61 lines
2.5 KiB
Markdown
61 lines
2.5 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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> **Note**: Starting with version 0.7.0, haiku.rag uses LanceDB instead of SQLite. If you have an existing SQLite database, use `haiku-rag migrate old_database.sqlite` to migrate your data safely.
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## Features
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- **Local LanceDB**: No need to run additional servers
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- **Support for various embedding providers**: Ollama, VoyageAI, OpenAI or add your own
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- **Native Hybrid Search**: Vector search combined with full-text search using native LanceDB RRF reranking
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- **Reranking**: Optional result reranking with MixedBread AI or Cohere
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- **Question Answering**: Built-in QA agents using Ollama, OpenAI, or Anthropic.
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- **File monitoring**: Automatically index files when run as a server
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- **Extended file format support**: Parse 40+ file formats including PDF, DOCX, HTML, Markdown, code files and more. Or add a URL!
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- **MCP server**: Exposes functionality as MCP tools
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- **CLI commands**: Access all functionality from your terminal
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- **Python client**: Call `haiku.rag` from your own python applications
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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 search "query"
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haiku-rag ask "Who is the author of haiku.rag?"
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haiku-rag migrate old_database.sqlite # Migrate from SQLite
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```
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## Documentation
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- [Installation](installation.md) - Install haiku.rag with different providers
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- [Configuration](configuration.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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## 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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