Installation

Choose Your Package

haiku.rag is available in two packages:

Full Package (Recommended)

uv pip install haiku.rag

The full package includes all features and extras:

Document processing (Docling) - PDF, DOCX, PPTX, images, and 40+ file formats
All embedding providers - VoyageAI
All rerankers - MixedBread AI, Cohere, Zero Entropy

This is the easiest way to get started with all features enabled.

Slim Package (Minimal Dependencies)

# Minimal installation (no document processing)
uv pip install haiku.rag-slim

# With document processing
uv pip install haiku.rag-slim[docling]

# With specific providers
uv pip install haiku.rag-slim[docling,voyageai,mxbai]

The slim package has minimal dependencies and lets you install only what you need:

docling - PDF, DOCX, PPTX, images, and other document formats
voyageai - VoyageAI embeddings
mxbai - MixedBread AI reranking
cohere - Cohere reranking
zeroentropy - Zero Entropy reranking

Built-in providers (no extras needed):

Ollama (default embedding provider)
OpenAI (GPT models for QA and embeddings)
Anthropic (Claude models for QA)

See Configuration for configuring providers including advanced options like vLLM.

Requirements

Python 3.12+
Ollama (for default embeddings and QA)

Pre-download Models (Optional)

You can prefetch all required runtime models before first use:

haiku-rag download-models

This will download:

Docling models for document processing
HuggingFace tokenizer models for chunking
Any Ollama models referenced by your current configuration

Remote Processing (Optional)

When using haiku.rag-slim, you can skip installing the docling extra and instead use docling-serve for remote document processing. This is useful for:

Keeping dependencies minimal
Offloading heavy document processing to a dedicated service
Production deployments with separate processing infrastructure

See Remote processing for setup instructions and Document Processing for configuration options.

Docker

docker pull ghcr.io/ggozad/haiku.rag:latest

Run the container with all services:

docker run -p 8000:8000 -p 8001:8001 -v $(pwd)/data:/data ghcr.io/ggozad/haiku.rag:latest

This starts the MCP server on port 8001, with data persisted to ./data.

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