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. -
EUCLIDEAN_
DISTANCE : Calculate the Euclidean distance between two vectors. -
Cosine Similarity and Cosine Distance: Calculate the cosine similarity and distance metrics.
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SCALAR_
VECTOR_ : Multiply a vector by a scalar value.MUL -
VECTOR_
ADD : Add two vectors. -
VECTOR_
ELEMENTS_ : Sum all the elements in a vector.SUM -
VECTOR_
KTH_ : Find the kth element of a vector.ELEMENT -
VECTOR_
MUL : Multiply two vectors element-wise. -
VECTOR_
NUM_ : Count the number of elements in a vector.ELEMENTS -
VECTOR_
SORT : Sort a vector. -
VECTOR_
SUB : Subtract one vector from a second vector.
Last modified: April 30, 2024