[Backport to 2.11] throw exception if remote model doesn't return 2xx status code; fix p…

ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/remote/AwsConnectorExecutor.java

@@ -101,6 +101,9 @@ public void invokeRemoteModel(MLInput mlInput, Map<String, String> parameters, S
                 throw new OpenSearchStatusException("No response from model", RestStatus.BAD_REQUEST);
             }
             String modelResponse = responseBuilder.toString();
+            if (statusCode < 200 || statusCode >= 300) {
+                throw new OpenSearchStatusException(modelResponse, RestStatus.fromCode(statusCode));
+            }
 
             ModelTensors tensors = processOutput(modelResponse, connector, scriptService, parameters);
             tensors.setStatusCode(statusCode);

ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/remote/ConnectorUtils.java

@@ -102,7 +102,7 @@ private static RemoteInferenceInputDataSet processTextDocsInput(TextDocsInputDat
                     docs.add(null);
                 }
             }
-            if (preProcessFunction.contains("${parameters")) {
+            if (preProcessFunction.contains("${parameters.")) {
                 StringSubstitutor substitutor = new StringSubstitutor(parameters, "${parameters.", "}");
                 preProcessFunction = substitutor.replace(preProcessFunction);
             }
@@ -164,7 +164,7 @@ public static ModelTensors processOutput(String modelResponse, Connector connect
         // execute user defined painless script.
         Optional<String> processedResponse = executePostProcessFunction(scriptService, postProcessFunction, modelResponse);
         String response = processedResponse.orElse(modelResponse);
-        boolean scriptReturnModelTensor = postProcessFunction != null && processedResponse.isPresent();
+        boolean scriptReturnModelTensor = postProcessFunction != null && processedResponse.isPresent() && org.opensearch.ml.common.utils.StringUtils.isJson(response);
         if (responseFilter == null) {
             connector.parseResponse(response, modelTensors, scriptReturnModelTensor);
         } else {

ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/remote/HttpJsonConnectorExecutor.java

@@ -16,6 +16,8 @@
 import org.apache.http.entity.StringEntity;
 import org.apache.http.impl.client.CloseableHttpClient;
 import org.apache.http.util.EntityUtils;
+import org.opensearch.OpenSearchStatusException;
+import org.opensearch.core.rest.RestStatus;
 import org.opensearch.ml.common.connector.Connector;
 import org.opensearch.ml.common.connector.HttpConnector;
 import org.opensearch.ml.common.exception.MLException;
@@ -103,9 +105,13 @@ public void invokeRemoteModel(MLInput mlInput, Map<String, String> parameters, S
                 return null;
             });
             String modelResponse = responseRef.get();
+            Integer statusCode = statusCodeRef.get();
+            if (statusCode < 200 || statusCode >= 300) {
+                throw new OpenSearchStatusException(modelResponse, RestStatus.fromCode(statusCode));
+            }
 
             ModelTensors tensors = processOutput(modelResponse, connector, scriptService, parameters);
-            tensors.setStatusCode(statusCodeRef.get());
+            tensors.setStatusCode(statusCode);
             tensorOutputs.add(tensors);
         } catch (RuntimeException e) {
             log.error("Fail to execute http connector", e);

ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/remote/RemoteConnectorExecutor.java

@@ -32,8 +32,15 @@ default ModelTensorOutput executePredict(MLInput mlInput) {
 
         if (mlInput.getInputDataset() instanceof TextDocsInputDataSet) {
             TextDocsInputDataSet textDocsInputDataSet = (TextDocsInputDataSet) mlInput.getInputDataset();
-            List<String> textDocs = new ArrayList<>(textDocsInputDataSet.getDocs());
-            preparePayloadAndInvokeRemoteModel(MLInput.builder().algorithm(FunctionName.TEXT_EMBEDDING).inputDataset(TextDocsInputDataSet.builder().docs(textDocs).build()).build(), tensorOutputs);
+            int processedDocs = 0;
+            while(processedDocs < textDocsInputDataSet.getDocs().size()) {
+                List<String> textDocs = textDocsInputDataSet.getDocs().subList(processedDocs, textDocsInputDataSet.getDocs().size());
+                List<ModelTensors> tempTensorOutputs = new ArrayList<>();
+                preparePayloadAndInvokeRemoteModel(MLInput.builder().algorithm(FunctionName.TEXT_EMBEDDING).inputDataset(TextDocsInputDataSet.builder().docs(textDocs).build()).build(), tempTensorOutputs);
+                processedDocs += Math.max(tempTensorOutputs.size(), 1);
+                tensorOutputs.addAll(tempTensorOutputs);
+            }
+
         } else {
             preparePayloadAndInvokeRemoteModel(mlInput, tensorOutputs);
         }

ml-algorithms/src/test/java/org/opensearch/ml/engine/algorithms/remote/AwsConnectorExecutorTest.java

