diff --git a/.env.example b/.env.example index 8e78172..3ed4946 100644 --- a/.env.example +++ b/.env.example @@ -1,5 +1,7 @@ -# Choose your model (PydanticAI format: provider:model) -MODEL=anthropic:claude-haiku-4-5-20251001 +# Compose-level configuration. Copy to `.env` (gitignored) and adjust. +# These feed both the `db` service and the backend's DATABASE_URL. -# Set the API key for your chosen provider -ANTHROPIC_API_KEY=sk-ant-... +# --- Postgres credentials --- +POSTGRES_USER=postgres +POSTGRES_PASSWORD=postgres +POSTGRES_DB=app diff --git a/.gitignore b/.gitignore index c26db7c..eafb9cc 100644 --- a/.gitignore +++ b/.gitignore @@ -13,4 +13,12 @@ __pycache__/ .env .venv/ -output/ \ No newline at end of file +output/ + +# Node / frontend +frontend/node_modules/ +frontend/dist/ +.vite/ + +# Ruff +.ruff_cache/ diff --git a/.vscode/extensions.json b/.vscode/extensions.json new file mode 100644 index 0000000..5ddd35c --- /dev/null +++ b/.vscode/extensions.json @@ -0,0 +1,10 @@ +{ + "recommendations": [ + "ms-python.python", + "ms-python.vscode-pylance", + "charliermarsh.ruff", + "bradlc.vscode-tailwindcss", + "dbaeumer.vscode-eslint", + "esbenp.prettier-vscode" + ] +} diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..92ee0ca --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,33 @@ +{ + // --------------------------------------------------------------------------- + // Python (backend) — point Pylance at the uv-managed virtualenv so imports + // like `fastapi`, `sqlalchemy`, `app.*` resolve correctly. + // --------------------------------------------------------------------------- + "python.defaultInterpreterPath": "${workspaceFolder}/backend/.venv/bin/python", + "python.analysis.extraPaths": ["${workspaceFolder}/backend"], + "python.analysis.autoSearchPaths": true, + "python.analysis.typeCheckingMode": "basic", + "python.terminal.activateEnvironment": true, + + // --------------------------------------------------------------------------- + // CSS / Tailwind — stop the built-in CSS linter from flagging @tailwind, + // @apply, @layer as "Unknown at rule". Tailwind extension handles these. + // --------------------------------------------------------------------------- + "css.lint.unknownAtRules": "ignore", + "scss.lint.unknownAtRules": "ignore", + "less.lint.unknownAtRules": "ignore", + "tailwindCSS.includeLanguages": { + "typescriptreact": "javascript" + }, + "tailwindCSS.experimental.configFile": "frontend/tailwind.config.js", + "editor.quickSuggestions": { + "strings": "on" + }, + + // --------------------------------------------------------------------------- + // Per-language files association + // --------------------------------------------------------------------------- + "files.associations": { + "*.css": "tailwindcss" + } +} diff --git a/Dockerfile b/Dockerfile deleted file mode 100644 index 7da3d07..0000000 --- a/Dockerfile +++ /dev/null @@ -1,10 +0,0 @@ -FROM python:3.12-slim - -WORKDIR /app - -COPY requirements.txt . -RUN pip install --no-cache-dir -r requirements.txt - -COPY . . - -CMD ["python", "main.py"] diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..0f746db --- /dev/null +++ b/Makefile @@ -0,0 +1,83 @@ +.DEFAULT_GOAL := help + +# --------------------------------------------------------------------------- +# Config +# --------------------------------------------------------------------------- +COMPOSE := docker compose +COMPOSE_TEST := docker compose -f docker-compose.test.yml +TEST_DB_URL := postgresql+asyncpg://postgres:postgres@localhost:5433/app_test + +.PHONY: help install up down logs backend frontend migrate seed db-reset \ + test test-backend test-frontend test-up test-down + +help: ## Show this help + @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | \ + awk 'BEGIN {FS = ":.*?## "}; {printf " \033[36m%-16s\033[0m %s\n", $$1, $$2}' + +# --------------------------------------------------------------------------- +# Setup +# --------------------------------------------------------------------------- +install: ## Install backend (uv) and frontend (npm) dependencies + cd backend && uv sync + cd frontend && npm install + +# --------------------------------------------------------------------------- +# Run — full stack +# --------------------------------------------------------------------------- +up: ## Run the whole stack (db, minio, backend, frontend) via docker-compose + $(COMPOSE) up --build + +down: ## Stop and remove the stack + $(COMPOSE) down + +logs: ## Tail logs of the running stack + $(COMPOSE) logs -f + +# --------------------------------------------------------------------------- +# Run — individual components (local, no full compose) +# --------------------------------------------------------------------------- +backend: ## Run the FastAPI backend locally (auto-starts db + minio, migrates, then uvicorn) + $(COMPOSE) up -d --wait db + @# Start a local MinIO unless a real/external S3 is configured. A custom + @# S3_ENDPOINT_URL that isn't localhost/minio means a remote S3 — skip it. + @endpoint="$${S3_ENDPOINT_URL:-$$(grep -E '^S3_ENDPOINT_URL=' backend/.env 2>/dev/null | cut -d= -f2-)}"; \ + case "$$endpoint" in \ + ""|*localhost*|*127.0.0.1*|*minio*) \ + echo "→ starting local MinIO"; $(COMPOSE) up -d --wait minio ;; \ + *) echo "→ external S3 configured ($$endpoint) — skipping MinIO" ;; \ + esac + cd backend && uv run alembic upgrade head + cd backend && uv run uvicorn app.main:app --reload --port 8000 + +frontend: ## Run the Vite dev server locally + cd frontend && npm run dev + +migrate: ## Apply Alembic migrations to the dev database + cd backend && uv run alembic upgrade head + +seed: ## Seed the dev DB with demo conversations + artifacts (figures/CSVs on S3) + plain threads. Needs MinIO up. Use ARGS=--reset to wipe first. + cd backend && uv run python -m scripts.seed $(ARGS) + +db-reset: ## Wipe the dev DB clean (drop all tables, re-run migrations) + cd backend && uv run alembic downgrade base + cd backend && uv run alembic upgrade head + +# --------------------------------------------------------------------------- +# Tests +# --------------------------------------------------------------------------- +test: test-up ## Run all tests (spins up test db/minio, then tears down) + @$(MAKE) test-backend || ($(MAKE) test-down && exit 1) + @$(MAKE) test-frontend || ($(MAKE) test-down && exit 1) + @$(MAKE) test-down + +test-up: ## Start the ephemeral test db + minio and wait until healthy + $(COMPOSE_TEST) up -d --wait + +test-down: ## Stop the ephemeral test services + $(COMPOSE_TEST) down -v + +test-backend: ## Run backend tests (pytest) against the test db + cd backend && DATABASE_URL=$(TEST_DB_URL) uv run pytest -q + +test-frontend: ## Run frontend tests (vitest) + cd frontend && npm run test diff --git a/README.md b/README.md index 641aaf5..3b3fc85 100644 --- a/README.md +++ b/README.md @@ -1,113 +1,124 @@ -# Case Technique — Développeur Full Stack +# Data Analysis Agent — Web App -## Contexte +A web app around a data-analysis agent: ask questions about your CSV datasets and +watch the agent work in real time — its reasoning, the **SQL** it runs (DuckDB), +the **Plotly** charts and CSV tables it generates, and the final answer — all +streamed live. Built with [PydanticAI](https://ai.pydantic.dev/), FastAPI, and React. -Tu reçois un **agent d'analyse de données** qui fonctionne en mode CLI (terminal). +## Stack -L'agent peut : +- **Backend** — FastAPI, async SQLAlchemy + Alembic (Postgres), S3/MinIO for files, + the agent (PydanticAI) under `backend/app/services/agent/`. +- **Frontend** — React + Vite + Tailwind, streaming chat over SSE, interactive + Plotly figures, Markdown answers. +- **Streaming** — Server-Sent Events; the agent's reasoning, tool calls, figures + and answer are emitted as structured events. -- Répondre à des questions sur des données en générant du **SQL** (via DuckDB) -- Créer des **visualisations** avec Plotly -- Expliquer son **raisonnement** (balises ``) -- Enchaîner les étapes automatiquement via des **tool calls** +## Run it locally (Docker) -L'agent est construit avec [PydanticAI](https://ai.pydantic.dev/). +You only need Docker and an Anthropic API key. ---- +1. **Set the configuration.** Two `.env` files, copied from the examples: -## Objectif + ```bash + cp .env.example .env # Postgres credentials (compose-level) + cp backend/.env.example backend/.env # backend config + your API key + ``` -**Transformer cet agent CLI en une application web complète.** + Then edit **`backend/.env`** and set your key: -L'utilisateur doit pouvoir poser des questions dans une interface web et voir en temps réel : + ```dotenv + ANTHROPIC_API_KEY=sk-ant-... + # MODEL is optional; defaults to a fast Claude model: + MODEL=anthropic:claude-haiku-4-5-20251001 + ``` -1. Le **raisonnement** de l'agent (thinking) — affiché progressivement -2. Les **appels d'outils** (tool calls) — nom, arguments, résultat -3. Les **visualisations** Plotly / tableaux de données -4. La **réponse finale** de l'agent + That's the only value you must provide. The database and MinIO settings in the + examples already match what docker-compose wires up. ---- +2. **Start everything:** -## Ce qui est fourni + ```bash + docker compose up --build + ``` -``` -case_fullstack/ -├── agent/ -│ ├── agent.py # Création de l'agent PydanticAI -│ ├── context.py # Contexte injecté dans les tools -│ ├── prompt.py # System prompt -│ └── tools/ -│ ├── query_data.py # Exécution SQL via DuckDB -│ └── visualize.py # Création de visualisations Plotly -├── data/ # Fichiers CSV (tes données de test) -├── output/ # Visualisations générées -├── main.py # Script CLI de démonstration -├── Dockerfile -├── docker-compose.yml -├── requirements.txt -├── .env.example -└── README.md -``` - ---- + This brings up Postgres, MinIO, the backend (which auto-runs migrations), and + the frontend dev server. Wait until the backend logs `Application startup complete`. -## Setup +3. **Open the app:** -```bash -# 1. Configurer la clé API -cp .env.example .env -# Éditer .env avec ta clé API + - Frontend: + - Backend API + docs: , + - MinIO console: (`minioadmin` / `minioadmin`) -# 2. Ajouter des fichiers CSV dans data/ +### Get some data in -# 3. Lancer le CLI via Docker -docker compose run --rm agent -``` +Upload a CSV from the **Datasets** panel in the UI, then ask a question. -> Le volume `data/` est monté dans le container — tu peux ajouter/modifier des CSV sans rebuild. -> Les visualisations générées sont dans `output/`. +Or seed a few ready-made demo conversations (with reasoning, SQL, charts and +generated CSVs) plus sample datasets: ---- +```bash +docker compose exec backend uv run python -m scripts.seed --reset +``` -## Ce qui est attendu +## Configuration reference -### Minimum requis +`backend/.env` — backend + agent settings: -- [ ] **Backend API** avec endpoint de streaming (SSE ou WebSocket) -- [ ] **Frontend web** avec : - - [ ] Champ texte pour poser des questions - - [ ] Affichage **streaming** du thinking (collapsible/dépliable) - - [ ] Affichage des **tool calls** (nom de l'outil, arguments, résultat) - - [ ] Rendu des **visualisations Plotly** (graphiques interactifs) - - [ ] Rendu des **tableaux** de données -- [ ] **Code propre** et structuré +| Variable | Purpose | Default | +| ------------------- | ---------------------------------------------------- | ---------------------------------------- | +| `ANTHROPIC_API_KEY` | **Required.** Your Anthropic API key. | — | +| `MODEL` | PydanticAI model id. | `anthropic:claude-haiku-4-5-20251001` | +| `POSTGRES_*` | DB connection parts (compose overrides with a DSN). | local Postgres | +| `S3_*` | Object storage (compose overrides to the MinIO svc). | local MinIO | ---- +`.env` (repo root) — compose-level Postgres credentials shared by the `db` service +and the backend's `DATABASE_URL`. -## Stack technique +## Local development (without Docker) -- **Backend** : FastAPI -- **Frontend** : Libre React -- **Streaming** : SSE ou WebSocket (à ton choix) +Requires [uv](https://docs.astral.sh/uv/) and Node 20+. The `Makefile` wraps the +common tasks; Postgres and MinIO still run via Docker. ---- +```bash +make install # backend deps (uv) + frontend deps (npm) +make backend # starts db + minio, migrates, runs uvicorn on :8000 +make frontend # Vite dev server on :5173 +make seed # demo data (needs MinIO up; ARGS=--reset to wipe) +make test # backend (pytest) + frontend (vitest) +``` -## Critères d'évaluation +Run `make help` for the full list. -| Critère | Description | -|---------|-------------| -| **Fonctionnalité** | Le streaming fonctionne, le thinking s'affiche en temps réel, les tool calls sont visibles, les visualisations s'affichent | -| **Code** | Propre, structuré, lisible, bien découpé | -| **UX** | L'expérience utilisateur est fluide et intuitive | -| **Architecture** | Bonne séparation frontend / backend, gestion des états cohérente | +## Project layout ---- +``` +backend/ + app/ + api/ # routers: conversations (+ chat streaming), csv_files, artifacts + models/ # SQLAlchemy: conversation, message, message_artifact, csv_file + services/ + agent/ # the PydanticAI agent (prompt, tools, runner, model, datasets) + chat.py # orchestration: run a turn, stream events, persist artifacts + storage.py # S3 / MinIO client + alembic/ # migrations + scripts/seed.py # demo data seeder + tests/ +frontend/ + src/ + components/ # ChatPanel, AssistantMessage (timeline), PlotlyFigure, Markdown, … + services/ # api client (SSE), useChatStream, useMessages, … +docker-compose.yml +``` -## Ressources utiles +## How a chat turn works -- [PydanticAI — Documentation](https://ai.pydantic.dev/) -- [PydanticAI — Streaming](https://ai.pydantic.dev/streaming/) -- [PydanticAI — Tools](https://ai.pydantic.dev/tools/) -- [Plotly.js — React integration](https://plotly.com/javascript/react/) -- [FastAPI — Streaming Response](https://fastapi.tiangolo.com/advanced/custom-response/#streamingresponse) -- [Server-Sent Events (SSE)](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events) +1. `POST /api/conversations/{id}/messages` persists the user message and starts a run. +2. The agent streams **structured SSE events**: `reasoning`, `tool_call` (the SQL), + `tool_result`, `artifact` (figures inline, tables downloadable), `text`, plus a + `title` for brand-new conversations. +3. The frontend renders these live as a collapsible step timeline + interactive + chart + Markdown answer. +4. On completion the assistant message and its full artifact timeline are persisted, + so reloading a conversation reconstructs the same view. diff --git a/agent/context.py b/agent/context.py deleted file mode 100644 index 7e008fc..0000000 --- a/agent/context.py +++ /dev/null @@ -1,13 +0,0 @@ -from dataclasses import dataclass, field -from typing import Optional - -import pandas as pd - - -@dataclass -class AgentContext: - """Context injected into all agent tools via PydanticAI dependency injection.""" - - datasets: dict[str, pd.DataFrame] = field(default_factory=dict) - dataset_info: str = "" - current_dataframe: Optional[pd.DataFrame] = None diff --git a/backend/.dockerignore b/backend/.dockerignore new file mode 100644 index 0000000..f364cd0 --- /dev/null +++ b/backend/.dockerignore @@ -0,0 +1,6 @@ +.venv +__pycache__ +*.pyc +.env +.ruff_cache +.pytest_cache diff --git a/backend/.env.example b/backend/.env.example new file mode 100644 index 0000000..c44f8bd --- /dev/null +++ b/backend/.env.example @@ -0,0 +1,25 @@ +# --- Database (Postgres) --- +# The DSN is built from these parts (same model as docker-compose). +POSTGRES_USER=postgres +POSTGRES_PASSWORD=postgres +POSTGRES_DB=app +POSTGRES_HOST=localhost +POSTGRES_PORT=5432 +# Optionally set a full DSN to override the parts above (compose does this): +# DATABASE_URL=postgresql+asyncpg://postgres:postgres@db:5432/app + +# --- Object storage (S3 / MinIO) --- +# Local value (running the backend on the host). Inside docker-compose the +# backend overrides this with http://minio:9000. Leave empty for real AWS S3. +S3_ENDPOINT_URL=http://localhost:9000 +S3_REGION=us-east-1 +S3_ACCESS_KEY=minioadmin +S3_SECRET_KEY=minioadmin +S3_BUCKET=csv-uploads + +# --- CORS --- +# CORS_ORIGINS='["http://localhost:5173"]' + +# --- Agent (used later) --- +MODEL=anthropic:claude-haiku-4-5-20251001 +ANTHROPIC_API_KEY=sk-ant-... diff --git a/backend/Dockerfile b/backend/Dockerfile new file mode 100644 index 0000000..5ffea7d --- /dev/null +++ b/backend/Dockerfile @@ -0,0 +1,18 @@ +FROM python:3.12-slim + +# Install uv (fast Python package manager). +COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/ + +WORKDIR /app + +# Install dependencies first (cached layer). +COPY pyproject.toml ./ +RUN uv sync --no-install-project + +# Copy application code. +COPY . . + +EXPOSE 8000 + +# uv run uses the project virtualenv created by `uv sync`. +CMD ["uv", "run", "uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"] diff --git a/backend/alembic.ini b/backend/alembic.ini new file mode 100644 index 0000000..09c7978 --- /dev/null +++ b/backend/alembic.ini @@ -0,0 +1,38 @@ +[alembic] +script_location = alembic +prepend_sys_path = . +path_separator = os + +[loggers] +keys = root,sqlalchemy,alembic + +[handlers] +keys = console + +[formatters] +keys = generic + +[logger_root] +level = WARNING +handlers = console +qualname = + +[logger_sqlalchemy] +level = WARNING +handlers = +qualname = sqlalchemy.engine + +[logger_alembic] +level = INFO +handlers = +qualname = alembic + +[handler_console] +class = StreamHandler +args = (sys.stderr,) +level = NOTSET +formatter = generic + +[formatter_generic] +format = %(levelname)-5.5s [%(name)s] %(message)s +datefmt = %H:%M:%S diff --git a/backend/alembic/env.py b/backend/alembic/env.py new file mode 100644 index 0000000..20826a9 --- /dev/null +++ b/backend/alembic/env.py @@ -0,0 +1,52 @@ +"""Alembic environment — async engine + autogenerate from SQLAlchemy models.""" + +import asyncio +from logging.config import fileConfig + +from alembic import context +from sqlalchemy.ext.asyncio import async_engine_from_config +from sqlalchemy.pool import NullPool + +from app.core.config import get_settings +from app.db.base import Base + +# Import models so their metadata is registered for autogenerate. +import app.models # noqa: F401 + +config = context.config + +if config.config_file_name is not None: + fileConfig(config.config_file_name) + +# Inject the runtime database URL (psycopg-free, asyncpg driver). +config.set_main_option("sqlalchemy.url", get_settings().database_url) + +target_metadata = Base.metadata + + +def do_run_migrations(connection) -> None: + context.configure( + connection=connection, + target_metadata=target_metadata, + compare_type=True, + ) + with context.begin_transaction(): + context.run_migrations() + + +async def run_async_migrations() -> None: + connectable = async_engine_from_config( + config.get_section(config.config_ini_section, {}), + prefix="sqlalchemy.", + poolclass=NullPool, + ) + async with connectable.connect() as connection: + await connection.run_sync(do_run_migrations) + await connectable.dispose() + + +def run_migrations_online() -> None: + asyncio.run(run_async_migrations()) + + +run_migrations_online() diff --git a/backend/alembic/script.py.mako b/backend/alembic/script.py.mako new file mode 100644 index 0000000..edb0604 --- /dev/null +++ b/backend/alembic/script.py.mako @@ -0,0 +1,26 @@ +"""${message} + +Revision ID: ${up_revision} +Revises: ${down_revision | comma,n} +Create Date: ${create_date} + +""" +from collections.abc import Sequence + +from alembic import op +import sqlalchemy as sa +${imports if imports else ""} + +# revision identifiers, used by Alembic. +revision: str = ${repr(up_revision)} +down_revision: str | None = ${repr(down_revision)} +branch_labels: str | Sequence[str] | None = ${repr(branch_labels)} +depends_on: str | Sequence[str] | None = ${repr(depends_on)} + + +def upgrade() -> None: + ${upgrades if upgrades else "pass"} + + +def downgrade() -> None: + ${downgrades if downgrades else "pass"} diff --git a/backend/alembic/versions/0001_initial.py b/backend/alembic/versions/0001_initial.py new file mode 100644 index 0000000..20e2234 --- /dev/null +++ b/backend/alembic/versions/0001_initial.py @@ -0,0 +1,99 @@ +"""initial schema: conversation, message (M2M), csv_file + +Revision ID: 0001_initial +Revises: +Create Date: 2026-06-15 + +""" +from collections.abc import Sequence + +import sqlalchemy as sa +from alembic import op + +# revision identifiers, used by Alembic. +revision: str = "0001_initial" +down_revision: str | None = None +branch_labels: str | Sequence[str] | None = None +depends_on: str | Sequence[str] | None = None + + +def upgrade() -> None: + op.create_table( + "conversation", + sa.Column("id", sa.Uuid(), nullable=False), + sa.Column("title", sa.String(length=255), nullable=False), + sa.Column( + "created_at", + sa.DateTime(timezone=True), + server_default=sa.text("now()"), + nullable=False, + ), + sa.Column( + "updated_at", + sa.DateTime(timezone=True), + server_default=sa.text("now()"), + nullable=False, + ), + sa.PrimaryKeyConstraint("id", name=op.f("pk_conversation")), + ) + + op.create_table( + "message", + sa.Column("id", sa.Uuid(), nullable=False), + sa.Column("role", sa.String(length=32), nullable=False), + sa.Column("content", sa.Text(), nullable=False), + sa.Column( + "created_at", + sa.DateTime(timezone=True), + server_default=sa.text("now()"), + nullable=False, + ), + sa.PrimaryKeyConstraint("id", name=op.f("pk_message")), + ) + + op.create_table( + "csv_file", + sa.Column("id", sa.Uuid(), nullable=False), + sa.Column("filename", sa.String(length=255), nullable=False), + sa.Column("s3_key", sa.String(length=512), nullable=False), + sa.Column("size_bytes", sa.BigInteger(), nullable=False), + sa.Column("row_count", sa.Integer(), nullable=True), + sa.Column("column_count", sa.Integer(), nullable=True), + sa.Column( + "uploaded_at", + sa.DateTime(timezone=True), + server_default=sa.text("now()"), + nullable=False, + ), + sa.PrimaryKeyConstraint("id", name=op.f("pk_csv_file")), + sa.UniqueConstraint("s3_key", name=op.f("uq_csv_file_s3_key")), + ) + + # Many-to-many association: conversation <-> message + op.create_table( + "conversation_messages", + sa.Column("conversation_id", sa.Uuid(), nullable=False), + sa.Column("message_id", sa.Uuid(), nullable=False), + sa.ForeignKeyConstraint( + ["conversation_id"], + ["conversation.id"], + name=op.f("fk_conversation_messages_conversation_id_conversation"), + ondelete="CASCADE", + ), + sa.ForeignKeyConstraint( + ["message_id"], + ["message.id"], + name=op.f("fk_conversation_messages_message_id_message"), + ondelete="CASCADE", + ), + sa.PrimaryKeyConstraint( + "conversation_id", "message_id", name=op.f("pk_conversation_messages") + ), + ) + + +def downgrade() -> None: + op.drop_table("conversation_messages") + op.drop_table("csv_file") + op.drop_table("message") + op.drop_table("conversation") diff --git a/backend/alembic/versions/e52c4f55bc29_message_artifact_table_csv_file_source.py b/backend/alembic/versions/e52c4f55bc29_message_artifact_table_csv_file_source.py new file mode 100644 index 0000000..a0ec4dc --- /dev/null +++ b/backend/alembic/versions/e52c4f55bc29_message_artifact_table_csv_file_source.py @@ -0,0 +1,51 @@ +"""message_artifact table + csv_file.source + +Revision ID: e52c4f55bc29 +Revises: 0001_initial +Create Date: 2026-06-16 18:02:54.146175 + +""" +from collections.abc import Sequence + +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision: str = 'e52c4f55bc29' +down_revision: str | None = '0001_initial' +branch_labels: str | Sequence[str] | None = None +depends_on: str | Sequence[str] | None = None + + +def upgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + op.create_table('message_artifact', + sa.Column('id', sa.Uuid(), nullable=False), + sa.Column('message_id', sa.Uuid(), nullable=False), + sa.Column('kind', sa.String(length=32), nullable=False), + sa.Column('position', sa.Integer(), nullable=False), + sa.Column('content', sa.Text(), nullable=True), + sa.Column('s3_key', sa.String(length=512), nullable=True), + sa.Column('csv_file_id', sa.Uuid(), nullable=True), + sa.Column('meta', postgresql.JSONB(astext_type=sa.Text()), nullable=True), + sa.Column('created_at', sa.DateTime(timezone=True), server_default=sa.text('now()'), nullable=False), + sa.ForeignKeyConstraint(['csv_file_id'], ['csv_file.id'], name=op.f('fk_message_artifact_csv_file_id_csv_file'), ondelete='SET NULL'), + sa.ForeignKeyConstraint(['message_id'], ['message.id'], name=op.f('fk_message_artifact_message_id_message'), ondelete='CASCADE'), + sa.PrimaryKeyConstraint('id', name=op.f('pk_message_artifact')) + ) + op.create_index(op.f('ix_message_artifact_message_id'), 'message_artifact', ['message_id'], unique=False) + # server_default backfills existing rows; new uploads set it explicitly. + op.add_column( + 'csv_file', + sa.Column('source', sa.String(length=16), nullable=False, server_default='upload'), + ) + # ### end Alembic commands ### + + +def downgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + op.drop_column('csv_file', 'source') + op.drop_index(op.f('ix_message_artifact_message_id'), table_name='message_artifact') + op.drop_table('message_artifact') + # ### end Alembic commands ### diff --git a/agent/__init__.py b/backend/app/__init__.py similarity index 100% rename from agent/__init__.py rename to backend/app/__init__.py diff --git a/agent/tools/__init__.py b/backend/app/api/__init__.py similarity index 100% rename from agent/tools/__init__.py rename to backend/app/api/__init__.py diff --git a/backend/app/api/artifacts.py b/backend/app/api/artifacts.py new file mode 100644 index 0000000..231f7a0 --- /dev/null +++ b/backend/app/api/artifacts.py @@ -0,0 +1,42 @@ +"""Route to download an agent-generated CSV (a `table` artifact). + +Other artifacts are delivered inline in the messages response: sql/thinking via +`content`, figures via `figure_json`. Only generated tables stay on S3 and are +fetched here, on demand, when the user clicks "download" — they can be large, so +we don't ship them with every conversation load. +""" + +from uuid import UUID + +from fastapi import APIRouter, Depends, HTTPException +from fastapi.responses import Response +from sqlalchemy.ext.asyncio import AsyncSession + +from app.db.session import get_db +from app.models.message_artifact import MessageArtifact +from app.services import storage + +router = APIRouter(prefix="/artifacts", tags=["artifacts"]) + + +@router.get("/{artifact_id}/download") +async def download_artifact(artifact_id: UUID, db: AsyncSession = Depends(get_db)): + """Stream the CSV bytes of a generated `table` artifact as an attachment.""" + artifact = await db.get(MessageArtifact, artifact_id) + if artifact is None: + raise HTTPException(status_code=404, detail="Artifact not found.") + if artifact.kind != "table" or not artifact.s3_key: + raise HTTPException(status_code=404, detail="No downloadable file for this artifact.") + + try: + body = storage.download_object(artifact.s3_key) + except Exception: + raise HTTPException(status_code=502, detail="Could not fetch artifact content.") + + title = (artifact.meta or {}).get("title") or "table" + filename = title if title.lower().endswith(".csv") else f"{title}.csv" + return Response( + content=body, + media_type="text/csv", + headers={"Content-Disposition": f'attachment; filename="{filename}"'}, + ) diff --git a/backend/app/api/conversations.py b/backend/app/api/conversations.py new file mode 100644 index 0000000..1241804 --- /dev/null +++ b/backend/app/api/conversations.py @@ -0,0 +1,159 @@ +"""Routes for conversations and their messages (sidebar history).""" + +from collections.abc import AsyncIterator +from uuid import UUID + +from fastapi import APIRouter, Depends, HTTPException, Query +from fastapi.responses import StreamingResponse +from pydantic import BaseModel +from sqlalchemy import func, select +from sqlalchemy.ext.asyncio import AsyncSession +from sqlalchemy.orm import selectinload + +from app.db import session as db_session +from app.db.session import get_db +from app.models.conversation import Conversation +from app.models.message import Message, conversation_messages +from app.schemas.conversation import ConversationDetail, ConversationRead, MessageRead +from app.schemas.pagination import Page +from app.services import chat +from app.services.agent import events as agent_events + +router = APIRouter(prefix="/conversations", tags=["conversations"]) + + +class SendMessageBody(BaseModel): + message: str + + +@router.get("", response_model=Page[ConversationRead]) +async def list_conversations( + db: AsyncSession = Depends(get_db), + limit: int = Query(25, ge=1, le=100), + offset: int = Query(0, ge=0), +): + total = await db.scalar(select(func.count()).select_from(Conversation)) + result = await db.execute( + select(Conversation).order_by(Conversation.updated_at.desc()).limit(limit).offset(offset) + ) + return Page( + items=list(result.scalars().all()), + total=total or 0, + limit=limit, + offset=offset, + ) + + +@router.post("", response_model=ConversationRead, status_code=201) +async def create_conversation(db: AsyncSession = Depends(get_db)): + conversation = Conversation() + db.add(conversation) + await db.commit() + await db.refresh(conversation) + return conversation + + +@router.get("/{conversation_id}", response_model=ConversationDetail) +async def get_conversation(conversation_id: UUID, db: AsyncSession = Depends(get_db)): + result = await db.execute( + select(Conversation) + .where(Conversation.id == conversation_id) + .options(selectinload(Conversation.messages).selectinload(Message.artifacts)) + ) + conversation = result.scalar_one_or_none() + if conversation is None: + raise HTTPException(status_code=404, detail="Conversation not found.") + return { + "id": conversation.id, + "title": conversation.title, + "created_at": conversation.created_at, + "updated_at": conversation.updated_at, + "messages": [chat.serialize_message(m) for m in conversation.messages], + } + + +@router.get("/{conversation_id}/messages", response_model=Page[MessageRead]) +async def list_messages( + conversation_id: UUID, + db: AsyncSession = Depends(get_db), + limit: int = Query(10, ge=1, le=100), + offset: int = Query(0, ge=0), +): + if await db.get(Conversation, conversation_id) is None: + raise HTTPException(status_code=404, detail="Conversation not found.") + + where_conv = conversation_messages.c.conversation_id == conversation_id + total = await db.scalar( + select(func.count()) + .select_from(conversation_messages) + .where(where_conv) + ) + result = await db.execute( + select(Message) + .join(conversation_messages, conversation_messages.c.message_id == Message.id) + .where(where_conv) + .options(selectinload(Message.artifacts)) + .order_by(Message.created_at.desc()) + .limit(limit) + .offset(offset) + ) + return Page( + items=[chat.serialize_message(m) for m in result.scalars().all()], + total=total or 0, + limit=limit, + offset=offset, + ) + + +@router.post("/{conversation_id}/messages") +async def send_message(conversation_id: UUID, body: SendMessageBody): + """Send a user message and stream the agent's reply as Server-Sent Events. + + The conversation existence is checked up front (so a missing id fails with a + clean 404), then the run streams. Persistence (user message, assistant + message + artifacts) happens inside the stream via the chat service. + """ + if not body.message.strip(): + raise HTTPException(status_code=400, detail="Message must not be empty.") + + # Verify the conversation exists before committing to a streaming response, + # using a short-lived session (the stream owns its own session below). + async with db_session.new_session() as session: + if await session.get(Conversation, conversation_id) is None: + raise HTTPException(status_code=404, detail="Conversation not found.") + + async def event_stream() -> AsyncIterator[str]: + # A dedicated session for the whole stream: the request-scoped session + # from Depends() is already closed by the time this body runs. + async with db_session.new_session() as session: + conversation = await session.get( + Conversation, + conversation_id, + options=[selectinload(Conversation.messages)], + ) + if conversation is None: # deleted between the check and now + yield agent_events.to_sse(agent_events.error("Conversation not found.")) + yield agent_events.to_sse(agent_events.done()) + return + try: + async for event in chat.run_chat(session, conversation, body.message): + yield agent_events.to_sse(event) + yield agent_events.to_sse(agent_events.done()) + except Exception as exc: # surface failures to the client, then end + yield agent_events.to_sse(agent_events.error(str(exc))) + yield agent_events.to_sse(agent_events.done()) + + return StreamingResponse( + event_stream(), + media_type="text/event-stream", + headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"}, + ) + + +@router.delete("/{conversation_id}", status_code=204) +async def delete_conversation(conversation_id: UUID, db: AsyncSession = Depends(get_db)): + conversation = await db.get(Conversation, conversation_id) + if conversation is None: + raise HTTPException(status_code=404, detail="Conversation not found.") + await db.delete(conversation) + await db.commit() diff --git a/backend/app/api/csv_files.py b/backend/app/api/csv_files.py new file mode 100644 index 0000000..4a03d5c --- /dev/null +++ b/backend/app/api/csv_files.py @@ -0,0 +1,103 @@ +"""Routes for uploading and listing CSV files (stored on S3).""" + +import io +from uuid import UUID, uuid4 + +import pandas as pd +from fastapi import APIRouter, Depends, File, HTTPException, Query, UploadFile +from sqlalchemy import func, select +from sqlalchemy.ext.asyncio import AsyncSession + +from app.db.session import get_db +from app.models.csv_file import CsvFile +from app.schemas.csv_file import CsvFileRead +from app.schemas.pagination import Page +from app.services import storage + +router = APIRouter(prefix="/csv", tags=["csv"]) + + +@router.get("", response_model=Page[CsvFileRead]) +async def list_csv_files( + db: AsyncSession = Depends(get_db), + limit: int = Query(25, ge=1, le=100), + offset: int = Query(0, ge=0), +): + total = await db.scalar(select(func.count()).select_from(CsvFile)) + result = await db.execute( + select(CsvFile).order_by(CsvFile.uploaded_at.desc()).limit(limit).offset(offset) + ) + return Page( + items=list(result.scalars().all()), + total=total or 0, + limit=limit, + offset=offset, + ) + + +@router.post("", response_model=list[CsvFileRead], status_code=201) +async def upload_csv_files( + files: list[UploadFile] = File(...), + db: AsyncSession = Depends(get_db), +): + """Upload one or more CSVs: store each on S3 and persist its metadata. + + The whole batch is validated before anything is persisted, so a single + invalid file rejects the request without leaving partial state behind. + """ + if not files: + raise HTTPException(status_code=400, detail="No files were uploaded.") + + # Validate and read every file up front so a single bad file rejects the + # whole batch before anything is written to S3 or Postgres. + bodies: list[tuple[str, bytes]] = [] + for file in files: + if not file.filename or not file.filename.lower().endswith(".csv"): + raise HTTPException( + status_code=400, detail="Only .csv files are accepted." + ) + body = await file.read() + if not body.strip(): + raise HTTPException( + status_code=400, detail=f"{file.filename} is empty." + ) + bodies.append((file.filename, body)) + + prepared: list[CsvFile] = [] + for filename, body in bodies: + # Best-effort row/column count (does not fail the upload if unparsable). + row_count: int | None = None + column_count: int | None = None + try: + df = pd.read_csv(io.BytesIO(body)) + row_count, column_count = int(df.shape[0]), int(df.shape[1]) + except Exception: + pass + + s3_key = f"{uuid4()}/{filename}" + storage.upload_csv(s3_key, body) + + csv_file = CsvFile( + filename=filename, + s3_key=s3_key, + size_bytes=len(body), + row_count=row_count, + column_count=column_count, + ) + db.add(csv_file) + prepared.append(csv_file) + + await db.commit() + for csv_file in prepared: + await db.refresh(csv_file) + return prepared + + +@router.delete("/{csv_id}", status_code=204) +async def delete_csv_file(csv_id: UUID, db: AsyncSession = Depends(get_db)): + csv_file = await db.get(CsvFile, csv_id) + if csv_file is None: + raise HTTPException(status_code=404, detail="CSV not found.") + storage.delete_object(csv_file.s3_key) + await db.delete(csv_file) + await db.commit() diff --git a/backend/app/core/config.py b/backend/app/core/config.py new file mode 100644 index 0000000..66d6eb2 --- /dev/null +++ b/backend/app/core/config.py @@ -0,0 +1,55 @@ +"""Application configuration loaded from environment variables.""" + +from functools import lru_cache + +from pydantic import Field, computed_field +from pydantic_settings import BaseSettings, SettingsConfigDict + + +class Settings(BaseSettings): + model_config = SettingsConfigDict(env_file=".env", extra="ignore") + + # --- App --- + app_name: str = "Data Analysis Agent API" + cors_origins: list[str] = ["http://localhost:5173"] + + # --- Database (Postgres) --- + # Same model as docker-compose: the DSN is built from the POSTGRES_* parts. + # A full DATABASE_URL, if provided, takes precedence over the parts. + postgres_user: str = "postgres" + postgres_password: str = "postgres" + postgres_db: str = "app" + postgres_host: str = "localhost" + postgres_port: int = 5432 + database_url_override: str | None = Field(default=None, alias="DATABASE_URL") + + @computed_field + @property + def database_url(self) -> str: + if self.database_url_override: + return self.database_url_override + return ( + f"postgresql+asyncpg://{self.postgres_user}:{self.postgres_password}" + f"@{self.postgres_host}:{self.postgres_port}/{self.postgres_db}" + ) + + # --- Object storage (S3 / MinIO) --- + s3_endpoint_url: str | None = "http://localhost:9000" # None => real AWS S3 + s3_region: str = "us-east-1" + s3_access_key: str = "minioadmin" + s3_secret_key: str = "minioadmin" + s3_bucket: str = "csv-uploads" + + # --- Agent / chat --- + # How many of the most recent messages are sent to the agent as context. + chat_history_window: int = 10 + # LLM model id (pydantic-ai form, e.g. "anthropic:claude-haiku-4-5-..."). + model: str = "anthropic:claude-haiku-4-5-20251001" + # Anthropic API key. Read from .env / environment; injected into the agent's + # provider explicitly (rather than relying on a global os.environ). + anthropic_api_key: str | None = None + + +@lru_cache +def get_settings() -> Settings: + return Settings() diff --git a/backend/app/db/base.py b/backend/app/db/base.py new file mode 100644 index 0000000..3ec1d4d --- /dev/null +++ b/backend/app/db/base.py @@ -0,0 +1,16 @@ +"""SQLAlchemy declarative base + naming conventions for clean migrations.""" + +from sqlalchemy import MetaData +from sqlalchemy.orm import DeclarativeBase + +# Consistent constraint naming so Alembic autogenerate produces stable names. +NAMING_CONVENTION = { + "ix": "ix_%(column_0_label)s", + "uq": "uq_%(table_name)s_%(column_0_name)s", + "fk": "fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s", + "pk": "pk_%(table_name)s", +} + + +class Base(DeclarativeBase): + metadata = MetaData(naming_convention=NAMING_CONVENTION) diff --git a/backend/app/db/session.py b/backend/app/db/session.py new file mode 100644 index 0000000..11b4faf --- /dev/null +++ b/backend/app/db/session.py @@ -0,0 +1,34 @@ +"""Async database engine and session factory.""" + +from collections.abc import AsyncGenerator + +from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine + +from app.core.config import get_settings + +settings = get_settings() + +engine = create_async_engine(settings.database_url, echo=False, future=True) + +AsyncSessionLocal = async_sessionmaker( + bind=engine, + class_=AsyncSession, + expire_on_commit=False, + autoflush=False, +) + + +async def get_db() -> AsyncGenerator[AsyncSession, None]: + """FastAPI dependency yielding an async DB session.""" + async with AsyncSessionLocal() as session: + yield session + + +def new_session() -> AsyncSession: + """Open a standalone session outside the request/response cycle. + + Used by the streaming chat endpoint, whose generator runs after the route + has returned (so the Depends()-injected session is already closed). Tests + override this to bind to their NullPool engine. + """ + return AsyncSessionLocal() diff --git a/backend/app/main.py b/backend/app/main.py new file mode 100644 index 0000000..aca2e0f --- /dev/null +++ b/backend/app/main.py @@ -0,0 +1,42 @@ +"""FastAPI application entry point.""" + +from contextlib import asynccontextmanager + +from fastapi import FastAPI +from fastapi.middleware.cors import CORSMiddleware + +from app.api import artifacts, conversations, csv_files +from app.core.config import get_settings +from app.services import storage + +settings = get_settings() + + +@asynccontextmanager +async def lifespan(app: FastAPI): + # Ensure the S3 bucket exists on startup (idempotent). + try: + storage.ensure_bucket() + except Exception as exc: # pragma: no cover - startup best effort + print(f"[startup] could not ensure S3 bucket: {exc}") + yield + + +app = FastAPI(title=settings.app_name, lifespan=lifespan) + +app.add_middleware( + CORSMiddleware, + allow_origins=settings.cors_origins, + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +app.include_router(csv_files.router, prefix="/api") +app.include_router(conversations.router, prefix="/api") +app.include_router(artifacts.router, prefix="/api") + + +@app.get("/api/health") +async def health(): + return {"status": "ok"} diff --git a/backend/app/models/__init__.py b/backend/app/models/__init__.py new file mode 100644 index 0000000..da80247 --- /dev/null +++ b/backend/app/models/__init__.py @@ -0,0 +1,14 @@ +"""SQLAlchemy models — imported here so Alembic autogenerate sees them all.""" + +from app.models.conversation import Conversation +from app.models.csv_file import CsvFile +from app.models.message import Message, conversation_messages +from app.models.message_artifact import MessageArtifact + +__all__ = [ + "Conversation", + "Message", + "CsvFile", + "MessageArtifact", + "conversation_messages", +] diff --git a/backend/app/models/conversation.py b/backend/app/models/conversation.py new file mode 100644 index 0000000..bfd339b --- /dev/null +++ b/backend/app/models/conversation.py @@ -0,0 +1,30 @@ +"""Conversation model — a chat thread shown in the sidebar history.""" + +from datetime import datetime +from uuid import UUID, uuid4 + +from sqlalchemy import DateTime, String, func +from sqlalchemy.orm import Mapped, mapped_column, relationship + +from app.db.base import Base +from app.models.message import conversation_messages + + +class Conversation(Base): + __tablename__ = "conversation" + + id: Mapped[UUID] = mapped_column(primary_key=True, default=uuid4) + title: Mapped[str] = mapped_column(String(255), default="New conversation") + created_at: Mapped[datetime] = mapped_column( + DateTime(timezone=True), server_default=func.now() + ) + updated_at: Mapped[datetime] = mapped_column( + DateTime(timezone=True), server_default=func.now(), onupdate=func.now() + ) + + # Many-to-many with Message via the association table. + messages: Mapped[list["Message"]] = relationship( # noqa: F821 + secondary=conversation_messages, + back_populates="conversations", + order_by="Message.created_at", + ) diff --git a/backend/app/models/csv_file.py b/backend/app/models/csv_file.py new file mode 100644 index 0000000..96420f7 --- /dev/null +++ b/backend/app/models/csv_file.py @@ -0,0 +1,26 @@ +"""CsvFile model — metadata for a CSV uploaded to S3.""" + +from datetime import datetime +from uuid import UUID, uuid4 + +from sqlalchemy import BigInteger, DateTime, Integer, String, func +from sqlalchemy.orm import Mapped, mapped_column + +from app.db.base import Base + + +class CsvFile(Base): + __tablename__ = "csv_file" + + id: Mapped[UUID] = mapped_column(primary_key=True, default=uuid4) + filename: Mapped[str] = mapped_column(String(255)) + # "upload" (user-provided) | "generated" (produced by an agent run). + source: Mapped[str] = mapped_column(String(16), default="upload") + # Object key in the S3 bucket. + s3_key: Mapped[str] = mapped_column(String(512), unique=True) + size_bytes: Mapped[int] = mapped_column(BigInteger, default=0) + row_count: Mapped[int | None] = mapped_column(Integer, nullable=True) + column_count: Mapped[int | None] = mapped_column(Integer, nullable=True) + uploaded_at: Mapped[datetime] = mapped_column( + DateTime(timezone=True), server_default=func.now() + ) diff --git a/backend/app/models/message.py b/backend/app/models/message.py new file mode 100644 index 0000000..29388fd --- /dev/null +++ b/backend/app/models/message.py @@ -0,0 +1,49 @@ +"""Message model + the association table linking it to conversations (M2M).""" + +from datetime import datetime +from uuid import UUID, uuid4 + +from sqlalchemy import Column, DateTime, ForeignKey, String, Table, Text, func +from sqlalchemy.orm import Mapped, mapped_column, relationship + +from app.db.base import Base + +# Association table for the Conversation <-> Message many-to-many relation. +conversation_messages = Table( + "conversation_messages", + Base.metadata, + Column( + "conversation_id", + ForeignKey("conversation.id", ondelete="CASCADE"), + primary_key=True, + ), + Column( + "message_id", + ForeignKey("message.id", ondelete="CASCADE"), + primary_key=True, + ), +) + + +class Message(Base): + __tablename__ = "message" + + id: Mapped[UUID] = mapped_column(primary_key=True, default=uuid4) + # "user" | "assistant" + role: Mapped[str] = mapped_column(String(32)) + content: Mapped[str] = mapped_column(Text, default="") + created_at: Mapped[datetime] = mapped_column( + DateTime(timezone=True), server_default=func.now() + ) + + conversations: Mapped[list["Conversation"]] = relationship( # noqa: F821 + secondary=conversation_messages, + back_populates="messages", + ) + + # Things an agent run produced for this (assistant) message, in order. + artifacts: Mapped[list["MessageArtifact"]] = relationship( # noqa: F821 + back_populates="message", + cascade="all, delete-orphan", + order_by="MessageArtifact.position", + ) diff --git a/backend/app/models/message_artifact.py b/backend/app/models/message_artifact.py new file mode 100644 index 0000000..2560e27 --- /dev/null +++ b/backend/app/models/message_artifact.py @@ -0,0 +1,52 @@ +"""MessageArtifact model — a single thing an agent run produced. + +An assistant Message carries 0..N artifacts (reasoning, SQL, query result, +figure, table) in order. Light payloads (sql, thinking) are stored inline in +`content`; heavy payloads (figure JSON, generated CSV) live on S3 and are +referenced by `s3_key`. A generated table also links to the CsvFile created +for it via `csv_file_id`. +""" + +from datetime import datetime +from uuid import UUID, uuid4 + +from sqlalchemy import DateTime, ForeignKey, Integer, String, Text, func +from sqlalchemy.dialects.postgresql import JSONB +from sqlalchemy.orm import Mapped, mapped_column, relationship + +from app.db.base import Base + +# Allowed artifact kinds (kept as a plain tuple — validated at the API layer). +ARTIFACT_KINDS = ("thinking", "sql", "query_result", "figure", "table") + + +class MessageArtifact(Base): + __tablename__ = "message_artifact" + + id: Mapped[UUID] = mapped_column(primary_key=True, default=uuid4) + message_id: Mapped[UUID] = mapped_column( + ForeignKey("message.id", ondelete="CASCADE"), index=True + ) + # One of ARTIFACT_KINDS. + kind: Mapped[str] = mapped_column(String(32)) + # Order of this artifact within its message. + position: Mapped[int] = mapped_column(Integer, default=0) + + # Light payloads stored inline (SQL text, thinking text, result preview). + content: Mapped[str | None] = mapped_column(Text, nullable=True) + # Heavy payloads on S3 (figure JSON, generated CSV). + s3_key: Mapped[str | None] = mapped_column(String(512), nullable=True) + # For a generated `table`, the CsvFile row created for it. + csv_file_id: Mapped[UUID | None] = mapped_column( + ForeignKey("csv_file.id", ondelete="SET NULL"), nullable=True + ) + # Free-form metadata: title, description, shape, etc. + meta: Mapped[dict | None] = mapped_column(JSONB, nullable=True) + + created_at: Mapped[datetime] = mapped_column( + DateTime(timezone=True), server_default=func.now() + ) + + message: Mapped["Message"] = relationship(back_populates="artifacts") # noqa: F821 + # The CsvFile created for a generated `table` artifact (if any). + csv_file: Mapped["CsvFile | None"] = relationship() # noqa: F821 diff --git a/backend/app/schemas/__init__.py b/backend/app/schemas/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/app/schemas/conversation.py b/backend/app/schemas/conversation.py new file mode 100644 index 0000000..5393afd --- /dev/null +++ b/backend/app/schemas/conversation.py @@ -0,0 +1,51 @@ +"""API schemas for conversations and messages.""" + +from datetime import datetime +from uuid import UUID + +from pydantic import BaseModel, ConfigDict + + +class ArtifactRead(BaseModel): + """An item an agent run produced, attached to an assistant message. + + Payload delivery by kind: + - sql / thinking / query_result: inline in `content`. + - figure: the Plotly JSON is inlined in `figure_json` (loading a + conversation needs no per-figure request). + - table: NOT inlined (can be large); the frontend previews from `meta` and + downloads via `GET /artifacts/{id}/content`. + """ + + model_config = ConfigDict(from_attributes=True) + + id: UUID + kind: str # thinking | sql | query_result | figure | table + position: int + content: str | None = None + figure_json: str | None = None + csv_file_id: UUID | None = None + meta: dict | None = None + + +class MessageRead(BaseModel): + model_config = ConfigDict(from_attributes=True) + + id: UUID + role: str + content: str + created_at: datetime + artifacts: list[ArtifactRead] = [] + + +class ConversationRead(BaseModel): + model_config = ConfigDict(from_attributes=True) + + id: UUID + title: str + created_at: datetime + updated_at: datetime + + +class ConversationDetail(ConversationRead): + messages: list[MessageRead] = [] diff --git a/backend/app/schemas/csv_file.py b/backend/app/schemas/csv_file.py new file mode 100644 index 0000000..de15f54 --- /dev/null +++ b/backend/app/schemas/csv_file.py @@ -0,0 +1,18 @@ +"""API schemas for CSV files.""" + +from datetime import datetime +from uuid import UUID + +from pydantic import BaseModel, ConfigDict + + +class CsvFileRead(BaseModel): + model_config = ConfigDict(from_attributes=True) + + id: UUID + filename: str + source: str # "upload" | "generated" + size_bytes: int + row_count: int | None + column_count: int | None + uploaded_at: datetime diff --git a/backend/app/schemas/pagination.py b/backend/app/schemas/pagination.py new file mode 100644 index 0000000..3ae8a9a --- /dev/null +++ b/backend/app/schemas/pagination.py @@ -0,0 +1,16 @@ +"""Generic pagination envelope shared by list endpoints.""" + +from typing import Generic, TypeVar + +from pydantic import BaseModel + +T = TypeVar("T") + + +class Page(BaseModel, Generic[T]): + """A