FEATURE: Summarize the steps when max steps limit reached

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

@@ -34,6 +34,7 @@
 import static org.opensearch.ml.engine.algorithms.agent.AgentUtils.parseLLMOutput;
 import static org.opensearch.ml.engine.algorithms.agent.AgentUtils.substitute;
 import static org.opensearch.ml.engine.algorithms.agent.PromptTemplate.CHAT_HISTORY_PREFIX;
+import static org.opensearch.ml.engine.algorithms.agent.PromptTemplate.SUMMARY_PROMPT_TEMPLATE;
 
 import java.security.PrivilegedActionException;
 import java.util.ArrayList;
@@ -125,9 +126,11 @@ public class MLChatAgentRunner implements MLAgentRunner {
     public static final String INJECT_DATETIME_FIELD = "inject_datetime";
     public static final String DATETIME_FORMAT_FIELD = "datetime_format";
     public static final String SYSTEM_PROMPT_FIELD = "system_prompt";
+    public static final String SUMMARIZE_WHEN_MAX_ITERATION = "summarize_when_max_iteration";
 
     private static final String DEFAULT_MAX_ITERATIONS = "10";
     private static final String MAX_ITERATIONS_MESSAGE = "Agent reached maximum iterations (%d) without completing the task";
+    private static final String MAX_ITERATIONS_SUMMARY_MESSAGE = MAX_ITERATIONS_MESSAGE + ". Here's a summary of the steps taken:\n\n%s";
 
     private Client client;
     private Settings settings;
@@ -322,7 +325,6 @@ private void runReAct(
 
         StringBuilder scratchpadBuilder = new StringBuilder();
         List<String> interactions = new CopyOnWriteArrayList<>();
-
         StringSubstitutor tmpSubstitutor = new StringSubstitutor(Map.of(SCRATCHPAD, scratchpadBuilder.toString()), "${parameters.", "}");
         AtomicReference<String> newPrompt = new AtomicReference<>(tmpSubstitutor.replace(prompt));
         tmpParameters.put(PROMPT, newPrompt.get());
@@ -414,7 +416,10 @@ private void runReAct(
                             additionalInfo,
                             lastThought,
                             maxIterations,
-                            tools
+                            tools,
+                            tmpParameters,
+                            llm,
+                            tenantId
                         );
                         return;
                     }
@@ -513,7 +518,10 @@ private void runReAct(
                             additionalInfo,
                             lastThought,
                             maxIterations,
-                            tools
+                            tools,
+                            tmpParameters,
+                            llm,
+                            tenantId
                         );
                         return;
                     }
@@ -885,11 +893,70 @@ private void handleMaxIterationsReached(
         Map<String, Object> additionalInfo,
         AtomicReference<String> lastThought,
         int maxIterations,
+        Map<String, Tool> tools,
+        Map<String, String> parameters,
+        LLMSpec llmSpec,
+        String tenantId
+    ) {
+        boolean shouldSummarize = Boolean.parseBoolean(parameters.getOrDefault(SUMMARIZE_WHEN_MAX_ITERATION, "false"));
+
+        if (shouldSummarize && !traceTensors.isEmpty()) {
+            generateLLMSummary(traceTensors, llmSpec, tenantId, ActionListener.wrap(summary -> {
+                String summaryResponse = String.format(Locale.ROOT, MAX_ITERATIONS_SUMMARY_MESSAGE, maxIterations, summary);
+                sendTraditionalMaxIterationsResponse(
+                    sessionId,
+                    listener,
+                    question,
+                    parentInteractionId,
+                    verbose,
+                    traceDisabled,
+                    traceTensors,
+                    conversationIndexMemory,
+                    traceNumber,
+                    additionalInfo,
+                    summaryResponse,
+                    tools
+                );
+            }, e -> {
+                log.error("Failed to generate LLM summary", e);
+                listener.onFailure(e);
+                cleanUpResource(tools);
+            }));
+        } else {
+            String response = (lastThought.get() != null && !lastThought.get().isEmpty() && !"null".equals(lastThought.get()))
+                ? String.format("%s. Last thought: %s", String.format(MAX_ITERATIONS_MESSAGE, maxIterations), lastThought.get())
+                : String.format(MAX_ITERATIONS_MESSAGE, maxIterations);
+            sendTraditionalMaxIterationsResponse(
+                sessionId,
+                listener,
+                question,
+                parentInteractionId,
+                verbose,
+                traceDisabled,
+                traceTensors,
+                conversationIndexMemory,
+                traceNumber,
+                additionalInfo,
+                response,
+                tools
+            );
+        }
+    }
+
+    private void sendTraditionalMaxIterationsResponse(
+        String sessionId,
+        ActionListener<Object> listener,
+        String question,
+        String parentInteractionId,
+        boolean verbose,
+        boolean traceDisabled,
+        List<ModelTensors> traceTensors,
+        ConversationIndexMemory conversationIndexMemory,
+        AtomicInteger traceNumber,
+        Map<String, Object> additionalInfo,
+        String response,
         Map<String, Tool> tools
     ) {
-        String incompleteResponse = (lastThought.get() != null && !lastThought.get().isEmpty() && !"null".equals(lastThought.get()))
-            ? String.format("%s. Last thought: %s", String.format(MAX_ITERATIONS_MESSAGE, maxIterations), lastThought.get())
-            : String.format(MAX_ITERATIONS_MESSAGE, maxIterations);
         sendFinalAnswer(
             sessionId,
             listener,
@@ -901,11 +968,86 @@ private void handleMaxIterationsReached(
             conversationIndexMemory,
             traceNumber,
             additionalInfo,
-            incompleteResponse
+            response
         );
         cleanUpResource(tools);
     }
 
