simbarag/CLAUDE.md

CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Project Overview

SimbaRAG is a RAG (Retrieval-Augmented Generation) conversational AI system for querying information about Simba (a cat). It ingests documents from Paperless-NGX, stores embeddings in ChromaDB, and uses LLMs (Ollama or OpenAI) to answer questions.

Commands

Development

# Start dev environment with hot reload
docker compose -f docker-compose.dev.yml up --build

# View logs
docker compose -f docker-compose.dev.yml logs -f raggr

Database Migrations (Aerich/Tortoise ORM)

# Generate migration (must run in Docker with DB access)
docker compose -f docker-compose.dev.yml exec raggr aerich migrate --name describe_change

# Apply migrations (auto-runs on startup, manual if needed)
docker compose -f docker-compose.dev.yml exec raggr aerich upgrade

# View migration history
docker compose exec raggr aerich history

Frontend

cd raggr-frontend
yarn install
yarn build      # Production build
yarn dev        # Dev server (rarely needed, backend serves frontend)

Production

docker compose build raggr
docker compose up -d

Architecture

┌─────────────────────────────────────────────────────────────┐
│                     Docker Compose                          │
├─────────────────────────────────────────────────────────────┤
│  raggr (port 8080)              │  postgres (port 5432)    │
│  ├── Quart backend              │  PostgreSQL 16           │
│  ├── React frontend (served)    │                          │
│  └── ChromaDB (volume)          │                          │
└─────────────────────────────────────────────────────────────┘

Backend (root directory):

• app.py - Quart application entry, serves API and static frontend
• main.py - RAG logic, document indexing, LLM interaction, LangChain agent
• llm.py - LLM client with Ollama primary, OpenAI fallback
• aerich_config.py - Database migration configuration
• blueprints/ - API routes organized as Quart blueprints
  • users/ - OIDC auth, JWT tokens, RBAC with LDAP groups
  • conversation/ - Chat conversations and message history
  • rag/ - Document indexing endpoints (admin-only)
• config/ - Configuration modules
  • oidc_config.py - OIDC authentication configuration
• utils/ - Reusable utilities
  • chunker.py - Document chunking for embeddings
  • cleaner.py - PDF cleaning and summarization
  • image_process.py - Image description with LLM
  • request.py - Paperless-NGX API client
• scripts/ - Administrative and utility scripts
  • add_user.py - Create users manually
  • user_message_stats.py - User message statistics
  • manage_vectorstore.py - Vector store management CLI
  • inspect_vector_store.py - Inspect ChromaDB contents
  • query.py - Query generation utilities
• migrations/ - Database migration files

Frontend (raggr-frontend/):

• React 19 with Rsbuild bundler
• Tailwind CSS for styling
• Built to dist/, served by backend at /

Auth Flow: LLDAP → Authelia (OIDC) → Backend JWT → Frontend localStorage

Key Patterns

• All endpoints are async (async def)
• Use @jwt_refresh_token_required for authenticated endpoints
• Use @admin_required for admin-only endpoints (checks lldap_admin group)
• Tortoise ORM models in blueprints/*/models.py
• Frontend API services in raggr-frontend/src/api/

Environment Variables

See .env.example. Key ones:

• DATABASE_URL - PostgreSQL connection
• OIDC_* - Authelia OIDC configuration
• OLLAMA_URL - Local LLM server
• OPENAI_API_KEY - Fallback LLM
• PAPERLESS_TOKEN / BASE_URL - Document source