MarkLogic 12 EA 1 Product Documentation
vec.euclideanDistance

vec.euclideanDistance(
   vector1 as vec.vector,
   vector2 as vec.vector
) as Number

Summary

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

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

Example

  const vec1 = vec.vector([3.14,1.59,2.65])
  const vec2 = vec.vector([3.58,9.79,3.23])

  vec.euclideanDistance(vec1,vec2)

  => 8.23225343535979

Example

  const vec1 = vec.vector(xdmp.toJSON(fn.doc('pronethalol.json')).xpath('/data/array-node{embedding}'))
  const vec2 = vec.vector(fn.head(fn.doc('cell_renewal.json')).xpath('/data/array-node{embedding}'))

  vec.euclideanDistance(vec1,vec2)

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

Example

'use strict';

const op = require('/MarkLogic/optic');

// construct a query vector from the JSON array node 'emb' in document 'embedding104576.json'
const qv = vec.vector(fn.head(fn.doc('embedding104576.json')).xpath("/array-node('emb')"))

const view = op.fromView('vecs','wiki_vectors')
               .bind(op.as('euclDistance',op.vec.euclideanDistance(op.col('embedding'),qv)))
               .select([op.col('title'),op.col('text'),op.col('euclDistance')])
               .orderBy(op.asc(op.col('euclDistance')))
               .limit(30)
               .result()

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'

MarkLogic 12 EA 1 Product Documentationvec.euclideanDistance

Summary

Example

Example

Example

MarkLogic 12 EA 1 Product Documentation
vec.euclideanDistance