rmuir commented on code in PR #11946:
URL: https://github.com/apache/lucene/pull/11946#discussion_r1050228933
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lucene/core/src/java/org/apache/lucene/search/KnnVectorQuery.java:
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@@ -76,12 +91,29 @@ public KnnVectorQuery(String field, float[] target, int k) {
* @throws IllegalArgumentException if <code>k</code> is less than 1
*/
public KnnVectorQuery(String field, float[] target, int k, Query filter) {
+ this(field, target, k, Float.NEGATIVE_INFINITY, filter);
+ }
+
+ /**
+ * Find the <code>k</code> nearest documents to the target vector according
to the vectors in the
+ * given field. <code>target</code> vector.
+ *
+ * @param field a field that has been indexed as a {@link KnnVectorField}.
+ * @param target the target of the search
+ * @param k the number of documents to find (the upper bound)
+ * @param similarityThreshold the minimum acceptable value of similarity
Review Comment:
ben for the normal scoring, you can look at tests for similarities package.
none of these have any 0 to 1 range or anything like that. instead requirements
are that score increases semimonotonically as term frequency increases,
decreases wrt documents length, etc. these guarantees allow optimizations such
as block max wand to be applied safely. but theres no defined range at all.
instead lots of crazy floating point hacks so that we can safely get really
good performance.
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