|
| 1 | +/* |
| 2 | + * Copyright OpenSearch Contributors |
| 3 | + * SPDX-License-Identifier: Apache-2.0 |
| 4 | + */ |
| 5 | +package org.opensearch.neuralsearch.mappingtransformer; |
| 6 | + |
| 7 | +import java.io.IOException; |
| 8 | +import java.net.URISyntaxException; |
| 9 | +import java.nio.file.Files; |
| 10 | +import java.nio.file.Path; |
| 11 | +import java.util.List; |
| 12 | +import java.util.Locale; |
| 13 | +import java.util.Map; |
| 14 | +import java.util.Objects; |
| 15 | + |
| 16 | +import org.apache.hc.core5.http.HttpHeaders; |
| 17 | +import org.apache.hc.core5.http.message.BasicHeader; |
| 18 | +import org.opensearch.client.Request; |
| 19 | +import org.opensearch.client.Response; |
| 20 | +import org.opensearch.common.xcontent.XContentHelper; |
| 21 | +import org.opensearch.common.xcontent.XContentType; |
| 22 | +import org.junit.After; |
| 23 | +import org.junit.Assume; |
| 24 | +import org.junit.Before; |
| 25 | +import org.opensearch.neuralsearch.BaseNeuralSearchIT; |
| 26 | +import org.opensearch.neuralsearch.util.RemoteModelTestUtils; |
| 27 | + |
| 28 | +import com.google.common.collect.ImmutableList; |
| 29 | + |
| 30 | +import lombok.SneakyThrows; |
| 31 | +import lombok.extern.log4j.Log4j2; |
| 32 | + |
| 33 | +import static org.opensearch.neuralsearch.util.TestUtils.DEFAULT_USER_AGENT; |
| 34 | + |
| 35 | +/** |
| 36 | + * End-to-end integration test for the semantic field mapping transformation with a remote dense |
| 37 | + * (text embedding) model served by the TorchServe Docker mock. It verifies two things: |
| 38 | + * <ol> |
| 39 | + * <li>The semantic field mapping is transformed correctly using the embedding dimension and |
| 40 | + * space type resolved from the deployed remote model.</li> |
| 41 | + * <li>The remote model actually generates embeddings during ingestion (real inference through |
| 42 | + * the ML Commons connector), not just a metadata-only mapping change.</li> |
| 43 | + * </ol> |
| 44 | + * This test is only exercised by the {@code remoteModelIntegTest} Gradle task (class name matches |
| 45 | + * the {@code *RemoteModelIT*} filter) and is skipped when TorchServe is not available. |
| 46 | + */ |
| 47 | +@Log4j2 |
| 48 | +public class SemanticMappingTransformerRemoteModelIT extends BaseNeuralSearchIT { |
| 49 | + |
| 50 | + private static final String INDEX_NAME = "semantic_field_remote_dense_model_index"; |
| 51 | + private static final int EMBEDDING_DIMENSION = 128; // tiny BERT model served by TorchServe emits 128-dim embeddings |
| 52 | + |
| 53 | + // Nested semantic field structure produced by mappingtransformer/SemanticIndexMappings.json |
| 54 | + private static final String LEVEL_1_FIELD = "products"; |
| 55 | + private static final String SEMANTIC_INFO_FIELD = "product_description_semantic_info"; |
| 56 | + private static final String EMBEDDING_FIELD = "embedding"; |
| 57 | + |
| 58 | + private final String createIndexRequestBody = Files.readString( |
| 59 | + Path.of(Objects.requireNonNull(classLoader.getResource("mappingtransformer/SemanticIndexMappings.json")).toURI()) |
| 60 | + ); |
| 61 | + private final String expectedIndexMappingTemplate = Files.readString( |
| 62 | + Path.of(Objects.requireNonNull(classLoader.getResource("mappingtransformer/expectedIndexMappingWithRemoteDenseModel.json")).toURI()) |
| 63 | + ); |
| 64 | + private final String ingestDoc = Files.readString( |
| 65 | + Path.of(Objects.requireNonNull(classLoader.getResource("mappingtransformer/ingest_doc_remote_dense_model.json")).toURI()) |
| 66 | + ); |
| 67 | + |
| 68 | + private String connectorId; |
| 69 | + private String remoteModelId; |
| 70 | + private boolean isTorchServeAvailable = false; |
| 71 | + |
| 72 | + public SemanticMappingTransformerRemoteModelIT() throws IOException, URISyntaxException {} |
| 73 | + |
| 74 | + @Before |
| 75 | + @Override |
| 76 | + public void setUp() throws Exception { |
| 77 | + super.setUp(); |
| 78 | + updateClusterSettings(); |
| 79 | + |
| 80 | + // Configure ML Commons to trust localhost endpoints for remote models |
| 81 | + updateClusterSettings("plugins.ml_commons.only_run_on_ml_node", false); |
| 82 | + updateClusterSettings("plugins.ml_commons.connector.private_ip_enabled", true); |
| 83 | + updateClusterSettings("plugins.ml_commons.allow_registering_model_via_url", true); |
