Using Vector Functions
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SingleStore provides a native vector data type and supports similarity searches over vector data using k-nearest neighbor search (kNN) and indexed Approximate Nearest Neighbor (ANN) search.
Resources
The following resources provide information about SingleStore's vector processing:
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      Vector Type: The native vector data type. 
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      Working with Vector Data: Vector similarity search. 
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      How to Bulk Load Vectors: Load larger vector data sets using a binary format. 
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      Vector Indexing: Indexed Approximate Nearest Neighbor (ANN) vector similarity search. 
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      Hybrid Search - Re-ranking and Blending Searches: Combine indexed (ANN) vector similarity search and full-text search. 
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      Tuning Vector Indexes and Queries: Adjust the index building and searching parameters to meet your application's needs for query accuracy and performance. 
The following functions are available for the vector type:
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      DOT_ PRODUCT: Calculate the dot product of two vectors. 
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      EUCLIDEAN_ DISTANCE: Calculate the Euclidean distance between two vectors. 
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      Cosine Similarity and Cosine Distance: Calculate the cosine similarity and distance metrics. 
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      SCALAR_ VECTOR_ MUL: Multiply a vector by a scalar value. 
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      VECTOR_ ADD: Add two vectors. 
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      VECTOR_ ELEMENTS_ SUM: Sum all the elements in a vector. 
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      VECTOR_ KTH_ ELEMENT: Find the kth element of a vector. 
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      VECTOR_ MUL: Multiply two vectors element-wise. 
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      VECTOR_ NUM_ ELEMENTS: Count the number of elements in a vector. 
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      VECTOR_ SORT: Sort a vector. 
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      VECTOR_ SUB: Subtract one vector from a second vector. 
Last modified: April 30, 2024