fix(practice): generate replies from coach hints

backend/ruoyi-modules/ruoyi-aihr/src/main/java/org/dromara/aihr/domain/AihrPracticeDto.java

@@ -246,7 +246,12 @@ public final class AihrPracticeDto {
                                        String tenantId, Set<String> allowedTools, Long createdAt) {
     }
 
-    public record TurnRequest(String sessionId, Integer roundIndex, String traineeText, String traineeAudioUrl, Long traineeAudioOssId, Boolean regenerate, String style) {
+    public record TurnRequest(String sessionId, Integer roundIndex, String traineeText, String traineeAudioUrl,
+                              Long traineeAudioOssId, Boolean regenerate, String style, Boolean suggestReply) {
+        public TurnRequest(String sessionId, Integer roundIndex, String traineeText, String traineeAudioUrl,
+                           Long traineeAudioOssId, Boolean regenerate, String style) {
+            this(sessionId, roundIndex, traineeText, traineeAudioUrl, traineeAudioOssId, regenerate, style, false);
+        }
     }
 
     public record TurnResponse(
@@ -258,8 +263,13 @@ public final class AihrPracticeDto {
         Boolean redFlag,
         Integer roundIndex,
         Boolean finished,
-        String message
+        String message,
+        String suggestedReply
     ) {
+        public TurnResponse(String customerText, String customerAudioUrl, Integer emotion, Integer trust,
+                            String coachHint, Boolean redFlag, Integer roundIndex, Boolean finished, String message) {
+            this(customerText, customerAudioUrl, emotion, trust, coachHint, redFlag, roundIndex, finished, message, "");
+        }
     }
 
     public record FinishRequest(String sessionId, Long assignmentId) {

backend/ruoyi-modules/ruoyi-aihr/src/main/java/org/dromara/aihr/service/AihrPracticeLlmService.java

@@ -14,7 +14,7 @@ import java.util.Optional;
 
 /**
  * 三角色对练的 LLM 编排：AI 客户回复生成 + /finish 单次结构化评分（TechSpec 第 6 章 P0）。
- * 所有方法失败返回 empty，由 AihrPracticeSeedService 用 seed 数据兜底，前后端契约不变。
+ * 所有方法失败返回 empty；正式训练回合由 seed 兜底，显式请求的 LLM 回复建议则向用户报告生成失败。
  */
 @Service
 @RequiredArgsConstructor
@@ -26,6 +26,7 @@ public class AihrPracticeLlmService {
     private static final double SCORE_TEMPERATURE = 0.0;
     private static final double DAILY_DRILL_TEMPERATURE = 0.0;
     private static final double PREP_CARD_TEMPERATURE = 0.0;
+    private static final double REPLY_SUGGESTION_TEMPERATURE = 0.4;
     private static final int MAX_CUSTOMER_CHARS = 80;
 
     private final AihrModelSeedService modelService;
@@ -104,6 +105,33 @@ public class AihrPracticeLlmService {
             .filter(text -> !text.isBlank());
     }
 
+    public Optional<String> suggestedReply(
+        String scenario, String goal, String customerText, String traineeText, String coachHint
+    ) {
+        String system = """
+            你是物业员工话术教练。根据老师傅提示，生成员工可直接回复当前业主的一句话。
+            规则：
+            1. 只输出 JSON：{"reply":""}，不要解释或 markdown。
+            2. reply 必须是员工第一人称直接对业主说的话，不得出现“建议、应该、员工、老师傅”等指导语。
+            3. 落实提示中的动作、责任和反馈节点，不照抄提示。
+            4. 不编造政策、金额、姓名或处理结果；可以承诺员工本人将核实和反馈的动作。
+            5. 一句话，不超过100字。
+            """;
+        String user = """
+            场景：%s
+            训练目标：%s
+            业主当前说：%s
+            员工上一句：%s
+            老师傅提示：%s
+            """.formatted(scenario, goal, customerText, traineeText, coachHint);
+        return modelService.tryChat(
+                AihrSensitiveText.forModel(system),
+                AihrSensitiveText.forModel(user),
+                REPLY_SUGGESTION_TEMPERATURE)
+            .flatMap(this::parseSuggestedReply)
+            .filter(reply -> !reply.equals(coachHint.trim()));
+    }
+
     /**
      * 生成下一句业主回应，并让模型同步给出回合情绪、信任、红线和教练提示；解析失败由调用方回退 seed。
      */
@@ -308,6 +336,16 @@ public class AihrPracticeLlmService {
         }
     }
 
