{"repo":"sqliteai/sqlite-rag","free":true,"listed":false,"github":"https://github.com/sqliteai/sqlite-rag","clone":"git clone https://github.com/sqliteai/sqlite-rag.git","description":"A hybrid search engine built on SQLite with SQLite AI and SQLite Vector extensions","language":"Python","stars":78,"topics":["ai","sqlite","vector-search"],"license":null,"category":"databases-storage","readme_excerpt":"SQLite RAG A hybrid search engine built on SQLite with SQLite AI and SQLite Vector extensions. SQLite RAG combines vector similarity search with full-text search (FTS5 extension) using Reciprocal Rank Fusion (RRF) for enhanced document retrieval. Features - Hybrid Search : Combines vector embeddings with full-text search for optimal results - SQLite-based : Built on SQLite with AI and Vector extensions for reliability and performance - Multi-format Text Support : Process text file formats including PDF, DOCX, Markdown, code files - Recursive Character Text Splitter : Token-aware text chunking with configurable overlap - Interactive CLI : Command-line interface with interactive REPL mode - Flexible Configuration : Customizable embedding models, search weights, and chunking parameters Installation Prerequisites SQLite RAG requires SQLite with extension loading support. If you encounter extension loading issues (e.g., 'sqlite3.Connection' object has no attribute 'enable load extension' ), follow the setup guides for macOS or Windows. Install SQLite RAG Quick Start Download the model Embedding Gemma from Hugging Face chosen as default model: SQLite RAG comes preconfigured to work with the Embedding Gemma model. When you add a document or text, it automatically creates a new database (if one does not already exist) and uses default settings, so you can get started immediately without manual setup. For help run: CLI Commands Configuration Settings are stored in the database and sho","default_branch":null,"files":null,"tree":[],"storefront":"/r/sqliteai","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/sqliteai/sqlite-rag/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}