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[Backport 2.x] Added rescorer in hybrid query (opensearch-project#917) (
opensearch-project#924) * Added rescorer in hybrid query (opensearch-project#917) * Initial version for rescorer Signed-off-by: Martin Gaievski <[email protected]> (cherry picked from commit 9f4a49a) Signed-off-by: Martin Gaievski <[email protected]>
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...ling-upgrade/src/test/java/org/opensearch/neuralsearch/bwc/HybridSearchWithRescoreIT.java
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| /* | ||
| * Copyright OpenSearch Contributors | ||
| * SPDX-License-Identifier: Apache-2.0 | ||
| */ | ||
| package org.opensearch.neuralsearch.bwc; | ||
|
|
||
| import org.opensearch.index.query.MatchQueryBuilder; | ||
| import org.opensearch.index.query.QueryBuilder; | ||
| import org.opensearch.index.query.QueryBuilders; | ||
| import org.opensearch.knn.index.query.rescore.RescoreContext; | ||
| import org.opensearch.neuralsearch.query.HybridQueryBuilder; | ||
| import org.opensearch.neuralsearch.query.NeuralQueryBuilder; | ||
|
|
||
| import java.nio.file.Files; | ||
| import java.nio.file.Path; | ||
| import java.util.Arrays; | ||
| import java.util.List; | ||
| import java.util.Map; | ||
| import java.util.Objects; | ||
|
|
||
| import static org.opensearch.neuralsearch.util.TestUtils.NODES_BWC_CLUSTER; | ||
| import static org.opensearch.neuralsearch.util.TestUtils.PARAM_NAME_WEIGHTS; | ||
| import static org.opensearch.neuralsearch.util.TestUtils.TEXT_EMBEDDING_PROCESSOR; | ||
| import static org.opensearch.neuralsearch.util.TestUtils.DEFAULT_NORMALIZATION_METHOD; | ||
| import static org.opensearch.neuralsearch.util.TestUtils.DEFAULT_COMBINATION_METHOD; | ||
| import static org.opensearch.neuralsearch.util.TestUtils.getModelId; | ||
|
|
||
| public class HybridSearchWithRescoreIT extends AbstractRollingUpgradeTestCase { | ||
|
|
||
| private static final String PIPELINE_NAME = "nlp-hybrid-with-rescore-pipeline"; | ||
| private static final String SEARCH_PIPELINE_NAME = "nlp-search-with_rescore-pipeline"; | ||
| private static final String TEST_FIELD = "passage_text"; | ||
| private static final String TEXT = "Hello world"; | ||
| private static final String TEXT_MIXED = "Hi planet"; | ||
| private static final String TEXT_UPGRADED = "Hi earth"; | ||
| private static final String QUERY = "Hi world"; | ||
| private static final int NUM_DOCS_PER_ROUND = 1; | ||
| private static final String VECTOR_EMBEDDING_FIELD = "passage_embedding"; | ||
| protected static final String RESCORE_QUERY = "hi"; | ||
| private static String modelId = ""; | ||
|
|
||
| /** | ||
| * Test normalization with hybrid query and rescore. This test is required as rescore will not be compatible with version lower than 2.15 | ||
| */ | ||
| public void testHybridQueryWithRescore_whenIndexWithMultipleShards_E2EFlow() throws Exception { | ||
| waitForClusterHealthGreen(NODES_BWC_CLUSTER); | ||
| switch (getClusterType()) { | ||
| case OLD: | ||
| modelId = uploadTextEmbeddingModel(); | ||
| loadModel(modelId); | ||
| createPipelineProcessor(modelId, PIPELINE_NAME); | ||
| createIndexWithConfiguration( | ||
| getIndexNameForTest(), | ||
| Files.readString(Path.of(classLoader.getResource("processor/IndexMappings.json").toURI())), | ||
| PIPELINE_NAME | ||
| ); | ||
| addDocument(getIndexNameForTest(), "0", TEST_FIELD, TEXT, null, null); | ||
| createSearchPipeline( | ||
| SEARCH_PIPELINE_NAME, | ||
| DEFAULT_NORMALIZATION_METHOD, | ||
| DEFAULT_COMBINATION_METHOD, | ||
| Map.of(PARAM_NAME_WEIGHTS, Arrays.toString(new float[] { 0.3f, 0.7f })) | ||
| ); | ||
| break; | ||
| case MIXED: | ||
| modelId = getModelId(getIngestionPipeline(PIPELINE_NAME), TEXT_EMBEDDING_PROCESSOR); | ||
