[AC-AISVC-RES-01~15] docs(progress): 新增策略路由进度文档

- 记录会话进度
- 记录实现的模块和测试
- 记录变更历史

ai-service-admin/src/App.vue

@@ -13,6 +13,7 @@
           <span class="logo-text">AI Robot</span>
         </div>
         <nav class="main-nav">
+          <div class="nav-row">
             <router-link to="/dashboard" class="nav-item" :class="{ active: isActive('/dashboard') }">
               <el-icon><Odometer /></el-icon>
               <span>控制台</span>
@@ -29,7 +30,8 @@
               <el-icon><Monitor /></el-icon>
               <span>会话监控</span>
             </router-link>
-          <div class="nav-divider"></div>
+          </div>
+          <div class="nav-row">
             <router-link to="/admin/embedding" class="nav-item" :class="{ active: isActive('/admin/embedding') }">
               <el-icon><Connection /></el-icon>
               <span>嵌入模型</span>
@@ -66,6 +68,11 @@
               <el-icon><Grid /></el-icon>
               <span>槽位定义</span>
             </router-link>
+            <router-link to="/admin/scene-slot-bundles" class="nav-item" :class="{ active: isActive('/admin/scene-slot-bundles') }">
+              <el-icon><Collection /></el-icon>
+              <span>场景槽位包</span>
+            </router-link>
+          </div>
         </nav>
       </div>
       <div class="header-right">
@@ -98,7 +105,7 @@ import { ref, onMounted } from 'vue'
 import { useRoute } from 'vue-router'
 import { useTenantStore } from '@/stores/tenant'
 import { getTenantList, type Tenant } from '@/api/tenant'
-import { Odometer, FolderOpened, Cpu, Monitor, Connection, ChatDotSquare, Document, Aim, Share, Warning, Setting, ChatLineRound, Grid } from '@element-plus/icons-vue'
+import { Odometer, FolderOpened, Cpu, Monitor, Connection, ChatDotSquare, Document, Aim, Share, Warning, Setting, ChatLineRound, Grid, Collection } from '@element-plus/icons-vue'
 import { ElMessage } from 'element-plus'
 
 const route = useRoute()
@@ -168,7 +175,8 @@ onMounted(() => {
   align-items: center;
   justify-content: space-between;
   padding: 0 24px;
-  height: 60px;
+  height: auto;
+  min-height: 60px;
   background-color: var(--bg-secondary, #FFFFFF);
   border-bottom: 1px solid var(--border-color, #E2E8F0);
   box-shadow: 0 1px 3px rgba(0, 0, 0, 0.04);
@@ -208,6 +216,12 @@ onMounted(() => {
 }
 
 .main-nav {
+  display: flex;
+  flex-direction: column;
+  gap: 4px;
+}
+
+.nav-row {
   display: flex;
   align-items: center;
   gap: 4px;

ai-service-admin/src/api/kb.ts

@@ -36,3 +36,19 @@ export function deleteDocument(docId: string) {
     method: 'delete'
   })
 }
+
+export function batchUploadDocuments(data: FormData) {
+  return request({
+    url: '/admin/kb/documents/batch-upload',
+    method: 'post',
+    data
+  })
+}
+
+export function jsonBatchUpload(kbId: string, data: FormData) {
+  return request({
+    url: `/admin/kb/${kbId}/documents/json-batch`,
+    method: 'post',
+    data
+  })
+}

ai-service-admin/src/api/metadata-schema.ts

@@ -11,7 +11,7 @@ import type {
 
 export const metadataSchemaApi = {
   list: (status?: 'draft' | 'active' | 'deprecated', fieldRole?: FieldRole) =>
-    request<MetadataFieldListResponse>({ 
+    request<MetadataFieldDefinition[]>({ 
       method: 'GET', 
       url: '/admin/metadata-schemas', 
       params: { 

ai-service-admin/src/api/mid-platform.ts

@@ -35,6 +35,8 @@ export interface ToolCallTrace {
   error_code?: string
   args_digest?: string
   result_digest?: string
+  arguments?: Record<string, unknown>
+  result?: unknown
 }
 
 export interface DialogueResponse {
@@ -219,3 +221,16 @@ export function sendSharedMessage(shareToken: string, userMessage: string): Prom
     data: { user_message: userMessage }
   })
 }
+
+export interface CancelFlowResponse {
+  success: boolean
+  message: string
+  session_id: string
+}
+
+export function cancelActiveFlow(sessionId: string): Promise<CancelFlowResponse> {
+  return request({
+    url: `/mid/sessions/${sessionId}/cancel-flow`,
+    method: 'post'
+  })
+}

ai-service-admin/src/api/scene-slot-bundle.ts

@@ -0,0 +1,48 @@
+import request from '@/utils/request'
+import type {
+  SceneSlotBundle,
+  SceneSlotBundleWithDetails,
+  SceneSlotBundleCreateRequest,
+  SceneSlotBundleUpdateRequest,
+} from '@/types/scene-slot-bundle'
+
+export const sceneSlotBundleApi = {
+  list: (status?: string) =>
+    request<SceneSlotBundle[]>({
+      method: 'GET',
+      url: '/admin/scene-slot-bundles',
+      params: status ? { status } : {},
+    }),
+
+  get: (id: string) =>
+    request<SceneSlotBundleWithDetails>({
+      method: 'GET',
+      url: `/admin/scene-slot-bundles/${id}`,
+    }),
+
+  getBySceneKey: (sceneKey: string) =>
+    request<SceneSlotBundle>({
+      method: 'GET',
+      url: `/admin/scene-slot-bundles/by-scene/${sceneKey}`,
+    }),
+
+  create: (data: SceneSlotBundleCreateRequest) =>
+    request<SceneSlotBundle>({
+      method: 'POST',
+      url: '/admin/scene-slot-bundles',
+      data,
+    }),
+
+  update: (id: string, data: SceneSlotBundleUpdateRequest) =>
+    request<SceneSlotBundle>({
+      method: 'PUT',
+      url: `/admin/scene-slot-bundles/${id}`,
+      data,
+    }),
+
+  delete: (id: string) =>
+    request<void>({
+      method: 'DELETE',
+      url: `/admin/scene-slot-bundles/${id}`,
+    }),
+}

ai-service-admin/src/components/metadata/MetadataForm.vue

@@ -99,17 +99,19 @@ const deprecatedFields = computed(() => {
 })
 
 const visibleFields = computed(() => {
+  // Show all fields except deprecated in edit mode, so users can change draft to active
   if (props.isNewObject) {
     return activeFields.value
   }
-  return allFields.value.filter(f => f.status !== 'draft')
+  return allFields.value.filter(f => f.status !== 'deprecated')
 })
 
 const loadFields = async () => {
   loading.value = true
   try {
     const res = await metadataSchemaApi.getByScope(props.scope, props.showDeprecated)
-    allFields.value = res.items || []
+    // Handle both formats: direct array or { items: array }
+    allFields.value = Array.isArray(res) ? res : (res.items || [])
     emit('fields-loaded', allFields.value)
     
     applyDefaults()

ai-service-admin/src/router/index.ts

@@ -17,12 +17,6 @@ const routes: Array<RouteRecordRaw> = [
     component: () => import('@/views/dashboard/index.vue'),
     meta: { title: '控制台' }
   },
-  {
-    path: '/kb',
-    name: 'KBManagement',
-    component: () => import('@/views/kb/index.vue'),
-    meta: { title: '知识库管理' }
-  },
   {
     path: '/rag-lab',
     name: 'RagLab',
@@ -71,6 +65,12 @@ const routes: Array<RouteRecordRaw> = [
     component: () => import('@/views/admin/slot-definition/index.vue'),
     meta: { title: '槽位定义管理' }
   },
+  {
+    path: '/admin/scene-slot-bundles',
+    name: 'SceneSlotBundle',
+    component: () => import('@/views/admin/scene-slot-bundle/index.vue'),
+    meta: { title: '场景槽位包管理' }
+  },
   {
     path: '/admin/intent-rules',
     name: 'IntentRule',

ai-service-admin/src/types/metadata.ts

@@ -15,6 +15,7 @@ export interface MetadataFieldDefinition {
   scope: MetadataScope[]
   is_filterable: boolean
   is_rank_feature: boolean
+  usage_description?: string
   field_roles: FieldRole[]
   status: MetadataFieldStatus
   created_at?: string
@@ -31,6 +32,7 @@ export interface MetadataFieldCreateRequest {
   scope: MetadataScope[]
   is_filterable?: boolean
   is_rank_feature?: boolean
+  usage_description?: string
   field_roles?: FieldRole[]
   status: MetadataFieldStatus
 }
@@ -43,6 +45,7 @@ export interface MetadataFieldUpdateRequest {
   scope?: MetadataScope[]
   is_filterable?: boolean
   is_rank_feature?: boolean
+  usage_description?: string
   field_roles?: FieldRole[]
   status?: MetadataFieldStatus
 }

ai-service-admin/src/types/scene-slot-bundle.ts

@@ -0,0 +1,80 @@
+/**
+ * Scene Slot Bundle Types.
+ * [AC-SCENE-SLOT-01] 场景-槽位映射配置类型定义
+ */
+
+export type SceneSlotBundleStatus = 'draft' | 'active' | 'deprecated'
+export type AskBackOrder = 'priority' | 'required_first' | 'parallel'
+
+export interface SceneSlotBundle {
+  id: string
+  tenant_id: string
+  scene_key: string
+  scene_name: string
+  description: string | null
+  required_slots: string[]
+  optional_slots: string[]
+  slot_priority: string[] | null
+  completion_threshold: number
+  ask_back_order: AskBackOrder
+  status: SceneSlotBundleStatus
+  version: number
+  created_at: string | null
+  updated_at: string | null
+}
+
+export interface SlotDetail {
+  slot_key: string
+  type: string
+  required: boolean
+  ask_back_prompt: string | null
+  linked_field_id: string | null
+}
+
+export interface SceneSlotBundleWithDetails extends SceneSlotBundle {
+  required_slot_details: SlotDetail[]
+  optional_slot_details: SlotDetail[]
+}
+
+export interface SceneSlotBundleCreateRequest {
+  scene_key: string
+  scene_name: string
+  description?: string
+  required_slots?: string[]
+  optional_slots?: string[]
+  slot_priority?: string[]
+  completion_threshold?: number
+  ask_back_order?: AskBackOrder
+  status?: SceneSlotBundleStatus
+}
+
+export interface SceneSlotBundleUpdateRequest {
+  scene_name?: string
+  description?: string
+  required_slots?: string[]
+  optional_slots?: string[]
+  slot_priority?: string[]
+  completion_threshold?: number
+  ask_back_order?: AskBackOrder
+  status?: SceneSlotBundleStatus
+}
+
+export const SCENE_SLOT_BUNDLE_STATUS_OPTIONS = [
+  { value: 'draft', label: '草稿', description: '配置中，未启用' },
+  { value: 'active', label: '已启用', description: '运行时可用' },
+  { value: 'deprecated', label: '已废弃', description: '不再使用' },
+]
+
+export const ASK_BACK_ORDER_OPTIONS = [
+  { value: 'priority', label: '按优先级', description: '按 slot_priority 定义的顺序追问' },
+  { value: 'required_first', label: '必填优先', description: '优先追问必填槽位' },
+  { value: 'parallel', label: '并行追问', description: '一次追问多个缺失槽位' },
+]
+
+export function getStatusLabel(status: SceneSlotBundleStatus): string {
+  return SCENE_SLOT_BUNDLE_STATUS_OPTIONS.find(o => o.value === status)?.label || status
+}
+
+export function getAskBackOrderLabel(order: AskBackOrder): string {
+  return ASK_BACK_ORDER_OPTIONS.find(o => o.value === order)?.label || order
+}

ai-service-admin/src/types/script-flow.ts

@@ -39,6 +39,10 @@ export interface FlowStep {
   intent_description?: string
   script_constraints?: string[]
   expected_variables?: string[]
+  allowed_kb_ids?: string[]
+  preferred_kb_ids?: string[]
+  kb_query_hint?: string
+  max_kb_calls_per_step?: number
 }
 
 export interface NextCondition {

ai-service-admin/src/types/slot-definition.ts

@@ -2,13 +2,31 @@ export type SlotType = 'string' | 'number' | 'boolean' | 'enum' | 'array_enum'
 export type ExtractStrategy = 'rule' | 'llm' | 'user_input'
 export type SlotSource = 'user_confirmed' | 'rule_extracted' | 'llm_inferred' | 'default'
 
+/**
+ * [AC-MRS-07-UPGRADE] 提取失败类型
+ */
+export type ExtractFailureType = 
+  | 'EXTRACT_EMPTY' 
+  | 'EXTRACT_PARSE_FAIL' 
+  | 'EXTRACT_VALIDATION_FAIL' 
+  | 'EXTRACT_RUNTIME_ERROR'
+
 export interface SlotDefinition {
   id: string
   tenant_id: string
   slot_key: string
+  display_name?: string
+  description?: string
   type: SlotType
   required: boolean
+  /**
+   * [AC-MRS-07-UPGRADE] 兼容字段：单提取策略，已废弃
+   */
   extract_strategy?: ExtractStrategy
+  /**
+   * [AC-MRS-07-UPGRADE] 提取策略链：有序数组，按顺序执行直到成功
+   */
+  extract_strategies?: ExtractStrategy[]
   validation_rule?: string
   ask_back_prompt?: string
   default_value?: string | number | boolean | string[]
@@ -29,8 +47,17 @@ export interface LinkedField {
 export interface SlotDefinitionCreateRequest {
   tenant_id?: string
   slot_key: string
+  display_name?: string
+  description?: string
   type: SlotType
   required: boolean
+  /**
+   * [AC-MRS-07-UPGRADE] 提取策略链
+   */
+  extract_strategies?: ExtractStrategy[]
+  /**
+   * [AC-MRS-07-UPGRADE] 兼容字段
+   */
   extract_strategy?: ExtractStrategy
   validation_rule?: string
   ask_back_prompt?: string
@@ -39,8 +66,17 @@ export interface SlotDefinitionCreateRequest {
 }
 
 export interface SlotDefinitionUpdateRequest {
+  display_name?: string
+  description?: string
   type?: SlotType
   required?: boolean
+  /**
+   * [AC-MRS-07-UPGRADE] 提取策略链
+   */
+  extract_strategies?: ExtractStrategy[]
+  /**
+   * [AC-MRS-07-UPGRADE] 兼容字段
+   */
   extract_strategy?: ExtractStrategy
   validation_rule?: string
   ask_back_prompt?: string
@@ -56,6 +92,31 @@ export interface RuntimeSlotValue {
   updated_at?: string
 }
 
+/**
+ * [AC-MRS-07-UPGRADE] 策略链执行步骤结果
+ */
+export interface StrategyStepResult {
+  strategy: ExtractStrategy
+  success: boolean
+  value?: string | number | boolean | string[]
+  failure_type?: ExtractFailureType
+  failure_reason?: string
+  execution_time_ms: number
+}
+
+/**
+ * [AC-MRS-07-UPGRADE] 策略链执行结果
+ */
+export interface StrategyChainResult {
+  slot_key: string
+  success: boolean
+  final_value?: string | number | boolean | string[]
+  final_strategy?: ExtractStrategy
+  steps: StrategyStepResult[]
+  total_execution_time_ms: number
+  ask_back_prompt?: string
+}
+
 export const SLOT_TYPE_OPTIONS = [
   { value: 'string', label: '文本' },
   { value: 'number', label: '数字' },
@@ -69,3 +130,42 @@ export const EXTRACT_STRATEGY_OPTIONS = [
   { value: 'llm', label: 'LLM 推断', description: '通过大语言模型推断槽位值' },
   { value: 'user_input', label: '用户输入', description: '通过追问提示语让用户主动输入' }
 ]
+
+/**
+ * [AC-MRS-07-UPGRADE] 提取策略链验证
+ */
+export function validateExtractStrategies(strategies: ExtractStrategy[]): { valid: boolean; error?: string } {
+  if (!strategies || strategies.length === 0) {
+    return { valid: true } // 空数组视为有效
+  }
+
+  const validStrategies = new Set(['rule', 'llm', 'user_input'])
+  
+  // 检查重复
+  const uniqueStrategies = new Set(strategies)
+  if (uniqueStrategies.size !== strategies.length) {
+    return { valid: false, error: '提取策略链中不允许重复的策略' }
+  }
+  
+  // 检查有效性
+  const invalid = strategies.filter(s => !validStrategies.has(s))
+  if (invalid.length > 0) {
+    return { valid: false, error: `无效的提取策略: ${invalid.join(', ')}` }
+  }
+  
+  return { valid: true }
+}
+
+/**
+ * [AC-MRS-07-UPGRADE] 获取有效的策略链
+ * 优先使用 extract_strategies，如果不存在则兼容读取 extract_strategy
+ */
+export function getEffectiveStrategies(slot: SlotDefinition): ExtractStrategy[] {
+  if (slot.extract_strategies && slot.extract_strategies.length > 0) {
+    return slot.extract_strategies
+  }
+  if (slot.extract_strategy) {
+    return [slot.extract_strategy]
+  }
+  return []
+}

ai-service-admin/src/utils/request.ts

@@ -4,7 +4,7 @@ import { useTenantStore } from '@/stores/tenant'
 
 const service = axios.create({
   baseURL: import.meta.env.VITE_APP_BASE_API || '/api',
-  timeout: 60000
+  timeout: 180000
 })
 
 service.interceptors.request.use(

ai-service-admin/src/views/admin/knowledge-base/components/DocumentList.vue

@@ -5,6 +5,14 @@
         <el-icon><Upload /></el-icon>
         上传文档
       </el-button>
+      <el-button type="success" @click="handleBatchUploadClick">
+        <el-icon><Upload /></el-icon>
+        批量上传
+      </el-button>
+      <el-button type="warning" @click="handleJsonUploadClick">
+        <el-icon><Upload /></el-icon>
+        JSON上传
+      </el-button>
       <input
         ref="fileInputRef"
         type="file"
@@ -12,6 +20,20 @@
         style="display: none"
         @change="handleFileSelect"
       />
+      <input
+        ref="batchFileInputRef"
+        type="file"
+        accept=".zip"
+        style="display: none"
+        @change="handleBatchFileSelect"
+      />
+      <input
+        ref="jsonFileInputRef"
+        type="file"
+        accept=".json,.jsonl,.txt"
+        style="display: none"
+        @change="handleJsonFileSelect"
+      />
     </div>
 
     <el-table :data="documents" v-loading="loading" stripe>
@@ -165,6 +187,8 @@ const pagination = ref({
 })
 
 const fileInputRef = ref<HTMLInputElement>()
+const batchFileInputRef = ref<HTMLInputElement>()
+const jsonFileInputRef = ref<HTMLInputElement>()
 const uploadDialogVisible = ref(false)
 const editDialogVisible = ref(false)
 const uploading = ref(false)
@@ -261,6 +285,14 @@ const handleUploadClick = () => {
   fileInputRef.value?.click()
 }
 
+const handleBatchUploadClick = () => {
+  batchFileInputRef.value?.click()
+}
+
+const handleJsonUploadClick = () => {
+  jsonFileInputRef.value?.click()
+}
+
 const handleFileSelect = (event: Event) => {
   const target = event.target as HTMLInputElement
   const file = target.files?.[0]
@@ -289,6 +321,140 @@ const handleFileSelect = (event: Event) => {
   }
 }
 
+const handleBatchFileSelect = async (event: Event) => {
+  const target = event.target as HTMLInputElement
+  const file = target.files?.[0]
+  if (!file) return
+
+  if (!file.name.toLowerCase().endsWith('.zip')) {
+    ElMessage.error('请上传 .zip 格式的压缩包')
+    if (batchFileInputRef.value) {
+      batchFileInputRef.value.value = ''
+    }
+    return
+  }
+
+  const maxSize = 100 * 1024 * 1024
+  if (file.size > maxSize) {
+    ElMessage.error('压缩包大小不能超过 100MB')
+    if (batchFileInputRef.value) {
+      batchFileInputRef.value.value = ''
+    }
+    return
+  }
+
+  try {
+    loading.value = true
+    const formData = new FormData()
+    formData.append('file', file)
+    formData.append('kb_id', props.kbId)
+
+    const baseUrl = import.meta.env.VITE_APP_BASE_API || '/api'
+    const apiKey = import.meta.env.VITE_APP_API_KEY || ''
+    const response = await fetch(`${baseUrl}/admin/kb/documents/batch-upload`, {
+      method: 'POST',
+      headers: {
+        'X-Tenant-Id': tenantStore.currentTenantId,
+        'X-API-Key': apiKey
+      },
+      body: formData
+    })
+
+    const result = await response.json()
+    if (result.success) {
+      const { total, succeeded, failed, results } = result
+      ElMessage.success(`批量上传完成！成功: ${succeeded}, 失败: ${failed}, 总计: ${total}`)
+      
+      // 收集所有成功的任务ID，稍后串行轮询
+      const jobIds: string[] = []
+      for (const item of results) {
+        if (item.status === 'created' && item.jobId) {
+          jobIds.push(item.jobId)
+        }
+      }
+      
+      // 串行轮询所有任务，避免并发请求触发限流
+      if (jobIds.length > 0) {
+        pollJobStatusSequential(jobIds)
+      }
+      
+      emit('upload-success')
+      loadDocuments()
+    } else {
+      ElMessage.error(result.message || '批量上传失败')
+    }
+  } catch (error) {
+    ElMessage.error('批量上传失败，请重试')
+    console.error('Batch upload error:', error)
+  } finally {
+    loading.value = false
+    if (batchFileInputRef.value) {
+      batchFileInputRef.value.value = ''
+    }
+  }
+}
+
+const handleJsonFileSelect = async (event: Event) => {
+  const target = event.target as HTMLInputElement
+  const file = target.files?.[0]
+  if (!file) return
+
+  const fileName = file.name.toLowerCase()
+  if (!fileName.endsWith('.json') && !fileName.endsWith('.jsonl') && !fileName.endsWith('.txt')) {
+    ElMessage.error('请上传 .json、.jsonl 或 .txt 格式的文件')
+    if (jsonFileInputRef.value) {
+      jsonFileInputRef.value.value = ''
+    }
+    return
+  }
+
+  try {
+    loading.value = true
+    ElMessage.info('正在上传 JSON 文件...')
+    
+    const formData = new FormData()
+    formData.append('file', file)
+    
+    const baseUrl = import.meta.env.VITE_APP_BASE_API || '/api'
+    const apiKey = import.meta.env.VITE_APP_API_KEY || ''
+    const response = await fetch(`${baseUrl}/admin/kb/${props.kbId}/documents/json-batch?tenant_id=${tenantStore.currentTenantId}`, {
+      method: 'POST',
+      headers: {
+        'X-Tenant-Id': tenantStore.currentTenantId,
+        'X-API-Key': apiKey
+      },
+      body: formData
+    })
+
+    const result = await response.json()
+    if (result.success) {
+      ElMessage.success(`JSON 批量上传成功！成功: ${result.succeeded}, 失败: ${result.failed}`)
+      
+      if (result.valid_metadata_fields) {
+        console.log('有效的元数据字段:', result.valid_metadata_fields)
+      }
+      
+      if (result.failed > 0) {
+        const failedItems = result.results.filter((r: any) => !r.success)
+        console.warn('失败的项目:', failedItems)
+      }
+      
+      emit('upload-success')
+      loadDocuments()
+    } else {
+      ElMessage.error(result.message || 'JSON 批量上传失败')
+    }
+  } catch (error) {
+    ElMessage.error('JSON 批量上传失败，请重试')
+    console.error('JSON batch upload error:', error)
+  } finally {
+    loading.value = false
+    if (jsonFileInputRef.value) {
+      jsonFileInputRef.value.value = ''
+    }
+  }
+}
+
 const handleUpload = async () => {
   if (!selectedFile.value) {
     ElMessage.warning('请选择文件')
@@ -310,11 +476,13 @@ const handleUpload = async () => {
     formData.append('kb_id', props.kbId)
     formData.append('metadata', JSON.stringify(uploadForm.value.metadata))
 
-    const baseUrl = import.meta.env.VITE_API_BASE_URL || ''
+    const baseUrl = import.meta.env.VITE_APP_BASE_API || '/api'
+    const apiKey = import.meta.env.VITE_APP_API_KEY || ''
     const response = await fetch(`${baseUrl}/admin/kb/documents`, {
       method: 'POST',
       headers: {
-        'X-Tenant-Id': tenantStore.currentTenantId
+        'X-Tenant-Id': tenantStore.currentTenantId,
+        'X-API-Key': apiKey
       },
       body: formData
     })
@@ -356,12 +524,14 @@ const handleSaveMetadata = async () => {
 
   saving.value = true
   try {
-    const baseUrl = import.meta.env.VITE_API_BASE_URL || ''
+    const baseUrl = import.meta.env.VITE_APP_BASE_API || '/api'
+    const apiKey = import.meta.env.VITE_APP_API_KEY || ''
     const response = await fetch(`${baseUrl}/admin/kb/documents/${currentEditDoc.value.docId}/metadata`, {
       method: 'PUT',
       headers: {
         'Content-Type': 'application/json',
-        'X-Tenant-Id': tenantStore.currentTenantId
+        'X-Tenant-Id': tenantStore.currentTenantId,
+        'X-API-Key': apiKey
       },
       body: JSON.stringify({ metadata: editForm.value.metadata })
     })
@@ -398,14 +568,64 @@ const pollJobStatus = async (jobId: string) => {
         ElMessage.error(`文档处理失败: ${job.errorMsg || '未知错误'}`)
         loadDocuments()
       } else {
-        setTimeout(poll, 3000)
+        setTimeout(poll, 10000)  // 10秒轮询一次，避免触发限流
       }
-    } catch (error) {
+    } catch (error: any) {
+      if (error.status === 429) {
+        console.warn('请求过于频繁，稍后重试')
+        setTimeout(poll, 15000)  // 被限流后等待15秒
+      } else {
         console.error('轮询任务状态失败', error)
       }
     }
+  }
 
-  setTimeout(poll, 2000)
+  setTimeout(poll, 5000)  // 首次轮询等待5秒
+}
+
+// 串行轮询多个任务，避免并发触发限流
+const pollJobStatusSequential = async (jobIds: string[]) => {
+  const pendingJobs = new Set(jobIds)
+  const maxPolls = 60
+  let pollCount = 0
+
+  const pollAll = async () => {
+    if (pollCount >= maxPolls || pendingJobs.size === 0) return
+    pollCount++
+
+    const completedJobs: string[] = []
+    
+    for (const jobId of pendingJobs) {
+      try {
+        const job: IndexJob = await getIndexJob(jobId)
+        if (job.status === 'completed') {
+          completedJobs.push(jobId)
+        } else if (job.status === 'failed') {
+          completedJobs.push(jobId)
+          console.error(`文档处理失败: ${job.errorMsg || '未知错误'}`)
+        }
+      } catch (error: any) {
+        if (error.status === 429) {
+          console.warn('请求过于频繁，稍后重试')
+          break  // 被限流时停止本轮轮询，等待下次
+        } else {
+          console.error('轮询任务状态失败', error)
+        }
+      }
+    }
+
+    // 移除已完成的任务
+    completedJobs.forEach(jobId => pendingJobs.delete(jobId))
+
+    if (pendingJobs.size > 0) {
+      setTimeout(pollAll, 10000)  // 10秒后再次轮询
+    } else {
+      ElMessage.success('所有文档处理完成')
+      loadDocuments()
+    }
+  }
+
+  setTimeout(pollAll, 5000)  // 首次轮询等待5秒
 }
 
 const handleDelete = async (row: DocumentWithMetadata) => {
@@ -438,6 +658,8 @@ onMounted(() => {
 
 .list-header {
   margin-bottom: 16px;
+  display: flex;
+  gap: 12px;
 }
 
 .pagination-wrapper {

ai-service-admin/src/views/admin/metadata-schema/index.vue

@@ -211,6 +211,14 @@
             </el-form-item>
           </el-col>
         </el-row>
+        <el-form-item label="用途说明">
+          <el-input
+            v-model="formData.usage_description"
+            type="textarea"
+            :rows="2"
+            placeholder="描述该字段的业务用途和使用场景"
+          />
+        </el-form-item>
         <el-form-item label="默认值">
           <el-input v-model="formData.default_value" placeholder="可选默认值" />
         </el-form-item>
@@ -308,6 +316,7 @@ const formData = reactive({
   scope: [] as MetadataScope[],
   is_filterable: true,
   is_rank_feature: false,
+  usage_description: '',
   field_roles: [] as FieldRole[],
   status: 'draft' as MetadataFieldStatus
 })
@@ -354,8 +363,11 @@ const getRoleLabel = (role: FieldRole) => {
 const fetchFields = async () => {
   loading.value = true
   try {
-    const res = await metadataSchemaApi.list(filterStatus.value || undefined, filterRole.value || undefined)
-    fields.value = res.items || []
+    const res: any = await metadataSchemaApi.list(filterStatus.value || undefined, filterRole.value || undefined)
+    // 兼容多种后端返回格式：[] / {items: []} / {schemas: []} / {data: []}
+    fields.value = Array.isArray(res)
+      ? res
+      : (res?.items || res?.schemas || res?.data || [])
   } catch (error: any) {
     ElMessage.error(error.response?.data?.message || '获取元数据字段失败')
   } finally {
@@ -376,6 +388,7 @@ const handleCreate = () => {
     scope: [],
     is_filterable: true,
     is_rank_feature: false,
+    usage_description: '',
     field_roles: [],
     status: 'draft'
   })
@@ -395,6 +408,7 @@ const handleEdit = (field: MetadataFieldDefinition) => {
     scope: [...field.scope],
     is_filterable: field.is_filterable,
     is_rank_feature: field.is_rank_feature,
+    usage_description: field.usage_description || '',
     field_roles: field.field_roles || [],
     status: field.status
   })
@@ -472,6 +486,7 @@ const handleSubmit = async () => {
         scope: formData.scope,
         is_filterable: formData.is_filterable,
         is_rank_feature: formData.is_rank_feature,
+        usage_description: formData.usage_description || undefined,
         field_roles: formData.field_roles,
         status: formData.status
       }

ai-service-admin/src/views/admin/mid-platform-playground/index.vue

@@ -30,6 +30,12 @@
         <el-form-item>
           <el-button :loading="switchingMode" @click="handleSwitchMode">应用模式</el-button>
         </el-form-item>
+        <el-form-item>
+          <el-button type="warning" :loading="cancellingFlow" @click="handleCancelFlow">取消流程</el-button>
+        </el-form-item>
+        <el-form-item>
+          <el-button type="primary" @click="handleNewSession">新建会话</el-button>
+        </el-form-item>
       </el-form>
     </el-card>
 
@@ -98,14 +104,36 @@
 
             <div class="json-panel">
               <div class="json-title">tool_calls</div>
+              <div class="json-content">
+                <el-button
+                  class="copy-btn"
+                  size="small"
+                  text
+                  type="primary"
+                  @click="copyJson(lastTrace.tool_calls || [], 'tool_calls')"
+                >
+                  复制
+                </el-button>
                 <pre>{{ JSON.stringify(lastTrace.tool_calls || [], null, 2) }}</pre>
               </div>
+            </div>
 
             <div class="json-panel">
               <div class="json-title">metrics_snapshot</div>
+              <div class="json-content">
+                <el-button
+                  class="copy-btn"
+                  size="small"
+                  text
+                  type="primary"
+                  @click="copyJson(lastTrace.metrics_snapshot || {}, 'metrics_snapshot')"
+                >
+                  复制
+                </el-button>
                 <pre>{{ JSON.stringify(lastTrace.metrics_snapshot || {}, null, 2) }}</pre>
               </div>
             </div>
+          </div>
           <el-empty v-else description="暂无 Trace" :image-size="90" />
         </el-card>
 
@@ -180,6 +208,7 @@ import {
   createPublicShareToken,
   listShares,
   deleteShare,
+  cancelActiveFlow,
   type DialogueMessage,
   type DialogueResponse,
   type SessionMode,
@@ -205,6 +234,7 @@ const rollbackToLegacy = ref(false)
 const sending = ref(false)
 const switchingMode = ref(false)
 const reporting = ref(false)
+const cancellingFlow = ref(false)
 
 const userInput = ref('')
 const conversation = ref<ChatItem[]>([])
@@ -221,11 +251,11 @@ const shareForm = ref({
 
 const now = () => new Date().toLocaleTimeString()
 
-const tagType = (role: ChatRole) => {
+const tagType = (role: ChatRole): 'primary' | 'success' | 'info' | 'warning' | 'danger' => {
   if (role === 'user') return 'info'
   if (role === 'assistant') return 'success'
   if (role === 'human') return 'warning'
-  return ''
+  return 'primary'
 }
 
 const toHistory = (): DialogueMessage[] => {
@@ -289,6 +319,32 @@ const handleSwitchMode = async () => {
   }
 }
 
+const handleCancelFlow = async () => {
+  cancellingFlow.value = true
+  try {
+    const result = await cancelActiveFlow(sessionId.value)
+    if (result.success) {
+      ElMessage.success(result.message)
+    } else {
+      ElMessage.warning(result.message)
+    }
+  } catch {
+    ElMessage.error('取消流程失败')
+  } finally {
+    cancellingFlow.value = false
+  }
+}
+
+const handleNewSession = () => {
+  const newSessionId = `sess_${Date.now()}`
+  const newUserId = `user_${Date.now()}`
+  sessionId.value = newSessionId
+  userId.value = newUserId
+  conversation.value = []
+  lastTrace.value = null
+  ElMessage.success(`新会话已创建: ${newSessionId}`)
+}
+
 const handleReportMessages = async () => {
   if (!conversation.value.length) {
     ElMessage.warning('暂无可上报消息')
@@ -370,6 +426,12 @@ const copyShareUrl = (url: string) => {
   ElMessage.success('链接已复制到剪贴板')
 }
 
+const copyJson = (data: unknown, name: string) => {
+  const jsonStr = JSON.stringify(data, null, 2)
+  navigator.clipboard.writeText(jsonStr)
+  ElMessage.success(`${name} 已复制到剪贴板`)
+}
+
 const formatShareExpires = (expiresAt: string) => {
   const expires = new Date(expiresAt)
   return expires.toLocaleDateString()
@@ -462,6 +524,23 @@ onMounted(() => {
   margin-bottom: 4px;
 }
 
