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Yiorgis Gozadinos fa242c1c94
Tell the analysis and RAG capabilities about the databases 
A capability covering several databases received `source` on every document
and search result and never used it: asked how many documents were in each
database, the model read the titles and answered that there was one corpus
of 67,581. The instruction files enumerate what a result carries, and both
enumerations had gone stale.

The note follows what the capability opens rather than what the
configuration names, through `covers_several_databases`: an explicit
`db_path` or a lent client covering one database is instructed as before,
as is every `uri` or path deployment and every eval dataset. The analysis
note separates the three interfaces, since they differ: an
`analysis_search` result carries a `Database:` line, in-code `search` and
`list_documents` return `source`, and the mounted files carry neither.
2026-08-24 10:03:47 +03:00
..
haiku/rag Tell the analysis and RAG capabilities about the databases 2026-08-24 10:03:47 +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/