MongoDB reports the score directly as similarity value.
The scoring function must be specified in the index and therefore, Vector search methods do not consider the `Score.scoringFunction`.
The scoring function defaults to `ScoringFunction.unspecified()` as there is no information inside of search results how the score has been computed.

.Using `Score` and `Similarity` in a Repository Search Methods
====
[source,java]
----
interface CommentRepository extends Repository<Comment, String> {

  @VectorSearch(…)
  SearchResults<Comment> searchTop10ByEmbeddingNear(Vector vector, Score similarity);

  @VectorSearch(…)
  SearchResults<Comment> searchTop10ByEmbeddingNear(Vector vector, Similarity similarity);

  @VectorSearch(…)
  SearchResults<Comment> searchTop10ByEmbeddingNear(Vector vector, Range<Similarity> range);
}

repository.searchByEmbeddingNear(Vector.of(…), Score.of(0.9));                <1>

repository.searchByEmbeddingNear(Vector.of(…), Similarity.of(0.9));           <2>

repository.searchByEmbeddingNear(Vector.of(…), Similarity.between(0.5, 1));   <3>
----

<1> Run a search and return results with a similarity of `0.9` or greater.
<2> Return results with a similarity of `0.9` or greater.
<3> Return results with a similarity of between `0.5` and `1.0`  or greater.
====

