#Pgvector

In this article, you will learn how to implement vector similarity search in PostgreSQL using the pgvector extension, allowing you to find semantically similar results based on meaning rather than keyword matching.

Most "vector database comparison" posts you'll find online were written in 2023, when pgvector was a...

You can read the original post over on lukaswalter.dev. Developers often start RAG apps using...

RAG Without the Chatbot: pgvector + Ollama for Operational Data Most RAG tutorials start...

This post was originally published on Rivestack. Most pgvector tutorials are written for Python....

From Dev.to - postgresql: Opinion: Why You Should Use PostgreSQL 17 with pgvector Instead of Dedicated Vector Databases for RAG in 2026

Pure vector search is not always enough. If you have built a semantic search system with pgvector and...

How to add vector similarity search to an existing Postgres app using pgvector — covering extension setup, embedding generation, cosine similarity queries, and HNSW indexing for performance.

Latency, recall, cost, and ops weight at 1M vectors. Numbers from named benchmarks, the rest labeled illustrative. A decision tree at the end.

Introducing pgvector for Heroku Postgres. The extension adds the vector data type to Heroku Postgres along with additional functions to work with it.

We’re pleased to introduce the pgvector extension (https://github.com/pgvector/pgvector) on Heroku Postgres. In an era where large language models...

Learn how vector databases help AI systems find semantically similar content by storing and indexing data in high-dimensional vector form.