+    void generateLLMSummary(List<ModelTensors> stepsSummary, LLMSpec llmSpec, String tenantId, ActionListener<String> listener) {
+        if (stepsSummary == null || stepsSummary.isEmpty()) {
+            listener.onFailure(new IllegalArgumentException("Steps summary cannot be null or empty"));
+            return;
+        }
+
+        try {
+            Map<String, String> summaryParams = new HashMap<>();
+            if (llmSpec.getParameters() != null) {
+                summaryParams.putAll(llmSpec.getParameters());
+            }
+
+            // Convert ModelTensors to strings before joining
+            List<String> stepStrings = new ArrayList<>();
+            for (ModelTensors tensor : stepsSummary) {
+                if (tensor != null && tensor.getMlModelTensors() != null) {
+                    for (ModelTensor modelTensor : tensor.getMlModelTensors()) {
+                        if (modelTensor.getResult() != null) {
+                            stepStrings.add(modelTensor.getResult());
+                        } else if (modelTensor.getDataAsMap() != null && modelTensor.getDataAsMap().containsKey("response")) {
+                            stepStrings.add(String.valueOf(modelTensor.getDataAsMap().get("response")));
+                        }
+                    }
+                }
+            }
+            String summaryPrompt = String.format(Locale.ROOT, SUMMARY_PROMPT_TEMPLATE, String.join("\n", stepStrings));
+            summaryParams.put(PROMPT, summaryPrompt);
+            summaryParams.putIfAbsent(SYSTEM_PROMPT_FIELD, "");
+
+            ActionRequest request = new MLPredictionTaskRequest(
+                llmSpec.getModelId(),
+                RemoteInferenceMLInput
+                    .builder()
+                    .algorithm(FunctionName.REMOTE)
+                    .inputDataset(RemoteInferenceInputDataSet.builder().parameters(summaryParams).build())
+                    .build(),
+                null,
+                tenantId
+            );
+            client.execute(MLPredictionTaskAction.INSTANCE, request, ActionListener.wrap(response -> {
+                String summary = extractSummaryFromResponse(response);
+                if (summary != null) {
+                    listener.onResponse(summary);
+                } else {
+                    listener.onFailure(new RuntimeException("Empty or invalid LLM summary response"));
+                }
+            }, listener::onFailure));
+        } catch (Exception e) {
+            listener.onFailure(e);
+        }
+    }
+
+    public String extractSummaryFromResponse(MLTaskResponse response) {
+        try {
+            String outputString = outputToOutputString(response.getOutput());
+            if (outputString != null && !outputString.trim().isEmpty()) {
+                try {
+                    Map<String, Object> dataMap = gson.fromJson(outputString, Map.class);
+                    if (dataMap.containsKey("response")) {
+                        String summary = String.valueOf(dataMap.get("response"));
+                        if (summary != null && !summary.trim().isEmpty() && !"null".equals(summary)) {
+                            return summary.trim();
+                        }
+                    }
+                } catch (Exception jsonException) {
+                    return outputString.trim();
+                }
+            }
+            return null;
+        } catch (Exception e) {
+            log.error("Failed to extract summary from response", e);
+            throw new RuntimeException("Failed to extract summary from response", e);
+        }
+    }
+
     private void saveMessage(
         ConversationIndexMemory memory,
         String question,

