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: trueon 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-ingesterservice 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 tagand 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
1 maintainer
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