# haiku.rag

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Agentic RAG that answers questions about your own documents with citations to page numbers and section headings. Runs locally on an embedded database, no server required.

Built on [LanceDB](https://lancedb.com/), [Pydantic AI](https://ai.pydantic.dev/), and [Docling](https://docling-project.github.io/docling/). Full documentation at [ggozad.github.io/haiku.rag](https://ggozad.github.io/haiku.rag/).

## 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](https://docling-project.github.io/docling/concepts/docling_document/), 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
- **Multi-database search** — Search, ask, analyze, or chat across named databases with source attribution on results and citations
- **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](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)

```bash
pip install haiku.rag
```

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

Using [uv](https://docs.astral.sh/uv/)? `uv pip install haiku.rag`

### Slim Package (Minimal Dependencies)

```bash
pip install haiku.rag-slim
```

Install only the extras you need. See the [Installation](https://ggozad.github.io/haiku.rag/installation/) documentation for available options.

## Quick Start

> **Note**: Requires an embedding provider (Ollama, OpenAI, etc.). See the [Tutorial](https://ggozad.github.io/haiku.rag/tutorial/) for setup instructions.

```bash
# 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](https://ggozad.github.io/haiku.rag/configuration/) for customization options.

## Python API

```python
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](https://ggozad.github.io/haiku.rag/capabilities/).

## MCP Server

Use with AI assistants like Claude Desktop:

```bash
haiku-rag mcp --stdio
```

Add to your Claude Desktop configuration:

```json
{
  "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](examples/) for working examples:

- **[Docker Setup](examples/docker/)** - Complete Docker deployment with continuous ingestion (`haiku-ingester`) and MCP server
- **[Web Application](app/)** - Full-stack conversational RAG with CopilotKit frontend

## Documentation

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

- [Quickstart](https://ggozad.github.io/haiku.rag/tutorial/) - Provider setup and first ingestion
- [Installation](https://ggozad.github.io/haiku.rag/installation/) - Packages and extras
- [Configuration](https://ggozad.github.io/haiku.rag/configuration/) - YAML reference
- [CLI](https://ggozad.github.io/haiku.rag/cli/) - Command reference
- [Python API](https://ggozad.github.io/haiku.rag/python/) - Complete API docs
- [Capabilities](https://ggozad.github.io/haiku.rag/capabilities/) - Native Pydantic AI RAG and analysis capabilities
- [Tuning](https://ggozad.github.io/haiku.rag/tuning/) - Retrieval and answer-quality tuning
- [Ingester](https://ggozad.github.io/haiku.rag/ingester/) - Production ingester for continuous indexing from FS, HTTP, S3, and WebDAV
- [MCP](https://ggozad.github.io/haiku.rag/mcp/) - Model Context Protocol integration
- [Remote processing](https://ggozad.github.io/haiku.rag/remote-processing/) - Offload conversion to docling-serve
- [Applications](https://ggozad.github.io/haiku.rag/apps/) - Chat TUI, web app, and inspector
- [Benchmarks](https://ggozad.github.io/haiku.rag/benchmarks/) - Performance benchmarks
- [Changelog](https://ggozad.github.io/haiku.rag/changelog/) - Version history

## License

This project is licensed under the [MIT License](LICENSE).

<!-- mcp-name is used by the MCP registry to identify this server -->
mcp-name: io.github.ggozad/haiku-rag