
Amazon OpenSearch Service now enables semantic search using vector embeddings from Amazon Bedrock, allowing users to find relevant documents based on meaning rather than exact word matches, with hybrid search combining both techniques delivering superior results.


<div><p>This article explains semantic search capabilities in Amazon OpenSearch Service, comparing it with traditional lexical search and demonstrating practical applications.</p><ul><li>Semantic search uses vector embeddings to find contextually similar documents beyond keyword matching</li><li>OpenSearch supports both lexical and vector search techniques for comprehensive search experiences</li><li>Amazon Bedrock integration enables high-quality vector embeddings without fine-tuning</li><li>Cross-modal search allows finding images using textual descriptions via multi-modal embeddings</li><li>Hybrid search combining lexical and vector approaches improves result quality by approximately 15%</li><li>Interactive demos on OpenSearch Playground showcase semantic vs. lexical search differences</li><li>Vector engine for OpenSearch Serverless simplifies deployment of semantic search solutions</li></ul><p>OpenSearch's vector engine enables semantic search for better relevance, with hybrid approaches delivering superior results for production applications.</p></div>


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