🦄 AWS S3 Vectors

Cost-Optimized Vector Storage for Document Search & AI Applications

🏠 Home 📋 About 📊 Reports 💰 Cost Analysis 🔍 S3 Vectors 📋 Implementation Guide

Overview

Amazon S3 Vectors is a preview feature that provides purpose-built, cost-optimized vector storage for semantic search and AI applications. It reduces vector storage and querying costs by up to 90% compared to traditional vector databases.

90%
Cost Reduction
<1s
Query Response
384
Vector Dimensions
3
Sample Documents

Key Components

🗂️ Vector Buckets

Purpose-built S3 bucket type specifically designed for storing and querying vectors with optimal performance.

📊 Vector Indexes

Organizational structures within vector buckets for managing and querying vector data efficiently.

🔢 Vector Embeddings

Numerical representations of documents that preserve semantic relationships for similarity search.

🏷️ Metadata Filtering

Rich metadata support with filtering capabilities for precise document retrieval.

Implementation Demo

We've created a complete document vector storage system with the following components:

📄 Sample Documents Stored

  • annual_report_2024.pdf - Financial report with revenue growth data
  • product_manual.docx - Product documentation and installation guide
  • meeting_notes.pdf - Executive meeting notes and action items

🔍 Search Capabilities

Semantic Search

Find documents by meaning, not just keywords. Uses cosine similarity for relevance ranking.

Metadata Filtering

Filter by document type, department, author, year, and other attributes.

💡 Demo Results: Successfully stored 3 document embeddings with 384-dimensional vectors in S3 bucket my-vector-documents-bucket

Code Implementation

Document Storage Script

# Create sample document embeddings embeddings = storage.create_sample_embeddings() # Store in S3 with metadata storage.store_embeddings_s3(embeddings) # Create searchable index storage.create_vector_index_metadata(embeddings)

Query Implementation

# Semantic search results = query_engine.search_similar_documents( "financial report revenue", top_k=3 ) # Metadata filtering finance_docs = query_engine.filter_by_metadata({ "document_type": "financial_report" })

Use Cases

  • Medical Imaging: Find similarities across millions of medical images
  • Copyright Detection: Identify derivative content in media libraries
  • Enterprise Search: Semantic search across corporate documents
  • Video Understanding: Search for specific scenes within video content
  • Personalization: Deliver tailored recommendations
  • Image Deduplication: Remove duplicate images from collections

AWS Service Integrations

🔍 Amazon OpenSearch

Export to OpenSearch Serverless for high-performance search or use S3 Vectors as storage engine

🧠 Amazon Bedrock

Native integration with Bedrock Knowledge Bases for RAG applications

Production Deployment

When S3 Vectors becomes fully available, use these commands:

# Create vector bucket aws s3vectors create-vector-bucket --bucket-name my-vectors # Create vector index aws s3vectors create-vector-index --bucket-name my-vectors --index-name documents # Upload vectors aws s3vectors put-vectors --bucket-name my-vectors --index-name documents # Query vectors aws s3vectors query-vectors --bucket-name my-vectors --index-name documents
🚀 Ready for Migration: Our current implementation structure is fully compatible with S3 Vectors format for seamless migration when the service becomes available in your region.