I built a production-ready RAG backend (because most examples break in real life)

Yasir Mansoori — Tue, 05 May 2026 23:19:06 +0000

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 could actually use in a real product

Looking for feedback / contributors

Would love input on:

improving retrieval quality
reranking approaches
hybrid search strategies
cost + latency optimization

If you’re building in the RAG / LLM space, this might be useful.

Would appreciate your thoughts 🙌

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Yasir Mansoori — Fri, 14 Feb 2025 23:52:00 +0000

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https://www.npmjs.com/package/eventar

DEV Community: Yasir Mansoori

I built a production-ready RAG backend (because most examples break in real life)

Struggling with event management in React? Meet Eventar!