[Backport 2.x] Pagination in hybrid query (#1099)

* Pagination in Hybrid query (#1048) * Pagination in Hybrid query Signed-off-by: Varun Jain <[email protected]> * Remove unwanted code Signed-off-by: Varun Jain <[email protected]> * Adding hybrid query context dto Signed-off-by: Varun Jain <[email protected]> * Adding javadoc in hybridquerycontext and addressing few comments from review Signed-off-by: Varun Jain <[email protected]> * rename hybrid query extraction method Signed-off-by: Varun Jain <[email protected]> * Refactoring to optimize extractHybridQuery method calls Signed-off-by: Varun Jain <[email protected]> * Changes in tests to adapt with builder pattern in querybuilder Signed-off-by: Varun Jain <[email protected]> * Add mapper service mock in tests Signed-off-by: Varun Jain <[email protected]> * Fix error message of index.max_result_window setting Signed-off-by: Varun Jain <[email protected]> * Fix error message of index.max_result_window setting Signed-off-by: Varun Jain <[email protected]> * Fixing validation condition for lower bound Signed-off-by: Varun Jain <[email protected]> * fix tests Signed-off-by: Varun Jain <[email protected]> * Removing version check from doEquals and doHashCode method Signed-off-by: Varun Jain <[email protected]> --------- Signed-off-by: Varun Jain <[email protected]> * Update pagination_depth datatype from int to Integer (#1094) * Update pagination_depth datatype from int to Integer Signed-off-by: Varun Jain <[email protected]> --------- Signed-off-by: Varun Jain <[email protected]>
opensearch-project · Jan 14, 2025 · 96fa384 · 96fa384
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diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -15,6 +15,7 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 
 ## [Unreleased 2.x](https://github.com/opensearch-project/neural-search/compare/2.18...2.x)
 ### Features
+- Pagination in Hybrid query ([#1048](https://github.com/opensearch-project/neural-search/pull/1048))
 ### Enhancements
 - Explainability in hybrid query ([#970](https://github.com/opensearch-project/neural-search/pull/970))
 - Implement pruning for neural sparse ingestion pipeline and two phase search processor ([#988](https://github.com/opensearch-project/neural-search/pull/988))

diff --git a/src/main/java/org/opensearch/neuralsearch/common/MinClusterVersionUtil.java b/src/main/java/org/opensearch/neuralsearch/common/MinClusterVersionUtil.java
@@ -24,6 +24,7 @@ public final class MinClusterVersionUtil {
     private static final Version MINIMAL_SUPPORTED_VERSION_DEFAULT_MODEL_ID = Version.V_2_11_0;
     private static final Version MINIMAL_SUPPORTED_VERSION_RADIAL_SEARCH = Version.V_2_14_0;
     private static final Version MINIMAL_SUPPORTED_VERSION_QUERY_IMAGE_FIX = Version.V_2_19_0;
+    private static final Version MINIMAL_SUPPORTED_VERSION_PAGINATION_IN_HYBRID_QUERY = Version.V_2_19_0;
 
     // Note this minimal version will act as a override
     private static final Map<String, Version> MINIMAL_VERSION_NEURAL = ImmutableMap.<String, Version>builder()
@@ -41,6 +42,10 @@ public static boolean isClusterOnOrAfterMinReqVersionForRadialSearch() {
         return NeuralSearchClusterUtil.instance().getClusterMinVersion().onOrAfter(MINIMAL_SUPPORTED_VERSION_RADIAL_SEARCH);
     }
 
+    public static boolean isClusterOnOrAfterMinReqVersionForPaginationInHybridQuery() {
+        return NeuralSearchClusterUtil.instance().getClusterMinVersion().onOrAfter(MINIMAL_SUPPORTED_VERSION_PAGINATION_IN_HYBRID_QUERY);
+    }
+
     public static boolean isClusterOnOrAfterMinReqVersion(String key) {
         Version version;
         if (MINIMAL_VERSION_NEURAL.containsKey(key)) {

diff --git a/src/main/java/org/opensearch/neuralsearch/processor/NormalizationProcessor.java b/src/main/java/org/opensearch/neuralsearch/processor/NormalizationProcessor.java
@@ -93,6 +93,7 @@ private <Result extends SearchPhaseResult> void prepareAndExecuteNormalizationWo
             .combinationTechnique(combinationTechnique)
             .explain(explain)
             .pipelineProcessingContext(requestContextOptional.orElse(null))
+            .searchPhaseContext(searchPhaseContext)
             .build();
         normalizationWorkflow.execute(request);
     }