+.json-content {
+  position: relative;
+}
+
+.json-content .copy-btn {
+  position: absolute;
+  top: 4px;
+  right: 4px;
+  z-index: 10;
+  background: var(--el-bg-color);
+  opacity: 0.8;
+}
+
+.json-content .copy-btn:hover {
+  opacity: 1;
+}
+
 pre {
   background: var(--el-fill-color-lighter);
   padding: 8px;

ai-service-admin/src/views/admin/scene-slot-bundle/index.vue

@@ -0,0 +1,660 @@
+<template>
+  <div class="scene-slot-bundle-page">
+    <div class="page-header">
+      <div class="header-content">
+        <div class="title-section">
+          <h1 class="page-title">场景槽位包管理</h1>
+          <p class="page-desc">配置场景与槽位的映射关系，定义每个场景需要采集的槽位集合。[AC-SCENE-SLOT-01]</p>
+        </div>
+        <div class="header-actions">
+          <el-select v-model="filterStatus" placeholder="按状态筛选" clearable style="width: 140px;">
+            <el-option
+              v-for="opt in SCENE_SLOT_BUNDLE_STATUS_OPTIONS"
+              :key="opt.value"
+              :label="opt.label"
+              :value="opt.value"
+            />
+          </el-select>
+          <el-button type="primary" @click="handleCreate">
+            <el-icon><Plus /></el-icon>
+            新建场景槽位包
+          </el-button>
+        </div>
+      </div>
+    </div>
+
+    <el-card shadow="hover" class="bundle-card" v-loading="loading">
+      <el-table :data="bundles" stripe style="width: 100%">
+        <el-table-column prop="scene_key" label="场景标识" min-width="140">
+          <template #default="{ row }">
+            <code class="scene-key">{{ row.scene_key }}</code>
+          </template>
+        </el-table-column>
+        <el-table-column prop="scene_name" label="场景名称" min-width="120" />
+        <el-table-column prop="status" label="状态" width="100">
+          <template #default="{ row }">
+            <el-tag :type="getStatusTagType(row.status)" size="small">
+              {{ getStatusLabel(row.status) }}
+            </el-tag>
+          </template>
+        </el-table-column>
+        <el-table-column prop="required_slots" label="必填槽位" min-width="160">
+          <template #default="{ row }">
+            <div class="slot-tags">
+              <el-tag
+                v-for="slotKey in row.required_slots.slice(0, 3)"
+                :key="slotKey"
+                size="small"
+                type="danger"
+                class="slot-tag"
+              >
+                {{ slotKey }}
+              </el-tag>
+              <el-tag v-if="row.required_slots.length > 3" size="small" type="info">
+                +{{ row.required_slots.length - 3 }}
+              </el-tag>
+            </div>
+          </template>
+        </el-table-column>
+        <el-table-column prop="optional_slots" label="可选槽位" min-width="140">
+          <template #default="{ row }">
+            <div class="slot-tags" v-if="row.optional_slots.length > 0">
+              <el-tag
+                v-for="slotKey in row.optional_slots.slice(0, 3)"
+                :key="slotKey"
+                size="small"
+                type="info"
+                class="slot-tag"
+              >
+                {{ slotKey }}
+              </el-tag>
+              <el-tag v-if="row.optional_slots.length > 3" size="small" type="info">
+                +{{ row.optional_slots.length - 3 }}
+              </el-tag>
+            </div>
+            <span v-else class="no-value">-</span>
+          </template>
+        </el-table-column>
+        <el-table-column prop="completion_threshold" label="完成阈值" width="100">
+          <template #default="{ row }">
+            <span>{{ (row.completion_threshold * 100).toFixed(0) }}%</span>
+          </template>
+        </el-table-column>
+        <el-table-column prop="ask_back_order" label="追问策略" width="100">
+          <template #default="{ row }">
+            <span>{{ getAskBackOrderLabel(row.ask_back_order) }}</span>
+          </template>
+        </el-table-column>
+        <el-table-column label="操作" width="150" fixed="right">
+          <template #default="{ row }">
+            <el-button type="primary" link size="small" @click="handleEdit(row)">
+              <el-icon><Edit /></el-icon>
+              编辑
+            </el-button>
+            <el-button type="danger" link size="small" @click="handleDelete(row)">
+              <el-icon><Delete /></el-icon>
+              删除
+            </el-button>
+          </template>
+        </el-table-column>
+      </el-table>
+      <el-empty v-if="!loading && bundles.length === 0" description="暂无场景槽位包" />
+    </el-card>
+
+    <el-dialog
+      v-model="dialogVisible"
+      :title="isEdit ? '编辑场景槽位包' : '新建场景槽位包'"
+      width="800px"
+      :close-on-click-modal="false"
+      destroy-on-close
+    >
+      <el-form :model="formData" :rules="formRules" ref="formRef" label-width="100px">
+        <el-row :gutter="20">
+          <el-col :span="12">
+            <el-form-item label="场景标识" prop="scene_key">
+              <el-input
+                v-model="formData.scene_key"
+                placeholder="如：open_consult, refund_apply"
+                :disabled="isEdit"
+              />
+              <div class="field-hint">唯一标识，创建后不可修改</div>
+            </el-form-item>
+          </el-col>
+          <el-col :span="12">
+            <el-form-item label="场景名称" prop="scene_name">
+              <el-input v-model="formData.scene_name" placeholder="如：开放咨询、退款申请" />
+            </el-form-item>
+          </el-col>
+        </el-row>
+        
+        <el-form-item label="场景描述" prop="description">
+          <el-input
+            v-model="formData.description"
+            type="textarea"
+            :rows="2"
+            placeholder="描述该场景的业务背景和用途"
+          />
+        </el-form-item>
+
+        <el-row :gutter="20">
+          <el-col :span="12">
+            <el-form-item label="状态" prop="status">
+              <el-select v-model="formData.status" style="width: 100%;">
+                <el-option
+                  v-for="opt in SCENE_SLOT_BUNDLE_STATUS_OPTIONS"
+                  :key="opt.value"
+                  :label="opt.label"
+                  :value="opt.value"
+                >
+                  <div class="status-option">
+                    <span>{{ opt.label }}</span>
+                    <span class="status-desc">{{ opt.description }}</span>
+                  </div>
+                </el-option>
+              </el-select>
+            </el-form-item>
+          </el-col>
+          <el-col :span="12">
+            <el-form-item label="完成阈值" prop="completion_threshold">
+              <el-slider
+                v-model="formData.completion_threshold"
+                :min="0"
+                :max="1"
+                :step="0.1"
+                :format-tooltip="(val: number) => `${(val * 100).toFixed(0)}%`"
+              />
+              <div class="field-hint">必填槽位填充比例达到此值视为完成</div>
+            </el-form-item>
+          </el-col>
+        </el-row>
+
+        <el-form-item label="必填槽位" prop="required_slots">
+          <el-select
+            v-model="formData.required_slots"
+            multiple
+            filterable
+            style="width: 100%;"
+            placeholder="选择必填槽位"
+          >
+            <el-option
+              v-for="slot in availableSlots"
+              :key="slot.id"
+              :label="`${slot.slot_key} (${slot.ask_back_prompt || '无追问提示'})`"
+              :value="slot.slot_key"
+              :disabled="formData.optional_slots.includes(slot.slot_key)"
+            >
+              <div class="slot-option">
+                <span class="slot-key-label">{{ slot.slot_key }}</span>
+                <el-tag size="small" type="info">{{ slot.type }}</el-tag>
+              </div>
+            </el-option>
+          </el-select>
+          <div class="field-hint">缺失必填槽位时会触发追问，不会直接进行 KB 检索</div>
+        </el-form-item>
+
+        <el-form-item label="可选槽位" prop="optional_slots">
+          <el-select
+            v-model="formData.optional_slots"
+            multiple
+            filterable
+            style="width: 100%;"
+            placeholder="选择可选槽位"
+          >
+            <el-option
+              v-for="slot in availableSlots"
+              :key="slot.id"
+              :label="slot.slot_key"
+              :value="slot.slot_key"
+              :disabled="formData.required_slots.includes(slot.slot_key)"
+            >
+              <div class="slot-option">
+                <span class="slot-key-label">{{ slot.slot_key }}</span>
+                <el-tag size="small" type="info">{{ slot.type }}</el-tag>
+              </div>
+            </el-option>
+          </el-select>
+          <div class="field-hint">可选槽位用于增强检索效果，缺失时不阻塞流程</div>
+        </el-form-item>
+
+        <el-form-item label="槽位优先级" prop="slot_priority">
+          <div class="priority-editor">
+            <div class="priority-header">
+              <span class="priority-hint">拖拽调整槽位采集和追问的优先级顺序</span>
+              <el-button
+                v-if="formData.slot_priority && formData.slot_priority.length > 0"
+                type="danger"
+                link
+                size="small"
+                @click="formData.slot_priority = null"
+              >
+                清空
+              </el-button>
+            </div>
+            
+            <div v-if="allSlotsForPriority.length > 0" class="priority-list-wrapper">
+              <draggable
+                v-model="allSlotsForPriority"
+                item-key="slot_key"
+                handle=".drag-handle"
+                class="priority-list"
+                ghost-class="ghost"
+              >
+                <template #item="{ element, index }">
+                  <div class="priority-item" :class="{ 'in-priority': isInPriority(element.slot_key) }">
+                    <el-icon class="drag-handle"><Rank /></el-icon>
+                    <span class="priority-order">{{ index + 1 }}</span>
+                    <el-tag
+                      size="small"
+                      :type="formData.required_slots.includes(element.slot_key) ? 'danger' : 'info'"
+                    >
+                      {{ element.slot_key }}
+                    </el-tag>
+                    <el-tag size="small" type="warning" v-if="formData.required_slots.includes(element.slot_key)">
+                      必填
+                    </el-tag>
+                  </div>
+                </template>
+              </draggable>
+            </div>
+            <div v-else class="priority-empty">
+              <el-text type="info">请先添加必填或可选槽位</el-text>
+            </div>
+          </div>
+        </el-form-item>
+
+        <el-form-item label="追问策略" prop="ask_back_order">
+          <el-radio-group v-model="formData.ask_back_order">
+            <el-radio-button
+              v-for="opt in ASK_BACK_ORDER_OPTIONS"
+              :key="opt.value"
+              :value="opt.value"
+            >
+              {{ opt.label }}
+            </el-radio-button>
+          </el-radio-group>
+          <div class="field-hint">
+            {{ ASK_BACK_ORDER_OPTIONS.find(o => o.value === formData.ask_back_order)?.description }}
+          </div>
+        </el-form-item>
+      </el-form>
+      <template #footer>
+        <el-button @click="dialogVisible = false">取消</el-button>
+        <el-button type="primary" :loading="submitting" @click="handleSubmit">
+          {{ isEdit ? '保存' : '创建' }}
+        </el-button>
+      </template>
+    </el-dialog>
+  </div>
+</template>
+
+<script setup lang="ts">
+import { ref, reactive, onMounted, watch, computed } from 'vue'
+import { ElMessage, ElMessageBox } from 'element-plus'
+import { Plus, Edit, Delete, Rank } from '@element-plus/icons-vue'
+import type { FormInstance, FormRules } from 'element-plus'
+import draggable from 'vuedraggable'
+import { sceneSlotBundleApi } from '@/api/scene-slot-bundle'
+import { slotDefinitionApi } from '@/api/slot-definition'
+import {
+  SCENE_SLOT_BUNDLE_STATUS_OPTIONS,
+  ASK_BACK_ORDER_OPTIONS,
+  type SceneSlotBundle,
+  type SceneSlotBundleCreateRequest,
+  type SceneSlotBundleUpdateRequest,
+  type SceneSlotBundleStatus,
+  type AskBackOrder,
+  getStatusLabel,
+  getAskBackOrderLabel,
+} from '@/types/scene-slot-bundle'
+import type { SlotDefinition } from '@/types/slot-definition'
+
+const loading = ref(false)
+const bundles = ref<SceneSlotBundle[]>([])
+const availableSlots = ref<SlotDefinition[]>([])
+const filterStatus = ref<SceneSlotBundleStatus | ''>('')
+const dialogVisible = ref(false)
+const isEdit = ref(false)
+const submitting = ref(false)
+const formRef = ref<FormInstance>()
+
+const formData = reactive({
+  id: '',
+  scene_key: '',
+  scene_name: '',
+  description: '',
+  required_slots: [] as string[],
+  optional_slots: [] as string[],
+  slot_priority: null as string[] | null,
+  completion_threshold: 1.0,
+  ask_back_order: 'priority' as AskBackOrder,
+  status: 'draft' as SceneSlotBundleStatus,
+})
+
+const formRules: FormRules = {
+  scene_key: [
+    { required: true, message: '请输入场景标识', trigger: 'blur' },
+    { pattern: /^[a-z][a-z0-9_]*$/, message: '以小写字母开头，仅允许小写字母、数字、下划线', trigger: 'blur' }
+  ],
+  scene_name: [{ required: true, message: '请输入场景名称', trigger: 'blur' }],
+  status: [{ required: true, message: '请选择状态', trigger: 'change' }],
+}
+
+const getStatusTagType = (status: SceneSlotBundleStatus): 'success' | 'warning' | 'info' => {
+  const typeMap: Record<SceneSlotBundleStatus, 'success' | 'warning' | 'info'> = {
+    'active': 'success',
+    'draft': 'warning',
+    'deprecated': 'info',
+  }
+  return typeMap[status] || 'info'
+}
+
+const allSlotsForPriority = computed({
+  get: () => {
+    const allKeys = [...formData.required_slots, ...formData.optional_slots]
+    const priority = formData.slot_priority || allKeys
+    
+    const orderedSlots = priority
+      .filter(key => allKeys.includes(key))
+      .map(key => availableSlots.value.find(s => s.slot_key === key) || { slot_key: key })
+    
+    const remainingKeys = allKeys.filter(key => !priority.includes(key))
+    const remainingSlots = remainingKeys
+      .map(key => availableSlots.value.find(s => s.slot_key === key) || { slot_key: key })
+    
+    return [...orderedSlots, ...remainingSlots]
+  },
+  set: (value: { slot_key: string }[]) => {
+    formData.slot_priority = value.map(s => s.slot_key)
+  }
+})
+
+const isInPriority = (slotKey: string): boolean => {
+  if (!formData.slot_priority) return true
+  return formData.slot_priority.includes(slotKey)
+}
+
+const fetchBundles = async () => {
+  loading.value = true
+  try {
+    const res = await sceneSlotBundleApi.list(filterStatus.value || undefined)
+    bundles.value = res || []
+  } catch (error: any) {
+    ElMessage.error(error.response?.data?.message || '获取场景槽位包列表失败')
+  } finally {
+    loading.value = false
+  }
+}
+
+const fetchAvailableSlots = async () => {
+  try {
+    const res = await slotDefinitionApi.list()
+    availableSlots.value = res || []
+  } catch (error: any) {
+    console.error('获取槽位定义失败', error)
+  }
+}
+
+const handleCreate = () => {
+  isEdit.value = false
+  Object.assign(formData, {
+    id: '',
+    scene_key: '',
+    scene_name: '',
+    description: '',
+    required_slots: [],
+    optional_slots: [],
+    slot_priority: null,
+    completion_threshold: 1.0,
+    ask_back_order: 'priority',
+    status: 'draft',
+  })
+  dialogVisible.value = true
+}
+
+const handleEdit = (bundle: SceneSlotBundle) => {
+  isEdit.value = true
+  Object.assign(formData, {
+    id: bundle.id,
+    scene_key: bundle.scene_key,
+    scene_name: bundle.scene_name,
+    description: bundle.description || '',
+    required_slots: [...bundle.required_slots],
+    optional_slots: [...bundle.optional_slots],
+    slot_priority: bundle.slot_priority ? [...bundle.slot_priority] : null,
+    completion_threshold: bundle.completion_threshold,
+    ask_back_order: bundle.ask_back_order,
+    status: bundle.status,
+  })
+  dialogVisible.value = true
+}
+
+const handleDelete = async (bundle: SceneSlotBundle) => {
+  try {
+    await ElMessageBox.confirm(
+      `确定要删除场景槽位包「${bundle.scene_name}」吗？`,
+      '删除确认',
+      { type: 'warning' }
+    )
+    await sceneSlotBundleApi.delete(bundle.id)
+    ElMessage.success('删除成功')
+    fetchBundles()
+  } catch (error: any) {
+    if (error !== 'cancel') {
+      ElMessage.error(error.response?.data?.message || '删除失败')
+    }
+  }
+}
+
+const handleSubmit = async () => {
+  if (!formRef.value) return
+
+  await formRef.value.validate(async (valid) => {
+    if (!valid) return
+
+    submitting.value = true
+    try {
+      const data: SceneSlotBundleCreateRequest | SceneSlotBundleUpdateRequest = {
+        scene_name: formData.scene_name,
+        description: formData.description || undefined,
+        required_slots: formData.required_slots.length > 0 ? formData.required_slots : undefined,
+        optional_slots: formData.optional_slots.length > 0 ? formData.optional_slots : undefined,
+        slot_priority: formData.slot_priority || undefined,
+        completion_threshold: formData.completion_threshold,
+        ask_back_order: formData.ask_back_order,
+        status: formData.status,
+      }
+
+      if (isEdit.value) {
+        await sceneSlotBundleApi.update(formData.id, data as SceneSlotBundleUpdateRequest)
+        ElMessage.success('更新成功')
+      } else {
+        const createData = data as SceneSlotBundleCreateRequest
+        createData.scene_key = formData.scene_key
+        await sceneSlotBundleApi.create(createData)
+        ElMessage.success('创建成功')
+      }
+
+      dialogVisible.value = false
+      fetchBundles()
+    } catch (error: any) {
+      ElMessage.error(error.response?.data?.message || '操作失败')
+    } finally {
+      submitting.value = false
+    }
+  })
+}
+
+watch(filterStatus, () => {
+  fetchBundles()
+})
+
+onMounted(() => {
+  fetchBundles()
+  fetchAvailableSlots()
+})
+</script>
+
+<style scoped lang="scss">
+.scene-slot-bundle-page {
+  padding: 20px;
+}
+
+.page-header {
+  margin-bottom: 24px;
+}
+
+.header-content {
+  display: flex;
+  justify-content: space-between;
+  align-items: flex-start;
+}
+
+.title-section {
+  .page-title {
+    font-size: 24px;
+    font-weight: 600;
+    margin: 0 0 8px 0;
+    color: var(--el-text-color-primary);
+  }
+
+  .page-desc {
+    font-size: 14px;
+    color: var(--el-text-color-secondary);
+    margin: 0;
+  }
+}
+
+.header-actions {
+  display: flex;
+  align-items: center;
+  gap: 12px;
+}
+
+.bundle-card {
+  border-radius: 8px;
+}
+
+.scene-key {
+  padding: 2px 6px;
+  background-color: var(--el-fill-color-light);
+  border-radius: 4px;
+  font-family: monospace;
+  font-size: 12px;
+}
+
+.slot-tags {
+  display: flex;
+  flex-wrap: wrap;
+  gap: 4px;
+}
+
+.slot-tag {
+  margin: 0;
+}
+
+.no-value {
+  color: var(--el-text-color-placeholder);
+}
+
+.field-hint {
+  margin-top: 4px;
+  font-size: 12px;
+  color: var(--el-text-color-secondary);
+}
+
+.status-option {
+  display: flex;
+  flex-direction: column;
+  gap: 2px;
+
+  .status-desc {
+    font-size: 12px;
+    color: var(--el-text-color-secondary);
+  }
+}
+
+.slot-option {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+
+  .slot-key-label {
+    font-weight: 500;
+    font-family: monospace;
+  }
+}
+
+.priority-editor {
+  border: 1px solid var(--el-border-color);
+  border-radius: 4px;
+  padding: 12px;
+  background-color: var(--el-fill-color-light);
+}
+
+.priority-header {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  margin-bottom: 12px;
+}
+
+.priority-hint {
+  font-size: 12px;
+  color: var(--el-text-color-secondary);
+}
+
+.priority-list-wrapper {
+  max-height: 300px;
+  overflow-y: auto;
+}
+
+.priority-list {
+  display: flex;
+  flex-direction: column;
+  gap: 8px;
+}
+
+.priority-item {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+  padding: 8px 12px;
+  background-color: var(--el-bg-color);
+  border-radius: 4px;
+  border: 1px solid var(--el-border-color-light);
+  cursor: move;
+  transition: all 0.2s;
+
+  &:hover {
+    border-color: var(--el-color-primary-light-5);
+  }
+
+  &.in-priority {
+    background-color: var(--el-color-primary-light-9);
+  }
+}
+
+.drag-handle {
+  cursor: move;
+  color: var(--el-text-color-secondary);
+  font-size: 16px;
+}
+
+.priority-order {
+  width: 20px;
+  text-align: center;
+  font-weight: 600;
+  color: var(--el-text-color-secondary);
+}
+
+.priority-empty {
+  text-align: center;
+  padding: 20px;
+  color: var(--el-text-color-secondary);
+}
+
+.ghost {
+  opacity: 0.5;
+  background: var(--el-color-primary-light-8);
+}
+</style>

ai-service-admin/src/views/admin/script-flow/index.vue

@@ -185,11 +185,23 @@
                         v-model="element.expected_variables"
                         multiple
                         filterable
-                        allow-create
-                        default-first-option
-                        placeholder="输入变量名后回车添加"
+                        placeholder="选择期望提取的槽位"
                         style="width: 100%"
-                      />
+                      >
+                        <el-option
+                          v-for="slot in availableSlots"
+                          :key="slot.id"
+                          :label="`${slot.slot_key} (${slot.type})`"
+                          :value="slot.slot_key"
+                        >
+                          <div class="slot-option">
+                            <span class="slot-key">{{ slot.slot_key }}</span>
+                            <el-tag size="small" type="info">{{ slot.type }}</el-tag>
+                            <el-tag size="small" v-if="slot.required" type="danger">必填</el-tag>
+                          </div>
+                        </el-option>
+                      </el-select>
+                      <div class="field-hint">期望变量必须引用已定义的槽位，步骤完成时会检查这些槽位是否已填充</div>
                     </el-form-item>
                   </template>
 
@@ -218,13 +230,100 @@
                         v-model="element.expected_variables"
                         multiple
                         filterable
-                        allow-create
-                        default-first-option
-                        placeholder="输入变量名后回车添加"
+                        placeholder="选择期望提取的槽位"
+                        style="width: 100%"
+                      >
+                        <el-option
+                          v-for="slot in availableSlots"
+                          :key="slot.id"
+                          :label="`${slot.slot_key} (${slot.type})`"
+                          :value="slot.slot_key"
+                        >
+                          <div class="slot-option">
+                            <span class="slot-key">{{ slot.slot_key }}</span>
+                            <el-tag size="small" type="info">{{ slot.type }}</el-tag>
+                            <el-tag size="small" v-if="slot.required" type="danger">必填</el-tag>
+                          </div>
+                        </el-option>
+                      </el-select>
+                      <div class="field-hint">期望变量必须引用已定义的槽位，步骤完成时会检查这些槽位是否已填充</div>
+                    </el-form-item>
+                  </template>
+
+                  <el-divider content-position="left">知识库范围</el-divider>
+                  
+                  <div class="kb-binding-section">
+                    <el-form-item label="允许的知识库">
+                      <el-select
+                        v-model="element.allowed_kb_ids"
+                        multiple
+                        filterable
+                        clearable
+                        placeholder="选择允许检索的知识库（为空则使用默认策略）"
+                        style="width: 100%"
+                      >
+                        <el-option
+                          v-for="kb in availableKnowledgeBases"
+                          :key="kb.id"
+                          :label="kb.name"
+                          :value="kb.id"
+                        >
+                          <div class="kb-option">
+                            <span>{{ kb.name }}</span>
+                            <el-tag size="small" :type="getKbTypeTagType(kb.kbType)">{{ getKbTypeLabel(kb.kbType) }}</el-tag>
+                          </div>
+                        </el-option>
+                      </el-select>
+                      <div class="field-hint">限制该步骤只能从选定的知识库中检索信息，为空则使用默认检索策略</div>
+                    </el-form-item>
+
+                    <el-form-item label="优先知识库">
+                      <el-select
+                        v-model="element.preferred_kb_ids"
+                        multiple
+                        filterable
+                        clearable
+                        placeholder="选择优先检索的知识库"
+                        style="width: 100%"
+                      >
+                        <el-option
+                          v-for="kb in availableKnowledgeBases"
+                          :key="kb.id"
+                          :label="kb.name"
+                          :value="kb.id"
+                          :disabled="element.allowed_kb_ids && element.allowed_kb_ids.length > 0 && !element.allowed_kb_ids.includes(kb.id)"
+                        >
+                          <div class="kb-option">
+                            <span>{{ kb.name }}</span>
+                            <el-tag size="small" :type="getKbTypeTagType(kb.kbType)">{{ getKbTypeLabel(kb.kbType) }}</el-tag>
+                          </div>
+                        </el-option>
+                      </el-select>
+                      <div class="field-hint">检索时优先搜索这些知识库</div>
+                    </el-form-item>
+
+                    <el-row :gutter="16">
+                      <el-col :span="16">
+                        <el-form-item label="检索提示">
+                          <el-input
+                            v-model="element.kb_query_hint"
+                            placeholder="可选：描述本步骤的检索意图，帮助提高检索准确性"
+                          />
+                        </el-form-item>
+                      </el-col>
+                      <el-col :span="8">
+                        <el-form-item label="最大检索次数">
+                          <el-input-number
+                            v-model="element.max_kb_calls_per_step"
+                            :min="1"
+                            :max="5"
+                            placeholder="默认1"
                             style="width: 100%"
                           />
                         </el-form-item>
-                  </template>
+                      </el-col>
+                    </el-row>
+                  </div>
 
                   <el-row :gutter="16">
                     <el-col :span="8">
@@ -362,17 +461,24 @@ import {
   createScriptFlow,
   updateScriptFlow,
   deleteScriptFlow,
-  getScriptFlow
+  getScriptFlow,
 } from '@/api/script-flow'
+import { slotDefinitionApi } from '@/api/slot-definition'
+import { listKnowledgeBases } from '@/api/knowledge-base'
 import { MetadataForm } from '@/components/metadata'
 import { TIMEOUT_ACTION_OPTIONS, SCRIPT_MODE_OPTIONS } from '@/types/script-flow'
 import type { ScriptFlow, ScriptFlowDetail, ScriptFlowCreate, ScriptFlowUpdate, FlowStep, ScriptMode } from '@/types/script-flow'
+import type { SlotDefinition } from '@/types/slot-definition'
+import type { KnowledgeBase } from '@/types/knowledge-base'
+import { KB_TYPE_MAP } from '@/types/knowledge-base'
 import FlowPreview from './components/FlowPreview.vue'
 import SimulateDialog from './components/SimulateDialog.vue'
 import ConstraintManager from './components/ConstraintManager.vue'
 
 const loading = ref(false)
 const flows = ref<ScriptFlow[]>([])
+const availableSlots = ref<SlotDefinition[]>([])
+const availableKnowledgeBases = ref<KnowledgeBase[]>([])
 const dialogVisible = ref(false)
 const previewDrawer = ref(false)
 const simulateDialogVisible = ref(false)
@@ -425,16 +531,55 @@ const loadFlows = async () => {
   }
 }
 
+const loadAvailableSlots = async () => {
+  try {
+    const res = await slotDefinitionApi.list()
+    availableSlots.value = res || []
+  } catch (error) {
+    console.error('加载槽位定义失败', error)
+    availableSlots.value = []
+  }
+}
+
+const loadAvailableKnowledgeBases = async () => {
+  try {
+    const res = await listKnowledgeBases({ is_enabled: true })
+    availableKnowledgeBases.value = res.data || []
+  } catch (error) {
+    console.error('加载知识库列表失败', error)
+    availableKnowledgeBases.value = []
+  }
+}
+
+const getKbTypeLabel = (kbType: string): string => {
+  return KB_TYPE_MAP[kbType]?.label || kbType
+}
+
+const getKbTypeTagType = (kbType: string): string => {
+  const typeMap: Record<string, string> = {
+    product: 'primary',
+    faq: 'success',
+    script: 'warning',
+    policy: 'danger',
+    general: 'info'
+  }
+  return typeMap[kbType] || 'info'
+}
+
 const handleCreate = () => {
   isEdit.value = false
   currentEditId.value = ''
   formData.value = defaultFormData()
+  loadAvailableSlots()
+  loadAvailableKnowledgeBases()
   dialogVisible.value = true
 }
 
 const handleEdit = async (row: ScriptFlow) => {
   isEdit.value = true
   currentEditId.value = row.id
+  await loadAvailableSlots()
+  await loadAvailableKnowledgeBases()
   try {
     const detail = await getScriptFlow(row.id)
     formData.value = {
@@ -444,7 +589,11 @@ const handleEdit = async (row: ScriptFlow) => {
         ...step,
         script_mode: step.script_mode || 'fixed',
         script_constraints: step.script_constraints || [],
-        expected_variables: step.expected_variables || []
+        expected_variables: step.expected_variables || [],
+        allowed_kb_ids: step.allowed_kb_ids || [],
+        preferred_kb_ids: step.preferred_kb_ids || [],
+        kb_query_hint: step.kb_query_hint || '',
+        max_kb_calls_per_step: step.max_kb_calls_per_step || null
       })),
       is_enabled: detail.is_enabled,
       metadata: detail.metadata || {}
@@ -739,4 +888,35 @@ onMounted(() => {
   border-radius: 4px;
   margin-bottom: 8px;
 }
+
+.slot-option {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+}
+
+.slot-key {
+  font-weight: 500;
+  font-family: monospace;
+}
+
+.field-hint {
+  margin-top: 4px;
+  font-size: 12px;
+  color: var(--el-text-color-secondary);
+}
+
+.kb-binding-section {
+  padding: 12px;
+  background-color: var(--el-fill-color-lighter);
+  border-radius: 6px;
+  margin-bottom: 12px;
+}
+
+.kb-option {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  width: 100%;
+}
 </style>

ai-service-admin/src/views/admin/slot-definition/index.vue

@@ -23,7 +23,16 @@
       <el-table :data="slots" stripe style="width: 100%">
         <el-table-column prop="slot_key" label="槽位标识" min-width="140">
           <template #default="{ row }">
+            <div class="slot-key-cell">
               <code class="slot-key">{{ row.slot_key }}</code>
+              <span v-if="row.display_name" class="display-name">{{ row.display_name }}</span>
+            </div>
+          </template>
+        </el-table-column>
+        <el-table-column prop="description" label="说明" min-width="180">
+          <template #default="{ row }">
+            <span v-if="row.description" class="slot-description">{{ row.description }}</span>
+            <span v-else class="no-value">-</span>
           </template>
         </el-table-column>
         <el-table-column prop="type" label="类型" width="100">
@@ -38,11 +47,20 @@
             </el-tag>
           </template>
         </el-table-column>
-        <el-table-column prop="extract_strategy" label="提取策略" width="120">
+        <!-- [AC-MRS-07-UPGRADE] 策略链展示 -->
+        <el-table-column prop="extract_strategies" label="提取策略链" min-width="180">
           <template #default="{ row }">
-            <el-tag v-if="row.extract_strategy" size="small">
-              {{ getExtractStrategyLabel(row.extract_strategy) }}
+            <div v-if="getEffectiveStrategies(row).length > 0" class="strategy-chain">
+              <el-tag 
+                v-for="(strategy, idx) in getEffectiveStrategies(row)" 
+                :key="idx"
+                size="small"
+                :type="getStrategyTagType(strategy)"
+                class="strategy-tag"
+              >
+                {{ idx + 1 }}. {{ getExtractStrategyLabel(strategy) }}
               </el-tag>
+            </div>
             <span v-else class="no-value">-</span>
           </template>
         </el-table-column>
@@ -80,7 +98,7 @@
     <el-dialog
       v-model="dialogVisible"
       :title="isEdit ? '编辑槽位定义' : '新建槽位定义'"
-      width="650px"
+      width="700px"
       :close-on-click-modal="false"
       destroy-on-close
     >
@@ -96,6 +114,25 @@
               <div class="field-hint">仅允许小写字母、数字、下划线</div>
             </el-form-item>
           </el-col>
+          <el-col :span="12">
+            <el-form-item label="槽位名称" prop="display_name">
+              <el-input
+                v-model="formData.display_name"
+                placeholder="如：当前年级"
+              />
+              <div class="field-hint">给运营/教研看的中文名</div>
+            </el-form-item>
+          </el-col>
+        </el-row>
+        <el-form-item label="槽位说明" prop="description">
+          <el-input
+            v-model="formData.description"
+            type="textarea"
+            :rows="2"
+            placeholder="解释这个槽位采集什么、用于哪里，如：用于课程分层推荐和知识库过滤"
+          />
+        </el-form-item>
+        <el-row :gutter="20">
           <el-col :span="12">
             <el-form-item label="槽位类型" prop="type">
               <el-select v-model="formData.type" style="width: 100%;">
@@ -108,21 +145,74 @@
               </el-select>
             </el-form-item>
           </el-col>
-        </el-row>
-        <el-row :gutter="20">
           <el-col :span="12">
             <el-form-item label="是否必填" prop="required">
               <el-switch v-model="formData.required" />
             </el-form-item>
           </el-col>
-          <el-col :span="12">
-            <el-form-item label="提取策略" prop="extract_strategy">
-              <el-select v-model="formData.extract_strategy" style="width: 100%;" clearable placeholder="选择提取策略">
+        </el-row>
+        
+        <!-- [AC-MRS-07-UPGRADE] 提取策略链配置 -->
+        <el-form-item label="提取策略链" prop="extract_strategies">
+          <div class="strategy-chain-editor">
+            <div class="strategy-chain-header">
+              <span class="chain-hint">按优先级排序，系统将依次尝试直到成功</span>
+              <el-button 
+                v-if="formData.extract_strategies.length > 0"
+                type="danger" 
+                link 
+                size="small"
+                @click="clearStrategies"
+              >
+                清空
+              </el-button>
+            </div>
+            
+            <div v-if="formData.extract_strategies.length > 0" class="strategy-chain-list">
+              <draggable 
+                v-model="formData.extract_strategies" 
+                :item-key="(item: ExtractStrategy) => item"
+                handle=".drag-handle"
+                class="strategy-list"
+              >
+                <template #item="{ element, index }">
+                  <div class="strategy-item">
+                    <el-icon class="drag-handle"><Rank /></el-icon>
+                    <span class="strategy-order">{{ index + 1 }}</span>
+                    <el-tag size="small" :type="getStrategyTagType(element)">
+                      {{ getExtractStrategyLabel(element) }}
+                    </el-tag>
+                    <el-button 
+                      type="danger" 
+                      link 
+                      size="small"
+                      class="remove-btn"
+                      @click="removeStrategy(index)"
+                    >
+                      <el-icon><Close /></el-icon>
+                    </el-button>
+                  </div>
+                </template>
+              </draggable>
+            </div>
+            
+            <div v-else class="strategy-empty">
+              <el-text type="info">暂无策略，请从下方添加</el-text>
+            </div>
+            
+            <div class="strategy-add-section">
+              <el-select 
+                v-model="selectedStrategy" 
+                placeholder="选择要添加的策略"
+                style="width: 200px;"
+                clearable
+              >
                 <el-option
-                  v-for="opt in EXTRACT_STRATEGY_OPTIONS"
+                  v-for="opt in availableStrategies"
                   :key="opt.value"
                   :label="opt.label"
-                  :value="opt.value"
+                  :value="opt.value as ExtractStrategy"
+                  :disabled="isStrategySelected(opt.value as ExtractStrategy)"
                 >
                   <div class="extract-option">
                     <span>{{ opt.label }}</span>
@@ -130,9 +220,22 @@
                   </div>
                 </el-option>
               </el-select>
+              <el-button 
+                type="primary" 
+                :disabled="!selectedStrategy"
+                @click="addStrategy"
+              >
+                <el-icon><Plus /></el-icon>
+                添加
+              </el-button>
+            </div>
+            
+            <div v-if="strategyError" class="strategy-error">
+              <el-text type="danger">{{ strategyError }}</el-text>
+            </div>
+          </div>
         </el-form-item>
-          </el-col>
-        </el-row>
+        
         <el-form-item label="关联字段" prop="linked_field_id">
           <el-select
             v-model="formData.linked_field_id"
@@ -186,10 +289,11 @@
 </template>
 
 <script setup lang="ts">
-import { ref, reactive, onMounted, watch } from 'vue'
+import { ref, reactive, onMounted, watch, computed } from 'vue'
 import { ElMessage, ElMessageBox } from 'element-plus'
-import { Plus, Edit, Delete } from '@element-plus/icons-vue'
+import { Plus, Edit, Delete, Rank, Close } from '@element-plus/icons-vue'
 import type { FormInstance, FormRules } from 'element-plus'
+import draggable from 'vuedraggable'
 import { slotDefinitionApi } from '@/api/slot-definition'
 import { metadataSchemaApi } from '@/api/metadata-schema'
 import {
@@ -199,7 +303,9 @@ import {
   type SlotDefinitionCreateRequest,
   type SlotDefinitionUpdateRequest,
   type SlotType,
-  type ExtractStrategy
+  type ExtractStrategy,
+  validateExtractStrategies,
+  getEffectiveStrategies
 } from '@/types/slot-definition'
 import type { MetadataFieldDefinition } from '@/types/metadata'
 
@@ -212,12 +318,19 @@ const isEdit = ref(false)
 const submitting = ref(false)
 const formRef = ref<FormInstance>()
 
+// [AC-MRS-07-UPGRADE] 策略链相关
+const selectedStrategy = ref<ExtractStrategy | ''>('')
+const strategyError = ref('')
+
 const formData = reactive({
   id: '',
   slot_key: '',
+  display_name: '',
+  description: '',
   type: 'string' as SlotType,
   required: false,
-  extract_strategy: '' as ExtractStrategy | '',
+  // [AC-MRS-07-UPGRADE] 使用策略链数组
+  extract_strategies: [] as ExtractStrategy[],
   validation_rule: '',
   ask_back_prompt: '',
   default_value: '',
@@ -233,6 +346,42 @@ const formRules: FormRules = {
   required: [{ required: true, message: '请选择是否必填', trigger: 'change' }]
 }
 
+// [AC-MRS-07-UPGRADE] 计算可用策略（未选择的）
+const availableStrategies = computed(() => {
+  return EXTRACT_STRATEGY_OPTIONS
+})
+
+// [AC-MRS-07-UPGRADE] 检查策略是否已选择
+const isStrategySelected = (strategy: ExtractStrategy) => {
+  return formData.extract_strategies.includes(strategy)
+}
+
+// [AC-MRS-07-UPGRADE] 添加策略到链
+const addStrategy = () => {
+  if (!selectedStrategy.value) return
+  
+  if (isStrategySelected(selectedStrategy.value)) {
+    strategyError.value = '该策略已存在于链中'
+    return
+  }
+  
+  formData.extract_strategies.push(selectedStrategy.value)
+  selectedStrategy.value = ''
+  strategyError.value = ''
+}
+
+// [AC-MRS-07-UPGRADE] 从链中移除策略
+const removeStrategy = (index: number) => {
+  formData.extract_strategies.splice(index, 1)
+  strategyError.value = ''
+}
+
+// [AC-MRS-07-UPGRADE] 清空策略链
+const clearStrategies = () => {
+  formData.extract_strategies = []
+  strategyError.value = ''
+}
+
 const getTypeLabel = (type: SlotType) => {
   return SLOT_TYPE_OPTIONS.find(o => o.value === type)?.label || type
 }
@@ -241,6 +390,16 @@ const getExtractStrategyLabel = (strategy: ExtractStrategy) => {
   return EXTRACT_STRATEGY_OPTIONS.find(o => o.value === strategy)?.label || strategy
 }
 
+// [AC-MRS-07-UPGRADE] 获取策略标签类型
+const getStrategyTagType = (strategy: ExtractStrategy): any => {
+  const typeMap: Record<ExtractStrategy, any> = {
+    'rule': 'success',
+    'llm': 'warning',
+    'user_input': 'info'
+  }
+  return typeMap[strategy] || 'info'
+}
+
 const fetchSlots = async () => {
   loading.value = true
   try {
@@ -256,7 +415,8 @@ const fetchSlots = async () => {
 const fetchSlotFields = async () => {
   try {
     const res = await metadataSchemaApi.getByRole('slot', true)
-    slotFields.value = res.items || []
+    // 后端返回数组格式，兼容处理
+    slotFields.value = Array.isArray(res) ? res : (res.items || [])
   } catch (error: any) {
     console.error('获取槽位角色字段失败', error)
   }
@@ -267,14 +427,19 @@ const handleCreate = () => {
   Object.assign(formData, {
     id: '',
     slot_key: '',
+    display_name: '',
+    description: '',
     type: 'string',
     required: false,
-    extract_strategy: '',
+    // [AC-MRS-07-UPGRADE] 初始化为空数组
+    extract_strategies: [],
     validation_rule: '',
     ask_back_prompt: '',
     default_value: '',
     linked_field_id: ''
   })
+  selectedStrategy.value = ''
+  strategyError.value = ''
   dialogVisible.value = true
 }
 
@@ -283,14 +448,19 @@ const handleEdit = (slot: SlotDefinition) => {
   Object.assign(formData, {
     id: slot.id,
     slot_key: slot.slot_key,
+    display_name: slot.display_name || '',
+    description: slot.description || '',
     type: slot.type,
     required: slot.required,
-    extract_strategy: slot.extract_strategy || '',
+    // [AC-MRS-07-UPGRADE] 使用有效的策略链
+    extract_strategies: [...getEffectiveStrategies(slot)],
     validation_rule: slot.validation_rule || '',
     ask_back_prompt: slot.ask_back_prompt || '',
     default_value: slot.default_value ?? '',
     linked_field_id: slot.linked_field_id || ''
   })
+  selectedStrategy.value = ''
+  strategyError.value = ''
   dialogVisible.value = true
 }
 
@@ -317,13 +487,25 @@ const handleSubmit = async () => {
   await formRef.value.validate(async (valid) => {
     if (!valid) return
 
+    // [AC-MRS-07-UPGRADE] 校验策略链
+    if (formData.extract_strategies.length > 0) {
+      const validation = validateExtractStrategies(formData.extract_strategies)
+      if (!validation.valid) {
+        strategyError.value = validation.error || '策略链校验失败'
+        return
+      }
+    }
+
     submitting.value = true
     try {
       const data: SlotDefinitionCreateRequest | SlotDefinitionUpdateRequest = {
         slot_key: formData.slot_key,
+        display_name: formData.display_name || undefined,
+        description: formData.description || undefined,
         type: formData.type,
         required: formData.required,
-        extract_strategy: formData.extract_strategy || undefined,
+        // [AC-MRS-07-UPGRADE] 提交策略链
+        extract_strategies: formData.extract_strategies.length > 0 ? formData.extract_strategies : undefined,
         validation_rule: formData.validation_rule || undefined,
         ask_back_prompt: formData.ask_back_prompt || undefined,
         linked_field_id: formData.linked_field_id || undefined
@@ -409,6 +591,27 @@ onMounted(() => {
   font-size: 12px;
 }
 
+.slot-key-cell {
+  display: flex;
+  flex-direction: column;
+  gap: 2px;
+}
+
+.display-name {
+  font-size: 12px;
+  color: var(--el-text-color-secondary);
+}
+
+.slot-description {
+  color: var(--el-text-color-regular);
+  font-size: 13px;
+  overflow: hidden;
+  text-overflow: ellipsis;
+  white-space: nowrap;
+  display: block;
+  max-width: 180px;
+}
+
 .no-value {
   color: var(--el-text-color-placeholder);
 }
@@ -439,6 +642,91 @@ onMounted(() => {
   color: var(--el-text-color-secondary);
 }
 