page of results plus the metadata a client needs to load more.""" + + items: list[T] + total: int + limit: int + offset: int diff --git a/backend/app/services/__init__.py b/backend/app/services/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/app/services/agent/__init__.py b/backend/app/services/agent/__init__.py new file mode 100644 index 0000000..572d20b --- /dev/null +++ b/backend/app/services/agent/__init__.py @@ -0,0 +1,12 @@ +"""Data-analysis agent (the data scientist's territory). + +Migrated from the old root-level `agent/` package + CLI `main.py`. This subtree +owns the LLM agent: its prompt, tools, dependency context, and the runner that +loads datasets and streams a run. The rest of the backend treats it as a service +and only depends on its public entry points. +""" + +from app.services.agent.agent import create_agent +from app.services.agent.context import AgentContext + +__all__ = ["create_agent", "AgentContext"] diff --git a/agent/agent.py b/backend/app/services/agent/agent.py similarity index 56% rename from agent/agent.py rename to backend/app/services/agent/agent.py index fcefe8c..2a77fda 100644 --- a/agent/agent.py +++ b/backend/app/services/agent/agent.py @@ -1,19 +1,16 @@ -import os - from pydantic_ai import Agent -from agent.context import AgentContext -from agent.prompt import get_system_prompt -from agent.tools.query_data import query_data -from agent.tools.visualize import visualize +from app.services.agent.context import AgentContext +from app.services.agent.model import get_model +from app.services.agent.prompt import get_system_prompt +from app.services.agent.tools.query_data import query_data +from app.services.agent.tools.visualize import visualize def create_agent(dataset_info: str) -> Agent[AgentContext]: """Create the data analysis agent with query and visualization tools.""" - model = os.getenv("MODEL", "anthropic:claude-haiku-4-5-20251001") - agent: Agent[AgentContext] = Agent( - model=model, + model=get_model(), deps_type=AgentContext, system_prompt=get_system_prompt(dataset_info), retries=3, diff --git a/backend/app/services/agent/context.py b/backend/app/services/agent/context.py new file mode 100644 index 0000000..1f62589 --- /dev/null +++ b/backend/app/services/agent/context.py @@ -0,0 +1,29 @@ +from dataclasses import dataclass, field +from typing import Optional + +import pandas as pd + + +@dataclass +class Artifact: + """A visual artifact produced by the `visualize` tool, surfaced to the UI. + + kind="figure": `payload` is Plotly figure JSON (a string). + kind="table": `payload` is CSV text (a string). + """ + + kind: str # "figure" | "table" + title: str + payload: str + + +@dataclass +class AgentContext: + """Context injected into all agent tools via PydanticAI dependency injection.""" + + datasets: dict[str, pd.DataFrame] = field(default_factory=dict) + dataset_info: str = "" + current_dataframe: Optional[pd.DataFrame] = None + # Artifacts produced during the run (charts / tables), in order. The runner + # drains these to emit structured streaming events to the frontend. + artifacts: list[Artifact] = field(default_factory=list) diff --git a/backend/app/services/agent/datasets.py b/backend/app/services/agent/datasets.py new file mode 100644 index 0000000..f0681c6 --- /dev/null +++ b/backend/app/services/agent/datasets.py @@ -0,0 +1,61 @@ +"""Dataset loading for the agent. + +Datasets live as CSV objects on S3/MinIO (the user's uploads). This module turns +a list of (filename, s3_key) references into the in-memory DataFrames the agent +queries, plus the `dataset_info` string injected into its system prompt. + +It deliberately knows nothing about the database: callers pass the CSV +references they fetched, keeping this (the data scientist's territory) free of +SQLAlchemy. Downloading goes through the shared storage service so there is a +single S3 client in the codebase. +""" + +import io +import re + +import pandas as pd + +from app.services import storage + + +def table_name(filename: str) -> str: + """Derive a valid SQL table name from a filename (mirrors the old CLI).""" + stem = filename + if stem.lower().endswith(".csv"): + stem = stem[:-4] + return re.sub(r"[^a-zA-Z0-9_]", "_", stem).strip("_").lower() or "dataset" + + +def load_datasets(refs: list[tuple[str, str]]) -> dict[str, pd.DataFrame]: + """Download and parse every CSV reference into a {table_name: DataFrame} dict. + + Args: + refs: list of (filename, s3_key) tuples. A file that fails to download + or parse is skipped (logged), never crashing the whole run. + + Later refs win on a table-name clash, so names stay unique. + """ + datasets: dict[str, pd.DataFrame] = {} + for filename, s3_key in refs: + try: + body = storage.download_object(s3_key) + df = pd.read_csv(io.BytesIO(body)) + except Exception as exc: # noqa: BLE001 - best effort per file + print(f"[datasets] skipping {filename!r} ({s3_key}): {exc}") + continue + datasets[table_name(filename)] = df + return datasets + + +def describe(datasets: dict[str, pd.DataFrame]) -> str: + """Build the `dataset_info` string fed to the agent's system prompt.""" + if not datasets: + return "No datasets available." + lines: list[str] = [] + for name, df in datasets.items(): + cols = ", ".join(df.columns.tolist()) + lines.append( + f"- **{name}** ({df.shape[0]} rows, {df.shape[1]} columns)\n" + f" Columns: {cols}" + ) + return "\n".join(lines) diff --git a/backend/app/services/agent/events.py b/backend/app/services/agent/events.py new file mode 100644 index 0000000..1417176 --- /dev/null +++ b/backend/app/services/agent/events.py @@ -0,0 +1,83 @@ +"""Structured events streamed from an agent run to the frontend. + +The runner ([runner.py]) yields these as plain dicts; the API layer serializes +them as Server-Sent Events. Keeping the shapes here gives the frontend a single +contract to render against (reasoning, SQL, tool results, charts, tables, the +final answer). +""" + +from __future__ import annotations + +from typing import Any, Literal, TypedDict + +# Event type discriminator sent as the SSE `event:` field and the `type` key. +EventType = Literal[ + "reasoning", # delta of the agent's reasoning + "text", # delta of the final answer text + "tool_call", # the agent invoked a tool (query_data / visualize) + "tool_result", # a tool returned (summary string) + "artifact", # a figure or table was produced + "title", # an auto-generated conversation title (new conversations) + "done", # the run finished cleanly + "error", # the run failed +] + + +class Event(TypedDict, total=False): + type: EventType + # reasoning / text + delta: str + # tool_call + tool: str + args: dict[str, Any] + # tool_result + summary: str + # artifact + kind: str # "figure" | "table" + title: str + figure_json: str # Plotly JSON, inlined for figures (live rendering) + artifact_id: str # MessageArtifact id (set once persisted) + csv_file_id: str # for generated tables + # title + value: str + # error + message: str + + +def reasoning(delta: str) -> Event: + return {"type": "reasoning", "delta": delta} + + +def text(delta: str) -> Event: + return {"type": "text", "delta": delta} + + +def tool_call(tool: str, args: dict[str, Any]) -> Event: + return {"type": "tool_call", "tool": tool, "args": args} + + +def tool_result(tool: str, summary: str) -> Event: + return {"type": "tool_result", "tool": tool, "summary": summary} + + +def title(value: str) -> Event: + return {"type": "title", "value": value} + + +def done() -> Event: + return {"type": "done"} + + +def error(message: str) -> Event: + return {"type": "error", "message": message} + + +def to_sse(event: Event) -> str: + """Serialize an event as a Server-Sent Events frame. + + The event type goes in the `event:` field and the full payload (JSON) in + `data:`, so the frontend can dispatch on either. + """ + import json + + return f"event: {event['type']}\ndata: {json.dumps(event)}\n\n" diff --git a/backend/app/services/agent/model.py b/backend/app/services/agent/model.py new file mode 100644 index 0000000..e0986a5 --- /dev/null +++ b/backend/app/services/agent/model.py @@ -0,0 +1,31 @@ +"""Resolve the configured LLM model with credentials injected explicitly. + +The API key comes from Settings (read from .env by pydantic-settings), not from +a global os.environ. For Anthropic models we build an AnthropicModel whose +provider carries the key; any other provider string is passed through unchanged +so pydantic-ai resolves it the usual way. +""" + +from pydantic_ai.models import Model +from pydantic_ai.models.anthropic import AnthropicModel +from pydantic_ai.providers.anthropic import AnthropicProvider + +from app.core.config import get_settings + + +def get_model() -> Model | str: + """Return a configured Model (Anthropic, key injected) or a model string.""" + settings = get_settings() + model = settings.model + + if model.startswith("anthropic:"): + name = model.split(":", 1)[1] + provider = ( + AnthropicProvider(api_key=settings.anthropic_api_key) + if settings.anthropic_api_key + else AnthropicProvider() # falls back to ANTHROPIC_API_KEY in env + ) + return AnthropicModel(name, provider=provider) + + # Non-Anthropic providers: let pydantic-ai resolve the string itself. + return model diff --git a/agent/prompt.py b/backend/app/services/agent/prompt.py similarity index 100% rename from agent/prompt.py rename to backend/app/services/agent/prompt.py diff --git a/backend/app/services/agent/runner.py b/backend/app/services/agent/runner.py new file mode 100644 index 0000000..1eb2f9f --- /dev/null +++ b/backend/app/services/agent/runner.py @@ -0,0 +1,169 @@ +"""Run the agent and stream structured events. + +Uses pydantic-ai's `agent.iter()` to walk the run graph node by node so we can +surface what the agent is doing in real time: + +- text deltas, split into `reasoning` (inside ) and `text` (the answer) +- `tool_call` when query_data / visualize is invoked (carries the SQL or viz code) +- `tool_result` when a tool returns +- `artifact` when the visualize tool produced a figure or table + +The agent itself (prompt, tools, model) is untouched — this is pure observation. +""" + +from __future__ import annotations + +from collections.abc import AsyncIterator, Sequence + +from pydantic_ai import Agent +from pydantic_ai.messages import ( + FunctionToolCallEvent, + FunctionToolResultEvent, + ModelMessage, + PartDeltaEvent, + PartStartEvent, + TextPart, + TextPartDelta, + ThinkingPart, + ThinkingPartDelta, + ToolCallPart, +) + +from app.services.agent.context import AgentContext, Artifact +from app.services.agent.events import ( + Event, + reasoning, + text, + tool_call, + tool_result, +) + + +class _ThinkingSplitter: + """Split a stream of text deltas into `reasoning` vs `text` events. + + The system prompt wraps reasoning in .... We track + whether we're inside such a block across deltas (tags can land on chunk + boundaries) and tag each piece accordingly so the frontend can render the + reasoning trace separately from the final answer. + """ + + OPEN = "" + CLOSE = "" + + def __init__(self) -> None: + self._buf = "" + self._inside = False + + def feed(self, delta: str) -> list[Event]: + self._buf += delta + out: list[Event] = [] + # Process the buffer up to the point where a partial tag might still be + # forming at the tail, so we never split a tag across two emissions. + while self._buf: + tag = self.CLOSE if self._inside else self.OPEN + idx = self._buf.find(tag) + if idx == -1: + # No full tag. Hold back a tail that could be a partial tag. + keep = self._partial_tail_len(self._buf, tag) + emit, self._buf = self._buf[: len(self._buf) - keep], self._buf[len(self._buf) - keep :] + if emit: + out.append(reasoning(emit) if self._inside else text(emit)) + break + # Emit everything before the tag, then flip state and drop the tag. + emit = self._buf[:idx] + if emit: + out.append(reasoning(emit) if self._inside else text(emit)) + self._buf = self._buf[idx + len(tag) :] + self._inside = not self._inside + return out + + def flush(self) -> list[Event]: + out: list[Event] = [] + if self._buf: + out.append(reasoning(self._buf) if self._inside else text(self._buf)) + self._buf = "" + return out + + @staticmethod + def _partial_tail_len(buf: str, tag: str) -> int: + """Length of the longest suffix of `buf` that is a prefix of `tag`.""" + for k in range(min(len(tag) - 1, len(buf)), 0, -1): + if buf.endswith(tag[:k]): + return k + return 0 + + +async def stream_run( + agent: Agent[AgentContext], + prompt: str, + deps: AgentContext, + message_history: Sequence[ModelMessage] | None = None, +) -> AsyncIterator[Event]: + """Run the agent on `prompt`, yielding structured events as it progresses. + + The caller owns persistence: drain `deps.artifacts` / read the final output + from the events. This generator does not touch the database. + """ + splitter = _ThinkingSplitter() + emitted_artifacts = 0 + + def drain_artifacts() -> list[Event]: + nonlocal emitted_artifacts + events: list[Event] = [] + new: list[Artifact] = deps.artifacts[emitted_artifacts:] + for art in new: + event: Event = {"type": "artifact", "kind": art.kind, "title": art.title} + # Figures carry their Plotly JSON inline so the front renders them + # immediately; tables are downloaded on demand (payload omitted). + if art.kind == "figure": + event["figure_json"] = art.payload + events.append(event) + emitted_artifacts += len(new) + return events + + async with agent.iter(prompt, deps=deps, message_history=message_history) as run: + async for node in run: + if Agent.is_model_request_node(node): + # Stream the model's response: text/thinking deltas + tool calls. + async with node.stream(run.ctx) as stream: + async for event in stream: + if isinstance(event, PartStartEvent): + part = event.part + if isinstance(part, (TextPart, ThinkingPart)) and part.content: + is_think = isinstance(part, ThinkingPart) + for ev in ( + [reasoning(part.content)] if is_think + else splitter.feed(part.content) + ): + yield ev + elif isinstance(event, PartDeltaEvent): + delta = event.delta + if isinstance(delta, TextPartDelta) and delta.content_delta: + for ev in splitter.feed(delta.content_delta): + yield ev + elif isinstance(delta, ThinkingPartDelta) and delta.content_delta: + yield reasoning(delta.content_delta) + for ev in splitter.flush(): + yield ev + + elif Agent.is_call_tools_node(node): + # Stream tool invocations and their results. + async with node.stream(run.ctx) as stream: + async for event in stream: + if isinstance(event, FunctionToolCallEvent): + call: ToolCallPart = event.part + args = call.args_as_dict() if hasattr(call, "args_as_dict") else {} + yield tool_call(call.tool_name, args) + elif isinstance(event, FunctionToolResultEvent): + result_part = event.part + content = getattr(result_part, "content", "") + tool_name = getattr(result_part, "tool_name", "") or "" + yield tool_result(tool_name, str(content)) + # A tool may have produced an artifact (visualize). + for ev in drain_artifacts(): + yield ev + + # Final safety drain in case artifacts arrived after the last result event. + for ev in drain_artifacts(): + yield ev diff --git a/backend/app/services/agent/title.py b/backend/app/services/agent/title.py new file mode 100644 index 0000000..f754729 --- /dev/null +++ b/backend/app/services/agent/title.py @@ -0,0 +1,34 @@ +"""Generate a short conversation title from the first user message. + +A lightweight, tool-free LLM call (separate from the data-analysis agent) so a +brand-new conversation gets a meaningful sidebar label instead of the default +"New conversation". Lives in the agent package as it's a prompt/model concern. +""" + +from pydantic_ai import Agent + +from app.services.agent.model import get_model + +_TITLE_SYSTEM_PROMPT = ( + "You write very short, descriptive titles for a data-analysis chat. " + "Given the user's first message, reply with a title of at most 6 words. " + "No quotes, no trailing punctuation, no preamble — just the title." +) + +# Default fallback if the model call fails or returns nothing usable. +DEFAULT_TITLE = "New conversation" +MAX_TITLE_LEN = 80 + + +async def generate_title(first_message: str) -> str: + """Return a concise title for a conversation, or a sensible fallback.""" + agent: Agent[None] = Agent(model=get_model(), system_prompt=_TITLE_SYSTEM_PROMPT) + try: + result = await agent.run(first_message) + title = (result.output or "").strip().strip('"').strip() + except Exception as exc: # noqa: BLE001 - titling is best effort + print(f"[title] generation failed: {exc}") + return DEFAULT_TITLE + if not title: + return DEFAULT_TITLE + return title[:MAX_TITLE_LEN] diff --git a/backend/app/services/agent/tools/__init__.py b/backend/app/services/agent/tools/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/agent/tools/query_data.py b/backend/app/services/agent/tools/query_data.py similarity index 95% rename from agent/tools/query_data.py rename to backend/app/services/agent/tools/query_data.py index 5b60bc2..08d6e68 100644 --- a/agent/tools/query_data.py +++ b/backend/app/services/agent/tools/query_data.py @@ -1,7 +1,7 @@ import duckdb from pydantic_ai import RunContext -from agent.context import AgentContext +from app.services.agent.context import AgentContext async def query_data( diff --git a/agent/tools/visualize.py b/backend/app/services/agent/tools/visualize.py similarity index 78% rename from agent/tools/visualize.py rename to backend/app/services/agent/tools/visualize.py index b82a2f4..d724e24 100644 --- a/agent/tools/visualize.py +++ b/backend/app/services/agent/tools/visualize.py @@ -1,13 +1,12 @@ -import os -import re from typing import Literal import pandas as pd import plotly.express as px import plotly.graph_objects as go +import plotly.io as pio from pydantic_ai import RunContext -from agent.context import AgentContext +from app.services.agent.context import AgentContext, Artifact async def visualize( @@ -43,20 +42,19 @@ async def visualize( } exec(code, namespace) - safe_title = re.sub(r"[^\w\s-]", "", title).strip().replace(" ", "_").lower() - os.makedirs("output", exist_ok=True) - if result_type == "figure": fig = namespace.get("fig") if fig is None: return "Error: Code must create a 'fig' variable (plotly Figure)." - filepath = f"output/{safe_title}.html" - fig.write_html(filepath) + # Capture the figure as JSON so the frontend can render it with + # Plotly directly (no file on disk). + ctx.deps.artifacts.append( + Artifact(kind="figure", title=title, payload=pio.to_json(fig)) + ) return ( f"Figure created: {title}\n" - f"Saved to: {filepath}\n" f"Type: {type(fig).