RAG and Vector Search with pgvector and Amazon Bedrock (Part 4)

How to build retrieval-augmented generation that actually cites its sources — without a vector database subscription.

Most RAG tutorials reach for Pinecone, Chroma, or Weaviate as the vector store. Those are all fine services, but they add another cost line, another auth boundary, and a dependency you don't control. If you're already running Postgres — and for multi-tenant SaaS, you should be — the pgvector extension gives you vector similarity search inside your existing database, protected by the same Row-Level Security policies you already have.

This post covers the full query path in Sift: how a user's question becomes a vector, how pgvector finds the closest document chunks, and how Claude turns those chunks into a cited answer.

What RAG Actually Does

RAG and Vector Search with pgvector and Amazon Bedrock (Part 4)

Other newsrooms on this story

Related reading

Graph RAG vs Vector RAG: When to Use Each

The RAG era is ending for agentic AI — a new compilation-stage knowledge layer…

Advantages of Building a Vector Search Solution

I tested 7 vector databases for my RAG stack in 2026, here's the one nobody is…

How to build a production RAG pipeline in Python (without a vector database)

Chat With Your Documents Using Garudust Agent — No Vector Database Required