+    private Optional<String> parseSuggestedReply(String content) {
+        try {
+            String reply = truncateText(objectMapper.readTree(extractJson(content)).path("reply").asText(""), 160).trim();
+            return reply.isBlank() ? Optional.empty() : Optional.of(reply);
+        } catch (Exception e) {
+            log.warn("practice reply suggestion parse failed（处理错误已隐藏）");
+            return Optional.empty();
+        }
+    }
+
     private Optional<PracticeTurn> parseTurn(String content, int defaultEmotion, int defaultTrust, String defaultCoachHint) {
         try {
             JsonNode root = objectMapper.readTree(extractJson(content));

backend/ruoyi-modules/ruoyi-aihr/src/main/java/org/dromara/aihr/service/AihrPracticeSeedService.java

@@ -1185,6 +1185,10 @@ public class AihrPracticeSeedService {
     }
 
     private TurnResponse handleTurn(TurnRequest request, ActiveSession session, ScenarioSeed scenario, String style) {
+        if (request != null && Boolean.TRUE.equals(request.suggestReply())) {
+            int roundIndex = regenerateRoundIndex(session, request.roundIndex(), scenario.rounds().size());
+            return suggestReply(session, scenario, roundIndex);
+        }
         if (request != null && Boolean.TRUE.equals(request.regenerate())) {
             int roundIndex = regenerateRoundIndex(session, request.roundIndex(), scenario.rounds().size());
             return regenerateCoachHint(request, session, scenario, roundIndex, style);
@@ -1289,6 +1293,34 @@ public class AihrPracticeSeedService {
         return new TurnResponse(customerText, "", emotion, trust, coachHint, redFlag, nextRoundIndex, finished, "");
     }
 
+    private TurnResponse suggestReply(ActiveSession session, ScenarioSeed scenario, int roundIndex) {
+        RoundSeed currentRound = scenario.rounds().get(roundIndex);
+        int nextRoundIndex = roundIndex + 1;
+        boolean finished = nextRoundIndex >= scenario.rounds().size();
+        TurnEvidence evidence = turnEvidence(session, roundIndex);
+        String coachHint = evidence == null || isBlank(evidence.coachHint()) ? currentRound.coachHint() : evidence.coachHint();
+        String traineeText = traineeReply(session, roundIndex, "");
+        String customerText = finished ? currentRound.customer() : customerLine(session, nextRoundIndex, scenario.rounds().get(nextRoundIndex).customer());
+        if (practiceLlmService == null || isBlank(traineeText) || isBlank(coachHint)) {
+            throw new ServiceException("当前回合信息不完整，暂时无法生成回复");
+        }
+        String reply = practiceLlmService.suggestedReply(
+                scenario.name(), scenario.goal(), customerText, traineeText, coachHint)
+            .orElseThrow(() -> new ServiceException("回复生成失败，请稍后重试"));
+        return new TurnResponse(
+            finished ? "" : customerText,
+            "",
+            evidence == null ? currentRound.emotion() : evidence.emotion(),
+            evidence == null ? currentRound.trust() : evidence.trust(),
+            coachHint,
+            evidence != null && Boolean.TRUE.equals(evidence.redFlag()),
+            nextRoundIndex,
+            finished,
+            "",
+            reply
+        );
+    }
+
     private boolean hasRedFlag(String traineeText) {
         if (isBlank(traineeText)) {
             return false;

backend/ruoyi-modules/ruoyi-aihr/src/test/java/org/dromara/aihr/service/AihrPracticeLlmServiceTest.java

@@ -16,6 +16,33 @@ import static org.mockito.Mockito.when;
 @Tag("dev")
 class AihrPracticeLlmServiceTest {
 
