Database optimized for storing embeddings and performing similarity (nearest neighbor) searches efficiently at scale. On AWS, services like OpenSearch Service, Aurora (with pgvector), Neptune, and RDS for PostgreSQL can act as a vector store.
In AIF-C01 (Domain 3) it's a core component of RAG architectures: documents are converted into embeddings and indexed in a vector database to retrieve the most relevant context for a user query.
Vector database is a concept that can show up on the exam. Check the full guide to see it in context.