Getting started with pgvector and Amazon Aurora PostgreSQL

Amazon Aurora PostgreSQL-Compatible Edition 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 or images input into a large language model (LLM). With pgvector, you can query embeddings in your Aurora PostgreSQL database to perform efficient semantic similarity searches of these data types, represented as vectors, combined with other tabular data in Aurora. This enables the use of generative AI and other AI/ML systems to build new content, enable hyper-personalization, create interactive experiences, and more. In this session, you will learn how you can start using the pgvector extension with your Aurora PostgreSQL database.

***To learn more about the services featured in this talk, please visit: https://aws.amazon.com/rds/aurora/faqs/#Generative_AI
****To download a copy of the slide deck from this webinar visit: https://pages.awscloud.com/Getting-started-with-pgvector-and-Amazon-Aurora-PostgreSQL_2023_SN-0910-DAT_OD

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