This is an automated email from the ASF dual-hosted git repository. hossman pushed a commit to branch SOLR-17335 in repository https://gitbox.apache.org/repos/asf/solr.git
commit de4536cf738b35d3470d2f8caa2f3fa911fe3297 Author: Chris Hostetter <[email protected]> AuthorDate: Tue Jun 18 11:55:15 2024 -0700 Fix typos --- .../java/org/apache/solr/search/neural/VectorSimilarityQParser.java | 2 +- .../solr-ref-guide/modules/query-guide/pages/dense-vector-search.adoc | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/solr/core/src/java/org/apache/solr/search/neural/VectorSimilarityQParser.java b/solr/core/src/java/org/apache/solr/search/neural/VectorSimilarityQParser.java index 164cafc5192..e3ec2f242f7 100644 --- a/solr/core/src/java/org/apache/solr/search/neural/VectorSimilarityQParser.java +++ b/solr/core/src/java/org/apache/solr/search/neural/VectorSimilarityQParser.java @@ -52,7 +52,7 @@ public class VectorSimilarityQParser extends AbstractVectorQParserBase { if (null == minReturn) { throw new SolrException( SolrException.ErrorCode.BAD_REQUEST, - MIN_RETURN + " is requried to use Vector Similarity QParser"); + MIN_RETURN + " is required to use Vector Similarity QParser"); } final DenseVectorParser vectorBuilder = diff --git a/solr/solr-ref-guide/modules/query-guide/pages/dense-vector-search.adoc b/solr/solr-ref-guide/modules/query-guide/pages/dense-vector-search.adoc index df761eeaf8d..d4ad9756176 100644 --- a/solr/solr-ref-guide/modules/query-guide/pages/dense-vector-search.adoc +++ b/solr/solr-ref-guide/modules/query-guide/pages/dense-vector-search.adoc @@ -238,7 +238,7 @@ client.add(Arrays.asList(d1, d2)); == Query Time -Apache Solr provides two query parsers that work with dense vector fields, that each support differnet ways of matching documents based on vector similarity: The `knn` query parser, and the `vecSim` query parser. +Apache Solr provides two query parsers that work with dense vector fields, that each support different ways of matching documents based on vector similarity: The `knn` query parser, and the `vecSim` query parser. Both parsers return scores for retrieved documents that is the approximate distance to the target vector (defined by the similarityFunction configured at indexing time) and both support "Pre-Filtering" the document graph to reduce the number of candidate vectors evaluated (with out needing to compute their vector similarity distances). @@ -348,7 +348,7 @@ The `preFilter` parameter can be specified explicitly to reduce the number of ca [source,text] ?q={!vecSim f=vector minReturn=0.7 preFilter=inStock:true}[1.0, 2.0, 3.0, 4.0] -In the above example, only documents matching the Pre-Filter `inStock:true` will be candidates for consideration when evaluating the `knn` search against the specified vector. +In the above example, only documents matching the Pre-Filter `inStock:true` will be candidates for consideration when evaluating the `vecSim` search against the specified vector. The `preFilter` parameter may be blank (ex: `preFilter=""`) to indicate that no Pre-Filtering should be performed; or it may be multi-valued -- either through repetition, or via duplicated xref:local-params.adoc#parameter-dereferencing[Parameter References].