diff --git a/src/main/java/org/opensearch/neuralsearch/processor/NormalizationProcessorWorkflow.java b/src/main/java/org/opensearch/neuralsearch/processor/NormalizationProcessorWorkflow.java
@@ -19,6 +19,7 @@
 import org.apache.lucene.search.Sort;
 import org.apache.lucene.search.TopFieldDocs;
 import org.apache.lucene.search.FieldDoc;
+import org.opensearch.action.search.SearchPhaseContext;
 import org.opensearch.common.lucene.search.TopDocsAndMaxScore;
 import org.opensearch.neuralsearch.processor.combination.CombineScoresDto;
 import org.opensearch.neuralsearch.processor.combination.ScoreCombinationTechnique;
@@ -64,25 +65,30 @@ public void execute(
         final List<QuerySearchResult> querySearchResults,
         final Optional<FetchSearchResult> fetchSearchResultOptional,
         final ScoreNormalizationTechnique normalizationTechnique,
-        final ScoreCombinationTechnique combinationTechnique
+        final ScoreCombinationTechnique combinationTechnique,
+        final SearchPhaseContext searchPhaseContext
     ) {
         NormalizationProcessorWorkflowExecuteRequest request = NormalizationProcessorWorkflowExecuteRequest.builder()
             .querySearchResults(querySearchResults)
             .fetchSearchResultOptional(fetchSearchResultOptional)
             .normalizationTechnique(normalizationTechnique)
             .combinationTechnique(combinationTechnique)
             .explain(false)
+            .searchPhaseContext(searchPhaseContext)
             .build();
         execute(request);
     }
 
     public void execute(final NormalizationProcessorWorkflowExecuteRequest request) {
+        List<QuerySearchResult> querySearchResults = request.getQuerySearchResults();
+        Optional<FetchSearchResult> fetchSearchResultOptional = request.getFetchSearchResultOptional();
+
         // save original state
-        List<Integer> unprocessedDocIds = unprocessedDocIds(request.getQuerySearchResults());
+        List<Integer> unprocessedDocIds = unprocessedDocIds(querySearchResults);
 
         // pre-process data
         log.debug("Pre-process query results");
-        List<CompoundTopDocs> queryTopDocs = getQueryTopDocs(request.getQuerySearchResults());
+        List<CompoundTopDocs> queryTopDocs = getQueryTopDocs(querySearchResults);
 
         explain(request, queryTopDocs);
 
@@ -93,8 +99,9 @@ public void execute(final NormalizationProcessorWorkflowExecuteRequest request)
         CombineScoresDto combineScoresDTO = CombineScoresDto.builder()
             .queryTopDocs(queryTopDocs)
             .scoreCombinationTechnique(request.getCombinationTechnique())
-            .querySearchResults(request.getQuerySearchResults())
-            .sort(evaluateSortCriteria(request.getQuerySearchResults(), queryTopDocs))
+            .querySearchResults(querySearchResults)
+            .sort(evaluateSortCriteria(querySearchResults, queryTopDocs))
+            .fromValueForSingleShard(getFromValueIfSingleShard(request))
             .build();
 
         // combine
@@ -103,8 +110,26 @@ public void execute(final NormalizationProcessorWorkflowExecuteRequest request)
 
         // post-process data
         log.debug("Post-process query results after score normalization and combination");
-        updateOriginalQueryResults(combineScoresDTO);
-        updateOriginalFetchResults(request.getQuerySearchResults(), request.getFetchSearchResultOptional(), unprocessedDocIds);
+        updateOriginalQueryResults(combineScoresDTO, fetchSearchResultOptional.isPresent());
+        updateOriginalFetchResults(
+            querySearchResults,
+            fetchSearchResultOptional,
+            unprocessedDocIds,
+            combineScoresDTO.getFromValueForSingleShard()
+        );
+    }
+
+    /**
+     * Get value of from parameter when there is a single shard
+     * and fetch phase is already executed
+     * Ref https://github.com/opensearch-project/OpenSearch/blob/main/server/src/main/java/org/opensearch/search/SearchService.java#L715
+     */
+    private int getFromValueIfSingleShard(final NormalizationProcessorWorkflowExecuteRequest request) {
+        final SearchPhaseContext searchPhaseContext = request.getSearchPhaseContext();
+        if (searchPhaseContext.getNumShards() > 1 || request.fetchSearchResultOptional.isEmpty()) {
+            return -1;
+        }
+        return searchPhaseContext.getRequest().source().from();
     }
 
     /**
@@ -173,19 +198,33 @@ private List<CompoundTopDocs> getQueryTopDocs(final List<QuerySearchResult> quer
         return queryTopDocs;
     }
 
