Change text embedding processor to async mode for better isolation (o…

…pensearch-project#27) * Change text embedding processor to async mode Signed-off-by: Zan Niu <[email protected]> * Address review comments Signed-off-by: Zan Niu <[email protected]> Signed-off-by: Zan Niu <[email protected]>
zhichao-aws · Oct 27, 2022 · d538ad1 · d538ad1
1 parent 38de48d
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Showing 7 changed files with 121 additions and 131 deletions.
diff --git a/src/main/java/org/opensearch/neuralsearch/ml/MLCommonsClientAccessor.java b/src/main/java/org/opensearch/neuralsearch/ml/MLCommonsClientAccessor.java
@@ -8,14 +8,12 @@
 import java.util.ArrayList;
 import java.util.Arrays;
 import java.util.List;
-import java.util.concurrent.ExecutionException;
 import java.util.stream.Collectors;
 
 import lombok.NonNull;
 import lombok.RequiredArgsConstructor;
 import lombok.extern.log4j.Log4j2;
 
-import org.opensearch.action.ActionFuture;
 import org.opensearch.action.ActionListener;
 import org.opensearch.ml.client.MachineLearningNodeClient;
 import org.opensearch.ml.common.FunctionName;
@@ -109,29 +107,6 @@ public void inferenceSentences(
         }, listener::onFailure));
     }
 
-    /**
-     * Abstraction to call predict function of api of MLClient with provided targetResponseFilters. It uses the
-     * custom model provided as modelId and run the {@link FunctionName#TEXT_EMBEDDING}. The return will be sent
-     * using the actionListener which will have a {@link List} of {@link List} of {@link Float} in the order of
-     * inputText. We are not making this function generic enough to take any function or TaskType as currently we need
-     * to run only TextEmbedding tasks only. Please note this method is a blocking method, use this only when the processing
-     * needs block waiting for response, otherwise please use {@link #inferenceSentences(String, List, ActionListener)}
-     * instead.
-     * @param modelId {@link String}
-     * @param inputText {@link List} of {@link String} on which inference needs to happen.
-     * @return {@link List} of {@link List} of {@link String} represents the text embedding vector result.
-     * @throws ExecutionException If the underlying task failed, this exception will be thrown in the future.get().
-     * @throws InterruptedException If the thread is interrupted, this will be thrown.
-     */
-    public List<List<Float>> inferenceSentences(@NonNull final String modelId, @NonNull final List<String> inputText)
-        throws ExecutionException, InterruptedException {
-        final MLInput mlInput = createMLInput(TARGET_RESPONSE_FILTERS, inputText);
-        final ActionFuture<MLOutput> outputActionFuture = mlClient.predict(modelId, mlInput);
-        final List<List<Float>> vector = buildVectorFromResponse(outputActionFuture.get());
-        log.debug("Inference Response for input sentence {} is : {} ", inputText, vector);
-        return vector;
-    }
-
     private MLInput createMLInput(final List<String> targetResponseFilters, List<String> inputText) {
         final ModelResultFilter modelResultFilter = new ModelResultFilter(false, true, targetResponseFilters, null);
         final MLInputDataset inputDataset = new TextDocsInputDataSet(inputText, modelResultFilter);

diff --git a/src/main/java/org/opensearch/neuralsearch/processor/TextEmbeddingProcessor.java b/src/main/java/org/opensearch/neuralsearch/processor/TextEmbeddingProcessor.java
@@ -10,13 +10,14 @@
 import java.util.List;
 import java.util.Map;
 import java.util.Objects;
-import java.util.concurrent.ExecutionException;
+import java.util.function.BiConsumer;
 import java.util.function.Supplier;
 import java.util.stream.IntStream;
 
 import lombok.extern.log4j.Log4j2;
 
 import org.apache.commons.lang3.StringUtils;
+import org.opensearch.action.ActionListener;
 import org.opensearch.env.Environment;
 import org.opensearch.index.mapper.MapperService;
 import org.opensearch.ingest.AbstractProcessor;
@@ -80,17 +81,33 @@ private void validateEmbeddingConfiguration(Map<String, Object> fieldMap) {
 
     @Override
     public IngestDocument execute(IngestDocument ingestDocument) {
-        validateEmbeddingFieldsValue(ingestDocument);
-        Map<String, Object> knnMap = buildMapWithKnnKeyAndOriginalValue(ingestDocument);
+        return ingestDocument;
+    }
+
+    /**
+     * This method will be invoked by PipelineService to make async inference and then delegate the handler to
+     * process the inference response or failure.
+     * @param ingestDocument {@link IngestDocument} which is the document passed to processor.
+     * @param handler {@link BiConsumer} which is the handler which can be used after the inference task is done.
+     */
+    @Override
+    public void execute(IngestDocument ingestDocument, BiConsumer<IngestDocument, Exception> handler) {
+        // When received a bulk indexing request, the pipeline will be executed in this method, (see
+        // https://github.com/opensearch-project/OpenSearch/blob/main/server/src/main/java/org/opensearch/action/bulk/TransportBulkAction.java#L226).
+        // Before the pipeline execution, the pipeline will be marked as resolved (means executed),
+        // and then this overriding method will be invoked when executing the text embedding processor.
+        // After the inference completes, the handler will invoke the doInternalExecute method again to run actual write operation.
         try {
-            List<List<Float>> vectors = mlCommonsClientAccessor.inferenceSentences(this.modelId, createInferenceList(knnMap));
-            appendVectorFieldsToDocument(ingestDocument, knnMap, vectors);
-        } catch (ExecutionException | InterruptedException e) {
-            log.error("Text embedding processor failed with exception: ", e);
-            throw new RuntimeException("Text embedding processor failed with exception", e);
+            validateEmbeddingFieldsValue(ingestDocument);
+            Map<String, Object> knnMap = buildMapWithKnnKeyAndOriginalValue(ingestDocument);
+            mlCommonsClientAccessor.inferenceSentences(this.modelId, createInferenceList(knnMap), ActionListener.wrap(vectors -> {
+                appendVectorFieldsToDocument(ingestDocument, knnMap, vectors);
+                handler.accept(ingestDocument, null);
+            }, e -> { handler.accept(null, e); }));
+        } catch (Exception e) {
+            handler.accept(null, e);
         }
-        log.debug("Text embedding completed, returning ingestDocument!");
-        return ingestDocument;
+
     }
 
