diff --git a/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/remote/RemoteConnectorExecutor.java b/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/remote/RemoteConnectorExecutor.java
index 176fdb428e..2366b150d4 100644
--- a/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/remote/RemoteConnectorExecutor.java
+++ b/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/remote/RemoteConnectorExecutor.java
@@ -37,7 +37,11 @@ default ModelTensorOutput executePredict(MLInput mlInput) {
                 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);
+                int tensorCount = 0;
+                if (tempTensorOutputs.size() > 0 && tempTensorOutputs.get(0).getMlModelTensors() != null) {
+                    tensorCount = tempTensorOutputs.get(0).getMlModelTensors().size();
+                }
+                processedDocs += Math.max(tensorCount, 1);
                 tensorOutputs.addAll(tempTensorOutputs);
             }
 
diff --git a/ml-algorithms/src/test/java/org/opensearch/ml/engine/algorithms/remote/HttpJsonConnectorExecutorTest.java b/ml-algorithms/src/test/java/org/opensearch/ml/engine/algorithms/remote/HttpJsonConnectorExecutorTest.java
index c2e3b423f9..11ae20c470 100644
--- a/ml-algorithms/src/test/java/org/opensearch/ml/engine/algorithms/remote/HttpJsonConnectorExecutorTest.java
+++ b/ml-algorithms/src/test/java/org/opensearch/ml/engine/algorithms/remote/HttpJsonConnectorExecutorTest.java
@@ -184,7 +184,8 @@ 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(2, modelTensorOutput.getMlModelOutputs().size());
+        Assert.assertEquals(1, modelTensorOutput.getMlModelOutputs().size());
+        Assert.assertEquals(2, modelTensorOutput.getMlModelOutputs().get(0).getMlModelTensors().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());