-    private void updateOriginalQueryResults(final CombineScoresDto combineScoresDTO) {
+    private void updateOriginalQueryResults(final CombineScoresDto combineScoresDTO, final boolean isFetchPhaseExecuted) {
         final List<QuerySearchResult> querySearchResults = combineScoresDTO.getQuerySearchResults();
         final List<CompoundTopDocs> queryTopDocs = getCompoundTopDocs(combineScoresDTO, querySearchResults);
         final Sort sort = combineScoresDTO.getSort();
+        int totalScoreDocsCount = 0;
         for (int index = 0; index < querySearchResults.size(); index++) {
             QuerySearchResult querySearchResult = querySearchResults.get(index);
             CompoundTopDocs updatedTopDocs = queryTopDocs.get(index);
+            totalScoreDocsCount += updatedTopDocs.getScoreDocs().size();
             TopDocsAndMaxScore updatedTopDocsAndMaxScore = new TopDocsAndMaxScore(
                 buildTopDocs(updatedTopDocs, sort),
                 maxScoreForShard(updatedTopDocs, sort != null)
             );
+            // Fetch Phase had ran before the normalization phase, therefore update the from value in result of each shard.
+            // This will ensure the trimming of the search results.
+            if (isFetchPhaseExecuted) {
+                querySearchResult.from(combineScoresDTO.getFromValueForSingleShard());
+            }
             querySearchResult.topDocs(updatedTopDocsAndMaxScore, querySearchResult.sortValueFormats());
         }
+
+        final int from = querySearchResults.get(0).from();
+        if (from > totalScoreDocsCount) {
+            throw new IllegalArgumentException(
+                String.format(Locale.ROOT, "Reached end of search result, increase pagination_depth value to see more results")
+            );
+        }
     }
 
     private List<CompoundTopDocs> getCompoundTopDocs(CombineScoresDto combineScoresDTO, List<QuerySearchResult> querySearchResults) {
@@ -244,7 +283,8 @@ private TopDocs buildTopDocs(CompoundTopDocs updatedTopDocs, Sort sort) {
     private void updateOriginalFetchResults(
         final List<QuerySearchResult> querySearchResults,
         final Optional<FetchSearchResult> fetchSearchResultOptional,
-        final List<Integer> docIds
+        final List<Integer> docIds,
+        final int fromValueForSingleShard
     ) {
         if (fetchSearchResultOptional.isEmpty()) {
             return;
@@ -276,14 +316,21 @@ private void updateOriginalFetchResults(
 
         QuerySearchResult querySearchResult = querySearchResults.get(0);
         TopDocs topDocs = querySearchResult.topDocs().topDocs;
+        // Scenario to handle when calculating the trimmed length of updated search hits
+        // When normalization process runs after fetch phase, then search hits already fetched. Therefore, use the from value sent in the
+        // search request to calculate the effective length of updated search hits array.
+        int trimmedLengthOfSearchHits = topDocs.scoreDocs.length - fromValueForSingleShard;
         // iterate over the normalized/combined scores, that solves (1) and (3)
-        SearchHit[] updatedSearchHitArray = Arrays.stream(topDocs.scoreDocs).map(scoreDoc -> {
+        SearchHit[] updatedSearchHitArray = new SearchHit[trimmedLengthOfSearchHits];
+        for (int i = 0; i < trimmedLengthOfSearchHits; i++) {
+            // Read topDocs after the desired from length
+            ScoreDoc scoreDoc = topDocs.scoreDocs[i + fromValueForSingleShard];
             // get fetched hit content by doc_id
             SearchHit searchHit = docIdToSearchHit.get(scoreDoc.doc);
             // update score to normalized/combined value (3)
             searchHit.score(scoreDoc.score);
-            return searchHit;
-        }).toArray(SearchHit[]::new);
+            updatedSearchHitArray[i] = searchHit;
+        }
         SearchHits updatedSearchHits = new SearchHits(
             updatedSearchHitArray,
             querySearchResult.getTotalHits(),

