Haiku RAG

Agentic RAG built on LanceDB, Pydantic AI, and Docling.

New: vision and multimodal search. Picture-aware ingestion captures embedded figure bytes; vision-capable QA models receive them alongside text. Multimodal embedders put picture vectors in the same space as text, enabling text-as-query → figure hits and image-as-query retrieval.

Features

• Hybrid search — Vector + full-text with Reciprocal Rank Fusion
• Multimodal & cross-modal search — Multimodal embedders (vLLM, VoyageAI, Cohere) put picture vectors in the same space as text; supports text-as-query → figure hits and image-as-query
• Question answering — RAG capability with citations (page numbers, section headings)
• Vision QA — Vision-capable models receive figure bytes alongside chunk text; attach your own images to questions in ask, analyze, MCP, and the chat TUI
• Reranking — local cross-encoders, Cohere, Zero Entropy, or vLLM
• Analysis capability — Complex analytical tasks via sandboxed Python code execution (aggregation, computation, multi-document analysis)
• Evidence compaction — Optional capability that replaces earlier questions' search results on the request with the evidence they cited, so long conversations stop resending everything they retrieved
• Citation policy — Optional capability that requires every answer to declare what grounds it, including declaring that nothing does
• Conversational RAG — Chat TUI and web application for multi-turn conversations with session memory
• Document structure — Stores full DoclingDocument, enabling structure-aware context expansion
• Multiple providers — Embeddings: Ollama, OpenAI, VoyageAI, Cohere, LM Studio, vLLM (multimodal via multimodal: true on vLLM/VoyageAI/Cohere). QA: any model supported by Pydantic AI
• Local-first — Embedded LanceDB, no servers required. Also supports S3, GCS, Azure, and LanceDB Cloud
• CLI & Python API — Full functionality from command line or code
• MCP server — Expose as tools for AI assistants (Claude Desktop, etc.)
• Visual grounding — View chunks highlighted on original page images
• Production ingester — Long-lived haiku-ingester service with persistent SQLite queue, async worker pool with retries and a dead-letter queue, FS / HTTP / S3 / WebDAV source adapters, FastAPI control plane, and a browser dashboard for operators. See docs/ingester.md.
• Tags — Name database states with haiku-rag tag and roll back to them
• Inspector — TUI for browsing documents, chunks, and search results

Installation

Python 3.12 or newer required

Full Package (Recommended)

pip install haiku.rag

Includes all features: document processing, all embedding providers, and rerankers.

Using uv? uv pip install haiku.rag

Slim Package (Minimal Dependencies)

pip install haiku.rag-slim

Install only the extras you need. See the Installation documentation for available options.

Quick Start

Note

: Requires an embedding provider (Ollama, OpenAI, etc.). See the Tutorial for setup instructions.

# Index a PDF
haiku-rag add-src paper.pdf

# Search
haiku-rag search "attention mechanism"

# Ask questions with citations
haiku-rag ask "What datasets were used for evaluation?"

# Ask about an image (vision-capable model)
haiku-rag ask "Does this figure match the spec in the design doc?" --image figure.png

# Analyze — complex analytical tasks via code execution
haiku-rag analyze "How many documents mention transformers?"

# Interactive chat — multi-turn conversations with memory
haiku-rag chat

# Continuously ingest from configured sources (FS, HTTP, S3, WebDAV)
haiku-ingester serve

See Configuration for customization options.

Python API

from haiku.rag.client import HaikuRAG

async with HaikuRAG("knowledge.lancedb", create=True) as rag:
    # Index documents
    await rag.create_document_from_source("paper.pdf")
    await rag.create_document_from_source("https://arxiv.org/pdf/1706.03762")

    # Search — returns chunks with provenance
    results = await rag.search("self-attention")
    for result in results:
        print(f"{result.score:.2f} | p.{result.page_numbers} | {result.content[:100]}")

    # QA with citations
    answer, citations = await rag.ask("What is the complexity of self-attention?")
    print(answer)
    for cite in citations:
        print(f"  [{cite.chunk_id}] p.{cite.page_numbers}: {cite.content[:80]}")

For direct agent composition, see the capabilities documentation.

MCP Server

Use with AI assistants like Claude Desktop:

haiku-rag mcp --stdio

Add to your Claude Desktop configuration:

{
  "mcpServers": {
    "haiku-rag": {
      "command": "haiku-rag",
      "args": ["mcp", "--stdio"]
    }
  }
}

Provides tools for document management, search, QA, and analysis directly in your AI assistant.

Examples

See the examples directory for working examples:

• Docker Setup - Complete Docker deployment with continuous ingestion (haiku-ingester) and MCP server
• Web Application - Full-stack conversational RAG with CopilotKit frontend

Documentation

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

• Quickstart - Provider setup and first ingestion
• Installation - Packages and extras
• Configuration - YAML reference
• CLI - Command reference
• Python API - Complete API docs
• Capabilities - Native Pydantic AI RAG and analysis capabilities
• Tuning - Retrieval and answer-quality tuning
• Ingester - Production ingester for continuous indexing from FS, HTTP, S3, and WebDAV
• MCP - Model Context Protocol integration
• Remote processing - Offload conversion to docling-serve
• Applications - Chat TUI, web app, and inspector
• Benchmarks - Performance benchmarks
• Changelog - Version history

License

This project is licensed under the MIT License.

mcp-name: io.github.ggozad/haiku-rag