Amazon RDS for PostgreSQL now supports pgvector for simplified ML model integration
Amazon Relational Database Service (RDS) for PostgreSQL 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.
Amazon Relational Database Service (RDS) for PostgreSQL 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 Amazon RDS, 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.
The pgvector extension is available on all database instances in Amazon RDS running PostgreSQL 15.2 and higher in all AWS Regions, including the AWS GovCloud (US) Regions.
You can get started by launching a new Amazon RDS DB instance directly from the AWS Console or the AWS CLI. Learn more about pgvector in the AWS Database Blog and the Amazon RDS User Guide.