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Yiorgis Gozadinos 590719fca7
Read a document, chunk or picture from the database that names it 
`resolve_document` and `find_document` selected a document through a listing,
then dropped its source and looked the id up across the set. Ids repeat between
copies of a database, so a title that matched in one could be answered by
another's document.

`get_document_by_id` and `get_chunk_by_id` join `get_picture_bytes` in taking an
optional `source`, and all three route it through `clients_covering`, so a name
the client does not cover raises `UnknownDatabaseError` rather than being
answered by the database it does cover. Without a source the reads are as they
were, answering from the first database in configured order that holds the id.
2026-08-28 13:30:05 +03:00
..
haiku/rag Read a document, chunk or picture from the database that names it 2026-08-28 13:30:05 +03:00
LICENSE Restructure into uv workspace to support minimal and full installations 2025-11-04 17:59:12 +02:00
pyproject.toml vb 2026-08-24 16:06:03 +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/