Fixed exception for case when Hybrid query being wrapped into bool qu…

…ery (opensearch-project#490)

* Adding null check for case when hybrid query wrapped into bool query

Signed-off-by: Martin Gaievski <gaievski@amazon.com>
(cherry picked from commit b3c73bd)
Signed-off-by: Martin Gaievski <gaievski@amazon.com>

CHANGELOG.md

@@ -7,6 +7,7 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 ### Features
 ### Enhancements
 ### Bug Fixes
+Fixed exception for case when Hybrid query being wrapped into bool query ([#490](https://github.com/opensearch-project/neural-search/pull/490)
 ### Infrastructure
 ### Documentation
 ### Maintenance

src/main/java/org/opensearch/neuralsearch/query/HybridQueryScorer.java

@@ -83,7 +83,7 @@ public DocIdSetIterator iterator() {
      */
     @Override
     public float getMaxScore(int upTo) throws IOException {
-        return subScorers.stream().filter(scorer -> scorer.docID() <= upTo).map(scorer -> {
+        return subScorers.stream().filter(Objects::nonNull).filter(scorer -> scorer.docID() <= upTo).map(scorer -> {
             try {
                 return scorer.getMaxScore(upTo);
             } catch (IOException e) {

src/test/java/org/opensearch/neuralsearch/plugin/NeuralSearchTests.java

@@ -11,6 +11,7 @@
 import java.util.Map;
 import java.util.Optional;
 
+import org.opensearch.indices.IndicesService;
 import org.opensearch.ingest.IngestService;
 import org.opensearch.ingest.Processor;
 import org.opensearch.neuralsearch.processor.NeuralQueryEnricherProcessor;
@@ -66,7 +67,8 @@ public void testProcessors() {
             null,
             mock(IngestService.class),
             null,
-            null
+            null,
+            mock(IndicesService.class)
         );
         Map<String, Processor.Factory> processors = plugin.getProcessors(processorParams);
         assertNotNull(processors);

src/test/java/org/opensearch/neuralsearch/query/HybridQueryIT.java

@@ -22,6 +22,7 @@
 import org.junit.After;
 import org.junit.Before;
 import org.opensearch.index.query.BoolQueryBuilder;
+import org.opensearch.index.query.MatchQueryBuilder;
 import org.opensearch.index.query.QueryBuilders;
 import org.opensearch.index.query.TermQueryBuilder;
 import org.opensearch.knn.index.SpaceType;
@@ -33,6 +34,7 @@ public class HybridQueryIT extends BaseNeuralSearchIT {
     private static final String TEST_BASIC_INDEX_NAME = "test-neural-basic-index";
     private static final String TEST_BASIC_VECTOR_DOC_FIELD_INDEX_NAME = "test-neural-vector-doc-field-index";
     private static final String TEST_MULTI_DOC_INDEX_NAME = "test-neural-multi-doc-index";
+    private static final String TEST_MULTI_DOC_INDEX_NAME_ONE_SHARD = "test-neural-multi-doc-single-shard-index";
     private static final String TEST_QUERY_TEXT = "greetings";
     private static final String TEST_QUERY_TEXT2 = "salute";
     private static final String TEST_QUERY_TEXT3 = "hello";
@@ -188,6 +190,35 @@ public void testNoMatchResults_whenOnlyTermSubQueryWithoutMatch_thenEmptyResult(
         assertEquals(RELATION_EQUAL_TO, total.get("relation"));
     }
 