| 84 | + updateClusterSettings( |
| 85 | + "plugins.ml_commons.trusted_connector_endpoints_regex", |
| 86 | + List.of("^http://localhost:.*", "^http://127\\.0\\.0\\.1:.*", "^http://torchserve:.*") |
| 87 | + ); |
| 88 | + |
| 89 | + String torchServeEndpoint = System.getenv("TORCHSERVE_ENDPOINT"); |
| 90 | + if (torchServeEndpoint == null) { |
| 91 | + torchServeEndpoint = System.getProperty("tests.torchserve.endpoint"); |
| 92 | + } |
| 93 | + |
| 94 | + if (torchServeEndpoint == null || torchServeEndpoint.isEmpty()) { |
| 95 | + log.info("TorchServe endpoint not configured, tests will be skipped"); |
| 96 | + return; |
| 97 | + } |
| 98 | + |
| 99 | + isTorchServeAvailable = RemoteModelTestUtils.isRemoteEndpointAvailable(torchServeEndpoint); |
| 100 | + if (!isTorchServeAvailable) { |
| 101 | + log.info("TorchServe not available at {}, tests will be skipped", torchServeEndpoint); |
| 102 | + return; |
| 103 | + } |
| 104 | + |
| 105 | + log.info("TorchServe endpoint available at: {}", torchServeEndpoint); |
| 106 | + try { |
| 107 | + connectorId = createRemoteModelConnector(torchServeEndpoint); |
| 108 | + log.info("Created connector with ID: {}", connectorId); |
| 109 | + |
| 110 | + remoteModelId = deployRemoteModel(connectorId, "semantic-mapping-transformer-dense-remote"); |
| 111 | + log.info("Deployed remote text embedding model with ID: {}", remoteModelId); |
| 112 | + } catch (Exception e) { |
| 113 | + log.error("Failed to set up remote model: ", e); |
| 114 | + isTorchServeAvailable = false; |
| 115 | + } |
| 116 | + } |
| 117 | + |
| 118 | + @After |
| 119 | + @SneakyThrows |
| 120 | + public void tearDown() { |
| 121 | + super.tearDown(); |
| 122 | + |
| 123 | + try { |
| 124 | + deleteIndex(INDEX_NAME); |
| 125 | + } catch (Exception e) { |
| 126 | + log.debug("Index cleanup failed: {}", e.getMessage()); |
| 127 | + } |
| 128 | + |
| 129 | + if (remoteModelId != null || connectorId != null) { |
| 130 | + cleanupRemoteModelResources(connectorId, remoteModelId); |
| 131 | + } |
| 132 | + } |
| 133 | + |
| 134 | + /** |
| 135 | + * Verifies that the semantic field mapping is transformed using the dimension/space type from the |
| 136 | + * deployed remote dense model, and that the remote model produces real embeddings during ingestion. |
| 137 | + */ |
| 138 | + @SneakyThrows |
| 139 | + public void testTransformMappingWithRemoteDenseModel() { |
| 140 | + Assume.assumeTrue("TorchServe is not available, skipping test", isTorchServeAvailable); |
| 141 | + |
| 142 | + // 1. Create the semantic index. The mapping transformer resolves the embedding dimension and |
| 143 | + // space type from the deployed remote model to build the knn_vector sub-field. |
| 144 | + createSemanticIndexWithConfiguration(INDEX_NAME, createIndexRequestBody, remoteModelId); |
| 145 | + |
| 146 | + // 2. Assert the transformed index mapping matches the expected 128-dim dense mapping. |
| 147 | + final Map<String, Object> indexMapping = getIndexMapping(INDEX_NAME); |
| 148 | + final String expectedIndexMappingStr = String.format(Locale.ROOT, expectedIndexMappingTemplate, remoteModelId); |
| 149 | + final Map<String, Object> expectedIndexMappingMap = createParser(XContentType.JSON.xContent(), expectedIndexMappingStr).map(); |
| 150 | + org.assertj.core.api.Assertions.assertThat(indexMapping).isEqualTo(expectedIndexMappingMap); |
| 151 | + |
| 152 | + // 3. Ingest a document and verify the remote model actually generated embeddings. |
| 153 | + ingestDocument(INDEX_NAME, ingestDoc, "1"); |
| 154 | + assertEquals(1, getDocCount(INDEX_NAME)); |
| 155 | + |
| 156 | + @SuppressWarnings("unchecked") |
| 157 | + final Map<String, Object> source = (Map<String, Object>) getDocById(INDEX_NAME, "1").get("_source"); |
| 158 | + assertEmbeddingsGenerated(source); |
| 159 | + } |
| 160 | + |
| 161 | + /** |
| 162 | + * Verifies each nested product has a 128-dim, non-zero embedding produced by the remote model. |
| 163 | + */ |
| 164 | + @SuppressWarnings("unchecked") |
| 165 | + private void assertEmbeddingsGenerated(final Map<String, Object> source) { |
| 166 | + final List<Map<String, Object>> products = (List<Map<String, Object>>) source.get(LEVEL_1_FIELD); |
| 167 | + assertNotNull("Nested products should exist", products); |
