Haiku RAG

Agentic RAG built on LanceDB, Pydantic AI, and Docling.

Features

• Hybrid search — Vector + full-text with Reciprocal Rank Fusion
• Question answering — QA agents with citations (page numbers, section headings)
• Reranking — MxBAI, Cohere, Zero Entropy, or vLLM
• Research agents — Multi-agent workflows via pydantic-graph: plan, search, evaluate, synthesize
• RLM agent — Complex analytical tasks via sandboxed Python code execution (aggregation, computation, multi-document analysis)
• Conversational RAG — Chat TUI and web application for multi-turn conversations with session memory
• Document structure — Stores full DoclingDocument, enabling structure-aware context expansion
• Multiple providers — Embeddings: Ollama, OpenAI, VoyageAI, LM Studio, vLLM. QA/Research: any model supported by Pydantic AI
• Local-first — Embedded LanceDB, no servers required. Also supports S3, GCS, Azure, and LanceDB Cloud
• CLI & Python API — Full functionality from command line or code
• MCP server — Expose as tools for AI assistants (Claude Desktop, etc.)