Copilot commented on code in PR #4532:
URL: https://github.com/apache/solr/pull/4532#discussion_r3443372964
##########
solr/solr-ref-guide/modules/query-guide/pages/dense-vector-search.adoc:
##########
@@ -814,7 +814,37 @@ Some use cases where `includeTags` and/or `excludeTags`
may be more useful then
-=== Usage in Re-Ranking Query
+[[vector-reranking]]
+== Usage in Re-Ranking Query
+
+Dense vector similarity scores can be used to
xref:query-guide:query-re-ranking.adoc[re-rank] first pass query results.
+Possible use cases include:
+
+* Re-ranking approximate results from a quantized vector field using full
fidelity float vectors.
+* Re-ranking lexical search results with dense vector similarity scores.
+
+Details about using the ReRank Query Parser can be found in the
xref:query-guide:query-re-ranking.adoc[Query Re-Ranking] section.
+
+=== Re-Ranking with vectorSimilarity Function Query
+
+The
xref:query-guide:function-queries.adoc#vectorsimilarity-function[vectorSimilarity()]
function can be used with the `{!func}` query parser to re-rank by vector
similarity.
+When used as a function query, `vectorSimilarity()` computes the exact
similarity for only the candidate documents selected for re-ranking, without
traversing the index graph.
+
+Here is an example of re-ranking a lexical query using a `DenseVectorField`
named `vector`:
+
+[source,text]
+?q=title:phone&rq={!rerank reRankQuery=$rqq reRankDocs=100
reRankWeight=1}&rqq={!func}vectorSimilarity(vector,[1.0,2.0,3.0,4.0])
+
+NOTE: The default `reRankOperator` is `add`, which sums the first-pass score
and the vector similarity score.
+Since these scores may differ in magnitude, you can adjust `reRankWeight` to
control the balance between them, or use `reRankOperator=replace` to score
re-ranked documents by vector similarity alone.
Review Comment:
The NOTE admonition currently only applies to the first sentence; the
follow-up sentence becomes a normal paragraph. Use a NOTE block so both
sentences are part of the same admonition.
##########
solr/solr-ref-guide/modules/query-guide/pages/dense-vector-search.adoc:
##########
@@ -814,7 +814,37 @@ Some use cases where `includeTags` and/or `excludeTags`
may be more useful then
-=== Usage in Re-Ranking Query
+[[vector-reranking]]
+== Usage in Re-Ranking Query
+
+Dense vector similarity scores can be used to
xref:query-guide:query-re-ranking.adoc[re-rank] first pass query results.
+Possible use cases include:
+
+* Re-ranking approximate results from a quantized vector field using full
fidelity float vectors.
+* Re-ranking lexical search results with dense vector similarity scores.
+
+Details about using the ReRank Query Parser can be found in the
xref:query-guide:query-re-ranking.adoc[Query Re-Ranking] section.
+
+=== Re-Ranking with vectorSimilarity Function Query
+
+The
xref:query-guide:function-queries.adoc#vectorsimilarity-function[vectorSimilarity()]
function can be used with the `{!func}` query parser to re-rank by vector
similarity.
+When used as a function query, `vectorSimilarity()` computes the exact
similarity for only the candidate documents selected for re-ranking, without
traversing the index graph.
+
+Here is an example of re-ranking a lexical query using a `DenseVectorField`
named `vector`:
+
+[source,text]
+?q=title:phone&rq={!rerank reRankQuery=$rqq reRankDocs=100
reRankWeight=1}&rqq={!func}vectorSimilarity(vector,[1.0,2.0,3.0,4.0])
+
+NOTE: The default `reRankOperator` is `add`, which sums the first-pass score
and the vector similarity score.
+Since these scores may differ in magnitude, you can adjust `reRankWeight` to
control the balance between them, or use `reRankOperator=replace` to score
re-ranked documents by vector similarity alone.
+
+When using a quantized vector field type (such as
`ScalarQuantizedDenseVectorField`), the KNN first pass scores are computed on
the quantized vectors.
+Here is an example of re-ranking those results with exact float similarity
scores, where `topK` matches `reRankDocs`:
+
+[source,text]
+?q={!knn f=vector topK=100}[1.0,2.0,3.0,4.0]&rq={!rerank reRankQuery=$rqq
reRankDocs=100 reRankWeight=1
reRankOperator=replace}&rqq={!func}vectorSimilarity(vector,[1.0,2.0,3.0,4.0])
Review Comment:
This section suggests you can re-rank KNN results from a quantized vector
field with “exact float similarity” while using the same field (`f=vector`) in
both passes. Quantized fields inherently lose float precision, so this
example/wording is misleading unless you re-rank against a separate
non-quantized float vector field.
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