{"repo":"groovy-web/rag-system-pgvector","free":true,"listed":false,"github":"https://github.com/groovy-web/rag-system-pgvector","clone":"git clone https://github.com/groovy-web/rag-system-pgvector.git","description":"Production-ready RAG system using PostgreSQL + pgvector for semantic search","language":null,"stars":17,"topics":["ai-infrastructure","embeddings","llm","pgvector","postgresql","rag","retrieval-augmented-generation","semantic-search","typescript","vector-search"],"license":"MIT","category":"ai-agents","readme_excerpt":"RAG System with pgvector Production-ready Retrieval-Augmented Generation system using PostgreSQL + pgvector 🚀 Overview A complete, production-ready RAG (Retrieval-Augmented Generation) implementation using PostgreSQL with the pgvector extension. This system combines semantic search with LLM generation to build intelligent applications that can reason over your data. ✨ Features - Semantic Search : Vector similarity search with pgvector - Hybrid Search : Combine semantic and keyword search - Multiple Embeddings : Support for OpenAI, Cohere, and local models - Chunking Strategies : Smart document splitting for better retrieval - Reranking : Improve relevance with result reranking - Caching : Reduce costs with intelligent caching - Streaming : Real-time response streaming - TypeScript : Full type safety - Production Ready : Error handling, logging, monitoring - Scalable : Horizontal scaling with connection pooling 🏗️ Architecture 📦 Installation Prerequisites - PostgreSQL 15+ with pgvector extension - Node.js 18+ - OpenAI API key (or other embedding provider) Setup 1. Clone and install : 2. Database setup : 3. Environment configuration : Edit .env : 4. Run the example : 🎯 Quick Start Basic RAG Query Ingest Documents 📚 Advanced Features Custom Chunking Hybrid Search Reranking Streaming Responses Conversation Memory 🔧 Configuration RAG System Options Database Schema 🧪 Testing 📊 Monitoring & Analytics Query Logging Performance Metrics 🚀 Production Deployment Connection Pooli","default_branch":null,"files":null,"tree":[],"storefront":"/r/groovy-web","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/groovy-web/rag-system-pgvector/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."}