Improving document retrieval with sparse semantic encoders · OpenSearch
OpenSearch 2.11 introduced neural sparse search—a new efficient method of semantic retrieval. In this blog post, you’ll learn about using sparse encoders for semantic search. You’ll find that neural sparse search reduces costs, performs faster, and improves search relevance. We’re excited to share benchmarking results and show how neural sparse search outperforms other search methods. You can even try it out by building your own search engine in just five steps. To skip straight to the results, see Benchmarking results.
OpenSearch 2.11 introduced neural sparse search—a new efficient method of semantic retrieval. In this blog post, you’ll learn about using sparse encoders for semantic search. You’ll find that neural sparse search reduces costs, performs faster, and improves search relevance. We’re excited to share benchmarking results and show how neural sparse search outperforms other search methods. You can even try it out by building your own search engine in just five steps. To skip straight to the results, see Benchmarking results . What are dense and sparse vector embeddings? When
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