haiku.rag
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 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.
Features
- Local SQLite: No need to run additional servers
- Support for various embedding providers: Ollama, VoyageAI, OpenAI or add your own
- Hybrid Search: Vector search using
sqlite-vec
combined with full-text searchFTS5
, using Reciprocal Rank Fusion - Question Answering: Built-in QA agents using Ollama or OpenAI.
- File monitoring: Automatically index files when run as a server
- Extended file format support: Parse 40+ file formats including PDF, DOCX, HTML, Markdown, audio and more. Or add a URL!
- MCP server: Exposes functionality as MCP tools
- CLI commands: Access all functionality from your terminal
- Python client: Call
haiku.rag
from your own python applications
Quick Start
Install haiku.rag:
Use from Python:
from haiku.rag.client import HaikuRAG
async with HaikuRAG("database.db") as client:
# Add a document
doc = await client.create_document("Your content here")
# Search documents
results = await client.search("query")
# Ask questions
answer = await client.ask("Who is the author of haiku.rag?")
Or use the CLI:
haiku-rag add "Your document content"
haiku-rag search "query"
haiku-rag ask "Who is the author of haiku.rag?"
Documentation
- Installation - Install haiku.rag with different providers
- Configuration - Environment variables and settings
- CLI - Command line interface usage
- Question Answering - QA agents and natural language queries
- Server - File monitoring and server mode
- MCP - Model Context Protocol integration
- Python - Python API reference
License
This project is licensed under the MIT License.