+    @Test
+    void replySuggestionParsesOnlyTheReplyField() {
+        AihrModelSeedService modelService = mock(AihrModelSeedService.class);
+        when(modelService.tryChat(anyString(), anyString(), eq(0.4)))
+            .thenReturn(Optional.of("{\"reply\":\"我现在联系保洁负责人核实，十分钟内给您明确反馈。\"}"));
+        AihrPracticeLlmService service = new AihrPracticeLlmService(modelService, new ObjectMapper());
+
+        var reply = service.suggestedReply(
+            "日常保洁", "明确责任和反馈节点", "到底谁负责，什么时候回复？", "我再问问", "说明负责人和反馈时间"
+        );
+
+        assertTrue(reply.isPresent());
+        assertEquals("我现在联系保洁负责人核实，十分钟内给您明确反馈。", reply.get());
+    }
+
+    @Test
+    void replySuggestionRejectsCopiedCoachHint() {
+        AihrModelSeedService modelService = mock(AihrModelSeedService.class);
+        when(modelService.tryChat(anyString(), anyString(), eq(0.4)))
+            .thenReturn(Optional.of("{\"reply\":\"说明负责人和反馈时间\"}"));
+        AihrPracticeLlmService service = new AihrPracticeLlmService(modelService, new ObjectMapper());
+
+        assertTrue(service.suggestedReply(
+            "日常保洁", "明确责任和反馈节点", "什么时候回复？", "我再问问", "说明负责人和反馈时间"
+        ).isEmpty());
+    }
+
     @Test
     void dailyDrillAssessmentParsesStructuredModelResponse() {
         AihrModelSeedService modelService = mock(AihrModelSeedService.class);

backend/ruoyi-modules/ruoyi-aihr/src/test/java/org/dromara/aihr/service/AihrPracticeSeedServiceTest.java

@@ -1150,6 +1150,11 @@ public class AihrPracticeSeedServiceTest {
         assertEquals("训练回合已变化，请查看最新对话后再继续", skipped.getMessage());
         var helpBeforeValidTurn = service.recordHelp(session.sessionId(), 2, "employee-a");
         var firstTurn = service.turn(new TurnRequest(session.sessionId(), 0, "我先确认现场情况", null, null, false, null), "employee-a");
+        when(llm.suggestedReply(anyString(), anyString(), anyString(), anyString(), anyString()))
+            .thenReturn(Optional.of("我现在联系现场负责人核实，确认后第一时间给您反馈。"));
+        var suggestedReply = service.turn(new TurnRequest(
+            session.sessionId(), 0, "", null, null, false, null, true
+        ), "employee-a");
         var adjustedFirstTurn = service.turn(new TurnRequest(
             session.sessionId(), 0, "我先确认现场情况", null, null, true, "serious"
         ), "employee-a");
@@ -1157,7 +1162,9 @@ public class AihrPracticeSeedServiceTest {
 
         assertEquals(0, helpBeforeValidTurn.roundIndex());
         assertEquals(1, firstTurn.roundIndex());
+        assertEquals("我现在联系现场负责人核实，确认后第一时间给您反馈。", suggestedReply.suggestedReply());
         assertEquals(1, adjustedFirstTurn.roundIndex());
+        assertEquals(firstTurn.coachHint(), adjustedFirstTurn.coachHint());
         assertEquals(1, help.roundIndex());
     }
 

