mirror of
https://github.com/slhaf/Partner.git
synced 2026-05-12 08:43:02 +08:00
调整了 ActionInterventor 中数据构建方法的组织方式
This commit is contained in:
@@ -55,6 +55,9 @@ public class ActionInterventor extends PreRunningModule implements ActivateModel
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@InjectCapability
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private MemoryCapability memoryCapability;
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private final AssemblyHelper assemblyHelper = new AssemblyHelper();
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private final PromptHelper promptHelper = new PromptHelper();
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/**
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* 键: 本次调用uuid；
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* 值：本次调用对应的prompt；
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@@ -71,15 +74,15 @@ public class ActionInterventor extends PreRunningModule implements ActivateModel
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String uuid = context.getUuid();
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String userId = context.getUserId();
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RecognizerResult recognizerResult = interventionRecognizer
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.execute(buildRecognizerInput(userId, context.getInput())); // 此处的输入内容携带了所有 PhaserRecord
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.execute(assemblyHelper.buildRecognizerInput(userId, context.getInput())); // 此处的输入内容携带了所有 PhaserRecord
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if (!recognizerResult.isOk()) {
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setupNoInterventionPrompt(uuid);
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promptHelper.setupNoInterventionPrompt(uuid);
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return;
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}
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// 干预意图评估
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EvaluatorResult evaluatorResult = interventionEvaluator
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.execute(buildEvaluatorInput(recognizerResult, userId));
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.execute(assemblyHelper.buildEvaluatorInput(recognizerResult, userId));
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List<EvaluatedInterventionData> executingDataList = evaluatorResult.getExecutingDataList();
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List<EvaluatedInterventionData> preparedDataList = evaluatorResult.getPreparedDataList();
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@@ -90,10 +93,10 @@ public class ActionInterventor extends PreRunningModule implements ActivateModel
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invalidActionKeysFilter(preparedDataList);
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// 同步写入prompt，异步处理干预行为，‘异步’在 interventionHandler 中体现
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setupInterventionPrompt(uuid, executingDataList, preparedDataList);
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interventionHandler.execute(buildHandlerInput(executingDataList, preparedDataList, recognizerResult));
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promptHelper.setupInterventionPrompt(uuid, executingDataList, preparedDataList);
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interventionHandler.execute(assemblyHelper.buildHandlerInput(executingDataList, preparedDataList, recognizerResult));
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} else {
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setupInterventionIgnoredPrompt(uuid, executingDataList, preparedDataList);
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promptHelper.setupInterventionIgnoredPrompt(uuid, executingDataList, preparedDataList);
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}
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}
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@@ -136,137 +139,6 @@ public class ActionInterventor extends PreRunningModule implements ActivateModel
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return true;
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}
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/**
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* @param executingDataList 对应评估结果中的‘执行中行动’
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* @param preparedDataList 对应评估结果中的‘待执行行动’
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* @param recognizerResult 干预识别结果，包含‘执行中’‘待执行’两类评估结果各自对应的行动数据
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* @return 处理器输入
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*/
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private HandlerInput buildHandlerInput(List<EvaluatedInterventionData> executingDataList,
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List<EvaluatedInterventionData> preparedDataList, RecognizerResult recognizerResult) {
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HandlerInput input = new HandlerInput();
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Map<String, PhaserRecord> executingInterventions = recognizerResult.getExecutingInterventions();
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Map<String, ActionData> preparedInterventions = recognizerResult.getPreparedInterventions();
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List<ExecutingInterventionData> executing = setupInputDataList(executingDataList, executingInterventions,
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ExecutingInterventionData::new);
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List<PreparedInterventionData> prepared = setupInputDataList(preparedDataList, preparedInterventions,
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PreparedInterventionData::new);
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input.setExecuting(executing);
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input.setPrepared(prepared);
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return input;
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}
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/**
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* @param <I> HandlerInput 中 List 对应的泛型
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* @param evaluatedDataList 评估结果列表
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* @param interventionMap 干预识别结果中的 tendency:data 映射
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* @param factory 输入类型构建工厂
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* @return 处理器输入(干预列表)
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*/
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private <I> List<I> setupInputDataList(List<EvaluatedInterventionData> evaluatedDataList,
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Map<String, ?> interventionMap, Supplier<I> factory) {
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List<I> result = new ArrayList<>();
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for (EvaluatedInterventionData interventionData : evaluatedDataList) {
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I data = factory.get();
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if (data instanceof InterventionData inputData) {
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inputData.setTendency(interventionData.getTendency());
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inputData.setDescription(interventionData.getDescription());
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inputData.setInterventions(interventionData.getInterventionData());
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}
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if (data instanceof ExecutingInterventionData inputData) {
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inputData.setRecord((PhaserRecord) interventionMap.get(interventionData.getTendency()));