@@ -118,6 +118,36 @@ public void executePredict_RemoteInferenceInput_NullResponse() throws IOExceptio
         executor.executePredict(MLInput.builder().algorithm(FunctionName.REMOTE).inputDataset(inputDataSet).build());
     }
 
+    @Test
+    public void executePredict_RemoteInferenceInput_InvalidToken() throws IOException {
+        exceptionRule.expect(OpenSearchStatusException.class);
+        exceptionRule.expectMessage("{\"message\":\"The security token included in the request is invalid\"}");
+        String jsonString = "{\"message\":\"The security token included in the request is invalid\"}";
+        InputStream inputStream = new ByteArrayInputStream(jsonString.getBytes());
+        AbortableInputStream abortableInputStream = AbortableInputStream.create(inputStream);
+        when(response.responseBody()).thenReturn(Optional.of(abortableInputStream));
+        when(httpRequest.call()).thenReturn(response);
+        SdkHttpResponse httpResponse = mock(SdkHttpResponse.class);
+        when(httpResponse.statusCode()).thenReturn(403);
+        when(response.httpResponse()).thenReturn(httpResponse);
+        when(httpClient.prepareRequest(any())).thenReturn(httpRequest);
+
+        ConnectorAction predictAction = ConnectorAction.builder()
+                .actionType(ConnectorAction.ActionType.PREDICT)
+                .method("POST")
+                .url("http://test.com/mock")
+                .requestBody("{\"input\": \"${parameters.input}\"}")
+                .build();
+        Map<String, String> credential = ImmutableMap.of(ACCESS_KEY_FIELD, encryptor.encrypt("test_key"), SECRET_KEY_FIELD, encryptor.encrypt("test_secret_key"));
+        Map<String, String> parameters = ImmutableMap.of(REGION_FIELD, "us-west-2", SERVICE_NAME_FIELD, "sagemaker");
+        Connector connector = AwsConnector.awsConnectorBuilder().name("test connector").version("1").protocol("http").parameters(parameters).credential(credential).actions(Arrays.asList(predictAction)).build();
+        connector.decrypt((c) -> encryptor.decrypt(c));
+        AwsConnectorExecutor executor = spy(new AwsConnectorExecutor(connector, httpClient));
+
+        MLInputDataset inputDataSet = RemoteInferenceInputDataSet.builder().parameters(ImmutableMap.of("input", "test input data")).build();
+        executor.executePredict(MLInput.builder().algorithm(FunctionName.REMOTE).inputDataset(inputDataSet).build());
+    }
+
     @Test
     public void executePredict_RemoteInferenceInput() throws IOException {
         String jsonString = "{\"key\":\"value\"}";
@@ -176,7 +206,7 @@ public void executePredict_TextDocsInferenceInput() throws IOException {
         connector.decrypt((c) -> encryptor.decrypt(c));
         AwsConnectorExecutor executor = spy(new AwsConnectorExecutor(connector, httpClient));
 
-        MLInputDataset inputDataSet = TextDocsInputDataSet.builder().docs(ImmutableList.of("input", "test input data")).build();
+        MLInputDataset inputDataSet = TextDocsInputDataSet.builder().docs(ImmutableList.of("input")).build();
         ModelTensorOutput modelTensorOutput = executor.executePredict(MLInput.builder().algorithm(FunctionName.TEXT_EMBEDDING).inputDataset(inputDataSet).build());
         Assert.assertEquals(1, modelTensorOutput.getMlModelOutputs().size());
         Assert.assertEquals(1, modelTensorOutput.getMlModelOutputs().get(0).getMlModelTensors().size());

ml-algorithms/src/test/java/org/opensearch/ml/engine/algorithms/remote/HttpJsonConnectorExecutorTest.java

