[CI] Auto commit changes from spotless

Author: elasticsearchmachine

server/src/main/java/org/elasticsearch/search/retriever/rankdoc/RankDocsQuery.java

@@ -334,10 +334,10 @@ public Query rewrite(IndexSearcher searcher) throws IOException {
     public Weight createWeight(IndexSearcher searcher, ScoreMode scoreMode, float boost) throws IOException {
         Query combinedQuery;
         if (onlyRankDocs) {
-            combinedQuery = topQuery; 
+            combinedQuery = topQuery;
         } else {
             if (tailQuery == null) {
-                 combinedQuery = topQuery;
+                combinedQuery = topQuery;
             } else {
                 var combined = new BooleanQuery.Builder().add(topQuery, BooleanClause.Occur.SHOULD)
                     .add(tailQuery, BooleanClause.Occur.FILTER)

server/src/test/java/org/elasticsearch/index/query/RankDocsQueryBuilderTests.java

@@ -294,11 +294,7 @@ public void testRankDocsQueryMinScoreFiltering() throws IOException {
             }
 
             float minScore = 1.5f;
-            RankDoc[] rankDocs = new RankDoc[] {
-                new RankDoc(0, 2.0f, 0),
-                new RankDoc(1, 1.0f, 0),
-                new RankDoc(2, 1.6f, 0)
-            };
+            RankDoc[] rankDocs = new RankDoc[] { new RankDoc(0, 2.0f, 0), new RankDoc(1, 1.0f, 0), new RankDoc(2, 1.6f, 0) };
             Arrays.sort(rankDocs);
             for (int i = 0; i < rankDocs.length; i++) {
                 rankDocs[i].rank = i;
@@ -313,7 +309,7 @@ public void testRankDocsQueryMinScoreFiltering() throws IOException {
                 IndexSearcher searcher = newSearcher(reader);
                 TopDocs topDocs = searcher.search(query, 10);
 
-                long expectedTotalHits = 2; 
+                long expectedTotalHits = 2;
                 long expectedFilteredHits = 2;
                 assertEquals("Total hits should match filtered count", expectedTotalHits, topDocs.totalHits.value());
                 assertEquals("Number of score docs should match filtered count", expectedFilteredHits, topDocs.scoreDocs.length);

x-pack/plugin/rank-rrf/src/main/java/org/elasticsearch/xpack/rank/linear/LinearRetrieverBuilder.java

@@ -31,9 +31,9 @@
 import java.util.List;
 import java.util.Map;
 
+import static org.elasticsearch.index.query.RankDocsQueryBuilder.DEFAULT_MIN_SCORE;
 import static org.elasticsearch.xcontent.ConstructingObjectParser.constructorArg;
 import static org.elasticsearch.xcontent.ConstructingObjectParser.optionalConstructorArg;
-import static org.elasticsearch.index.query.RankDocsQueryBuilder.DEFAULT_MIN_SCORE;
 import static org.elasticsearch.xpack.rank.RankRRFFeatures.LINEAR_RETRIEVER_SUPPORTED;
 import static org.elasticsearch.xpack.rank.linear.LinearRetrieverComponent.DEFAULT_WEIGHT;
 
@@ -107,14 +107,20 @@ public static LinearRetrieverBuilder fromXContent(XContentParser parser, Retriev
     }
 
     LinearRetrieverBuilder(List<RetrieverSource> innerRetrievers, int rankWindowSize) {
-        this(innerRetrievers, rankWindowSize, getDefaultWeight(innerRetrievers.size()), getDefaultNormalizers(innerRetrievers.size()), DEFAULT_MIN_SCORE);
+        this(
+            innerRetrievers,
+            rankWindowSize,
+            getDefaultWeight(innerRetrievers.size()),
+            getDefaultNormalizers(innerRetrievers.size()),
+            DEFAULT_MIN_SCORE
+        );
     }
 
     public LinearRetrieverBuilder(
         List<RetrieverSource> innerRetrievers,
         int rankWindowSize,
         float[] weights,
-        ScoreNormalizer[] normalizers, 
+        ScoreNormalizer[] normalizers,
         float minScore
     ) {
         super(innerRetrievers, rankWindowSize);

x-pack/plugin/rank-rrf/src/test/java/org/elasticsearch/xpack/rank/linear/LinearRetrieverBuilderTests.java

