adamjq commented on code in PR #4532:
URL: https://github.com/apache/solr/pull/4532#discussion_r3494011849
##########
solr/solr-ref-guide/modules/query-guide/pages/dense-vector-search.adoc:
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@@ -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:
It's interesting that this was flagged by Copilot, because it's a source of
confusion in the existing documentation and I believe the claim made is
incorrect. This would be true if the field used BYTE encoding (meaning int8
vectors are externally supplied), but not if Solr is quantizing vectors.
`ScalarQuantizedDenseVectorField` builds the HNSW graphs using the scalar
quantized representations of embeddings. The original float embeddings remain
accessible via `FloatVectorValues` and the full precision vectors are used for
re-quantization during segment merges.
The code path for `{!func}vectorSimilarity` is:
1.
[VectorSimilaritySourceParser](https://github.com/apache/solr/blob/main/solr/core/src/java/org/apache/solr/search/VectorSimilaritySourceParser.java#L108)
- calls `getValueSource`
2.
[DenseVectorField.getValueSource](https://github.com/apache/solr/blob/main/solr/core/src/java/org/apache/solr/schema/DenseVectorField.java#L487)
returns `new FloatKnnVectorFieldSource(field.getName())`
3. [(Lucene)
FloatKnnVectorFieldSource](https://github.com/apache/lucene/blob/main/lucene/queries/src/java/org/apache/lucene/queries/function/valuesource/FloatKnnVectorFieldSource.java#L46)
- reads `getFloatVectorValues(fieldName);`
I was able to prove this locally via unit tests that do the following:
- Test 1: KNN vs KNN + rerank on DenseVectorField. Scores are identical as
both paths use exact floats.
- Test 2: KNN vs KNN + rerank on ScalarQuantizedDenseVectorField. Scores
differ as KNN uses quantized values and the rerank uses exact floats.
I can add the unit tests to the PR if that would be useful.
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