| int totalDocsCountMixed; | ||
| if (isFirstMixedRound()) { | ||
| totalDocsCountMixed = NUM_DOCS_PER_ROUND; | ||
| HybridQueryBuilder hybridQueryBuilder = getQueryBuilder(modelId, null, null); | ||
| QueryBuilder rescorer = QueryBuilders.matchQuery(TEST_FIELD, RESCORE_QUERY).boost(0.3f); | ||
| validateTestIndexOnUpgrade(totalDocsCountMixed, modelId, hybridQueryBuilder, rescorer); | ||
| addDocument(getIndexNameForTest(), "1", TEST_FIELD, TEXT_MIXED, null, null); | ||
| } else { | ||
| totalDocsCountMixed = 2 * NUM_DOCS_PER_ROUND; | ||
| HybridQueryBuilder hybridQueryBuilder = getQueryBuilder(modelId, null, null); | ||
| validateTestIndexOnUpgrade(totalDocsCountMixed, modelId, hybridQueryBuilder, null); | ||
| } | ||
| break; | ||
| case UPGRADED: | ||
| try { | ||
| modelId = getModelId(getIngestionPipeline(PIPELINE_NAME), TEXT_EMBEDDING_PROCESSOR); | ||
| int totalDocsCountUpgraded = 3 * NUM_DOCS_PER_ROUND; | ||
| loadModel(modelId); | ||
| addDocument(getIndexNameForTest(), "2", TEST_FIELD, TEXT_UPGRADED, null, null); | ||
| HybridQueryBuilder hybridQueryBuilder = getQueryBuilder(modelId, null, null); | ||
| QueryBuilder rescorer = QueryBuilders.matchQuery(TEST_FIELD, RESCORE_QUERY).boost(0.3f); | ||
| validateTestIndexOnUpgrade(totalDocsCountUpgraded, modelId, hybridQueryBuilder, rescorer); | ||
| hybridQueryBuilder = getQueryBuilder(modelId, Map.of("ef_search", 100), RescoreContext.getDefault()); | ||
| validateTestIndexOnUpgrade(totalDocsCountUpgraded, modelId, hybridQueryBuilder, rescorer); | ||
| } finally { | ||
| wipeOfTestResources(getIndexNameForTest(), PIPELINE_NAME, modelId, SEARCH_PIPELINE_NAME); | ||
| } | ||
| break; | ||
| default: | ||
| throw new IllegalStateException("Unexpected value: " + getClusterType()); | ||
| } | ||
| } | ||
|
|
||
| private void validateTestIndexOnUpgrade( | ||
| final int numberOfDocs, | ||
| final String modelId, | ||
| HybridQueryBuilder hybridQueryBuilder, | ||
| QueryBuilder rescorer | ||
| ) throws Exception { | ||
| int docCount = getDocCount(getIndexNameForTest()); | ||
| assertEquals(numberOfDocs, docCount); | ||
| loadModel(modelId); | ||
| Map<String, Object> searchResponseAsMap = search( | ||
| getIndexNameForTest(), | ||
| hybridQueryBuilder, | ||
| rescorer, | ||
| 1, | ||
| Map.of("search_pipeline", SEARCH_PIPELINE_NAME) | ||
| ); | ||
| assertNotNull(searchResponseAsMap); | ||
| int hits = getHitCount(searchResponseAsMap); | ||
| assertEquals(1, hits); | ||
| List<Double> scoresList = getNormalizationScoreList(searchResponseAsMap); | ||
| for (Double score : scoresList) { | ||
| assertTrue(0 <= score && score <= 2); | ||
| } | ||
| } | ||
|
|
||
| private HybridQueryBuilder getQueryBuilder( | ||
| final String modelId, | ||
| final Map<String, ?> methodParameters, | ||
| final RescoreContext rescoreContextForNeuralQuery | ||
| ) { | ||
| NeuralQueryBuilder neuralQueryBuilder = new NeuralQueryBuilder(); | ||
| neuralQueryBuilder.fieldName(VECTOR_EMBEDDING_FIELD); | ||
| neuralQueryBuilder.modelId(modelId); | ||
| neuralQueryBuilder.queryText(QUERY); | ||
| neuralQueryBuilder.k(5); | ||
| if (methodParameters != null) { | ||
| neuralQueryBuilder.methodParameters(methodParameters); | ||
| } | ||
| if (Objects.nonNull(rescoreContextForNeuralQuery)) { | ||
| neuralQueryBuilder.rescoreContext(rescoreContextForNeuralQuery); | ||
| } | ||
|
|
||
| MatchQueryBuilder matchQueryBuilder = new MatchQueryBuilder("text", QUERY); | ||
|
|
||
| HybridQueryBuilder hybridQueryBuilder = new HybridQueryBuilder(); | ||
| hybridQueryBuilder.add(matchQueryBuilder); | ||
| hybridQueryBuilder.add(neuralQueryBuilder); | ||
|
|
||
| return hybridQueryBuilder; | ||
| } | ||
| } |
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