Build agentic RAG systems with LanceDB vector storage, Pydantic AI agents, and Docling document parsing. For developers integrating retrieval-augmented generation into AI applications.
io.github.ggozad/haiku-rag
Local install
STDIO
No auth required
How models use it and what it is built for.
Build agentic RAG systems with LanceDB vector storage, Pydantic AI agents, and Docling document parsing. For developers integrating retrieval-augmented generation into AI applications.
Local install — runs as a subprocess.
Where to find authoritative docs and source for Ggozad Haiku Rag.
Paste any of these into Agent Studio after connecting Ggozad Haiku Rag.
Common questions about connecting and running Ggozad Haiku Rag.
What is haiku-rag and what does it do?
Haiku-rag is an opinionated RAG (Retrieval-Augmented Generation) framework that combines LanceDB for vector storage, Pydantic AI for agentic reasoning, and Docling for document parsing. It simplifies building AI systems that retrieve and reason over document collections.
How do I install and run haiku-rag?
Install via `uvx haiku-rag@0.44.0`. The server runs over stdio transport, making it compatible with MCP clients. Check the project repository for configuration and startup examples.
What document types can haiku-rag process?
Docling handles multiple document formats including PDFs, Word documents, and other structured formats. Refer to Docling's documentation for the complete list of supported file types.
Can I use haiku-rag with my own LLM or API keys?
Haiku-rag integrates Pydantic AI, which supports multiple LLM providers. Check the repository for environment variable configuration and authentication setup for your chosen provider.
Is haiku-rag suitable for production RAG systems?
Haiku-rag provides an opinionated, batteries-included approach to RAG. For production use, review the project's stability guarantees, scalability considerations with LanceDB, and whether the included defaults match your performance and cost requirements.
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