88 lines
2.8 KiB
Markdown
88 lines
2.8 KiB
Markdown
# Haiku SQLite RAG
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Retrieval-Augmented Generation (RAG) library on SQLite.
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`haiku.rag` is a Retrieval-Augmented Generation (RAG) library built to work on SQLite alone without the need for external vector databases. It uses [sqlite-vec](https://github.com/asg017/sqlite-vec) for storing the embeddings and performs semantic (vector) search as well as full-text search combined through Reciprocal Rank Fusion. Both open-source (Ollama) as well as commercial (OpenAI, VoyageAI) embedding providers are supported.
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## Features
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- **Local SQLite**: No external servers required
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- **Multiple embedding providers**: Ollama, VoyageAI, OpenAI
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- **Multiple QA providers**: Ollama, OpenAI, Anthropic
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- **Hybrid search**: Vector + full-text search with Reciprocal Rank Fusion
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- **Reranking**: Default search result reranking with MixedBread AI or Cohere
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- **Question answering**: Built-in QA agents on your documents
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- **File monitoring**: Auto-index files when run as server
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- **40+ file formats**: PDF, DOCX, HTML, Markdown, code files, URLs
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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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```bash
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# Install
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uv pip install haiku.rag
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# Add documents
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haiku-rag add "Your content here"
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haiku-rag add-src document.pdf
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# Search
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haiku-rag search "query"
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# Ask questions
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haiku-rag ask "Who is the author of haiku.rag?"
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# Ask questions with citations
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haiku-rag ask "Who is the author of haiku.rag?" --cite
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# Rebuild database (re-chunk and re-embed all documents)
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haiku-rag rebuild
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# Start server with file monitoring
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export MONITOR_DIRECTORIES="/path/to/docs"
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haiku-rag serve
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```
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## Python Usage
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```python
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from haiku.rag.client import HaikuRAG
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async with HaikuRAG("database.db") as client:
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# Add document
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doc = await client.create_document("Your content")
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# Search (reranking enabled by default)
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results = await client.search("query")
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for chunk, score in results:
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print(f"{score:.3f}: {chunk.content}")
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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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print(answer)
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# Ask questions with citations
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answer = await client.ask("Who is the author of haiku.rag?", cite=True)
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print(answer)
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```
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## MCP Server
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Use with AI assistants like Claude Desktop:
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```bash
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haiku-rag serve --stdio
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```
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Provides tools for document management and search directly in your AI assistant.
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## Documentation
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Full documentation at: https://ggozad.github.io/haiku.rag/
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- [Installation](https://ggozad.github.io/haiku.rag/installation/) - Provider setup
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- [Configuration](https://ggozad.github.io/haiku.rag/configuration/) - Environment variables
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- [CLI](https://ggozad.github.io/haiku.rag/cli/) - Command reference
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- [Python API](https://ggozad.github.io/haiku.rag/python/) - Complete API docs
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- [Benchmarks](https://ggozad.github.io/haiku.rag/benchmarks/) - Performance Benchmarks
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