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} else if (data instanceof PreparedInterventionData inputData) {
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inputData.setActionData((ActionData) interventionMap.get(interventionData.getTendency()));
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}
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result.add(data);
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}
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return result;
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}
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private void setupInterventionIgnoredPrompt(String uuid, List<EvaluatedInterventionData> executingDataList, List<EvaluatedInterventionData> preparedDataList) {
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List<EvaluatedInterventionData> total = Stream.concat(executingDataList.stream(), preparedDataList.stream()).toList();
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JSONArray reasons = new JSONArray();
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for (EvaluatedInterventionData data : total) {
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JSONObject reason = reasons.addObject();
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reason.put("[干预倾向]", data.getTendency());
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reason.put("[未采用原因]", data.getDescription());
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}
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synchronized (interventionPrompt) {
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interventionPrompt.put(uuid, Map.of(
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"[识别状态] <是否识别到干预已存在行动的意图>", "识别到，但都未采用",
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"[忽略原因] <各个意图被忽略的原因>", reasons.toString(),
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"[干预行动] <将对已存在行动做出的行为>", "无行为"));
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}
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}
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private void setupInterventionPrompt(String uuid, List<EvaluatedInterventionData> executingDataList,
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List<EvaluatedInterventionData> preparedDataList) {
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JSONArray contents = new JSONArray();
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List<EvaluatedInterventionData> temp = Stream.concat(executingDataList.stream(), preparedDataList.stream()).toList();
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for (EvaluatedInterventionData data : temp) {
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if (!data.isOk()) {
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continue;
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}
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String tendency = data.getTendency();
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JSONObject newElement = contents.addObject();
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newElement.put("[干预倾向]", tendency);
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JSONArray changes = newElement.putArray("[行动链变动情况]");
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for (MetaIntervention intervention : data.getInterventionData()) {
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JSONObject change = changes.addObject();
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change.put("[干预类型]", intervention.getType());
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change.put("[干预序号]", intervention.getOrder());
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change.putArray("[干预内容]").addAll(intervention.getActions());
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}
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}
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synchronized (interventionPrompt) {
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interventionPrompt.put(uuid, Map.of(
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"[识别状态] <是否识别到干预已存在行动的意图>", "识别到，将采用",
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"[干预内容] <将对已存在行动做出的行为>", contents.toString()));
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}
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}
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private void setupNoInterventionPrompt(String uuid) {
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interventionPrompt.put(uuid, Map.of(
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"[识别状态] <是否识别到干预已存在行动的意图>", "未识别到干预意图",
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"[干预行动] <将对已存在行动做出的行为>", "无行动"));
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}
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private EvaluatorInput buildEvaluatorInput(RecognizerResult recognizerResult, String userId) {
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EvaluatorInput input = new EvaluatorInput();
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input.setExecutingInterventions(recognizerResult.getExecutingInterventions());
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input.setPreparedInterventions(recognizerResult.getPreparedInterventions());
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input.setRecentMessages(cognationCapability.getChatMessages());
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input.setActivatedSlices(memoryCapability.getActivatedSlices(userId));
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return input;
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}
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private RecognizerInput buildRecognizerInput(String userId, String input) {
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RecognizerInput recognizerInput = new RecognizerInput();
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recognizerInput.setInput(input);
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recognizerInput.setUserDialogMapStr(memoryCapability.getUserDialogMapStr(userId));
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// 参考的对话列表大小或需调整
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recognizerInput.setRecentMessages(cognationCapability.getChatMessages());
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recognizerInput.setExecutingActions(actionCapability.listPhaserRecords());
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recognizerInput.setPreparedActions(actionCapability.listPreparedAction(userId));
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return recognizerInput;
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}
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@Override
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public String modelKey() {
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return "action_identifier";
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@@ -286,4 +158,146 @@ public class ActionInterventor extends PreRunningModule implements ActivateModel
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protected String moduleName() {
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return "[行动干预识别模块]";
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}
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private final class AssemblyHelper {
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private AssemblyHelper() {
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}
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/**
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* @param executingDataList 对应评估结果中的‘执行中行动’
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* @param preparedDataList 对应评估结果中的‘待执行行动’
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* @param recognizerResult 干预识别结果，包含‘执行中’‘待执行’两类评估结果各自对应的行动数据
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* @return 处理器输入
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*/