+    @SneakyThrows
+    public void testNestedQuery_whenHybridQueryIsWrappedIntoOtherQuery_thenSuccess() {
+        initializeIndexIfNotExist(TEST_MULTI_DOC_INDEX_NAME_ONE_SHARD);
+
+        MatchQueryBuilder matchQueryBuilder = QueryBuilders.matchQuery(TEST_TEXT_FIELD_NAME_1, TEST_QUERY_TEXT3);
+        MatchQueryBuilder matchQuery2Builder = QueryBuilders.matchQuery(TEST_TEXT_FIELD_NAME_1, TEST_QUERY_TEXT4);
+        HybridQueryBuilder hybridQueryBuilderOnlyTerm = new HybridQueryBuilder();
+        hybridQueryBuilderOnlyTerm.add(matchQueryBuilder);
+        hybridQueryBuilderOnlyTerm.add(matchQuery2Builder);
+        MatchQueryBuilder matchQuery3Builder = QueryBuilders.matchQuery(TEST_TEXT_FIELD_NAME_1, TEST_QUERY_TEXT3);
+        BoolQueryBuilder boolQueryBuilder = QueryBuilders.boolQuery().should(hybridQueryBuilderOnlyTerm).should(matchQuery3Builder);
+
+        Map<String, Object> searchResponseAsMap = search(
+            TEST_MULTI_DOC_INDEX_NAME_ONE_SHARD,
+            boolQueryBuilder,
+            null,
+            10,
+            Map.of("search_pipeline", SEARCH_PIPELINE)
+        );
+
+        assertTrue(getHitCount(searchResponseAsMap) > 0);
+        assertTrue(getMaxScore(searchResponseAsMap).isPresent());
+        assertTrue(getMaxScore(searchResponseAsMap).get() > 0.0f);
+
+        Map<String, Object> total = getTotalHits(searchResponseAsMap);
+        assertNotNull(total.get("value"));
+        assertTrue((int) total.get("value") > 0);
+    }
+
     private void initializeIndexIfNotExist(String indexName) throws IOException {
         if (TEST_BASIC_INDEX_NAME.equals(indexName) && !indexExists(TEST_BASIC_INDEX_NAME)) {
             prepareKnnIndex(
@@ -242,32 +273,45 @@ private void initializeIndexIfNotExist(String indexName) throws IOException {
                 TEST_MULTI_DOC_INDEX_NAME,
                 Collections.singletonList(new KNNFieldConfig(TEST_KNN_VECTOR_FIELD_NAME_1, TEST_DIMENSION, TEST_SPACE_TYPE))
             );
-            addKnnDoc(
-                TEST_MULTI_DOC_INDEX_NAME,
-                "1",
-                Collections.singletonList(TEST_KNN_VECTOR_FIELD_NAME_1),
-                Collections.singletonList(Floats.asList(testVector1).toArray()),
-                Collections.singletonList(TEST_TEXT_FIELD_NAME_1),
-                Collections.singletonList(TEST_DOC_TEXT1)
-            );
-            addKnnDoc(
-                TEST_MULTI_DOC_INDEX_NAME,
-                "2",
-                Collections.singletonList(TEST_KNN_VECTOR_FIELD_NAME_1),
-                Collections.singletonList(Floats.asList(testVector2).toArray())
-            );
-            addKnnDoc(
-                TEST_MULTI_DOC_INDEX_NAME,
-                "3",
-                Collections.singletonList(TEST_KNN_VECTOR_FIELD_NAME_1),
-                Collections.singletonList(Floats.asList(testVector3).toArray()),
-                Collections.singletonList(TEST_TEXT_FIELD_NAME_1),
-                Collections.singletonList(TEST_DOC_TEXT2)
+            addDocsToIndex(TEST_MULTI_DOC_INDEX_NAME);
+        }
+
+        if (TEST_MULTI_DOC_INDEX_NAME_ONE_SHARD.equals(indexName) && !indexExists(TEST_MULTI_DOC_INDEX_NAME_ONE_SHARD)) {
+            prepareKnnIndex(
+                TEST_MULTI_DOC_INDEX_NAME_ONE_SHARD,
+                Collections.singletonList(new KNNFieldConfig(TEST_KNN_VECTOR_FIELD_NAME_1, TEST_DIMENSION, TEST_SPACE_TYPE)),
+                1
             );
-            assertEquals(3, getDocCount(TEST_MULTI_DOC_INDEX_NAME));
+            addDocsToIndex(TEST_MULTI_DOC_INDEX_NAME_ONE_SHARD);
         }
     }
 
+    private void addDocsToIndex(final String testMultiDocIndexName) {
+        addKnnDoc(
+            testMultiDocIndexName,
+            "1",
+            Collections.singletonList(TEST_KNN_VECTOR_FIELD_NAME_1),
+            Collections.singletonList(Floats.asList(testVector1).toArray()),
+            Collections.singletonList(TEST_TEXT_FIELD_NAME_1),
+            Collections.singletonList(TEST_DOC_TEXT1)
+        );
+        addKnnDoc(
+            testMultiDocIndexName,
+            "2",
+            Collections.singletonList(TEST_KNN_VECTOR_FIELD_NAME_1),
+            Collections.singletonList(Floats.asList(testVector2).toArray())
+        );
+        addKnnDoc(
+            testMultiDocIndexName,
+            "3",
+            Collections.singletonList(TEST_KNN_VECTOR_FIELD_NAME_1),
+            Collections.singletonList(Floats.asList(testVector3).toArray()),
+            Collections.singletonList(TEST_TEXT_FIELD_NAME_1),
+            Collections.singletonList(TEST_DOC_TEXT2)
+        );
+        assertEquals(3, getDocCount(testMultiDocIndexName));
+    }
+
     private List<Map<String, Object>> getNestedHits(Map<String, Object> searchResponseAsMap) {
         Map<String, Object> hitsMap = (Map<String, Object>) searchResponseAsMap.get("hits");
         return (List<Map<String, Object>>) hitsMap.get("hits");

src/test/java/org/opensearch/neuralsearch/query/HybridQueryScorerTests.java

@@ -6,7 +6,9 @@
 package org.opensearch.neuralsearch.query;
 
 import static org.apache.lucene.search.DocIdSetIterator.NO_MORE_DOCS;
+import static org.mockito.ArgumentMatchers.anyInt;
 import static org.mockito.Mockito.mock;
+import static org.mockito.Mockito.when;
 
 import java.io.IOException;
 import java.util.Arrays;
@@ -21,6 +23,7 @@
 import org.apache.lucene.search.DocIdSetIterator;
 import org.apache.lucene.search.MatchAllDocsQuery;
 import org.apache.lucene.search.MatchNoDocsQuery;
+import org.apache.lucene.search.Scorer;
 import org.apache.lucene.search.Weight;
 import org.apache.lucene.tests.util.TestUtil;
 
