Amazon Aurora PostgreSQL now supports pgvector for vector storage and similarity search

Amazon Aurora PostgreSQL-Compatible Edition now supports the pgvector extension to store embeddings from machine learning (ML) models in your database and to perform efficient similarity searches. Embeddings are numerical representations (vectors) created from generative AI that capture the semantic meaning of text input into a large language model (LLM). pgvector can store and search embeddings from Amazon Bedrock, Amazon SageMaker, and more.

By using pgvector on Aurora PostgreSQL, you can simply set up, operate, and scale databases for your ML-enabled applications. The pgvector extension allows you to build ML capabilities into your e-commerce, media, health applications, and more to find similar items within a catalog. For example, a streaming service can use pgvector to provide a list of film recommendations similar to the one you just watched. Aurora machine learning enables you to add ML-based predictions to applications via the familiar SQL programming language, so you don't need to learn separate tools or have prior machine learning experience.

The pgvector extension is available on Aurora PostgreSQL 15.3, 14.8, 13.11, 12.15 and higher in AWS Regions including the AWS GovCloud (US) Regions.

You can get started by launching a new Amazon Aurora DB instance directly from the AWS Console or the AWS CLI. Learn more about pgvector in the AWS Database Blog. To get started with Amazon Aurora, take a look at our getting started page.

If you are interested in learning more about this launch, you can watch our team's demo on AWS On Air.