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JavaScript vec.cosineDistance

MarkLogic 12 Product Documentation
vec:cosine-distance

vec:cosine-distance(
   $vector1 as vec:vector,
   $vector2 as vec:vector
) as xs:double

Summary

Returns the cosine distance between two vectors. The vectors must be of the same dimension.

Parameters
vector1 The vector from which to calculate the cosine distance to vector2.
vector2 The vector from which to calculate the cosine distance to vector1.

Example

  xquery version "1.0-ml";
  let $vec1 := vec:vector((3.14,1.59,2.65))
  let $vec2 := vec:vector((3.58,9.79,3.23))

  return vec:cosine-distance($vec1,$vec2);

  => 0.26440817117691

Example

  xquery version "1.0-ml";

  let $vec1 := vec:vector(fn:head(fn:doc('pronethalol.json'))/data/array-node{embedding})
  let $vec2 := vec:vector(fn:head(fn:doc('cell_renewal.json'))/data/array-node{embedding})

  return vec:cosine-distance($vec1,$vec2)

  => The cosine distance between vectors in JSON arrays named 'embedding'
      in documents 'pronethalol.json' and 'cell_renewal.json'

Example

xquery version "1.0-ml";
import module namespace op = 'http://marklogic.com/optic'
           at 'MarkLogic/optic.xqy';
import module namespace ovec = 'http://marklogic.com/optic/expression/vec'
            at 'MarkLogic/optic/optic-vec.xqy';
  
  (: construct a query vector from the JSON array node 'emb' in document 'embedding104576.json' :)
  let $qv := vec:vector(fn:head(fn:doc('embedding104576.json'))/array-node('emb'))

  let $view := op:from-view('vecs','wiki_vectors')
             =>op:bind(op:as('cosineDist',ovec:cosine-distance(op:col('embedding'),$qv)))
             =>op:select((op:col('title'),op:col('text'),op:col('cosineDist')))
             =>op:order-by(op:asc(op:col('cosineDist')))
             =>op:limit(30)
             =>op:result()
  return $view;

  =>
  // Performs a linear scan of vectors in the 'embedding' column in the 'wiki_vectors' view to find the top
  //  30 matches to the query vector $qv

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