Retrieval Augmented Generation Based on SQLite

Haiku SQLite RAG A Retrieval-Augmented Generation (RAG) library on SQLite. Features Local SQLite : No need to run additional servers : No need to run additional servers Support for various embedding providers : You can use Ollama, VoyageAI, OpenAI or add your own : You can use Ollama, VoyageAI, OpenAI or add your own Hybrid Search : Vector search using sqlite-vec combined with full-text search FTS5 , using Reciprocal Rank Fusion : Vector search using combined with full-text search , using Reciprocal Rank Fusion File monitoring when run as a server automatically indexing your files when run as a server automatically indexing your files Extended file format Support : Parse 40+ file formats including PDF, DOCX, HTML, Markdown, audio and more. Or add a url! : Parse 40+ file formats including PDF, DOCX, HTML, Markdown, audio and more. Or add a url! MCP server Exposes functionality as MCP tools. Exposes functionality as MCP tools. CLI commands Access all functionality from your terminal Access all functionality from your terminal Python client Call haiku.rag from your own python applications. Installation uv pip install haiku.rag By default Ollama (with the mxbai-embed-large model) is used for the embeddings. For other providers use: VoyageAI : uv pip install haiku.rag --extra voyageai : OpenAI: uv pip install haiku.rag --extra openai Configuration You can set the directories to monitor using the MONITOR_DIRECTORIES environment variable (as comma separated values) : # Monitor single directory export MONITOR_DIRECTORIES= " /path/to/documents,/another_path/to/documents " If you want to use an alternative embeddings provider (Ollama being the default) you will need to set the provider details through environment variables: By default: EMBEDDINGS_PROVIDER= " ollama " EMBEDDINGS_MODEL= " mxbai-embed-large " # or any other model EMBEDDINGS_VECTOR_DIM=1024 For VoyageAI: EMBEDDINGS_PROVIDER= " voyageai " EMBEDDINGS_MODEL= " voyage-3.5 " # or any other model EMBEDDINGS_VECTOR_DIM=1024 VOYAGE_API_KEY= " your-api-key " For OpenAI: EMBEDDINGS_PROVIDER= " openai " EMBEDDINGS_MODEL= " text-embedding-3-small " # or text-embedding-3-large EMBEDDINGS_VECTOR_DIM=1536 OPENAI_API_KEY= " your-api-key " Command Line Interface haiku.rag includes a CLI application for managing documents and performing searches from the command line: Available Commands # List all documents haiku-rag list # Add document from text haiku-rag add " Your document content here " # Add document from file or URL haiku-rag add-src /path/to/document.pdf haiku-rag add-src https://example.com/article.html # Get and display a specific document haiku-rag get 1 # Delete a document by ID haiku-rag delete 1 # Search documents haiku-rag search " machine learning " # Search with custom options haiku-rag search " python programming " --limit 10 --k 100 # Start file monitoring & MCP server (default HTTP transport) haiku-rag serve # --stdio for stdio transport or --sse for SSE transport All commands support the --db option to specify a custom database path. Run haiku-rag command -h to see additional parameters for a command. File Monitoring & MCP server You can start the server (using Streamble HTTP, stdio or SSE transports) with: # Start with default HTTP transport haiku-rag serve # --stdio for stdio transport or --sse for SSE transport You need to have set the MONITOR_DIRECTORIES environment variable for monitoring to take place. File monitoring haiku.rag can watch directories for changes and automatically update the document store: Startup : Scan all monitored directories and add any new files : Scan all monitored directories and add any new files File Added/Modified : Automatically parse and add/update the document in the database : Automatically parse and add/update the document in the database File Deleted: Remove the corresponding document from the database MCP Server haiku.rag includes a Model Context Protocol (MCP) server that exposes RAG functionality as tools for AI assistants like Claude Desktop. The MCP server provides the following tools: add_document_from_file - Add documents from local file paths - Add documents from local file paths add_document_from_url - Add documents from URLs - Add documents from URLs add_document_from_text - Add documents from raw text content - Add documents from raw text content search_documents - Search documents using hybrid search - Search documents using hybrid search get_document - Retrieve specific documents by ID - Retrieve specific documents by ID list_documents - List all documents with pagination - List all documents with pagination delete_document - Delete documents by ID Using haiku.rag from python Managing documents from pathlib import Path from haiku . rag . client import HaikuRAG # Use as async context manager (recommended) async with HaikuRAG ( "path/to/database.db" ) as client : # Create document from text doc = await client . create_document ( content = "Your document content here" , uri = "doc://example" , metadata = { "source" : "manual" , "topic" : "example" } ) # Create document from file (auto-parses content) doc = await client . create_document_from_source ( "path/to/document.pdf" ) # Create document from URL doc = await client . create_document_from_source ( "https://example.com/article.html" ) # Retrieve documents doc = await client . get_document_by_id ( 1 ) doc = await client . get_document_by_uri ( "file:///path/to/document.pdf" ) # List all documents with pagination docs = await client . list_documents ( limit = 10 , offset = 0 ) # Update document content doc . content = "Updated content" await client . update_document ( doc ) # Delete document await client . delete_document ( doc . id ) # Search documents using hybrid search (vector + full-text) results = await client . search ( "machine learning algorithms" , limit = 5 ) for chunk , score in results : print ( f"Score: { score :.3f } " ) print ( f"Content: { chunk . content } " ) print ( f"Document ID: { chunk . document_id } " ) print ( "---" ) Searching documents