+// [AC-MRS-07-UPGRADE] 策略链样式
+.strategy-chain {
+  display: flex;
+  flex-wrap: wrap;
+  gap: 4px;
+}
+
+.strategy-tag {
+  margin-right: 4px;
+}
+
+.strategy-chain-editor {
+  border: 1px solid var(--el-border-color);
+  border-radius: 4px;
+  padding: 12px;
+  background-color: var(--el-fill-color-light);
+}
+
+.strategy-chain-header {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  margin-bottom: 12px;
+}
+
+.chain-hint {
+  font-size: 12px;
+  color: var(--el-text-color-secondary);
+}
+
+.strategy-chain-list {
+  margin-bottom: 16px;
+}
+
+.strategy-list {
+  display: flex;
+  flex-direction: column;
+  gap: 8px;
+}
+
+.strategy-item {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+  padding: 8px 12px;
+  background-color: var(--el-bg-color);
+  border-radius: 4px;
+  border: 1px solid var(--el-border-color-light);
+}
+
+.drag-handle {
+  cursor: move;
+  color: var(--el-text-color-secondary);
+  font-size: 16px;
+}
+
+.strategy-order {
+  width: 20px;
+  text-align: center;
+  font-weight: 600;
+  color: var(--el-text-color-secondary);
+}
+
+.remove-btn {
+  margin-left: auto;
+}
+
+.strategy-empty {
+  text-align: center;
+  padding: 20px;
+  color: var(--el-text-color-secondary);
+}
+
+.strategy-add-section {
+  display: flex;
+  gap: 8px;
+  align-items: center;
+  padding-top: 12px;
+  border-top: 1px dashed var(--el-border-color);
+}
+
+.strategy-error {
+  margin-top: 8px;
+}
+
 .extract-option {
   display: flex;
   flex-direction: column;

ai-service-admin/src/views/dashboard/index.vue

@@ -410,6 +410,24 @@
                 <p>配置知识库、意图规则等的动态元数据字段定义。</p>
               </div>
             </div>
+            <div class="help-item" @click="navigateTo('/admin/slot-definitions')">
+              <div class="help-icon success">
+                <el-icon><Grid /></el-icon>
+              </div>
+              <div class="help-text">
+                <h4>槽位定义</h4>
+                <p>定义槽位类型、提取策略、校验规则和追问提示。</p>
+              </div>
+            </div>
+            <div class="help-item" @click="navigateTo('/admin/scene-slot-bundles')">
+              <div class="help-icon warning">
+                <el-icon><Collection /></el-icon>
+              </div>
+              <div class="help-text">
+                <h4>场景槽位包</h4>
+                <p>配置场景与槽位的映射关系，定义每个场景需要采集的槽位。</p>
+              </div>
+            </div>
           </div>
         </el-card>
       </el-col>
@@ -420,7 +438,7 @@
 <script setup lang="ts">
 import { ref, reactive, computed, onMounted } from 'vue'
 import { useRouter } from 'vue-router'
-import { FolderOpened, Document, ChatDotSquare, Monitor, Cpu, InfoFilled, Connection, Timer, DataLine, Aim, DocumentCopy, Share, Warning, Setting } from '@element-plus/icons-vue'
+import { FolderOpened, Document, ChatDotSquare, Monitor, Cpu, InfoFilled, Connection, Timer, DataLine, Aim, DocumentCopy, Share, Warning, Setting, Grid, Collection } from '@element-plus/icons-vue'
 import { getDashboardStats, type DashboardStats } from '@/api/dashboard'
 
 const router = useRouter()

ai-service-admin/src/views/kb/index.vue

@@ -11,6 +11,14 @@
             <el-icon><Upload /></el-icon>
             上传文档
           </el-button>
+          <el-button type="success" @click="handleBatchUploadClick">
+            <el-icon><Upload /></el-icon>
+            批量上传
+          </el-button>
+          <el-button type="warning" @click="handleJsonUploadClick">
+            <el-icon><Upload /></el-icon>
+            JSON上传
+          </el-button>
         </div>
       </div>
     </div>
@@ -85,6 +93,8 @@
     </el-dialog>
 
     <input ref="fileInput" type="file" style="display: none" @change="handleFileChange" />
+    <input ref="batchFileInput" type="file" accept=".zip" style="display: none" @change="handleBatchFileChange" />
+    <input ref="jsonFileInput" type="file" accept=".json,.jsonl,.txt" style="display: none" @change="handleJsonFileChange" />
   </div>
 </template>
 
@@ -92,7 +102,7 @@
 import { ref, onMounted, onUnmounted } from 'vue'
 import { ElMessage, ElMessageBox } from 'element-plus'
 import { Upload, Document, View, Delete } from '@element-plus/icons-vue'
-import { uploadDocument, listDocuments, getIndexJob, deleteDocument } from '@/api/kb'
+import { uploadDocument, listDocuments, getIndexJob, deleteDocument, batchUploadDocuments, jsonBatchUpload } from '@/api/kb'
 
 interface DocumentItem {
   docId: string
@@ -242,11 +252,21 @@ onUnmounted(() => {
 })
 
 const fileInput = ref<HTMLInputElement | null>(null)
+const batchFileInput = ref<HTMLInputElement | null>(null)
+const jsonFileInput = ref<HTMLInputElement | null>(null)
 
 const handleUploadClick = () => {
   fileInput.value?.click()
 }
 
+const handleBatchUploadClick = () => {
+  batchFileInput.value?.click()
+}
+
+const handleJsonUploadClick = () => {
+  jsonFileInput.value?.click()
+}
+
 const handleFileChange = async (event: Event) => {
   const target = event.target as HTMLInputElement
   const file = target.files?.[0]
@@ -281,6 +301,111 @@ const handleFileChange = async (event: Event) => {
     target.value = ''
   }
 }
+
+const handleBatchFileChange = async (event: Event) => {
+  const target = event.target as HTMLInputElement
+  const file = target.files?.[0]
+  if (!file) return
+
+  if (!file.name.toLowerCase().endsWith('.zip')) {
+    ElMessage.error('请上传 .zip 格式的压缩包')
+    target.value = ''
+    return
+  }
+
+  const formData = new FormData()
+  formData.append('file', file)
+  formData.append('kb_id', 'kb_default')
+
+  try {
+    loading.value = true
+    const res: any = await batchUploadDocuments(formData)
+    
+    const { total, succeeded, failed, results } = res
+    
+    if (succeeded > 0) {
+      ElMessage.success(`批量上传成功！成功: ${succeeded}, 失败: ${failed}, 总计: ${total}`)
+      
+      for (const result of results) {
+        if (result.status === 'created') {
+          const newDoc: DocumentItem = {
+            docId: result.docId,
+            name: result.fileName,
+            status: 'pending',
+            jobId: result.jobId,
+            createTime: new Date().toLocaleString('zh-CN')
+          }
+          tableData.value.unshift(newDoc)
+          startPolling(result.jobId)
+        }
+      }
+    } else {
+      ElMessage.error('批量上传失败，请检查压缩包格式')
+    }
+    
+    if (failed > 0) {
+      const failedItems = results.filter((r: any) => r.status === 'failed')
+      console.error('Failed uploads:', failedItems)
+    }
+    
+    fetchDocuments()
+  } catch (error: any) {
+    ElMessage.error(error.message || '批量上传失败')
+    console.error('Batch upload error:', error)
+  } finally {
+    loading.value = false
+    target.value = ''
+  }
+}
+
+const handleJsonFileChange = async (event: Event) => {
+  const target = event.target as HTMLInputElement
+  const file = target.files?.[0]
+  if (!file) return
+
+  const fileName = file.name.toLowerCase()
+  if (!fileName.endsWith('.json') && !fileName.endsWith('.jsonl') && !fileName.endsWith('.txt')) {
+    ElMessage.error('请上传 .json、.jsonl 或 .txt 格式的文件')
+    target.value = ''
+    return
+  }
+
+  const kbId = '75c465fe-277d-455d-a30b-4b168adcc03b'
+  
+  const formData = new FormData()
+  formData.append('file', file)
+  formData.append('tenant_id', 'szmp@ash@2026')
+
+  try {
+    loading.value = true
+    ElMessage.info('正在上传 JSON 文件...')
+    
+    const res: any = await jsonBatchUpload(kbId, formData)
+    
+    if (res.success) {
+      ElMessage.success(`JSON 批量上传成功！成功: ${res.succeeded}, 失败: ${res.failed}`)
+      
+      if (res.valid_metadata_fields) {
+        console.log('有效的元数据字段:', res.valid_metadata_fields)
+      }
+      
+      if (res.failed > 0) {
+        const failedItems = res.results.filter((r: any) => !r.success)
+        console.warn('失败的项目:', failedItems)
+      }
+      
+      fetchDocuments()
+    } else {
+      ElMessage.error('JSON 批量上传失败')
+    }
+  } catch (error: any) {
+    ElMessage.error(error.message || 'JSON 批量上传失败')
+    console.error('JSON batch upload error:', error)
+  } finally {
+    loading.value = false
+    target.value = ''
+  }
+}
 </script>
 
 <style scoped>
@@ -324,6 +449,7 @@ const handleFileChange = async (event: Event) => {
 .header-actions {
   display: flex;
   align-items: center;
+  gap: 12px;
 }
 
 .table-card {

ai-service/app/api/admin/__init__.py

@@ -2,6 +2,8 @@
 Admin API routes for AI Service management.
 [AC-ASA-01, AC-ASA-02, AC-ASA-05, AC-ASA-07, AC-ASA-08, AC-AISVC-50] Admin management endpoints.
 [AC-MRS-07,08,16] Slot definition management endpoints.
+[AC-SCENE-SLOT-01] Scene slot bundle management endpoints.
+[AC-AISVC-RES-01~15] Retrieval strategy management endpoints.
 """
 
 from app.api.admin.api_key import router as api_key_router
@@ -18,6 +20,8 @@ from app.api.admin.metadata_schema import router as metadata_schema_router
 from app.api.admin.monitoring import router as monitoring_router
 from app.api.admin.prompt_templates import router as prompt_templates_router
 from app.api.admin.rag import router as rag_router
+from app.api.admin.retrieval_strategy import router as retrieval_strategy_router
+from app.api.admin.scene_slot_bundle import router as scene_slot_bundle_router
 from app.api.admin.script_flows import router as script_flows_router
 from app.api.admin.sessions import router as sessions_router
 from app.api.admin.slot_definition import router as slot_definition_router
@@ -38,6 +42,8 @@ __all__ = [
     "monitoring_router",
     "prompt_templates_router",
     "rag_router",
+    "retrieval_strategy_router",
+    "scene_slot_bundle_router",
     "script_flows_router",
     "sessions_router",
     "slot_definition_router",

ai-service/app/api/admin/intent_rules.py

@@ -2,6 +2,8 @@
 Intent Rule Management API.
 [AC-AISVC-65~AC-AISVC-68] Intent rule CRUD endpoints.
 [AC-AISVC-96] Intent rule testing endpoint.
+[AC-AISVC-116] Fusion config management endpoints.
+[AC-AISVC-114] Intent vector generation endpoint.
 """
 
 import logging
@@ -14,6 +16,7 @@ from sqlalchemy.ext.asyncio import AsyncSession
 
 from app.core.database import get_session
 from app.models.entities import IntentRuleCreate, IntentRuleUpdate
+from app.services.intent.models import DEFAULT_FUSION_CONFIG, FusionConfig
 from app.services.intent.rule_service import IntentRuleService
 from app.services.intent.tester import IntentRuleTester
 
@@ -21,6 +24,8 @@ logger = logging.getLogger(__name__)
 
 router = APIRouter(prefix="/admin/intent-rules", tags=["Intent Rules"])
 
+_fusion_config = FusionConfig()
+
 
 def get_tenant_id(x_tenant_id: str = Header(..., alias="X-Tenant-Id")) -> str:
     """Extract tenant ID from header."""
@@ -204,3 +209,109 @@ async def test_rule(
     result = await tester.test_rule(rule, [body.message], all_rules)
 
     return result.to_dict()
+
+
+class FusionConfigUpdate(BaseModel):
+    """Request body for updating fusion config."""
+
+    w_rule: float | None = None
+    w_semantic: float | None = None
+    w_llm: float | None = None
+    semantic_threshold: float | None = None
+    conflict_threshold: float | None = None
+    gray_zone_threshold: float | None = None
+    min_trigger_threshold: float | None = None
+    clarify_threshold: float | None = None
+    multi_intent_threshold: float | None = None
+    llm_judge_enabled: bool | None = None
+    semantic_matcher_enabled: bool | None = None
+    semantic_matcher_timeout_ms: int | None = None
+    llm_judge_timeout_ms: int | None = None
+    semantic_top_k: int | None = None
+
+
+@router.get("/fusion-config")
+async def get_fusion_config() -> dict[str, Any]:
+    """
+    [AC-AISVC-116] Get current fusion configuration.
+    """
+    logger.info("[AC-AISVC-116] Getting fusion config")
+    return _fusion_config.to_dict()
+
+
+@router.put("/fusion-config")
+async def update_fusion_config(
+    body: FusionConfigUpdate,
+) -> dict[str, Any]:
+    """
+    [AC-AISVC-116] Update fusion configuration.
+    """
+    global _fusion_config
+
+    logger.info(f"[AC-AISVC-116] Updating fusion config: {body.model_dump()}")
+
+    current_dict = _fusion_config.to_dict()
+    update_dict = body.model_dump(exclude_none=True)
+    current_dict.update(update_dict)
+    _fusion_config = FusionConfig.from_dict(current_dict)
+
+    return _fusion_config.to_dict()
+
+
+@router.post("/{rule_id}/generate-vector")
+async def generate_intent_vector(
+    rule_id: uuid.UUID,
+    tenant_id: str = Depends(get_tenant_id),
+    session: AsyncSession = Depends(get_session),
+) -> dict[str, Any]:
+    """
+    [AC-AISVC-114] Generate intent vector for a rule.
+    
+    Uses the rule's semantic_examples to generate an average vector.
+    If no semantic_examples exist, returns an error.
+    """
+    logger.info(
+        f"[AC-AISVC-114] Generating intent vector for tenant={tenant_id}, rule_id={rule_id}"
+    )
+
+    service = IntentRuleService(session)
+    rule = await service.get_rule(tenant_id, rule_id)
+
+    if not rule:
+        raise HTTPException(status_code=404, detail="Intent rule not found")
+
+    if not rule.semantic_examples:
+        raise HTTPException(
+            status_code=400,
+            detail="Rule has no semantic_examples. Please add semantic_examples first."
+        )
+
+    try:
+        from app.core.dependencies import get_embedding_provider
+        embedding_provider = get_embedding_provider()
+        
+        vectors = await embedding_provider.embed_batch(rule.semantic_examples)
+        
+        import numpy as np
+        avg_vector = np.mean(vectors, axis=0).tolist()
+        
+        update_data = IntentRuleUpdate(intent_vector=avg_vector)
+        updated_rule = await service.update_rule(tenant_id, rule_id, update_data)
+        
+        logger.info(
+            f"[AC-AISVC-114] Generated intent vector for rule={rule_id}, "
+            f"dimension={len(avg_vector)}"
+        )
+        
+        return {
+            "id": str(updated_rule.id),
+            "intent_vector": updated_rule.intent_vector,
+            "semantic_examples": updated_rule.semantic_examples,
+        }
+        
+    except Exception as e:
+        logger.error(f"[AC-AISVC-114] Failed to generate intent vector: {e}")
+        raise HTTPException(
+            status_code=500,
+            detail=f"Failed to generate intent vector: {str(e)}"
+        )

ai-service/app/api/admin/kb.py

@@ -6,28 +6,35 @@ Knowledge Base management endpoints.
 
 import logging
 import uuid
+import json
+import hashlib
 from dataclasses import dataclass
 from typing import Annotated, Any, Optional
 
 import tiktoken
-from fastapi import APIRouter, BackgroundTasks, Depends, File, Form, Query, UploadFile
+from fastapi import APIRouter, BackgroundTasks, Depends, File, Form, HTTPException, Query, UploadFile
 from fastapi.responses import JSONResponse
 from sqlalchemy import select
 from sqlalchemy.ext.asyncio import AsyncSession
 
+from app.core.config import get_settings
 from app.core.database import get_session
 from app.core.exceptions import MissingTenantIdException
 from app.core.tenant import get_tenant_id
 from app.models import ErrorResponse
 from app.models.entities import (
+    Document,
+    DocumentStatus,
     IndexJob,
     IndexJobStatus,
     KBType,
+    KnowledgeBase,
     KnowledgeBaseCreate,
     KnowledgeBaseUpdate,
 )
 from app.services.kb import KBService
 from app.services.knowledge_base_service import KnowledgeBaseService
+from app.services.metadata_field_definition_service import MetadataFieldDefinitionService
 
 logger = logging.getLogger(__name__)
 
@@ -457,6 +464,7 @@ async def list_documents(
             "kbId": doc.kb_id,
             "fileName": doc.file_name,
             "status": doc.status,
+            "metadata": doc.doc_metadata,
             "jobId": str(latest_job.id) if latest_job else None,
             "createdAt": doc.created_at.isoformat() + "Z",
             "updatedAt": doc.updated_at.isoformat() + "Z",
@@ -585,6 +593,7 @@ async def upload_document(
         file_name=file.filename or "unknown",
         file_content=file_content,
         file_type=file.content_type,
+        metadata=metadata_dict,
     )
 
     await kb_service.update_doc_count(tenant_id, kb_id, delta=1)
@@ -915,3 +924,488 @@ async def delete_document(
             "message": "Document deleted",
         }
     )
+
+
+@router.put(
+    "/documents/{doc_id}/metadata",
+    operation_id="updateDocumentMetadata",
+    summary="Update document metadata",
+    description="[AC-ASA-08] Update metadata for a specific document.",
+    responses={
+        200: {"description": "Metadata updated"},
+        404: {"description": "Document not found"},
+        401: {"description": "Unauthorized", "model": ErrorResponse},
+        403: {"description": "Forbidden", "model": ErrorResponse},
+    },
+)
+async def update_document_metadata(
+    tenant_id: Annotated[str, Depends(get_current_tenant_id)],
+    session: Annotated[AsyncSession, Depends(get_session)],
+    doc_id: str,
+    body: dict,
+) -> JSONResponse:
+    """
+    [AC-ASA-08] Update document metadata.
+    """
+    import json
+    
+    metadata = body.get("metadata")
+    
+    if metadata is not None and not isinstance(metadata, dict):
+        try:
+            metadata = json.loads(metadata) if isinstance(metadata, str) else metadata
+        except json.JSONDecodeError:
+            return JSONResponse(
+                status_code=400,
+                content={
+                    "code": "INVALID_METADATA",
+                    "message": "Invalid JSON format for metadata",
+                },
+            )
+    
+    logger.info(
+        f"[AC-ASA-08] Updating document metadata: tenant={tenant_id}, doc_id={doc_id}"
+    )
+
+    from sqlalchemy import select
+    from app.models.entities import Document
+    
+    stmt = select(Document).where(
+        Document.tenant_id == tenant_id,
+        Document.id == doc_id,
+    )
+    result = await session.execute(stmt)
+    document = result.scalar_one_or_none()
+
+    if not document:
+        return JSONResponse(
+            status_code=404,
+            content={
+                "code": "DOCUMENT_NOT_FOUND",
+                "message": f"Document {doc_id} not found",
+            },
+        )
+
+    document.doc_metadata = metadata
+    await session.commit()
+
+    return JSONResponse(
+        content={
+            "success": True,
+            "message": "Metadata updated",
+            "metadata": document.doc_metadata,
+        }
+    )
+
+
+@router.post(
+    "/documents/batch-upload",
+    operation_id="batchUploadDocuments",
+    summary="Batch upload documents from zip",
+    description="Upload a zip file containing multiple folders, each with a markdown file and metadata.json",
+    responses={
+        200: {"description": "Batch upload result"},
+        400: {"description": "Bad Request - invalid zip or missing files"},
+        401: {"description": "Unauthorized", "model": ErrorResponse},
+        403: {"description": "Forbidden", "model": ErrorResponse},
+    },
+)
+async def batch_upload_documents(
+    tenant_id: Annotated[str, Depends(get_current_tenant_id)],
+    session: Annotated[AsyncSession, Depends(get_session)],
+    background_tasks: BackgroundTasks,
+    file: UploadFile = File(...),
+    kb_id: str = Form(...),
+) -> JSONResponse:
+    """
+    Batch upload documents from a zip file.
+    
+    Zip structure:
+    - Each folder contains one .md file and one metadata.json
+    - metadata.json uses field_key from MetadataFieldDefinition as keys
+    
+    Example metadata.json:
+    {
+        "grade": "高一",
+        "subject": "数学",
+        "type": "痛点"
+    }
+    """
+    import json
+    import tempfile
+    import zipfile
+    from pathlib import Path
+    
+    from app.services.metadata_field_definition_service import MetadataFieldDefinitionService
+    
+    logger.info(
+        f"[BATCH-UPLOAD] Starting batch upload: tenant={tenant_id}, "
+        f"kb_id={kb_id}, filename={file.filename}"
+    )
+    
+    if not file.filename or not file.filename.lower().endswith('.zip'):
+        return JSONResponse(
+            status_code=400,
+            content={
+                "code": "INVALID_FORMAT",
+                "message": "Only .zip files are supported",
+            },
+        )
+    
+    kb_service = KnowledgeBaseService(session)
+    kb = await kb_service.get_knowledge_base(tenant_id, kb_id)
+    if not kb:
+        return JSONResponse(
+            status_code=404,
+            content={
+                "code": "KB_NOT_FOUND",
+                "message": f"Knowledge base {kb_id} not found",
+            },
+        )
+    
+    file_content = await file.read()
+    
+    results = []
+    succeeded = 0
+    failed = 0
+    
+    with tempfile.TemporaryDirectory() as temp_dir:
+        zip_path = Path(temp_dir) / "upload.zip"
+        with open(zip_path, "wb") as f:
+            f.write(file_content)
+        
+        try:
+            with zipfile.ZipFile(zip_path, 'r') as zf:
+                zf.extractall(temp_dir)
+        except zipfile.BadZipFile as e:
+            return JSONResponse(
+                status_code=400,
+                content={
+                    "code": "INVALID_ZIP",
+                    "message": f"Invalid zip file: {str(e)}",
+                },
+            )
+        
+        extracted_path = Path(temp_dir)
+        
+        # 列出解压后的所有内容，用于调试
+        all_items = list(extracted_path.iterdir())
+        logger.info(f"[BATCH-UPLOAD] Extracted items: {[item.name for item in all_items]}")
+        
+        # 递归查找所有包含 content.txt/md 和 metadata.json 的文件夹
+        def find_document_folders(path: Path) -> list[Path]:
+            """递归查找所有包含文档文件的文件夹"""
+            doc_folders = []
+            
+            # 检查当前文件夹是否包含文档文件
+            content_files = (
+                list(path.glob("*.md")) + 
+                list(path.glob("*.markdown")) + 
+                list(path.glob("*.txt"))
+            )
+            
+            if content_files:
+                # 这个文件夹包含文档文件，是一个文档文件夹
+                doc_folders.append(path)
+                logger.info(f"[BATCH-UPLOAD] Found document folder: {path.name}, files: {[f.name for f in content_files]}")
+            
+            # 递归检查子文件夹
+            for subfolder in [p for p in path.iterdir() if p.is_dir()]:
+                doc_folders.extend(find_document_folders(subfolder))
+            
+            return doc_folders
+        
+        folders = find_document_folders(extracted_path)
+        
+        if not folders:
+            logger.error(f"[BATCH-UPLOAD] No document folders found in zip. Items found: {[item.name for item in all_items]}")
+            return JSONResponse(
+                status_code=400,
+                content={
+                    "code": "NO_DOCUMENTS_FOUND",
+                    "message": "压缩包中没有找到包含 .txt/.md 文件的文件夹",
+                    "details": {
+                        "expected_structure": "每个文件夹应包含 content.txt (或 .md) 和 metadata.json",
+                        "found_items": [item.name for item in all_items],
+                    },
+                },
+            )
+        
+        logger.info(f"[BATCH-UPLOAD] Found {len(folders)} document folders")
+        
+        for folder in folders:
+            folder_name = folder.name if folder != extracted_path else "root"
+            
+            content_files = (
+                list(folder.glob("*.md")) + 
+                list(folder.glob("*.markdown")) + 
+                list(folder.glob("*.txt"))
+            )
+            
+            if not content_files:
+                # 这种情况不应该发生，因为我们已经过滤过了
+                failed += 1
+                results.append({
+                    "folder": folder_name,
+                    "status": "failed",
+                    "error": "No content file found",
+                })
+                continue
+            
+            content_file = content_files[0]
+            metadata_file = folder / "metadata.json"
+            
+            metadata_dict = {}
+            if metadata_file.exists():
+                try:
+                    with open(metadata_file, 'r', encoding='utf-8') as f:
+                        metadata_dict = json.load(f)
+                except json.JSONDecodeError as e:
+                    failed += 1
+                    results.append({
+                        "folder": folder_name,
+                        "status": "failed",
+                        "error": f"Invalid metadata.json: {str(e)}",
+                    })
+                    continue
+            else:
+                logger.warning(f"[BATCH-UPLOAD] No metadata.json in folder {folder_name}, using empty metadata")
+            
+            field_def_service = MetadataFieldDefinitionService(session)
+            is_valid, validation_errors = await field_def_service.validate_metadata_for_create(
+                tenant_id, metadata_dict, "kb_document"
+            )
+            
+            if not is_valid:
+                failed += 1
+                results.append({
+                    "folder": folder_name,
+                    "status": "failed",
+                    "error": f"Metadata validation failed: {validation_errors}",
+                })
+                continue
+            
+            try:
+                with open(content_file, 'rb') as f:
+                    doc_content = f.read()
+                
+                file_ext = content_file.suffix.lower()
+                if file_ext == '.txt':
+                    file_type = "text/plain"
+                else:
+                    file_type = "text/markdown"
+                
+                doc_kb_service = KBService(session)
+                document, job = await doc_kb_service.upload_document(
+                    tenant_id=tenant_id,
+                    kb_id=kb_id,
+                    file_name=content_file.name,
+                    file_content=doc_content,
+                    file_type=file_type,
+                    metadata=metadata_dict,
+                )
+                
+                await kb_service.update_doc_count(tenant_id, kb_id, delta=1)
+                await session.commit()
+                
+                background_tasks.add_task(
+                    _index_document,
+                    tenant_id,
+                    kb_id,
+                    str(job.id),
+                    str(document.id),
+                    doc_content,
+                    content_file.name,
+                    metadata_dict,
+                )
+                
+                succeeded += 1
+                results.append({
+                    "folder": folder_name,
+                    "docId": str(document.id),
+                    "jobId": str(job.id),
+                    "status": "created",
+                    "fileName": content_file.name,
+                })
+                
+                logger.info(
+                    f"[BATCH-UPLOAD] Created document: folder={folder_name}, "
+                    f"doc_id={document.id}, job_id={job.id}"
+                )
+                
+            except Exception as e:
+                failed += 1
+                results.append({
+                    "folder": folder_name,
+                    "status": "failed",
+                    "error": str(e),
+                })
+                logger.error(f"[BATCH-UPLOAD] Failed to create document: folder={folder_name}, error={e}")
+    
+    logger.info(
+        f"[BATCH-UPLOAD] Completed: total={len(results)}, succeeded={succeeded}, failed={failed}"
+    )
+    
+    return JSONResponse(
+        content={
+            "success": True,
+            "total": len(results),
+            "succeeded": succeeded,
+            "failed": failed,
+            "results": results,
+        }
+    )
+
+
+@router.post(
+    "/{kb_id}/documents/json-batch",
+    summary="[AC-KB-03] JSON批量上传文档",
+    description="上传JSONL格式文件，每行一个JSON对象，包含text和元数据字段",
+)
+async def upload_json_batch(
+    kb_id: str,
+    tenant_id: str = Query(..., description="租户ID"),
+    file: UploadFile = File(..., description="JSONL格式文件，每行一个JSON对象"),
+    session: AsyncSession = Depends(get_session),
+    background_tasks: BackgroundTasks = None,
+):
+    """
+    JSON批量上传文档
+    
+    文件格式：JSONL (每行一个JSON对象)
+    必填字段：text - 需要录入知识库的文本内容
+    可选字段：元数据字段（如grade, subject, kb_scene等）
+    
+    示例：
+    {"text": "课程内容...", "grade": "初二", "subject": "数学", "kb_scene": "课程咨询"}
+    {"text": "另一条课程内容...", "grade": "初三", "info_type": "课程概述"}
+    """
+    kb = await session.get(KnowledgeBase, kb_id)
+    if not kb:
+        raise HTTPException(status_code=404, detail="知识库不存在")
+    
+    if kb.tenant_id != tenant_id:
+        raise HTTPException(status_code=403, detail="无权访问此知识库")
+    
+    valid_field_keys = set()
+    try:
+        field_defs = await MetadataFieldDefinitionService(session).get_fields(
+            tenant_id=tenant_id,
+            include_inactive=False,
+        )
+        valid_field_keys = {f.field_key for f in field_defs}
+        logger.info(f"[AC-KB-03] Valid metadata fields for tenant {tenant_id}: {valid_field_keys}")
+    except Exception as e:
+        logger.warning(f"[AC-KB-03] Failed to get metadata fields: {e}")
+    
+    content = await file.read()
+    try:
+        text_content = content.decode("utf-8")
+    except UnicodeDecodeError:
+        try:
+            text_content = content.decode("gbk")
+        except UnicodeDecodeError:
+            raise HTTPException(status_code=400, detail="文件编码不支持，请使用UTF-8编码")
+    
+    lines = text_content.strip().split("\n")
+    if not lines:
+        raise HTTPException(status_code=400, detail="文件内容为空")
+    
+    results = []
+    succeeded = 0
+    failed = 0
+    
+    kb_service = KBService(session)
+    
+    for line_num, line in enumerate(lines, 1):
+        line = line.strip()
+        if not line:
+            continue
+        
+        try:
+            json_obj = json.loads(line)
+        except json.JSONDecodeError as e:
+            failed += 1
+            results.append({
+                "line": line_num,
+                "success": False,
+                "error": f"JSON解析失败: {e}",
+            })
+            continue
+        
+        text = json_obj.get("text")
+        if not text:
+            failed += 1
+            results.append({
+                "line": line_num,
+                "success": False,
+                "error": "缺少必填字段: text",
+            })
+            continue
+        
+        metadata = {}
+        for key, value in json_obj.items():
+            if key == "text":
+                continue
+            if valid_field_keys and key not in valid_field_keys:
+                logger.debug(f"[AC-KB-03] Skipping invalid metadata field: {key}")
+                continue
+            if value is not None:
+                metadata[key] = value
+        
+        try:
+            file_name = f"json_batch_line_{line_num}.txt"
+            file_content = text.encode("utf-8")
+            
+            document, job = await kb_service.upload_document(
+                tenant_id=tenant_id,
+                kb_id=kb_id,
+                file_name=file_name,
+                file_content=file_content,
+                file_type="text/plain",
+                metadata=metadata,
+            )
+            
+            if background_tasks:
+                background_tasks.add_task(
+                    _index_document,
+                    tenant_id,
+                    kb_id,
+                    str(job.id),
+                    str(document.id),
+                    file_content,
+                    file_name,
+                    metadata,
+                )
+            
+            succeeded += 1
+            results.append({
+                "line": line_num,
+                "success": True,
+                "doc_id": str(document.id),
+                "job_id": str(job.id),
+                "metadata": metadata,
+            })
+        except Exception as e:
+            failed += 1
+            results.append({
+                "line": line_num,
+                "success": False,
+                "error": str(e),
+            })
+            logger.error(f"[AC-KB-03] Failed to upload document at line {line_num}: {e}")
+    
+    await session.commit()
+    
+    logger.info(f"[AC-KB-03] JSON batch upload completed: kb_id={kb_id}, total={len(lines)}, succeeded={succeeded}, failed={failed}")
+    
+    return JSONResponse(
+        content={
+            "success": True,
+            "total": len(lines),
+            "succeeded": succeeded,
+            "failed": failed,
+            "valid_metadata_fields": list(valid_field_keys) if valid_field_keys else [],
+            "results": results,
+        }
+    )

ai-service/app/api/admin/metadata_field_definition.py

@@ -51,6 +51,7 @@ def _field_to_dict(f: MetadataFieldDefinition) -> dict[str, Any]:
         "scope": f.scope,
         "is_filterable": f.is_filterable,
         "is_rank_feature": f.is_rank_feature,
+        "usage_description": f.usage_description,
         "field_roles": f.field_roles or [],
         "status": f.status,
         "version": f.version,

ai-service/app/api/admin/monitoring.py

@@ -407,6 +407,7 @@ async def get_conversation_detail(
         "guardrailTriggered": user_msg.guardrail_triggered,
         "guardrailWords": user_msg.guardrail_words,
         "executionSteps": execution_steps,
+        "routeTrace": user_msg.route_trace,
         "createdAt": user_msg.created_at.isoformat(),
     }
 
@@ -659,8 +660,56 @@ async def _process_export(
         except Exception as e:
             logger.error(f"[AC-AISVC-110] Export failed: task_id={task_id}, error={e}")
 
-            task = await session.get(ExportTask, task_id)
+            task = task_status.get(ExportTask, task_id)
             if task:
                 task.status = ExportTaskStatus.FAILED.value
                 task.error_message = str(e)
                 await session.commit()
+
+
+@router.get("/clarification-metrics")
+async def get_clarification_metrics(
+    tenant_id: str = Depends(get_tenant_id),
+    total_requests: int = Query(100, ge=1, description="Total requests for rate calculation"),
+) -> dict[str, Any]:
+    """
+    [AC-CLARIFY] Get clarification metrics.
+    
+    Returns:
+    - clarify_trigger_rate: 澄清触发率
+    - clarify_converge_rate: 澄清后收敛率
+    - misroute_rate: 误入流程率
+    """
+    from app.services.intent.clarification import get_clarify_metrics
+    
+    metrics = get_clarify_metrics()
+    counts = metrics.get_metrics()
+    rates = metrics.get_rates(total_requests)
+    
+    return {
+        "counts": counts,
+        "rates": rates,
+        "thresholds": {
+            "t_high": 0.75,
+            "t_low": 0.45,
+            "max_retry": 3,
+        },
+    }
+
+
+@router.post("/clarification-metrics/reset")
+async def reset_clarification_metrics(
+    tenant_id: str = Depends(get_tenant_id),
+) -> dict[str, Any]:
+    """
+    [AC-CLARIFY] Reset clarification metrics.
+    """
+    from app.services.intent.clarification import get_clarify_metrics
+    
+    metrics = get_clarify_metrics()
+    metrics.reset()
+    
+    return {
+        "status": "reset",
+        "message": "Clarification metrics have been reset.",
+    }

ai-service/app/api/admin/prompt_templates.py

@@ -17,6 +17,17 @@ from app.models.entities import PromptTemplateCreate, PromptTemplateUpdate
 from app.services.prompt.template_service import PromptTemplateService
 from app.services.monitoring.prompt_monitor import PromptMonitor
 
+
+class PromptTemplateUpdateAPI(BaseModel):
+    """API model for updating prompt template with metadata field mapping."""
+    name: str | None = None
+    scene: str | None = None
+    description: str | None = None
+    system_instruction: str | None = None
+    variables: list[dict[str, Any]] | None = None
+    is_default: bool | None = None
+    metadata: dict[str, Any] | None = None
+
 logger = logging.getLogger(__name__)
 
 router = APIRouter(prefix="/admin/prompt-templates", tags=["Prompt Management"])
@@ -108,29 +119,47 @@ async def get_template_detail(
 @router.put("/{tpl_id}")
 async def update_template(
     tpl_id: uuid.UUID,
-    body: PromptTemplateUpdate,
+    body: PromptTemplateUpdateAPI,
     tenant_id: str = Depends(get_tenant_id),
     session: AsyncSession = Depends(get_session),
 ) -> dict[str, Any]:
     """
     [AC-AISVC-53] Update prompt template (creates a new version).
+    [AC-IDSMETA-16] Return current_content and metadata for frontend display.
     """
     logger.info(f"[AC-AISVC-53] Updating template for tenant={tenant_id}, id={tpl_id}")
 
     service = PromptTemplateService(session)
-    template = await service.update_template(tenant_id, tpl_id, body)
+
+    # Convert API model to entity model (metadata -> metadata_)
+    update_data = PromptTemplateUpdate(
+        name=body.name,
+        scene=body.scene,
+        description=body.description,
+        system_instruction=body.system_instruction,
+        variables=body.variables,
+        is_default=body.is_default,
+        metadata_=body.metadata,
+    )
+
+    template = await service.update_template(tenant_id, tpl_id, update_data)
 
     if not template:
         raise HTTPException(status_code=404, detail="Template not found")
 
     published_version = await service.get_published_version_info(tenant_id, template.id)
 