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private HandlerInput buildHandlerInput(List<EvaluatedInterventionData> executingDataList,
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List<EvaluatedInterventionData> preparedDataList, RecognizerResult recognizerResult) {
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HandlerInput input = new HandlerInput();
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Map<String, PhaserRecord> executingInterventions = recognizerResult.getExecutingInterventions();
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Map<String, ActionData> preparedInterventions = recognizerResult.getPreparedInterventions();
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List<ExecutingInterventionData> executing = setupInputDataList(executingDataList, executingInterventions,
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ExecutingInterventionData::new);
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List<PreparedInterventionData> prepared = setupInputDataList(preparedDataList, preparedInterventions,
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PreparedInterventionData::new);
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input.setExecuting(executing);
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input.setPrepared(prepared);
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return input;
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}
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/**
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* @param <I> HandlerInput 中 List 对应的泛型
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* @param evaluatedDataList 评估结果列表
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* @param interventionMap 干预识别结果中的 tendency:data 映射
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* @param factory 输入类型构建工厂
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* @return 处理器输入(干预列表)
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*/
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private <I> List<I> setupInputDataList(List<EvaluatedInterventionData> evaluatedDataList,
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Map<String, ?> interventionMap, Supplier<I> factory) {
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List<I> result = new ArrayList<>();
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for (EvaluatedInterventionData interventionData : evaluatedDataList) {
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I data = factory.get();
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if (data instanceof InterventionData inputData) {
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inputData.setTendency(interventionData.getTendency());
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inputData.setDescription(interventionData.getDescription());
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inputData.setInterventions(interventionData.getInterventionData());
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}
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if (data instanceof ExecutingInterventionData inputData) {
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inputData.setRecord((PhaserRecord) interventionMap.get(interventionData.getTendency()));
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} else if (data instanceof PreparedInterventionData inputData) {
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inputData.setActionData((ActionData) interventionMap.get(interventionData.getTendency()));
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}
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result.add(data);
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}
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return result;
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}
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private RecognizerInput buildRecognizerInput(String userId, String input) {
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RecognizerInput recognizerInput = new RecognizerInput();
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recognizerInput.setInput(input);
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recognizerInput.setUserDialogMapStr(memoryCapability.getUserDialogMapStr(userId));
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// 参考的对话列表大小或需调整
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recognizerInput.setRecentMessages(cognationCapability.getChatMessages());
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recognizerInput.setExecutingActions(actionCapability.listPhaserRecords());
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recognizerInput.setPreparedActions(actionCapability.listPreparedAction(userId));
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return recognizerInput;
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}
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private EvaluatorInput buildEvaluatorInput(RecognizerResult recognizerResult, String userId) {
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EvaluatorInput input = new EvaluatorInput();
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input.setExecutingInterventions(recognizerResult.getExecutingInterventions());
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input.setPreparedInterventions(recognizerResult.getPreparedInterventions());
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input.setRecentMessages(cognationCapability.getChatMessages());
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input.setActivatedSlices(memoryCapability.getActivatedSlices(userId));
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return input;
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}
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}
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private final class PromptHelper {
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private PromptHelper() {
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}
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private void setupInterventionIgnoredPrompt(String uuid, List<EvaluatedInterventionData> executingDataList, List<EvaluatedInterventionData> preparedDataList) {
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List<EvaluatedInterventionData> total = Stream.concat(executingDataList.stream(), preparedDataList.stream()).toList();
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JSONArray reasons = new JSONArray();
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for (EvaluatedInterventionData data : total) {
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JSONObject reason = reasons.addObject();
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reason.put("[干预倾向]", data.getTendency());
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reason.put("[未采用原因]", data.getDescription());
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}
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synchronized (interventionPrompt) {
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interventionPrompt.put(uuid, Map.of(
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"[识别状态] <是否识别到干预已存在行动的意图>", "识别到，但都未采用",
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"[忽略原因] <各个意图被忽略的原因>", reasons.toString(),
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"[干预行动] <将对已存在行动做出的行为>", "无行为"));
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}
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}
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private void setupInterventionPrompt(String uuid, List<EvaluatedInterventionData> executingDataList,
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List<EvaluatedInterventionData> preparedDataList) {
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JSONArray contents = new JSONArray();
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List<EvaluatedInterventionData> temp = Stream.concat(executingDataList.stream(), preparedDataList.stream()).toList();
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for (EvaluatedInterventionData data : temp) {
