haiku.rag — Agentic RAG CLI + MCP Server

Main

• Start with one PDF and verify citations (--cite) before scaling to directory monitoring or research agents.

• Use the MCP server mode when you want assistants like Claude Desktop to manage documents/search/QA as tools rather than pasted context.

• Keep provider swaps explicit: embeddings and QA models are pluggable; document which provider you used for each dataset to make runs reproducible.

Source-backed notes

• README states it is built on LanceDB, Pydantic AI, and Docling, and includes both CLI and Python API entrypoints.
• README documents MCP server usage: haiku-rag serve --mcp --stdio and a sample mcpServers JSON config.
• README lists multiple features including hybrid search, citations with page numbers/section headings, and local-first embedded LanceDB storage.

FAQ

• Do I need an embedding provider?: Yes — README says you must configure one (Ollama/OpenAI/etc.) before indexing/searching.
• Can I use it from an MCP client?: Yes — run serve --mcp --stdio and add it to your client config.
• Is there a slim install?: Yes — README mentions haiku.rag-slim plus extras; use it when you want fewer deps.