__name__}\n" f"Traces: {len(fig.data)}" ) @@ -64,12 +62,13 @@ async def visualize( elif result_type == "table": result = namespace.get("result", df) - filepath = f"output/{safe_title}.csv" - result.to_csv(filepath, index=False) + # Capture the table as CSV text for download / preview in the UI. + ctx.deps.artifacts.append( + Artifact(kind="table", title=title, payload=result.to_csv(index=False)) + ) return ( f"Table created: {title}\n" - f"Saved to: {filepath}\n" f"Shape: {result.shape[0]} rows x {result.shape[1]} columns\n" f"Preview:\n{result.head(10).to_string(index=False)}" ) diff --git a/backend/app/services/chat.py b/backend/app/services/chat.py new file mode 100644 index 0000000..a2f763b --- /dev/null +++ b/backend/app/services/chat.py @@ -0,0 +1,255 @@ +"""Chat orchestration: bridge the DB/persistence world and the agent runner. + +This is where the database lives (the agent package stays DB-free). It loads the +datasets, builds the agent + conversation history, streams the run, and persists +the assistant message together with its artifacts (figures/tables on S3, a +CsvFile row for each generated table). +""" + +from __future__ import annotations + +import io +from collections.abc import AsyncIterator +from dataclasses import dataclass +from uuid import UUID, uuid4 + +import pandas as pd +from pydantic_ai.messages import ( + ModelMessage, + ModelRequest, + ModelResponse, + TextPart, + UserPromptPart, +) +from sqlalchemy import select +from sqlalchemy.ext.asyncio import AsyncSession + +from app.core.config import get_settings +from app.models.conversation import Conversation +from app.models.csv_file import CsvFile +from app.models.message import Message +from app.models.message_artifact import MessageArtifact +from app.services import storage +from app.services.agent import AgentContext, create_agent +from app.services.agent import events as agent_events +from app.services.agent.datasets import describe, load_datasets +from app.services.agent.events import Event +from app.services.agent.runner import stream_run +from app.services.agent.title import generate_title + +settings = get_settings() + + +def serialize_artifact(artifact: MessageArtifact) -> dict: + """Build the API representation of an artifact, inlining figure JSON. + + Figures are small enough to embed (so a conversation loads in one request); + their Plotly JSON is fetched from S3 here. Tables are left for on-demand + download via the artifact content endpoint. + """ + figure_json = None + if artifact.kind == "figure" and artifact.s3_key: + try: + figure_json = storage.download_object(artifact.s3_key).decode() + except Exception as exc: # noqa: BLE001 - a missing figure shouldn't 500 + print(f"[chat] could not load figure {artifact.id}: {exc}") + return { + "id": artifact.id, + "kind": artifact.kind, + "position": artifact.position, + "content": artifact.content, + "figure_json": figure_json, + "csv_file_id": artifact.csv_file_id, + "meta": artifact.meta, + } + + +def serialize_message(message: Message) -> dict: + """Build the API representation of a message including its artifacts.""" + return { + "id": message.id, + "role": message.role, + "content": message.content, + "created_at": message.created_at, + "artifacts": [serialize_artifact(a) for a in message.artifacts], + } + + +async def _load_dataset_context() -> AgentContext: + """Fetch every uploaded CSV from the DB + S3 and build the agent context.""" + # A fresh short-lived session: dataset loading is read-only and independent + # of the request/stream session lifecycle. + from app.db.session import new_session + + async with new_session() as session: + rows = ( + await session.execute(select(CsvFile.filename, CsvFile.s3_key)) + ).all() + refs = [(filename, s3_key) for filename, s3_key in rows] + datasets = load_datasets(refs) + info = describe(datasets) + return AgentContext(datasets=datasets, dataset_info=info) + + +def _build_history(messages: list[Message]) -> list[ModelMessage]: + """Turn stored messages (oldest→newest) into pydantic-ai message history.""" + history: list[ModelMessage] = [] + for msg in messages: + if msg.role == "user": + history.append(ModelRequest(parts=[UserPromptPart(content=msg.content)])) + else: + history.append(ModelResponse(parts=[TextPart(content=msg.content)])) + return history + + +async def _recent_messages(session: AsyncSession, conversation_id: UUID) -> list[Message]: + """The most recent N messages of a conversation, returned oldest→newest.""" + from app.models.message import conversation_messages + + window = settings.chat_history_window + result = await session.execute( + select(Message) + .join(conversation_messages, conversation_messages.c.message_id == Message.id) + .where(conversation_messages.c.conversation_id == conversation_id) + .order_by(Message.created_at.desc()) + .limit(window) + ) + return list(reversed(result.scalars().all())) + + +@dataclass +class _ArtifactSpec: + """An artifact to persist, collected from the event stream in order. + + `payload` holds heavy content (figure JSON / generated CSV) for figure/table + kinds; `content` holds light inline text (sql / thinking / query_result). + """ + + kind: str + title: str = "" + content: str | None = None + payload: str | None = None + + +async def _persist_assistant( + session: AsyncSession, + conversation: Conversation, + text: str, + specs: list[_ArtifactSpec], +) -> Message: + """Persist the assistant message + its artifacts (S3 + CsvFile as needed).""" + assistant = Message(role="assistant", content=text) + conversation.messages.append(assistant) + await session.flush() # assign assistant.id + + for position, spec in enumerate(specs): + ma = MessageArtifact( + message_id=assistant.id, + kind=spec.kind, + position=position, + content=spec.content, + meta={"title": spec.title} if spec.title else None, + ) + if spec.kind == "figure" and spec.payload is not None: + # Heavy payload (Plotly JSON) → S3. + key = f"artifacts/{assistant.id}/{uuid4()}.json" + storage.upload_object(key, spec.payload.encode(), "application/json") + ma.s3_key = key + elif spec.kind == "table" and spec.payload is not None: + # Generated CSV → S3 + a CsvFile row (source=generated), reusable. + body = spec.payload.encode() + filename = f"{spec.title or 'table'}.csv" + key = f"artifacts/{assistant.id}/{uuid4()}.csv" + storage.upload_object(key, body, "text/csv") + ma.s3_key = key + try: + df = pd.read_csv(io.BytesIO(body)) + rows, cols = int(df.shape[0]), int(df.shape[1]) + except Exception: + rows = cols = None + ma.meta = {"title": spec.title, "shape": f"{rows}×{cols}" if rows is not None else None} + csv_file = CsvFile( + filename=filename, + source="generated", + s3_key=key, + size_bytes=len(body), + row_count=rows, + column_count=cols, + ) + session.add(csv_file) + await session.flush() + ma.csv_file_id = csv_file.id + session.add(ma) + + await session.commit() + return assistant + + +async def run_chat( + session: AsyncSession, + conversation: Conversation, + user_message: str, +) -> AsyncIterator[Event]: + """Persist the user message, stream the agent run, persist the assistant reply. + + Yields structured events for SSE; the caller serializes them. The user + message is persisted before streaming so a dropped connection still records + the question. + """ + # A brand-new conversation (no prior messages) gets an auto-generated title. + is_new = len(conversation.messages) == 0 + + # 1. Persist the user message. + user_msg = Message(role="user", content=user_message) + conversation.messages.append(user_msg) + await session.commit() + + # 1b. For a new conversation, generate + persist a title and announce it. + if is_new: + new_title = await generate_title(user_message) + conversation.title = new_title + await session.commit() + yield agent_events.title(new_title) + + # 2. Build context: datasets + recent history (excludes the message we just + # added — it becomes the prompt). + ctx = await _load_dataset_context() + recent = await _recent_messages(session, conversation.id) + history = _build_history([m for m in recent if m.id != user_msg.id]) + agent = create_agent(ctx.dataset_info) + + # 3. Stream the run, accumulating the final answer text AND the ordered + # timeline of artifacts to persist (reasoning, SQL, query results, + # figures, tables) so a reloaded conversation shows the same steps. + final_text_parts: list[str] = [] + specs: list[_ArtifactSpec] = [] + artifact_cursor = 0 # next unconsumed entry in ctx.artifacts (figures/tables) + + async for event in stream_run(agent, user_message, ctx, message_history=history or None): + etype = event["type"] + if etype == "reasoning": + # Coalesce consecutive reasoning deltas into one thinking artifact. + if specs and specs[-1].kind == "thinking": + specs[-1].content = (specs[-1].content or "") + event.get("delta", "") + else: + specs.append(_ArtifactSpec(kind="thinking", content=event.get("delta", ""))) + elif etype == "tool_call": + if event.get("tool") == "query_data": + specs.append(_ArtifactSpec(kind="sql", content=str(event["args"].get("sql", "")))) + elif etype == "tool_result": + if event.get("tool") == "query_data" and event.get("summary"): + specs.append(_ArtifactSpec(kind="query_result", content=event["summary"])) + elif etype == "artifact": + # Pull the real payload from ctx.artifacts (events omit CSV bytes). + if artifact_cursor < len(ctx.artifacts): + art = ctx.artifacts[artifact_cursor] + artifact_cursor += 1 + specs.append( + _ArtifactSpec(kind=art.kind, title=art.title, payload=art.payload) + ) + elif etype == "text": + final_text_parts.append(event.get("delta", "")) + yield event + + # 4. Persist the assistant message + its full artifact timeline. + await _persist_assistant(session, conversation, "".join(final_text_parts), specs) diff --git a/backend/app/services/storage.py b/backend/app/services/storage.py new file mode 100644 index 0000000..2de60fd --- /dev/null +++ b/backend/app/services/storage.py @@ -0,0 +1,61 @@ +"""S3 / MinIO object storage client for CSV uploads.""" + +import boto3 +from botocore.client import Config +from botocore.exceptions import ClientError + +from app.core.config import get_settings + +settings = get_settings() + + +def get_s3_client(): + """Return a boto3 S3 client. Works with both MinIO and real AWS S3.""" + return boto3.client( + "s3", + endpoint_url=settings.s3_endpoint_url, # None => real AWS + region_name=settings.s3_region, + aws_access_key_id=settings.s3_access_key, + aws_secret_access_key=settings.s3_secret_key, + config=Config(signature_version="s3v4"), + ) + + +def ensure_bucket() -> None: + """Create the upload bucket if it does not exist (idempotent).""" + client = get_s3_client() + try: + client.head_bucket(Bucket=settings.s3_bucket) + except ClientError: + client.create_bucket(Bucket=settings.s3_bucket) + + +def upload_csv(key: str, body: bytes, content_type: str = "text/csv") -> None: + """Upload raw bytes to the bucket under the given key.""" + upload_object(key, body, content_type) + + +def upload_object(key: str, body: bytes, content_type: str = "application/octet-stream") -> None: + """Upload raw bytes to the bucket under the given key (any content type). + + Used for both CSV uploads and agent-generated artifacts (figure JSON, + generated CSVs). + """ + client = get_s3_client() + client.put_object( + Bucket=settings.s3_bucket, + Key=key, + Body=body, + ContentType=content_type, + ) + + +def download_object(key: str) -> bytes: + """Fetch the raw bytes stored under the given key.""" + client = get_s3_client() + return client.get_object(Bucket=settings.s3_bucket, Key=key)["Body"].read() + + +def delete_object(key: str) -> None: + client = get_s3_client() + client.delete_object(Bucket=settings.s3_bucket, Key=key) diff --git a/backend/pyproject.toml b/backend/pyproject.toml new file mode 100644 index 0000000..ccd691b --- /dev/null +++ b/backend/pyproject.toml @@ -0,0 +1,46 @@ +[project] +name = "case-fullstack-backend" +version = "0.1.0" +description = "Backend API for the data analysis agent web app" +requires-python = ">=3.12" +dependencies = [ + # Web framework + "fastapi>=0.115.0", + "uvicorn[standard]>=0.32.0", + "python-multipart>=0.0.12", # file uploads (CSV) + # Database (Postgres + async ORM + migrations) + "sqlalchemy[asyncio]>=2.0.36", + "asyncpg>=0.30.0", + "alembic>=1.14.0", + # Object storage (S3 / MinIO) + "boto3>=1.35.0", + # Config + "pydantic>=2.10.0", + "pydantic-settings>=2.6.0", + "python-dotenv>=1.0.0", + # Agent stack (reused later — folder agent/ untouched for now) + "pydantic-ai-slim[openai,anthropic]>=0.0.36", + "duckdb>=0.9.0", + "plotly>=5.0.0", + "pandas>=2.0.0", +] + +[dependency-groups] +dev = [ + "ruff>=0.8.0", + "pytest>=8.3.0", + "pytest-asyncio>=0.24.0", + "httpx>=0.28.0", # async test client for FastAPI +] + +[tool.pytest.ini_options] +asyncio_mode = "auto" +asyncio_default_fixture_loop_scope = "session" +testpaths = ["tests"] + +[tool.ruff] +line-length = 100 +target-version = "py312" + +[tool.uv] +package = false diff --git a/backend/pyrightconfig.json b/backend/pyrightconfig.json new file mode 100644 index 0000000..625ec59 --- /dev/null +++ b/backend/pyrightconfig.json @@ -0,0 +1,8 @@ +{ + "venvPath": ".", + "venv": ".venv", + "include": ["app", "alembic"], + "extraPaths": ["."], + "typeCheckingMode": "basic", + "reportMissingImports": "warning" +} diff --git a/backend/scripts/__init__.py b/backend/scripts/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/scripts/seed.py b/backend/scripts/seed.py new file mode 100644 index 0000000..748951e --- /dev/null +++ b/backend/scripts/seed.py @@ -0,0 +1,364 @@ +"""Seed the local dev database with realistic demo conversations + artifacts. + +Creates a small set of demo conversations whose assistant turns carry a full +agent process — reasoning, SQL, an interactive Plotly figure, and (sometimes) a +generated CSV table — so the chat UI can be built and reviewed without running a +live agent. Real Plotly JSON and generated CSVs are uploaded to MinIO/S3 so the +figures render and the table downloads work end to end. + +Also seeds a handful of `upload`-source CsvFile rows (datasets the user +"uploaded"), with real bytes on S3 so they're consistent with the rest. + +Usage (from the repo root): + make seed + # or: + cd backend && uv run python -m scripts.seed + # wipe existing rows first: + cd backend && uv run python -m scripts.seed --reset + +DEV-only helper. Refuses to run unless the database looks local, and needs +MinIO/S3 reachable (the dev stack's `minio` service). +""" + +import argparse +import asyncio +from datetime import datetime, timedelta, timezone +from uuid import uuid4 + +import pandas as pd +import plotly.express as px +import plotly.io as pio +from sqlalchemy import delete, func, select + +from app.core.config import get_settings +from app.db.session import AsyncSessionLocal +from app.models.conversation import Conversation +from app.models.csv_file import CsvFile +from app.models.message import Message, conversation_messages +from app.models.message_artifact import MessageArtifact +from app.services import storage + +# A fixed base time keeps generated timestamps reproducible across runs. +BASE = datetime(2026, 1, 1, tzinfo=timezone.utc) + + +# --------------------------------------------------------------------------- +# Demo content +# --------------------------------------------------------------------------- +# Small in-memory datasets the demo answers are built from. Used to produce +# real Plotly figures and CSV exports. +def _sales_df() -> pd.DataFrame: + return pd.DataFrame( + { + "region": ["North", "South", "East", "West"], + "total_sales": [12000, 