Author: Steve Dille

Learn how to use binary quantization with reranking (HNSW+BQ) in pgvector to scale vector search to hundreds of millions or billions of vectors on Amazon Aurora PostgreSQL, with practical guidance on index sizing, recall validation, and the scenarios where the approach works best.

April, 2026: Aurora Serverless v2 has been renamed Aurora serverless. No action required. pgvector is the open source PostgreSQL extension for vector similarity search that powers generative artificial intelligence (AI) applications using techniques such as semantic search and retrieval-augmented generation (RAG). Amazon Aurora PostgreSQL-Compatible Edition has supported pgvector 0.5.1 since 2023. Amazon Aurora now supports pgvector version 0.7.0, […]

April, 2026: Aurora Serverless v2 has been renamed Aurora serverless. No action required. Because organizations store a large amount of their data in relational databases, there is a clear impetus to augment these datasets using generative artificial intelligence (AI) foundation models to elevate end-user experiences. In this post, we explore how to integrate Amazon Aurora […]

April, 2026: Aurora Serverless v2 has been renamed Aurora serverless. No action required. Generative AI has increased the possibilities for businesses to build applications that require searching and comparison of unstructured data types such as text, images, and video. Embeddings, or vectors, capture the meaning and context of this unstructured data in a machine-readable form, […]

April, 2026: Aurora Serverless v2 has been renamed Aurora serverless. No action required. Amazon Bedrock is the easiest way to build and scale generative AI applications with foundational models (FMs). FMs are trained on vast quantities of data, allowing them to be used to answer questions on a variety of subjects. However, if you want […]