storing-and-querying-vectors

To store vector embeddings in Amazon S3, you can create a dedicated S3 Vectors bucket and index with the correct embedding dimension and distance metric, then ingest embeddings and run similarity queries using put-vectors and query-vectors operations.

S3 Vectors is suitable for RAG vector storage but may not fit sustained high QPS workloads. This skill validates your QPS requirements and guides you on when to choose alternatives like OpenSearch for sustained high query volumes.

When creating an S3 Vectors index, you must define immutable creation-time parameters including embedding dimension, distance metric, non-filterable metadata keys, and encryption choices, which cannot be modified after index creation.

Migrating embeddings to S3 Vectors requires aligning the vector dimensions and distance metrics between your source database and the new S3 Vectors index to ensure controlled migration and accurate similarity search results.

Yes, querying vectors in S3 Vectors supports query-time filters and metadata retrieval, allowing you to narrow down semantic search results using specific metadata attributes alongside top-k similarity scoring.

You should not use S3 Vectors if your workload requires sustained high queries per second beyond its throughput capacity, or if you need to change vector dimensions and distance metrics after index creation due to immutable parameter constraints.