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

@@ -140,4 +140,7 @@ public class PromptTemplate {
             - Avoid making assumptions and relying on implicit knowledge.
             - Your response must be self-contained and ready for the planner to use without modification. Never end with a question.
             - Break complex searches into simpler queries when appropriate.""";
+
+    public static final String SUMMARY_PROMPT_TEMPLATE =
+        "Please provide a concise summary of the following agent execution steps. Focus on what the agent was trying to accomplish and what progress was made:\n\n%s\n\nPlease respond in the following JSON format:\n{\"response\": \"your summary here\"}";
 }

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

@@ -1118,4 +1118,145 @@ public void testConstructLLMParams_DefaultValues() {
         Assert.assertTrue(result.containsKey(AgentUtils.RESPONSE_FORMAT_INSTRUCTION));
         Assert.assertTrue(result.containsKey(AgentUtils.TOOL_RESPONSE));
     }
+
+    @Test
+    public void testMaxIterationsWithSummaryEnabled() {
+        // Create LLM spec with max_iteration = 1 to simplify test
+        LLMSpec llmSpec = LLMSpec.builder().modelId("MODEL_ID").parameters(Map.of("max_iteration", "1")).build();
+        MLToolSpec firstToolSpec = MLToolSpec.builder().name(FIRST_TOOL).type(FIRST_TOOL).build();
+        final MLAgent mlAgent = MLAgent
+            .builder()
+            .name("TestAgent")
+            .type(MLAgentType.CONVERSATIONAL.name())
+            .llm(llmSpec)
+            .memory(mlMemorySpec)
+            .tools(Arrays.asList(firstToolSpec))
+            .build();
+
+        // Reset and setup fresh mocks
+        Mockito.reset(client);
+        Mockito.reset(firstTool);
+        when(firstTool.getName()).thenReturn(FIRST_TOOL);
+        when(firstTool.validate(Mockito.anyMap())).thenReturn(true);
+        Mockito.doAnswer(generateToolResponse("First tool response")).when(firstTool).run(Mockito.anyMap(), any());
+
+        // First call: LLM response without final_answer to trigger max iterations
+        // Second call: Summary LLM response with result field instead of dataAsMap
+        Mockito
+            .doAnswer(getLLMAnswer(ImmutableMap.of("thought", "I need to analyze the data", "action", FIRST_TOOL)))
+            .doAnswer(invocation -> {
+                ActionListener<Object> listener = invocation.getArgument(2);
+                ModelTensor modelTensor = ModelTensor.builder().result("Summary: Analysis step was attempted").build();
+                ModelTensors modelTensors = ModelTensors.builder().mlModelTensors(Arrays.asList(modelTensor)).build();
+                ModelTensorOutput mlModelTensorOutput = ModelTensorOutput.builder().mlModelOutputs(Arrays.asList(modelTensors)).build();
+                MLTaskResponse mlTaskResponse = MLTaskResponse.builder().output(mlModelTensorOutput).build();
+                listener.onResponse(mlTaskResponse);
+                return null;
+            })
+            .when(client)
+            .execute(any(ActionType.class), any(ActionRequest.class), isA(ActionListener.class));
+
+        Map<String, String> params = new HashMap<>();
+        params.put(MLAgentExecutor.PARENT_INTERACTION_ID, "parent_interaction_id");
+        params.put(MLChatAgentRunner.SUMMARIZE_WHEN_MAX_ITERATION, "true");
+
+        mlChatAgentRunner.run(mlAgent, params, agentActionListener);
+
+        // Verify response is captured
+        verify(agentActionListener).onResponse(objectCaptor.capture());
+        Object capturedResponse = objectCaptor.getValue();
+        assertTrue(capturedResponse instanceof ModelTensorOutput);
+
+        ModelTensorOutput modelTensorOutput = (ModelTensorOutput) capturedResponse;
+        List<ModelTensor> agentOutput = modelTensorOutput.getMlModelOutputs().get(1).getMlModelTensors();
+        assertEquals(1, agentOutput.size());
+
+        // Verify the response contains summary message
