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BM25Similarity interface

  • Reference
Package:
@azure/search-documents

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).

Extends
Similarity

Properties

b

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.
k1

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.
odatatype

Polymorphic discriminator, which specifies the different types this object can be

Property Details

b

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.

b?: number

Property Value

number

k1

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.

k1?: number

Property Value

number

odatatype

Polymorphic discriminator, which specifies the different types this object can be

odatatype: "#Microsoft.Azure.Search.BM25Similarity"

Property Value

"#Microsoft.Azure.Search.BM25Similarity"