+    # Get latest version content for current_content
+    latest_version = await service._get_latest_version(template.id)
+
     return {
         "id": str(template.id),
         "name": template.name,
         "scene": template.scene,
         "description": template.description,
         "is_default": template.is_default,
+        "current_content": latest_version.system_instruction if latest_version else None,
+        "metadata": template.metadata_,
         "published_version": published_version,
         "created_at": template.created_at.isoformat(),
         "updated_at": template.updated_at.isoformat(),

ai-service/app/api/admin/scene_slot_bundle.py

@@ -0,0 +1,265 @@
+"""
+Scene Slot Bundle API.
+[AC-SCENE-SLOT-01] 场景-槽位映射配置管理接口
+"""
+
+import logging
+from typing import Annotated, Any
+
+from fastapi import APIRouter, Depends, Query
+from fastapi.responses import JSONResponse
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.core.database import get_session
+from app.core.exceptions import MissingTenantIdException
+from app.core.tenant import get_tenant_id
+from app.models.entities import SceneSlotBundleCreate, SceneSlotBundleUpdate
+from app.services.scene_slot_bundle_service import SceneSlotBundleService
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(prefix="/admin/scene-slot-bundles", tags=["SceneSlotBundle"])
+
+
+def get_current_tenant_id() -> str:
+    """Get current tenant ID from context."""
+    tenant_id = get_tenant_id()
+    if not tenant_id:
+        raise MissingTenantIdException()
+    return tenant_id
+
+
+def _bundle_to_dict(bundle: Any) -> dict[str, Any]:
+    """Convert bundle to dict"""
+    return {
+        "id": str(bundle.id),
+        "tenant_id": str(bundle.tenant_id),
+        "scene_key": bundle.scene_key,
+        "scene_name": bundle.scene_name,
+        "description": bundle.description,
+        "required_slots": bundle.required_slots,
+        "optional_slots": bundle.optional_slots,
+        "slot_priority": bundle.slot_priority,
+        "completion_threshold": bundle.completion_threshold,
+        "ask_back_order": bundle.ask_back_order,
+        "status": bundle.status,
+        "version": bundle.version,
+        "created_at": bundle.created_at.isoformat() if bundle.created_at else None,
+        "updated_at": bundle.updated_at.isoformat() if bundle.updated_at else None,
+    }
+
+
+@router.get(
+    "",
+    operation_id="listSceneSlotBundles",
+    summary="List scene slot bundles",
+    description="获取场景槽位包列表",
+)
+async def list_scene_slot_bundles(
+    tenant_id: Annotated[str, Depends(get_current_tenant_id)],
+    session: Annotated[AsyncSession, Depends(get_session)],
+    status: Annotated[str | None, Query(
+        description="按状态过滤: draft/active/deprecated"
+    )] = None,
+) -> JSONResponse:
+    """
+    列出场景槽位包
+    """
+    logger.info(
+        f"Listing scene slot bundles: tenant={tenant_id}, status={status}"
+    )
+    
+    service = SceneSlotBundleService(session)
+    bundles = await service.list_bundles(tenant_id, status)
+    
+    return JSONResponse(
+        content=[_bundle_to_dict(b) for b in bundles]
+    )
+
+
+@router.post(
+    "",
+    operation_id="createSceneSlotBundle",
+    summary="Create scene slot bundle",
+    description="[AC-SCENE-SLOT-01] 创建新的场景槽位包",
+    status_code=201,
+)
+async def create_scene_slot_bundle(
+    tenant_id: Annotated[str, Depends(get_current_tenant_id)],
+    session: Annotated[AsyncSession, Depends(get_session)],
+    bundle_create: SceneSlotBundleCreate,
+) -> JSONResponse:
+    """
+    [AC-SCENE-SLOT-01] 创建场景槽位包
+    """
+    logger.info(
+        f"[AC-SCENE-SLOT-01] Creating scene slot bundle: "
+        f"tenant={tenant_id}, scene_key={bundle_create.scene_key}"
+    )
+    
+    service = SceneSlotBundleService(session)
+    
+    try:
+        bundle = await service.create_bundle(tenant_id, bundle_create)
+        await session.commit()
+    except ValueError as e:
+        return JSONResponse(
+            status_code=400,
+            content={
+                "error_code": "VALIDATION_ERROR",
+                "message": str(e),
+            }
+        )
+    
+    return JSONResponse(
+        status_code=201,
+        content=_bundle_to_dict(bundle)
+    )
+
+
+@router.get(
+    "/by-scene/{scene_key}",
+    operation_id="getSceneSlotBundleBySceneKey",
+    summary="Get scene slot bundle by scene key",
+    description="根据场景标识获取槽位包",
+)
+async def get_scene_slot_bundle_by_scene_key(
+    tenant_id: Annotated[str, Depends(get_current_tenant_id)],
+    session: Annotated[AsyncSession, Depends(get_session)],
+    scene_key: str,
+) -> JSONResponse:
+    """
+    根据场景标识获取槽位包
+    """
+    logger.info(
+        f"Getting scene slot bundle by scene_key: tenant={tenant_id}, scene_key={scene_key}"
+    )
+    
+    service = SceneSlotBundleService(session)
+    bundle = await service.get_bundle_by_scene_key(tenant_id, scene_key)
+    
+    if not bundle:
+        return JSONResponse(
+            status_code=404,
+            content={
+                "error_code": "NOT_FOUND",
+                "message": f"Scene slot bundle with scene_key '{scene_key}' not found",
+            }
+        )
+    
+    return JSONResponse(content=_bundle_to_dict(bundle))
+
+
+@router.get(
+    "/{id}",
+    operation_id="getSceneSlotBundle",
+    summary="Get scene slot bundle by ID",
+    description="获取单个场景槽位包详情（含槽位详情）",
+)
+async def get_scene_slot_bundle(
+    tenant_id: Annotated[str, Depends(get_current_tenant_id)],
+    session: Annotated[AsyncSession, Depends(get_session)],
+    id: str,
+) -> JSONResponse:
+    """
+    获取单个场景槽位包详情
+    """
+    logger.info(
+        f"Getting scene slot bundle: tenant={tenant_id}, id={id}"
+    )
+    
+    service = SceneSlotBundleService(session)
+    bundle = await service.get_bundle_with_slot_details(tenant_id, id)
+    
+    if not bundle:
+        return JSONResponse(
+            status_code=404,
+            content={
+                "error_code": "NOT_FOUND",
+                "message": f"Scene slot bundle {id} not found",
+            }
+        )
+    
+    return JSONResponse(content=bundle)
+
+
+@router.put(
+    "/{id}",
+    operation_id="updateSceneSlotBundle",
+    summary="Update scene slot bundle",
+    description="更新场景槽位包",
+)
+async def update_scene_slot_bundle(
+    tenant_id: Annotated[str, Depends(get_current_tenant_id)],
+    session: Annotated[AsyncSession, Depends(get_session)],
+    id: str,
+    bundle_update: SceneSlotBundleUpdate,
+) -> JSONResponse:
+    """
+    更新场景槽位包
+    """
+    logger.info(
+        f"Updating scene slot bundle: tenant={tenant_id}, id={id}"
+    )
+    
+    service = SceneSlotBundleService(session)
+    
+    try:
+        bundle = await service.update_bundle(tenant_id, id, bundle_update)
+    except ValueError as e:
+        return JSONResponse(
+            status_code=400,
+            content={
+                "error_code": "VALIDATION_ERROR",
+                "message": str(e),
+            }
+        )
+    
+    if not bundle:
+        return JSONResponse(
+            status_code=404,
+            content={
+                "error_code": "NOT_FOUND",
+                "message": f"Scene slot bundle {id} not found",
+            }
+        )
+    
+    await session.commit()
+    
+    return JSONResponse(content=_bundle_to_dict(bundle))
+
+
+@router.delete(
+    "/{id}",
+    operation_id="deleteSceneSlotBundle",
+    summary="Delete scene slot bundle",
+    description="[AC-SCENE-SLOT-01] 删除场景槽位包",
+    status_code=204,
+)
+async def delete_scene_slot_bundle(
+    tenant_id: Annotated[str, Depends(get_current_tenant_id)],
+    session: Annotated[AsyncSession, Depends(get_session)],
+    id: str,
+) -> JSONResponse:
+    """
+    [AC-SCENE-SLOT-01] 删除场景槽位包
+    """
+    logger.info(
+        f"[AC-SCENE-SLOT-01] Deleting scene slot bundle: tenant={tenant_id}, id={id}"
+    )
+    
+    service = SceneSlotBundleService(session)
+    success = await service.delete_bundle(tenant_id, id)
+    
+    if not success:
+        return JSONResponse(
+            status_code=404,
+            content={
+                "error_code": "NOT_FOUND",
+                "message": f"Scene slot bundle not found: {id}",
+            }
+        )
+    
+    await session.commit()
+    
+    return JSONResponse(status_code=204, content=None)

ai-service/app/api/admin/slot_definition.py

@@ -38,9 +38,13 @@ def _slot_to_dict(slot: dict[str, Any] | Any) -> dict[str, Any]:
         "id": str(slot.id),
         "tenant_id": str(slot.tenant_id),
         "slot_key": slot.slot_key,
+        "display_name": slot.display_name,
+        "description": slot.description,
         "type": slot.type,
         "required": slot.required,
+        # [AC-MRS-07-UPGRADE] 返回新旧字段
         "extract_strategy": slot.extract_strategy,
+        "extract_strategies": slot.extract_strategies,
         "validation_rule": slot.validation_rule,
         "ask_back_prompt": slot.ask_back_prompt,
         "default_value": slot.default_value,

ai-service/app/api/admin/strategy.py

@@ -0,0 +1,342 @@
+"""
+Retrieval Strategy API Endpoints.
+[AC-AISVC-RES-01~15] API for strategy management and configuration.
+
+Endpoints:
+- GET /strategy/retrieval/current - Get current strategy configuration
+- POST /strategy/retrieval/switch - Switch strategy configuration
+- POST /strategy/retrieval/validate - Validate strategy configuration
+- POST /strategy/retrieval/rollback - Rollback to default strategy
+"""
+
+import logging
+from typing import Any
+
+from fastapi import APIRouter, Depends, HTTPException
+from pydantic import BaseModel, Field
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.core.database import get_session
+from app.core.tenant import get_tenant_id
+from app.services.retrieval.routing_config import (
+    RagRuntimeMode,
+    StrategyType,
+    RoutingConfig,
+)
+from app.services.retrieval.strategy_router import (
+    get_strategy_router,
+    RollbackRecord,
+)
+from app.services.retrieval.mode_router import get_mode_router
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(prefix="/strategy/retrieval", tags=["Retrieval Strategy"])
+
+
+class RoutingConfigRequest(BaseModel):
+    """Request model for routing configuration."""
+    enabled: bool | None = Field(default=None, description="Enable strategy routing")
+    strategy: StrategyType | None = Field(default=None, description="Retrieval strategy")
+    grayscale_percentage: float | None = Field(default=None, ge=0.0, le=1.0, description="Grayscale percentage")
+    grayscale_allowlist: list[str] | None = Field(default=None, description="Grayscale allowlist")
+    rag_runtime_mode: RagRuntimeMode | None = Field(default=None, description="RAG runtime mode")
+    react_trigger_confidence_threshold: float | None = Field(default=None, ge=0.0, le=1.0)
+    react_trigger_complexity_score: float | None = Field(default=None, ge=0.0, le=1.0)
+    react_max_steps: int | None = Field(default=None, ge=3, le=10)
+    direct_fallback_on_low_confidence: bool | None = Field(default=None)
+    direct_fallback_confidence_threshold: float | None = Field(default=None, ge=0.0, le=1.0)
+    performance_budget_ms: int | None = Field(default=None, ge=1000)
+    performance_degradation_threshold: float | None = Field(default=None, ge=0.0, le=1.0)
+
+
+class RoutingConfigResponse(BaseModel):
+    """Response model for routing configuration."""
+    enabled: bool
+    strategy: StrategyType
+    grayscale_percentage: float
+    grayscale_allowlist: list[str]
+    rag_runtime_mode: RagRuntimeMode
+    react_trigger_confidence_threshold: float
+    react_trigger_complexity_score: float
+    react_max_steps: int
+    direct_fallback_on_low_confidence: bool
+    direct_fallback_confidence_threshold: float
+    performance_budget_ms: int
+    performance_degradation_threshold: float
+
+
+class ValidationResponse(BaseModel):
+    """Response model for configuration validation."""
+    is_valid: bool
+    errors: list[str]
+    warnings: list[str] = Field(default_factory=list)
+
+
+class RollbackRequest(BaseModel):
+    """Request model for strategy rollback."""
+    reason: str = Field(..., description="Reason for rollback")
+    tenant_id: str | None = Field(default=None, description="Optional tenant ID for audit")
+
+
+class RollbackResponse(BaseModel):
+    """Response model for strategy rollback."""
+    success: bool
+    previous_strategy: StrategyType
+    current_strategy: StrategyType
+    reason: str
+    rollback_records: list[dict[str, Any]] = Field(default_factory=list)
+
+
+class RollbackRecordResponse(BaseModel):
+    """Response model for rollback record."""
+    timestamp: float
+    from_strategy: StrategyType
+    to_strategy: StrategyType
+    reason: str
+    tenant_id: str | None
+    request_id: str | None
+
+
+class CurrentStrategyResponse(BaseModel):
+    """Response model for current strategy."""
+    config: RoutingConfigResponse
+    current_strategy: StrategyType
+    rollback_records: list[RollbackRecordResponse] = Field(default_factory=list)
+
+
+@router.get(
+    "/current",
+    operation_id="getCurrentRetrievalStrategy",
+    summary="Get current retrieval strategy configuration",
+    description="[AC-AISVC-RES-01] Returns the current strategy configuration and recent rollback records.",
+    response_model=CurrentStrategyResponse,
+)
+async def get_current_strategy() -> CurrentStrategyResponse:
+    """
+    [AC-AISVC-RES-01] Get current retrieval strategy configuration.
+    """
+    strategy_router = get_strategy_router()
+    config = strategy_router.config
+    rollback_records = strategy_router.get_rollback_records(limit=5)
+    
+    return CurrentStrategyResponse(
+        config=RoutingConfigResponse(
+            enabled=config.enabled,
+            strategy=config.strategy,
+            grayscale_percentage=config.grayscale_percentage,
+            grayscale_allowlist=config.grayscale_allowlist,
+            rag_runtime_mode=config.rag_runtime_mode,
+            react_trigger_confidence_threshold=config.react_trigger_confidence_threshold,
+            react_trigger_complexity_score=config.react_trigger_complexity_score,
+            react_max_steps=config.react_max_steps,
+            direct_fallback_on_low_confidence=config.direct_fallback_on_low_confidence,
+            direct_fallback_confidence_threshold=config.direct_fallback_confidence_threshold,
+            performance_budget_ms=config.performance_budget_ms,
+            performance_degradation_threshold=config.performance_degradation_threshold,
+        ),
+        current_strategy=strategy_router.current_strategy,
+        rollback_records=[
+            RollbackRecordResponse(
+                timestamp=r.timestamp,
+                from_strategy=r.from_strategy,
+                to_strategy=r.to_strategy,
+                reason=r.reason,
+                tenant_id=r.tenant_id,
+                request_id=r.request_id,
+            )
+            for r in rollback_records
+        ],
+    )
+
+
+@router.post(
+    "/switch",
+    operation_id="switchRetrievalStrategy",
+    summary="Switch retrieval strategy configuration",
+    description="[AC-AISVC-RES-02, AC-AISVC-RES-03] Update strategy configuration with hot reload support.",
+    response_model=RoutingConfigResponse,
+)
+async def switch_strategy(
+    request: RoutingConfigRequest,
+    session: AsyncSession = Depends(get_session),
+) -> RoutingConfigResponse:
+    """
+    [AC-AISVC-RES-02, AC-AISVC-RES-03, AC-AISVC-RES-15] 
+    Switch retrieval strategy configuration.
+    
+    Supports:
+    - Strategy selection (default/enhanced)
+    - Grayscale release (percentage/allowlist)
+    - Mode selection (direct/react/auto)
+    - Hot reload of routing parameters
+    """
+    strategy_router = get_strategy_router()
+    mode_router = get_mode_router()
+    current_config = strategy_router.config
+    
+    new_config = RoutingConfig(
+        enabled=request.enabled if request.enabled is not None else current_config.enabled,
+        strategy=request.strategy if request.strategy is not None else current_config.strategy,
+        grayscale_percentage=request.grayscale_percentage if request.grayscale_percentage is not None else current_config.grayscale_percentage,
+        grayscale_allowlist=request.grayscale_allowlist if request.grayscale_allowlist is not None else current_config.grayscale_allowlist,
+        rag_runtime_mode=request.rag_runtime_mode if request.rag_runtime_mode is not None else current_config.rag_runtime_mode,
+        react_trigger_confidence_threshold=request.react_trigger_confidence_threshold if request.react_trigger_confidence_threshold is not None else current_config.react_trigger_confidence_threshold,
+        react_trigger_complexity_score=request.react_trigger_complexity_score if request.react_trigger_complexity_score is not None else current_config.react_trigger_complexity_score,
+        react_max_steps=request.react_max_steps if request.react_max_steps is not None else current_config.react_max_steps,
+        direct_fallback_on_low_confidence=request.direct_fallback_on_low_confidence if request.direct_fallback_on_low_confidence is not None else current_config.direct_fallback_on_low_confidence,
+        direct_fallback_confidence_threshold=request.direct_fallback_confidence_threshold if request.direct_fallback_confidence_threshold is not None else current_config.direct_fallback_confidence_threshold,
+        performance_budget_ms=request.performance_budget_ms if request.performance_budget_ms is not None else current_config.performance_budget_ms,
+        performance_degradation_threshold=request.performance_degradation_threshold if request.performance_degradation_threshold is not None else current_config.performance_degradation_threshold,
+    )
+    
+    is_valid, errors = new_config.validate()
+    if not is_valid:
+        raise HTTPException(
+            status_code=400,
+            detail={"errors": errors},
+        )
+    
+    strategy_router.update_config(new_config)
+    mode_router.update_config(new_config)
+    
+    logger.info(
+        f"[AC-AISVC-RES-02, AC-AISVC-RES-15] Strategy switched: "
+        f"strategy={new_config.strategy.value}, mode={new_config.rag_runtime_mode.value}"
+    )
+    
+    return RoutingConfigResponse(
+        enabled=new_config.enabled,
+        strategy=new_config.strategy,
+        grayscale_percentage=new_config.grayscale_percentage,
+        grayscale_allowlist=new_config.grayscale_allowlist,
+        rag_runtime_mode=new_config.rag_runtime_mode,
+        react_trigger_confidence_threshold=new_config.react_trigger_confidence_threshold,
+        react_trigger_complexity_score=new_config.react_trigger_complexity_score,
+        react_max_steps=new_config.react_max_steps,
+        direct_fallback_on_low_confidence=new_config.direct_fallback_on_low_confidence,
+        direct_fallback_confidence_threshold=new_config.direct_fallback_confidence_threshold,
+        performance_budget_ms=new_config.performance_budget_ms,
+        performance_degradation_threshold=new_config.performance_degradation_threshold,
+    )
+
+
+@router.post(
+    "/validate",
+    operation_id="validateRetrievalStrategy",
+    summary="Validate retrieval strategy configuration",
+    description="[AC-AISVC-RES-06] Validate strategy configuration for completeness and consistency.",
+    response_model=ValidationResponse,
+)
+async def validate_strategy(
+    request: RoutingConfigRequest,
+) -> ValidationResponse:
+    """
+    [AC-AISVC-RES-06] Validate strategy configuration.
+    
+    Checks:
+    - Parameter value ranges
+    - Configuration consistency
+    - Performance budget constraints
+    """
+    warnings = []
+    
+    config = RoutingConfig(
+        enabled=request.enabled if request.enabled is not None else True,
+        strategy=request.strategy if request.strategy is not None else StrategyType.DEFAULT,
+        grayscale_percentage=request.grayscale_percentage if request.grayscale_percentage is not None else 0.0,
+        grayscale_allowlist=request.grayscale_allowlist or [],
+        rag_runtime_mode=request.rag_runtime_mode if request.rag_runtime_mode is not None else RagRuntimeMode.AUTO,
+        react_trigger_confidence_threshold=request.react_trigger_confidence_threshold if request.react_trigger_confidence_threshold is not None else 0.6,
+        react_trigger_complexity_score=request.react_trigger_complexity_score if request.react_trigger_complexity_score is not None else 0.5,
+        react_max_steps=request.react_max_steps if request.react_max_steps is not None else 5,
+        direct_fallback_on_low_confidence=request.direct_fallback_on_low_confidence if request.direct_fallback_on_low_confidence is not None else True,
+        direct_fallback_confidence_threshold=request.direct_fallback_confidence_threshold if request.direct_fallback_confidence_threshold is not None else 0.4,
+        performance_budget_ms=request.performance_budget_ms if request.performance_budget_ms is not None else 5000,
+        performance_degradation_threshold=request.performance_degradation_threshold if request.performance_degradation_threshold is not None else 0.2,
+    )
+    
+    is_valid, errors = config.validate()
+    
+    if config.grayscale_percentage > 0.5:
+        warnings.append("grayscale_percentage > 50% may affect production stability")
+    
+    if config.react_max_steps > 7:
+        warnings.append("react_max_steps > 7 may cause high latency")
+    
+    if config.direct_fallback_confidence_threshold > config.react_trigger_confidence_threshold:
+        warnings.append(
+            "direct_fallback_confidence_threshold > react_trigger_confidence_threshold "
+            "may cause frequent fallbacks"
+        )
+    
+    if config.performance_budget_ms < 3000:
+        warnings.append("performance_budget_ms < 3000ms may be too aggressive for complex queries")
+    
+    logger.info(
+        f"[AC-AISVC-RES-06] Strategy validation: is_valid={is_valid}, "
+        f"errors={len(errors)}, warnings={len(warnings)}"
+    )
+    
+    return ValidationResponse(
+        is_valid=is_valid,
+        errors=errors,
+        warnings=warnings,
+    )
+
+
+@router.post(
+    "/rollback",
+    operation_id="rollbackRetrievalStrategy",
+    summary="Rollback to default retrieval strategy",
+    description="[AC-AISVC-RES-07] Force rollback to default strategy with audit logging.",
+    response_model=RollbackResponse,
+)
+async def rollback_strategy(
+    request: RollbackRequest,
+    session: AsyncSession = Depends(get_session),
+) -> RollbackResponse:
+    """
+    [AC-AISVC-RES-07] Rollback to default strategy.
+    
+    Records the rollback event for audit and monitoring.
+    """
+    strategy_router = get_strategy_router()
+    mode_router = get_mode_router()
+    
+    previous_strategy = strategy_router.current_strategy
+    
+    strategy_router.rollback(
+        reason=request.reason,
+        tenant_id=request.tenant_id,
+    )
+    
+    new_config = RoutingConfig(
+        strategy=StrategyType.DEFAULT,
+        rag_runtime_mode=RagRuntimeMode.AUTO,
+    )
+    mode_router.update_config(new_config)
+    
+    rollback_records = strategy_router.get_rollback_records(limit=5)
+    
+    logger.info(
+        f"[AC-AISVC-RES-07] Strategy rollback: "
+        f"from={previous_strategy.value}, to=DEFAULT, reason={request.reason}"
+    )
+    
+    return RollbackResponse(
+        success=True,
+        previous_strategy=previous_strategy,
+        current_strategy=StrategyType.DEFAULT,
+        reason=request.reason,
+        rollback_records=[
+            {
+                "timestamp": r.timestamp,
+                "from_strategy": r.from_strategy.value,
+                "to_strategy": r.to_strategy.value,
+                "reason": r.reason,
+                "tenant_id": r.tenant_id,
+            }
+            for r in rollback_records
+        ],
+    )

ai-service/app/api/mid/dialogue.py

@@ -55,6 +55,18 @@ from app.services.mid.segment_humanizer import HumanizeConfig, SegmentHumanizer
 from app.services.mid.timeout_governor import TimeoutGovernor
 from app.services.mid.tool_registry import ToolRegistry
 from app.services.mid.trace_logger import TraceLogger
+from app.services.prompt.template_service import PromptTemplateService
+from app.services.prompt.variable_resolver import VariableResolver
+from app.services.intent.clarification import (
+    ClarificationEngine,
+    ClarifyReason,
+    ClarifySessionManager,
+    ClarifyState,
+    HybridIntentResult,
+    IntentCandidate,
+    T_HIGH,
+    T_LOW,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -118,6 +130,7 @@ _kb_search_dynamic_registered: bool = False
 _intent_hint_registered: bool = False
 _high_risk_check_registered: bool = False
 _memory_recall_registered: bool = False
+_metadata_discovery_registered: bool = False
 
 
 def ensure_kb_search_dynamic_registered(
@@ -134,7 +147,7 @@ def ensure_kb_search_dynamic_registered(
     config = KbSearchDynamicConfig(
         enabled=True,
         top_k=5,
-        timeout_ms=2000,
+        timeout_ms=10000,
         min_score_threshold=0.5,
     )
 
@@ -222,6 +235,26 @@ def ensure_memory_recall_registered(
     logger.info("[AC-IDMP-13] memory_recall tool registered to registry")
 
 
+def ensure_metadata_discovery_registered(
+    registry: ToolRegistry,
+    session: AsyncSession,
+) -> None:
+    """Ensure metadata_discovery tool is registered."""
+    global _metadata_discovery_registered
+    if _metadata_discovery_registered:
+        return
+
+    from app.services.mid.metadata_discovery_tool import register_metadata_discovery_tool
+
+    register_metadata_discovery_tool(
+        registry=registry,
+        session=session,
+        timeout_governor=get_timeout_governor(),
+    )
+    _metadata_discovery_registered = True
+    logger.info("[MetadataDiscovery] metadata_discovery tool registered to registry")
+
+
 def get_output_guardrail_executor() -> OutputGuardrailExecutor:
     """Get or create OutputGuardrailExecutor instance."""
     if "output_guardrail_executor" not in _mid_services:
@@ -259,6 +292,16 @@ def get_runtime_observer() -> RuntimeObserver:
     return _mid_services["runtime_observer"]
 
 
+def get_clarification_engine() -> ClarificationEngine:
+    """Get or create ClarificationEngine instance."""
+    if "clarification_engine" not in _mid_services:
+        _mid_services["clarification_engine"] = ClarificationEngine(
+            t_high=T_HIGH,
+            t_low=T_LOW,
+        )
+    return _mid_services["clarification_engine"]
+
+
 @router.post(
     "/dialogue/respond",
     operation_id="respondDialogue",
@@ -288,6 +331,7 @@ async def respond_dialogue(
     default_kb_tool_runner: DefaultKbToolRunner = Depends(get_default_kb_tool_runner),
     segment_humanizer: SegmentHumanizer = Depends(get_segment_humanizer),
     runtime_observer: RuntimeObserver = Depends(get_runtime_observer),
+    clarification_engine: ClarificationEngine = Depends(get_clarification_engine),
 ) -> DialogueResponse:
     """
     [AC-MARH-01~12] Generate dialogue response with segments and trace.
@@ -316,7 +360,8 @@ async def respond_dialogue(
 
     logger.info(
         f"[AC-MARH-01] Dialogue request: tenant={tenant_id}, "
-        f"session={dialogue_request.session_id}, request_id={request_id}"
+        f"session={dialogue_request.session_id}, request_id={request_id}, "
+        f"user_message={dialogue_request.user_message[:100] if dialogue_request.user_message else 'None'}..."
     )
 
     runtime_ctx = runtime_observer.start_observation(
@@ -340,6 +385,7 @@ async def respond_dialogue(
     ensure_intent_hint_registered(tool_registry, session)
     ensure_high_risk_check_registered(tool_registry, session)
     ensure_memory_recall_registered(tool_registry, session)
+    ensure_metadata_discovery_registered(tool_registry, session)
 
     try:
         interrupt_ctx = interrupt_context_enricher.enrich(
@@ -541,7 +587,15 @@ async def respond_dialogue(
 
     except Exception as e:
         latency_ms = int((time.time() - start_time) * 1000)
-        logger.error(f"[AC-IDMP-06] Dialogue error: {e}")
+        import traceback
+        logger.error(
+            f"[AC-IDMP-06] Dialogue error: {e}\n"
+            f"Exception type: {type(e).__name__}\n"
+            f"Request details: session_id={dialogue_request.session_id}, "
+            f"user_message={dialogue_request.user_message[:200] if dialogue_request.user_message else 'None'}, "
+            f"scene={dialogue_request.scene}, user_id={dialogue_request.user_id}\n"
+            f"Traceback:\n{traceback.format_exc()}"
+        )
 
         trace_logger.update_trace(
             request_id=request_id,
@@ -593,7 +647,7 @@ async def _match_intent(
             return IntentMatch(
                 intent_id=str(result.rule.id),
                 intent_name=result.rule.name,
-                confidence=0.8,
+                confidence=1.0,
                 response_type=result.rule.response_type,
                 target_kb_ids=result.rule.target_kb_ids,
                 flow_id=str(result.rule.flow_id) if result.rule.flow_id else None,
@@ -608,6 +662,227 @@ async def _match_intent(
         return None
 
 
+async def _match_intent_hybrid(
+    tenant_id: str,
+    request: DialogueRequest,
+    session: AsyncSession,
+    clarification_engine: ClarificationEngine,
+) -> HybridIntentResult:
+    """
+    [AC-CLARIFY] Hybrid intent matching with clarification support.
+    
+    Returns HybridIntentResult with unified confidence and candidates.
+    """
+    try:
+        from app.services.intent.router import IntentRouter
+        from app.services.intent.rule_service import IntentRuleService
+
+        rule_service = IntentRuleService(session)
+        rules = await rule_service.get_enabled_rules_for_matching(tenant_id)
+
+        if not rules:
+            return HybridIntentResult(
+                intent=None,
+                confidence=0.0,
+                candidates=[],
+            )
+
+        router = IntentRouter()
+        
+        try:
+            fusion_result = await router.match_hybrid(
+                message=request.user_message,
+                rules=rules,
+                tenant_id=tenant_id,
+            )
+            
+            hybrid_result = HybridIntentResult.from_fusion_result(fusion_result)
+            
+            logger.info(
+                f"[AC-CLARIFY] Hybrid intent match: "
+                f"intent={hybrid_result.intent.intent_name if hybrid_result.intent else None}, "
+                f"confidence={hybrid_result.confidence:.3f}, "
+                f"need_clarify={hybrid_result.need_clarify}"
+            )
+            
+            return hybrid_result
+            
+        except Exception as e:
+            logger.warning(f"[AC-CLARIFY] Hybrid match failed, fallback to rule: {e}")
+            
+            result = router.match(request.user_message, rules)
+            
+            if result:
+                confidence = clarification_engine.compute_confidence(
+                    rule_score=1.0,
+                    semantic_score=0.0,
+                    llm_score=0.0,
+                )
+                candidate = IntentCandidate(
+                    intent_id=str(result.rule.id),
+                    intent_name=result.rule.name,
+                    confidence=confidence,
+                    response_type=result.rule.response_type,
+                    target_kb_ids=result.rule.target_kb_ids,
+                    flow_id=str(result.rule.flow_id) if result.rule.flow_id else None,
+                    fixed_reply=result.rule.fixed_reply,
+                    transfer_message=result.rule.transfer_message,
+                )
+                return HybridIntentResult(
+                    intent=candidate,
+                    confidence=confidence,
+                    candidates=[candidate],
+                )
+            
+            return HybridIntentResult(
+                intent=None,
+                confidence=0.0,
+                candidates=[],
+            )
+
+    except Exception as e:
+        logger.warning(f"[AC-CLARIFY] Intent match failed: {e}")
+        return HybridIntentResult(
+            intent=None,
+            confidence=0.0,
+            candidates=[],
+        )
+
+
+async def _handle_clarification(
+    tenant_id: str,
+    request: DialogueRequest,
+    hybrid_result: HybridIntentResult,
+    clarification_engine: ClarificationEngine,
+    trace: TraceInfo,
+    session: AsyncSession,
+    required_slots: list[str] | None = None,
+    filled_slots: dict[str, Any] | None = None,
+) -> DialogueResponse | None:
+    """
+    [AC-CLARIFY] Handle clarification logic.
+    
+    Returns DialogueResponse if clarification is needed, None otherwise.
+    """
+    existing_state = ClarifySessionManager.get_session(request.session_id)
+    
+    if existing_state and not existing_state.is_max_retry():
+        logger.info(
+            f"[AC-CLARIFY] Processing clarify response: "
+            f"session={request.session_id}, retry={existing_state.retry_count}"
+        )
+        
+        new_result = clarification_engine.process_clarify_response(
+            user_message=request.user_message,
+            state=existing_state,
+        )
+        
+        if not new_result.need_clarify:
+            ClarifySessionManager.clear_session(request.session_id)
+            
+            if new_result.intent:
+                return None
+        
+        clarify_prompt = clarification_engine.generate_clarify_prompt(existing_state)
+        
+        return DialogueResponse(
+            segments=[Segment(text=clarify_prompt, delay_after=0)],
+            trace=TraceInfo(
+                mode=ExecutionMode.FIXED,
+                request_id=trace.request_id,
+                generation_id=trace.generation_id,
+                fallback_reason_code=f"clarify_retry_{existing_state.retry_count}",
+                intent=existing_state.candidates[0].intent_name if existing_state.candidates else None,
+            ),
+        )
+    
+    should_clarify, clarify_state = clarification_engine.should_trigger_clarify(
+        result=hybrid_result,
+        required_slots=required_slots,
+        filled_slots=filled_slots,
+    )
+    
+    if not should_clarify or not clarify_state:
+        return None
+    
+    logger.info(
+        f"[AC-CLARIFY] Clarification triggered: "
+        f"session={request.session_id}, reason={clarify_state.reason}, "
+        f"confidence={hybrid_result.confidence:.3f}"
+    )
+    
+    ClarifySessionManager.set_session(request.session_id, clarify_state)
+    
+    clarify_prompt = clarification_engine.generate_clarify_prompt(clarify_state)
+    
+    return DialogueResponse(
+        segments=[Segment(text=clarify_prompt, delay_after=0)],
+        trace=TraceInfo(
+            mode=ExecutionMode.FIXED,
+            request_id=trace.request_id,
+            generation_id=trace.generation_id,
+            fallback_reason_code=f"clarify_{clarify_state.reason.value}",
+            intent=hybrid_result.intent.intent_name if hybrid_result.intent else None,
+        ),
+    )
+
+
+async def _check_hard_block(
+    tenant_id: str,
+    request: DialogueRequest,
+    hybrid_result: HybridIntentResult,
+    clarification_engine: ClarificationEngine,
+    trace: TraceInfo,
+    required_slots: list[str] | None = None,
+    filled_slots: dict[str, Any] | None = None,
+) -> DialogueResponse | None:
+    """
+    [AC-CLARIFY] Check hard block conditions.
+    
+    Hard blocks:
+    1. confidence < T_high: block entering new flow
+    2. required_slots missing: block flow progression
+    
+    Returns DialogueResponse if blocked, None otherwise.
+    """
+    is_blocked, block_reason = clarification_engine.check_hard_block(
+        result=hybrid_result,
+        required_slots=required_slots,
+        filled_slots=filled_slots,
+    )
+    
+    if not is_blocked:
+        return None
+    
+    logger.info(
+        f"[AC-CLARIFY] Hard block triggered: "
+        f"session={request.session_id}, reason={block_reason}, "
+        f"confidence={hybrid_result.confidence:.3f}"
+    )
+    
+    clarify_state = ClarifyState(
+        reason=block_reason,
+        asked_slot=required_slots[0] if required_slots and block_reason == ClarifyReason.MISSING_SLOT else None,
+        candidates=hybrid_result.candidates,
+    )
+    
+    ClarifySessionManager.set_session(request.session_id, clarify_state)
+    clarification_engine._metrics.record_clarify_trigger()
+    
+    clarify_prompt = clarification_engine.generate_clarify_prompt(clarify_state)
+    
+    return DialogueResponse(
+        segments=[Segment(text=clarify_prompt, delay_after=0)],
+        trace=TraceInfo(
+            mode=ExecutionMode.FIXED,
+            request_id=trace.request_id,
+            generation_id=trace.generation_id,
+            fallback_reason_code=f"hard_block_{block_reason.value}",
+            intent=hybrid_result.intent.intent_name if hybrid_result.intent else None,
+        ),
+    )
+
+
 async def _handle_legacy_response(
     tenant_id: str,
     request: DialogueRequest,
@@ -793,18 +1068,32 @@ async def _execute_agent_mode(
     session: AsyncSession | None = None,
     tool_registry: ToolRegistry | None = None,
 ) -> DialogueResponse:
-    """[AC-MARH-05, AC-MARH-06, AC-MARH-07, AC-IDMP-13] Execute agent mode with ReAct loop, KB tool, and memory recall."""
+    """
+    [AC-MARH-05, AC-MARH-06, AC-MARH-07, AC-IDMP-13, AC-MRS-SLOT-META-03] 
+    Execute agent mode with ReAct loop, KB tool, memory recall, and slot state aggregation.
+    """
     from app.services.llm.factory import get_llm_config_manager
+    from app.services.mid.slot_state_aggregator import SlotStateAggregator
+
+    logger.info(
+        f"[DEBUG-AGENT] Starting _execute_agent_mode: tenant={tenant_id}, "
+        f"session={request.session_id}, scene={request.scene}, user_id={request.user_id}, "
+        f"user_message={request.user_message[:100] if request.user_message else 'None'}..."
+    )
 
     try:
         llm_manager = get_llm_config_manager()
         llm_client = llm_manager.get_client()
+        logger.info(f"[DEBUG-AGENT] LLM client obtained successfully")
     except Exception as e:
         logger.warning(f"[AC-MARH-07] Failed to get LLM client: {e}")
         llm_client = None
 
     base_context = {"history": [h.model_dump() for h in request.history]} if request.history else {}
 
+    if request.scene:
+        base_context["scene"] = request.scene
+
     if interrupt_ctx and interrupt_ctx.consumed:
         base_context["interrupted_content"] = interrupt_ctx.interrupted_content
         base_context["interrupted_segment_ids"] = interrupt_ctx.interrupted_segment_ids
@@ -813,8 +1102,50 @@ async def _execute_agent_mode(
             f"{len(interrupt_ctx.interrupted_content or '')} chars"
         )
 
+    # [AC-MRS-SLOT-META-03] 初始化槽位状态聚合器
+    slot_state = None
     memory_context = ""
     memory_missing_slots: list[str] = []
+    scene_slot_context = None
+
+    flow_status = None
+    is_flow_runtime = False
+    if session:
+        from app.services.flow.engine import FlowEngine
+        flow_engine = FlowEngine(session)
+        flow_status = await flow_engine.get_flow_status(tenant_id, request.session_id)
+        is_flow_runtime = bool(flow_status and flow_status.get("status") == "active")
+
+    logger.info(
+        f"[AC-IDMP-02] 运行轨道判定: session={request.session_id}, "
+        f"is_flow_runtime={is_flow_runtime}, flow_id={flow_status.get('flow_id') if flow_status else None}, "
+        f"current_step={flow_status.get('current_step') if flow_status else None}"
+    )
+
+    # [AC-SCENE-SLOT-02] 加载场景槽位包（仅流程态加载）
+    if is_flow_runtime and session and request.scene:
+        from app.services.mid.scene_slot_bundle_loader import SceneSlotBundleLoader
+
+        scene_loader = SceneSlotBundleLoader(session)
+        scene_slot_context = await scene_loader.load_scene_context(
+            tenant_id=tenant_id,
+            scene_key=request.scene,
+        )
+
+        if scene_slot_context:
+            logger.info(
+                f"[AC-SCENE-SLOT-02] 场景槽位包加载成功: "
+                f"scene={request.scene}, required={len(scene_slot_context.required_slots)}, "
+                f"optional={len(scene_slot_context.optional_slots)}, "
+                f"threshold={scene_slot_context.completion_threshold}"
+            )
+            base_context["scene_slot_context"] = {
+                "scene_key": scene_slot_context.scene_key,
+                "scene_name": scene_slot_context.scene_name,
+                "required_slots": scene_slot_context.get_required_slot_keys(),
+                "optional_slots": scene_slot_context.get_optional_slot_keys(),
+            }
+
     if session and request.user_id:
         memory_recall_tool = MemoryRecallTool(
             session=session,
@@ -855,11 +1186,111 @@ async def _execute_agent_mode(
                 f"[AC-IDMP-13] Memory recall fallback: reason={memory_result.fallback_reason_code}"
             )
 
+        # [AC-MRS-SLOT-META-03] 仅流程态执行槽位聚合/提取
+        if is_flow_runtime:
+            slot_aggregator = SlotStateAggregator(
+                session=session,
+                tenant_id=tenant_id,
+                session_id=request.session_id,
+            )
+            slot_state = await slot_aggregator.aggregate(
+                memory_slots=memory_result.slots,
+                current_input_slots=None,  # 可从 request 中解析
+                context=base_context,
+                scene_slot_context=scene_slot_context,  # [AC-SCENE-SLOT-02] 传入场景槽位上下文
+            )
+
+            logger.info(
+                f"[AC-MRS-SLOT-META-03] 流程态槽位聚合完成: "
+                f"filled={len(slot_state.filled_slots)}, "
+                f"missing={len(slot_state.missing_required_slots)}, "
+                f"mappings={slot_state.slot_to_field_map}"
+            )
+
+            # [AC-MRS-SLOT-EXTRACT-01] 自动提取槽位
+            if slot_state and slot_state.missing_required_slots:
+                from app.services.mid.slot_extraction_integration import SlotExtractionIntegration
+
+                extraction_integration = SlotExtractionIntegration(
+                    session=session,
+                    tenant_id=tenant_id,
+                    session_id=request.session_id,
+                )
+
+                extraction_result = await extraction_integration.extract_and_fill(
+                    user_input=request.user_message,
+                    slot_state=slot_state,
+                )
+
+                if extraction_result.extracted_slots:
+                    slot_state = await slot_aggregator.aggregate(
+                        memory_slots=memory_result.slots,
+                        current_input_slots=extraction_result.extracted_slots,
+                        context=base_context,
+                    )
+
+                    logger.info(
+                        f"[AC-MRS-SLOT-EXTRACT-01] 流程态自动提槽完成: "
+                        f"extracted={list(extraction_result.extracted_slots.keys())}, "
+                        f"time_ms={extraction_result.total_execution_time_ms:.2f}"
+                    )
+        else:
+            logger.info(
+                f"[AC-MRS-SLOT-META-03] 当前为通用问答轨道，跳过槽位聚合与缺槽追问: "
+                f"session={request.session_id}"
+            )
+
     kb_hits = []
     kb_success = False
     kb_fallback_reason = None
     kb_applied_filter = {}
     kb_missing_slots = []
+    kb_dynamic_result = None
+    step_kb_binding_trace: dict[str, Any] | None = None
+
+    # [Step-KB-Binding] 获取当前流程步骤的 KB 配置（仅流程态）
+    step_kb_config = None
+    if is_flow_runtime and session and flow_status:
+        current_step_no = flow_status.get("current_step")
+        flow_id = flow_status.get("flow_id")
+
+        if flow_id and current_step_no:
+            from app.models.entities import ScriptFlow
+            from sqlalchemy import select
+
+            stmt = select(ScriptFlow).where(ScriptFlow.id == flow_id)
+            result = await session.execute(stmt)
+            flow = result.scalar_one_or_none()
+
+            if flow and flow.steps:
+                step_idx = current_step_no - 1
+                if 0 <= step_idx < len(flow.steps):
+                    current_step = flow.steps[step_idx]
+
+                    # 构建 StepKbConfig
+                    from app.services.mid.kb_search_dynamic_tool import StepKbConfig
+                    step_kb_config = StepKbConfig(
+                        allowed_kb_ids=current_step.get("allowed_kb_ids"),
+                        preferred_kb_ids=current_step.get("preferred_kb_ids"),
+                        kb_query_hint=current_step.get("kb_query_hint"),
+                        max_kb_calls=current_step.get("max_kb_calls_per_step", 1),
+                        step_id=f"{flow_id}_step_{current_step_no}",
+                    )
+
+                    step_kb_binding_trace = {
+                        "flow_id": str(flow_id),
+                        "flow_name": flow_status.get("flow_name"),
+                        "current_step": current_step_no,
+                        "step_id": step_kb_config.step_id,
+                        "allowed_kb_ids": step_kb_config.allowed_kb_ids,
+                        "preferred_kb_ids": step_kb_config.preferred_kb_ids,
+                    }
+
+                    logger.info(
+                        f"[Step-KB-Binding] 步骤知识库配置加载成功: "
+                        f"flow={flow_status.get('flow_name')}, step={current_step_no}, "
+                        f"allowed_kb_ids={step_kb_config.allowed_kb_ids}"
+                    )
 
     if session and tool_registry:
         kb_tool = KbSearchDynamicTool(
@@ -868,17 +1299,21 @@ async def _execute_agent_mode(
             config=KbSearchDynamicConfig(
                 enabled=True,
                 top_k=5,
-                timeout_ms=2000,
+                timeout_ms=10000,
                 min_score_threshold=0.5,
             ),
         )
 