+        String response = (String) agentOutput.get(0).getDataAsMap().get("response");
+        assertTrue(
+            response.startsWith("Agent reached maximum iterations (1) without completing the task. Here's a summary of the steps taken:")
+        );
+        assertTrue(response.contains("Summary: Analysis step was attempted"));
+    }
+
+    @Test
+    public void testMaxIterationsWithSummaryDisabled() {
+        // Create LLM spec with max_iteration = 1 and summary disabled
+        LLMSpec llmSpec = LLMSpec.builder().modelId("MODEL_ID").parameters(Map.of("max_iteration", "1")).build();
+        MLToolSpec firstToolSpec = MLToolSpec.builder().name(FIRST_TOOL).type(FIRST_TOOL).build();
+        final MLAgent mlAgent = MLAgent
+            .builder()
+            .name("TestAgent")
+            .type(MLAgentType.CONVERSATIONAL.name())
+            .llm(llmSpec)
+            .memory(mlMemorySpec)
+            .tools(Arrays.asList(firstToolSpec))
+            .build();
+
+        // Reset client mock for this test
+        Mockito.reset(client);
+        // Mock LLM response that doesn't contain final_answer
+        Mockito
+            .doAnswer(getLLMAnswer(ImmutableMap.of("thought", "I need to use the tool", "action", FIRST_TOOL)))
+            .when(client)
+            .execute(any(ActionType.class), any(ActionRequest.class), isA(ActionListener.class));
+
+        Map<String, String> params = new HashMap<>();
+        params.put(MLAgentExecutor.PARENT_INTERACTION_ID, "parent_interaction_id");
+        params.put(MLChatAgentRunner.SUMMARIZE_WHEN_MAX_ITERATION, "false");
+
+        mlChatAgentRunner.run(mlAgent, params, agentActionListener);
+
+        // Verify response is captured
+        verify(agentActionListener).onResponse(objectCaptor.capture());
+        Object capturedResponse = objectCaptor.getValue();
+        assertTrue(capturedResponse instanceof ModelTensorOutput);
+
+        ModelTensorOutput modelTensorOutput = (ModelTensorOutput) capturedResponse;
+        List<ModelTensor> agentOutput = modelTensorOutput.getMlModelOutputs().get(1).getMlModelTensors();
+        assertEquals(1, agentOutput.size());
+
+        // Verify the response contains traditional max iterations message
+        String response = (String) agentOutput.get(0).getDataAsMap().get("response");
+        assertEquals("Agent reached maximum iterations (1) without completing the task. Last thought: I need to use the tool", response);
+    }
+
+    @Test
+    public void testExtractSummaryFromResponse() {
+        MLTaskResponse response = MLTaskResponse
+            .builder()
+            .output(
+                ModelTensorOutput
+                    .builder()
+                    .mlModelOutputs(
+                        Arrays
+                            .asList(
+                                ModelTensors
+                                    .builder()
+                                    .mlModelTensors(
+                                        Arrays
+                                            .asList(
+                                                ModelTensor.builder().dataAsMap(ImmutableMap.of("response", "Valid summary text")).build()
+                                            )
+                                    )
+                                    .build()
+                            )
+                    )
+                    .build()
+            )
+            .build();
+
+        String result = mlChatAgentRunner.extractSummaryFromResponse(response);
+        assertEquals("Valid summary text", result);
+    }
+
+    @Test
+    public void testGenerateLLMSummaryWithNullSteps() {
+        LLMSpec llmSpec = LLMSpec.builder().modelId("MODEL_ID").build();
+        ActionListener<String> listener = Mockito.mock(ActionListener.class);
+
+        mlChatAgentRunner.generateLLMSummary(null, llmSpec, "tenant", listener);
+
+        verify(listener).onFailure(any(IllegalArgumentException.class));
+    }
 }