18500, 9300, 22100], + } + ) + + +def _mau_df() -> pd.DataFrame: + return pd.DataFrame( + { + "month": ["Jan", "Feb", "Mar", "Apr", "May", "Jun"], + "active_users": [1200, 1450, 1600, 1580, 1900, 2300], + } + ) + + +def _churn_df() -> pd.DataFrame: + return pd.DataFrame( + { + "plan": ["Basic", "Pro", "Enterprise"], + "churn_rate": [0.18, 0.09, 0.04], + } + ) + + +def _figure_json(kind: str, df: pd.DataFrame, title: str) -> str: + """Build a real Plotly figure and return it as JSON (string).""" + if kind == "bar": + fig = px.bar(df, x=df.columns[0], y=df.columns[1], title=title) + elif kind == "line": + fig = px.line(df, x=df.columns[0], y=df.columns[1], markers=True, title=title) + else: # pie + fig = px.pie(df, names=df.columns[0], values=df.columns[1], title=title) + fig.update_layout(template="plotly_white") + return pio.to_json(fig) + + +# Each demo conversation is a list of (user_prompt, assistant_answer, steps), +# where steps describe the artifacts to attach to the assistant message. +# A step is (kind, content_or_None, figure_spec_or_None, table_df_or_None, title). +def _demo_conversations() -> list[dict]: + sales, mau, churn = _sales_df(), _mau_df(), _churn_df() + return [ + { + "title": "Total sales by region", + "turns": [ + { + "user": "What are the total sales by region? Show a bar chart.", + "answer": ( + "The West region leads with $22.1k in sales, followed by " + "South ($18.5k). East trails at $9.3k." + ), + "steps": [ + ("thinking", "I need to aggregate sales by region, then plot a bar chart.", None, None, ""), + ("sql", "SELECT region, SUM(amount) AS total_sales\nFROM sales\nGROUP BY region\nORDER BY total_sales DESC", None, None, ""), + ("query_result", "4 rows × 2 columns. Columns: region, total_sales", None, None, ""), + ("figure", None, ("bar", sales), None, "Total sales by region"), + ], + }, + ], + }, + { + "title": "Monthly active users trend", + "turns": [ + { + "user": "Plot monthly active users over time.", + "answer": ( + "Monthly active users grew from 1,200 in January to 2,300 in " + "June — a steady upward trend with a small dip in April." + ), + "steps": [ + ("thinking", "Pull MAU per month ordered chronologically, then a line chart.", None, None, ""), + ("sql", "SELECT month, COUNT(DISTINCT user_id) AS active_users\nFROM events\nGROUP BY month\nORDER BY month", None, None, ""), + ("figure", None, ("line", mau), None, "Monthly active users"), + ], + }, + { + "user": "Export that as a CSV.", + "answer": "Here is the monthly active users table as a downloadable CSV.", + "steps": [ + ("thinking", "Return the MAU table as a CSV export.", None, None, ""), + ("table", None, None, mau, "monthly_active_users"), + ], + }, + ], + }, + { + "title": "Churn rate by plan", + "turns": [ + { + "user": "Which plan has the highest churn? Show a pie chart and export the data.", + "answer": ( + "The Basic plan has by far the highest churn at 18%, versus 9% " + "for Pro and just 4% for Enterprise." + ), + "steps": [ + ("thinking", "Compute churn per plan, visualize as a pie, then export the table.", None, None, ""), + ("sql", "SELECT plan, AVG(churned::int) AS churn_rate\nFROM subscriptions\nGROUP BY plan", None, None, ""), + ("figure", None, ("pie", churn), None, "Churn rate by plan"), + ("table", None, None, churn, "churn_by_plan"), + ], + }, + ], + }, + { + "title": "Dataset overview", + "turns": [ + { + "user": "How many rows are in the sales dataset?", + "answer": "The sales dataset has 4 aggregated regions covering all transactions.", + "steps": [ + ("thinking", "A simple COUNT(*) answers this.", None, None, ""), + ("sql", "SELECT COUNT(*) AS n FROM sales", None, None, ""), + ("query_result", "1 row × 1 column. Columns: n", None, None, ""), + ], + }, + ], + }, + ] + + +# Plain (artifact-free) conversations so "load older messages" pagination stays +# testable on at least one long thread. +N_PLAIN_CONVERSATIONS = 4 +N_PLAIN_MESSAGES = 60 +PLAIN_PROMPTS = [ + "What is the total revenue by region?", + "Show me the top 10 customers by spend.", + "Summarize the key trends in this file.", + "How many distinct products are there?", +] + +# Uploaded datasets (source=upload), with real CSV bytes on S3. +UPLOAD_DATASETS = { + "sales.csv": _sales_df, + "monthly_active_users.csv": _mau_df, + "subscriptions.csv": _churn_df, +} + + +def _guard_dev_database() -> None: + """Refuse to seed anything that doesn't look like a local dev/test DB.""" + url = get_settings().database_url + if not any(host in url for host in ("localhost", "127.0.0.1", "@db:")): + raise SystemExit( + f"Refusing to seed a non-local database:\n {url}\n" + "Point DATABASE_URL at a local dev DB before seeding." + ) + + +async def _reset(session) -> None: + """Delete all seeded rows (association + artifacts first to satisfy FKs).""" + await session.execute(delete(MessageArtifact)) + await session.execute(delete(conversation_messages)) + await session.execute(delete(Message)) + await session.execute(delete(Conversation)) + await session.execute(delete(CsvFile)) + await session.commit() + + +def _upload_csv_dataset(filename: str, df: pd.DataFrame, source: str) -> tuple[str, int, int, int]: + """Upload a CSV to S3, return (s3_key, size_bytes, rows, cols).""" + body = df.to_csv(index=False).encode() + key = f"{'uploads' if source == 'upload' else 'artifacts/seed'}/{uuid4()}/{filename}" + storage.upload_object(key, body, "text/csv") + return key, len(body), int(df.shape[0]), int(df.shape[1]) + + +async def _seed_uploaded_csvs(session) -> None: + for i, (filename, make_df) in enumerate(UPLOAD_DATASETS.items()): + key, size, rows, cols = _upload_csv_dataset(filename, make_df(), "upload") + session.add( + CsvFile( + filename=filename, + source="upload", + s3_key=key, + size_bytes=size, + row_count=rows, + column_count=cols, + uploaded_at=BASE + timedelta(minutes=i), + ) + ) + await session.commit() + print(f" ✓ {len(UPLOAD_DATASETS)} uploaded csv_file rows (bytes on S3)") + + +def _build_artifacts(session, steps: list[tuple], at: datetime) -> list[MessageArtifact]: + """Turn step specs into MessageArtifact rows, uploading heavy payloads.""" + artifacts: list[MessageArtifact] = [] + for position, (kind, content, figure_spec, table_df, title) in enumerate(steps): + ma = MessageArtifact(kind=kind, position=position, content=content) + if title: + ma.meta = {"title": title} + if kind == "figure" and figure_spec is not None: + fkind, df = figure_spec + key = f"artifacts/seed/{uuid4()}.json" + storage.upload_object(key, _figure_json(fkind, df, title).encode(), "application/json") + ma.s3_key = key + elif kind == "table" and table_df is not None: + key, size, rows, cols = _upload_csv_dataset(f"{title}.csv", table_df, "generated") + ma.s3_key = key + ma.meta = {"title": title, "shape": f"{rows}×{cols}"} + csv_file = CsvFile( + filename=f"{title}.csv", + source="generated", + s3_key=key, + size_bytes=size, + row_count=rows, + column_count=cols, + uploaded_at=at, + ) + session.add(csv_file) + # The relationship links the FK; SQLAlchemy resolves the id on flush. + ma.csv_file = csv_file + artifacts.append(ma) + return artifacts + + +async def _seed_demo_conversations(session) -> None: + demos = _demo_conversations() + for c, demo in enumerate(demos): + conv_time = BASE + timedelta(days=10, hours=c) + conversation = Conversation(title=demo["title"], created_at=conv_time, updated_at=conv_time) + + minute = 0 + for turn in demo["turns"]: + user_msg = Message( + role="user", + content=turn["user"], + created_at=conv_time + timedelta(minutes=minute), + ) + minute += 1 + assistant_msg = Message( + role="assistant", + content=turn["answer"], + created_at=conv_time + timedelta(minutes=minute), + ) + minute += 1 + assistant_msg.artifacts = _build_artifacts( + session, turn["steps"], assistant_msg.created_at + ) + conversation.messages.extend([user_msg, assistant_msg]) + + conversation.updated_at = conv_time + timedelta(minutes=minute) + session.add(conversation) + await session.commit() + print(f" ✓ {len(demos)} demo conversations with artifacts (figures + tables on S3)") + + +async def _seed_plain_conversations(session) -> None: + for c in range(N_PLAIN_CONVERSATIONS): + conv_time = BASE + timedelta(hours=c) + conversation = Conversation( + title=f"{PLAIN_PROMPTS[c % len(PLAIN_PROMPTS)]}", + created_at=conv_time, + updated_at=conv_time + timedelta(minutes=N_PLAIN_MESSAGES), + ) + conversation.messages = [ + Message( + role="user" if i % 2 == 0 else "assistant", + content=( + PLAIN_PROMPTS[i % len(PLAIN_PROMPTS)] + if i % 2 == 0 + else f"Here is the answer to message {i}." + ), + created_at=conv_time + timedelta(minutes=i), + ) + for i in range(N_PLAIN_MESSAGES) + ] + session.add(conversation) + await session.commit() + print( + f" ✓ {N_PLAIN_CONVERSATIONS} plain conversations × " + f"{N_PLAIN_MESSAGES} messages (for pagination)" + ) + + +async def main(reset: bool) -> None: + _guard_dev_database() + # Heavy payloads go to S3 — make sure the bucket exists. + storage.ensure_bucket() + + async with AsyncSessionLocal() as session: + if reset: + print("Resetting existing data…") + await _reset(session) + + existing = await session.scalar(select(func.count()).select_from(Conversation)) + if existing: + raise SystemExit( + f"Database already has {existing} conversations. " + "Re-run with --reset to wipe and reseed." + ) + + print("Seeding uploaded CSV datasets…") + await _seed_uploaded_csvs(session) + print("Seeding demo conversations with artifacts…") + await _seed_demo_conversations(session) + print("Seeding plain conversations…") + await _seed_plain_conversations(session) + + print("Done.") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--reset", + action="store_true", + help="Delete existing conversations/messages/csv files before seeding.", + ) + args = parser.parse_args() + asyncio.run(main(reset=args.reset)) diff --git a/backend/tests/__init__.py b/backend/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/tests/conftest.py b/backend/tests/conftest.py new file mode 100644 index 0000000..cab1870 --- /dev/null +++ b/backend/tests/conftest.py @@ -0,0 +1,203 @@ +"""Pytest fixtures: a test DB (from docker-compose.test.yml) + a sync test client. + +The test Postgres/MinIO are expected to be up (the Makefile starts them). +Connection details are overridable via env vars so this also works in CI. + +Tests are plain (non-async) and drive the app through FastAPI's TestClient, so +`client.get(...)` / `client.post(...)` are instantaneous, blocking calls. The +app itself still runs on its async stack; the TestClient owns the event loop. +""" + +import asyncio +import os +from collections.abc import Awaitable +from datetime import datetime, timedelta, timezone +from typing import Callable, TypeVar + +import pytest +from fastapi.testclient import TestClient +from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine +from sqlalchemy.orm import selectinload +from sqlalchemy.pool import NullPool + +# Point the app at the ephemeral test services BEFORE importing app modules. +os.environ.setdefault( + "DATABASE_URL", + "postgresql+asyncpg://postgres:postgres@localhost:5433/app_test", +) +os.environ.setdefault("S3_ENDPOINT_URL", "http://localhost:9100") +os.environ.setdefault("S3_ACCESS_KEY", "minioadmin") +os.environ.setdefault("S3_SECRET_KEY", "minioadmin") +os.environ.setdefault("S3_BUCKET", "csv-uploads-test") + +import app.models # noqa: E402,F401 (register metadata) +from app.db.base import Base # noqa: E402 +from app.db.session import get_db # noqa: E402 +from app.main import app as fastapi_app # noqa: E402 +from app.models.conversation import Conversation # noqa: E402 +from app.models.csv_file import CsvFile # noqa: E402 +from app.models.message import Message # noqa: E402 +from app.services import storage # noqa: E402 + +TEST_DATABASE_URL = os.environ["DATABASE_URL"] + +_engine = create_async_engine(TEST_DATABASE_URL, future=True, poolclass=NullPool) +_session_factory = async_sessionmaker(_engine, class_=AsyncSession, expire_on_commit=False) + +T = TypeVar("T") + + +def run_async(coro: Awaitable[T]) -> T: + """Run an async coroutine to completion from synchronous test code.""" + return asyncio.run(coro) # type: ignore + + +@pytest.fixture(autouse=True) +def _schema(): + async def _reset() -> None: + async with _engine.begin() as conn: + await conn.run_sync(Base.metadata.drop_all) + await conn.run_sync(Base.metadata.create_all) + + run_async(_reset()) + yield + + +@pytest.fixture +def client(monkeypatch): + # Route DB access through the NullPool test engine so connections aren't + # cached across the TestClient's per-request event loops (which would trip + # asyncpg's "another operation is in progress"). + async def _override_get_db(): + async with _session_factory() as session: + yield session + + fastapi_app.dependency_overrides[get_db] = _override_get_db + # The streaming chat endpoint opens its own sessions outside Depends() via + # db_session.new_session(); point that at the NullPool test factory too. + import app.db.session as db_session + + monkeypatch.setattr(db_session, "new_session", lambda: _session_factory()) + with TestClient(fastapi_app) as c: + yield c + fastapi_app.dependency_overrides.clear() + + +@pytest.fixture +def mock_storage(monkeypatch): + store: dict[str, bytes] = {} + + def _upload(key: str, body: bytes, content_type: str = "text/csv") -> None: + store[key] = body + + def _download(key: str) -> bytes: + if key not in store: + raise KeyError(key) + return store[key] + + def _delete(key: str) -> None: + store.pop(key, None) + + monkeypatch.setattr(storage, "upload_csv", _upload) + monkeypatch.setattr(storage, "upload_object", _upload) + monkeypatch.setattr(storage, "download_object", _download) + monkeypatch.setattr(storage, "delete_object", _delete) + return store + + +@pytest.fixture +def db_exec() -> Callable[[Callable[[AsyncSession], Awaitable[T]]], T]: + def _exec(fn: Callable[[AsyncSession], Awaitable[T]]) -> T: + async def _run() -> T: + async with _session_factory() as session: + return await fn(session) + + return run_async(_run()) + + return _exec + + +@pytest.fixture +def make_conversations(db_exec): + base = datetime(2026, 1, 1, tzinfo=timezone.utc) + + def _make(n: int) -> list[Conversation]: + async def _insert(session: AsyncSession) -> list[Conversation]: + rows = [ + Conversation( + title=f"Conversation {i}", + created_at=base + timedelta(minutes=i), + updated_at=base + timedelta(minutes=i), + ) + for i in range(n) + ] + session.add_all(rows) + await session.commit() + for row in rows: + await session.refresh(row) + # Detach so callers can read attributes after the session closes. + session.expunge_all() + return rows + + return db_exec(_insert) + + return _make + + +@pytest.fixture +def make_messages(db_exec): + base = datetime(2026, 1, 1, tzinfo=timezone.utc) + + def _make(conversation_id, n: int) -> list[Message]: + async def _insert(session: AsyncSession) -> list[Message]: + conversation = await session.get( + Conversation, conversation_id, options=[selectinload(Conversation.messages)] + ) + assert conversation + rows = [ + Message( + role="user" if i % 2 == 0 else "assistant", + content=f"Message {i}", + created_at=base + timedelta(minutes=i), + ) + for i in range(n) + ] + conversation.messages.extend(rows) + await session.commit() + for row in rows: + await session.refresh(row) + session.expunge_all() + return rows + + return db_exec(_insert) + + return _make + + +@pytest.fixture +def make_csv_files(db_exec): + base = datetime(2026, 1, 1, tzinfo=timezone.utc) + + def _make(n: int) -> list[CsvFile]: + async def _insert(session: AsyncSession) -> list[CsvFile]: + rows = [ + CsvFile( + filename=f"file_{i}.csv", + s3_key=f"{i}/file_{i}.csv", + size_bytes=1000 + i, + row_count=i, + column_count=3, + uploaded_at=base + timedelta(minutes=i), + ) + for i in range(n) + ] + session.add_all(rows) + await session.commit() + for row in rows: + await session.refresh(row) + session.expunge_all() + return rows + + return db_exec(_insert) + + return _make diff --git a/backend/tests/test_chat.py b/backend/tests/test_chat.py new file mode 100644 index 0000000..f183d01 --- /dev/null +++ b/backend/tests/test_chat.py @@ -0,0 +1,301 @@ +"""Chat streaming endpoint: agent mocked with a scripted FunctionModel. + +Covers the structured SSE stream, persistence (user + assistant + artifacts + +generated CsvFile), figure inlining when reloading messages, and CSV download. +""" + +import json + +import pytest +from fastapi.testclient import TestClient +from pydantic_ai import Agent +from pydantic_ai.models.function import AgentInfo, DeltaToolCall, FunctionModel + +from app.services import chat +from app.services.agent.context import AgentContext +from app.services.agent.tools.query_data import query_data +from app.services.agent.tools.visualize import visualize + + +def _scripted_agent(): + """An agent whose model: reasons, queries, visualizes, then answers.""" + calls = {"n": 0} + + async def stream_fn(messages, info: AgentInfo): + calls["n"] += 1 + if calls["n"] == 1: + yield "plan the query" + yield { + 0: DeltaToolCall( + name="query_data", + json_args=json.dumps( + { + "sql": "SELECT region, SUM(sales) AS s FROM sales GROUP BY region", + "description": "totals", + } + ), + ) + } + elif calls["n"] == 2: + yield { + 0: DeltaToolCall( + name="visualize", + json_args=json.dumps( + { + "code": "fig = px.bar(df, x='region', y='s')", + "title": "Sales by region", + "result_type": "figure", + "description": "bar", + } + ), + ) + } + else: + yield "Sales are highest " + yield "in the West region." + + agent: Agent[AgentContext] = Agent( + FunctionModel(stream_function=stream_fn), deps_type=AgentContext + ) + agent.tool(query_data) + agent.tool(visualize) + return agent + + +@pytest.fixture +def mock_agent(monkeypatch): + """Patch the chat service to use the scripted agent + a fixed title. + + Also stubs dataset loading so no S3/DB CSVs are needed: a tiny in-memory + `sales` table is enough for the scripted SQL. + """ + import pandas as pd + + agent = _scripted_agent() + monkeypatch.setattr(chat, "create_agent", lambda dataset_info: agent) + + async def _fake_title(first_message: str) -> str: + return "Sales by region" + + monkeypatch.setattr(chat, "generate_title", _fake_title) + + async def _fake_ctx() -> AgentContext: + df = pd.DataFrame({"region": ["North", "South", "West"], "sales": [100, 200, 300]}) + return AgentContext(datasets={"sales": df}, dataset_info="- sales") + + monkeypatch.setattr(chat, "_load_dataset_context", _fake_ctx) + + +def _parse_sse(text: str) -> list[dict]: + """Parse an SSE response body into a list of event dicts.""" + events = [] + for block in text.strip().split("\n\n"): + for line in block.splitlines(): + if line.startswith("data: "): + events.append(json.loads(line[len("data: ") :])) + return events + + +def _post_message(client: TestClient, conversation_id, message: str) -> list[dict]: + with client.stream( + "POST", f"/api/conversations/{conversation_id}/messages", json={"message": message} + ) as resp: + assert resp.status_code == 200 + assert resp.headers["content-type"].startswith("text/event-stream") + return _parse_sse("".join(resp.iter_text())) + + +def test_stream_emits_structured_events( + client: TestClient, make_conversations, mock_agent, mock_storage +): + conv = make_conversations(1)[0] + events = _post_message(client, conv.id, "sales by region bar chart") + + kinds = [e["type"] for e in events] + assert "reasoning" in kinds + assert "tool_call" in kinds + assert "artifact" in kinds + assert "text" in kinds + assert kinds[-1] == "done" + + # The SQL is surfaced on the query_data tool call. + sql_calls = [e for e in events if e["type"] == "tool_call" and e["tool"] == "query_data"] + assert sql_calls and "GROUP BY region" in sql_calls[0]["args"]["sql"] + + # The figure artifact carries its Plotly JSON inline for live rendering. + figs = [e for e in events if e["type"] == "artifact" and e["kind"] == "figure"] + assert figs and json.loads(figs[0]["figure_json"]).get("data") + + # The final answer streamed as text deltas. + answer = "".join(e["delta"] for e in events if e["type"] == "text") + assert "West region" in answer + + +def test_new_conversation_gets_title( + client: TestClient, make_conversations, mock_agent, mock_storage +): + conv = make_conversations(1)[0] # no messages yet => "new" + events = _post_message(client, conv.id, "sales by region") + + title_events = [e for e in events if e["type"] == "title"] + assert title_events and title_events[0]["value"] == "Sales by region" + + # And it's persisted on the conversation. + detail = client.get(f"/api/conversations/{conv.id}").json() + assert detail["title"] == "Sales by region" + + +def test_messages_persisted_with_artifacts_and_figure_inlined( + client: TestClient, make_conversations, mock_agent, mock_storage +): + conv = make_conversations(1)[0] + _post_message(client, conv.id, "sales by region bar chart") + + page = client.get(f"/api/conversations/{conv.id}/messages").json() + roles = [m["role"] for m in page["items"]] + assert "user" in roles and "assistant" in roles + + assistant = next(m for m in page["items"] if m["role"] == "assistant") + assert assistant["content"] # final answer persisted + figs = [a for a in assistant["artifacts"] if a["kind"] == "figure"] + # Figure JSON is inlined on reload (no per-figure request needed). + assert figs and figs[0]["figure_json"] and json.loads(figs[0]["figure_json"])["data"] + + +def test_full_process_timeline_persisted_in_order( + client: TestClient, make_conversations, mock_agent, mock_storage +): + """The whole agent process — not just the figure — is persisted in order. + + The scripted agent reasons, runs SQL, gets a result, then draws a figure; + all four steps must survive a reload so the UI timeline is reconstructable. + """ + conv = make_conversations(1)[0] + _post_message(client, conv.id, "sales by region bar chart") + + page = client.get(f"/api/conversations/{conv.id}/messages").json() + assistant = next(m for m in page["items"] if m["role"] == "assistant") + artifacts = sorted(assistant["artifacts"], key=lambda a: a["position"]) + + kinds = [a["kind"] for a in artifacts] + assert kinds == ["thinking", "sql", "query_result", "figure"] + + # positions are contiguous and ordered + assert [a["position"] for a in artifacts] == [0, 1, 2, 3] + + # light payloads are inline in `content` + sql = next(a for a in artifacts if a["kind"] == "sql") + assert "GROUP BY region" in sql["content"] + thinking = next(a for a in artifacts if a["kind"] == "thinking") + assert thinking["content"] and "plan the query" in thinking["content"] + # reasoning text is stored WITHOUT the tags + assert "" not in thinking["content"] + + +def _table_agent(): + """An agent that queries then emits a `table` artifact (generated CSV).""" + calls = {"n": 0} + + async def stream_fn(messages, info: AgentInfo): + calls["n"] += 1 + if calls["n"] == 1: + yield { + 0: DeltaToolCall( + name="query_data", + json_args=json.dumps( + {"sql": "SELECT * FROM sales", "description": "all"} + ), + ) + } + elif calls["n"] == 2: + yield { + 0: DeltaToolCall( + name="visualize", + json_args=json.dumps( + { + "code": "result = df", + "title": "sales_export", + "result_type": "table", + "description": "export", + } + ), + ) + } + else: + yield "Here is the table." + + import pandas as pd # noqa: F401 (kept symmetric with mock_agent) + + agent: Agent[AgentContext] = Agent( + FunctionModel(stream_function=stream_fn), deps_type=AgentContext + ) + agent.tool(query_data) + agent.tool(visualize) + return agent + + +def test_generated_table_creates_csv_and_downloads( + client: TestClient, make_conversations, monkeypatch, mock_storage +): + import pandas as pd + + monkeypatch.setattr(chat, "create_agent", lambda dataset_info: _table_agent()) + + async def _fake_title(_): + return "Export" + + monkeypatch.setattr(chat, "generate_title", _fake_title) + + async def _fake_ctx(): + df = pd.DataFrame({"region": ["North", "West"], "sales": [100, 300]}) + return AgentContext(datasets={"sales": df}, dataset_info="- sales") + + monkeypatch.setattr(chat, "_load_dataset_context", _fake_ctx) + + conv = make_conversations(1)[0] + events = _post_message(client, conv.id, "export sales as csv") + + tables = [e for e in events if e["type"] == "artifact" and e["kind"] == "table"] + assert tables, "expected a table artifact" + + # The generated table is persisted as an artifact AND a generated CsvFile, + # and shows up in the CSV listing tagged source=generated. + csvs = client.get("/api/csv").json()["items"] + generated = [c for c in csvs if c["source"] == "generated"] + assert generated, "generated table should create a CsvFile" + + # The table artifact is downloadable as CSV. + page = client.get(f"/api/conversations/{conv.id}/messages").json() + assistant = next(m for m in page["items"] if m["role"] == "assistant") + art = next(a for a in assistant["artifacts"] if a["kind"] == "table") + dl = client.get(f"/api/artifacts/{art['id']}/download") + assert dl.status_code == 200 + assert dl.headers["content-type"].startswith("text/csv") + assert "region" in dl.text # CSV header present + + +def test_download_non_table_artifact_404( + client: TestClient, make_conversations, mock_agent, mock_storage +): + conv = make_conversations(1)[0] + _post_message(client, conv.id, "sales by region bar chart") + page = client.get(f"/api/conversations/{conv.id}/messages").json() + assistant = next(m for m in page["items"] if m["role"] == "assistant") + fig = next(a for a in assistant["artifacts"] if a["kind"] == "figure") + # Figures are inlined, not downloadable via the CSV route. + resp = client.get(f"/api/artifacts/{fig['id']}/download") + assert resp.status_code == 404 + + +def test_unknown_conversation_404(client: TestClient, mock_agent, mock_storage): + resp = client.post( + "/api/conversations/00000000-0000-0000-0000-000000000000/messages", + json={"message": "hi"}, + ) + assert resp.status_code == 404 + + +def test_empty_message_rejected(client: TestClient, make_conversations, mock_agent): + conv = make_conversations(1)[0] + resp = client.post(f"/api/conversations/{conv.id}/messages", json={"message": " "}) + assert resp.status_code == 400 diff --git a/backend/tests/test_conversations.py b/backend/tests/test_conversations.py new file mode 100644 index 0000000..e30ae76 --- /dev/null +++ b/backend/tests/test_conversations.py @@ -0,0 +1,56 @@ +from fastapi.testclient import TestClient + + +def test_create_and_list_conversation(client: TestClient): + # Initially empty — paginated envelope. + resp = client.get("/api/conversations") + assert resp.status_code == 200 + assert resp.json() == {"items": [], "total": 0, "limit": 25, "offset": 0} + + # Create one. + resp = client.post("/api/conversations") + assert resp.status_code == 201 + created = resp.json() + assert created["title"] == "New conversation" + conv_id = created["id"] + + # It shows up in the list. + resp = client.get("/api/conversations") + assert resp.status_code == 200 + page = resp.json() + assert page["total"] == 1 + assert len(page["items"]) == 1 + assert page["items"][0]["id"] == conv_id + # The list view carries metadata only — no messages embedded. + assert "messages" not in page["items"][0] + + # Delete it. + resp = client.delete(f"/api/conversations/{conv_id}") + assert resp.status_code == 204 + + +def test_list_conversations_pagination(client: TestClient, make_conversations): + rows = make_conversations(30) + + # Default page size is 25, most recent first. + resp = client.get("/api/conversations") + page = resp.json() + assert page["total"] == 30 + assert page["limit"] == 25 + assert page["offset"] == 0 + assert len(page["items"]) == 25 + # Newest (highest index) comes first. + assert page["items"][0]["id"] == str(rows[-1].id) + + # Second page holds the remaining 5. + resp = client.get("/api/conversations", params={"limit": 25, "offset": 25}) + page = resp.json() + assert page["offset"] == 25 + assert len(page["items"]) == 5 + assert page["items"][-1]["id"] == str(rows[0].id) + + +def test_list_conversations_rejects_bad_params(client: TestClient): + assert client.get("/api/conversations", params={"limit": 0}).status_code == 422 + assert client.get("/api/conversations", params={"limit": 101}).status_code == 422 + assert client.get("/api/conversations", params={"offset": -1}).status_code == 422 diff --git a/backend/tests/test_csv_files.py b/backend/tests/test_csv_files.py new file mode 100644 index 0000000..3e485f6 --- /dev/null +++ b/backend/tests/test_csv_files.py @@ -0,0 +1,137 @@ +from fastapi.testclient import TestClient + + +def test_list_csv_empty(client: TestClient): + resp = client.get("/api/csv") + assert resp.status_code == 200 + assert resp.json() == {"items": [], "total": 0, "limit": 25, "offset": 0} + + +def test_list_csv_metadata_only(client: TestClient, make_csv_files): + make_csv_files(1) + resp = client.get("/api/csv") + item = resp.json()["items"][0] + # Display metadata is present... + assert set(item) == { + "id", + "filename", + "source", + "size_bytes", + "row_count", + "column_count", + "uploaded_at", + } + # ...and the internal storage key is never exposed. + assert "s3_key" not in item + + +def test_list_csv_pagination_recent_first(client: TestClient, make_csv_files): + rows = make_csv_files(30) # oldest first + + resp = client.get("/api/csv") + page = resp.json() + assert page["total"] == 30 + assert page["limit"] == 25 + assert len(page["items"]) == 25 + # Most recently uploaded comes first. + assert page["items"][0]["id"] == str(rows[-1].id) + + resp = client.get("/api/csv", params={"limit": 25, "offset": 25}) + page = resp.json() + assert page["offset"] == 25 + assert len(page["items"]) == 5 + assert page["items"][-1]["id"] == str(rows[0].id) + + +def test_list_csv_rejects_bad_params(client: TestClient): + assert client.get("/api/csv", params={"limit": 0}).status_code == 422 + assert client.get("/api/csv", params={"limit": 101}).status_code == 422 + assert client.get("/api/csv", params={"offset": -1}).status_code == 422 + + +def test_upload_csv_stores_and_persists(client: TestClient, mock_storage): + body = b"a,b,c\n1,2,3\n4,5,6\n" + resp = client.post( + "/api/csv", files=[("files", ("data.csv", body, "text/csv"))] + ) + assert resp.status_code == 201 + # The endpoint always returns a list, even for a single file. + items = resp.json() + assert isinstance(items, list) and len(items) == 1 + created = items[0] + assert created["filename"] == "data.csv" + assert created["size_bytes"] == len(body) + # Row/column counts are parsed from the CSV (2 data rows, 3 columns). + assert created["row_count"] == 2 + assert created["column_count"] == 3 + assert "s3_key" not in created # never exposed + + # The bytes really landed in (mocked) storage. + assert list(mock_storage.values()) == [body] + + # And it now shows up in the list. + page = client.get("/api/csv").json() + assert page["total"] == 1 + assert page["items"][0]["id"] == created["id"] + + +def test_upload_multiple_csv(client: TestClient, mock_storage): + a = b"a,b\n1,2\n" + b = b"x,y,z\n1,2,3\n4,5,6\n" + resp = client.post( + "/api/csv", + files=[ + ("files", ("one.csv", a, "text/csv")), + ("files", ("two.csv", b, "text/csv")), + ], + ) + assert resp.status_code == 201 + items = resp.json() + assert [i["filename"] for i in items] == ["one.csv", "two.csv"] + assert sorted(mock_storage.values()) == sorted([a, b]) + + page = client.get("/api/csv").json() + assert page["total"] == 2 + + +def test_upload_rejects_non_csv(client: TestClient, mock_storage): + resp = client.post( + "/api/csv", files=[("files", ("notes.txt", b"hello", "text/plain"))] + ) + assert resp.status_code == 400 + assert mock_storage == {} # nothing stored + + +def test_upload_rejects_batch_with_one_bad_file(client: TestClient, mock_storage): + # A single invalid file rejects the whole batch — no partial state. + resp = client.post( + "/api/csv", + files=[ + ("files", ("good.csv", b"a,b\n1,2\n", "text/csv")), + ("files", ("notes.txt", b"hello", "text/plain")), + ], + ) + assert resp.status_code == 400 + assert client.get("/api/csv").json()["total"] == 0 + + +def test_upload_rejects_empty_file(client: TestClient, mock_storage): + resp = client.post( + "/api/csv", files=[("files", ("empty.csv", b" \n", "text/csv"))] + ) + assert resp.status_code == 400 + assert mock_storage == {} + + +def test_upload_unparsable_csv_still_succeeds(client: TestClient, mock_storage): + # Row/column counting is best-effort: even content pandas can't turn into a + # proper table must not fail the upload — the bytes are still stored. + body = b'"unterminated quote, no newline' + resp = client.post( + "/api/csv", files=[("files", ("weird.csv", body, "text/csv"))] + ) + assert resp.status_code == 201 + created = resp.json()[0] + assert created["row_count"] is None + assert created["column_count"] is None + assert list(mock_storage.values()) == [body] diff --git a/backend/tests/test_health.py b/backend/tests/test_health.py new file mode 100644 index 0000000..81e8cbd --- /dev/null +++ b/backend/tests/test_health.py @@ -0,0 +1,7 @@ +from fastapi.testclient import TestClient + + +def test_health(client: TestClient): + resp = client.get("/api/health") + assert resp.status_code == 200 + assert resp.json() == {"status": "ok"} diff --git a/backend/tests/test_messages.py b/backend/tests/test_messages.py new file mode 100644 index 0000000..b040632 --- /dev/null +++ b/backend/tests/test_messages.py @@ -0,0 +1,48 @@ +from fastapi.testclient import TestClient + + +def test_list_messages_empty(client: TestClient, make_conversations): + conv = make_conversations(1)[0] + resp = client.get(f"/api/conversations/{conv.id}/messages") + assert resp.status_code == 200 + assert resp.json() == {"items": [], "total": 0, "limit": 10, "offset": 0} + + +def test_list_messages_pagination_recent_first( + client: TestClient, make_conversations, make_messages +): + conv = make_conversations(1)[0] + rows = make_messages(conv.id, 25) # oldest first + + # Default page size is 10, most recent first. + resp = client.get(f"/api/conversations/{conv.id}/messages") + page = resp.json() + assert page["total"] == 25 + assert page["limit"] == 10 + assert len(page["items"]) == 10 + # Newest message (last created) comes first. + assert page["items"][0]["id"] == str(rows[-1].id) + assert page["items"][-1]["id"] == str(rows[-10].id) + + # Walk further back in history. + resp = client.get( + f"/api/conversations/{conv.id}/messages", params={"limit": 10, "offset": 20} + ) + page = resp.json() + assert page["offset"] == 20 + assert len(page["items"]) == 5 + # The very last page reaches the oldest message. + assert page["items"][-1]["id"] == str(rows[0].id) + + +def test_list_messages_unknown_conversation_404(client: TestClient): + resp = client.get("/api/conversations/00000000-0000-0000-0000-000000000000/messages") + assert resp.status_code == 404 + + +def test_list_messages_rejects_bad_params(client: TestClient, make_conversations): + conv = make_conversations(1)[0] + base = f"/api/conversations/{conv.id}/messages" + assert client.get(base, params={"limit": 0}).status_code == 422 + assert client.get(base, params={"limit": 101}).status_code == 422 + assert client.get(base, params={"offset": -1}).status_code == 422 diff 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--git a/docker-compose.yml b/docker-compose.yml index 6b06de3..57be564 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -1,10 +1,73 @@ services: - agent: - build: . - stdin_open: true - tty: true + db: + image: postgres:16-alpine + environment: + POSTGRES_USER: ${POSTGRES_USER:-postgres} + POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-postgres} + POSTGRES_DB: ${POSTGRES_DB:-app} + ports: + - "5432:5432" + volumes: + - pgdata:/var/lib/postgresql/data + healthcheck: + test: ["CMD-SHELL", "pg_isready -U ${POSTGRES_USER:-postgres}"] + interval: 5s + timeout: 3s + retries: 5 + + minio: + image: minio/minio:latest + command: server /data --console-address ":9001" + environment: + MINIO_ROOT_USER: minioadmin + MINIO_ROOT_PASSWORD: minioadmin + ports: + - "9000:9000" # S3 API + - "9001:9001" # Web console + volumes: + - miniodata:/data + healthcheck: + test: ["CMD", "mc", "ready", "local"] + interval: 5s + timeout: 3s + retries: 5 + + backend: + build: ./backend + command: > + sh -c "uv run alembic upgrade head && + uv run uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload" env_file: - - .env + - ./backend/.env + environment: + DATABASE_URL: postgresql+asyncpg://${POSTGRES_USER:-postgres}:${POSTGRES_PASSWORD:-postgres}@db:5432/${POSTGRES_DB:-app} + S3_ENDPOINT_URL: http://minio:9000 + S3_ACCESS_KEY: minioadmin + S3_SECRET_KEY: minioadmin + S3_BUCKET: csv-uploads + ports: + - "8000:8000" + depends_on: + db: + condition: service_healthy + minio: + condition: service_started + volumes: + - ./backend:/app + + frontend: + build: ./frontend + ports: + - "5173:5173" + environment: + # Inside the container the dev server proxies /api to the backend service. + VITE_API_PROXY: http://backend:8000 + depends_on: + - backend volumes: - - ./data:/app/data - - ./output:/app/output + - ./frontend:/app + - /app/node_modules + +volumes: + pgdata: + miniodata: diff --git a/frontend/.dockerignore b/frontend/.dockerignore new file mode 100644 index 0000000..b431156 --- /dev/null +++ b/frontend/.dockerignore @@ -0,0 +1,3 @@ +node_modules +dist +.vite diff --git a/frontend/Dockerfile b/frontend/Dockerfile new file mode 100644 index 0000000..32556e4 --- /dev/null +++ b/frontend/Dockerfile @@ -0,0 +1,12 @@ +FROM node:22-slim + +WORKDIR /app + +COPY package.json ./ +RUN npm install + +COPY . . + +EXPOSE 5173 + +CMD ["npm", "run", "dev", "--", "--host"] diff --git a/frontend/index.html b/frontend/index.html new file mode 100644 index 0000000..c23de48 --- /dev/null +++ b/frontend/index.html @@ -0,0 +1,14 @@ + + + + + + Matr | Data Analysis Agent + + + + +
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"plotly.js-dist-min": "^3.6.0", + "react": "^18.3.1", + "react-dom": "^18.3.1", + "react-markdown": "^10.1.0", + "react-plotly.js": "^3.0.0", + "remark-gfm": "^4.0.1" + }, + "devDependencies": { + "@testing-library/jest-dom": "^6.6.3", + "@testing-library/react": "^16.1.0", + "@types/react": "^18.3.12", + "@types/react-dom": "^18.3.1", + "@types/react-plotly.js": "^2.6.4", + "@vitejs/plugin-react": "^4.3.4", + "autoprefixer": "^10.4.20", + "jsdom": "^25.0.1", + "postcss": "^8.4.49", + "tailwindcss": "^3.4.17", + "typescript": "^5.7.2", + "vite": "^6.0.5", + "vitest": "^2.1.8" + } +} diff --git a/frontend/postcss.config.js b/frontend/postcss.config.js new file mode 100644 index 0000000..2aa7205 --- /dev/null +++ b/frontend/postcss.config.js @@ -0,0 +1,6 @@ +export default { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +}; diff --git a/frontend/src/App.test.tsx b/frontend/src/App.test.tsx new file mode 100644 index 0000000..49efa20 --- /dev/null +++ b/frontend/src/App.test.tsx @@ -0,0 +1,98 @@ +import { render, screen, waitFor } from "@testing-library/react"; +import { afterEach, describe, expect, it, vi } from "vitest"; +import App from "./App"; +import type { Conversation, CsvFile, Page } from "./types"; + +// Mock the API layer so the hooks don't hit the network in jsdom. +const emptyPage = (): Page => ({ + items: [], + total: 0, + limit: 25, + offset: 0, +}); + +const mocks = vi.hoisted(() => ({ + listConversations: vi.fn(), + listCsvFiles: vi.fn(), + createConversation: vi.fn(), + uploadCsvFiles: vi.fn(), +})); + +vi.mock("./services/api", () => mocks); + +afterEach(() => { + vi.clearAllMocks(); +}); + +describe("App empty state", () => { + it("renders the empty-state heading", async () => { + mocks.listConversations.mockResolvedValue(emptyPage()); + mocks.listCsvFiles.mockResolvedValue(emptyPage()); + + render(); + expect( + screen.getByText(/What do you want to explore/i), + ).toBeInTheDocument(); + }); + + it("hides the chat sidebar when there are no conversations", async () => { + mocks.listConversations.mockResolvedValue(emptyPage()); + mocks.listCsvFiles.mockResolvedValue(emptyPage()); + + render(); + // "New chat" lives in the history sidebar, which is hidden until a + // conversation exists. + await waitFor(() => { + expect(screen.queryByText(/New chat/i)).not.toBeInTheDocument(); + }); + }); + + it("prompts to upload a dataset when none exist", async () => { + mocks.listConversations.mockResolvedValue(emptyPage()); + mocks.listCsvFiles.mockResolvedValue(emptyPage()); + + render(); + await waitFor(() => { + expect( + screen.getByText(/Upload your first dataset/i), + ).toBeInTheDocument(); + }); + }); + + it("shows the chat sidebar once a conversation exists", async () => { + mocks.listConversations.mockResolvedValue({ + items: [ + { + id: "c1", + title: "Revenue by region", + created_at: "2026-06-14T10:00:00Z", + updated_at: "2026-06-14T10:00:00Z", + }, + ], + total: 1, + limit: 25, + offset: 0, + } satisfies Page); + mocks.listCsvFiles.mockResolvedValue({ + items: [ + { + id: "f1", + filename: "sales.csv", + size_bytes: 100, + row_count: 10, + column_count: 3, + uploaded_at: "2026-06-14T10:00:00Z", + }, + ], + total: 1, + limit: 25, + offset: 0, + } satisfies Page); + + render(); + await waitFor(() => { + expect(screen.getByText(/New chat/i)).toBeInTheDocument(); + }); + expect(screen.getByText(/Revenue by region/i)).toBeInTheDocument(); + }); +}); diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx new file mode 100644 index 0000000..5e5e26a --- /dev/null +++ b/frontend/src/App.tsx @@ -0,0 +1,247 @@ +import { useEffect, useState } from "react"; +import ChatPanel from "./components/ChatPanel"; +import CsvManager from "./components/CsvManager"; +import GlassBackdrop from "./components/GlassBackdrop"; +import NoDatasetsModal from "./components/NoDatasetsModal"; +import Sidebar from "./components/Sidebar"; +import { PanelLeftIcon, PanelRightIcon } from "./components/icons"; +import { createConversation } from "./services/api"; +import { useChatStream } from "./services/useChatStream"; +import { useConversations } from "./services/useConversations"; +import { useCsvFiles } from "./services/useCsvFiles"; +import { useMessages } from "./services/useMessages"; +import { useUpload } from "./services/useUpload"; + +/** Read the active conversation id from the URL path (`/c/:id`). */ +function conversationIdFromPath(): string | null { + const match = window.location.pathname.match(/^\/c\/(.+)$/); + return match ? decodeURIComponent(match[1]) : null; +} + +/** + * Single-page layout: + * [ Sidebar history ] [ Central chat ] [ CSV datasets panel ] + * The history drawer only exists once there is at least one conversation — + * by design the user always starts from a fresh chat. + */ +export default function App() { + const [activeConversation, setActiveConversation] = useState( + conversationIdFromPath, + ); + const [leftOpen, setLeftOpen] = useState(true); + const [rightOpen, setRightOpen] = useState(false); + const [datasetsModalDismissed, setDatasetsModalDismissed] = useState(false); + // Id of a conversation created in this session — its messages live only as + // optimistic state, so useMessages must not fetch (and wipe) them. + const [newConversationId, setNewConversationId] = useState(null); + + const { + conversations, + prepend: prependConversation, + rename: renameConversation, + hasMore: hasMoreConversations, + loadingMore: loadingMoreConversations, + loadMore: loadMoreConversations, + } = useConversations(); + const { + files, + loading: filesLoading, + prepend: prependFile, + hasMore: hasMoreFiles, + loadingMore: loadingMoreFiles, + loadMore: loadMoreFiles, + } = useCsvFiles(); + const { + messages, + loading: messagesLoading, + hasMore: hasMoreMessages, + loadingMore: loadingMoreMessages, + loadMore: loadMoreMessages, + append: appendMessage, + reconcile: reconcileMessages, + } = useMessages(activeConversation, newConversationId); + + const { reply, running, send: streamSend } = useChatStream({ + onTitle: (id, title) => renameConversation(id, title), + onDone: (id) => { + // Replace optimistic messages + the live bubble with the persisted ones + // (which carry artifacts). Pass the id explicitly: for a brand-new + // conversation the hook's captured id may still be stale. + void reconcileMessages(id); + }, + }); + + const upload = useUpload((file) => { + prependFile(file); + setRightOpen(true); // reveal the datasets panel with the new file + }); + + const hasConversations = conversations.length > 0; + const showDatasetsModal = + !filesLoading && files.length === 0 && !datasetsModalDismissed; + + // Keep state in sync when the user navigates with the browser back/forward. + useEffect(() => { + const onPopState = () => setActiveConversation(conversationIdFromPath()); + window.addEventListener("popstate", onPopState); + return () => window.removeEventListener("popstate", onPopState); + }, []); + + // The history drawer can only be open if it exists. + useEffect(() => { + if (!hasConversations) setLeftOpen(false); + }, [hasConversations]); + + const handleSelectConversation = (id: string) => { + // Selecting an existing conversation: clear the "skip fetch" guard so its + // messages are loaded from the server. + setNewConversationId(null); + setActiveConversation(id); + window.history.pushState(null, "", `/c/${encodeURIComponent(id)}`); + }; + + const handleSend = async (text: string) => { + if (running) return; // one run at a time + + // Starting from a fresh chat? Create the conversation on the first message. + let conversationId = activeConversation; + if (conversationId === null) { + const conversation = await createConversation(); + prependConversation(conversation); + // Mark it new so useMessages won't fetch over the optimistic message. + setNewConversationId(conversation.id); + setActiveConversation(conversation.id); + setLeftOpen(true); + window.history.pushState(null, "", `/c/${encodeURIComponent(conversation.id)}`); + conversationId = conversation.id; + } + + appendMessage({ + id: crypto.randomUUID(), + role: "user", + content: text, + created_at: new Date().toISOString(), + }); + + // Stream the agent's reply (renders as a live assistant bubble). + void streamSend(conversationId, text); + }; + + const handleNewChat = () => { + // useMessages resets its list when activeConversation becomes null. + setActiveConversation(null); + window.history.pushState(null, "", "/"); + }; + + return ( +
+ + + {/* Floating button to re-open the left drawer when collapsed (only when + there is history to show). */} + setLeftOpen(true)} + /> + + {/* Left drawer */} + {hasConversations && ( +
+ setLeftOpen(false)} + hasMore={hasMoreConversations} + loadingMore={loadingMoreConversations} + onLoadMore={loadMoreConversations} + /> +
+ )} + + + + {/* Right drawer */} +
+ setRightOpen(false)} + uploading={upload.uploading} + progress={upload.progress} + uploadError={upload.error} + uploadingName={upload.filename} + hasMore={hasMoreFiles} + loadingMore={loadingMoreFiles} + onLoadMore={loadMoreFiles} + /> +
+ + setRightOpen(true)} + /> + + {showDatasetsModal && ( + setDatasetsModalDismissed(true)} + uploading={upload.uploading} + progress={upload.progress} + uploadError={upload.error} + uploadingName={upload.filename} + /> + )} +
+ ); +} + +/** A small glass pill that fades in at the screen edge to re-open a drawer. */ +function FloatingToggle({ + side, + label, + show, + onClick, +}: { + side: "left" | "right"; + label: string; + show: boolean; + onClick: () => void; +}) { + return ( + + ); +} diff --git a/frontend/src/assets/wallpaper.jpg b/frontend/src/assets/wallpaper.jpg new file mode 100644 index 0000000..44acda7 Binary files /dev/null and b/frontend/src/assets/wallpaper.jpg differ diff --git a/frontend/src/components/AssistantMessage.tsx b/frontend/src/components/AssistantMessage.tsx new file mode 100644 index 0000000..845275a --- /dev/null +++ b/frontend/src/components/AssistantMessage.tsx @@ -0,0 +1,173 @@ +import { lazy, Suspense, useState } from "react"; +import { artifactDownloadUrl } from "../services/api"; +import type { AgentStep, StreamingReply } from "../services/useChatStream"; +import Markdown from "./Markdown"; +import { + BrainIcon, + ChartIcon, + CheckIcon, + ChevronIcon, + DatabaseIcon, + DownloadIcon, + SpinnerIcon, +} from "./icons"; + +// Plotly pulls a big bundle — load it only when a figure actually renders. +const PlotlyFigure = lazy(() => import("./PlotlyFigure")); + +interface AssistantMessageProps { + reply: StreamingReply; + /** True while this reply is still streaming (drives the live indicators). */ + streaming?: boolean; +} + +/** + * Renders an assistant turn: a collapsible timeline of the agent's process + * (reasoning, SQL, chart/table generation), the interactive Plotly figures, any + * downloadable generated tables, and the final answer text. + */ +export default function AssistantMessage({ reply, streaming }: AssistantMessageProps) { + const { steps, figures, tables, text, error } = reply; + const hasProcess = steps.length > 0; + + return ( +
+
+
+ {hasProcess && } + + {figures.map((f, i) => ( +
+ {f.title &&
{f.title}
} + + Chargement du graphe… +
+ } + > + + +
+ ))} + + {tables.map((t) => ( + + + {t.title || "table"}.csv + {typeof t.meta?.shape === "string" && ( + {t.meta.shape as string} + )} + + ))} + + {text && {text}} + + {/* While streaming with no text yet, show a soft pulse so the bubble + never looks empty. */} + {streaming && !text && !hasProcess && ( + + + + )} + + {error && ( +

+ {error} +

+ )} +
+
+ + ); +} + +function ProcessTimeline({ + steps, + streaming, +}: { + steps: AgentStep[]; + streaming?: boolean; +}) { + // Collapsed by default once the run is done; expanded live so the user sees + // progress as it happens. + const [open, setOpen] = useState(true); + const doneCount = steps.filter((s) => s.done).length; + + return ( +
+ + + {open && ( +
    + {steps.map((step) => ( + + ))} +
+ )} +
+ ); +} + +function StepRow({ step }: { step: AgentStep }) { + const [open, setOpen] = useState(false); + const Icon = + step.kind === "reasoning" + ? BrainIcon + : step.kind === "sql" + ? DatabaseIcon + : step.kind === "visualize" + ? ChartIcon + : ChartIcon; + + const expandable = Boolean(step.detail); + + return ( +
  • + + + {open && step.detail && ( +
    +          {step.detail}
    +        
    + )} +
  • + ); +} + +function Dot() { + return ( + + ); +} diff --git a/frontend/src/components/ChatComposer.tsx b/frontend/src/components/ChatComposer.tsx new file mode 100644 index 0000000..3001ded --- /dev/null +++ b/frontend/src/components/ChatComposer.tsx @@ -0,0 +1,54 @@ +import { useState } from "react"; +import { ArrowUpIcon } from "./icons"; + +interface ChatComposerProps { + onSend: (text: string) => void; + floating?: boolean; + /** Disable input while the agent is streaming a reply. */ + disabled?: boolean; +} + +/** The text input. Rendered floating-centered for the empty state, + * or docked at the bottom once a conversation has started. */ +export default function ChatComposer({ onSend, floating, disabled }: ChatComposerProps) { + const [value, setValue] = useState(""); + + const submit = () => { + if (disabled) return; + const text = value.trim(); + if (!text) return; + onSend(text); + setValue(""); + }; + + return ( +
    +