This article explains the role of vector databases in generative AI applications and AWS solutions for implementing them.

• Vector databases store and query embeddings to enable semantic search for RAG applications
• Retrieval Augmented Generation (RAG) enriches LLM prompts with domain-specific, contextually relevant data
• Embeddings are numeric vectors representing semantic meaning in multi-dimensional space
• AWS offers vector capabilities across multiple database services: Aurora PostgreSQL, RDS PostgreSQL, OpenSearch, MemoryDB, Neptune Analytics, DocumentDB
• Knowledge Bases for Amazon Bedrock automates chunking, embedding generation, and vector storage
• Key considerations: chunking strategy, embedding dimensions, exact vs approximate search, data governance
• Aurora PostgreSQL with pgvector ideal for existing PostgreSQL users; OpenSearch for distributed, hybrid search needs
• MemoryDB delivers fastest vector search performance; Neptune Analytics optimal for GraphRAG use cases

Vector databases enable generative AI applications to leverage proprietary domain-specific data for accurate, contextually relevant outputs while maintaining data governance and security.