haiku.rag/haiku_rag_slim
Yiorgis Gozadinos 110adf23ee
Make toc.json item_range the line slice it documents
build_toc stored item positions, while the sandbox instructions describe
item_range as a slice into items.jsonl; a gap in positions pulled the next
heading into a section. Ranges are now indices into the position-ordered
items, and get_document_section slices by index too. docs/mcp.md names the
tools that take sources.
2026-09-07 10:27:02 +03:00
..
haiku/rag Make toc.json item_range the line slice it documents 2026-09-07 10:27:02 +03:00
LICENSE
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

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/