Automating Vector Embedding Generation in Amazon Aurora PostgreSQL with Amazon Bedrock: Approaches for Custom Vector Database Solutions

Amazon Aurora PostgreSQL users can automate vector embedding generation with Amazon Bedrock. Embeddings are mathematical representations that enable semantic search, recommendation systems, and natural language processing tasks. Organizations can choose between Amazon Bedrock's managed RAG solutions and custom vector database solutions using PostgreSQL with the pgvector extension. The post explores several approaches for automating embedding generation in Aurora PostgreSQL with pgvector, including detecting data changes, sending content to Amazon Bedrock, receiving generated embeddings, and storing them alongside original data.