MarkLogic 12 EA 1 Product Documentationvec:cosine-similarity
vec:cosine-similarity( $vector1 as vec:vector, $vector2 as vec:vector ) as xs:double
Summary
Returns the cosine similarity between two vectors. The vectors must be of the same dimension.
| Parameters | |
|---|---|
| vector1 | The vector from which to calculate the cosine similarity with vector2. |
| vector2 | The vector from which to calculate the cosine similarity with vector1. |
Example
xquery version "1.0-ml"; let $vec1 := vec:vector((3.14,1.59,2.65)) let $vec2 := vec:vector((3.58,9.79,3.23)) return vec:cosine-similarity($vec1,$vec2); => 0.73559182882309
Example
xquery version "1.0-ml";
let $vec1 := vec:vector(fn:head(fn:doc('pronethalol.json'))/data/array-node{embedding})
let $vec2 := vec:vector(fn:head(fn:doc('cell_renewal.json'))/data/array-node{embedding})
return vec:cosine-similarity($vec1,$vec2)
=> The cosine similarity 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 $view := op:from-view('vecs','wiki_vectors')
=>op:bind(op:as('cosineSim',ovec:cosine-similarity(op:col('embedding'),$qv)))
=>op:select((op:col('title'),op:col('text'),op:col('cosineSim')))
=>op:order-by(op:desc(op:col('cosineSim')))
=>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
:)