docs/API_INTEGRATION.md

@@ -8,7 +8,7 @@
 |---|---|---|
 | AI面试 `/recruit/interview` | `POST /api/recruit/interview/start`、`/answer`、`/finish`、`GET /records`、`POST /records/{sessionId}/review` | 已接入真实模型优先链路：配置 chat 模型后 `/start` 动态生成面试题，`/finish` 按前端提交的真实回答做结构化评分；未配置模型时使用本地 Rubric 估分，不返回固定候选人分数。AI 分数只作辅助参考，HR/管理员可提交 `reviewedScore/reviewNote` 完成人工复核；记录同时保留 AI 分、人工复核分、最终采用分和复核状态 |
 | 候选人入职主体关联 `/recruit/interview` | `GET/POST /api/recruit/interview/candidate-links` | HR/管理员在当前租户范围内把本地候选人 ID 关联到已同步的在职 `ext_party_id`；只保存外部主体 ID，不复制姓名、部门等组织字段，重复关联同一主体幂等，候选人更换主体或同一主体已关联其他候选人会拒绝 |
-| 三角色对练 `/train/practice` | `POST /api/train/practice/start`、`/turn`、`/finish` | 已接入编排 API；数据库启用 chat 模型后，`/turn` 客户回复按人设走真 LLM 生成（seed 剧本作剧情锚点），`/finish` 走单次 temperature=0 结构化评分（五维分、导师改写、点评）。APP 员工请求由服务端绑定当前登录手机号并强制 `mode=mobile`，不信任客户端的身份或模式；`turn/finish` 只允许该员工操作本人活动/已完成会话。`superadmin` 或 `hr_operator` 的管理端预览可复用同一路径，但服务端固定为 `operator:{userId}` 与 `mode=preview`，忽略客户端 `extPartyId/mode/assignmentId`；预览记录不进入员工历史、主管复盘、成长、试点或正式校准统计。其他后台身份拒绝。已完成会话的重复 `/finish` 只读取持久化结果，绝不按当前场景目录重算或回写历史快照；模型未配置或调用失败自动回退 seed，契约不变。 |
+| 三角色对练 `/train/practice` | `POST /api/train/practice/start`、`/turn`、`/finish` | 已接入编排 API；数据库启用 chat 模型后，`/turn` 客户回复按人设走真 LLM 生成（seed 剧本作剧情锚点），`suggestReply=true` 时服务端按当前会话、回合和老师傅提示生成一条可编辑但不自动发送的员工回复，失败明确提示且不复制指导语；`/finish` 走单次 temperature=0 结构化评分（五维分、导师改写、点评）。APP 员工请求由服务端绑定当前登录手机号并强制 `mode=mobile`，不信任客户端的身份或模式；`turn/finish` 只允许该员工操作本人活动/已完成会话。`superadmin` 或 `hr_operator` 的管理端预览可复用同一路径，但服务端固定为 `operator:{userId}` 与 `mode=preview`，忽略客户端 `extPartyId/mode/assignmentId`；预览记录不进入员工历史、主管复盘、成长、试点或正式校准统计。其他后台身份拒绝。已完成会话的重复 `/finish` 只读取持久化结果，绝不按当前场景目录重算或回写历史快照；正式训练回合模型未配置或调用失败自动回退 seed，契约不变。 |
 | 训练场景运营 `/train/scenarios` | 内容读取 `GET /api/train/practice/scenarios`、`GET /scenarios/{id}`；管理写入 `POST /scenarios`、`PATCH /scenarios/{id}/enabled`、`PATCH /scenarios/{id}/review-status`；目录 `GET /curriculum/capabilities`、`GET /curriculum/matrix` | 读取默认只返回当前租户“已发布且启用”的场景，且不返回审核人、稳定用户 ID 或审核时间；仅 `superadmin/hr_operator` 可请求 `includeDisabled=true`、编辑、启停和推进审核。状态只能按 `草稿 → 待业务审核 → 已发布 → 已下线` 流转；发布时服务端重新读取场景并校验住宅范围、固定五岗位、成长能力项、业务来源/SOP、服务红线、目标、成功标准、至少两轮回合和完整五维 Rubric，不信任客户端传入的发布内容。每次编辑已发布内容会退回待业务审核并刷新内容版本/哈希及审核留痕。运营可以选择风险级别，但服务端会对收费、安全、投诉等受控词及全部回合文本自动升级为“高风险”；待评估内容不可发布。高风险场景先记录第一位审核人的稳定用户 ID 和显示名并保持待业务审核，只有第二位不同的已认证运营人员复核同一内容版本后才发布；迁移重跑不得清空等待复核的首审记录。员工可见和可启动路径始终同时要求 `review_status=已发布 AND enabled=1`。 |
 | M2 训练预习与求助 | `GET /api/train/practice/scenarios/{id}/prep-card`、`POST /api/train/practice/sessions/{sessionId}/help` | 均要求已登录；预习卡只使用当前租户已启用的 `prep_card/json_prep` 模板，模型返回必须严格为 3 条要点、3 条红线、2 条话术，否则回退场景卡。求助接口只允许 APP 员工访问其本人、同租户的活动训练会话；客户端可传 `roundIndex` 仅为兼容字段，服务端按会话实际进度落库，返回 `{sessionId,scenarioId,roundIndex,recordedAt}`。迁移表为 `aihr_practice_help_event`，不在请求路径执行 DDL。该表已随 2026-07-24 完整包发布并纳入远端 `62/62` 结构预检；完整训练写入与正式试点仍需另验。 |
 | 正式试点数据导出 | `GET /api/train/practice/export?startDate=YYYY-MM-DD&endDate=YYYY-MM-DD` | 起止日期必填且包含结束日；只统计窗口内能通过唯一手机号或外部 ID 映射到在职组织快照的正式会话，排除重复手机号和身份碰撞。完训定义为每人至少 10 次，校准必须关联同一窗口内正式会话；CSV 同时给出校准命中数、SOP 可用数、满意度响应数/平均分，以及明细级 AI 分、人工校准分、校准人、校准时间、最终采用分、满意度分和意见，避免用四舍五入后的比率反推门禁状态；汇总和明细均携带正式人员及项目口径，不混入历史 seed/开发身份 |

