msokolov commented on code in PR #11946:
URL: https://github.com/apache/lucene/pull/11946#discussion_r1050171394
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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:
> I don't know what CR means. Change request?
sorry, yes like a PR but from a parallel universe (code review actually)
So .. theoretical considerations aside, what's the alternative here -- we
would treat the threshold as a "vector similarity" and internally convert it to
a score. I mean that seems to make sense -- all the conversions are invertible,
right? I think we'd want to add a normalize method to VectorSimilarity for this
internal use.
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