PGVector Explained: Boost Semantic Search and Retrieval with Postgres

Learn how PGVector integrates embeddings into Postgres for fast and accurate semantic search and RAG workflows.

PGVector is an extension for PostgreSQL that lets you store, manage, and query vector data — specifically embedding vectors. In our AI assistant project, PGVector is crucial for retrieval-augmented generation (RAG), where we embed documents, user history, or any text into vectors, store them in the database, and later search them efficiently to provide context-aware responses.

Free medium member — visit here!

Here’s how it works: when you input text (like a paragraph, sentence, or document), the system converts it into a vector representation using an embedding model. This vector captures the semantic meaning of the text. PGVector enables you to insert these vectors into a Postgres table and then perform similarity searches (like finding the closest matching text) using operations like cosine similarity or Euclidean distance. This makes it possible to retrieve relevant documents or knowledge from a large dataset quickly and accurately.

Disclaimer: This post is part of our comprehensive guide “Building an AI Assistant: Essential Tools and Concepts”. Each topic…