mobile-uni/src/pages/user/practice/index.vue

@@ -344,7 +344,13 @@
                   >
                     {{ speechButtonLabel(`turn-${index}`) }}
                   </button>
-                  <button class="bubble-action" @click="adoptCoachHint(turn.text)">用这句话回复</button>
+                  <button
+                    class="bubble-action"
+                    :disabled="replySuggestionLoading !== null || busy"
+                    @click="adoptCoachHint(turn, index)"
+                  >
+                    {{ replySuggestionLoading === index ? '生成中…' : '用这句话回复' }}
+                  </button>
                 </view>
               </view>
             </view>
@@ -556,6 +562,7 @@ const helpRefs = ref<string[]>([]);
 const lastTraineeText = ref('');
 const lastCoachRoundIndex = ref<number | null>(null);
 const adjustingStyle = ref('');
+const replySuggestionLoading = ref<number | null>(null);
 const emotionScore = ref(0);
 const trustScore = ref(0);
 const redFlagActive = ref(false);
@@ -808,12 +815,30 @@ const handleComposerSend = (text: string) => {
   void submit();
 };
 
-/** 旧 H5「建议回复」逻辑的会话版:把老师傅建议填入输入框,员工可改后发送。 */
-const adoptCoachHint = (text: string) => {
-  const value = (text || '').trim();
-  if (!value) return;
-  composerRef.value?.setDraft(value);
-  uni.showToast({ title: '已填入输入框，可修改后发送', icon: 'none' });
+const adoptCoachHint = async (turn: PracticeTurn, index: number) => {
+  if (busy.value || replySuggestionLoading.value !== null || !sessionId.value || !Number.isInteger(turn.roundIndex)) return;
+  const request = requests.begin('reply-suggestion');
+  replySuggestionLoading.value = index;
+  message.value = '';
+  try {
+    const data = await submitPracticeTurn({
+      sessionId: sessionId.value,
+      roundIndex: turn.roundIndex as number,
+      traineeText: '',
+      suggestReply: true
+    });
+    if (!requests.isCurrent(request)) return;
+    const value = (data.suggestedReply || '').trim();
+    if (!value) throw new Error(data.message || '未生成可用回复');
+    composerRef.value?.setDraft(value);
+    uni.showToast({ title: '已生成回复，可修改后发送', icon: 'none' });
+  } catch (error) {
+    if (!requests.isCurrent(request)) return;
+    handleError(error, '回复生成失败');
+    uni.showToast({ title: message.value || '回复生成失败', icon: 'none' });
+  } finally {
+    if (requests.isCurrent(request)) replySuggestionLoading.value = null;
+  }
 };
 
 const submitTranscribed = async (text: string, audioUrl: string, ossId?: string) => {
@@ -953,6 +978,7 @@ const clearPracticeData = () => {
   lastTraineeText.value = '';
   lastCoachRoundIndex.value = null;
   adjustingStyle.value = '';
+  replySuggestionLoading.value = null;
   emotionScore.value = 0;
   trustScore.value = 0;
   redFlagActive.value = false;
@@ -975,9 +1001,9 @@ const requireLogin = () => {
   return false;
 };
 
