Spotless

Author: carlosdelest

x-pack/plugin/esql/src/main/java/org/elasticsearch/xpack/esql/expression/function/vector/Knn.java

@@ -128,7 +128,8 @@ public Knn(
                     type = "double",
                     valueHint = { "3.5" },
                     description = "Applies the specified oversampling for rescoring quantized vectors. "
-                        + "See [oversampling and rescoring quantized vectors](docs-content://solutions/search/vector/knn.md#dense-vector-knn-search-rescoring) for details."
+                        + "See [oversampling and rescoring quantized vectors]"
+                        + "(docs-content://solutions/search/vector/knn.md#dense-vector-knn-search-rescoring) for details."
                 ), },
             description = "(Optional) kNN additional options as <<esql-function-named-params,function named parameters>>."
                 + " See <<query-dsl-knn-query,knn query>> for more information.",

x-pack/plugin/esql/src/main/java/org/elasticsearch/xpack/esql/querydsl/query/KnnQuery.java

@@ -64,8 +64,9 @@ protected String innerToString() {
 
     @Override
     public boolean equals(Object o) {
-        if (!(o instanceof KnnQuery knnQuery)) return false;
         if (super.equals(o) == false) return false;
+
+        KnnQuery knnQuery = (KnnQuery) o;
         return Objects.equals(field, knnQuery.field)
             && Objects.deepEquals(query, knnQuery.query)
             && Objects.equals(options, knnQuery.options);

x-pack/plugin/esql/src/test/java/org/elasticsearch/xpack/esql/optimizer/LocalPhysicalPlanOptimizerTests.java

@@ -1348,11 +1348,11 @@ public void testMultiMatchOptionsPushDown() {
     }
 
     public void testKnnOptionsPushDown() {
-        String query =
-            """
-                from test
-                | where KNN(dense_vector, [0.1, 0.2, 0.3], { "k": 5, "similarity": 0.001, "num_candidates": 10, "rescore_oversample": 7, "boost": 3.5 })
-                """;
+        String query = """
+            from test
+            | where KNN(dense_vector, [0.1, 0.2, 0.3],
+                { "k": 5, "similarity": 0.001, "num_candidates": 10, "rescore_oversample": 7, "boost": 3.5 })
+            """;
         var analyzer = makeAnalyzer("mapping-all-types.json");
         var plan = plannerOptimizer.plan(query, IS_SV_STATS, analyzer);