haiku.rag MCP server

haiku.rag

Agentic RAG that answers questions about your own documents with citations to page numbers and section headings. Runs locally on an embedded database, no server required.

Built on LanceDB, Pydantic AI, and Docling. Full documentation at ggozad.github.io/haiku.rag.

Features

• Hybrid search — Vector + full-text with Reciprocal Rank Fusion
• Multimodal & cross-modal search — Multimodal embedders (vLLM, VoyageAI, Cohere) put picture vectors in the same space as text; supports text-as-query → figure hits and image-as-query
• Question answering — RAG capability with citations (page numbers, section headings)
• Vision QA — Vision-capable models receive figure bytes alongside chunk text; attach your own images to questions in ask and the chat TUI
• Reranking — local cross-encoders, Cohere, Zero Entropy, or vLLM
• Code execution — The RAG capability runs sandboxed Python over the documents for aggregation, computation and multi-document analysis
• Evidence compaction — Optional capability that replaces earlier questions' search results on the request with the evidence they cited, so long conversations stop resending everything they retrieved
• Citation policy — Optional capability that requires every answer to declare what grounds it, including declaring that nothing does
• 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, Cohere, LM Studio, vLLM (multimodal via multimodal: true on vLLM/VoyageAI/Cohere). QA: any model supported by Pydantic AI
• Multi-database search — Search, ask, or chat across named databases with source attribution on results and citations

Installation

Python 3.12 or newer required

Full Package (Recommended)

pip install haiku.rag

Includes all features: document processing, all embedding providers, and rerankers.

Using uv? uv pip install haiku.rag

Slim Package (Minimal Dependencies)

pip install haiku.rag-slim

Install only the extras you need. See the Installation documentation for available options.

Shortened. The full README is on GitHub.