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.
Open-source vector similarity search for Postgres
Store your vectors with the rest of your data. Supports:exact and approximate nearest neighbor search
single-precision, half-precision, binary, and sparse vectors
L2 distance, inner product, cosine distance, L1 distance, Hamming distance, and Jaccard distance
any language with a Postgres client
Plus ACID compliance, point-in-time recovery, JOINs, and all of the other great features of Postgres
