Package 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
    Description
    BM25SimilarityAlgorithm()
     

  • Method Summary

    Modifier and Type
    Method
    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 Details

    • BM25SimilarityAlgorithm

      public BM25SimilarityAlgorithm()

  • Method Details

    • 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 BM25SimilarityAlgorithm 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 BM25SimilarityAlgorithm object itself.