Integrate explainability for hybrid query into RRF processor

Signed-off-by: Martin Gaievski <[email protected]>

src/main/java/org/opensearch/neuralsearch/processor/AbstractScoreHybridizationProcessor.java

@@ -0,0 +1,65 @@
+/*
+ * Copyright OpenSearch Contributors
+ * SPDX-License-Identifier: Apache-2.0
+ */
+package org.opensearch.neuralsearch.processor;
+
+import org.opensearch.action.search.SearchPhaseContext;
+import org.opensearch.action.search.SearchPhaseResults;
+import org.opensearch.search.SearchPhaseResult;
+import org.opensearch.search.internal.SearchContext;
+import org.opensearch.search.pipeline.PipelineProcessingContext;
+import org.opensearch.search.pipeline.SearchPhaseResultsProcessor;
+
+import java.util.Optional;
+
+/**
+ * Base class for all score hybridization processors. This class is responsible for executing the score hybridization process.
+ * It is a pipeline processor that is executed after the query phase and before the fetch phase.
+ */
+public abstract class AbstractScoreHybridizationProcessor implements SearchPhaseResultsProcessor {
+    /**
+     * Method abstracts functional aspect of score normalization and score combination. Exact methods for each processing stage
+     * are set as part of class constructor. This method is called when there is no pipeline context
+     * @param searchPhaseResult {@link SearchPhaseResults} DTO that has query search results. Results will be mutated as part of this method execution
+     * @param searchPhaseContext {@link SearchContext}
+     */
+    @Override
+    public <Result extends SearchPhaseResult> void process(
+        final SearchPhaseResults<Result> searchPhaseResult,
+        final SearchPhaseContext searchPhaseContext
+    ) {
+        hybridizeScores(searchPhaseResult, searchPhaseContext, Optional.empty());
+    }
+
+    /**
+     * Method abstracts functional aspect of score normalization and score combination. Exact methods for each processing stage
+     * are set as part of class constructor. This method is called when there is pipeline context
+     * @param searchPhaseResult {@link SearchPhaseResults} DTO that has query search results. Results will be mutated as part of this method execution
+     * @param searchPhaseContext {@link SearchContext}
+     * @param requestContext {@link PipelineProcessingContext} processing context of search pipeline
+     * @param <Result>
+     */
+    @Override
+    public <Result extends SearchPhaseResult> void process(
+        final SearchPhaseResults<Result> searchPhaseResult,
+        final SearchPhaseContext searchPhaseContext,
+        final PipelineProcessingContext requestContext
+    ) {
+        hybridizeScores(searchPhaseResult, searchPhaseContext, Optional.ofNullable(requestContext));
+    }
+
+    /**
+     * Method abstracts functional aspect of score normalization and score combination. Exact methods for each processing stage
+     * are set as part of class constructor
+     * @param searchPhaseResult
+     * @param searchPhaseContext
+     * @param requestContextOptional
+     * @param <Result>
+     */
+    abstract <Result extends SearchPhaseResult> void hybridizeScores(
+        SearchPhaseResults<Result> searchPhaseResult,
+        SearchPhaseContext searchPhaseContext,
+        Optional<PipelineProcessingContext> requestContextOptional
+    );
+}

src/main/java/org/opensearch/neuralsearch/processor/NormalizationProcessor.java

@@ -19,9 +19,7 @@
 import org.opensearch.neuralsearch.processor.normalization.ScoreNormalizationTechnique;
 import org.opensearch.search.SearchPhaseResult;
 import org.opensearch.search.fetch.FetchSearchResult;
-import org.opensearch.search.internal.SearchContext;
 import org.opensearch.search.pipeline.PipelineProcessingContext;
-import org.opensearch.search.pipeline.SearchPhaseResultsProcessor;
 import org.opensearch.search.query.QuerySearchResult;
 
 import lombok.AllArgsConstructor;
@@ -33,7 +31,7 @@
  */
 @Log4j2
 @AllArgsConstructor
-public class NormalizationProcessor implements SearchPhaseResultsProcessor {
+public class NormalizationProcessor extends AbstractScoreHybridizationProcessor {
     public static final String TYPE = "normalization-processor";
 
     private final String tag;
@@ -42,38 +40,8 @@ public class NormalizationProcessor implements SearchPhaseResultsProcessor {
     private final ScoreCombinationTechnique combinationTechnique;
     private final NormalizationProcessorWorkflow normalizationWorkflow;
 