@@ -169,6 +172,63 @@ public void testWithRandomDocuments_whenMultipleScorersAndSomeScorersEmpty_thenR
         testWithQuery(docs, scores, hybridQueryScorer);
     }
 
+    @SneakyThrows
+    public void testMaxScore_whenMultipleScorers_thenSuccessful() {
+        int maxDocId = TestUtil.nextInt(random(), 10, 10_000);
+        Pair<int[], float[]> docsAndScores = generateDocuments(maxDocId);
+        int[] docs = docsAndScores.getLeft();
+        float[] scores = docsAndScores.getRight();
+
+        Weight weight = mock(Weight.class);
+
+        HybridQueryScorer hybridQueryScorerWithAllNonNullSubScorers = new HybridQueryScorer(
+            weight,
+            Arrays.asList(
+                scorer(docs, scores, fakeWeight(new MatchAllDocsQuery())),
+                scorer(docs, scores, fakeWeight(new MatchNoDocsQuery()))
+            )
+        );
+
+        float maxScore = hybridQueryScorerWithAllNonNullSubScorers.getMaxScore(Integer.MAX_VALUE);
+        assertTrue(maxScore > 0.0f);
+
+        HybridQueryScorer hybridQueryScorerWithSomeNullSubScorers = new HybridQueryScorer(
+            weight,
+            Arrays.asList(null, scorer(docs, scores, fakeWeight(new MatchAllDocsQuery())), null)
+        );
+
+        maxScore = hybridQueryScorerWithSomeNullSubScorers.getMaxScore(Integer.MAX_VALUE);
+        assertTrue(maxScore > 0.0f);
+
+        HybridQueryScorer hybridQueryScorerWithAllNullSubScorers = new HybridQueryScorer(weight, Arrays.asList(null, null));
+
+        maxScore = hybridQueryScorerWithAllNullSubScorers.getMaxScore(Integer.MAX_VALUE);
+        assertEquals(0.0f, maxScore, 0.0f);
+    }
+
+    @SneakyThrows
+    public void testMaxScoreFailures_whenScorerThrowsException_thenFail() {
+        int maxDocId = TestUtil.nextInt(random(), 10, 10_000);
+        Pair<int[], float[]> docsAndScores = generateDocuments(maxDocId);
+        int[] docs = docsAndScores.getLeft();
+        float[] scores = docsAndScores.getRight();
+
+        Weight weight = mock(Weight.class);
+
+        Scorer scorer = mock(Scorer.class);
+        when(scorer.getWeight()).thenReturn(fakeWeight(new MatchAllDocsQuery()));
+        when(scorer.iterator()).thenReturn(iterator(docs));
+        when(scorer.getMaxScore(anyInt())).thenThrow(new IOException("Test exception"));
+
+        HybridQueryScorer hybridQueryScorerWithAllNonNullSubScorers = new HybridQueryScorer(weight, Arrays.asList(scorer));
+
+        RuntimeException runtimeException = expectThrows(
+            RuntimeException.class,
+            () -> hybridQueryScorerWithAllNonNullSubScorers.getMaxScore(Integer.MAX_VALUE)
+        );
+        assertTrue(runtimeException.getMessage().contains("Test exception"));
+    }
+
     private Pair<int[], float[]> generateDocuments(int maxDocId) {
         final int numDocs = RandomizedTest.randomIntBetween(1, maxDocId / 2);
         final int[] docs = new int[numDocs];

src/test/java/org/opensearch/neuralsearch/query/HybridQueryWeightTests.java

@@ -61,11 +61,11 @@ public void testScorerIterator_whenExecuteQuery_thenScorerIteratorSuccessful() {
             List.of(QueryBuilders.termQuery(TEXT_FIELD_NAME, TERM_QUERY_TEXT).toQuery(mockQueryShardContext))
         );
         IndexSearcher searcher = newSearcher(reader);
-        Weight weight = searcher.createWeight(hybridQueryWithTerm, ScoreMode.COMPLETE, 1.0f);
+        Weight weight = hybridQueryWithTerm.createWeight(searcher, ScoreMode.TOP_SCORES, 1.0f);
 
         assertNotNull(weight);
 
-        LeafReaderContext leafReaderContext = reader.getContext().leaves().get(0);
+        LeafReaderContext leafReaderContext = searcher.getIndexReader().leaves().get(0);
         Scorer scorer = weight.scorer(leafReaderContext);
 
         assertNotNull(scorer);