| 168 | + assertEquals("Both nested products should be present", 2, products.size()); |
| 169 | + |
| 170 | + for (final Map<String, Object> product : products) { |
| 171 | + final Map<String, Object> semanticInfo = (Map<String, Object>) product.get(SEMANTIC_INFO_FIELD); |
| 172 | + assertNotNull("Semantic info should exist for each product", semanticInfo); |
| 173 | + |
| 174 | + final List<Number> embedding = (List<Number>) semanticInfo.get(EMBEDDING_FIELD); |
| 175 | + assertNotNull("Remote model should generate an embedding", embedding); |
| 176 | + assertEquals("Embedding dimension should match the remote model", EMBEDDING_DIMENSION, embedding.size()); |
| 177 | + |
| 178 | + final boolean hasNonZeroValues = embedding.stream().anyMatch(value -> value.doubleValue() != 0.0); |
| 179 | + assertTrue("Embedding should contain non-zero values", hasNonZeroValues); |
| 180 | + } |
| 181 | + } |
| 182 | + |
| 183 | + /** |
| 184 | + * Registers and deploys the remote model with a full text-embedding {@code model_config}. The |
| 185 | + * mapping transformer reads {@code embedding_dimension} and {@code additional_config.space_type} |
| 186 | + * from this config, so they must match the TorchServe dense handler (128-dim, l2). |
| 187 | + */ |
| 188 | + @Override |
| 189 | + protected String deployRemoteModel(final String connectorId, final String modelName) throws Exception { |
| 190 | + final String requestBody = String.format(Locale.ROOT, """ |
| 191 | + { |
| 192 | + "name": "%s", |
| 193 | + "function_name": "remote", |
| 194 | + "description": "Remote dense text embedding model for semantic mapping transformer IT", |
| 195 | + "connector_id": "%s", |
| 196 | + "model_config": { |
| 197 | + "model_type": "TEXT_EMBEDDING", |
| 198 | + "embedding_dimension": 128, |
| 199 | + "framework_type": "sentence_transformers", |
| 200 | + "additional_config": { |
| 201 | + "space_type": "l2" |
| 202 | + } |
| 203 | + } |
| 204 | + } |
| 205 | + """, modelName, connectorId); |
| 206 | + |
| 207 | + final Request registerRequest = new Request("POST", "/_plugins/_ml/models/_register"); |
| 208 | + registerRequest.setJsonEntity(requestBody); |
| 209 | + final Response registerResponse = client().performRequest(registerRequest); |
| 210 | + final Map<String, Object> registerResponseMap = XContentHelper.convertToMap( |
| 211 | + XContentType.JSON.xContent(), |
| 212 | + registerResponse.getEntity().getContent(), |
| 213 | + false |
| 214 | + ); |
| 215 | + final String modelId = (String) registerResponseMap.get("model_id"); |
| 216 | + |
| 217 | + final Request deployRequest = new Request("POST", "/_plugins/_ml/models/" + modelId + "/_deploy"); |
| 218 | + client().performRequest(deployRequest); |
| 219 | + |
| 220 | + waitForModelToBeReady(modelId); |
| 221 | + return modelId; |
| 222 | + } |
| 223 | + |
| 224 | + /** |
| 225 | + * Creates a TorchServe connector for symmetric (dense) text embedding, reusing the shared |
| 226 | + * connector template used by {@link org.opensearch.neuralsearch.ml.SymmetricRemoteModelIT}. |
| 227 | + */ |
| 228 | + @Override |
| 229 | + protected String createRemoteModelConnector(final String endpoint) throws Exception { |
| 230 | + final String connectorName = "semantic-mapping-transformer-dense-connector-" + System.currentTimeMillis(); |
| 231 | + final String connectorTemplate = Files.readString( |
| 232 | + Path.of(Objects.requireNonNull(classLoader.getResource("symmetric/RemoteTorchServeConnector.json")).toURI()) |
| 233 | + ); |
| 234 | + final String requestBody = String.format(Locale.ROOT, connectorTemplate, connectorName, endpoint); |
| 235 | + |
| 236 | + final Response response = makeRequest( |
| 237 | + client(), |
| 238 | + "POST", |
| 239 | + "/_plugins/_ml/connectors/_create", |
| 240 | + null, |
| 241 | + toHttpEntity(requestBody), |
| 242 | + ImmutableList.of(new BasicHeader(HttpHeaders.USER_AGENT, DEFAULT_USER_AGENT)) |
| 243 | + ); |
| 244 | + final Map<String, Object> responseMap = XContentHelper.convertToMap( |
| 245 | + XContentType.JSON.xContent(), |
| 246 | + response.getEntity().getContent(), |
| 247 | + false |
| 248 | + ); |
| 249 | + return (String) responseMap.get("connector_id"); |
| 250 | + } |
| 251 | +} |
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