+        # [AC-MRS-SLOT-META-03] 传入 slot_state 进行 KB 检索
+        # [Step-KB-Binding] 传入 step_kb_config 进行步骤级别的 KB 约束
         kb_dynamic_result = await kb_tool.execute(
             query=request.user_message,
             tenant_id=tenant_id,
-            scene="open_consult",
             top_k=5,
             context=base_context,
+            slot_state=slot_state,
+            step_kb_config=step_kb_config,
+            slot_policy="flow_strict" if is_flow_runtime else "agent_relaxed",
         )
 
         kb_success = kb_dynamic_result.success
@@ -894,9 +1329,43 @@ async def _execute_agent_mode(
             )
 
         logger.info(
-            f"[AC-MARH-05] KB dynamic search: success={kb_success}, "
+            f"[AC-MARH-05] KB动态检索完成: success={kb_success}, "
             f"hits={len(kb_hits)}, filter={kb_applied_filter}, "
-            f"missing_slots={kb_missing_slots}"
+            f"missing_slots={kb_missing_slots}, track={'flow' if is_flow_runtime else 'agent'}"
+        )
+
+        # [AC-MRS-SLOT-META-03] 处理缺失必填槽位 -> 追问闭环（仅流程态）
+        if is_flow_runtime and kb_fallback_reason == "MISSING_REQUIRED_SLOTS" and kb_missing_slots:
+            ask_back_text = await _generate_ask_back_for_missing_slots(
+                slot_state=slot_state,
+                missing_slots=kb_missing_slots,
+                session=session,
+                tenant_id=tenant_id,
+                session_id=request.session_id,
+                scene_slot_context=scene_slot_context,  # [AC-SCENE-SLOT-02] 传入场景槽位上下文
+            )
+            
+            logger.info(
+                f"[AC-MRS-SLOT-META-03] 流程态缺槽追问文案已生成: "
+                f"{ask_back_text[:50]}..."
+            )
+            
+            return DialogueResponse(
+                segments=[Segment(text=ask_back_text, delay_after=0)],
+                trace=TraceInfo(
+                    mode=ExecutionMode.AGENT,
+                    request_id=trace.request_id,
+                    generation_id=trace.generation_id,
+                    fallback_reason_code="missing_required_slots",
+                    kb_tool_called=True,
+                    kb_hit=False,
+                    # [AC-SCENE-SLOT-02] 场景槽位追踪
+                    scene=request.scene,
+                    scene_slot_context=base_context.get("scene_slot_context"),
+                    missing_slots=[s.get("slot_key") for s in kb_missing_slots] if kb_missing_slots else None,
+                    ask_back_triggered=True,
+                    slot_sources=slot_state.slot_sources if slot_state else None,
+                ),
             )
     else:
         kb_result = await default_kb_tool_runner.execute(
@@ -933,7 +1402,18 @@ async def _execute_agent_mode(
         timeout_governor=timeout_governor,
         llm_client=llm_client,
         tool_registry=tool_registry,
+        template_service=PromptTemplateService,
+        variable_resolver=VariableResolver(),
         tenant_id=tenant_id,
+        user_id=request.user_id,
+        session_id=request.session_id,
+        scene=request.scene,
+    )
+
+    logger.info(
+        f"[DEBUG-AGENT] Calling orchestrator.execute with: "
+        f"user_message={request.user_message[:100] if request.user_message else 'None'}, "
+        f"context_keys={list(base_context.keys())}, llm_client={llm_client is not None}"
     )
 
     final_answer, react_ctx, agent_trace = await orchestrator.execute(
@@ -941,6 +1421,12 @@ async def _execute_agent_mode(
         context=base_context,
     )
 
+    logger.info(
+        f"[DEBUG-AGENT] orchestrator.execute completed: "
+        f"final_answer={final_answer[:200] if final_answer else 'None'}, "
+        f"iterations={react_ctx.iteration}, tool_calls={len(react_ctx.tool_calls) if react_ctx.tool_calls else 0}"
+    )
+
     runtime_observer.record_react(request_id, react_ctx.iteration, react_ctx.tool_calls)
 
     trace_logger.update_trace(
@@ -964,10 +1450,114 @@ async def _execute_agent_mode(
             kb_tool_called=True,
             kb_hit=kb_success and len(kb_hits) > 0,
             fallback_reason_code=kb_fallback_reason,
+            # [AC-SCENE-SLOT-02] 场景槽位追踪
+            scene=request.scene,
+            scene_slot_context=base_context.get("scene_slot_context"),
+            missing_slots=[s.get("slot_key") for s in kb_missing_slots] if kb_missing_slots else None,
+            ask_back_triggered=False,
+            slot_sources=slot_state.slot_sources if slot_state else None,
+            kb_filter_sources=kb_dynamic_result.filter_sources if kb_dynamic_result else None,
+            # [Step-KB-Binding] 步骤知识库绑定追踪
+            step_kb_binding=kb_dynamic_result.step_kb_binding if kb_dynamic_result else step_kb_binding_trace,
         ),
     )
 
 
+async def _generate_ask_back_for_missing_slots(
+    slot_state: Any,
+    missing_slots: list[dict[str, str]],
+    session: AsyncSession,
+    tenant_id: str,
+    session_id: str | None = None,
+    max_ask_back_slots: int = 2,
+    scene_slot_context: Any = None,  # [AC-SCENE-SLOT-02] 场景槽位上下文
+) -> str:
+    """
+    [AC-MRS-SLOT-META-03, AC-MRS-SLOT-ASKBACK-01] 为缺失的必填槽位生成追问响应
+    [AC-SCENE-SLOT-02] 支持场景槽位包配置的追问策略
+    
+    支持批量追问多个缺失槽位，优先追问必填槽位
+    """
+    if not missing_slots:
+        return "请提供更多信息以便我更好地帮助您。"
+    
+    # [AC-SCENE-SLOT-02] 如果有场景槽位上下文，使用场景配置的追问策略
+    if scene_slot_context:
+        ask_back_order = getattr(scene_slot_context, 'ask_back_order', 'priority')
+        
+        if ask_back_order == "parallel":
+            prompts = []
+            for missing in missing_slots[:max_ask_back_slots]:
+                ask_back_prompt = missing.get("ask_back_prompt")
+                if ask_back_prompt:
+                    prompts.append(ask_back_prompt)
+                else:
+                    slot_key = missing.get("slot_key", "相关信息")
+                    prompts.append(f"您的{slot_key}")
+            
+            if len(prompts) == 1:
+                return prompts[0]
+            elif len(prompts) == 2:
+                return f"为了更好地为您服务，请告诉我{prompts[0]}和{prompts[1]}。"
+            else:
+                all_but_last = "、".join(prompts[:-1])
+                return f"为了更好地为您服务，请告诉我{all_but_last}，以及{prompts[-1]}。"
+    
+    if len(missing_slots) == 1:
+        first_missing = missing_slots[0]
+        ask_back_prompt = first_missing.get("ask_back_prompt")
+        if ask_back_prompt:
+            return ask_back_prompt
+        slot_key = first_missing.get("slot_key", "相关信息")
+        label = first_missing.get("label", slot_key)
+        return f"为了更好地为您提供帮助，请告诉我您的{label}。"
+    
+    try:
+        from app.services.mid.batch_ask_back_service import (
+            BatchAskBackConfig,
+            BatchAskBackService,
+        )
+        
+        config = BatchAskBackConfig(
+            max_ask_back_slots_per_turn=max_ask_back_slots,
+            prefer_required=True,
+            merge_prompts=True,
+        )
+        
+        ask_back_service = BatchAskBackService(
+            session=session,
+            tenant_id=tenant_id,
+            session_id=session_id or "",
+            config=config,
+        )
+        
+        result = await ask_back_service.generate_batch_ask_back(
+            missing_slots=missing_slots,
+        )
+        
+        if result.has_ask_back():
+            return result.get_prompt()
+    except Exception as e:
+        logger.warning(f"[AC-MRS-SLOT-ASKBACK-01] Batch ask-back failed, fallback to single: {e}")
+    
+    prompts = []
+    for missing in missing_slots[:max_ask_back_slots]:
+        ask_back_prompt = missing.get("ask_back_prompt")
+        if ask_back_prompt:
+            prompts.append(ask_back_prompt)
+        else:
+            label = missing.get("label", missing.get("slot_key", "相关信息"))
+            prompts.append(f"您的{label}")
+    
+    if len(prompts) == 1:
+        return prompts[0]
+    elif len(prompts) == 2:
+        return f"为了更好地为您服务，请告诉我{prompts[0]}和{prompts[1]}。"
+    else:
+        all_but_last = "、".join(prompts[:-1])
+        return f"为了更好地为您服务，请告诉我{all_but_last}，以及{prompts[-1]}。"
+
+
 async def _execute_micro_flow_mode(
     tenant_id: str,
     request: DialogueRequest,

ai-service/app/api/mid/sessions.py

@@ -8,6 +8,7 @@ from typing import Annotated
 
 from fastapi import APIRouter, Depends, Path
 from fastapi.responses import JSONResponse
+from pydantic import BaseModel
 from sqlalchemy.ext.asyncio import AsyncSession
 
 from app.core.database import get_session
@@ -26,6 +27,82 @@ router = APIRouter(prefix="/mid", tags=["Mid Platform Sessions"])
 _session_modes: dict[str, SessionMode] = {}
 
 
+class CancelFlowResponse(BaseModel):
+    """Response for cancel flow operation."""
+
+    success: bool
+    message: str
+    session_id: str
+
+
+@router.post(
+    "/sessions/{sessionId}/cancel-flow",
+    operation_id="cancelActiveFlow",
+    summary="Cancel active flow",
+    description="""
+    Cancel the active flow for a session.
+    
+    Use this when you encounter "Session already has an active flow" error.
+    """,
+    responses={
+        200: {"description": "Flow cancelled successfully", "model": CancelFlowResponse},
+        404: {"description": "No active flow found"},
+    },
+)
+async def cancel_active_flow(
+    sessionId: Annotated[str, Path(description="Session ID")],
+    session: Annotated[AsyncSession, Depends(get_session)],
+) -> CancelFlowResponse:
+    """
+    Cancel the active flow for a session.
+    
+    This endpoint allows you to cancel any active flow instance
+    so that a new flow can be started.
+    """
+    tenant_id = get_tenant_id()
+    
+    if not tenant_id:
+        from app.core.exceptions import MissingTenantIdException
+        raise MissingTenantIdException()
+    
+    logger.info(
+        f"[Cancel Flow] Cancelling active flow: tenant={tenant_id}, session={sessionId}"
+    )
+    
+    try:
+        from app.services.flow.engine import FlowEngine
+        
+        flow_engine = FlowEngine(session)
+        cancelled = await flow_engine.cancel_flow(
+            tenant_id=tenant_id,
+            session_id=sessionId,
+            reason="User requested cancellation via API",
+        )
+        
+        if cancelled:
+            logger.info(f"[Cancel Flow] Flow cancelled: session={sessionId}")
+            return CancelFlowResponse(
+                success=True,
+                message="Active flow cancelled successfully",
+                session_id=sessionId,
+            )
+        else:
+            logger.info(f"[Cancel Flow] No active flow found: session={sessionId}")
+            return CancelFlowResponse(
+                success=True,
+                message="No active flow found for this session",
+                session_id=sessionId,
+            )
+    
+    except Exception as e:
+        logger.error(f"[Cancel Flow] Failed to cancel flow: {e}")
+        return CancelFlowResponse(
+            success=False,
+            message=f"Failed to cancel flow: {str(e)}",
+            session_id=sessionId,
+        )
+
+
 @router.post(
     "/sessions/{sessionId}/mode",
     operation_id="switchSessionMode",

ai-service/app/core/config.py

@@ -64,6 +64,7 @@ class Settings(BaseSettings):
     redis_enabled: bool = True
     dashboard_cache_ttl: int = 60
     stats_counter_ttl: int = 7776000
+    slot_state_cache_ttl: int = 1800
 
     frontend_base_url: str = "http://localhost:3000"
 

ai-service/app/core/database.py

@@ -20,7 +20,7 @@ engine = create_async_engine(
     settings.database_url,
     pool_size=settings.database_pool_size,
     max_overflow=settings.database_max_overflow,
-    echo=settings.debug,
+    echo=False,
     pool_pre_ping=True,
 )
 

ai-service/app/core/middleware.py

@@ -114,7 +114,12 @@ class ApiKeyMiddleware(BaseHTTPMiddleware):
                 from app.core.database import async_session_maker
                 async with async_session_maker() as session:
                     await service.initialize(session)
+                    if service._initialized and len(service._keys_cache) > 0:
                         logger.info(f"[AC-AISVC-50] API key service lazy initialized with {len(service._keys_cache)} keys")
+                    elif service._initialized and len(service._keys_cache) == 0:
+                        logger.warning("[AC-AISVC-50] API key service initialized but no keys found in database")
+                    else:
+                        logger.error("[AC-AISVC-50] API key service lazy initialization failed")
             except Exception as e:
                 logger.error(f"[AC-AISVC-50] Failed to initialize API key service: {e}")
 

ai-service/app/core/qdrant_client.py

@@ -272,20 +272,24 @@ class QdrantClient:
         score_threshold: float | None = None,
         vector_name: str = "full",
         with_vectors: bool = False,
+        metadata_filter: dict[str, Any] | None = None,
+        kb_ids: list[str] | None = None,
     ) -> list[dict[str, Any]]:
         """
-        [AC-AISVC-10] Search vectors in tenant's collection.
+        [AC-AISVC-10] Search vectors in tenant's collections.
         Returns results with score >= score_threshold if specified.
-        Searches both old format (with @) and new format (with _) for backward compatibility.
+        Searches all collections for the tenant (multi-KB support).
 
         Args:
             tenant_id: Tenant identifier
             query_vector: Query vector for similarity search
-            limit: Maximum number of results
+            limit: Maximum number of results per collection
             score_threshold: Minimum score threshold for results
             vector_name: Name of the vector to search (for multi-vector collections)
                          Default is "full" for 768-dim vectors in Matryoshka setup.
             with_vectors: Whether to return vectors in results (for two-stage reranking)
+            metadata_filter: Optional metadata filter to apply during search
+            kb_ids: Optional list of knowledge base IDs to restrict search to specific KBs
         """
         client = await self.get_client()
 
@@ -293,21 +297,36 @@ class QdrantClient:
             f"[AC-AISVC-10] Starting search: tenant_id={tenant_id}, "
             f"limit={limit}, score_threshold={score_threshold}, vector_dim={len(query_vector)}, vector_name={vector_name}"
         )
+        if metadata_filter:
+            logger.info(f"[AC-AISVC-10] Metadata filter: {metadata_filter}")
 
-        collection_names = [self.get_collection_name(tenant_id)]
-        if '@' in tenant_id:
-            old_format = f"{self._collection_prefix}{tenant_id}"
-            new_format = f"{self._collection_prefix}{tenant_id.replace('@', '_')}"
-            collection_names = [new_format, old_format]
+        # 构建 Qdrant filter
+        qdrant_filter = None
+        if metadata_filter:
+            qdrant_filter = self._build_qdrant_filter(metadata_filter)
+            logger.info(f"[AC-AISVC-10] Qdrant filter: {qdrant_filter}")
 
-        logger.info(f"[AC-AISVC-10] Will search in collections: {collection_names}")
+        # 获取该租户的所有 collections
+        collection_names = await self._get_tenant_collections(client, tenant_id)
+        
+        # 如果指定了 kb_ids，则只搜索指定的知识库 collections
+        if kb_ids:
+            target_collections = []
+            for kb_id in kb_ids:
+                kb_collection_name = self.get_kb_collection_name(tenant_id, kb_id)
+                if kb_collection_name in collection_names:
+                    target_collections.append(kb_collection_name)
+                else:
+                    logger.warning(f"[AC-AISVC-10] KB collection not found: {kb_collection_name} for kb_id={kb_id}")
+            collection_names = target_collections
+            logger.info(f"[AC-AISVC-10] Restricted to {len(collection_names)} KB collections: {collection_names}")
+        else:
+            logger.info(f"[AC-AISVC-10] Will search in {len(collection_names)} collections: {collection_names}")
 
         all_hits = []
 
         for collection_name in collection_names:
             try:
-                logger.info(f"[AC-AISVC-10] Searching in collection: {collection_name}")
-
                 exists = await client.collection_exists(collection_name)
                 if not exists:
                     logger.warning(f"[AC-AISVC-10] Collection {collection_name} does not exist")
@@ -321,6 +340,7 @@ class QdrantClient:
                         limit=limit,
                         with_vectors=with_vectors,
                         score_threshold=score_threshold,
+                        query_filter=qdrant_filter,
                     )
                 except Exception as e:
                     if "vector name" in str(e).lower() or "Not existing vector" in str(e) or "using" in str(e).lower():
@@ -334,6 +354,7 @@ class QdrantClient:
                             limit=limit,
                             with_vectors=with_vectors,
                             score_threshold=score_threshold,
+                            query_filter=qdrant_filter,
                         )
                     else:
                         raise
@@ -348,6 +369,7 @@ class QdrantClient:
                         "id": str(result.id),
                         "score": result.score,
                         "payload": result.payload or {},
+                        "collection": collection_name,  # 添加 collection 信息
                     }
                     if with_vectors and result.vector:
                         hit["vector"] = result.vector
@@ -358,10 +380,6 @@ class QdrantClient:
                     logger.info(
                         f"[AC-AISVC-10] Search in collection {collection_name}: {len(hits)} results for tenant={tenant_id}"
                     )
-                    for i, h in enumerate(hits[:3]):
-                        logger.debug(
-                            f"[AC-AISVC-10] Hit {i+1}: id={h['id']}, score={h['score']:.4f}"
-                        )
                 else:
                     logger.warning(
                         f"[AC-AISVC-10] Collection {collection_name} returned no hits (filtered or empty)"
@@ -370,9 +388,10 @@ class QdrantClient:
                 logger.warning(
                     f"[AC-AISVC-10] Collection {collection_name} not found or error: {e}"
                 )
-                continue
 
-        all_hits = sorted(all_hits, key=lambda x: x["score"], reverse=True)[:limit]
+        # 按分数排序并返回 top results
+        all_hits.sort(key=lambda x: x["score"], reverse=True)
+        all_hits = all_hits[:limit]
 
         logger.info(
             f"[AC-AISVC-10] Search returned {len(all_hits)} total results for tenant={tenant_id}"
@@ -386,6 +405,113 @@ class QdrantClient:
 
         return all_hits
 
+    async def _get_tenant_collections(
+        self,
+        client: AsyncQdrantClient,
+        tenant_id: str,
+    ) -> list[str]:
+        """
+        获取指定租户的所有 collections。
+        优先从 Redis 缓存获取，未缓存则从 Qdrant 查询并缓存。
+        
+        Args:
+            client: Qdrant client
+            tenant_id: 租户 ID
+            
+        Returns:
+            Collection 名称列表
+        """
+        import time
+        start_time = time.time()
+        
+        # 1. 尝试从缓存获取
+        from app.services.metadata_cache_service import get_metadata_cache_service
+        cache_service = await get_metadata_cache_service()
+        cache_key = f"collections:{tenant_id}"
+        
+        try:
+            # 确保 Redis 连接已初始化
+            redis_client = await cache_service._get_redis()
+            if redis_client and cache_service._enabled:
+                cached = await redis_client.get(cache_key)
+                if cached:
+                    import json
+                    collections = json.loads(cached)
+                    logger.info(
+                        f"[AC-AISVC-10] Cache hit: Found {len(collections)} collections "
+                        f"for tenant={tenant_id} in {(time.time() - start_time)*1000:.2f}ms"
+                    )
+                    return collections
+        except Exception as e:
+            logger.warning(f"[AC-AISVC-10] Cache get error: {e}")
+        
+        # 2. 从 Qdrant 查询
+        safe_tenant_id = tenant_id.replace('@', '_')
+        prefix = f"{self._collection_prefix}{safe_tenant_id}"
+        
+        try:
+            collections = await client.get_collections()
+            tenant_collections = [
+                c.name for c in collections.collections
+                if c.name.startswith(prefix)
+            ]
+            
+            # 按名称排序
+            tenant_collections.sort()
+            
+            db_time = (time.time() - start_time) * 1000
+            logger.info(
+                f"[AC-AISVC-10] Found {len(tenant_collections)} collections from Qdrant "
+                f"for tenant={tenant_id} in {db_time:.2f}ms: {tenant_collections}"
+            )
+            
+            # 3. 缓存结果（5分钟 TTL）
+            try:
+                redis_client = await cache_service._get_redis()
+                if redis_client and cache_service._enabled:
+                    import json
+                    await redis_client.setex(
+                        cache_key,
+                        300,  # 5分钟
+                        json.dumps(tenant_collections)
+                    )
+                    logger.info(f"[AC-AISVC-10] Cached collections for tenant={tenant_id}")
+            except Exception as e:
+                logger.warning(f"[AC-AISVC-10] Cache set error: {e}")
+            
+            return tenant_collections
+        except Exception as e:
+            logger.error(f"[AC-AISVC-10] Failed to get collections for tenant={tenant_id}: {e}")
+            return [self.get_collection_name(tenant_id)]
+
+    def _build_qdrant_filter(
+        self,
+        metadata_filter: dict[str, Any],
+    ) -> Any:
+        """
+        构建 Qdrant 过滤条件。
+        
+        Args:
+            metadata_filter: 元数据过滤条件，如 {"grade": "三年级", "subject": "语文"}
+            
+        Returns:
+            Qdrant Filter 对象
+        """
+        from qdrant_client.models import FieldCondition, Filter, MatchValue
+        
+        must_conditions = []
+        
+        for key, value in metadata_filter.items():
+            # 支持嵌套 metadata 字段，如 metadata.grade
+            field_path = f"metadata.{key}"
+            condition = FieldCondition(
+                key=field_path,
+                match=MatchValue(value=value),
+            )
+            must_conditions.append(condition)
+        
+        return Filter(must=must_conditions) if must_conditions else None
+
     async def delete_collection(self, tenant_id: str) -> bool:
         """
         [AC-AISVC-10] Delete tenant's collection.

ai-service/app/main.py

@@ -29,6 +29,8 @@ from app.api.admin import (
     monitoring_router,
     prompt_templates_router,
     rag_router,
+    retrieval_strategy_router,
+    scene_slot_bundle_router,
     script_flows_router,
     sessions_router,
     slot_definition_router,
@@ -55,6 +57,11 @@ logging.basicConfig(
     format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
 )
 
+logging.getLogger("sqlalchemy.engine").setLevel(logging.WARNING)
+logging.getLogger("sqlalchemy.pool").setLevel(logging.WARNING)
+logging.getLogger("sqlalchemy.dialects").setLevel(logging.WARNING)
+logging.getLogger("sqlalchemy.orm").setLevel(logging.WARNING)
+
 logger = logging.getLogger(__name__)
 
 
@@ -88,6 +95,28 @@ async def lifespan(app: FastAPI):
     except Exception as e:
         logger.error(f"[AC-AISVC-50] API key initialization FAILED: {e}", exc_info=True)
 
+    try:
+        from app.services.mid.tool_guide_registry import init_tool_guide_registry
+
+        logger.info("[ToolGuideRegistry] Starting tool guides initialization...")
+        tool_guide_registry = init_tool_guide_registry()
+        logger.info(f"[ToolGuideRegistry] Tool guides loaded: {tool_guide_registry.list_tools()}")
+    except Exception as e:
+        logger.error(f"[ToolRegistry] Tools initialization FAILED: {e}", exc_info=True)
+
+    # [AC-AISVC-29] 预初始化 Embedding 服务，避免首次查询时的延迟
+    try:
+        from app.services.embedding import get_embedding_provider
+
+        logger.info("[AC-AISVC-29] Pre-initializing embedding service...")
+        embedding_provider = await get_embedding_provider()
+        logger.info(
+            f"[AC-AISVC-29] Embedding service pre-initialized: "
+            f"provider={embedding_provider.PROVIDER_NAME}"
+        )
+    except Exception as e:
+        logger.error(f"[AC-AISVC-29] Embedding service pre-initialization FAILED: {e}", exc_info=True)
+
     yield
 
     await close_db()
@@ -171,6 +200,8 @@ app.include_router(metadata_schema_router)
 app.include_router(monitoring_router)
 app.include_router(prompt_templates_router)
 app.include_router(rag_router)
+app.include_router(retrieval_strategy_router)
+app.include_router(scene_slot_bundle_router)
 app.include_router(script_flows_router)
 app.include_router(sessions_router)
 app.include_router(slot_definition_router)

ai-service/app/models/entities.py

@@ -41,6 +41,7 @@ class ChatMessage(SQLModel, table=True):
     [AC-AISVC-13] Chat message entity with tenant isolation.
     Messages are scoped by (tenant_id, session_id) for multi-tenant security.
     [v0.7.0] Extended with monitoring fields for Dashboard statistics.
+    [v0.8.0] Extended with route_trace for hybrid routing observability.
     """
 
     __tablename__ = "chat_messages"
@@ -90,6 +91,11 @@ class ChatMessage(SQLModel, table=True):
         sa_column=Column("guardrail_words", JSON, nullable=True),
         description="[v0.7.0] Guardrail trigger details: words, categories, strategy"
     )
+    route_trace: dict[str, Any] | None = Field(
+        default=None,
+        sa_column=Column("route_trace", JSON, nullable=True),
+        description="[v0.8.0] Intent routing trace log for hybrid routing observability"
+    )
 
 
 class ChatSessionCreate(SQLModel):
@@ -227,6 +233,7 @@ class Document(SQLModel, table=True):
     file_type: str | None = Field(default=None, description="File MIME type")
     status: str = Field(default=DocumentStatus.PENDING.value, description="Document status")
     error_msg: str | None = Field(default=None, description="Error message if failed")
+    doc_metadata: dict | None = Field(default=None, sa_type=JSON, description="Document metadata as JSON")
     created_at: datetime = Field(default_factory=datetime.utcnow, description="Upload time")
     updated_at: datetime = Field(default_factory=datetime.utcnow, description="Last update time")
 
@@ -421,6 +428,7 @@ class IntentRule(SQLModel, table=True):
     [AC-AISVC-65] Intent rule entity with tenant isolation.
     Supports keyword and regex matching for intent recognition.
     [AC-IDSMETA-16] Extended with metadata field for unified storage structure.
+    [v0.8.0] Extended with intent_vector and semantic_examples for hybrid routing.
     """
 
     __tablename__ = "intent_rules"
@@ -458,6 +466,16 @@ class IntentRule(SQLModel, table=True):
         sa_column=Column("metadata", JSON, nullable=True),
         description="[AC-IDSMETA-16] Structured metadata for the intent rule"
     )
+    intent_vector: list[float] | None = Field(
+        default=None,
+        sa_column=Column("intent_vector", JSON, nullable=True),
+        description="[v0.8.0] Pre-computed intent vector for semantic matching"
+    )
+    semantic_examples: list[str] | None = Field(
+        default=None,
+        sa_column=Column("semantic_examples", JSON, nullable=True),
+        description="[v0.8.0] Semantic example sentences for dynamic vector computation"
+    )
     created_at: datetime = Field(default_factory=datetime.utcnow, description="Creation time")
     updated_at: datetime = Field(default_factory=datetime.utcnow, description="Last update time")
 
@@ -475,6 +493,8 @@ class IntentRuleCreate(SQLModel):
     fixed_reply: str | None = None
     transfer_message: str | None = None
     metadata_: dict[str, Any] | None = None
+    intent_vector: list[float] | None = None
+    semantic_examples: list[str] | None = None
 
 
 class IntentRuleUpdate(SQLModel):
@@ -491,6 +511,8 @@ class IntentRuleUpdate(SQLModel):
     transfer_message: str | None = None
     is_enabled: bool | None = None
     metadata_: dict[str, Any] | None = None
+    intent_vector: list[float] | None = None
+    semantic_examples: list[str] | None = None
 
 
 class IntentMatchResult:
@@ -810,6 +832,24 @@ class FlowStep(SQLModel):
         default=None,
         description="RAG configuration for this step: {'enabled': true, 'tag_filter': {'grade': '${context.grade}', 'type': '痛点'}}"
     )
+    allowed_kb_ids: list[str] | None = Field(
+        default=None,
+        description="[Step-KB-Binding] Allowed knowledge base IDs for this step. If set, KB search will be restricted to these KBs."
+    )
+    preferred_kb_ids: list[str] | None = Field(
+        default=None,
+        description="[Step-KB-Binding] Preferred knowledge base IDs for this step. These KBs will be searched first."
+    )
+    kb_query_hint: str | None = Field(
+        default=None,
+        description="[Step-KB-Binding] Query hint for KB search in this step, helps improve retrieval accuracy."
+    )
+    max_kb_calls_per_step: int | None = Field(
+        default=None,
+        ge=1,
+        le=5,
+        description="[Step-KB-Binding] Max KB calls allowed per step. Default is 1 if not set."
+    )
 
 
 class ScriptFlowCreate(SQLModel):
@@ -1078,6 +1118,7 @@ class MetadataFieldDefinition(SQLModel, table=True):
     )
     is_filterable: bool = Field(default=True, description="是否可用于过滤")
     is_rank_feature: bool = Field(default=False, description="是否用于排序特征")
+    usage_description: str | None = Field(default=None, description="用途说明")
     field_roles: list[str] = Field(
         default_factory=list,
         sa_column=Column("field_roles", JSON, nullable=False, server_default="'[]'"),
@@ -1104,6 +1145,7 @@ class MetadataFieldDefinitionCreate(SQLModel):
     scope: list[str] = Field(default_factory=lambda: [MetadataScope.KB_DOCUMENT.value])
     is_filterable: bool = Field(default=True)
     is_rank_feature: bool = Field(default=False)
+    usage_description: str | None = None
     field_roles: list[str] = Field(default_factory=list)
     status: str = Field(default=MetadataFieldStatus.DRAFT.value)
 
@@ -1118,6 +1160,7 @@ class MetadataFieldDefinitionUpdate(SQLModel):
     scope: list[str] | None = None
     is_filterable: bool | None = None
     is_rank_feature: bool | None = None
+    usage_description: str | None = None
     field_roles: list[str] | None = None
     status: str | None = None
 
@@ -1131,6 +1174,17 @@ class ExtractStrategy(str, Enum):
     USER_INPUT = "user_input"
 
 
+class ExtractFailureType(str, Enum):
+    """
+    [AC-MRS-07-UPGRADE] 提取失败类型
+    统一失败分类，用于追踪和日志
+    """
+    EXTRACT_EMPTY = "EXTRACT_EMPTY"              # 提取结果为空
+    EXTRACT_PARSE_FAIL = "EXTRACT_PARSE_FAIL"    # 解析失败
+    EXTRACT_VALIDATION_FAIL = "EXTRACT_VALIDATION_FAIL"  # 校验失败
+    EXTRACT_RUNTIME_ERROR = "EXTRACT_RUNTIME_ERROR"      # 运行时错误
+
+
 class SlotValueSource(str, Enum):
     """
     [AC-MRS-09] 槽位值来源
@@ -1145,6 +1199,7 @@ class SlotDefinition(SQLModel, table=True):
     """
     [AC-MRS-07,08] 槽位定义表
     独立的槽位定义模型，与元数据字段解耦但可复用
+    [AC-MRS-07-UPGRADE] 支持提取策略链 extract_strategies
     """
 
     __tablename__ = "slot_definitions"
@@ -1162,14 +1217,31 @@ class SlotDefinition(SQLModel, table=True):
         min_length=1,
         max_length=100,
     )
+    display_name: str | None = Field(
+        default=None,
+        description="槽位名称，给运营/教研看的中文名，例：grade -> '当前年级'",
+        max_length=100,
+    )
+    description: str | None = Field(
+        default=None,
+        description="槽位说明，解释这个槽位采集什么、用于哪里",
+        max_length=500,
+    )
     type: str = Field(
         default=MetadataFieldType.STRING.value,
         description="槽位类型: string/number/boolean/enum/array_enum"
     )
     required: bool = Field(default=False, description="是否必填槽位")
+    # [AC-MRS-07-UPGRADE] 保留旧字段用于兼容读取
     extract_strategy: str | None = Field(
         default=None,
-        description="提取策略: rule/llm/user_input"
+        description="[兼容字段] 提取策略: rule/llm/user_input，已废弃，请使用 extract_strategies"
+    )
+    # [AC-MRS-07-UPGRADE] 新增策略链字段
+    extract_strategies: list[str] | None = Field(
+        default=None,
+        sa_column=Column("extract_strategies", JSON, nullable=True),
+        description="[AC-MRS-07-UPGRADE] 提取策略链：有序数组，元素为 rule/llm/user_input，按顺序执行直到成功"
     )
     validation_rule: str | None = Field(
         default=None,
@@ -1192,14 +1264,72 @@ class SlotDefinition(SQLModel, table=True):
     created_at: datetime = Field(default_factory=datetime.utcnow, description="创建时间")
     updated_at: datetime = Field(default_factory=datetime.utcnow, description="更新时间")
 
+    def get_effective_strategies(self) -> list[str]:
+        """
+        [AC-MRS-07-UPGRADE] 获取有效的提取策略链
+        优先使用 extract_strategies，如果不存在则兼容读取 extract_strategy
+        """
+        if self.extract_strategies and len(self.extract_strategies) > 0:
+            return self.extract_strategies
+        if self.extract_strategy:
+            return [self.extract_strategy]
+        return []
+
+    def validate_strategies(self) -> tuple[bool, str]:
+        """
+        [AC-MRS-07-UPGRADE] 校验提取策略链的有效性
+
+        Returns:
+            Tuple of (是否有效, 错误信息)
+        """
+        valid_strategies = {"rule", "llm", "user_input"}
+        strategies = self.get_effective_strategies()
+
+        if not strategies:
+            return True, ""  # 空策略链视为有效（使用默认行为）
+
+        # 校验至少1个策略
+        if len(strategies) == 0:
+            return False, "提取策略链不能为空"
+
+        # 校验不允许重复策略
+        if len(strategies) != len(set(strategies)):
+            return False, "提取策略链中不允许重复的策略"
+
+        # 校验策略值有效
+        invalid = [s for s in strategies if s not in valid_strategies]
+        if invalid:
+            return False, f"无效的提取策略: {invalid}，有效值为: {list(valid_strategies)}"
+
+        return True, ""
+
 
 class SlotDefinitionCreate(SQLModel):
     """[AC-MRS-07,08] 创建槽位定义"""
 
     slot_key: str = Field(..., min_length=1, max_length=100)
+    display_name: str | None = Field(
+        default=None,
+        description="槽位名称，给运营/教研看的中文名",
+        max_length=100,
+    )
+    description: str | None = Field(
+        default=None,
+        description="槽位说明，解释这个槽位采集什么、用于哪里",
+        max_length=500,
+    )
     type: str = Field(default=MetadataFieldType.STRING.value)
     required: bool = Field(default=False)
-    extract_strategy: str | None = None
+    # [AC-MRS-07-UPGRADE] 支持策略链
+    extract_strategies: list[str] | None = Field(
+        default=None,
+        description="提取策略链：有序数组，元素为 rule/llm/user_input，按顺序执行直到成功"
+    )
+    # [AC-MRS-07-UPGRADE] 保留旧字段用于兼容
+    extract_strategy: str | None = Field(
+        default=None,
+        description="[兼容字段] 单提取策略，已废弃，请使用 extract_strategies"
+    )
     validation_rule: str | None = None
     ask_back_prompt: str | None = None
     default_value: dict[str, Any] | None = None
@@ -1209,9 +1339,28 @@ class SlotDefinitionCreate(SQLModel):
 class SlotDefinitionUpdate(SQLModel):
     """[AC-MRS-07] 更新槽位定义"""
 
