This pattern deploys two Lambda functions backed by Amazon S3 Vectors and Amazon Bedrock to implement a serverless Retrieval-Augmented Generation (RAG) pipeline.

The Ingest function takes text documents, generates vector embeddings using Bedrock Titan Embeddings V2, and stores them in an S3 vector index with metadata. The Query function takes a natural language question, embeds it, performs a similarity search against S3 Vectors, and uses Bedrock Claude to generate an answer grounded in the retrieved context.

S3 Vectors provides purpose-built, cost-optimized vector storage with sub-second query latency — no vector database infrastructure to manage.