_eda_service.py`
Service: http://localhost:8001
Health: http://localhost:8001/health | Redis (caching), Dataset loaded in memory | **M** - Large datasets may cause memory issues; 10k row sampling used for correlations | +| **3. Data Quality Validation** | `mcp-server/refinery_agent.py` (POST /execute)
Actions: check_schema_consistency, check_missing_values, check_distributions, check_drift
`mcp-server/REFINERY_AGENT_GUIDE.md`
`mcp-server/dq/` modules | `python -m uvicorn refinery_agent:app --port 8005`
OR via orchestrator workflow | Evidently library, Dataset files | **L** - Read-only operations; comprehensive validation framework | +| **4. Feature Engineering Pipeline** | `mcp-server/refinery_agent.py` (feature engineering actions)
`mcp-server/fe/` modules
Actions: basic_impute_missing_values, basic_scale_numeric_features, basic_encode_categorical_features | Via POST /execute to http://localhost:8005
Dual-mode operation (data_quality vs feature_engineering) | Scikit-learn pipelines, Feature metadata | **M** - Transformation logic; mode validation prevents accidental transforms | +| **5. Model Training & Evaluation** | `mcp-server/ml_agent.py` (POST /train_validation_test, /class_imbalance, /baseline_sanity)
`mcp-server/ML_WORKFLOW_GUIDE.md`
`mcp-server/mlruns/` (MLflow) | `python -m uvicorn ml_agent:app --port 8002`
Service: http://localhost:8002 | MLflow backend, Training datasets, Scikit-learn | **H** - Model persistence, hyperparameter tuning; GPU/CPU resource management | +| **6. Workflow Orchestration** | `mcp-server/master_orchestrator_api.py` (POST /workflows/start, GET /runs/{run_id}/status)
`mcp-server/orchestrator/workflow_manager.py`
`mcp-server/orchestrator/` (agent_registry, sla_monitor) | `python start_master_orchestrator.py`
API: http://localhost:8000
Docs: http://localhost:8000/docs | MongoDB (run persistence), Redis (locks), Kafka (events) | **M** - Deadlock monitoring, graceful cancellation; task dependencies managed | +| **7. Real-time Observability Dashboard** | `dashboard-ui/src/` (React SPA)
`dashboard-ui/package.json`
Backend: WebSocket `/ws/events` | `cd dashboard-ui && npm run dev`
UI: http://localhost:3000 | FastAPI WebSocket, Kafka events stream, Recent runs API | **M** - WebSocket connection stability; event streaming from Kafka | + +--- + +## Runnable Surfaces & Entrypoints + +### Core Services (Microservices Architecture) + +| Service | Entrypoint Script | Port | Health Check | Description | +|---------|------------------|------|-------------|-------------| +| **Master Orchestrator** | `mcp-server/start_master_orchestrator.py`
OR `python master_orchestrator_api.py` | 8000 | `/health` | Workflow coordination, task dispatch, artifact management | +| **EDA Agent** | `mcp-server/start_eda_service.py`
OR `python eda_agent.py` | 8001 | `/health` | Data loading, statistical analysis, visualization, outlier detection | +| **Refinery Agent** | `python -m uvicorn refinery_agent:app --port 8005`
OR via Docker | 8005 | `/health` | Data quality validation, feature engineering, drift detection | +| **ML Agent** | `python -m uvicorn ml_agent:app --port 8002` | 8002 | `/health` | Model training, cross-validation, baseline checks, experiment tracking | +| **MCP Server** | `mcp-server/server.py` (MCP protocol)
OR `python launch_server.py` | N/A | N/A | Claude Desktop integration for conversational data analysis | +| **Dashboard UI** | `cd dashboard-ui && npm run dev` | 3000 | N/A | React-based real-time monitoring interface | + +### Infrastructure Dependencies + +| Component | Start Command | Required By | Notes | +|-----------|--------------|-------------|-------| +| **MongoDB** | `docker-compose up -d mongodb` | Master Orchestrator | Run persistence, task history | +| **Redis** | `docker-compose up -d redis` | All agents | Caching, distributed locks, translation queue | +| **Kafka** | `docker-compose up -d kafka` | Master Orchestrator, Dashboard | Event streaming, task routing | +| **Nginx** (Optional) | `docker-compose up -d nginx` | Production deployment | Load balancing, reverse proxy | + +### Docker Deployment + +```bash +# Start all services with infrastructure +cd mcp-server +docker-compose up -d + +# Production deployment +python production_deployment.py +``` + +**Docker Compose Files:** +- `mcp-server/docker-compose.yml` - Full multi-service deployment +- `mcp-server/docker-compose.local.yml` - Local development variant +- `docker-compose.yml` (root) - Simplified deployment + +### Scripts & Utilities + +| Script | Purpose | Usage | +|--------|---------|-------| +| `mcp-server/install_orchestrator.py` | Setup dependencies, create directories, verify infrastructure | `python install_orchestrator.py` | +| `mcp-server/production_deployment.py` | Production-ready deployment with monitoring | `python production_deployment.py` | +| `mcp-server/connectivity_tester.py` | Test inter-service connectivity | `python connectivity_tester.py` | +| `mcp-server/verify_setup.py` | Verify Python dependencies and configuration | `python verify_setup.py` | +| `mcp-server/bug_hunter.py` | Diagnostic tool for troubleshooting | `python bug_hunter.py` | +| `mcp-server/test_*.py` | Test suites (pytest) | `pytest test_refinery_e2e.py` | + +--- + +## Data Dependencies & Flow + +``` +User → Master Orchestrator (8000) + ↓ + Kafka Events + ↓ + ┌────┴────┬────────┬─────────┐ + ↓ ↓ ↓ ↓ +EDA Agent Refinery ML Agent Dashboard + (8001) (8005) (8002) (3000) + ↓ ↓ ↓ ↓ + Redis MongoDB MLflow WebSocket +``` + +**Key Data Stores:** +- **MongoDB**: `deepline` database - workflow runs, task status, artifacts metadata +- **Redis**: Caching layer, distributed locks, translation queue (`translation:q`) +- **MLflow**: `mcp-server/mlruns/` - experiment tracking, model registry +- **Local Filesystem**: + - `artifacts/` - workflow artifacts (reports, visualizations) + - `snapshots/` - data versioning + - `mcp-server/data_sources/` - uploaded datasets + +**Configuration:** +- Primary: `mcp-server/config.yaml` (comprehensive settings) +- Environment: `.env` files (production deployment) +- Claude Desktop: `claude_desktop_config.json` (MCP integration) + +--- + +## Risk Assessment Summary + +| Risk Level | Count | Mitigation Strategy | +|------------|-------|-------------------| +| **High (H)** | 1 | Model training - Resource quotas in `resource-quota.yaml`, GPU/CPU agent routing | +| **Medium (M)** | 5 | Multiple agents - Health checks, deadlock monitoring, graceful cancellation, SLA monitoring | +| **Low (L)** | 1 | Data quality - Read-only operations, comprehensive validation | + +--- + +## Assumptions & Uncertainties + +### Assumptions (<20% uncertainty): +1. **Primary use case**: Data scientists performing end-to-end analysis workflows via API/UI +2. **Deployment target**: Single-node or small-cluster deployment with Docker Compose +3. **Data scale**: Datasets up to 100k rows (configurable: `max_rows_processed: 100000`) +4. **Concurrency**: 1 workflow at a time (configurable: `max_concurrent_workflows: 1`) +5. **Authentication**: Development mode - production deployment includes API key support + +### Uncertainties Identified: +1. **Missing Mission Definition Module** (mentioned in COMPREHENSIVE_SYSTEM_AUDIT_REPORT.md) - No implementation found in codebase +2. **Data Governance Implementation** - Configuration exists but enforcement unclear +3. **Kafka/MongoDB fallback behavior** - System continues with reduced functionality (per CONNECTIVITY_TEST_REPORT.md) +4. **Multi-tenant support** - Not implemented; roadmap item per README.md +5. **Hybrid API translation** - LLM integration unclear; references Claude/Llama2 but implementation incomplete + +--- + +## Architecture Principles + +1. **Microservices**: Each agent is independently deployable FastAPI service +2. **Async-first**: Non-blocking translation, background task processing +3. **Resilience**: Deadlock detection, automatic retry with exponential backoff, graceful cancellation +4. **Observability**: OpenTelemetry integration, Kafka event streams, health endpoints +5. **Configuration-driven**: YAML-based tuning, environment variable overrides +6. **License Model**: Hybrid Apache 2.0 (clients/SDKs) + BUSL 1.1 (server core) + +--- + +## Next Steps for Users + +1. **Quick Start**: Follow `docs/INSTALLATION.md` for Windows/Linux setup +2. **First Workflow**: Upload CSV → Run EDA → Generate quality report (see `docs/EXAMPLES.md`) +3. **Configuration**: Tune thresholds in `config.yaml` per `docs/CONFIGURATION.md` +4. **Advanced**: Build custom workflows with DSL or natural language API +5. **Production**: Use `production_deployment.py` for containerized deployment + +--- + +*Report generated: 2025-10-13* +*Repository: DeepExtrema/Sherlock-Multiagent-Data-Scientist* +*Version: 2.1.0* From 700de5c72b1d122762b473a9b68ddad670da6716 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Mon, 13 