Haiku RAG
An opinionated local-first RAG system built on LanceDB, Pydantic AI, and Docling that provides hybrid vector/full-text search, citation-aware Q&A, multi-agent research workflows, and an MCP server for integration with AI assistants like Claude Desktop.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
RAG pipeline MCP using Claude Haiku. Inherits security from vector store and LLM provider. Embeddings of sensitive data require same protection as source data.
⚡ Reliability
Best When
You want a production-quality, local-first RAG system with strong document structure awareness, citations, and MCP integration for AI assistant workflows.
Avoid When
You need a managed RAG-as-a-service solution or your documents are primarily unstructured web content rather than PDFs and structured documents.
Use Cases
- • Indexing and querying a personal or enterprise document library with citation-backed answers (page numbers, section headings)
- • Running multi-agent research workflows that plan, search, evaluate, and synthesize across a document corpus
- • Integrating a local document knowledge base into Claude Desktop or other MCP clients via the built-in MCP server
Not For
- • Cloud-first teams that need managed vector database infrastructure without self-hosting
- • Simple keyword search use cases where a full RAG pipeline adds unnecessary complexity
- • Non-Python shops or teams without Python 3.12+ capability
Interface
Authentication
No authentication on the MCP server or local API. External embedding and model providers require their own API keys configured via environment variables.
Pricing
MIT licensed. LanceDB is embedded (free). Costs are pass-through to embedding and LLM providers. Optional cloud storage (S3, GCS, Azure) incurs cloud provider costs.
Agent Metadata
Known Gotchas
- ⚠ Python 3.12+ required — older environments need upgrade before use
- ⚠ No MCP server authentication — local deployment assumed; exposing over network is unsafe without adding auth
- ⚠ Docling document processing can be slow for large PDFs; agents may time out on initial indexing requests
- ⚠ Two packages (haiku.rag vs haiku.rag-slim) with different dependency profiles can cause confusion
Alternatives
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Scores are editorial opinions as of 2026-03-07.