haiku.rag/docs/index.md

haiku.rag

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

Features

• Hybrid search — Vector + full-text with Reciprocal Rank Fusion
• Reranking — MxBAI, Cohere, Zero Entropy, or vLLM
• Question answering — QA agents with citations (page numbers, section headings)
• Research agents — Multi-agent workflows via pydantic-graph: plan, search, evaluate, synthesize
• Conversational RAG — Chat TUI and web application for multi-turn conversations with session memory
• Document structure — Stores full DoclingDocument, enabling structure-aware context expansion and visual grounding
• Multiple providers — Embeddings: Ollama, OpenAI, VoyageAI, LM Studio, vLLM. QA/Research: any model supported by Pydantic AI
• Local-first — Embedded LanceDB, no servers required. Also supports S3, GCS, Azure, and LanceDB Cloud
• MCP server — Expose as tools for AI assistants (Claude Desktop, etc.)
• File monitoring — Watch directories and auto-index on changes
• Inspector — TUI for browsing documents, chunks, and search results
• CLI & Python API — Full functionality from command line or code

Quick Start

Install haiku.rag:

uv pip install haiku.rag

Use from Python:

from haiku.rag.client import HaikuRAG

async with HaikuRAG("database.lancedb", create=True) as client:
    # Add a document
    doc = await client.create_document("Your content here")

    # Search documents
    results = await client.search("query")

    # Ask questions (returns answer and citations)
    answer, citations = await client.ask("Who is the author of haiku.rag?")

Or use the CLI:

haiku-rag add "Your document content"
haiku-rag add "Your document content" --meta author=alice
haiku-rag add-src /path/to/document.pdf --title "Q3 Financial Report" --meta source=manual
haiku-rag search "query"
haiku-rag ask "Who is the author of haiku.rag?"
haiku-rag chat  # Interactive conversation mode

Documentation

• Getting started - Tutorial
• Installation - Install haiku.rag with different providers
• Architecture - System overview and data flow
• Configuration - Environment variables and settings
• CLI - Command line interface usage
• Python - Python API reference
• Custom Pipelines - Build custom processing workflows
• Agents - QA, chat, and research agents
• Applications - Chat TUI, web app, and inspector
• Server - File monitoring and server mode
• MCP - Model Context Protocol integration
• Remote processing - Remote document processing with docling-serve

License

This project is licensed under the MIT License.