How to Use pgvector with Python: A Complete Guide

You've decided to use PostgreSQL for your vector embeddings. Smart move. Now you need to wire it up from Python — and if you've landed here, you've probably already noticed that there are a few different libraries involved, the syntax isn't immediately obvious, and the official pgvector docs give you the C extension but leave the Python story somewhat scattered. This guide covers the whole picture: installing the Python client, connecting with both psycopg3 and SQLAlchemy, storing and querying embeddings, building indexes, and wiring it up into a real RAG pipeline. By the end you'll have a working setup you can actually ship. What You Need Before You Start You'll need: A PostgreSQL database with the vector extension enabled Python 3.8+ The pgvector Python package If you're running PostgreSQL locally, install the pgvector extension from the pgvector GitHub repo and run CREATE EXTENSION vector;. If you're using a managed PostgreSQL service, the extension is typically pre-installed — on R