SearchModelFactory.BM25Similarity Method
Definition
Important
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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).
public static Azure.Search.Documents.Indexes.Models.BM25Similarity BM25Similarity(double? k1 = default, double? b = default);static member BM25Similarity : Nullable<double> * Nullable<double> -> Azure.Search.Documents.Indexes.Models.BM25SimilarityPublic Shared Function BM25Similarity (Optional k1 As Nullable(Of Double) = Nothing, Optional b As Nullable(Of Double) = Nothing) As BM25SimilarityParameters
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.
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
A new BM25Similarity instance for mocking.
