com.azure.search.documents.indexes.models

Class BM25SimilarityAlgorithm

java.lang.Object

com.azure.search.documents.indexes.models.SimilarityAlgorithm

com.azure.search.documents.indexes.models.BM25SimilarityAlgorithm

public final class BM25SimilarityAlgorithm
extends SimilarityAlgorithm

Ranking function based on the Okapi BM25 similarity algorithm. BM25 is a TF-IDF-like algorithm that includes length normalization (controlled by the 'b' parameter) as well as term frequency saturation (controlled by the 'k1' parameter).

Constructor Summary

Constructors

Constructor and Description
BM25SimilarityAlgorithm()

Method Summary

Modifier and Type	Method and Description
Double	getB() Get the b property: This property controls how the length of a document affects the relevance score.
Double	getK1() Get the k1 property: This property controls the scaling function between the term frequency of each matching terms and the final relevance score of a document-query pair.
BM25SimilarityAlgorithm	setB(Double b) Set the b property: This property controls how the length of a document affects the relevance score.
BM25SimilarityAlgorithm	setK1(Double k1) Set the k1 property: This property controls the scaling function between the term frequency of each matching terms and the final relevance score of a document-query pair.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

BM25SimilarityAlgorithm

public BM25SimilarityAlgorithm()

Method Detail

getK1

public Double getK1()

Get the k1 property: This property controls the scaling function between the term frequency of each matching terms and the final relevance score of a document-query pair. By default, a value of 1.2 is used. A value of 0.0 means the score does not scale with an increase in term frequency.

Returns:

the k1 value.

setK1

public BM25SimilarityAlgorithm setK1(Double k1)

Set the k1 property: This property controls the scaling function between the term frequency of each matching terms and the final relevance score of a document-query pair. By default, a value of 1.2 is used. A value of 0.0 means the score does not scale with an increase in term frequency.

Parameters:

k1 - the k1 value to set.

Returns:

the BM25Similarity object itself.

getB

public Double getB()

Get the b property: This property controls how the length of a document affects the relevance score. By default, a value of 0.75 is used. A value of 0.0 means no length normalization is applied, while a value of 1.0 means the score is fully normalized by the length of the document.

Returns:

the b value.

setB

public BM25SimilarityAlgorithm setB(Double b)

Set the b property: This property controls how the length of a document affects the relevance score. By default, a value of 0.75 is used. A value of 0.0 means no length normalization is applied, while a value of 1.0 means the score is fully normalized by the length of the document.

Parameters:

b - the b value to set.

Returns:

the BM25Similarity object itself.