@@ -22,9 +22,7 @@ public void testCombineInnerRetrieverResultsCalculatesWeightedSum() throws IOExc
         ScoreNormalizer[] normalizers = new ScoreNormalizer[] { IdentityScoreNormalizer.INSTANCE, IdentityScoreNormalizer.INSTANCE };
         float minScore = 0f;
 
-        LinearRetrieverBuilder builder = new LinearRetrieverBuilder(
-            sources, rankWindowSize, weights, normalizers, minScore
-        );
+        LinearRetrieverBuilder builder = new LinearRetrieverBuilder(sources, rankWindowSize, weights, normalizers, minScore);
 
         ScoreDoc[] left = new ScoreDoc[] { new ScoreDoc(5, 1.0f, 0), new ScoreDoc(6, 2.0f, 0) };
         ScoreDoc[] right = new ScoreDoc[] { new ScoreDoc(5, 3.0f, 0), new ScoreDoc(6, 1.0f, 0), new ScoreDoc(7, 0.5f, 0) };
@@ -56,39 +54,34 @@ public void testCombineAndFilterWithMinScore() throws IOException {
         ScoreNormalizer[] normalizers = new ScoreNormalizer[] { IdentityScoreNormalizer.INSTANCE, IdentityScoreNormalizer.INSTANCE };
         final float minScoreThreshold = 1.5f;
 
-        LinearRetrieverBuilder builder = new LinearRetrieverBuilder(
-            sources, rankWindowSize, weights, normalizers, minScoreThreshold
-        );
-        assertEquals(minScoreThreshold, builder.getMinScore(), 0f); 
+        LinearRetrieverBuilder builder = new LinearRetrieverBuilder(sources, rankWindowSize, weights, normalizers, minScoreThreshold);
+        assertEquals(minScoreThreshold, builder.getMinScore(), 0f);
 
         ScoreDoc[] left = new ScoreDoc[] { new ScoreDoc(0, 1.0f, 0), new ScoreDoc(1, 0.8f, 0) };
         ScoreDoc[] right = new ScoreDoc[] { new ScoreDoc(0, 2.0f, 0), new ScoreDoc(1, 0.6f, 0), new ScoreDoc(2, 1.8f, 0) };
 
         RankDoc[] combinedRankDocs = builder.combineInnerRetrieverResults(List.of(left, right), false);
         assertEquals("Combined docs before filtering", 3, combinedRankDocs.length);
 
-        List<RankDoc> filteredDocs = Stream.of(combinedRankDocs)
-                                            .filter(rd -> rd.score >= builder.getMinScore())
-                                            .sorted()
-                                            .toList();
+        List<RankDoc> filteredDocs = Stream.of(combinedRankDocs).filter(rd -> rd.score >= builder.getMinScore()).sorted().toList();
 
         assertEquals("Filtered docs count", 2, filteredDocs.size());
 
         boolean foundDoc0 = false;
         boolean foundDoc2 = false;
         for (RankDoc scoreDoc : filteredDocs) {
-             assertTrue("Score should be >= minScore", scoreDoc.score >= minScoreThreshold);
+            assertTrue("Score should be >= minScore", scoreDoc.score >= minScoreThreshold);
             if (scoreDoc.doc == 0) {
-                 assertEquals("Doc 0 score", 3.0f, scoreDoc.score, 0.001f);
+                assertEquals("Doc 0 score", 3.0f, scoreDoc.score, 0.001f);
                 foundDoc0 = true;
             } else if (scoreDoc.doc == 2) {
-                 assertEquals("Doc 2 score", 1.8f, scoreDoc.score, 0.001f);
+                assertEquals("Doc 2 score", 1.8f, scoreDoc.score, 0.001f);
                 foundDoc2 = true;
             } else {
-                 fail("Unexpected document ID returned: " + scoreDoc.doc + " (should have been filtered)");
+                fail("Unexpected document ID returned: " + scoreDoc.doc + " (should have been filtered)");
             }
         }
         assertTrue("Document 0 should have been found", foundDoc0);
         assertTrue("Document 2 should have been found", foundDoc2);
     }
-} 
+}