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Signed-off-by: Junqiu Lei <junqiu@amazon.com>
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qa/restart-upgrade/src/test/java/org/opensearch/neuralsearch/bwc/KnnRadialSearchIT.java
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,82 @@ | ||
| /* | ||
| * Copyright OpenSearch Contributors | ||
| * SPDX-License-Identifier: Apache-2.0 | ||
| */ | ||
| package org.opensearch.neuralsearch.bwc; | ||
|
|
||
| import java.nio.file.Files; | ||
| import java.nio.file.Path; | ||
| import java.util.Map; | ||
| import static org.opensearch.neuralsearch.TestUtils.NODES_BWC_CLUSTER; | ||
| import static org.opensearch.neuralsearch.TestUtils.TEXT_IMAGE_EMBEDDING_PROCESSOR; | ||
| import static org.opensearch.neuralsearch.TestUtils.getModelId; | ||
| import org.opensearch.neuralsearch.query.NeuralQueryBuilder; | ||
|
|
||
| public class KnnRadialSearchIT extends AbstractRestartUpgradeRestTestCase { | ||
| private static final String PIPELINE_NAME = "nlp-ingest-pipeline"; | ||
| private static final String TEST_FIELD = "passage_text"; | ||
| private static final String TEST_IMAGE_FIELD = "passage_image"; | ||
| private static final String TEXT = "Hello world"; | ||
| private static final String TEXT_1 = "Hello world a"; | ||
| private static final String TEST_IMAGE_TEXT = "/9j/4AAQSkZJRgABAQAASABIAAD"; | ||
| private static final String TEST_IMAGE_TEXT_1 = "/9j/4AAQSkZJRgbdwoeicfhoid"; | ||
|
|
||
| // Test rolling-upgrade with kNN radial search | ||
| // Create Text Image Embedding Processor, Ingestion Pipeline and add document | ||
| // Validate radial query, pipeline and document count in restart-upgrade scenario | ||
| public void testKnnRadialSearch_E2EFlow() throws Exception { | ||
| waitForClusterHealthGreen(NODES_BWC_CLUSTER); | ||
|
|
||
| if (isRunningAgainstOldCluster()) { | ||
| String modelId = uploadTextEmbeddingModel(); | ||
| loadModel(modelId); | ||
| createPipelineForTextImageProcessor(modelId, PIPELINE_NAME); | ||
| createIndexWithConfiguration( | ||
| getIndexNameForTest(), | ||
| Files.readString(Path.of(classLoader.getResource("processor/IndexMappingMultipleShard.json").toURI())), | ||
| PIPELINE_NAME | ||
| ); | ||
| addDocument(getIndexNameForTest(), "0", TEST_FIELD, TEXT, TEST_IMAGE_FIELD, TEST_IMAGE_TEXT); | ||
| } else { | ||
| String modelId = null; | ||
| try { | ||
| modelId = getModelId(getIngestionPipeline(PIPELINE_NAME), TEXT_IMAGE_EMBEDDING_PROCESSOR); | ||
| loadModel(modelId); | ||
| addDocument(getIndexNameForTest(), "1", TEST_FIELD, TEXT_1, TEST_IMAGE_FIELD, TEST_IMAGE_TEXT_1); | ||
| validateIndexQuery(modelId); | ||
| } finally { | ||
| wipeOfTestResources(getIndexNameForTest(), PIPELINE_NAME, modelId, null); | ||
| } | ||
| } | ||
| } | ||
|
|
||
| private void validateIndexQuery(final String modelId) { | ||
| NeuralQueryBuilder neuralQueryBuilderWithMinScoreQuery = new NeuralQueryBuilder( | ||
| "passage_embedding", | ||
| TEXT, | ||
| TEST_IMAGE_TEXT, | ||
| modelId, | ||
| null, | ||
| null, | ||
| 0.01f, | ||
| null, | ||
| null | ||
| ); | ||
| Map<String, Object> responseWithMinScoreQuery = search(getIndexNameForTest(), neuralQueryBuilderWithMinScoreQuery, 1); | ||
| assertNotNull(responseWithMinScoreQuery); | ||
|
|
||
| NeuralQueryBuilder neuralQueryBuilderWithMaxDistanceQuery = new NeuralQueryBuilder( | ||
| "passage_embedding", | ||
| TEXT, | ||
| TEST_IMAGE_TEXT, | ||
| modelId, | ||
| null, | ||
| 100000f, | ||
| null, | ||
| null, | ||
| null | ||
| ); | ||
| Map<String, Object> responseWithMaxDistanceQuery = search(getIndexNameForTest(), neuralQueryBuilderWithMaxDistanceQuery, 1); | ||
| assertNotNull(responseWithMaxDistanceQuery); | ||
| } | ||
| } |
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108 changes: 108 additions & 0 deletions
108
qa/rolling-upgrade/src/test/java/org/opensearch/neuralsearch/bwc/KnnRadialSearchIT.java
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,108 @@ | ||
| /* | ||
| * Copyright OpenSearch Contributors | ||
| * SPDX-License-Identifier: Apache-2.0 | ||
| */ | ||
| package org.opensearch.neuralsearch.bwc; | ||
|
|
||
| import java.nio.file.Files; | ||
| import java.nio.file.Path; | ||
| import java.util.Map; | ||
| import static org.opensearch.neuralsearch.TestUtils.NODES_BWC_CLUSTER; | ||
| import static org.opensearch.neuralsearch.TestUtils.TEXT_IMAGE_EMBEDDING_PROCESSOR; | ||
