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Copy file name to clipboardExpand all lines: solutions/search/vector.md
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## Dense vector search
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Dense neural embeddings capture semantic meaning by translating content into fixed-length vectors of floating-point bumbers. Similar content maps to nearby points in the vector space, making them ideal for:
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Dense neural embeddings capture semantic meaning by translating content into fixed-length vectors of floating-point numbers. Similar content maps to nearby points in the vector space, making them ideal for:
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- Finding semantically similar content
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- Matching questions with answers
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- Image similarity search
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- Domain-specific search
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- Large-scale deployments
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[Learn more about sparse vector search with ELSER](vector/sparse-vector.md).
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[Learn more about sparse vector search with ELSER](vector/sparse-vector.md).
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