     void appendVectorFieldsToDocument(IngestDocument ingestDocument, Map<String, Object> knnMap, List<List<Float>> vectors) {

diff --git a/src/test/java/org/opensearch/neuralsearch/ml/MLCommonsClientAccessorTests.java b/src/test/java/org/opensearch/neuralsearch/ml/MLCommonsClientAccessorTests.java
@@ -5,25 +5,17 @@
 
 package org.opensearch.neuralsearch.ml;
 
-import static org.mockito.ArgumentMatchers.any;
-import static org.mockito.ArgumentMatchers.anyString;
-import static org.mockito.Mockito.mock;
-import static org.mockito.Mockito.when;
-
 import java.nio.ByteBuffer;
 import java.util.ArrayList;
 import java.util.Arrays;
 import java.util.Collections;
 import java.util.List;
 
-import lombok.SneakyThrows;
-
 import org.junit.Before;
 import org.mockito.InjectMocks;
 import org.mockito.Mock;
 import org.mockito.Mockito;
 import org.mockito.MockitoAnnotations;
-import org.opensearch.action.ActionFuture;
 import org.opensearch.action.ActionListener;
 import org.opensearch.ml.client.MachineLearningNodeClient;
 import org.opensearch.ml.common.input.MLInput;
@@ -35,8 +27,6 @@
 import org.opensearch.neuralsearch.constants.TestCommonConstants;
 import org.opensearch.test.OpenSearchTestCase;
 
-import com.google.common.collect.ImmutableList;
-
 public class MLCommonsClientAccessorTests extends OpenSearchTestCase {
 
     @Mock
@@ -124,26 +114,6 @@ public void testInferenceSentences_whenExceptionFromMLClient_thenFailure() {
         Mockito.verifyNoMoreInteractions(resultListener);
     }
 
-    @SneakyThrows
-    public void test_blockingInferenceSentences() {
-        ActionFuture actionFuture = mock(ActionFuture.class);
-        when(client.predict(anyString(), any(MLInput.class))).thenReturn(actionFuture);
-        List<ModelTensors> tensorsList = new ArrayList<>();
-
-        List<ModelTensor> tensors = new ArrayList<>();
-        ModelTensor tensor = mock(ModelTensor.class);
-        when(tensor.getData()).thenReturn(TestCommonConstants.PREDICT_VECTOR_ARRAY);
-        tensors.add(tensor);
-
-        ModelTensors modelTensors = new ModelTensors(tensors);
-        tensorsList.add(modelTensors);
-
-        ModelTensorOutput mlOutput = new ModelTensorOutput(tensorsList);
-        when(actionFuture.get()).thenReturn(mlOutput);
-        List<List<Float>> result = accessor.inferenceSentences("modelId", ImmutableList.of("mock"));
-        assertEquals(TestCommonConstants.PREDICT_VECTOR_ARRAY[0], result.get(0).get(0));
-    }
-
     private ModelTensorOutput createModelTensorOutput(final Float[] output) {
         final List<ModelTensors> tensorsList = new ArrayList<>();
         final List<ModelTensor> mlModelTensorList = new ArrayList<>();

diff --git a/src/test/java/org/opensearch/neuralsearch/processor/TextEmbeddingProcessorIT.java b/src/test/java/org/opensearch/neuralsearch/processor/TextEmbeddingProcessorIT.java
@@ -7,7 +7,6 @@
 
 import java.nio.file.Files;
 import java.nio.file.Path;
-import java.util.Locale;
 import java.util.Map;
 
 import org.apache.http.HttpHeaders;
@@ -36,13 +35,8 @@ public void testTextEmbeddingProcessor() throws Exception {
     }
 
     private String uploadTextEmbeddingModel() throws Exception {
-        String currentPath = System.getProperty("user.dir");
-        Path testClusterPath = Path.of(currentPath).getParent().resolveSibling("testclusters/integTest-0/data");
-        Path path = Path.of(testClusterPath + "/all-MiniLM-L6-v2.zip");
-        Files.copy(Path.of(classLoader.getResource("model/all-MiniLM-L6-v2.zip").toURI()), path);
         String requestBody = Files.readString(Path.of(classLoader.getResource("processor/UploadModelRequestBody.json").toURI()));
-        String request = String.format(Locale.getDefault(), requestBody, path);
-        return uploadModel(request);
+        return uploadModel(requestBody);
     }
 
     private void createTextEmbeddingIndex() throws Exception {