Bringing RAG's to Postgres with PGVector

Learn how to store, update, and delete embeddings in real-time using PostgreSQL with the PGVector extension, demonstrating user-specific RAG with FastAPI, LangChain, and OpenAI.

Overview

When building RAGs, a common pain point is: where do you store your embeddings? How do you update or delete them in real-time? What if you want isolated knowledge per user?
That’s where PGVector comes in — an open-source vector similarity search extension for Postgres that makes all of this way easier.
With PGVector, you can keep everything in your DB, with minimal infra overhead, real-time updates, and native Postgres support.
I’ll show how I used PGVector in a RAG system with user-specific context and real-time updates — all running with FastAPI, LangChain, and OpenAI.

Tech stack