storing-and-querying-vectors

What problem does it solve?

This Skill helps you store vector embeddings and run semantic similarity search without managing a high-throughput vector database.

Core Features & Use Cases

• Vector bucket and index setup: Create S3 Vectors vector buckets and indexes with the correct embedding dimension and distance metric.
• Embedding ingestion and querying: Generate embeddings (via Bedrock when needed) and insert or query them using put-vectors and query-vectors.
• Cost- and workload-aware decisioning: Guides when to use S3 Vectors versus alternatives like OpenSearch for sustained high QPS, and provides troubleshooting for common failure modes.

Quick Start

Use this skill to create an S3 Vectors vector bucket and index, store your embeddings, and run a semantic query with top-k results for a given question or text prompt.