+    display_name: str | None = Field(
+        default=None,
+        description="槽位名称，给运营/教研看的中文名",
+        max_length=100,
+    )
+    description: str | None = Field(
+        default=None,
+        description="槽位说明，解释这个槽位采集什么、用于哪里",
+        max_length=500,
+    )
     type: str | None = None
     required: bool | None = None
-    extract_strategy: str | None = None
+    # [AC-MRS-07-UPGRADE] 支持策略链
+    extract_strategies: list[str] | None = Field(
+        default=None,
+        description="提取策略链：有序数组，元素为 rule/llm/user_input，按顺序执行直到成功"
+    )
+    # [AC-MRS-07-UPGRADE] 保留旧字段用于兼容
+    extract_strategy: str | None = Field(
+        default=None,
+        description="[兼容字段] 单提取策略，已废弃，请使用 extract_strategies"
+    )
     validation_rule: str | None = None
     ask_back_prompt: str | None = None
     default_value: dict[str, Any] | None = None
@@ -1522,3 +1671,107 @@ class MidAuditLog(SQLModel, table=True):
     high_risk_scenario: str | None = Field(default=None, description="触发的高风险场景")
     latency_ms: int | None = Field(default=None, description="总耗时(ms)")
     created_at: datetime = Field(default_factory=datetime.utcnow, description="创建时间", index=True)
+
+
+class SceneSlotBundleStatus(str, Enum):
+    """[AC-SCENE-SLOT-01] 场景槽位包状态"""
+    DRAFT = "draft"
+    ACTIVE = "active"
+    DEPRECATED = "deprecated"
+
+
+class SceneSlotBundle(SQLModel, table=True):
+    """
+    [AC-SCENE-SLOT-01] 场景-槽位映射配置
+    定义每个场景需要采集的槽位集合
+
+    三层关系：
+    - 层1：slot ↔ metadata（通过 linked_field_id）
+    - 层2：scene ↔ slot_bundle（本模型）
+    - 层3：step.expected_variables ↔ slot_key（话术步骤引用）
+    """
+
+    __tablename__ = "scene_slot_bundles"
+    __table_args__ = (
+        Index("ix_scene_slot_bundles_tenant", "tenant_id"),
+        Index("ix_scene_slot_bundles_tenant_scene", "tenant_id", "scene_key", unique=True),
+        Index("ix_scene_slot_bundles_tenant_status", "tenant_id", "status"),
+    )
+
+    id: uuid.UUID = Field(default_factory=uuid.uuid4, primary_key=True)
+    tenant_id: str = Field(..., description="Tenant ID for multi-tenant isolation", index=True)
+    scene_key: str = Field(
+        ...,
+        description="场景标识，如 'open_consult', 'refund_apply', 'course_recommend'",
+        min_length=1,
+        max_length=100,
+    )
+    scene_name: str = Field(
+        ...,
+        description="场景名称，如 '开放咨询', '退款申请', '课程推荐'",
+        min_length=1,
+        max_length=100,
+    )
+    description: str | None = Field(
+        default=None,
+        description="场景描述"
+    )
+    required_slots: list[str] = Field(
+        default_factory=list,
+        sa_column=Column("required_slots", JSON, nullable=False),
+        description="必填槽位 slot_key 列表"
+    )
+    optional_slots: list[str] = Field(
+        default_factory=list,
+        sa_column=Column("optional_slots", JSON, nullable=False),
+        description="可选槽位 slot_key 列表"
+    )
+    slot_priority: list[str] | None = Field(
+        default=None,
+        sa_column=Column("slot_priority", JSON, nullable=True),
+        description="槽位采集优先级顺序（slot_key 列表）"
+    )
+    completion_threshold: float = Field(
+        default=1.0,
+        ge=0.0,
+        le=1.0,
+        description="完成阈值（0.0-1.0），必填槽位填充比例达到此值视为完成"
+    )
+    ask_back_order: str = Field(
+        default="priority",
+        description="追问顺序策略: priority/required_first/parallel"
+    )
+    status: str = Field(
+        default=SceneSlotBundleStatus.DRAFT.value,
+        description="状态: draft/active/deprecated"
+    )
+    version: int = Field(default=1, description="版本号")
+    created_at: datetime = Field(default_factory=datetime.utcnow, description="创建时间")
+    updated_at: datetime = Field(default_factory=datetime.utcnow, description="更新时间")
+
+
+class SceneSlotBundleCreate(SQLModel):
+    """[AC-SCENE-SLOT-01] 创建场景槽位包"""
+
+    scene_key: str = Field(..., min_length=1, max_length=100)
+    scene_name: str = Field(..., min_length=1, max_length=100)
+    description: str | None = None
+    required_slots: list[str] = Field(default_factory=list)
+    optional_slots: list[str] = Field(default_factory=list)
+    slot_priority: list[str] | None = None
+    completion_threshold: float = Field(default=1.0, ge=0.0, le=1.0)
+    ask_back_order: str = Field(default="priority")
+    status: str = Field(default=SceneSlotBundleStatus.DRAFT.value)
+
+
+class SceneSlotBundleUpdate(SQLModel):
+    """[AC-SCENE-SLOT-01] 更新场景槽位包"""
+
+    scene_name: str | None = Field(default=None, min_length=1, max_length=100)
+    description: str | None = None
+    required_slots: list[str] | None = None
+    optional_slots: list[str] | None = None
+    slot_priority: list[str] | None = None
+    completion_threshold: float | None = Field(default=None, ge=0.0, le=1.0)
+    ask_back_order: str | None = None
+    status: str | None = None

ai-service/app/models/mid/__init__.py

@@ -73,6 +73,7 @@ class DialogueRequest(BaseModel):
     history: list[HistoryMessage] = Field(default_factory=list, description="已送达历史")
     interrupted_segments: list[InterruptedSegment] | None = Field(default=None, description="打断的分段")
     feature_flags: FeatureFlags | None = Field(default=None, description="特性开关")
+    scene: str | None = Field(default=None, description="场景标识，用于KB过滤，如 'open_consult', 'after_sale'")
 
 
 class Segment(BaseModel):
@@ -127,6 +128,7 @@ class TraceInfo(BaseModel):
     )
     tools_used: list[str] | None = Field(default=None, description="使用的工具列表")
     tool_calls: list[ToolCallTraceModel] | None = Field(default=None, description="工具调用追踪")
+    step_kb_binding: dict[str, Any] | None = Field(default=None, description="[Step-KB-Binding] 步骤知识库绑定信息")
 
 
 class DialogueResponse(BaseModel):

ai-service/app/models/mid/schemas.py

@@ -86,6 +86,7 @@ class DialogueRequest(BaseModel):
     humanize_config: HumanizeConfigRequest | None = Field(
         default=None, description="Humanize config for segment delay"
     )
+    scene: str | None = Field(default=None, description="Scene identifier for KB filtering, e.g., 'open_consult', 'after_sale'")
 
 
 class Segment(BaseModel):
@@ -122,6 +123,8 @@ class ToolCallTrace(BaseModel):
     error_code: str | None = Field(default=None, description="Error code if failed")
     args_digest: str | None = Field(default=None, description="Arguments digest for logging")
     result_digest: str | None = Field(default=None, description="Result digest for logging")
+    arguments: dict[str, Any] | None = Field(default=None, description="Full tool call arguments")
+    result: Any = Field(default=None, description="Full tool call result")
 
 
 class SegmentStats(BaseModel):
@@ -133,7 +136,7 @@ class SegmentStats(BaseModel):
 
 class TraceInfo(BaseModel):
     """[AC-MARH-02, AC-MARH-03, AC-MARH-05, AC-MARH-06, AC-MARH-07, AC-MARH-11,
-    AC-MARH-12, AC-MARH-18, AC-MARH-19, AC-MARH-20] Trace info for observability."""
+    AC-MARH-12, AC-MARH-18, AC-MARH-19, AC-MARH-20, AC-SCENE-SLOT-02] Trace info for observability."""
     mode: ExecutionMode = Field(..., description="Execution mode")
     intent: str | None = Field(default=None, description="Matched intent")
     request_id: str | None = Field(
@@ -156,6 +159,17 @@ class TraceInfo(BaseModel):
     high_risk_policy_set: list[HighRiskScenario] | None = Field(default=None, description="Active high-risk policy set")
     tools_used: list[str] | None = Field(default=None, description="Tools used in this request")
     tool_calls: list[ToolCallTrace] | None = Field(default=None, description="Tool call traces")
+    duration_ms: int = Field(default=0, ge=0, description="Execution duration in milliseconds")
+    created_at: str | None = Field(default=None, description="Creation timestamp")
+    # [AC-SCENE-SLOT-02] 场景槽位追踪字段
+    scene: str | None = Field(default=None, description="当前场景标识")
+    scene_slot_context: dict[str, Any] | None = Field(default=None, description="场景槽位上下文信息")
+    missing_slots: list[str] | None = Field(default=None, description="缺失的必填槽位列表")
+    ask_back_triggered: bool | None = Field(default=False, description="是否触发了追问")
+    slot_sources: dict[str, str] | None = Field(default=None, description="槽位值来源映射")
+    kb_filter_sources: dict[str, str] | None = Field(default=None, description="KB 过滤条件来源映射")
+    # [Step-KB-Binding] 步骤知识库绑定追踪
+    step_kb_binding: dict[str, Any] | None = Field(default=None, description="步骤知识库绑定信息，包含 step_id, allowed_kb_ids, used_kb_ids 等")
 
 
 class DialogueResponse(BaseModel):

ai-service/app/models/mid/tool_trace.py

@@ -43,6 +43,8 @@ class ToolCallTrace:
     - error_code: 错误码
     - args_digest: 参数摘要（脱敏）
     - result_digest: 结果摘要
+    - arguments: 完整参数
+    - result: 完整结果
     """
     tool_name: str
     duration_ms: int
@@ -53,6 +55,8 @@ class ToolCallTrace:
     error_code: str | None = None
     args_digest: str | None = None
     result_digest: str | None = None
+    arguments: dict[str, Any] | None = None
+    result: Any = None
     started_at: datetime = field(default_factory=datetime.utcnow)
     completed_at: datetime | None = None
 
@@ -74,6 +78,10 @@ class ToolCallTrace:
             result["args_digest"] = self.args_digest
         if self.result_digest:
             result["result_digest"] = self.result_digest
+        if self.arguments:
+            result["arguments"] = self.arguments
+        if self.result is not None:
+            result["result"] = self.result
         return result
 
     @staticmethod

ai-service/app/schemas/metadata.py

@@ -139,7 +139,16 @@ class SlotDefinitionResponse(BaseModel):
     slot_key: str = Field(..., description="槽位键名")
     type: str = Field(..., description="槽位类型")
     required: bool = Field(default=False, description="是否必填槽位")
-    extract_strategy: str | None = Field(default=None, description="提取策略")
+    # [AC-MRS-07-UPGRADE] 保留旧字段用于兼容
+    extract_strategy: str | None = Field(
+        default=None,
+        description="[兼容字段] 单提取策略，已废弃"
+    )
+    # [AC-MRS-07-UPGRADE] 新增策略链字段
+    extract_strategies: list[str] | None = Field(
+        default=None,
+        description="[AC-MRS-07-UPGRADE] 提取策略链：有序数组，元素为 rule/llm/user_input"
+    )
     validation_rule: str | None = Field(default=None, description="校验规则")
     ask_back_prompt: str | None = Field(default=None, description="追问提示语模板")
     default_value: dict[str, Any] | None = Field(default=None, description="默认值")
@@ -157,9 +166,15 @@ class SlotDefinitionCreateRequest(BaseModel):
     slot_key: str = Field(..., min_length=1, max_length=100, description="槽位键名")
     type: str = Field(default="string", description="槽位类型")
     required: bool = Field(default=False, description="是否必填槽位")
+    # [AC-MRS-07-UPGRADE] 支持策略链
+    extract_strategies: list[str] | None = Field(
+        default=None,
+        description="[AC-MRS-07-UPGRADE] 提取策略链：有序数组，元素为 rule/llm/user_input，按顺序执行直到成功"
+    )
+    # [AC-MRS-07-UPGRADE] 保留旧字段用于兼容
     extract_strategy: str | None = Field(
         default=None,
-        description="提取策略: rule/llm/user_input"
+        description="[兼容字段] 单提取策略，已废弃，请使用 extract_strategies"
     )
     validation_rule: str | None = Field(default=None, description="校验规则")
     ask_back_prompt: str | None = Field(default=None, description="追问提示语模板")
@@ -172,7 +187,16 @@ class SlotDefinitionUpdateRequest(BaseModel):
 
     type: str | None = None
     required: bool | None = None
-    extract_strategy: str | None = None
+    # [AC-MRS-07-UPGRADE] 支持策略链
+    extract_strategies: list[str] | None = Field(
+        default=None,
+        description="[AC-MRS-07-UPGRADE] 提取策略链：有序数组，元素为 rule/llm/user_input，按顺序执行直到成功"
+    )
+    # [AC-MRS-07-UPGRADE] 保留旧字段用于兼容
+    extract_strategy: str | None = Field(
+        default=None,
+        description="[兼容字段] 单提取策略，已废弃，请使用 extract_strategies"
+    )
     validation_rule: str | None = None
     ask_back_prompt: str | None = None
     default_value: dict[str, Any] | None = None

ai-service/app/schemas/retrieval_strategy.py

@@ -0,0 +1,198 @@
+"""
+Retrieval Strategy Schemas for AI Service.
+[AC-AISVC-RES-01~15] Request/Response models aligned with OpenAPI contract.
+"""
+
+from enum import Enum
+from typing import Any
+
+from pydantic import BaseModel, Field, model_validator
+
+
+class StrategyType(str, Enum):
+    DEFAULT = "default"
+    ENHANCED = "enhanced"
+
+
+class ReactMode(str, Enum):
+    REACT = "react"
+    NON_REACT = "non_react"
+
+
+class RolloutMode(str, Enum):
+    OFF = "off"
+    PERCENTAGE = "percentage"
+    ALLOWLIST = "allowlist"
+
+
+class ValidationCheckType(str, Enum):
+    METADATA_CONSISTENCY = "metadata_consistency"
+    EMBEDDING_PREFIX = "embedding_prefix"
+    RRF_CONFIG = "rrf_config"
+    PERFORMANCE_BUDGET = "performance_budget"
+
+
+class RolloutConfig(BaseModel):
+    """
+    [AC-AISVC-RES-03] Grayscale rollout configuration.
+    """
+
+    mode: RolloutMode = Field(..., description="Rollout mode: off, percentage, or allowlist")
+    percentage: float | None = Field(
+        default=None,
+        ge=0,
+        le=100,
+        description="Percentage of traffic for grayscale (0-100)",
+    )
+    allowlist: list[str] | None = Field(
+        default=None,
+        description="List of tenant IDs in allowlist",
+    )
+
+    @model_validator(mode="after")
+    def validate_rollout_config(self) -> "RolloutConfig":
+        if self.mode == RolloutMode.PERCENTAGE and self.percentage is None:
+            raise ValueError("percentage is required when mode is 'percentage'")
+        if self.mode == RolloutMode.ALLOWLIST and (
+            self.allowlist is None or len(self.allowlist) == 0
+        ):
+            raise ValueError("allowlist is required when mode is 'allowlist'")
+        return self
+
+
+class RetrievalStrategyStatus(BaseModel):
+    """
+    [AC-AISVC-RES-01] Current retrieval strategy status.
+    """
+
+    active_strategy: StrategyType = Field(
+        ...,
+        alias="active_strategy",
+        description="Current active strategy: default or enhanced",
+    )
+    react_mode: ReactMode = Field(
+        ...,
+        alias="react_mode",
+        description="ReAct mode: react or non_react",
+    )
+    rollout: RolloutConfig = Field(..., description="Grayscale rollout configuration")
+
+    model_config = {"populate_by_name": True}
+
+
+class RetrievalStrategySwitchRequest(BaseModel):
+    """
+    [AC-AISVC-RES-02, AC-AISVC-RES-03, AC-AISVC-RES-05] Request to switch retrieval strategy.
+    """
+
+    target_strategy: StrategyType = Field(
+        ...,
+        alias="target_strategy",
+        description="Target strategy to switch to",
+    )
+    react_mode: ReactMode | None = Field(
+        default=None,
+        alias="react_mode",
+        description="ReAct mode to use",
+    )
+    rollout: RolloutConfig | None = Field(
+        default=None,
+        description="Grayscale rollout configuration",
+    )
+    reason: str | None = Field(
+        default=None,
+        description="Reason for strategy switch",
+    )
+
+    model_config = {"populate_by_name": True}
+
+
+class RetrievalStrategySwitchResponse(BaseModel):
+    """
+    [AC-AISVC-RES-02] Response after strategy switch.
+    """
+
+    previous: RetrievalStrategyStatus = Field(..., description="Previous strategy status")
+    current: RetrievalStrategyStatus = Field(..., description="Current strategy status")
+
+
+class RetrievalStrategyValidationRequest(BaseModel):
+    """
+    [AC-AISVC-RES-04, AC-AISVC-RES-06, AC-AISVC-RES-08] Request to validate strategy.
+    """
+
+    strategy: StrategyType = Field(..., description="Strategy to validate")
+    react_mode: ReactMode | None = Field(
+        default=None,
+        description="ReAct mode to validate",
+    )
+    checks: list[ValidationCheckType] | None = Field(
+        default=None,
+        description="List of checks to perform",
+    )
+
+    model_config = {"populate_by_name": True}
+
+
+class ValidationResult(BaseModel):
+    """
+    [AC-AISVC-RES-06] Single validation check result.
+    """
+
+    check: str = Field(..., description="Check name")
+    passed: bool = Field(..., description="Whether the check passed")
+    message: str | None = Field(default=None, description="Additional message")
+
+
+class RetrievalStrategyValidationResponse(BaseModel):
+    """
+    [AC-AISVC-RES-06] Validation response with all check results.
+    """
+
+    passed: bool = Field(..., description="Whether all checks passed")
+    results: list[ValidationResult] = Field(..., description="Individual check results")
+
+
+class RetrievalStrategyRollbackResponse(BaseModel):
+    """
+    [AC-AISVC-RES-07] Response after strategy rollback.
+    """
+
+    current: RetrievalStrategyStatus = Field(..., description="Current strategy status before rollback")
+    rollback_to: RetrievalStrategyStatus = Field(..., description="Strategy status after rollback")
+
+
+class StrategyAuditLog(BaseModel):
+    """
+    [AC-AISVC-RES-07] Audit log entry for strategy operations.
+    """
+
+    timestamp: str = Field(..., description="ISO timestamp of the operation")
+    operation: str = Field(..., description="Operation type: switch, rollback, validate")
+    previous_strategy: str | None = Field(default=None, description="Previous strategy")
+    new_strategy: str | None = Field(default=None, description="New strategy")
+    previous_react_mode: str | None = Field(default=None, description="Previous react mode")
+    new_react_mode: str | None = Field(default=None, description="New react mode")
+    reason: str | None = Field(default=None, description="Reason for the operation")
+    operator: str | None = Field(default=None, description="Operator who performed the operation")
+    tenant_id: str | None = Field(default=None, description="Tenant ID if applicable")
+    metadata: dict[str, Any] | None = Field(default=None, description="Additional metadata")
+
+
+class StrategyMetrics(BaseModel):
+    """
+    [AC-AISVC-RES-03, AC-AISVC-RES-08] Metrics for strategy operations.
+    """
+
+    strategy: StrategyType = Field(..., description="Current strategy")
+    react_mode: ReactMode = Field(..., description="Current react mode")
+    total_requests: int = Field(default=0, description="Total requests count")
+    successful_requests: int = Field(default=0, description="Successful requests count")
+    failed_requests: int = Field(default=0, description="Failed requests count")
+    avg_latency_ms: float = Field(default=0.0, description="Average latency in ms")
+    p99_latency_ms: float = Field(default=0.0, description="P99 latency in ms")
+    direct_route_count: int = Field(default=0, description="Direct route count")
+    react_route_count: int = Field(default=0, description="React route count")
+    auto_route_count: int = Field(default=0, description="Auto route count")
+    fallback_count: int = Field(default=0, description="Fallback to default count")
+    last_updated: str | None = Field(default=None, description="Last update timestamp")

ai-service/app/services/api_key.py

@@ -81,7 +81,6 @@ class ApiKeyService:
             return
         except Exception as e:
             logger.warning(f"[AC-AISVC-50] Full API key schema load failed, fallback to legacy columns: {e}")
-            await session.rollback()
 
         # Backward-compat fallback for environments without new columns
         try:

ai-service/app/services/cache/scene_slot_bundle_cache.py

@@ -0,0 +1,274 @@
+"""
+Scene Slot Bundle Cache Service.
+[AC-SCENE-SLOT-03] 场景槽位包缓存服务
+
+职责：
+1. 缓存场景槽位包配置，减少数据库查询
+2. 支持缓存失效和刷新
+3. 支持租户隔离
+"""
+
+import json
+import logging
+from dataclasses import dataclass, field
+from datetime import datetime, timedelta
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+try:
+    import redis.asyncio as redis
+    REDIS_AVAILABLE = True
+except ImportError:
+    REDIS_AVAILABLE = False
+
+
+@dataclass
+class CachedSceneSlotBundle:
+    """缓存的场景槽位包"""
+    scene_key: str
+    scene_name: str
+    description: str | None
+    required_slots: list[str]
+    optional_slots: list[str]
+    slot_priority: list[str] | None
+    completion_threshold: float
+    ask_back_order: str
+    status: str
+    version: int
+    cached_at: datetime = field(default_factory=datetime.utcnow)
+    
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "scene_key": self.scene_key,
+            "scene_name": self.scene_name,
+            "description": self.description,
+            "required_slots": self.required_slots,
+            "optional_slots": self.optional_slots,
+            "slot_priority": self.slot_priority,
+            "completion_threshold": self.completion_threshold,
+            "ask_back_order": self.ask_back_order,
+            "status": self.status,
+            "version": self.version,
+            "cached_at": self.cached_at.isoformat(),
+        }
+    
+    @classmethod
+    def from_dict(cls, data: dict[str, Any]) -> "CachedSceneSlotBundle":
+        return cls(
+            scene_key=data["scene_key"],
+            scene_name=data["scene_name"],
+            description=data.get("description"),
+            required_slots=data.get("required_slots", []),
+            optional_slots=data.get("optional_slots", []),
+            slot_priority=data.get("slot_priority"),
+            completion_threshold=data.get("completion_threshold", 1.0),
+            ask_back_order=data.get("ask_back_order", "priority"),
+            status=data.get("status", "draft"),
+            version=data.get("version", 1),
+            cached_at=datetime.fromisoformat(data["cached_at"]) if data.get("cached_at") else datetime.utcnow(),
+        )
+
+
+class SceneSlotBundleCache:
+    """
+    [AC-SCENE-SLOT-03] 场景槽位包缓存
+    
+    使用 Redis 或内存缓存场景槽位包配置
+    """
+    
+    CACHE_PREFIX = "scene_slot_bundle"
+    CACHE_TTL_SECONDS = 300  # 5分钟缓存
+    
+    def __init__(
+        self,
+        redis_client: Any = None,
+        ttl_seconds: int = 300,
+    ):
+        self._redis = redis_client
+        self._ttl = ttl_seconds or self.CACHE_TTL_SECONDS
+        self._memory_cache: dict[str, tuple[CachedSceneSlotBundle, datetime]] = {}
+    
+    def _get_cache_key(self, tenant_id: str, scene_key: str) -> str:
+        """生成缓存键"""
+        return f"{self.CACHE_PREFIX}:{tenant_id}:{scene_key}"
+    
+    async def get(
+        self,
+        tenant_id: str,
+        scene_key: str,
+    ) -> CachedSceneSlotBundle | None:
+        """
+        获取缓存的场景槽位包
+        
+        Args:
+            tenant_id: 租户 ID
+            scene_key: 场景标识
+            
+        Returns:
+            缓存的场景槽位包或 None
+        """
+        cache_key = self._get_cache_key(tenant_id, scene_key)
+        
+        if self._redis and REDIS_AVAILABLE:
+            try:
+                cached_data = await self._redis.get(cache_key)
+                if cached_data:
+                    data = json.loads(cached_data)
+                    return CachedSceneSlotBundle.from_dict(data)
+            except Exception as e:
+                logger.warning(
+                    f"[AC-SCENE-SLOT-03] Redis get failed: {e}, falling back to memory cache"
+                )
+        
+        if cache_key in self._memory_cache:
+            cached_bundle, cached_at = self._memory_cache[cache_key]
+            if datetime.utcnow() - cached_at < timedelta(seconds=self._ttl):
+                return cached_bundle
+            else:
+                del self._memory_cache[cache_key]
+        
+        return None
+    
+    async def set(
+        self,
+        tenant_id: str,
+        scene_key: str,
+        bundle: CachedSceneSlotBundle,
+    ) -> bool:
+        """
+        设置缓存
+        
+        Args:
+            tenant_id: 租户 ID
+            scene_key: 场景标识
+            bundle: 要缓存的场景槽位包
+            
+        Returns:
+            是否成功
+        """
+        cache_key = self._get_cache_key(tenant_id, scene_key)
+        
+        if self._redis and REDIS_AVAILABLE:
+            try:
+                await self._redis.setex(
+                    cache_key,
+                    self._ttl,
+                    json.dumps(bundle.to_dict()),
+                )
+                return True
+            except Exception as e:
+                logger.warning(
+                    f"[AC-SCENE-SLOT-03] Redis set failed: {e}, falling back to memory cache"
+                )
+        
+        self._memory_cache[cache_key] = (bundle, datetime.utcnow())
+        return True
+    
+    async def delete(
+        self,
+        tenant_id: str,
+        scene_key: str,
+    ) -> bool:
+        """
+        删除缓存
+        
+        Args:
+            tenant_id: 租户 ID
+            scene_key: 场景标识
+            
+        Returns:
+            是否成功
+        """
+        cache_key = self._get_cache_key(tenant_id, scene_key)
+        
+        if self._redis and REDIS_AVAILABLE:
+            try:
+                await self._redis.delete(cache_key)
+            except Exception as e:
+                logger.warning(
+                    f"[AC-SCENE-SLOT-03] Redis delete failed: {e}"
+                )
+        
+        if cache_key in self._memory_cache:
+            del self._memory_cache[cache_key]
+        
+        return True
+    
+    async def delete_by_tenant(self, tenant_id: str) -> bool:
+        """
+        删除租户下所有缓存
+        
+        Args:
+            tenant_id: 租户 ID
+            
+        Returns:
+            是否成功
+        """
+        pattern = f"{self.CACHE_PREFIX}:{tenant_id}:*"
+        
+        if self._redis and REDIS_AVAILABLE:
+            try:
+                keys = []
+                async for key in self._redis.scan_iter(match=pattern):
+                    keys.append(key)
+                if keys:
+                    await self._redis.delete(*keys)
+            except Exception as e:
+                logger.warning(
+                    f"[AC-SCENE-SLOT-03] Redis delete by tenant failed: {e}"
+                )
+        
+        keys_to_delete = [
+            k for k in self._memory_cache
+            if k.startswith(f"{self.CACHE_PREFIX}:{tenant_id}:")
+        ]
+        for key in keys_to_delete:
+            del self._memory_cache[key]
+        
+        return True
+    
+    async def invalidate_on_update(
+        self,
+        tenant_id: str,
+        scene_key: str,
+    ) -> bool:
+        """
+        当场景槽位包更新时使缓存失效
+        
+        Args:
+            tenant_id: 租户 ID
+            scene_key: 场景标识
+            
+        Returns:
+            是否成功
+        """
+        logger.info(
+            f"[AC-SCENE-SLOT-03] Invalidating cache for scene slot bundle: "
+            f"tenant={tenant_id}, scene={scene_key}"
+        )
+        return await self.delete(tenant_id, scene_key)
+
+
+_scene_slot_bundle_cache: SceneSlotBundleCache | None = None
+
+
+def get_scene_slot_bundle_cache() -> SceneSlotBundleCache:
+    """获取场景槽位包缓存实例"""
+    global _scene_slot_bundle_cache
+    if _scene_slot_bundle_cache is None:
+        _scene_slot_bundle_cache = SceneSlotBundleCache()
+    return _scene_slot_bundle_cache
+
+
+def init_scene_slot_bundle_cache(redis_client: Any = None, ttl_seconds: int = 300) -> None:
+    """初始化场景槽位包缓存"""
+    global _scene_slot_bundle_cache
+    _scene_slot_bundle_cache = SceneSlotBundleCache(
+        redis_client=redis_client,
+        ttl_seconds=ttl_seconds,
+    )
+    logger.info(
+        f"[AC-SCENE-SLOT-03] Scene slot bundle cache initialized: "
+        f"ttl={ttl_seconds}s, redis={redis_client is not None}"
+    )

ai-service/app/services/cache/slot_state_cache.py

@@ -0,0 +1,397 @@
+"""
+Slot State Cache Layer.
+槽位状态缓存层 - 提供会话级槽位状态持久化
+
+[AC-MRS-SLOT-CACHE-01] 多轮状态持久化
+
+Features:
+- L1: In-memory cache (process-level, 5 min TTL)
+- L2: Redis cache (shared, configurable TTL)
+- Automatic fallback on cache miss
+- Support for slot value source priority
+
+Key format: slot_state:{tenant_id}:{session_id}
+TTL: Configurable (default 30 minutes)
+"""
+
+import json
+import logging
+import time
+from dataclasses import asdict, dataclass, field
+from datetime import datetime
+from typing import Any
+
+import redis.asyncio as redis
+
+from app.core.config import get_settings
+from app.models.mid.schemas import SlotSource
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class CachedSlotValue:
+    """
+    缓存的槽位值
+    
+    Attributes:
+        value: 槽位值
+        source: 值来源 (user_confirmed, rule_extracted, llm_inferred, default, context)
+        confidence: 置信度
+        updated_at: 更新时间戳
+    """
+    value: Any
+    source: str
+    confidence: float = 1.0
+    updated_at: float = field(default_factory=time.time)
+    
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "value": self.value,
+            "source": self.source,
+            "confidence": self.confidence,
+            "updated_at": self.updated_at,
+        }
+    
+    @classmethod
+    def from_dict(cls, data: dict[str, Any]) -> "CachedSlotValue":
+        return cls(
+            value=data["value"],
+            source=data["source"],
+            confidence=data.get("confidence", 1.0),
+            updated_at=data.get("updated_at", time.time()),
+        )
+
+
+@dataclass
+class CachedSlotState:
+    """
+    缓存的槽位状态
+    
+    Attributes:
+        filled_slots: 已填充的槽位值字典 {slot_key: CachedSlotValue}
+        slot_to_field_map: 槽位到元数据字段的映射
+        created_at: 创建时间
+        updated_at: 最后更新时间
+    """
+    filled_slots: dict[str, CachedSlotValue] = field(default_factory=dict)
+    slot_to_field_map: dict[str, str] = field(default_factory=dict)
+    created_at: float = field(default_factory=time.time)
+    updated_at: float = field(default_factory=time.time)
+    
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "filled_slots": {
+                k: v.to_dict() for k, v in self.filled_slots.items()
+            },
+            "slot_to_field_map": self.slot_to_field_map,
+            "created_at": self.created_at,
+            "updated_at": self.updated_at,
+        }
+    
+    @classmethod
+    def from_dict(cls, data: dict[str, Any]) -> "CachedSlotState":
+        filled_slots = {}
+        for k, v in data.get("filled_slots", {}).items():
+            if isinstance(v, dict):
+                filled_slots[k] = CachedSlotValue.from_dict(v)
+            else:
+                filled_slots[k] = CachedSlotValue(value=v, source="unknown")
+        
+        return cls(
+            filled_slots=filled_slots,
+            slot_to_field_map=data.get("slot_to_field_map", {}),
+            created_at=data.get("created_at", time.time()),
+            updated_at=data.get("updated_at", time.time()),
+        )
+    
+    def get_simple_filled_slots(self) -> dict[str, Any]:
+        """获取简化的已填充槽位字典（仅值）"""
+        return {k: v.value for k, v in self.filled_slots.items()}
+    
+    def get_slot_sources(self) -> dict[str, str]:
+        """获取槽位来源字典"""
+        return {k: v.source for k, v in self.filled_slots.items()}
+    
+    def get_slot_confidence(self) -> dict[str, float]:
+        """获取槽位置信度字典"""
+        return {k: v.confidence for k, v in self.filled_slots.items()}
+
+
+class SlotStateCache:
+    """
+    [AC-MRS-SLOT-CACHE-01] 槽位状态缓存层
+    
+    提供会话级槽位状态持久化，支持：
+    - L1: 内存缓存（进程级，5分钟 TTL）
+    - L2: Redis 缓存（共享，可配置 TTL）
+    - 自动降级（Redis 不可用时仅使用内存缓存）
+    - 槽位值来源优先级合并
+    
+    Key format: slot_state:{tenant_id}:{session_id}
+    TTL: Configurable via settings.slot_state_cache_ttl (default 1800 seconds = 30 minutes)
+    """
+    
+    _local_cache: dict[str, tuple[CachedSlotState, float]] = {}
+    _local_cache_ttl = 300
+    
+    SOURCE_PRIORITY = {
+        SlotSource.USER_CONFIRMED.value: 100,
+        "user_confirmed": 100,
+        SlotSource.RULE_EXTRACTED.value: 80,
+        "rule_extracted": 80,
+        SlotSource.LLM_INFERRED.value: 60,
+        "llm_inferred": 60,
+        "context": 40,
+        SlotSource.DEFAULT.value: 20,
+        "default": 20,
+        "unknown": 0,
+    }
+    
+    def __init__(self, redis_client: redis.Redis | None = None):
+        self._redis = redis_client
+        self._settings = get_settings()
+        self._enabled = self._settings.redis_enabled
+        self._cache_ttl = getattr(self._settings, "slot_state_cache_ttl", 1800)
+    
+    async def _get_client(self) -> redis.Redis | None:
+        """Get or create Redis client."""
+        if not self._enabled:
+            return None
+        if self._redis is None:
+            try:
+                self._redis = redis.from_url(
+                    self._settings.redis_url,
+                    encoding="utf-8",
+                    decode_responses=True,
+                )
+            except Exception as e:
+                logger.warning(f"[SlotStateCache] Failed to connect to Redis: {e}")
+                self._enabled = False
+                return None
+        return self._redis
+    
+    def _make_key(self, tenant_id: str, session_id: str) -> str:
+        """Generate cache key."""
+        return f"slot_state:{tenant_id}:{session_id}"
+    
+    def _make_local_key(self, tenant_id: str, session_id: str) -> str:
+        """Generate local cache key."""
+        return f"{tenant_id}:{session_id}"
+    
+    def _get_source_priority(self, source: str) -> int:
+        """Get priority for a source."""
+        return self.SOURCE_PRIORITY.get(source, 0)
+    
+    async def get(
+        self,
+        tenant_id: str,
+        session_id: str,
+    ) -> CachedSlotState | None:
+        """
+        Get cached slot state (L1 -> L2).
+        
+        Args:
+            tenant_id: Tenant ID for isolation
+            session_id: Session ID
+            
+        Returns:
+            CachedSlotState or None if not found
+        """
+        local_key = self._make_local_key(tenant_id, session_id)
+        if local_key in self._local_cache:
+            state, timestamp = self._local_cache[local_key]
+            if time.time() - timestamp < self._local_cache_ttl:
+                logger.debug(f"[SlotStateCache] L1 hit: {local_key}")
+                return state
+            else:
+                del self._local_cache[local_key]
+        
+        client = await self._get_client()
+        if client is None:
+            return None
+        
+        key = self._make_key(tenant_id, session_id)
+        
+        try:
+            data = await client.get(key)
+            if data:
+                logger.debug(f"[SlotStateCache] L2 hit: {key}")
+                state_dict = json.loads(data)
+                state = CachedSlotState.from_dict(state_dict)
+                
+                self._local_cache[local_key] = (state, time.time())
+                
+                return state
+            return None
+        except Exception as e:
+            logger.warning(f"[SlotStateCache] Failed to get from cache: {e}")
+            return None
+    
+    async def set(
+        self,
+        tenant_id: str,
+        session_id: str,
+        state: CachedSlotState,
+    ) -> bool:
+        """
+        Set slot state to cache (L1 + L2).
+        
+        Args:
+            tenant_id: Tenant ID for isolation
+            session_id: Session ID
+            state: CachedSlotState to cache
+            
+        Returns:
+            True if successful
+        """
+        local_key = self._make_local_key(tenant_id, session_id)
+        state.updated_at = time.time()
+        self._local_cache[local_key] = (state, time.time())
+        
+        client = await self._get_client()
+        if client is None:
+            return False
+        
+        key = self._make_key(tenant_id, session_id)
+        
+        try:
+            await client.setex(
+                key,
+                self._cache_ttl,
+                json.dumps(state.to_dict(), default=str),
+            )
+            logger.debug(f"[SlotStateCache] Set cache: {key}")
+            return True
+        except Exception as e:
+            logger.warning(f"[SlotStateCache] Failed to set cache: {e}")
+            return False
+    
+    async def merge_and_set(
+        self,
+        tenant_id: str,
+        session_id: str,
+        new_slots: dict[str, CachedSlotValue],
+        slot_to_field_map: dict[str, str] | None = None,
+    ) -> CachedSlotState:
+        """
+        Merge new slot values with cached state and save.
+        
+        Priority: user_confirmed > rule_extracted > llm_inferred > context > default
+        
+        Args:
+            tenant_id: Tenant ID
+            session_id: Session ID
+            new_slots: New slot values to merge
+            slot_to_field_map: Slot to field mapping
+            
+        Returns:
+            Updated CachedSlotState
+        """
+        state = await self.get(tenant_id, session_id)
+        if state is None:
+            state = CachedSlotState()
+        
+        for slot_key, new_value in new_slots.items():
+            if slot_key in state.filled_slots:
+                existing = state.filled_slots[slot_key]
+                existing_priority = self._get_source_priority(existing.source)
+                new_priority = self._get_source_priority(new_value.source)
+                
+                if new_priority >= existing_priority:
+                    state.filled_slots[slot_key] = new_value
+                    logger.debug(
+                        f"[SlotStateCache] Slot '{slot_key}' updated: "
+                        f"{existing.source}({existing_priority}) -> "
+                        f"{new_value.source}({new_priority})"
+                    )
+            else:
+                state.filled_slots[slot_key] = new_value
+                logger.debug(
+                    f"[SlotStateCache] Slot '{slot_key}' added: "
+                    f"source={new_value.source}, value={new_value.value}"
+                )
+        
+        if slot_to_field_map:
+            state.slot_to_field_map.update(slot_to_field_map)
+        
+        await self.set(tenant_id, session_id, state)
+        
+        return state
+    
+    async def delete(
+        self,
+        tenant_id: str,
+        session_id: str,
+    ) -> bool:
+        """
+        Delete slot state from cache (L1 + L2).
+        
+        Args:
+            tenant_id: Tenant ID for isolation
+            session_id: Session ID
+            
+        Returns:
+            True if successful
+        """
+        local_key = self._make_local_key(tenant_id, session_id)
+        if local_key in self._local_cache:
+            del self._local_cache[local_key]
+        
+        client = await self._get_client()
+        if client is None:
+            return False
+        
+        key = self._make_key(tenant_id, session_id)
+        
+        try:
+            await client.delete(key)
+            logger.debug(f"[SlotStateCache] Deleted cache: {key}")
+            return True
+        except Exception as e:
+            logger.warning(f"[SlotStateCache] Failed to delete cache: {e}")
+            return False
+    
+    async def clear_slot(
+        self,
+        tenant_id: str,
+        session_id: str,
+        slot_key: str,
+    ) -> bool:
+        """
+        Clear a specific slot from cached state.
+        
+        Args:
+            tenant_id: Tenant ID
+            session_id: Session ID
+            slot_key: Slot key to clear
+            
+        Returns:
+            True if successful
+        """
+        state = await self.get(tenant_id, session_id)
+        if state is None:
+            return True
+        
+        if slot_key in state.filled_slots:
+            del state.filled_slots[slot_key]
+            await self.set(tenant_id, session_id, state)
+            logger.debug(f"[SlotStateCache] Cleared slot: {slot_key}")
+        
+        return True
+    
+    async def close(self) -> None:
+        """Close Redis connection."""
+        if self._redis:
+            await self._redis.close()
+
+
+_slot_state_cache: SlotStateCache | None = None
+
+
+def get_slot_state_cache() -> SlotStateCache:
+    """Get singleton SlotStateCache instance."""
+    global _slot_state_cache
+    if _slot_state_cache is None:
+        _slot_state_cache = SlotStateCache()
+    return _slot_state_cache

ai-service/app/services/embedding/factory.py

@@ -12,6 +12,9 @@ import logging
 from pathlib import Path
 from typing import Any
 
+import redis
+
+from app.core.config import get_settings
 from app.services.embedding.base import EmbeddingException, EmbeddingProvider
 from app.services.embedding.nomic_provider import NomicEmbeddingProvider
 from app.services.embedding.ollama_provider import OllamaEmbeddingProvider
@@ -20,6 +23,7 @@ from app.services.embedding.openai_provider import OpenAIEmbeddingProvider
 logger = logging.getLogger(__name__)
 
