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OpenSearch is the flexible, scalable, open-source way to build solutions for data-intensive applications. Explore, enrich, and visualize your data with built-in performance, developer-friendly tools, and powerful integrations for machine learning, data processing, and more.

Current Version: 2.11.1 / Nov 30, 2023

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Featured Image for Semantic Search with OpenSearch: Architecture options and Benchmarks
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Unlike traditional lexical search algorithms such as BM25, which only take keywords into account, semantic search improves search relevance by understanding the context and semantic meaning of search terms and context. In general, semantic search has two key elements: 1. Embedding generation: A machine learning (ML) model, usually a deep neural network model (for example, TAS-B) is used to generate embeddings for both search terms and content; 2. k-NN: Searches return results based on embedding proximity using a vector search algorithm like k-nearest neighbors (k-NN).