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Chris McDonough 09e78ef46a Thread extract_items off the asyncio event loop 
Moves CPU-bound docling document item extraction into worker threads
via asyncio.to_thread so the event loop stays responsive during
ingestion. The extract_items calls are hoisted out of the write lock
(they are pure computation with no DB I/O) and run with placeholder
document IDs that are patched after the DB create returns.

Fixes #453.
2026-06-22 13:19:38 -04:00
..
haiku/rag Thread extract_items off the asyncio event loop 2026-06-22 13:19:38 -04:00
LICENSE
pyproject.toml vb 2026-06-22 15:18:35 +03:00
README.md

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

Document Processing:

  • docling - PDF, DOCX, HTML, and 40+ file formats

Embedding Providers:

  • voyageai - VoyageAI embeddings

Rerankers:

  • mxbai - MixedBread AI
  • cohere - Cohere
  • zeroentropy - Zero Entropy

Model Providers:

  • OpenAI/Ollama - included in core (OpenAI-compatible APIs)
  • anthropic - Anthropic Claude
  • groq - Groq
  • google - Google Gemini
  • mistral - Mistral AI
  • bedrock - AWS Bedrock
  • vertexai - Google Vertex AI
# Common combinations
uv pip install haiku.rag-slim[docling,anthropic,mxbai]
uv pip install haiku.rag-slim[docling,groq,logfire]

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/