| import static org.opensearch.neuralsearch.TestUtils.getModelId; | ||
| import org.opensearch.neuralsearch.query.NeuralQueryBuilder; | ||
|
|
||
| public class KnnRadialSearchIT extends AbstractRollingUpgradeTestCase { | ||
| private static final String PIPELINE_NAME = "nlp-ingest-pipeline"; | ||
| private static final String TEST_FIELD = "passage_text"; | ||
| private static final String TEST_IMAGE_FIELD = "passage_image"; | ||
| private static final String TEXT = "Hello world"; | ||
| private static final String TEXT_MIXED = "Hello world mixed"; | ||
| private static final String TEXT_UPGRADED = "Hello world upgraded"; | ||
| private static final String TEST_IMAGE_TEXT = "/9j/4AAQSkZJRgABAQAASABIAAD"; | ||
| private static final String TEST_IMAGE_TEXT_MIXED = "/9j/4AAQSkZJRgbdwoeicfhoid"; | ||
| private static final String TEST_IMAGE_TEXT_UPGRADED = "/9j/4AAQSkZJR8eydhgfwceocvlk"; | ||
|
|
||
| private static final int NUM_DOCS_PER_ROUND = 1; | ||
| private static String modelId = ""; | ||
|
|
||
| // Test rolling-upgrade with kNN radial search | ||
| // Create Text Image Embedding Processor, Ingestion Pipeline and add document | ||
| // Validate radial query, pipeline and document count in rolling-upgrade scenario | ||
| public void testKnnRadialSearch_E2EFlow() throws Exception { | ||
| waitForClusterHealthGreen(NODES_BWC_CLUSTER); | ||
| switch (getClusterType()) { | ||
| case OLD: | ||
| modelId = uploadTextImageEmbeddingModel(); | ||
| loadModel(modelId); | ||
| createPipelineForTextImageProcessor(modelId, PIPELINE_NAME); | ||
| createIndexWithConfiguration( | ||
| getIndexNameForTest(), | ||
| Files.readString(Path.of(classLoader.getResource("processor/IndexMappings.json").toURI())), | ||
| PIPELINE_NAME | ||
| ); | ||
| addDocument(getIndexNameForTest(), "0", TEST_FIELD, TEXT, TEST_IMAGE_FIELD, TEST_IMAGE_TEXT); | ||
| break; | ||
| case MIXED: | ||
| modelId = getModelId(getIngestionPipeline(PIPELINE_NAME), TEXT_IMAGE_EMBEDDING_PROCESSOR); | ||
| int totalDocsCountMixed; | ||
| if (isFirstMixedRound()) { | ||
| totalDocsCountMixed = NUM_DOCS_PER_ROUND; | ||
| validateIndexQueryOnUpgrade(totalDocsCountMixed, modelId, TEXT, TEST_IMAGE_TEXT); | ||
| addDocument(getIndexNameForTest(), "1", TEST_FIELD, TEXT_MIXED, TEST_IMAGE_FIELD, TEST_IMAGE_TEXT_MIXED); | ||
| } else { | ||
| totalDocsCountMixed = 2 * NUM_DOCS_PER_ROUND; | ||
| validateIndexQueryOnUpgrade(totalDocsCountMixed, modelId, TEXT_MIXED, TEST_IMAGE_TEXT_MIXED); | ||
| } | ||
| break; | ||
| case UPGRADED: | ||
| try { | ||
| modelId = getModelId(getIngestionPipeline(PIPELINE_NAME), TEXT_IMAGE_EMBEDDING_PROCESSOR); | ||
| int totalDocsCountUpgraded = 3 * NUM_DOCS_PER_ROUND; | ||
| loadModel(modelId); | ||
| addDocument(getIndexNameForTest(), "2", TEST_FIELD, TEXT_UPGRADED, TEST_IMAGE_FIELD, TEST_IMAGE_TEXT_UPGRADED); | ||
| validateIndexQueryOnUpgrade(totalDocsCountUpgraded, modelId, TEXT_UPGRADED, TEST_IMAGE_TEXT_UPGRADED); | ||
| } finally { | ||
| wipeOfTestResources(getIndexNameForTest(), PIPELINE_NAME, modelId, null); | ||
| } | ||
| break; | ||
| default: | ||
| throw new IllegalStateException("Unexpected value: " + getClusterType()); | ||
| } | ||
| } | ||
|
|
||
| private void validateIndexQueryOnUpgrade(final int numberOfDocs, final String modelId, final String text, final String imageText) | ||
| throws Exception { | ||
| int docCount = getDocCount(getIndexNameForTest()); | ||
| assertEquals(numberOfDocs, docCount); | ||
| loadModel(modelId); | ||
|
|
||
| NeuralQueryBuilder neuralQueryBuilderWithMinScoreQuery = new NeuralQueryBuilder( | ||
| "passage_embedding", | ||
| text, | ||
| imageText, | ||
| modelId, | ||
| null, | ||
| null, | ||
| 0.01f, | ||
| null, | ||
| null | ||
| ); | ||
| Map<String, Object> responseWithMinScore = search(getIndexNameForTest(), neuralQueryBuilderWithMinScoreQuery, 1); | ||
| assertNotNull(responseWithMinScore); | ||
|
|
||
| NeuralQueryBuilder neuralQueryBuilderWithMaxDistanceQuery = new NeuralQueryBuilder( | ||
| "passage_embedding", | ||
| text, | ||
| imageText, | ||
| modelId, | ||
| null, | ||
| 100000f, | ||
| null, | ||
| null, | ||
| null | ||
| ); | ||
| Map<String, Object> responseWithMaxScore = search(getIndexNameForTest(), neuralQueryBuilderWithMaxDistanceQuery, 1); | ||
| assertNotNull(responseWithMaxScore); | ||
| } | ||
| } |
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