msokolov commented on code in PR #11946:
URL: https://github.com/apache/lucene/pull/11946#discussion_r1049804451
##########
lucene/core/src/java/org/apache/lucene/search/KnnVectorQuery.java:
##########
@@ -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:
OK, with the current CR, orthogonal vectors will have a DOT_PRODUCT
"score" of 0.5, which could be surprising. However, this is similar to how
result scores are treated elsewhere in Lucene - their value ranges are not
well-defined; the only guarantee is that higher scores are "more relevant". I
guess practically speaking, as a user, I think I am going to have to do
empirical work to know what threshold to use; these are not likely going to be
motivated by some a priori knowledge of what a "good" dot-product is, and given
that I'd like to just be able to work with some kind of abstracted score in a
known range (0 = worst, 1 = best).Conversely, if we were to switch to using
vector similarities that would correspond more directly to the underlying
functions, we would have to clearly define them (today we don't actually
explain this anywhere, I guess we'd need to document) and maybe provide methods
for computing them. Also they would be weird too, just in a different way. For
exam
ple, how would we explain 8-bit dot-product? Would it be the 8-bit dot-product
score normalized by 2^15?
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