Oct 2025 10:09:20 +0000 Subject: [PATCH 3/3] Add README.md for reports directory with usage guide Co-authored-by: DeepExtrema <175066046+DeepExtrema@users.noreply.github.com> --- reports/README.md | 130 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 130 insertions(+) create mode 100644 reports/README.md diff --git a/reports/README.md b/reports/README.md new file mode 100644 index 0000000..2e7b5d5 --- /dev/null +++ b/reports/README.md @@ -0,0 +1,130 @@ +# Reports Directory + +This directory contains automated analysis and mapping reports for the Sherlock Multiagent Data Scientist platform. + +## Files + +### `purpose-map.md` +**Human-readable purpose and surface mapping document** + +Contains: +- Single-sentence MVP purpose statement +- Feature & User Journey Mapping table with 7 journeys + - Journey description + - Primary files involved + - Entry commands + - Data dependencies + - Risk assessment (H/M/L) +- Runnable surfaces documentation + - Core services with entrypoints + - Infrastructure dependencies + - Docker deployment options + - Scripts and utilities +- Data dependencies & flow diagram +- Risk assessment summary +- Assumptions & uncertainties + +### `feature-surface.json` +**Machine-readable feature surface mapping** + +Structured JSON containing: +- Repository metadata +- 7 features with detailed specifications: + - Endpoints (methods, paths, services, ports) + - Entry commands + - Data dependencies + - Risk levels and notes +- Runnable surfaces: + - 6 services (FastAPI, React SPA, MCP protocol) + - 4 infrastructure components (MongoDB, Redis, Kafka, Nginx) + - Docker deployment configurations + - Utility scripts +- Data flow and storage architecture +- Configuration details +- Risk summary +- Assumptions (15% uncertainty) +- Identified uncertainties + +## Generation Details + +- **Generated**: 2025-10-13 +- **Generator**: A0 Purpose & Surface Mapper +- **Repository Version**: 2.1.0 +- **Uncertainty Level**: 15% + +## Usage + +### View Human-Readable Report +```bash +cat reports/purpose-map.md +``` + +### Parse Machine-Readable JSON +```python +import json + +with open('reports/feature-surface.json') as f: + data = json.load(f) + +# Access features +for feature in data['features']: + print(f"{feature['name']}: {feature['risk_level']} risk") + +# Access services +for service in data['runnable_surfaces']['services']: + print(f"{service['name']} on port {service['port']}") +``` + +### Quick Stats +```bash +# Validate JSON +python -m json.tool reports/feature-surface.json > /dev/null && echo "✓ Valid JSON" + +# Count features +python -c "import json; print(f\"{len(json.load(open('reports/feature-surface.json'))['features'])} features mapped\")" + +# Line counts +wc -l reports/* +``` + +## Key Findings + +### MVP Purpose +Sherlock is an **end-to-end, orchestrator-driven data science platform** that enables users to perform exploratory data analysis, data quality validation, feature engineering, and model training through microservices agents coordinated by a master orchestrator with real-time observability. + +### Architecture +- **Type**: Microservices +- **Services**: 6 (Master Orchestrator, EDA, Refinery, ML, MCP, Dashboard) +- **Infrastructure**: MongoDB, Redis, Kafka, Nginx +- **License**: Hybrid (Apache 2.0 for clients, BUSL 1.1 for server) + +### Risk Distribution +- **High**: 1 feature (Model Training) +- **Medium**: 5 features (Upload, EDA, Feature Eng, Orchestration, Dashboard) +- **Low**: 1 feature (Data Quality) + +### Deployment +Multiple options: +1. Individual services: `python start_.py` +2. Docker Compose: `docker-compose up -d` +3. Production: `python production_deployment.py` + +## Assumptions + +1. Primary use case: Data scientists performing end-to-end analysis workflows +2. Deployment: Single-node or small-cluster with Docker Compose +3. Data scale: Up to 100k rows (configurable) +4. Concurrency: 1 workflow at a time (configurable) +5. Mode: Development (production has API key support) + +## Identified Uncertainties + +1. Mission Definition Module (mentioned but not implemented) +2. Data Governance enforcement details +3. Kafka/MongoDB fallback behavior +4. Multi-tenant support (roadmap item) +5. Hybrid API/LLM integration completeness + +--- + +*For questions about these reports, refer to the problem statement that generated them or examine the source documentation in `/docs`, `README.md`, and agent files in `/mcp-server`.*