haiku.rag
Reduces time-to-insight when querying proprietary codebases or documentation by combining retrieval, agentic reasoning, and structured parsing.
Python RAG toolkit built on LanceDB, Pydantic AI, and Docling. Ingests documents (including PDFs and embedded figures), runs hybrid vector plus full-text search with reranking, and answers questions with page-and-section citations. Works local-first with an embedded database, exposes a CLI, Python API, chat TUI, and an MCP server so assistants like Claude Desktop can use it as a tool.
- Index PDFs and documents, then search them with hybrid vector + full-text retrieval
- Ask questions over a document set and get answers with page and section citations
- Run analytical queries across many documents via the sandboxed analysis skill
Reduces time-to-insight when querying proprietary codebases or documentation by combining retrieval, agentic reasoning, and structured parsing. Lowers hallucination risk through grounded retrieval and typed outputs.
Development teams building internal knowledge systems or code search tools that need reliable, schema-validated answers from large document sets.
https://github.com/ggozad/haiku.rag
By ggozad
How to Get It
pip install haiku.rag
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
After installing, paste this into Claude:
Help me index PDFs and documents, then search them with hybrid vector + full-text retrieval
Trust Signals Auto-scanned
Community Pulse Active
Discussed on Hacker News, Reddit
- Retrieval Augmented Generation Based on SQLite — Hacker News · 88 pts
- I've been running production Bedrock workloads since pre-release. This weekend I — Reddit · 37 pts
- [Haiku] The Cheesecake Rag — Reddit · 21 pts
3 mentions across 2 sources
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 527 GitHub stars; contributors unknown; last commit 1d ago; license MIT.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
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Evaluation
Scored from trust signals (evidence-eval-v1): 527 GitHub stars; contributors unknown; last commit 1d ago; license MIT.