add memory id and interation id for non-verbose (#2004)

Signed-off-by: Jing Zhang <jngz@amazon.com>
(cherry picked from commit b84b130)

ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/agent/MLChatAgentRunner.java

@@ -411,6 +411,23 @@ private void runReAct(
                             );
 
                         List<ModelTensors> finalModelTensors = new ArrayList<>();
+                        finalModelTensors
+                            .add(
+                                ModelTensors
+                                    .builder()
+                                    .mlModelTensors(
+                                        List
+                                            .of(
+                                                ModelTensor.builder().name(MLAgentExecutor.MEMORY_ID).result(sessionId).build(),
+                                                ModelTensor
+                                                    .builder()
+                                                    .name(MLAgentExecutor.PARENT_INTERACTION_ID)
+                                                    .result(parentInteractionId)
+                                                    .build()
+                                            )
+                                    )
+                                    .build()
+                            );
                         finalModelTensors
                             .add(
                                 ModelTensors
@@ -599,6 +616,23 @@ private void runReAct(
                             listener.onResponse(ModelTensorOutput.builder().mlModelOutputs(cotModelTensors).build());
                         } else {
                             List<ModelTensors> finalModelTensors = new ArrayList<>();
+                            finalModelTensors
+                                .add(
+                                    ModelTensors
+                                        .builder()
+                                        .mlModelTensors(
+                                            List
+                                                .of(
+                                                    ModelTensor.builder().name(MLAgentExecutor.MEMORY_ID).result(sessionId).build(),
+                                                    ModelTensor
+                                                        .builder()
+                                                        .name(MLAgentExecutor.PARENT_INTERACTION_ID)
+                                                        .result(parentInteractionId)
+                                                        .build()
+                                                )
+                                        )
+                                        .build()
+                                );
                             finalModelTensors
                                 .add(
                                     ModelTensors

ml-algorithms/src/test/java/org/opensearch/ml/engine/algorithms/agent/MLChatAgentRunnerTest.java

@@ -268,6 +268,46 @@ public void testParsingJsonBlockFromResponse3() {
         assertEquals("parsed final answer", modelTensor2.getResult());
     }
 
+    @Test
+    public void testParsingJsonBlockFromResponse4() {
+        // Prepare the response with JSON block
+        String jsonBlock = "{\"thought\":\"parsed thought\", \"action\":\"parsed action\", "
+            + "\"action_input\":\"parsed action input\", \"final_answer\":\"parsed final answer\"}";
+        String responseWithJsonBlock = "Some text```json" + jsonBlock + "```More text";
+
+        // Mock LLM response to not contain "thought" but contain "response" with JSON block
+        Map<String, String> llmResponse = new HashMap<>();
+        llmResponse.put("response", responseWithJsonBlock);
+        doAnswer(getLLMAnswer(llmResponse))
+            .when(client)
+            .execute(any(ActionType.class), any(ActionRequest.class), isA(ActionListener.class));
+
+        // Create an MLAgent and run the MLChatAgentRunner
+        MLAgent mlAgent = createMLAgentWithTools();
+        Map<String, String> params = new HashMap<>();
+        params.put(MLAgentExecutor.PARENT_INTERACTION_ID, "parent_interaction_id");
+        params.put("verbose", "false");
+        mlChatAgentRunner.run(mlAgent, params, agentActionListener);
+
+        // Capture the response passed to the listener
+        ArgumentCaptor<Object> responseCaptor = ArgumentCaptor.forClass(Object.class);
+        verify(agentActionListener).onResponse(responseCaptor.capture());
+
+        // Extract the captured response
+        Object capturedResponse = responseCaptor.getValue();
+        assertTrue(capturedResponse instanceof ModelTensorOutput);
+        ModelTensorOutput modelTensorOutput = (ModelTensorOutput) capturedResponse;
+
+        ModelTensor memoryIdModelTensor = modelTensorOutput.getMlModelOutputs().get(0).getMlModelTensors().get(0);
+        ModelTensor parentInteractionModelTensor = modelTensorOutput.getMlModelOutputs().get(0).getMlModelTensors().get(1);
+
+        // Verify that the parsed values from JSON block are correctly set
+        assertEquals("memory_id", memoryIdModelTensor.getName());
+        assertEquals("conversation_id", memoryIdModelTensor.getResult());
+        assertEquals("parent_interaction_id", parentInteractionModelTensor.getName());
+        assertEquals("parent_interaction_id", parentInteractionModelTensor.getResult());
+    }
+
     @Test
     public void testRunWithIncludeOutputNotSet() {
         LLMSpec llmSpec = LLMSpec.builder().modelId("MODEL_ID").build();
@@ -283,7 +323,7 @@ public void testRunWithIncludeOutputNotSet() {
         mlChatAgentRunner.run(mlAgent, new HashMap<>(), agentActionListener);
         Mockito.verify(agentActionListener).onResponse(objectCaptor.capture());
         ModelTensorOutput modelTensorOutput = (ModelTensorOutput) objectCaptor.getValue();
-        List<ModelTensor> agentOutput = modelTensorOutput.getMlModelOutputs().get(0).getMlModelTensors();
+        List<ModelTensor> agentOutput = modelTensorOutput.getMlModelOutputs().get(1).getMlModelTensors();
         assertEquals(1, agentOutput.size());
         // Respond with last tool output
         assertEquals("This is the final answer", agentOutput.get(0).getDataAsMap().get("response"));
@@ -312,7 +352,7 @@ public void testRunWithIncludeOutputMLModel() {
         mlChatAgentRunner.run(mlAgent, new HashMap<>(), agentActionListener);
         Mockito.verify(agentActionListener).onResponse(objectCaptor.capture());
         ModelTensorOutput modelTensorOutput = (ModelTensorOutput) objectCaptor.getValue();
-        List<ModelTensor> agentOutput = modelTensorOutput.getMlModelOutputs().get(0).getMlModelTensors();
+        List<ModelTensor> agentOutput = modelTensorOutput.getMlModelOutputs().get(1).getMlModelTensors();
         assertEquals(1, agentOutput.size());
         // Respond with last tool output
         assertEquals("This is the final answer", agentOutput.get(0).getDataAsMap().get("response"));
@@ -334,7 +374,7 @@ public void testRunWithIncludeOutputSet() {
         mlChatAgentRunner.run(mlAgent, params, agentActionListener);
         Mockito.verify(agentActionListener).onResponse(objectCaptor.capture());
         ModelTensorOutput modelTensorOutput = (ModelTensorOutput) objectCaptor.getValue();
-        List<ModelTensor> agentOutput = modelTensorOutput.getMlModelOutputs().get(0).getMlModelTensors();
+        List<ModelTensor> agentOutput = modelTensorOutput.getMlModelOutputs().get(1).getMlModelTensors();
         assertEquals(1, agentOutput.size());
         // Respond with last tool output
         assertEquals("This is the final answer", agentOutput.get(0).getDataAsMap().get("response"));