 EMBEDDING_CONFIG_FILE = Path("config/embedding_config.json")
+EMBEDDING_CONFIG_REDIS_KEY = "ai_service:config:embedding"
 
 
 class EmbeddingProviderFactory:
@@ -170,8 +174,32 @@ class EmbeddingConfigManager:
         self._config = self._default_config.copy()
         self._provider: EmbeddingProvider | None = None
 
+        self._settings = get_settings()
+        self._redis_client: redis.Redis | None = None
+
+        self._load_from_redis()
         self._load_from_file()
 
+    def _load_from_redis(self) -> None:
+        """Load configuration from Redis if exists."""
+        try:
+            if not self._settings.redis_enabled:
+                return
+            self._redis_client = redis.from_url(
+                self._settings.redis_url,
+                encoding="utf-8",
+                decode_responses=True,
+            )
+            saved_raw = self._redis_client.get(EMBEDDING_CONFIG_REDIS_KEY)
+            if not saved_raw:
+                return
+            saved = json.loads(saved_raw)
+            self._provider_name = saved.get("provider", self._default_provider)
+            self._config = saved.get("config", self._default_config.copy())
+            logger.info(f"Loaded embedding config from Redis: provider={self._provider_name}")
+        except Exception as e:
+            logger.warning(f"Failed to load embedding config from Redis: {e}")
+
     def _load_from_file(self) -> None:
         """Load configuration from file if exists."""
         try:
@@ -184,6 +212,28 @@ class EmbeddingConfigManager:
         except Exception as e:
             logger.warning(f"Failed to load embedding config from file: {e}")
 
+    def _save_to_redis(self) -> None:
+        """Save configuration to Redis."""
+        try:
+            if not self._settings.redis_enabled:
+                return
+            if self._redis_client is None:
+                self._redis_client = redis.from_url(
+                    self._settings.redis_url,
+                    encoding="utf-8",
+                    decode_responses=True,
+                )
+            self._redis_client.set(
+                EMBEDDING_CONFIG_REDIS_KEY,
+                json.dumps({
+                    "provider": self._provider_name,
+                    "config": self._config,
+                }, ensure_ascii=False),
+            )
+            logger.info(f"Saved embedding config to Redis: provider={self._provider_name}")
+        except Exception as e:
+            logger.warning(f"Failed to save embedding config to Redis: {e}")
+
     def _save_to_file(self) -> None:
         """Save configuration to file."""
         try:
@@ -262,6 +312,7 @@ class EmbeddingConfigManager:
             self._config = config
             self._provider = new_provider_instance
 
+            self._save_to_redis()
             self._save_to_file()
 
             logger.info(f"Updated embedding config: provider={provider}")

ai-service/app/services/flow/engine.py

@@ -322,7 +322,7 @@ class FlowEngine:
         stmt = select(FlowInstance).where(
             FlowInstance.tenant_id == tenant_id,
             FlowInstance.session_id == session_id,
-        ).order_by(col(FlowInstance.created_at).desc())
+        ).order_by(col(FlowInstance.started_at).desc())
         result = await self._session.execute(stmt)
         instance = result.scalar_one_or_none()
 

ai-service/app/services/intent/clarification.py

@@ -0,0 +1,385 @@
+"""
+Clarification mechanism for intent recognition.
+[AC-CLARIFY] 澄清机制实现
+
+核心功能：
+1. 统一置信度计算
+2. 硬拦截规则（confidence检查、required_slots检查）
+3. 澄清状态管理
+4. 埋点指标收集
+"""
+
+import logging
+import time
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Any
+import uuid
+
+logger = logging.getLogger(__name__)
+
+
+T_HIGH = 0.75
+T_LOW = 0.45
+MAX_CLARIFY_RETRY = 3
+
+
+class ClarifyReason(str, Enum):
+    INTENT_AMBIGUITY = "intent_ambiguity"
+    MISSING_SLOT = "missing_slot"
+    LOW_CONFIDENCE = "low_confidence"
+    MULTI_INTENT = "multi_intent"
+
+
+class ClarifyMetrics:
+    _instance = None
+    
+    def __new__(cls):
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+            cls._instance._clarify_trigger_count = 0
+            cls._instance._clarify_converge_count = 0
+            cls._instance._misroute_count = 0
+        return cls._instance
+    
+    def record_clarify_trigger(self) -> None:
+        self._clarify_trigger_count += 1
+        logger.debug(f"[AC-CLARIFY-METRICS] clarify_trigger_count: {self._clarify_trigger_count}")
+    
+    def record_clarify_converge(self) -> None:
+        self._clarify_converge_count += 1
+        logger.debug(f"[AC-CLARIFY-METRICS] clarify_converge_count: {self._clarify_converge_count}")
+    
+    def record_misroute(self) -> None:
+        self._misroute_count += 1
+        logger.debug(f"[AC-CLARIFY-METRICS] misroute_count: {self._misroute_count}")
+    
+    def get_metrics(self) -> dict[str, int]:
+        return {
+            "clarify_trigger_rate": self._clarify_trigger_count,
+            "clarify_converge_rate": self._clarify_converge_count,
+            "misroute_rate": self._misroute_count,
+        }
+    
+    def get_rates(self, total_requests: int) -> dict[str, float]:
+        if total_requests == 0:
+            return {
+                "clarify_trigger_rate": 0.0,
+                "clarify_converge_rate": 0.0,
+                "misroute_rate": 0.0,
+            }
+        
+        return {
+            "clarify_trigger_rate": self._clarify_trigger_count / total_requests,
+            "clarify_converge_rate": self._clarify_converge_count / total_requests if self._clarify_trigger_count > 0 else 0.0,
+            "misroute_rate": self._misroute_count / total_requests,
+        }
+    
+    def reset(self) -> None:
+        self._clarify_trigger_count = 0
+        self._clarify_converge_count = 0
+        self._misroute_count = 0
+
+
+def get_clarify_metrics() -> ClarifyMetrics:
+    return ClarifyMetrics()
+
+
+@dataclass
+class IntentCandidate:
+    intent_id: str
+    intent_name: str
+    confidence: float
+    response_type: str | None = None
+    target_kb_ids: list[str] | None = None
+    flow_id: str | None = None
+    fixed_reply: str | None = None
+    transfer_message: str | None = None
+    
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "intent_id": self.intent_id,
+            "intent_name": self.intent_name,
+            "confidence": self.confidence,
+            "response_type": self.response_type,
+            "target_kb_ids": self.target_kb_ids,
+            "flow_id": self.flow_id,
+            "fixed_reply": self.fixed_reply,
+            "transfer_message": self.transfer_message,
+        }
+
+
+@dataclass
+class HybridIntentResult:
+    intent: IntentCandidate | None
+    confidence: float
+    candidates: list[IntentCandidate] = field(default_factory=list)
+    need_clarify: bool = False
+    clarify_reason: ClarifyReason | None = None
+    missing_slots: list[str] = field(default_factory=list)
+    
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "intent": self.intent.to_dict() if self.intent else None,
+            "confidence": self.confidence,
+            "candidates": [c.to_dict() for c in self.candidates],
+            "need_clarify": self.need_clarify,
+            "clarify_reason": self.clarify_reason.value if self.clarify_reason else None,
+            "missing_slots": self.missing_slots,
+        }
+    
+    @classmethod
+    def from_fusion_result(cls, fusion_result: Any) -> "HybridIntentResult":
+        candidates = []
+        if fusion_result.clarify_candidates:
+            for c in fusion_result.clarify_candidates:
+                candidates.append(IntentCandidate(
+                    intent_id=str(c.id),
+                    intent_name=c.name,
+                    confidence=0.0,
+                    response_type=getattr(c, "response_type", None),
+                    target_kb_ids=getattr(c, "target_kb_ids", None),
+                    flow_id=str(c.flow_id) if getattr(c, "flow_id", None) else None,
+                    fixed_reply=getattr(c, "fixed_reply", None),
+                    transfer_message=getattr(c, "transfer_message", None),
+                ))
+        
+        if fusion_result.final_intent:
+            final_candidate = IntentCandidate(
+                intent_id=str(fusion_result.final_intent.id),
+                intent_name=fusion_result.final_intent.name,
+                confidence=fusion_result.final_confidence,
+                response_type=fusion_result.final_intent.response_type,
+                target_kb_ids=fusion_result.final_intent.target_kb_ids,
+                flow_id=str(fusion_result.final_intent.flow_id) if fusion_result.final_intent.flow_id else None,
+                fixed_reply=fusion_result.final_intent.fixed_reply,
+                transfer_message=fusion_result.final_intent.transfer_message,
+            )
+            if not any(c.intent_id == final_candidate.intent_id for c in candidates):
+                candidates.insert(0, final_candidate)
+        
+        clarify_reason = None
+        if fusion_result.need_clarify:
+            if fusion_result.decision_reason == "multi_intent":
+                clarify_reason = ClarifyReason.MULTI_INTENT
+            elif fusion_result.decision_reason == "gray_zone":
+                clarify_reason = ClarifyReason.INTENT_AMBIGUITY
+            else:
+                clarify_reason = ClarifyReason.LOW_CONFIDENCE
+        
+        return cls(
+            intent=candidates[0] if candidates else None,
+            confidence=fusion_result.final_confidence,
+            candidates=candidates,
+            need_clarify=fusion_result.need_clarify,
+            clarify_reason=clarify_reason,
+        )
+
+
+@dataclass
+class ClarifyState:
+    reason: ClarifyReason
+    asked_slot: str | None = None
+    retry_count: int = 0
+    candidates: list[IntentCandidate] = field(default_factory=list)
+    asked_intent_ids: list[str] = field(default_factory=list)
+    created_at: float = field(default_factory=time.time)
+    
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "reason": self.reason.value,
+            "asked_slot": self.asked_slot,
+            "retry_count": self.retry_count,
+            "candidates": [c.to_dict() for c in self.candidates],
+            "asked_intent_ids": self.asked_intent_ids,
+            "created_at": self.created_at,
+        }
+    
+    def increment_retry(self) -> "ClarifyState":
+        self.retry_count += 1
+        return self
+    
+    def is_max_retry(self) -> bool:
+        return self.retry_count >= MAX_CLARIFY_RETRY
+
+
+class ClarificationEngine:
+    
+    def __init__(
+        self,
+        t_high: float = T_HIGH,
+        t_low: float = T_LOW,
+        max_retry: int = MAX_CLARIFY_RETRY,
+    ):
+        self._t_high = t_high
+        self._t_low = t_low
+        self._max_retry = max_retry
+        self._metrics = get_clarify_metrics()
+    
+    def compute_confidence(
+        self,
+        rule_score: float = 0.0,
+        semantic_score: float = 0.0,
+        llm_score: float = 0.0,
+        w_rule: float = 0.5,
+        w_semantic: float = 0.3,
+        w_llm: float = 0.2,
+    ) -> float:
+        total_weight = w_rule + w_semantic + w_llm
+        if total_weight == 0:
+            return 0.0
+        
+        weighted_score = (
+            rule_score * w_rule +
+            semantic_score * w_semantic +
+            llm_score * w_llm
+        )
+        
+        return min(1.0, max(0.0, weighted_score / total_weight))
+    
+    def check_hard_block(
+        self,
+        result: HybridIntentResult,
+        required_slots: list[str] | None = None,
+        filled_slots: dict[str, Any] | None = None,
+    ) -> tuple[bool, ClarifyReason | None]:
+        if result.confidence < self._t_high:
+            return True, ClarifyReason.LOW_CONFIDENCE
+        
+        if required_slots and filled_slots is not None:
+            missing = [s for s in required_slots if s not in filled_slots]
+            if missing:
+                return True, ClarifyReason.MISSING_SLOT
+        
+        return False, None
+    
+    def should_trigger_clarify(
+        self,
+        result: HybridIntentResult,
+        required_slots: list[str] | None = None,
+        filled_slots: dict[str, Any] | None = None,
+    ) -> tuple[bool, ClarifyState | None]:
+        if result.confidence >= self._t_high:
+            if required_slots and filled_slots is not None:
+                missing = [s for s in required_slots if s not in filled_slots]
+                if missing:
+                    self._metrics.record_clarify_trigger()
+                    return True, ClarifyState(
+                        reason=ClarifyReason.MISSING_SLOT,
+                        asked_slot=missing[0],
+                        candidates=result.candidates,
+                    )
+            return False, None
+        
+        if result.confidence < self._t_low:
+            self._metrics.record_clarify_trigger()
+            return True, ClarifyState(
+                reason=ClarifyReason.LOW_CONFIDENCE,
+                candidates=result.candidates,
+            )
+        
+        self._metrics.record_clarify_trigger()
+        
+        reason = result.clarify_reason or ClarifyReason.INTENT_AMBIGUITY
+        return True, ClarifyState(
+            reason=reason,
+            candidates=result.candidates,
+        )
+    
+    def generate_clarify_prompt(
+        self,
+        state: ClarifyState,
+        slot_label: str | None = None,
+    ) -> str:
+        if state.reason == ClarifyReason.MISSING_SLOT:
+            slot_name = slot_label or state.asked_slot or "相关信息"
+            return f"为了更好地为您服务，请告诉我您的{slot_name}。"
+        
+        if state.reason == ClarifyReason.LOW_CONFIDENCE:
+            return "抱歉，我不太理解您的意思，能否请您详细描述一下您的需求？"
+        
+        if state.reason == ClarifyReason.MULTI_INTENT and len(state.candidates) > 1:
+            candidates = state.candidates[:3]
+            if len(candidates) == 2:
+                return (
+                    f"请问您是想「{candidates[0].intent_name}」"
+                    f"还是「{candidates[1].intent_name}」？"
+                )
+            else:
+                options = "、".join([f"「{c.intent_name}」" for c in candidates[:-1]])
+                return f"请问您是想{options}，还是「{candidates[-1].intent_name}」？"
+        
+        if state.reason == ClarifyReason.INTENT_AMBIGUITY and len(state.candidates) > 1:
+            candidates = state.candidates[:2]
+            return (
+                f"请问您是想「{candidates[0].intent_name}」"
+                f"还是「{candidates[1].intent_name}」？"
+            )
+        
+        return "请问您具体想了解什么？"
+    
+    def process_clarify_response(
+        self,
+        user_message: str,
+        state: ClarifyState,
+        intent_router: Any = None,
+        rules: list[Any] | None = None,
+    ) -> HybridIntentResult:
+        state.increment_retry()
+        
+        if state.is_max_retry():
+            self._metrics.record_misroute()
+            return HybridIntentResult(
+                intent=None,
+                confidence=0.0,
+                need_clarify=False,
+            )
+        
+        if state.reason == ClarifyReason.MISSING_SLOT:
+            self._metrics.record_clarify_converge()
+            return HybridIntentResult(
+                intent=state.candidates[0] if state.candidates else None,
+                confidence=0.8,
+                candidates=state.candidates,
+                need_clarify=False,
+            )
+        
+        return HybridIntentResult(
+            intent=None,
+            confidence=0.0,
+            candidates=state.candidates,
+            need_clarify=True,
+            clarify_reason=state.reason,
+        )
+    
+    def get_metrics(self) -> dict[str, int]:
+        return self._metrics.get_metrics()
+    
+    def get_rates(self, total_requests: int) -> dict[str, float]:
+        return self._metrics.get_rates(total_requests)
+
+
+class ClarifySessionManager:
+    _sessions: dict[str, ClarifyState] = {}
+    
+    @classmethod
+    def get_session(cls, session_id: str) -> ClarifyState | None:
+        return cls._sessions.get(session_id)
+    
+    @classmethod
+    def set_session(cls, session_id: str, state: ClarifyState) -> None:
+        cls._sessions[session_id] = state
+        logger.debug(f"[AC-CLARIFY] Session state set: session={session_id}, reason={state.reason}")
+    
+    @classmethod
+    def clear_session(cls, session_id: str) -> None:
+        if session_id in cls._sessions:
+            del cls._sessions[session_id]
+            logger.debug(f"[AC-CLARIFY] Session state cleared: session={session_id}")
+    
+    @classmethod
+    def has_active_clarify(cls, session_id: str) -> bool:
+        state = cls._sessions.get(session_id)
+        if state:
+            return not state.is_max_retry()
+        return False

ai-service/app/services/intent/fusion_policy.py

@@ -0,0 +1,254 @@
+"""
+[v0.8.0] Fusion policy for hybrid intent routing.
+[AC-AISVC-115~AC-AISVC-117] Fusion decision logic for three-way matching.
+"""
+
+import logging
+from collections.abc import Callable
+from typing import TYPE_CHECKING
+
+from app.services.intent.models import (
+    FusionConfig,
+    FusionResult,
+    LlmJudgeResult,
+    RouteTrace,
+    RuleMatchResult,
+    SemanticMatchResult,
+)
+
+if TYPE_CHECKING:
+    from app.models.entities import IntentRule
+
+logger = logging.getLogger(__name__)
+
+DecisionCondition = Callable[[RuleMatchResult, SemanticMatchResult, LlmJudgeResult], bool]
+
+
+class FusionPolicy:
+    """
+    [AC-AISVC-115] Fusion decision policy for hybrid routing.
+
+    Decision priority:
+    1. rule_high_confidence: RuleMatcher hit with score=1.0
+    2. llm_judge: LlmJudge triggered and returned valid intent
+    3. semantic_override: RuleMatcher missed but SemanticMatcher high confidence
+    4. rule_semantic_agree: Both match same intent
+    5. semantic_fallback: SemanticMatcher medium confidence
+    6. rule_fallback: Only rule matched
+    7. no_match: All low confidence
+    """
+
+    DECISION_PRIORITY: list[tuple[str, DecisionCondition]] = [
+        ("rule_high_confidence", lambda r, s, llm: r.score == 1.0 and r.rule is not None),
+        ("llm_judge", lambda r, s, llm: llm.triggered and llm.intent_id is not None),
+        (
+            "semantic_override",
+            lambda r, s, llm: r.score == 0
+            and s.top_score > 0.7
+            and not s.skipped
+            and len(s.candidates) > 0,
+        ),
+        (
+            "rule_semantic_agree",
+            lambda r, s, llm: r.score > 0
+            and s.top_score > 0.5
+            and not s.skipped
+            and len(s.candidates) > 0
+            and r.rule_id == s.candidates[0].rule.id,
+        ),
+        (
+            "semantic_fallback",
+            lambda r, s, llm: s.top_score > 0.5 and not s.skipped and len(s.candidates) > 0,
+        ),
+        ("rule_fallback", lambda r, s, llm: r.score > 0),
+        ("no_match", lambda r, s, llm: True),
+    ]
+
+    def __init__(self, config: FusionConfig | None = None):
+        """
+        Initialize fusion policy with configuration.
+
+        Args:
+            config: Fusion configuration, uses default if not provided
+        """
+        self._config = config or FusionConfig()
+
+    def fuse(
+        self,
+        rule_result: RuleMatchResult,
+        semantic_result: SemanticMatchResult,
+        llm_result: LlmJudgeResult | None,
+    ) -> FusionResult:
+        """
+        [AC-AISVC-115] Execute fusion decision.
+
+        Args:
+            rule_result: Rule matching result
+            semantic_result: Semantic matching result
+            llm_result: LLM judge result (may be None)
+
+        Returns:
+            FusionResult with final intent, confidence, and trace
+        """
+        trace = self._build_trace(rule_result, semantic_result, llm_result)
+
+        final_intent = None
+        final_confidence = 0.0
+        decision_reason = "no_match"
+
+        effective_llm_result = llm_result or LlmJudgeResult.empty()
+
+        for reason, condition in self.DECISION_PRIORITY:
+            if condition(rule_result, semantic_result, effective_llm_result):
+                decision_reason = reason
+                break
+
+        if decision_reason == "rule_high_confidence":
+            final_intent = rule_result.rule
+            final_confidence = 1.0
+        elif decision_reason == "llm_judge" and llm_result:
+            final_intent = self._find_rule_by_id(
+                llm_result.intent_id, rule_result, semantic_result
+            )
+            final_confidence = llm_result.score
+        elif decision_reason == "semantic_override":
+            final_intent = semantic_result.candidates[0].rule
+            final_confidence = semantic_result.top_score
+        elif decision_reason == "rule_semantic_agree":
+            final_intent = rule_result.rule
+            final_confidence = self._calculate_weighted_confidence(
+                rule_result, semantic_result, llm_result
+            )
+        elif decision_reason == "semantic_fallback":
+            final_intent = semantic_result.candidates[0].rule
+            final_confidence = semantic_result.top_score
+        elif decision_reason == "rule_fallback":
+            final_intent = rule_result.rule
+            final_confidence = rule_result.score
+
+        need_clarify = final_confidence < self._config.clarify_threshold
+        clarify_candidates = None
+        if need_clarify and len(semantic_result.candidates) > 1:
+            clarify_candidates = [c.rule for c in semantic_result.candidates[:3]]
+
+        trace.fusion = {
+            "weights": {
+                "w_rule": self._config.w_rule,
+                "w_semantic": self._config.w_semantic,
+                "w_llm": self._config.w_llm,
+            },
+            "final_confidence": final_confidence,
+            "decision_reason": decision_reason,
+            "need_clarify": need_clarify,
+        }
+
+        logger.info(
+            f"[AC-AISVC-115] Fusion decision: reason={decision_reason}, "
+            f"confidence={final_confidence:.3f}, need_clarify={need_clarify}"
+        )
+
+        return FusionResult(
+            final_intent=final_intent,
+            final_confidence=final_confidence,
+            decision_reason=decision_reason,
+            need_clarify=need_clarify,
+            clarify_candidates=clarify_candidates,
+            trace=trace,
+        )
+
+    def _build_trace(
+        self,
+        rule_result: RuleMatchResult,
+        semantic_result: SemanticMatchResult,
+        llm_result: LlmJudgeResult | None,
+    ) -> RouteTrace:
+        """
+        [AC-AISVC-122] Build route trace log.
+        """
+        return RouteTrace(
+            rule_match={
+                "rule_id": str(rule_result.rule_id) if rule_result.rule_id else None,
+                "rule_name": rule_result.rule.name if rule_result.rule else None,
+                "match_type": rule_result.match_type,
+                "matched_text": rule_result.matched_text,
+                "score": rule_result.score,
+                "duration_ms": rule_result.duration_ms,
+            },
+            semantic_match={
+                "top_candidates": [
+                    {
+                        "rule_id": str(c.rule.id),
+                        "rule_name": c.rule.name,
+                        "score": c.score,
+                    }
+                    for c in semantic_result.candidates
+                ],
+                "top_score": semantic_result.top_score,
+                "duration_ms": semantic_result.duration_ms,
+                "skipped": semantic_result.skipped,
+                "skip_reason": semantic_result.skip_reason,
+            },
+            llm_judge={
+                "triggered": llm_result.triggered if llm_result else False,
+                "intent_id": llm_result.intent_id if llm_result else None,
+                "intent_name": llm_result.intent_name if llm_result else None,
+                "score": llm_result.score if llm_result else 0.0,
+                "reasoning": llm_result.reasoning if llm_result else None,
+                "duration_ms": llm_result.duration_ms if llm_result else 0,
+                "tokens_used": llm_result.tokens_used if llm_result else 0,
+            },
+            fusion={},
+        )
+
+    def _calculate_weighted_confidence(
+        self,
+        rule_result: RuleMatchResult,
+        semantic_result: SemanticMatchResult,
+        llm_result: LlmJudgeResult | None,
+    ) -> float:
+        """
+        [AC-AISVC-116] Calculate weighted confidence.
+
+        Formula:
+        final_confidence = (w_rule * rule_score + w_semantic * semantic_score + w_llm * llm_score) / total_weight
+
+        Returns:
+            Weighted confidence in [0.0, 1.0]
+        """
+        rule_score = rule_result.score
+        semantic_score = semantic_result.top_score if not semantic_result.skipped else 0.0
+        llm_score = llm_result.score if llm_result and llm_result.triggered else 0.0
+
+        total_weight = self._config.w_rule + self._config.w_semantic
+        if llm_result and llm_result.triggered:
+            total_weight += self._config.w_llm
+
+        if total_weight == 0:
+            return 0.0
+
+        confidence = (
+            self._config.w_rule * rule_score
+            + self._config.w_semantic * semantic_score
+            + self._config.w_llm * llm_score
+        ) / total_weight
+
+        return min(1.0, max(0.0, confidence))
+
+    def _find_rule_by_id(
+        self,
+        intent_id: str | None,
+        rule_result: RuleMatchResult,
+        semantic_result: SemanticMatchResult,
+    ) -> "IntentRule | None":
+        """Find rule by ID from rule or semantic results."""
+        if not intent_id:
+            return None
+
+        if rule_result.rule_id and str(rule_result.rule_id) == intent_id:
+            return rule_result.rule
+
+        for candidate in semantic_result.candidates:
+            if str(candidate.rule.id) == intent_id:
+                return candidate.rule
+
+        return None

ai-service/app/services/intent/llm_judge.py

@@ -0,0 +1,246 @@
+"""
+LLM judge for intent arbitration.
+[AC-AISVC-118, AC-AISVC-119] LLM-based intent arbitration.
+"""
+
+import asyncio
+import json
+import logging
+import time
+from typing import TYPE_CHECKING, Any
+
+from app.services.intent.models import (
+    FusionConfig,
+    LlmJudgeInput,
+    LlmJudgeResult,
+    RuleMatchResult,
+    SemanticMatchResult,
+)
+
+if TYPE_CHECKING:
+    from app.services.llm.base import LLMClient
+
+logger = logging.getLogger(__name__)
+
+
+class LlmJudge:
+    """
+    [AC-AISVC-118] LLM-based intent arbitrator.
+
+    Triggered when:
+    - Rule vs Semantic conflict
+    - Gray zone (low confidence)
+    - Multiple intent candidates with similar scores
+    """
+
+    JUDGE_PROMPT = """你是一个意图识别仲裁器。根据用户消息和候选意图，判断最匹配的意图。
+
+用户消息：{message}
+
+候选意图：
+{candidates}
+
+请返回 JSON 格式（不要包含```json标记）：
+{{
+  "intent_id": "最匹配的意图ID",
+  "intent_name": "意图名称",
+  "confidence": 0.0-1.0之间的置信度,
+  "reasoning": "判断理由"
+}}"""
+
+    def __init__(
+        self,
+        llm_client: "LLMClient",
+        config: FusionConfig,
+    ):
+        """
+        Initialize LLM judge.
+
+        Args:
+            llm_client: LLM client for generating responses
+            config: Fusion configuration
+        """
+        self._llm_client = llm_client
+        self._config = config
+
+    def should_trigger(
+        self,
+        rule_result: RuleMatchResult,
+        semantic_result: SemanticMatchResult,
+        config: FusionConfig | None = None,
+    ) -> tuple[bool, str]:
+        """
+        [AC-AISVC-118] Check if LLM judge should be triggered.
+
+        Trigger conditions:
+        1. Conflict: Rule and Semantic match different intents with close scores
+        2. Gray zone: Max confidence in gray zone range
+        3. Multi-intent: Multiple candidates with similar scores
+
+        Args:
+            rule_result: Rule matching result
+            semantic_result: Semantic matching result
+            config: Optional config override
+
+        Returns:
+            Tuple of (should_trigger, trigger_reason)
+        """
+        effective_config = config or self._config
+
+        if not effective_config.llm_judge_enabled:
+            return False, "disabled"
+
+        rule_score = rule_result.score
+        semantic_score = semantic_result.top_score
+
+        if rule_score > 0 and semantic_score > 0:
+            if semantic_result.candidates:
+                top_semantic_rule_id = semantic_result.candidates[0].rule.id
+                if rule_result.rule_id != top_semantic_rule_id:
+                    if abs(rule_score - semantic_score) < effective_config.conflict_threshold:
+                        logger.info(
+                            f"[AC-AISVC-118] LLM judge triggered: rule_semantic_conflict, "
+                            f"rule_id={rule_result.rule_id}, semantic_id={top_semantic_rule_id}, "
+                            f"rule_score={rule_score}, semantic_score={semantic_score}"
+                        )
+                        return True, "rule_semantic_conflict"
+
+        max_score = max(rule_score, semantic_score)
+        if effective_config.min_trigger_threshold < max_score < effective_config.gray_zone_threshold:
+            logger.info(
+                f"[AC-AISVC-118] LLM judge triggered: gray_zone, "
+                f"max_score={max_score}"
+            )
+            return True, "gray_zone"
+
+        if len(semantic_result.candidates) >= 2:
+            top1_score = semantic_result.candidates[0].score
+            top2_score = semantic_result.candidates[1].score
+            if abs(top1_score - top2_score) < effective_config.multi_intent_threshold:
+                logger.info(
+                    f"[AC-AISVC-118] LLM judge triggered: multi_intent, "
+                    f"top1_score={top1_score}, top2_score={top2_score}"
+                )
+                return True, "multi_intent"
+
+        return False, ""
+
+    async def judge(
+        self,
+        input_data: LlmJudgeInput,
+        tenant_id: str,
+    ) -> LlmJudgeResult:
+        """
+        [AC-AISVC-119] Perform LLM arbitration.
+
+        Args:
+            input_data: Judge input with message and candidates
+            tenant_id: Tenant ID for isolation
+
+        Returns:
+            LlmJudgeResult with arbitration decision
+        """
+        start_time = time.time()
+
+        candidates_text = "\n".join([
+            f"- ID: {c['id']}, 名称: {c['name']}, 描述: {c.get('description', 'N/A')}"
+            for c in input_data.candidates
+        ])
+
+        prompt = self.JUDGE_PROMPT.format(
+            message=input_data.message,
+            candidates=candidates_text,
+        )
+
+        try:
+            from app.services.llm.base import LLMConfig
+
+            response = await asyncio.wait_for(
+                self._llm_client.generate(
+                    messages=[{"role": "user", "content": prompt}],
+                    config=LLMConfig(
+                        max_tokens=200,
+                        temperature=0,
+                    ),
+                ),
+                timeout=self._config.llm_judge_timeout_ms / 1000,
+            )
+
+            result = self._parse_response(response.content or "")
+            duration_ms = int((time.time() - start_time) * 1000)
+
+            tokens_used = 0
+            if response.usage:
+                tokens_used = response.usage.get("total_tokens", 0)
+
+            logger.info(
+                f"[AC-AISVC-119] LLM judge completed for tenant={tenant_id}, "
+                f"intent_id={result.get('intent_id')}, confidence={result.get('confidence', 0):.3f}, "
+                f"duration={duration_ms}ms, tokens={tokens_used}"
+            )
+
+            return LlmJudgeResult(
+                intent_id=result.get("intent_id"),
+                intent_name=result.get("intent_name"),
+                score=float(result.get("confidence", 0.5)),
+                reasoning=result.get("reasoning"),
+                duration_ms=duration_ms,
+                tokens_used=tokens_used,
+                triggered=True,
+            )
+
+        except asyncio.TimeoutError:
+            duration_ms = int((time.time() - start_time) * 1000)
+            logger.warning(
+                f"[AC-AISVC-119] LLM judge timeout for tenant={tenant_id}, "
+                f"timeout={self._config.llm_judge_timeout_ms}ms"
+            )
+            return LlmJudgeResult(
+                intent_id=None,
+                intent_name=None,
+                score=0.0,
+                reasoning="LLM timeout",
+                duration_ms=duration_ms,
+                tokens_used=0,
+                triggered=True,
+            )
+        except Exception as e:
+            duration_ms = int((time.time() - start_time) * 1000)
+            logger.error(
+                f"[AC-AISVC-119] LLM judge error for tenant={tenant_id}: {e}"
+            )
+            return LlmJudgeResult(
+                intent_id=None,
+                intent_name=None,
+                score=0.0,
+                reasoning=f"LLM error: {str(e)}",
+                duration_ms=duration_ms,
+                tokens_used=0,
+                triggered=True,
+            )
+
+    def _parse_response(self, content: str) -> dict[str, Any]:
+        """
+        Parse LLM response to extract JSON result.
+
+        Args:
+            content: LLM response content
+
+        Returns:
+            Parsed dictionary with intent_id, intent_name, confidence, reasoning
+        """
+        try:
+            cleaned = content.strip()
+            if cleaned.startswith("```json"):
+                cleaned = cleaned[7:]
+            if cleaned.startswith("```"):
+                cleaned = cleaned[3:]
+            if cleaned.endswith("```"):
+                cleaned = cleaned[:-3]
+            cleaned = cleaned.strip()
+
+            result: dict[str, Any] = json.loads(cleaned)
+            return result
+        except json.JSONDecodeError as e:
+            logger.warning(f"[AC-AISVC-119] Failed to parse LLM response: {e}")
+            return {}

ai-service/app/services/intent/models.py

@@ -0,0 +1,226 @@
+"""
+Intent routing data models.
+[AC-AISVC-111~AC-AISVC-125] Data models for hybrid routing.
+"""
+
+import uuid
+from dataclasses import dataclass, field
+from typing import Any
+
+
+@dataclass
+class RuleMatchResult:
+    """
+    [AC-AISVC-112] Result of rule matching.
+    Contains matched rule and score.
+    """
+    rule_id: uuid.UUID | None
+    rule: Any | None
+    match_type: str | None
+    matched_text: str | None
+    score: float
+    duration_ms: int
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "rule_id": str(self.rule_id) if self.rule_id else None,
+            "rule_name": self.rule.name if self.rule else None,
+            "match_type": self.match_type,
+            "matched_text": self.matched_text,
+            "score": self.score,
+            "duration_ms": self.duration_ms,
+        }
+
+
+@dataclass
+class SemanticCandidate:
+    """
+    [AC-AISVC-113] Semantic match candidate.
+    """
+    rule: Any
+    score: float
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "rule_id": str(self.rule.id),
+            "rule_name": self.rule.name,
+            "score": self.score,
+        }
+
+
+@dataclass
+class SemanticMatchResult:
+    """
+    [AC-AISVC-113] Result of semantic matching.
+    """
+    candidates: list[SemanticCandidate]
+    top_score: float
+    duration_ms: int
+    skipped: bool
+    skip_reason: str | None
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "top_candidates": [c.to_dict() for c in self.candidates],
+            "top_score": self.top_score,
+            "duration_ms": self.duration_ms,
+            "skipped": self.skipped,
+            "skip_reason": self.skip_reason,
+        }
+
+
+@dataclass
+class LlmJudgeInput:
+    """
+    [AC-AISVC-119] Input for LLM judge.
+    """
+    message: str
+    candidates: list[dict[str, Any]]
+    conflict_type: str
+
+
+@dataclass
+class LlmJudgeResult:
+    """
+    [AC-AISVC-119] Result of LLM judge.
+    """
+    intent_id: str | None
+    intent_name: str | None
+    score: float
+    reasoning: str | None
+    duration_ms: int
+    tokens_used: int
+    triggered: bool
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "triggered": self.triggered,
+            "intent_id": self.intent_id,
+            "intent_name": self.intent_name,
+            "score": self.score,
+            "reasoning": self.reasoning,
+            "duration_ms": self.duration_ms,
+            "tokens_used": self.tokens_used,
+        }
+
+    @classmethod
+    def empty(cls) -> "LlmJudgeResult":
+        return cls(
+            intent_id=None,
+            intent_name=None,
+            score=0.0,
+            reasoning=None,
+            duration_ms=0,
+            tokens_used=0,
+            triggered=False,
+        )
+
+
+@dataclass
+class FusionConfig:
+    """
+    [AC-AISVC-116] Fusion configuration.
+    """
+    w_rule: float = 0.5
+    w_semantic: float = 0.3
+    w_llm: float = 0.2
+    semantic_threshold: float = 0.7
+    conflict_threshold: float = 0.2
+    gray_zone_threshold: float = 0.6
+    min_trigger_threshold: float = 0.3
+    clarify_threshold: float = 0.4
+    multi_intent_threshold: float = 0.15
+    llm_judge_enabled: bool = True
+    semantic_matcher_enabled: bool = True
+    semantic_matcher_timeout_ms: int = 100
+    llm_judge_timeout_ms: int = 2000
+    semantic_top_k: int = 3
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "w_rule": self.w_rule,
+            "w_semantic": self.w_semantic,
+            "w_llm": self.w_llm,
+            "semantic_threshold": self.semantic_threshold,
+            "conflict_threshold": self.conflict_threshold,
+            "gray_zone_threshold": self.gray_zone_threshold,
+            "min_trigger_threshold": self.min_trigger_threshold,
+            "clarify_threshold": self.clarify_threshold,
+            "multi_intent_threshold": self.multi_intent_threshold,
+            "llm_judge_enabled": self.llm_judge_enabled,
+            "semantic_matcher_enabled": self.semantic_matcher_enabled,
+            "semantic_matcher_timeout_ms": self.semantic_matcher_timeout_ms,
+            "llm_judge_timeout_ms": self.llm_judge_timeout_ms,
+            "semantic_top_k": self.semantic_top_k,
+        }
+
+    @classmethod
+    def from_dict(cls, data: dict[str, Any]) -> "FusionConfig":
+        return cls(
+            w_rule=data.get("w_rule", 0.5),
+            w_semantic=data.get("w_semantic", 0.3),
+            w_llm=data.get("w_llm", 0.2),
+            semantic_threshold=data.get("semantic_threshold", 0.7),
+            conflict_threshold=data.get("conflict_threshold", 0.2),
+            gray_zone_threshold=data.get("gray_zone_threshold", 0.6),
+            min_trigger_threshold=data.get("min_trigger_threshold", 0.3),
+            clarify_threshold=data.get("clarify_threshold", 0.4),
+            multi_intent_threshold=data.get("multi_intent_threshold", 0.15),
+            llm_judge_enabled=data.get("llm_judge_enabled", True),
+            semantic_matcher_enabled=data.get("semantic_matcher_enabled", True),
+            semantic_matcher_timeout_ms=data.get("semantic_matcher_timeout_ms", 100),
+            llm_judge_timeout_ms=data.get("llm_judge_timeout_ms", 2000),
+            semantic_top_k=data.get("semantic_top_k", 3),
+        )
+
+
+@dataclass
+class RouteTrace:
+    """
+    [AC-AISVC-122] Route trace log.
+    """
+    rule_match: dict[str, Any] = field(default_factory=dict)
+    semantic_match: dict[str, Any] = field(default_factory=dict)
+    llm_judge: dict[str, Any] = field(default_factory=dict)
+    fusion: dict[str, Any] = field(default_factory=dict)
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "rule_match": self.rule_match,
+            "semantic_match": self.semantic_match,
+            "llm_judge": self.llm_judge,
+            "fusion": self.fusion,
+        }
+
+
+@dataclass
+class FusionResult:
+    """
+    [AC-AISVC-115] Fusion decision result.
+    """
+    final_intent: Any | None
+    final_confidence: float
+    decision_reason: str
+    need_clarify: bool
+    clarify_candidates: list[Any] | None
+    trace: RouteTrace
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "final_intent": {
+                "id": str(self.final_intent.id),
+                "name": self.final_intent.name,
+                "response_type": self.final_intent.response_type,
+            } if self.final_intent else None,
+            "final_confidence": self.final_confidence,
+            "decision_reason": self.decision_reason,
+            "need_clarify": self.need_clarify,
+            "clarify_candidates": [
+                {"id": str(c.id), "name": c.name}
+                for c in (self.clarify_candidates or [])
+            ],
+            "trace": self.trace.to_dict(),
+        }
+
+
+DEFAULT_FUSION_CONFIG = FusionConfig()

ai-service/app/services/intent/router.py

@@ -1,14 +1,30 @@
 """
 Intent router for AI Service.
 [AC-AISVC-69, AC-AISVC-70] Intent matching engine with keyword and regex support.
+[v0.8.0] Upgraded to hybrid routing with RuleMatcher + SemanticMatcher + LlmJudge + FusionPolicy.
 """
 
 import logging
 import re
+import time
 from dataclasses import dataclass
-from typing import Any
+from typing import TYPE_CHECKING, Any
 
 from app.models.entities import IntentRule
+from app.services.intent.models import (
+    FusionConfig,
+    FusionResult,
+    LlmJudgeInput,
+    LlmJudgeResult,
+    RouteTrace,
+    RuleMatchResult,
+    SemanticMatchResult,
+)
+
+if TYPE_CHECKING:
+    from app.services.intent.fusion_policy import FusionPolicy
+    from app.services.intent.llm_judge import LlmJudge
+    from app.services.intent.semantic_matcher import SemanticMatcher
 
 logger = logging.getLogger(__name__)
 
@@ -38,38 +54,36 @@ class IntentMatchResult:
         }
 
 
-class IntentRouter:
+class RuleMatcher:
     """
-    [AC-AISVC-69] Intent matching engine.
-
-    Matching algorithm:
-    1. Load rules ordered by priority DESC
-    2. For each rule, try keyword matching first
-    3. If no keyword match, try regex pattern matching
-    4. Return first match (highest priority)
-    5. If no match, return None (fallback to default RAG)
+    [v0.8.0] Rule matcher for keyword and regex matching.
+    Extracted from IntentRouter for hybrid routing.
     """
 
-    def __init__(self):
-        pass
-
-    def match(
-        self,
-        message: str,
-        rules: list[IntentRule],
-    ) -> IntentMatchResult | None:
+    def match(self, message: str, rules: list[IntentRule]) -> RuleMatchResult:
         """
-        [AC-AISVC-69] Match user message against intent rules.
+        [AC-AISVC-112] Match user message against intent rules.
+        Returns RuleMatchResult with score (1.0 for match, 0.0 for no match).
 