-    /**
-     * Method abstracts functional aspect of score normalization and score combination. Exact methods for each processing stage
-     * are set as part of class constructor. This method is called when there is no pipeline context
-     * @param searchPhaseResult {@link SearchPhaseResults} DTO that has query search results. Results will be mutated as part of this method execution
-     * @param searchPhaseContext {@link SearchContext}
-     */
     @Override
-    public <Result extends SearchPhaseResult> void process(
-        final SearchPhaseResults<Result> searchPhaseResult,
-        final SearchPhaseContext searchPhaseContext
-    ) {
-        prepareAndExecuteNormalizationWorkflow(searchPhaseResult, searchPhaseContext, Optional.empty());
-    }
-
-    /**
-     * Method abstracts functional aspect of score normalization and score combination. Exact methods for each processing stage
-     * are set as part of class constructor
-     * @param searchPhaseResult {@link SearchPhaseResults} DTO that has query search results. Results will be mutated as part of this method execution
-     * @param searchPhaseContext {@link SearchContext}
-     * @param requestContext {@link PipelineProcessingContext} processing context of search pipeline
-     * @param <Result>
-     */
-    @Override
-    public <Result extends SearchPhaseResult> void process(
-        final SearchPhaseResults<Result> searchPhaseResult,
-        final SearchPhaseContext searchPhaseContext,
-        final PipelineProcessingContext requestContext
-    ) {
-        prepareAndExecuteNormalizationWorkflow(searchPhaseResult, searchPhaseContext, Optional.ofNullable(requestContext));
-    }
-
-    private <Result extends SearchPhaseResult> void prepareAndExecuteNormalizationWorkflow(
+    <Result extends SearchPhaseResult> void hybridizeScores(
         SearchPhaseResults<Result> searchPhaseResult,
         SearchPhaseContext searchPhaseContext,
         Optional<PipelineProcessingContext> requestContextOptional

src/main/java/org/opensearch/neuralsearch/processor/RRFProcessor.java

@@ -12,11 +12,12 @@
 import java.util.Objects;
 import java.util.Optional;
 
+import com.google.common.annotations.VisibleForTesting;
 import lombok.Getter;
-import org.opensearch.ml.repackage.com.google.common.annotations.VisibleForTesting;
 import org.opensearch.neuralsearch.processor.combination.ScoreCombinationTechnique;
 import org.opensearch.neuralsearch.processor.normalization.ScoreNormalizationTechnique;
 import org.opensearch.search.fetch.FetchSearchResult;
+import org.opensearch.search.pipeline.PipelineProcessingContext;
 import org.opensearch.search.query.QuerySearchResult;
 
 import org.opensearch.action.search.QueryPhaseResultConsumer;
@@ -25,7 +26,6 @@
 import org.opensearch.action.search.SearchPhaseResults;
 import org.opensearch.search.SearchPhaseResult;
 import org.opensearch.search.internal.SearchContext;
-import org.opensearch.search.pipeline.SearchPhaseResultsProcessor;
 
 import lombok.AllArgsConstructor;
 import lombok.extern.log4j.Log4j2;
@@ -39,7 +39,7 @@
  */
 @Log4j2
 @AllArgsConstructor
-public class RRFProcessor implements SearchPhaseResultsProcessor {
+public class RRFProcessor extends AbstractScoreHybridizationProcessor {
     public static final String TYPE = "score-ranker-processor";
 
     @Getter
@@ -57,25 +57,28 @@ public class RRFProcessor implements SearchPhaseResultsProcessor {
      * @param searchPhaseContext {@link SearchContext}
      */
     @Override
-    public <Result extends SearchPhaseResult> void process(
-        final SearchPhaseResults<Result> searchPhaseResult,
-        final SearchPhaseContext searchPhaseContext
+    <Result extends SearchPhaseResult> void hybridizeScores(
+        SearchPhaseResults<Result> searchPhaseResult,
+        SearchPhaseContext searchPhaseContext,
+        Optional<PipelineProcessingContext> requestContextOptional
     ) {
         if (shouldSkipProcessor(searchPhaseResult)) {
             log.debug("Query results are not compatible with RRF processor");
             return;
         }
         List<QuerySearchResult> querySearchResults = getQueryPhaseSearchResults(searchPhaseResult);
         Optional<FetchSearchResult> fetchSearchResult = getFetchSearchResults(searchPhaseResult);
-
+        boolean explain = Objects.nonNull(searchPhaseContext.getRequest().source().explain())
+            && searchPhaseContext.getRequest().source().explain();
         // make data transfer object to pass in, execute will get object with 4 or 5 fields, depending
         // on coming from NormalizationProcessor or RRFProcessor
         NormalizationProcessorWorkflowExecuteRequest normalizationExecuteDTO = NormalizationProcessorWorkflowExecuteRequest.builder()
             .querySearchResults(querySearchResults)
             .fetchSearchResultOptional(fetchSearchResult)
             .normalizationTechnique(normalizationTechnique)
             .combinationTechnique(combinationTechnique)
-            .explain(false)
+            .explain(explain)
+            .pipelineProcessingContext(requestContextOptional.orElse(null))
             .build();
         normalizationWorkflow.execute(normalizationExecuteDTO);
     }

