Posted On: Oct 24, 2023
Amazon Aurora PostgreSQL-Compatible Edition now supports v0.5.0 of the pgvector extension to store embeddings from machine learning (ML) models in your database and to perform efficient similarity searches. This version includes Hierarchical Navigable Small World (HNSW) indexing support, parallelization of ivfflat index builds, and improves performance of its distance functions.
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. With pgvector on Amazon RDS, you can simply set up, operate, and scale databases for your GenAI applications. pgvector 0.5.0 adds support for HNSW indexing, which lets you execute similarity searches with low latency and produces highly relevant results. Additionally, HNSW in pgvector supports concurrent inserts, and updating/deleting vectors from the index. You can integrate your GenAI applications with pgvector using open-source frameworks like LangChain, simplifying searches over your vector data.
The pgvector extension version 0.5.0 is available on Aurora PostgreSQL 15.4, 14.9, 13.12, 12.16 and higher in all AWS Regions including the AWS GovCloud (US) Regions.
Amazon Aurora is designed for unparalleled high performance and availability at a global scale with full MySQL and PostgreSQL compatibility. It provides built-in security, continuous backups, serverless compute, up to 15 read replicas, automated multi-Region replication, and integrations with other AWS services. Learn more about pgvector in the AWS Database Blog. To get started with Amazon Aurora, take a look at our getting started page.