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Yiorgis Gozadinos 569947b28d
Separate fetching from ranking in search 
`search` fetched, reranked and truncated in one pass, with the reranker's
over-fetch and the reranking itself interleaved in the same branch. Searching
several databases needs to fuse their candidates before anything is ranked, so
the phases have to be separable.

`_fetch` returns one database's candidates, over-fetching only when a reranker
will re-order them. `_rank` orders and cuts them, leaving an image query's vector
ranking alone since there is no text for a reranker to score against. The
over-fetch multiplier is named rather than a literal 10 at the point of use.

Both check the query type before reading `client.reranker`, which is a
cached_property that builds the reranker on first access and loads model weights
for a local one. An image query never used it and must not start.

No behaviour change: the same suite passes, and the search outputs digest
identically to before.
2026-08-24 10:03:45 +03:00
..
haiku/rag Separate fetching from ranking in search 2026-08-24 10:03:45 +03:00
LICENSE Restructure into uv workspace to support minimal and full installations 2025-11-04 17:59:12 +02:00
pyproject.toml vb 2026-08-21 13:15:50 +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/