danvics/haiku.rag
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions
haiku.rag/haiku_rag_slim
History
Yiorgis Gozadinos f33b789a31
Ask across databases and name the source of each citation 
`ask(sources=[…])` scopes a question to some of the configured databases, carried
on the capability state so its search tool searches those. `Citation.source` names
the database a cited chunk came from, resolved from the search results the model
saw, which already carry it.

Context expansion routes each result through the database it came from: a
federating client has no repositories of its own.

The cite fallback, which looks up an id absent from this run's results, searches
only the selected databases. A chunk id says nothing about which database holds
it, so placing one means asking, and asking outside the selection would let a
question scoped to some databases cite another.

The loosely-specced client mocks in the capability tests now say they stand in for
a single-database client. A bare AsyncMock answers any attribute with a truthy
Mock, so `_federated` sent the fallback down the multi-database branch, and
`_source` reached a validated field.
2026-08-24 10:03:46 +03:00
..
haiku/rag Ask across databases and name the source of each citation 2026-08-24 10:03:46 +03:00
LICENSE
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/