This video from AWS re:Invent 2023 explores best practices for querying vector data in PostgreSQL for generative AI applications. Jonathan Katz, a Postgres core team member, dives deep into vector search and retrieval techniques using the pgvector extension. He covers key concepts like retrieval-augmented generation, embedding models, and approximate nearest neighbor searches. The talk focuses on optimizing performance and relevancy when working with large vector datasets in Postgres, comparing indexing methods like HNSW and IVFFlat. Katz also discusses strategies for filtering, storage considerations, and new features in Amazon Aurora that can accelerate vector queries. This comprehensive overview provides valuable insights for developers looking to implement efficient vector search capabilities in their PostgreSQL databases for AI/ML workloads.