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storing-and-querying-vectors

Store and query vector embeddings using Amazon S3 Vectors.

2.3k|234|Updated Apr 23, 2026
Search Tags:#embeddings#migration#rag#vector#semantic-search#knowledge-base#s3-vectors
One-click install
npx skills add https://github.com/aws/agent-toolkit-for-aws --skill storing-and-querying-vectors-aws
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: storing-and-querying-vectors
Source: https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-data-analytics/skills/storing-and-querying-vectors
Command: npx skills add https://github.com/aws/agent-toolkit-for-aws --skill storing-and-querying-vectors-aws

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Store and query vector embeddings using Amazon S3 Vectors to enable scalable semantic search and retrieval-augmented workflows for AI applications.

Core Features & Use Cases

  • Store and index embeddings in a dedicated vector bucket.
  • Create immutable vector indexes with defined dimensions and distance metrics.
  • Enable semantic search, RAG workflows, and migrations across vector databases.

Quick Start

Use the storing-and-querying-vectors skill to configure a vector bucket, create an index, and ingest your first embedding vectors for semantic search.

Frequently Asked Questions about storing-and-querying-vectors

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I store and query vector embeddings for semantic search?▼

Store and query vector embeddings for semantic search by creating a dedicated vector bucket, defining an immutable index with specified dimensions and distance metrics, and ingesting embeddings using AWS CLI or MCP tools.

Can I use S3 Vectors to build a knowledge base for retrieval augmented generation?▼

S3 Vectors supports retrieval augmented generation by allowing you to store and index embeddings for scalable semantic search, enabling retrieval workflows for AI applications.

What is the process to migrate vectors across tenants and projects in S3 Vectors?▼

Migrate vectors across tenants and projects by configuring a vector bucket and creating immutable indexes with defined dimensions and distance metrics, using AWS CLI commands for vector operations.

Do I need to define dimensions and distance metrics when creating a vector index?▼

Defining dimensions and distance metrics is required when creating an immutable vector index in S3 Vectors to ensure accurate semantic search and retrieval.

Are S3 Vectors bucket and index settings immutable after creation?▼

Bucket and index settings in S3 Vectors are immutable after creation, enforcing consistent configuration for dimensions and distance metrics throughout the vector storage lifecycle.

What AWS tools can I use for vector operations in S3 Vectors?▼

AWS MCP tools or CLI commands such as create-bucket, create-index, put-vectors, and query-vectors are used for vector operations in S3 Vectors.

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