Priom7/RAG-System-Architecture-With-NodeJS
📦 Open Source ProjectPriom7
A production-ready RAG architecture implementation using Node.js, LangChain, MySQL, and Redis for optimized AI retrieval.
This repository serves as a practical implementation guide for modern RAG system architecture. It moves beyond basic tutorials by incorporating enterprise-grade patterns such as caching layers to reduce latency and costs, and parallel processing to handle concurrent data retrieval tasks. The system leverages LangChain for orchestration, allowing seamless integration between vector-based retrieval and LLM generation. Key technical features include a MySQL-backed storage strategy, AI-driven query refinement to improve retrieval accuracy, and support for local LLM execution via Ollama alongside cloud-based models like DeepSeek-R1. The codebase demonstrates how to structure a Node.js backend to handle complex document retrieval pipelines, making it an excellent resource for developers aiming to build production-ready AI agents that require high-fidelity, context-grounded responses.
💡Highlights
- ├─Node.js & LangChain integration
- ├─Redis caching for low latency
- └─MySQL-backed vector retrieval
🎯For
- ├─Full-stack Developers
- └─AI Engineers