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Yiorgis Gozadinos 2582f2c05a
Expand MCP search results and render the matched chunk's metadata 
Both search tools pass their results through HaikuRAG.expand_context, as
every other consumer of search results already did, so a client reads
the hit in its section rather than the chunk that matched. The rendering
gains an opt-in include_chunk_meta that shows the metadata stored with
the matched chunk beyond haiku.rag's structural keys, labelled as the
matched chunk's because an expanded passage spans several chunks and
only the anchor's metadata survives expansion. The capabilities' rendering
is unchanged.

Refs #599
2026-09-04 14:12:12 +03:00
..
haiku/rag Expand MCP search results and render the matched chunk's metadata 2026-09-04 14:12:12 +03:00
LICENSE Restructure into uv workspace to support minimal and full installations 2025-11-04 17:59:12 +02:00
pyproject.toml Move to fastmcp 4 and MCP SDK 2 2026-09-04 13:11:06 +03:00
README.md Give the docs an architecture page and one extras list 2026-08-20 15:07:06 +03:00

README.md

haiku.rag-slim

Opinionated agentic RAG powered by LanceDB, Pydantic AI, and Docling - Core package with minimal dependencies.

haiku.rag-slim is the core package for users who want to install only the dependencies they need. Document processing (docling), and reranker support are all optional extras.

For most users, we recommend installing haiku.rag instead, which includes all features out of the box.

Installation

Python 3.12 or newer required

Minimal Installation

uv pip install haiku.rag-slim

Core functionality with OpenAI/Ollama support, MCP server, and Logfire observability. Document processing (docling) is optional.

With Document Processing

uv pip install haiku.rag-slim[docling]

Adds support for 40+ file formats including PDF, DOCX, HTML, and more.

Available Extras

docling, tui, voyageai, cohere, zeroentropy, cross-encoder, jina, s3, ingester, and one per model provider: anthropic, google, groq, mistral, bedrock, vertexai. Ollama and any OpenAI-compatible endpoint need no extra.

What each provides, and which ones the full haiku.rag package already includes: Installation.

# Common combinations
uv pip install 'haiku.rag-slim[docling,anthropic,cross-encoder]'
uv pip install 'haiku.rag-slim[docling,groq]'

Usage

See the main haiku.rag repository for:

  • Quick start guide
  • CLI examples
  • Python API usage
  • MCP server setup

Documentation

Full documentation: https://ggozad.github.io/haiku.rag/