In this informative video, Steve Dille, a product manager for Amazon Aurora at AWS, introduces pgvector, a PostgreSQL extension that enables vector similarity search within databases. He explains how pgvector works with embeddings generated from large language models to perform semantic searches on text and images. The video covers common use cases for pgvector, including visual search in retail and recommendation systems. Dille also demonstrates a semantic search application using pgvector with retrieval augmented generation (RAG) to enhance AI model responses with proprietary data. The presentation highlights how pgvector in Aurora PostgreSQL can help developers leverage generative AI capabilities while maintaining data security and control.