PostgreSQL makes it easier to store and query vector data for artificial intelligence and machine learning (AI/ML) use cases with the pgvector extension. Learning best practices for vector search will help you deliver a high-performance experience to your customers. In this session, learn how to store data from Amazon Bedrock in an Amazon Aurora PostgreSQL-Compatible Edition database and learn SQL queries and tuning parameters to optimize the performance of your application when working with AI/ML data, vector data types, exact and approximate nearest neighbor search algorithms, and vector-optimized indexing.
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