diff --git a/...a/org/opensearch/neuralsearch/processor/NormalizationProcessorWorkflowExecuteRequest.java b/...a/org/opensearch/neuralsearch/processor/NormalizationProcessorWorkflowExecuteRequest.java
@@ -7,6 +7,7 @@
 import lombok.AllArgsConstructor;
 import lombok.Builder;
 import lombok.Getter;
+import org.opensearch.action.search.SearchPhaseContext;
 import org.opensearch.neuralsearch.processor.combination.ScoreCombinationTechnique;
 import org.opensearch.neuralsearch.processor.normalization.ScoreNormalizationTechnique;
 import org.opensearch.search.fetch.FetchSearchResult;
@@ -29,4 +30,5 @@ public class NormalizationProcessorWorkflowExecuteRequest {
     final ScoreCombinationTechnique combinationTechnique;
     boolean explain;
     final PipelineProcessingContext pipelineProcessingContext;
+    final SearchPhaseContext searchPhaseContext;
 }
diff --git a/src/main/java/org/opensearch/neuralsearch/processor/combination/CombineScoresDto.java b/src/main/java/org/opensearch/neuralsearch/processor/combination/CombineScoresDto.java
@@ -29,4 +29,5 @@ public class CombineScoresDto {
     private List<QuerySearchResult> querySearchResults;
     @Nullable
     private Sort sort;
+    private int fromValueForSingleShard;
 }
diff --git a/src/main/java/org/opensearch/neuralsearch/processor/combination/ScoreCombiner.java b/src/main/java/org/opensearch/neuralsearch/processor/combination/ScoreCombiner.java
@@ -70,14 +70,10 @@ public class ScoreCombiner {
     public void combineScores(final CombineScoresDto combineScoresDTO) {
         // iterate over results from each shard. Every CompoundTopDocs object has results from
         // multiple sub queries, doc ids may repeat for each sub query results
+        ScoreCombinationTechnique scoreCombinationTechnique = combineScoresDTO.getScoreCombinationTechnique();
+        Sort sort = combineScoresDTO.getSort();
         combineScoresDTO.getQueryTopDocs()
-            .forEach(
-                compoundQueryTopDocs -> combineShardScores(
-                    combineScoresDTO.getScoreCombinationTechnique(),
-                    compoundQueryTopDocs,
-                    combineScoresDTO.getSort()
-                )
-            );
+            .forEach(compoundQueryTopDocs -> combineShardScores(scoreCombinationTechnique, compoundQueryTopDocs, sort));
     }
 
     private void combineShardScores(

diff --git a/src/main/java/org/opensearch/neuralsearch/query/HybridQuery.java b/src/main/java/org/opensearch/neuralsearch/query/HybridQuery.java
@@ -34,17 +34,22 @@
 public final class HybridQuery extends Query implements Iterable<Query> {
 
     private final List<Query> subQueries;
+    private final HybridQueryContext queryContext;
 
     /**
      * Create new instance of hybrid query object based on collection of sub queries and filter query
      * @param subQueries collection of queries that are executed individually and contribute to a final list of combined scores
      * @param filterQueries list of filters that will be applied to each sub query. Each filter from the list is added as bool "filter" clause. If this is null sub queries will be executed as is
      */
-    public HybridQuery(final Collection<Query> subQueries, final List<Query> filterQueries) {
+    public HybridQuery(final Collection<Query> subQueries, final List<Query> filterQueries, final HybridQueryContext hybridQueryContext) {
         Objects.requireNonNull(subQueries, "collection of queries must not be null");
         if (subQueries.isEmpty()) {
             throw new IllegalArgumentException("collection of queries must not be empty");
         }
+        Integer paginationDepth = hybridQueryContext.getPaginationDepth();
+        if (Objects.nonNull(paginationDepth) && paginationDepth == 0) {
+            throw new IllegalArgumentException("pagination_depth must not be zero");
+        }
         if (Objects.isNull(filterQueries) || filterQueries.isEmpty()) {
             this.subQueries = new ArrayList<>(subQueries);
         } else {
@@ -57,10 +62,11 @@ public HybridQuery(final Collection<Query> subQueries, final List<Query> filterQ
             }
             this.subQueries = modifiedSubQueries;
         }
+        this.queryContext = hybridQueryContext;
     }
 
-    public HybridQuery(final Collection<Query> subQueries) {
-        this(subQueries, List.of());
+    public HybridQuery(final Collection<Query> subQueries, final HybridQueryContext hybridQueryContext) {
+        this(subQueries, List.of(), hybridQueryContext);
     }
 
     /**
@@ -128,7 +134,7 @@ public Query rewrite(IndexSearcher indexSearcher) throws IOException {
             return super.rewrite(indexSearcher);
         }
         final List<Query> rewrittenSubQueries = manager.getQueriesAfterRewrite(collectors);
-        return new HybridQuery(rewrittenSubQueries);
+        return new HybridQuery(rewrittenSubQueries, queryContext);
     }
 
     private Void rewriteQuery(Query query, HybridQueryExecutorCollector<IndexSearcher, Map.Entry<Query, Boolean>> collector) {
@@ -190,6 +196,10 @@ public Collection<Query> getSubQueries() {
         return Collections.unmodifiableCollection(subQueries);
     }
 
+    public HybridQueryContext getQueryContext() {
+        return queryContext;
+    }
+
     /**
      * Create the Weight used to score this query
      *