Build semantic search with native vector support in Amazon DynamoDB

This article demonstrates how to build semantic search applications using Amazon DynamoDB's new native vector search capability, eliminating the need for separate vector databases.

• Store vector embeddings alongside operational data in DynamoDB tables with vector indexes
• Generate embeddings using Amazon Bedrock and perform similarity searches with natural language queries
• Supports approximate nearest neighbor (ANN) search with configurable distance functions (DOT_PRODUCT, COSINE, EUCLIDEAN)
• Includes built-in filtering, serverless scaling, and pay-per-use billing based on vector write/search bytes
• Walkthrough builds semantic search over research paper abstracts using Python, Bedrock, and DynamoDB
• Vector indexes support up to 4,096 dimensions and work only with on-demand capacity mode
• Reduces architectural complexity, latency, and costs compared to maintaining separate operational and vector databases

Native vector search in DynamoDB simplifies building AI-powered applications by consolidating operational data and vector workloads into a single serverless database service.