后端与数据库

storing-and-querying-vectors

Name: storing-and-querying-vectors
Author: aws

aws/agent-toolk…-for-aws

Store and query vector embeddings using Amazon S3 Vectors, a cost-effective long-term vector storage service with its own API namespace (s3vectors). Triggers on: create S3 vector bucket, vector index, store embeddings, semantic search, RAG vector storage, similarity search, vector database, migrate from other vector databases. Do NOT use for: querying tabular data (use querying-data-lake), S3 object storage, or hundreds/thousands of sustained QPS (use OpenSearch).

6748 次安装
3300 星标
收录于 2026.05.06

安装命令

$ npx skills add https://github.com/aws/agent-toolkit-for-aws --skill storing-and-querying-vectors

SKILL.md

Store and Query Vectors with Amazon S3 Vectors

Overview

Amazon S3 Vectors is a cost-effective AWS service for storing and querying vector embeddings at scale. Optimized for long-term storage with subsecond latency for cold queries, as low as 100ms for warm queries.

Decision Guide

Hundreds/thousands of sustained queries per second (QPS): Wrong tool. Recommend OpenSearch.
Hybrid search, aggregations, faceted search: Recommend OpenSearch with S3 Vectors as storage engine. For OpenSearch integration, search AWS docs for "Using S3 Vectors with OpenSearch Service".
Tiered (bulk + hot): S3 Vectors for storage + OpenSearch Serverless for real-time. See references/limits-and-patterns.md.
Cost-effective storage, infrequent queries, RAG: S3 Vectors is the right fit. Proceed.

For latest guidance, search AWS docs for "S3 Vectors best practices".

Common Tasks

Classify the request before starting:

SKILL.md

Store and Query Vectors with Amazon S3 Vectors

Overview

Decision Guide

Common Tasks

相关 Skills