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Yiorgis Gozadinos d9ac221ca0
Refuse a chunk id that names a chunk in two databases 
A chunk id is unique within a database and says nothing across them, so a
database copied from another holds the same ids. `qualified_id` keys the
two in-memory identity sites on the database and the id together:
`merge_results` was dropping the second database's result when a query
repeated, and the arrival map that breaks fused score ties was ranking one
of the pair as the other.

Everything serialized records the id alone, so there ambiguity is refused
rather than qualified. `resolve_citations` raises `AmbiguousCitationError`
for a cited id held by two of the databases searched, where it used to
resolve to whichever result came last; `_register_citations` raises for one
already cited from another database in an earlier question. `_cite` turns
both into a `ModelRetry` asking for other evidence. The direct-id fallback
asks every database the question covers instead of taking the first that
answers, so an id no search returned is refused on the same terms.
`all_found` collects them and `first_found` reads its first, which document
reads keep doing on purpose.

Also drop a duplicated 0.77.0 heading from the changelog.
2026-08-25 17:38:25 +03:00
..
haiku/rag Refuse a chunk id that names a chunk in two databases 2026-08-25 17:38:25 +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/