# How PostgreSQL and pgvector Support Vector Search and RAG

DevFeed: [How PostgreSQL and pgvector Support Vector Search and RAG](<https://devfeed.tech/articles/you-probably-don-t-need-a-vector-database-17865.md>)

Original publisher: [Read original article](<https://encore.dev/blog/you-probably-dont-need-a-vector-database>)

Author: Ivan Cernja

Published: 2026-03-09T00:00:00Z

Content type: tutorial

Language: en

Sources: [Encore Updates](<https://devfeed.tech/sources/encore-updates.md>)

Topics: [PostgreSQL](<https://devfeed.tech/topics/postgresql.md>), [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [AI search](<https://devfeed.tech/topics/ai-search.md>), [Database](<https://devfeed.tech/topics/database.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [pgvector](<https://devfeed.tech/tags/pgvector.md>), [postgresql](<https://devfeed.tech/tags/postgresql.md>), [rag](<https://devfeed.tech/tags/rag.md>), [search](<https://devfeed.tech/tags/search.md>), [vector-search](<https://devfeed.tech/tags/vector-search.md>)

## AI overview

This tutorial explains embeddings, vector similarity search, and retrieval-augmented generation, then shows how to implement them with PostgreSQL and the pgvector extension instead of a separate vector database. It describes how embeddings represent semantic meaning and how vector search can find related content beyond exact keyword matches.

## Source excerpt

What vectors, similarity search, and RAG actually do under the hood, and why PostgreSQL handles most of it.