Most RAG (Retrieval-Augmented Generation) projects you see online are great demos. But try running them in production and you’ll quickly hit issues: no ingestion pipeline no async processing no scaling story no observability no proper deployment setup So I decided to build something that actually works beyond demos. Introducing Ragify An open-source, production-oriented RAG backend built with: Node.js + Express + TypeScript MongoDB for documents + logs Qdrant for vector search Redis + BullMQ for async ingestion OpenAI for embeddings + responses GitHub: https://github.com/open-loft/ragify What makes it different Instead of just “chat + embeddings”, Ragify focuses on the full pipeline: Upload → Queue → Chunk → Embed → Store → Retrieve → Generate Some key features: Async ingestion (doesn’t block uploads) Token-based chunking with overlap Streaming responses (SSE) Rate limiting + config validation Dockerized production setup Why I built this I wanted a backend that: I could self-host I could extend safely I…