68 lines
2.8 KiB
Markdown
68 lines
2.8 KiB
Markdown
# haiku.rag
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`haiku.rag` is a Retrieval-Augmented Generation (RAG) library built to work with LanceDB as a local vector database. It uses LanceDB for storing embeddings and performs semantic (vector) search as well as full-text search combined through native hybrid search with Reciprocal Rank Fusion. Both open-source (Ollama, MixedBread AI) as well as commercial (OpenAI, VoyageAI) embedding providers are supported.
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## Features
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- **Local LanceDB**: No external servers required, supports also LanceDB cloud storage, S3, Google Cloud & Azure
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- **Multiple embedding providers**: Ollama, LM Studio, VoyageAI, OpenAI, vLLM
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- **Multiple QA providers**: Any provider/model supported by Pydantic AI (Ollama, LM Studio, OpenAI, Anthropic, etc.)
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- **Native hybrid search**: Vector + full-text search with native LanceDB RRF reranking
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- **Reranking**: Optional result reranking with MixedBread AI, Cohere, Zero Entropy, or vLLM
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- **Question answering**: Built-in QA agents on your documents
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- **Research graph (multi‑agent)**: Plan → Search → Evaluate → Synthesize with agentic AI
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- **File monitoring**: Auto-index files when run as server
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- **Extended file format support**: Parse PDF, DOCX, HTML, Markdown, images, code files and more
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- **Flexible document processing**: Local processing with docling or remote with [docling-serve](remote-processing.md)
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- **MCP server**: Expose as tools for AI assistants
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- **CLI & Python API**: Use from command line or Python
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## Quick Start
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Install haiku.rag:
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```bash
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uv pip install haiku.rag
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```
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Use from Python:
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```python
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from haiku.rag.client import HaikuRAG
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async with HaikuRAG("database.lancedb") as client:
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# Add a document
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doc = await client.create_document("Your content here")
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# Search documents
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results = await client.search("query")
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# Ask questions
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answer = await client.ask("Who is the author of haiku.rag?")
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```
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Or use the CLI:
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```bash
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haiku-rag add "Your document content"
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haiku-rag add "Your document content" --meta author=alice
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haiku-rag add-src /path/to/document.pdf --title "Q3 Financial Report" --meta source=manual
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haiku-rag search "query"
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haiku-rag ask "Who is the author of haiku.rag?"
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```
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## Documentation
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- [Getting started](tutorial.md) - Tutorial
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- [Installation](installation.md) - Install haiku.rag with different providers
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- [Configuration](configuration/index.md) - Environment variables and settings
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- [CLI](cli.md) - Command line interface usage
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- [Server](server.md) - File monitoring and server mode
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- [MCP](mcp.md) - Model Context Protocol integration
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- [Python](python.md) - Python API reference
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- [Agents](agents.md) - QA agent and multi-agent research
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- [Remote processing](remote-processing.md) - Remote document processing with docling-serve
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## License
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This project is licensed under the [MIT License](https://raw.githubusercontent.com/ggozad/haiku.rag/main/LICENSE).
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