src/main/java/org/opensearch/neuralsearch/processor/combination/RRFScoreCombinationTechnique.java

@@ -6,27 +6,39 @@
 
 import lombok.ToString;
 import lombok.extern.log4j.Log4j2;
+import org.opensearch.neuralsearch.processor.explain.ExplainableTechnique;
 
-import java.util.Map;
+import java.util.List;
+import java.util.Objects;
+
+import static org.opensearch.neuralsearch.processor.explain.ExplanationUtils.describeCombinationTechnique;
 
 @Log4j2
 /**
  * Abstracts combination of scores based on reciprocal rank fusion algorithm
  */
 @ToString(onlyExplicitlyIncluded = true)
-public class RRFScoreCombinationTechnique implements ScoreCombinationTechnique {
+public class RRFScoreCombinationTechnique implements ScoreCombinationTechnique, ExplainableTechnique {
     @ToString.Include
     public static final String TECHNIQUE_NAME = "rrf";
 
     // Not currently using weights for RRF, no need to modify or verify these params
-    public RRFScoreCombinationTechnique(final Map<String, Object> params, final ScoreCombinationUtil combinationUtil) {}
+    public RRFScoreCombinationTechnique() {}
 
     @Override
     public float combine(final float[] scores) {
+        if (Objects.isNull(scores)) {
+            throw new IllegalArgumentException("scores array cannot be null");
+        }
         float sumScores = 0.0f;
         for (float score : scores) {
             sumScores += score;
         }
         return sumScores;
     }
+
+    @Override
+    public String describe() {
+        return describeCombinationTechnique(TECHNIQUE_NAME, List.of());
+    }
 }

src/main/java/org/opensearch/neuralsearch/processor/combination/ScoreCombinationFactory.java

@@ -27,7 +27,7 @@ public class ScoreCombinationFactory {
         GeometricMeanScoreCombinationTechnique.TECHNIQUE_NAME,
         params -> new GeometricMeanScoreCombinationTechnique(params, scoreCombinationUtil),
         RRFScoreCombinationTechnique.TECHNIQUE_NAME,
-        params -> new RRFScoreCombinationTechnique(params, scoreCombinationUtil)
+        params -> new RRFScoreCombinationTechnique()
     );
 
     /**

src/main/java/org/opensearch/neuralsearch/processor/normalization/RRFNormalizationTechnique.java

@@ -6,27 +6,34 @@
 
 import java.math.BigDecimal;
 import java.math.RoundingMode;
+import java.util.ArrayList;
+import java.util.HashMap;
 import java.util.List;
 import java.util.Map;
 import java.util.Objects;
 import java.util.Locale;
 import java.util.Set;
+import java.util.function.BiConsumer;
+import java.util.stream.IntStream;
 
 import org.apache.commons.lang3.Range;
 import org.apache.commons.lang3.math.NumberUtils;
-import org.apache.lucene.search.ScoreDoc;
-import org.apache.lucene.search.TopDocs;
 import org.opensearch.neuralsearch.processor.CompoundTopDocs;
 
 import lombok.ToString;
 import org.opensearch.neuralsearch.processor.NormalizeScoresDTO;
+import org.opensearch.neuralsearch.processor.explain.DocIdAtSearchShard;
+import org.opensearch.neuralsearch.processor.explain.ExplainableTechnique;
+import org.opensearch.neuralsearch.processor.explain.ExplanationDetails;
+
+import static org.opensearch.neuralsearch.processor.explain.ExplanationUtils.getDocIdAtQueryForNormalization;
 