-const appendTurn = (role: PracticeRole, text?: string, emotion = 0, synthesizeCustomerVoice = true, customerTurnIndex?: number) => {
+const appendTurn = (role: PracticeRole, text?: string, emotion = 0, synthesizeCustomerVoice = true, customerTurnIndex?: number, coachRoundIndex?: number) => {
   if (!text) return;
-  turns.value.push({ role, text });
+  turns.value.push({ role, text, roundIndex: coachRoundIndex });
   if (role === 'customer' && synthesizeCustomerVoice) {
     void prepareTurnVoice(turns.value.length - 1, text, emotion, customerTurnIndex);
   }
@@ -1586,7 +1612,7 @@ const replaceLastCoachHint = (text?: string) => {
   if (!text) return;
   for (let index = turns.value.length - 1; index >= 0; index -= 1) {
     if (turns.value[index].role === 'coach') {
-      turns.value[index] = { role: 'coach', text };
+      turns.value[index] = { ...turns.value[index], text };
       return;
     }
   }
@@ -1661,7 +1687,7 @@ const submit = async () => {
     emotionScore.value = data.emotion ?? emotionScore.value;
     trustScore.value = data.trust ?? trustScore.value;
     redFlagActive.value = Boolean(data.redFlag);
-    appendTurn('coach', data.redFlag ? data.coachHint || '先承接诉求，再给处理节点。' : data.coachHint);
+    appendTurn('coach', data.redFlag ? data.coachHint || '先承接诉求，再给处理节点。' : data.coachHint, 0, true, undefined, submittedRound);
     if (data.redFlag) {
       message.value = '命中红线，请按老师傅建议重说本轮';
       status.value = 'active';
@@ -1757,6 +1783,7 @@ const invalidateRequests = () => {
   feedbackSubmittingId.value = null;
   helpLoading.value = false;
   adjustingStyle.value = '';
+  replySuggestionLoading.value = null;
   satisfactionBusy.value = false;
   if (status.value === 'starting') status.value = 'idle';
   if (status.value === 'submitting') status.value = sessionId.value ? 'active' : 'idle';

mobile-uni/src/services/practice.ts

@@ -80,6 +80,7 @@ export const submitPracticeTurn = (params: {
   traineeAudioOssId?: number | string;
   regenerate?: boolean;
   style?: string;
+  suggestReply?: boolean;
 }) =>
   apiRequest<PracticeTurnResponse>({
     url: '/api/train/practice/turn',

mobile-uni/src/types/api.ts

@@ -510,6 +510,7 @@ export type PracticeRole = 'customer' | 'trainee' | 'coach';
 export interface PracticeTurn {
   role: PracticeRole;
   text: string;
+  roundIndex?: number;
 }
 
 export interface PracticeStartResponse {
@@ -548,6 +549,7 @@ export interface PracticeTurnResponse {
   roundIndex: number;
   finished: boolean;
   message?: string;
+  suggestedReply?: string;
 }
 
 export interface PracticeDimensionScore {

mobile-uni/tests/mobile-user-pages.test.mjs

@@ -341,6 +341,19 @@ test('训练页保留任务卡、对话区、回复区和结果区并互斥切
   assert.match(source, /result\.mentorRewrite/);
 });
 
+test('老师傅提示由 LLM 生成可发送回复后再填入输入框', async () => {
+  const [source, service] = await Promise.all([
+    pageSource('../src/pages/user/practice/index.vue'),
+    pageSource('../src/services/practice.ts')
+  ]);
+
+  assert.match(source, /suggestReply:\s*true/);
+  assert.match(source, /data\.suggestedReply/);
+  assert.match(source, /setDraft\(value\)/);
+  assert.doesNotMatch(source, /const adoptCoachHint[\s\S]{0,300}setDraft\(\(turn\.text/);
+  assert.match(service, /suggestReply\?:\s*boolean/);
+});
+
 test('底部固定导航页面复用安全操作区', async () => {
   const [base, practice, assign, review] = await Promise.all([
     pageSource('../src/styles/base.css'),