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…