@@ -20,6 +20,7 @@
 import org.junit.rules.ExpectedException;
 import org.mockito.Mock;
 import org.mockito.MockitoAnnotations;
+import org.opensearch.OpenSearchStatusException;
 import org.opensearch.cluster.ClusterStateTaskConfig;
 import org.opensearch.ingest.TestTemplateService;
 import org.opensearch.ml.common.FunctionName;
@@ -120,12 +121,34 @@ public void executePredict_TextDocsInput_NoPreprocessFunction() throws IOExcepti
         when(executor.getHttpClient()).thenReturn(httpClient);
         MLInputDataset inputDataSet = TextDocsInputDataSet.builder().docs(Arrays.asList("test doc1", "test doc2")).build();
         ModelTensorOutput modelTensorOutput = executor.executePredict(MLInput.builder().algorithm(FunctionName.REMOTE).inputDataset(inputDataSet).build());
-        Assert.assertEquals(1, modelTensorOutput.getMlModelOutputs().size());
+        Assert.assertEquals(2, modelTensorOutput.getMlModelOutputs().size());
         Assert.assertEquals("response", modelTensorOutput.getMlModelOutputs().get(0).getMlModelTensors().get(0).getName());
         Assert.assertEquals(1, modelTensorOutput.getMlModelOutputs().get(0).getMlModelTensors().get(0).getDataAsMap().size());
         Assert.assertEquals("test result", modelTensorOutput.getMlModelOutputs().get(0).getMlModelTensors().get(0).getDataAsMap().get("response"));
     }
 
+    @Test
+    public void executePredict_TextDocsInput_LimitExceed() throws IOException {
+        exceptionRule.expect(OpenSearchStatusException.class);
+        exceptionRule.expectMessage("{\"message\": \"Too many requests\"}");
+        ConnectorAction predictAction = ConnectorAction.builder()
+                .actionType(ConnectorAction.ActionType.PREDICT)
+                .method("POST")
+                .url("http://test.com/mock")
+                .requestBody("{\"input\": ${parameters.input}}")
+                .build();
+        when(httpClient.execute(any())).thenReturn(response);
+        HttpEntity entity = new StringEntity("{\"message\": \"Too many requests\"}");
+        when(response.getEntity()).thenReturn(entity);
+        StatusLine statusLine = new BasicStatusLine(new ProtocolVersion("HTTP", 1, 1), 429, "OK");
+        when(response.getStatusLine()).thenReturn(statusLine);
+        Connector connector = HttpConnector.builder().name("test connector").version("1").protocol("http").actions(Arrays.asList(predictAction)).build();
+        HttpJsonConnectorExecutor executor = spy(new HttpJsonConnectorExecutor(connector));
+        when(executor.getHttpClient()).thenReturn(httpClient);
+        MLInputDataset inputDataSet = TextDocsInputDataSet.builder().docs(Arrays.asList("test doc1", "test doc2")).build();
+        executor.executePredict(MLInput.builder().algorithm(FunctionName.REMOTE).inputDataset(inputDataSet).build());
+    }
+
     @Test
     public void executePredict_TextDocsInput() throws IOException {
         String preprocessResult1 = "{\"parameters\": { \"input\": \"test doc1\" } }";
@@ -161,7 +184,7 @@ public void executePredict_TextDocsInput() throws IOException {
         when(executor.getHttpClient()).thenReturn(httpClient);
         MLInputDataset inputDataSet = TextDocsInputDataSet.builder().docs(Arrays.asList("test doc1", "test doc2")).build();
         ModelTensorOutput modelTensorOutput = executor.executePredict(MLInput.builder().algorithm(FunctionName.REMOTE).inputDataset(inputDataSet).build());
-        Assert.assertEquals(1, modelTensorOutput.getMlModelOutputs().size());
+        Assert.assertEquals(2, modelTensorOutput.getMlModelOutputs().size());
         Assert.assertEquals("sentence_embedding", modelTensorOutput.getMlModelOutputs().get(0).getMlModelTensors().get(0).getName());
         Assert.assertArrayEquals(new Number[] {-0.014555434, -0.002135904, 0.0035105038}, modelTensorOutput.getMlModelOutputs().get(0).getMlModelTensors().get(0).getData());
         Assert.assertArrayEquals(new Number[] {-0.014555434, -0.002135904, 0.0035105038}, modelTensorOutput.getMlModelOutputs().get(0).getMlModelTensors().get(1).getData());

