haiku.rag-slim 0.14.1

Agentic Retrieval Augmented Generation (RAG) with LanceDB - Minimal dependencies

haiku.rag-slim

Retrieval-Augmented Generation (RAG) library built on LanceDB - 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), rerankers, and A2A 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

Agent Protocol:

• a2a - Agent-to-Agent protocol

# 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
• A2A agent configuration

Documentation

Full documentation: https://ggozad.github.io/haiku.rag/

• Installation - Provider setup
• Configuration - YAML configuration
• CLI - Command reference
• Python API - Complete API docs