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make memory optional in conversational agent #1

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make memory optional in conversational agent #1

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44 changes: 27 additions & 17 deletions ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/agent/MLChatAgentRunner.java
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Original file line number Diff line number Diff line change
Expand Up @@ -36,6 +36,7 @@
import java.util.concurrent.atomic.AtomicInteger;
import java.util.concurrent.atomic.AtomicReference;

import org.apache.commons.lang3.StringUtils;
import org.apache.commons.text.StringSubstitutor;
import org.opensearch.action.ActionRequest;
import org.opensearch.action.StepListener;
Expand Down Expand Up @@ -127,6 +128,11 @@ public void run(MLAgent mlAgent, Map<String, String> params, ActionListener<Obje
String title = params.get(MLAgentExecutor.QUESTION);
int messageHistoryLimit = getMessageHistoryLimit(params);

if (StringUtils.isEmpty(memoryType)) {
runAgent(mlAgent, params, listener, null, null);
return;
}

ConversationIndexMemory.Factory conversationIndexMemoryFactory = (ConversationIndexMemory.Factory) memoryFactoryMap.get(memoryType);
conversationIndexMemoryFactory.create(title, memoryId, appType, ActionListener.<ConversationIndexMemory>wrap(memory -> {
// TODO: call runAgent directly if messageHistoryLimit == 0
Expand All @@ -151,8 +157,8 @@ public void run(MLAgent mlAgent, Map<String, String> params, ActionListener<Obje
);
}

StringBuilder chatHistoryBuilder = new StringBuilder();
if (!messageList.isEmpty()) {
StringBuilder chatHistoryBuilder = new StringBuilder();
String chatHistoryPrefix = params.getOrDefault(PROMPT_CHAT_HISTORY_PREFIX, CHAT_HISTORY_PREFIX);
chatHistoryBuilder.append(chatHistoryPrefix);
for (Message message : messageList) {
Expand Down Expand Up @@ -220,7 +226,9 @@ private void runReAct(
AtomicReference<String> newPrompt = new AtomicReference<>(tmpSubstitutor.replace(prompt));
tmpParameters.put(PROMPT, newPrompt.get());

List<ModelTensors> traceTensors = createModelTensors(sessionId, parentInteractionId);
List<ModelTensors> traceTensors = (conversationIndexMemory == null)
? new ArrayList<>()
: createModelTensors(sessionId, parentInteractionId);
int maxIterations = Integer.parseInt(tmpParameters.getOrDefault(MAX_ITERATION, "3")) * 2;
for (int i = 0; i < maxIterations; i++) {
int finalI = i;
Expand Down Expand Up @@ -401,8 +409,8 @@ private void runReAct(
client.execute(MLPredictionTaskAction.INSTANCE, request, firstListener);
}

private static List<ModelTensors> createFinalAnswerTensors(List<ModelTensors> sessionId, List<ModelTensor> lastThought) {
List<ModelTensors> finalModelTensors = sessionId;
private static List<ModelTensors> createFinalAnswerTensors(List<ModelTensors> modelTensorsList, List<ModelTensor> lastThought) {
List<ModelTensors> finalModelTensors = modelTensorsList;
finalModelTensors.add(ModelTensors.builder().mlModelTensors(lastThought).build());
return finalModelTensors;
}
Expand Down Expand Up @@ -572,19 +580,21 @@ private void sendFinalAnswer(
private static List<ModelTensors> createModelTensors(String sessionId, String parentInteractionId) {
List<ModelTensors> cotModelTensors = new ArrayList<>();

cotModelTensors
.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()
);
if (!StringUtils.isEmpty(sessionId)) {
cotModelTensors
.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()
);
}
return cotModelTensors;
}

Expand Down
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