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if (!data.isOk()) {
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continue;
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}
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String tendency = data.getTendency();
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JSONObject newElement = contents.addObject();
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newElement.put("[干预倾向]", tendency);
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JSONArray changes = newElement.putArray("[行动链变动情况]");
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for (MetaIntervention intervention : data.getInterventionData()) {
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JSONObject change = changes.addObject();
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change.put("[干预类型]", intervention.getType());
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change.put("[干预序号]", intervention.getOrder());
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change.putArray("[干预内容]").addAll(intervention.getActions());
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}
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}
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synchronized (interventionPrompt) {
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interventionPrompt.put(uuid, Map.of(
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"[识别状态] <是否识别到干预已存在行动的意图>", "识别到，将采用",
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"[干预内容] <将对已存在行动做出的行为>", contents.toString()));
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}
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}
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private void setupNoInterventionPrompt(String uuid) {
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interventionPrompt.put(uuid, Map.of(
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"[识别状态] <是否识别到干预已存在行动的意图>", "未识别到干预意图",
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"[干预行动] <将对已存在行动做出的行为>", "无行动"));
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}
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}
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}
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@@ -1,14 +1,15 @@
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package work.slhaf.partner.module.modules.action.planner;
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import lombok.extern.slf4j.Slf4j;
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import work.slhaf.partner.api.agent.factory.capability.annotation.InjectCapability;
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import work.slhaf.partner.api.agent.factory.module.annotation.AfterExecute;
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import work.slhaf.partner.api.agent.factory.module.annotation.AgentModule;
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import work.slhaf.partner.api.agent.factory.module.annotation.Init;
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import work.slhaf.partner.api.agent.factory.module.annotation.InjectModule;
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import work.slhaf.partner.api.chat.pojo.Message;
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import work.slhaf.partner.common.thread.InteractionThreadPoolExecutor;
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import work.slhaf.partner.common.vector.VectorClient;
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import work.slhaf.partner.core.action.ActionCapability;
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import work.slhaf.partner.core.action.ActionCore;
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import work.slhaf.partner.core.action.entity.ActionData;
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import work.slhaf.partner.core.action.entity.ImmediateActionData;
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import work.slhaf.partner.core.action.entity.MetaAction;
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@@ -32,10 +33,12 @@ import work.slhaf.partner.runtime.interaction.data.context.PartnerRunningFlowCon
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import java.util.*;
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import java.util.concurrent.Callable;
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import java.util.concurrent.ExecutorService;
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/**
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* 负责针对本次输入生成基础的行动计划，在主模型传达意愿后，执行行动或者放入计划池
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*/
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@Slf4j
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@AgentModule(name = "action_planner", order = 2)
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public class ActionPlanner extends PreRunningModule {
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@@ -55,21 +58,25 @@ public class ActionPlanner extends PreRunningModule {
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@InjectModule
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private ActionConfirmer actionConfirmer;
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private InteractionThreadPoolExecutor executor;
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private ActionAssemblyHelper assemblyHelper;
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private ExecutorService executor;
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private final ActionAssemblyHelper assemblyHelper = new ActionAssemblyHelper();
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@Init
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public void init() {
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executor = InteractionThreadPoolExecutor.getInstance();
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assemblyHelper = new ActionAssemblyHelper();
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executor = actionCapability.getExecutor(ActionCore.ExecutorType.VIRTUAL);
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}
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@Override
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protected void doExecute(PartnerRunningFlowContext context) {
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List<Callable<Void>> tasks = new ArrayList<>();
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addConfirmTask(tasks, context);
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addNewActionTask(tasks, context);
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executor.invokeAll(tasks);
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try {
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List<Callable<Void>> tasks = new ArrayList<>();
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addConfirmTask(tasks, context);
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addNewActionTask(tasks, context);
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executor.invokeAll(tasks);
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} catch (Exception e) {
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log.error("执行异常", e);
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}
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}
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/**
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@@ -200,7 +207,7 @@ public class ActionPlanner extends PreRunningModule {
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return "[行动模块]";
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}
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private class ActionAssemblyHelper {
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private final class ActionAssemblyHelper {
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private ActionAssemblyHelper() {
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}
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Reference in New Issue
Block a user