📝 Project Summary: Multi-Agent AI Backend
✅ Status: Production Ready
System Overview
Production-ready Python backend with 4 intelligent agents orchestrated by LangGraph:
- Weather Agent: OpenWeatherMap API integration
- Document/Web Agent: Docling + DuckDuckGo search, RAG with ChromaDB
- Meeting Agent: Weather reasoning, scheduling, database operations
- NL-to-SQL Agent: Natural language queries to SQLite
Key Features
- Multi-provider LLM support (OpenAI, Google GenAI, Ollama)
- SQLite database (SQLModel ORM)
- DuckDuckGo search (no API key required)
- FastAPI REST endpoints
- LangGraph state management
- ChromaDB vector store for semantic search
Testing Results
- Weather queries: ✅ Working
- Meeting scheduling: ✅ Functional
- SQL generation: ✅ SQLite-specific syntax
- Tool calling/routing: ✅ Successful
Critical Fixes
- LangChain compatibility: pinned to 0.3.x
- DuckDB → SQLite: improved stability
- Custom SQL prompt for correct date handling
- Ollama integration: cost-free local LLM
- LLM fallback logic: smart API key detection
Main Files
- main.py: FastAPI application
- agents.py: LangGraph workflow (4 agents)
- tools.py: Weather, search, document tools
- models.py: SQLModel meeting schema
- database.py: SQLite connection
- seed_data.py: Sample data generator
- test_agents.py: Automated test suite
- OLLAMA_SETUP.md: Ollama configuration guide
Production Readiness
- Clean, modular architecture
- Comprehensive error handling
- Deterministic tool orchestration
- One-command startup
- Full documentation and setup guides
- Environment-based configuration
- Extensible agent framework
- Local LLM support for cost savings
Next Steps for User
- Configure API keys in
.env - Pull desired Ollama model:
ollama pull qwen3:0.6b - Seed database:
uv run python seed_data.py - Test:
uv run test_agents.py - Deploy:
uv run python main.py
Status: 🎉 Fully functional and verified!