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7 pages in this section.
Why native vector storage in S3 changes the cost and operations trade-offs of RAG - object-storage economics, higher latency, and no cluster to run.
Create a vector bucket and a vector index, generate an embedding with Bedrock Titan, then write and query your first vectors via the SDK.
Query nearest neighbors in an S3 Vectors index - top-k tuning, distance metrics, and metadata filtering - in boto3 and AWS SDK v3.
Build an ingestion pipeline that embeds and loads millions of vectors into S3 Vectors - batching, concurrency, retries, and idempotency - in boto3 and AWS SDK v3.
A checklist for correct, cost-aware, secure S3 Vectors use from the SDK - index sizing, metadata filtering, batching, query patterns, and security - across boto3 and AWS SDK v3.