 /**
  * Abstracts calculation of rank scores for each document returned as part of
  * reciprocal rank fusion. Rank scores are summed across subqueries in combination classes.
  */
 @ToString(onlyExplicitlyIncluded = true)
-public class RRFNormalizationTechnique implements ScoreNormalizationTechnique {
+public class RRFNormalizationTechnique implements ScoreNormalizationTechnique, ExplainableTechnique {
     @ToString.Include
     public static final String TECHNIQUE_NAME = "rrf";
     public static final int DEFAULT_RANK_CONSTANT = 60;
@@ -58,21 +65,49 @@ public RRFNormalizationTechnique(final Map<String, Object> params, final ScoreNo
     public void normalize(final NormalizeScoresDTO normalizeScoresDTO) {
         final List<CompoundTopDocs> queryTopDocs = normalizeScoresDTO.getQueryTopDocs();
         for (CompoundTopDocs compoundQueryTopDocs : queryTopDocs) {
-            if (Objects.isNull(compoundQueryTopDocs)) {
-                continue;
-            }
-            List<TopDocs> topDocsPerSubQuery = compoundQueryTopDocs.getTopDocs();
-            for (TopDocs topDocs : topDocsPerSubQuery) {
-                int docsCountPerSubQuery = topDocs.scoreDocs.length;
-                ScoreDoc[] scoreDocs = topDocs.scoreDocs;
-                for (int j = 0; j < docsCountPerSubQuery; j++) {
-                    // using big decimal approach to minimize error caused by floating point ops
-                    // score = 1.f / (float) (rankConstant + j + 1))
-                    scoreDocs[j].score = BigDecimal.ONE.divide(BigDecimal.valueOf(rankConstant + j + 1), 10, RoundingMode.HALF_UP)
-                        .floatValue();
-                }
-            }
+            processTopDocs(compoundQueryTopDocs, (docId, score) -> {});
+        }
+    }
+
+    @Override
+    public String describe() {
+        return String.format(Locale.ROOT, "%s, rank_constant %s", TECHNIQUE_NAME, rankConstant);
+    }
+
+    @Override
+    public Map<DocIdAtSearchShard, ExplanationDetails> explain(List<CompoundTopDocs> queryTopDocs) {
+        Map<DocIdAtSearchShard, List<Float>> normalizedScores = new HashMap<>();
+
+        for (CompoundTopDocs compoundQueryTopDocs : queryTopDocs) {
+            processTopDocs(
+                compoundQueryTopDocs,
+                (docId, score) -> normalizedScores.computeIfAbsent(docId, k -> new ArrayList<>()).add(score)
+            );
         }
+
+        return getDocIdAtQueryForNormalization(normalizedScores, this);
+    }
+
+    private void processTopDocs(CompoundTopDocs compoundQueryTopDocs, BiConsumer<DocIdAtSearchShard, Float> scoreProcessor) {
+        if (Objects.isNull(compoundQueryTopDocs)) {
+            return;
+        }
+
+        compoundQueryTopDocs.getTopDocs().forEach(topDocs -> {
+            IntStream.range(0, topDocs.scoreDocs.length).forEach(position -> {
+                float normalizedScore = calculateNormalizedScore(position);
+                DocIdAtSearchShard docIdAtSearchShard = new DocIdAtSearchShard(
+                    topDocs.scoreDocs[position].doc,
+                    compoundQueryTopDocs.getSearchShard()
+                );
+                scoreProcessor.accept(docIdAtSearchShard, normalizedScore);
+                topDocs.scoreDocs[position].score = normalizedScore;
+            });
+        });
+    }
+
+    private float calculateNormalizedScore(int position) {
+        return BigDecimal.ONE.divide(BigDecimal.valueOf(rankConstant + position + 1), 10, RoundingMode.HALF_UP).floatValue();
     }
 
     private int getRankConstant(final Map<String, Object> params) {
@@ -96,7 +131,7 @@ private void validateRankConstant(final int rankConstant) {
         }
     }
 
-    public static int getParamAsInteger(final Map<String, Object> parameters, final String fieldName) {
+    private static int getParamAsInteger(final Map<String, Object> parameters, final String fieldName) {
         try {
             return NumberUtils.createInteger(String.valueOf(parameters.get(fieldName)));
         } catch (NumberFormatException e) {