plugin/src/main/java/org/opensearch/ml/model/MLModelManager.java

@@ -933,8 +933,8 @@ public void deployModel(
                             CLUSTER_SERVICE,
                             clusterService
                         );
-                    // deploy remote model or model trained by built-in algorithm like kmeans
-                    if (mlModel.getConnector() != null) {
+                    // deploy remote model with internal connector or model trained by built-in algorithm like kmeans
+                    if (mlModel.getConnector() != null || FunctionName.REMOTE != mlModel.getAlgorithm()) {
                         setupPredictable(modelId, mlModel, params);
                         wrappedListener.onResponse("successful");
                         return;
@@ -943,6 +943,7 @@ public void deployModel(
                     GetRequest getConnectorRequest = new GetRequest();
                     FetchSourceContext fetchContext = new FetchSourceContext(true, null, null);
                     getConnectorRequest.index(ML_CONNECTOR_INDEX).id(mlModel.getConnectorId()).fetchSourceContext(fetchContext);
+                    // get connector and deploy remote model with standalone connector
                     client.get(getConnectorRequest, ActionListener.wrap(getResponse -> {
                         if (getResponse != null && getResponse.isExists()) {
                             try (

plugin/src/main/java/org/opensearch/ml/task/MLPredictTaskRunner.java

@@ -213,9 +213,9 @@ private void predict(String modelId, MLTask mlTask, MLInput mlInput, ActionListe
         FunctionName algorithm = mlInput.getAlgorithm();
         // run predict
         if (modelId != null) {
-            try {
-                Predictable predictor = mlModelManager.getPredictor(modelId);
-                if (predictor != null) {
+            Predictable predictor = mlModelManager.getPredictor(modelId);
+            if (predictor != null) {
+                try {
                     if (!predictor.isModelReady()) {
                         throw new IllegalArgumentException("Model not ready: " + modelId);
                     }
@@ -229,11 +229,12 @@ private void predict(String modelId, MLTask mlTask, MLInput mlInput, ActionListe
                     MLTaskResponse response = MLTaskResponse.builder().output(output).build();
                     internalListener.onResponse(response);
                     return;
-                } else if (algorithm == FunctionName.TEXT_EMBEDDING || algorithm == FunctionName.REMOTE) {
-                    throw new IllegalArgumentException("Model not ready to be used: " + modelId);
+                } catch (Exception e) {
+                    handlePredictFailure(mlTask, internalListener, e, false, modelId);
+                    return;
                 }
-            } catch (Exception e) {
-                handlePredictFailure(mlTask, internalListener, e, false, modelId);
+            } else if (algorithm == FunctionName.TEXT_EMBEDDING || algorithm == FunctionName.REMOTE) {
+                throw new IllegalArgumentException("Model not ready to be used: " + modelId);
             }
 
             // search model by model id.
@@ -252,6 +253,7 @@ private void predict(String modelId, MLTask mlTask, MLInput mlInput, ActionListe
                         GetResponse getResponse = r;
                         String algorithmName = getResponse.getSource().get(ALGORITHM_FIELD).toString();
                         MLModel mlModel = MLModel.parse(xContentParser, algorithmName);
+                        mlModel.setModelId(modelId);
                         User resourceUser = mlModel.getUser();
                         User requestUser = getUserContext(client);
                         if (!checkUserPermissions(requestUser, resourceUser, modelId)) {
@@ -263,7 +265,9 @@ private void predict(String modelId, MLTask mlTask, MLInput mlInput, ActionListe
                             return;
                         }
                         // run predict
-                        mlTaskManager.updateTaskStateAsRunning(mlTask.getTaskId(), mlTask.isAsync());
+                        if (mlTaskManager.contains(mlTask.getTaskId())) {
+                            mlTaskManager.updateTaskStateAsRunning(mlTask.getTaskId(), mlTask.isAsync());
+                        }
                         MLOutput output = mlEngine.predict(mlInput, mlModel);
                         if (output instanceof MLPredictionOutput) {
                             ((MLPredictionOutput) output).setStatus(MLTaskState.COMPLETED.name());