         Args:
             message: User input message
             rules: List of enabled rules ordered by priority DESC
 
         Returns:
-            IntentMatchResult if matched, None otherwise
+            RuleMatchResult with match details
         """
+        start_time = time.time()
+
         if not message or not rules:
-            return None
+            duration_ms = int((time.time() - start_time) * 1000)
+            return RuleMatchResult(
+                rule_id=None,
+                rule=None,
+                match_type=None,
+                matched_text=None,
+                score=0.0,
+                duration_ms=duration_ms,
+            )
 
         message_lower = message.lower()
 
@@ -79,22 +93,46 @@ class IntentRouter:
 
             keyword_result = self._match_keywords(message, message_lower, rule)
             if keyword_result:
+                duration_ms = int((time.time() - start_time) * 1000)
                 logger.info(
                     f"[AC-AISVC-69] Intent matched by keyword: "
                     f"rule={rule.name}, matched='{keyword_result.matched}'"
                 )
-                return keyword_result
+                return RuleMatchResult(
+                    rule_id=rule.id,
+                    rule=rule,
+                    match_type="keyword",
+                    matched_text=keyword_result.matched,
+                    score=1.0,
+                    duration_ms=duration_ms,
+                )
 
             regex_result = self._match_patterns(message, rule)
             if regex_result:
+                duration_ms = int((time.time() - start_time) * 1000)
                 logger.info(
                     f"[AC-AISVC-69] Intent matched by regex: "
                     f"rule={rule.name}, matched='{regex_result.matched}'"
                 )
-                return regex_result
+                return RuleMatchResult(
+                    rule_id=rule.id,
+                    rule=rule,
+                    match_type="regex",
+                    matched_text=regex_result.matched,
+                    score=1.0,
+                    duration_ms=duration_ms,
+                )
 
+        duration_ms = int((time.time() - start_time) * 1000)
         logger.debug("[AC-AISVC-70] No intent matched, will fallback to default RAG")
-        return None
+        return RuleMatchResult(
+            rule_id=None,
+            rule=None,
+            match_type=None,
+            matched_text=None,
+            score=0.0,
+            duration_ms=duration_ms,
+        )
 
     def _match_keywords(
         self,
@@ -153,6 +191,74 @@ class IntentRouter:
 
         return None
 
+
+class IntentRouter:
+    """
+    [AC-AISVC-69] Intent matching engine.
+    [v0.8.0] Upgraded to support hybrid routing.
+
+    Matching algorithm:
+    1. Load rules ordered by priority DESC
+    2. For each rule, try keyword matching first
+    3. If no keyword match, try regex pattern matching
+    4. Return first match (highest priority)
+    5. If no match, return None (fallback to default RAG)
+
+    Hybrid routing (match_hybrid):
+    1. Parallel execute RuleMatcher + SemanticMatcher
+    2. Conditionally trigger LlmJudge
+    3. Execute FusionPolicy for final decision
+    """
+
+    def __init__(
+        self,
+        rule_matcher: RuleMatcher | None = None,
+        semantic_matcher: "SemanticMatcher | None" = None,
+        llm_judge: "LlmJudge | None" = None,
+        fusion_policy: "FusionPolicy | None" = None,
+        config: FusionConfig | None = None,
+    ):
+        """
+        [v0.8.0] Initialize with optional dependencies for DI.
+
+        Args:
+            rule_matcher: Rule matcher for keyword/regex matching
+            semantic_matcher: Semantic matcher for vector similarity
+            llm_judge: LLM judge for arbitration
+            fusion_policy: Fusion policy for decision making
+            config: Fusion configuration
+        """
+        self._rule_matcher = rule_matcher or RuleMatcher()
+        self._semantic_matcher = semantic_matcher
+        self._llm_judge = llm_judge
+        self._fusion_policy = fusion_policy
+        self._config = config or FusionConfig()
+
+    def match(
+        self,
+        message: str,
+        rules: list[IntentRule],
+    ) -> IntentMatchResult | None:
+        """
+        [AC-AISVC-69] Match user message against intent rules.
+        Preserved for backward compatibility.
+
+        Args:
+            message: User input message
+            rules: List of enabled rules ordered by priority DESC
+
+        Returns:
+            IntentMatchResult if matched, None otherwise
+        """
+        result = self._rule_matcher.match(message, rules)
+        if result.rule:
+            return IntentMatchResult(
+                rule=result.rule,
+                match_type=result.match_type or "keyword",
+                matched=result.matched_text or "",
+            )
+        return None
+
     def match_with_stats(
         self,
         message: str,
@@ -168,3 +274,300 @@ class IntentRouter:
         if result:
             return result, str(result.rule.id)
         return None, None
+
+    async def match_hybrid(
+        self,
+        message: str,
+        rules: list[IntentRule],
+        tenant_id: str,
+        config: FusionConfig | None = None,
+    ) -> FusionResult:
+        """
+        [AC-AISVC-111] Hybrid routing entry point.
+
+        Flow:
+        1. Parallel execute RuleMatcher + SemanticMatcher
+        2. Check if LlmJudge should trigger
+        3. Execute FusionPolicy for final decision
+
+        Args:
+            message: User input message
+            rules: List of enabled rules ordered by priority DESC
+            tenant_id: Tenant ID for isolation
+            config: Optional fusion config override
+
+        Returns:
+            FusionResult with final intent, confidence, and trace
+        """
+        effective_config = config or self._config
+        start_time = time.time()
+
+        rule_result = self._rule_matcher.match(message, rules)
+
+        semantic_result = await self._execute_semantic_matcher(
+            message, rules, tenant_id, effective_config
+        )
+
+        llm_result = await self._conditionally_execute_llm_judge(
+            message, rule_result, semantic_result, tenant_id, effective_config
+        )
+
+        if self._fusion_policy:
+            fusion_result = self._fusion_policy.fuse(
+                rule_result, semantic_result, llm_result
+            )
+        else:
+            fusion_result = self._default_fusion(
+                rule_result, semantic_result, llm_result, effective_config
+            )
+
+        total_duration_ms = int((time.time() - start_time) * 1000)
+        fusion_result.trace.fusion["total_duration_ms"] = total_duration_ms
+
+        logger.info(
+            f"[AC-AISVC-111] Hybrid routing completed: "
+            f"decision={fusion_result.decision_reason}, "
+            f"confidence={fusion_result.final_confidence:.3f}, "
+            f"duration={total_duration_ms}ms"
+        )
+
+        return fusion_result
+
+    async def _execute_semantic_matcher(
+        self,
+        message: str,
+        rules: list[IntentRule],
+        tenant_id: str,
+        config: FusionConfig,
+    ) -> SemanticMatchResult:
+        """Execute semantic matcher if available and enabled."""
+        if not self._semantic_matcher:
+            return SemanticMatchResult(
+                candidates=[],
+                top_score=0.0,
+                duration_ms=0,
+                skipped=True,
+                skip_reason="not_configured",
+            )
+
+        if not config.semantic_matcher_enabled:
+            return SemanticMatchResult(
+                candidates=[],
+                top_score=0.0,
+                duration_ms=0,
+                skipped=True,
+                skip_reason="disabled",
+            )
+
+        try:
+            return await self._semantic_matcher.match(
+                message=message,
+                rules=rules,
+                tenant_id=tenant_id,
+                top_k=config.semantic_top_k,
+            )
+        except Exception as e:
+            logger.warning(f"[AC-AISVC-113] Semantic matcher failed: {e}")
+            return SemanticMatchResult(
+                candidates=[],
+                top_score=0.0,
+                duration_ms=0,
+                skipped=True,
+                skip_reason=f"error: {str(e)}",
+            )
+
+    async def _conditionally_execute_llm_judge(
+        self,
+        message: str,
+        rule_result: RuleMatchResult,
+        semantic_result: SemanticMatchResult,
+        tenant_id: str,
+        config: FusionConfig,
+    ) -> LlmJudgeResult | None:
+        """Conditionally execute LLM judge based on trigger conditions."""
+        if not self._llm_judge:
+            return None
+
+        if not config.llm_judge_enabled:
+            return None
+
+        should_trigger, trigger_reason = self._check_llm_trigger(
+            rule_result, semantic_result, config
+        )
+
+        if not should_trigger:
+            return None
+
+        logger.info(f"[AC-AISVC-118] LLM judge triggered: reason={trigger_reason}")
+
+        candidates = self._build_llm_candidates(rule_result, semantic_result)
+        if not candidates:
+            return None
+
+        try:
+            return await self._llm_judge.judge(
+                LlmJudgeInput(
+                    message=message,
+                    candidates=candidates,
+                    conflict_type=trigger_reason,
+                ),
+                tenant_id,
+            )
+        except Exception as e:
+            logger.warning(f"[AC-AISVC-119] LLM judge failed: {e}")
+            return LlmJudgeResult(
+                intent_id=None,
+                intent_name=None,
+                score=0.0,
+                reasoning=f"LLM error: {str(e)}",
+                duration_ms=0,
+                tokens_used=0,
+                triggered=True,
+            )
+
+    def _check_llm_trigger(
+        self,
+        rule_result: RuleMatchResult,
+        semantic_result: SemanticMatchResult,
+        config: FusionConfig,
+    ) -> tuple[bool, str]:
+        """
+        [AC-AISVC-118] Check if LLM judge should trigger.
+
+        Trigger conditions:
+        1. Conflict: RuleMatcher and SemanticMatcher match different intents
+        2. Gray zone: Max confidence in gray zone range
+        3. Multi-intent: Multiple candidates with close scores
+
+        Returns:
+            (should_trigger, trigger_reason)
+        """
+        rule_score = rule_result.score
+        semantic_score = semantic_result.top_score
+
+        if rule_score > 0 and semantic_score > 0 and not semantic_result.skipped:
+            if semantic_result.candidates:
+                top_semantic_rule_id = semantic_result.candidates[0].rule.id
+                if rule_result.rule_id != top_semantic_rule_id:
+                    if abs(rule_score - semantic_score) < config.conflict_threshold:
+                        return True, "rule_semantic_conflict"
+
+        max_score = max(rule_score, semantic_score)
+        if config.min_trigger_threshold < max_score < config.gray_zone_threshold:
+            return True, "gray_zone"
+
+        if len(semantic_result.candidates) >= 2:
+            top1_score = semantic_result.candidates[0].score
+            top2_score = semantic_result.candidates[1].score
+            if abs(top1_score - top2_score) < config.multi_intent_threshold:
+                return True, "multi_intent"
+
+        return False, ""
+
+    def _build_llm_candidates(
+        self,
+        rule_result: RuleMatchResult,
+        semantic_result: SemanticMatchResult,
+    ) -> list[dict[str, Any]]:
+        """Build candidate list for LLM judge."""
+        candidates = []
+
+        if rule_result.rule:
+            candidates.append({
+                "id": str(rule_result.rule_id),
+                "name": rule_result.rule.name,
+                "description": f"匹配方式: {rule_result.match_type}, 匹配内容: {rule_result.matched_text}",
+            })
+
+        for candidate in semantic_result.candidates[:3]:
+            if not any(c["id"] == str(candidate.rule.id) for c in candidates):
+                candidates.append({
+                    "id": str(candidate.rule.id),
+                    "name": candidate.rule.name,
+                    "description": f"语义相似度: {candidate.score:.2f}",
+                })
+
+        return candidates
+
+    def _default_fusion(
+        self,
+        rule_result: RuleMatchResult,
+        semantic_result: SemanticMatchResult,
+        llm_result: LlmJudgeResult | None,
+        config: FusionConfig,
+    ) -> FusionResult:
+        """Default fusion logic when FusionPolicy is not available."""
+        trace = RouteTrace(
+            rule_match=rule_result.to_dict(),
+            semantic_match=semantic_result.to_dict(),
+            llm_judge=llm_result.to_dict() if llm_result else {},
+            fusion={},
+        )
+
+        final_intent = None
+        final_confidence = 0.0
+        decision_reason = "no_match"
+
+        if rule_result.score == 1.0 and rule_result.rule:
+            final_intent = rule_result.rule
+            final_confidence = 1.0
+            decision_reason = "rule_high_confidence"
+        elif llm_result and llm_result.triggered and llm_result.intent_id:
+            final_intent = self._find_rule_by_id(
+                llm_result.intent_id, rule_result, semantic_result
+            )
+            final_confidence = llm_result.score
+            decision_reason = "llm_judge"
+        elif rule_result.score == 0 and semantic_result.top_score > config.semantic_threshold:
+            if semantic_result.candidates:
+                final_intent = semantic_result.candidates[0].rule
+                final_confidence = semantic_result.top_score
+                decision_reason = "semantic_override"
+        elif semantic_result.top_score > 0.5:
+            if semantic_result.candidates:
+                final_intent = semantic_result.candidates[0].rule
+                final_confidence = semantic_result.top_score
+                decision_reason = "semantic_fallback"
+
+        need_clarify = final_confidence < config.clarify_threshold
+        clarify_candidates = None
+        if need_clarify and len(semantic_result.candidates) > 1:
+            clarify_candidates = [c.rule for c in semantic_result.candidates[:3]]
+
+        trace.fusion = {
+            "weights": {
+                "w_rule": config.w_rule,
+                "w_semantic": config.w_semantic,
+                "w_llm": config.w_llm,
+            },
+            "final_confidence": final_confidence,
+            "decision_reason": decision_reason,
+        }
+
+        return FusionResult(
+            final_intent=final_intent,
+            final_confidence=final_confidence,
+            decision_reason=decision_reason,
+            need_clarify=need_clarify,
+            clarify_candidates=clarify_candidates,
+            trace=trace,
+        )
+
+    def _find_rule_by_id(
+        self,
+        intent_id: str | None,
+        rule_result: RuleMatchResult,
+        semantic_result: SemanticMatchResult,
+    ) -> IntentRule | None:
+        """Find rule by ID from rule or semantic results."""
+        if not intent_id:
+            return None
+
+        if rule_result.rule_id and str(rule_result.rule_id) == intent_id:
+            return rule_result.rule
+
+        for candidate in semantic_result.candidates:
+            if str(candidate.rule.id) == intent_id:
+                return candidate.rule
+
+        return None

ai-service/app/services/intent/rule_service.py

@@ -106,6 +106,8 @@ class IntentRuleService:
             is_enabled=True,
             hit_count=0,
             metadata_=create_data.metadata_,
+            intent_vector=create_data.intent_vector,
+            semantic_examples=create_data.semantic_examples,
         )
         self._session.add(rule)
         await self._session.flush()
@@ -195,6 +197,10 @@ class IntentRuleService:
             rule.is_enabled = update_data.is_enabled
         if update_data.metadata_ is not None:
             rule.metadata_ = update_data.metadata_
+        if update_data.intent_vector is not None:
+            rule.intent_vector = update_data.intent_vector
+        if update_data.semantic_examples is not None:
+            rule.semantic_examples = update_data.semantic_examples
 
         rule.updated_at = datetime.utcnow()
         await self._session.flush()
@@ -267,7 +273,7 @@ class IntentRuleService:
             select(IntentRule)
             .where(
                 IntentRule.tenant_id == tenant_id,
-                IntentRule.is_enabled == True,
+                IntentRule.is_enabled == True,  # noqa: E712
             )
             .order_by(col(IntentRule.priority).desc())
         )
@@ -300,6 +306,8 @@ class IntentRuleService:
             "is_enabled": rule.is_enabled,
             "hit_count": rule.hit_count,
             "metadata": rule.metadata_,
-            "created_at": rule.created_at.isoformat(),
-            "updated_at": rule.updated_at.isoformat(),
+            "intent_vector": rule.intent_vector,
+            "semantic_examples": rule.semantic_examples,
+            "created_at": rule.created_at.isoformat() if rule.created_at else None,
+            "updated_at": rule.updated_at.isoformat() if rule.updated_at else None,
         }

ai-service/app/services/intent/semantic_matcher.py

@@ -0,0 +1,233 @@
+"""
+Semantic matcher for intent recognition.
+[AC-AISVC-113, AC-AISVC-114] Vector-based semantic matching.
+"""
+
+import asyncio
+import logging
+import time
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from app.services.intent.models import (
+    FusionConfig,
+    SemanticCandidate,
+    SemanticMatchResult,
+)
+
+if TYPE_CHECKING:
+    from app.models.entities import IntentRule
+    from app.services.embedding.base import EmbeddingProvider
+
+logger = logging.getLogger(__name__)
+
+
+class SemanticMatcher:
+    """
+    [AC-AISVC-113] Semantic matcher using vector similarity.
+
+    Supports two matching modes:
+    - Mode A: Use pre-computed intent_vector for direct similarity calculation
+    - Mode B: Use semantic_examples for dynamic vector computation
+    """
+
+    def __init__(
+        self,
+        embedding_provider: "EmbeddingProvider",
+        config: FusionConfig,
+    ):
+        """
+        Initialize semantic matcher.
+
+        Args:
+            embedding_provider: Provider for generating embeddings
+            config: Fusion configuration
+        """
+        self._embedding_provider = embedding_provider
+        self._config = config
+
+    async def match(
+        self,
+        message: str,
+        rules: list["IntentRule"],
+        tenant_id: str,
+        top_k: int | None = None,
+    ) -> SemanticMatchResult:
+        """
+        [AC-AISVC-113] Perform vector semantic matching.
+
+        Args:
+            message: User message
+            rules: List of intent rules
+            tenant_id: Tenant ID for isolation
+            top_k: Number of top candidates to return
+
+        Returns:
+            SemanticMatchResult with candidates and scores
+        """
+        start_time = time.time()
+        effective_top_k = top_k or self._config.semantic_top_k
+
+        if not self._config.semantic_matcher_enabled:
+            return SemanticMatchResult(
+                candidates=[],
+                top_score=0.0,
+                duration_ms=0,
+                skipped=True,
+                skip_reason="disabled",
+            )
+
+        rules_with_semantic = [r for r in rules if self._has_semantic_config(r)]
+        if not rules_with_semantic:
+            duration_ms = int((time.time() - start_time) * 1000)
+            logger.debug(
+                f"[AC-AISVC-113] No rules with semantic config for tenant={tenant_id}"
+            )
+            return SemanticMatchResult(
+                candidates=[],
+                top_score=0.0,
+                duration_ms=duration_ms,
+                skipped=True,
+                skip_reason="no_semantic_config",
+            )
+
+        try:
+            message_vector = await asyncio.wait_for(
+                self._embedding_provider.embed(message),
+                timeout=self._config.semantic_matcher_timeout_ms / 1000,
+            )
+        except asyncio.TimeoutError:
+            duration_ms = int((time.time() - start_time) * 1000)
+            logger.warning(
+                f"[AC-AISVC-113] Embedding timeout for tenant={tenant_id}, "
+                f"timeout={self._config.semantic_matcher_timeout_ms}ms"
+            )
+            return SemanticMatchResult(
+                candidates=[],
+                top_score=0.0,
+                duration_ms=duration_ms,
+                skipped=True,
+                skip_reason="embedding_timeout",
+            )
+        except Exception as e:
+            duration_ms = int((time.time() - start_time) * 1000)
+            logger.error(
+                f"[AC-AISVC-113] Embedding error for tenant={tenant_id}: {e}"
+            )
+            return SemanticMatchResult(
+                candidates=[],
+                top_score=0.0,
+                duration_ms=duration_ms,
+                skipped=True,
+                skip_reason=f"embedding_error: {str(e)}",
+            )
+
+        candidates = []
+        for rule in rules_with_semantic:
+            try:
+                score = await self._calculate_similarity(message_vector, rule)
+                if score > 0:
+                    candidates.append(SemanticCandidate(rule=rule, score=score))
+            except Exception as e:
+                logger.warning(
+                    f"[AC-AISVC-114] Similarity calculation failed for rule={rule.id}: {e}"
+                )
+                continue
+
+        candidates.sort(key=lambda x: x.score, reverse=True)
+        candidates = candidates[:effective_top_k]
+
+        duration_ms = int((time.time() - start_time) * 1000)
+        logger.info(
+            f"[AC-AISVC-113] Semantic match completed for tenant={tenant_id}, "
+            f"candidates={len(candidates)}, top_score={candidates[0].score if candidates else 0:.3f}, "
+            f"duration={duration_ms}ms"
+        )
+
+        return SemanticMatchResult(
+            candidates=candidates,
+            top_score=candidates[0].score if candidates else 0.0,
+            duration_ms=duration_ms,
+            skipped=False,
+            skip_reason=None,
+        )
+
+    def _has_semantic_config(self, rule: "IntentRule") -> bool:
+        """
+        Check if rule has semantic configuration.
+
+        Args:
+            rule: Intent rule to check
+
+        Returns:
+            True if rule has intent_vector or semantic_examples
+        """
+        return bool(rule.intent_vector) or bool(rule.semantic_examples)
+
+    async def _calculate_similarity(
+        self,
+        message_vector: list[float],
+        rule: "IntentRule",
+    ) -> float:
+        """
+        [AC-AISVC-114] Calculate similarity between message and rule.
+
+        Mode A: Use pre-computed intent_vector
+        Mode B: Use semantic_examples for dynamic computation
+
+        Args:
+            message_vector: Message embedding vector
+            rule: Intent rule with semantic config
+
+        Returns:
+            Similarity score (0.0 ~ 1.0)
+        """
+        if rule.intent_vector:
+            return self._cosine_similarity(message_vector, rule.intent_vector)
+        elif rule.semantic_examples:
+            try:
+                example_vectors = await self._embedding_provider.embed_batch(
+                    rule.semantic_examples
+                )
+                similarities = [
+                    self._cosine_similarity(message_vector, v)
+                    for v in example_vectors
+                ]
+                return max(similarities) if similarities else 0.0
+            except Exception as e:
+                logger.warning(
+                    f"[AC-AISVC-114] Failed to compute example vectors for rule={rule.id}: {e}"
+                )
+                return 0.0
+        return 0.0
+
+    def _cosine_similarity(
+        self,
+        v1: list[float],
+        v2: list[float],
+    ) -> float:
+        """
+        Calculate cosine similarity between two vectors.
+
+        Args:
+            v1: First vector
+            v2: Second vector
+
+        Returns:
+            Cosine similarity (0.0 ~ 1.0)
+        """
+        if not v1 or not v2:
+            return 0.0
+
+        v1_arr = np.array(v1)
+        v2_arr = np.array(v2)
+
+        norm1 = np.linalg.norm(v1_arr)
+        norm2 = np.linalg.norm(v2_arr)
+
+        if norm1 == 0 or norm2 == 0:
+            return 0.0
+
+        similarity = float(np.dot(v1_arr, v2_arr) / (norm1 * norm2))
+        return max(0.0, min(1.0, similarity))

ai-service/app/services/kb.py

@@ -83,6 +83,7 @@ class KBService:
         file_name: str,
         file_content: bytes,
         file_type: str | None = None,
+        metadata: dict | None = None,
     ) -> tuple[Document, IndexJob]:
         """
         [AC-ASA-01] Upload document and create indexing job.
@@ -108,6 +109,7 @@ class KBService:
             file_size=len(file_content),
             file_type=file_type,
             status=DocumentStatus.PENDING.value,
+            doc_metadata=metadata,
         )
         self._session.add(document)
 

ai-service/app/services/llm/__init__.py

@@ -3,7 +3,14 @@ LLM Adapter module for AI Service.
 [AC-AISVC-02, AC-AISVC-06] Provides unified interface for LLM providers.
 """
 
-from app.services.llm.base import LLMClient, LLMConfig, LLMResponse, LLMStreamChunk
+from app.services.llm.base import (
+    LLMClient,
+    LLMConfig,
+    LLMResponse,
+    LLMStreamChunk,
+    ToolCall,
+    ToolDefinition,
+)
 from app.services.llm.openai_client import OpenAIClient
 
 __all__ = [
@@ -12,4 +19,6 @@ __all__ = [
     "LLMResponse",
     "LLMStreamChunk",
     "OpenAIClient",
+    "ToolCall",
+    "ToolDefinition",
 ]

ai-service/app/services/llm/base.py

@@ -28,18 +28,46 @@ class LLMConfig:
     extra_params: dict[str, Any] = field(default_factory=dict)
 
 
+@dataclass
+class ToolCall:
+    """
+    Represents a function call from the LLM.
+    Used in Function Calling mode.
+    """
+    id: str
+    name: str
+    arguments: dict[str, Any]
+    
+    def to_dict(self) -> dict[str, Any]:
+        import json
+        return {
+            "id": self.id,
+            "type": "function",
+            "function": {
+                "name": self.name,
+                "arguments": json.dumps(self.arguments, ensure_ascii=False),
+            }
+        }
+
+
 @dataclass
 class LLMResponse:
     """
     Response from LLM generation.
     [AC-AISVC-02] Contains generated content and metadata.
     """
-    content: str
-    model: str
+    content: str | None = None
+    model: str = ""
     usage: dict[str, int] = field(default_factory=dict)
     finish_reason: str = "stop"
+    tool_calls: list[ToolCall] = field(default_factory=list)
     metadata: dict[str, Any] = field(default_factory=dict)
     
+    @property
+    def has_tool_calls(self) -> bool:
+        """Check if response contains tool calls."""
+        return len(self.tool_calls) > 0
+
 
 @dataclass
 class LLMStreamChunk:
@@ -50,9 +78,33 @@ class LLMStreamChunk:
     delta: str
     model: str
     finish_reason: str | None = None
+    tool_calls_delta: list[dict[str, Any]] = field(default_factory=list)
     metadata: dict[str, Any] = field(default_factory=dict)
 
 
+@dataclass
+class ToolDefinition:
+    """
+    Tool definition for Function Calling.
+    Compatible with OpenAI/DeepSeek function calling format.
+    """
+    name: str
+    description: str
+    parameters: dict[str, Any]
+    type: str = "function"
+    
+    def to_openai_format(self) -> dict[str, Any]:
+        """Convert to OpenAI tools format."""
+        return {
+            "type": self.type,
+            "function": {
+                "name": self.name,
+                "description": self.description,
+                "parameters": self.parameters,
+            }
+        }
+
+
 class LLMClient(ABC):
     """
     Abstract base class for LLM clients.
@@ -67,6 +119,8 @@ class LLMClient(ABC):
         self,
         messages: list[dict[str, str]],
         config: LLMConfig | None = None,
+        tools: list[ToolDefinition] | None = None,
+        tool_choice: str | dict[str, Any] | None = None,
         **kwargs: Any,
     ) -> LLMResponse:
         """
@@ -76,10 +130,12 @@ class LLMClient(ABC):
         Args:
             messages: List of chat messages with 'role' and 'content'.
             config: Optional LLM configuration overrides.
+            tools: Optional list of tools for function calling.
+            tool_choice: Tool choice strategy ("auto", "none", or specific tool).
             **kwargs: Additional provider-specific parameters.
 
         Returns:
-            LLMResponse with generated content and metadata.
+            LLMResponse with generated content, tool_calls, and metadata.
 
         Raises:
             LLMException: If generation fails.
@@ -91,6 +147,8 @@ class LLMClient(ABC):
         self,
         messages: list[dict[str, str]],
         config: LLMConfig | None = None,
+        tools: list[ToolDefinition] | None = None,
+        tool_choice: str | dict[str, Any] | None = None,
         **kwargs: Any,
     ) -> AsyncGenerator[LLMStreamChunk, None]:
         """
@@ -100,6 +158,8 @@ class LLMClient(ABC):
         Args:
             messages: List of chat messages with 'role' and 'content'.
             config: Optional LLM configuration overrides.
+            tools: Optional list of tools for function calling.
+            tool_choice: Tool choice strategy ("auto", "none", or specific tool).
             **kwargs: Additional provider-specific parameters.
 
         Yields:

ai-service/app/services/llm/factory.py

@@ -11,12 +11,16 @@ from dataclasses import dataclass
 from pathlib import Path
 from typing import Any
 
+import redis
+
+from app.core.config import get_settings
 from app.services.llm.base import LLMClient, LLMConfig
 from app.services.llm.openai_client import OpenAIClient
 
 logger = logging.getLogger(__name__)
 
 LLM_CONFIG_FILE = Path("config/llm_config.json")
+LLM_CONFIG_REDIS_KEY = "ai_service:config:llm"
 
 
 @dataclass
@@ -286,6 +290,8 @@ class LLMConfigManager:
         from app.core.config import get_settings
 
         settings = get_settings()
+        self._settings = settings
+        self._redis_client: redis.Redis | None = None
 
         self._current_provider: str = settings.llm_provider
         self._current_config: dict[str, Any] = {
@@ -299,8 +305,75 @@ class LLMConfigManager:
         }
         self._client: LLMClient | None = None
 
+        self._load_from_redis()
         self._load_from_file()
 
+    def _load_from_redis(self) -> None:
+        """Load configuration from Redis if exists."""
+        try:
+            if not self._settings.redis_enabled:
+                return
+            self._redis_client = redis.from_url(
+                self._settings.redis_url,
+                encoding="utf-8",
+                decode_responses=True,
+            )
+            saved_raw = self._redis_client.get(LLM_CONFIG_REDIS_KEY)
+            if not saved_raw:
+                return
+            saved = json.loads(saved_raw)
+            self._current_provider = saved.get("provider", self._current_provider)
+            saved_config = saved.get("config", {})
+            if saved_config:
+                self._current_config.update(saved_config)
+            logger.info(f"[AC-ASA-16] Loaded LLM config from Redis: provider={self._current_provider}")
+        except Exception as e:
+            logger.warning(f"[AC-ASA-16] Failed to load LLM config from Redis: {e}")
+
+    def _save_to_redis(self) -> None:
+        """Save configuration to Redis."""
+        try:
+            if not self._settings.redis_enabled:
+                return
+            if self._redis_client is None:
+                self._redis_client = redis.from_url(
+                    self._settings.redis_url,
+                    encoding="utf-8",
+                    decode_responses=True,
+                )
+            self._redis_client.set(
+                LLM_CONFIG_REDIS_KEY,
+                json.dumps({
+                    "provider": self._current_provider,
+                    "config": self._current_config,
+                }, ensure_ascii=False),
+            )
+            logger.info(f"[AC-ASA-16] Saved LLM config to Redis: provider={self._current_provider}")
+        except Exception as e:
+            logger.warning(f"[AC-ASA-16] Failed to save LLM config to Redis: {e}")
+
+    def _load_from_redis(self) -> None:
+        """Load configuration from Redis if exists."""
+        try:
+            if not self._settings.redis_enabled:
+                return
+            self._redis_client = redis.from_url(
+                self._settings.redis_url,
+                encoding="utf-8",
+                decode_responses=True,
+            )
+            saved_raw = self._redis_client.get(LLM_CONFIG_REDIS_KEY)
+            if not saved_raw:
+                return
+            saved = json.loads(saved_raw)
+            self._current_provider = saved.get("provider", self._current_provider)
+            saved_config = saved.get("config", {})
+            if saved_config:
+                self._current_config.update(saved_config)
+            logger.info(f"[AC-ASA-16] Loaded LLM config from Redis: provider={self._current_provider}")
+        except Exception as e:
+            logger.warning(f"[AC-ASA-16] Failed to load LLM config from Redis: {e}")
+
     def _load_from_file(self) -> None:
         """Load configuration from file if exists."""
         try:
@@ -364,6 +437,7 @@ class LLMConfigManager:
         self._current_provider = provider
         self._current_config = validated_config
 
+        self._save_to_redis()
         self._save_to_file()
 
         logger.info(f"[AC-ASA-16] LLM config updated: provider={provider}")

ai-service/app/services/llm/openai_client.py

@@ -22,7 +22,14 @@ from tenacity import (
 
 from app.core.config import get_settings
 from app.core.exceptions import AIServiceException, ErrorCode, TimeoutException
-from app.services.llm.base import LLMClient, LLMConfig, LLMResponse, LLMStreamChunk
+from app.services.llm.base import (
+    LLMClient,
+    LLMConfig,
+    LLMResponse,
+    LLMStreamChunk,
+    ToolCall,
+    ToolDefinition,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -95,6 +102,8 @@ class OpenAIClient(LLMClient):
         messages: list[dict[str, str]],
         config: LLMConfig,
         stream: bool = False,
+        tools: list[ToolDefinition] | None = None,
+        tool_choice: str | dict[str, Any] | None = None,
         **kwargs: Any,
     ) -> dict[str, Any]:
         """Build request body for OpenAI API."""
@@ -106,6 +115,13 @@ class OpenAIClient(LLMClient):
             "top_p": config.top_p,
             "stream": stream,
         }
+        
+        if tools:
+            body["tools"] = [tool.to_openai_format() for tool in tools]
+        
+        if tool_choice:
+            body["tool_choice"] = tool_choice
+        
         body.update(config.extra_params)
         body.update(kwargs)
         return body
@@ -119,6 +135,8 @@ class OpenAIClient(LLMClient):
         self,
         messages: list[dict[str, str]],
         config: LLMConfig | None = None,
+        tools: list[ToolDefinition] | None = None,
+        tool_choice: str | dict[str, Any] | None = None,
         **kwargs: Any,
     ) -> LLMResponse:
         """
@@ -128,10 +146,12 @@ class OpenAIClient(LLMClient):
         Args:
             messages: List of chat messages with 'role' and 'content'.
             config: Optional LLM configuration overrides.
+            tools: Optional list of tools for function calling.
+            tool_choice: Tool choice strategy ("auto", "none", or specific tool).
             **kwargs: Additional provider-specific parameters.
 
         Returns:
-            LLMResponse with generated content and metadata.
+            LLMResponse with generated content, tool_calls, and metadata.
 
         Raises:
             LLMException: If generation fails.
@@ -140,9 +160,14 @@ class OpenAIClient(LLMClient):
         effective_config = config or self._default_config
         client = self._get_client(effective_config.timeout_seconds)
 
-        body = self._build_request_body(messages, effective_config, stream=False, **kwargs)
+        body = self._build_request_body(
+            messages, effective_config, stream=False, 
+            tools=tools, tool_choice=tool_choice, **kwargs
+        )
 
         logger.info(f"[AC-AISVC-02] Generating response with model={effective_config.model}")
+        if tools:
+            logger.info(f"[AC-AISVC-02] Function calling enabled with {len(tools)} tools")
         logger.info("[AC-AISVC-02] ========== FULL PROMPT TO AI ==========")
         for i, msg in enumerate(messages):
             role = msg.get("role", "unknown")
@@ -177,14 +202,18 @@ class OpenAIClient(LLMClient):
 
         try:
             choice = data["choices"][0]
-            content = choice["message"]["content"]
+            message = choice["message"]
+            content = message.get("content")
             usage = data.get("usage", {})
             finish_reason = choice.get("finish_reason", "stop")
             
+            tool_calls = self._parse_tool_calls(message)
+
             logger.info(
                 f"[AC-AISVC-02] Generated response: "
                 f"tokens={usage.get('total_tokens', 'N/A')}, "
-                f"finish_reason={finish_reason}"
+                f"finish_reason={finish_reason}, "
+                f"tool_calls={len(tool_calls)}"
             )
 
             return LLMResponse(
@@ -192,6 +221,7 @@ class OpenAIClient(LLMClient):
                 model=data.get("model", effective_config.model),
                 usage=usage,
                 finish_reason=finish_reason,
+                tool_calls=tool_calls,
                 metadata={"raw_response": data},
             )
 
@@ -202,10 +232,33 @@ class OpenAIClient(LLMClient):
                 details=[{"response": str(data)}],
             )
     
+    def _parse_tool_calls(self, message: dict[str, Any]) -> list[ToolCall]:
+        """Parse tool calls from LLM response message."""
+        tool_calls = []
+        raw_tool_calls = message.get("tool_calls", [])
+        
+        for tc in raw_tool_calls:
+            if tc.get("type") == "function":
+                func = tc.get("function", {})
+                try:
+                    arguments = json.loads(func.get("arguments", "{}"))
+                except json.JSONDecodeError:
+                    arguments = {}
+                
+                tool_calls.append(ToolCall(
+                    id=tc.get("id", ""),
+                    name=func.get("name", ""),
+                    arguments=arguments,
+                ))
+        
+        return tool_calls
+
     async def stream_generate(
         self,
         messages: list[dict[str, str]],
         config: LLMConfig | None = None,
+        tools: list[ToolDefinition] | None = None,
+        tool_choice: str | dict[str, Any] | None = None,
         **kwargs: Any,
     ) -> AsyncGenerator[LLMStreamChunk, None]:
         """
@@ -215,6 +268,8 @@ class OpenAIClient(LLMClient):
         Args:
             messages: List of chat messages with 'role' and 'content'.
             config: Optional LLM configuration overrides.
+            tools: Optional list of tools for function calling.
+            tool_choice: Tool choice strategy ("auto", "none", or specific tool).
             **kwargs: Additional provider-specific parameters.
 
         Yields:
@@ -227,9 +282,14 @@ class OpenAIClient(LLMClient):
         effective_config = config or self._default_config
         client = self._get_client(effective_config.timeout_seconds)
 
-        body = self._build_request_body(messages, effective_config, stream=True, **kwargs)
+        body = self._build_request_body(
+            messages, effective_config, stream=True,
+            tools=tools, tool_choice=tool_choice, **kwargs
+        )
 
         logger.info(f"[AC-AISVC-06] Starting streaming generation with model={effective_config.model}")
+        if tools:
+            logger.info(f"[AC-AISVC-06] Function calling enabled with {len(tools)} tools")
         logger.info("[AC-AISVC-06] ========== FULL PROMPT TO AI (STREAMING) ==========")
         for i, msg in enumerate(messages):
             role = msg.get("role", "unknown")

ai-service/app/services/metadata_cache_service.py

@@ -0,0 +1,202 @@
+"""
+元数据字段定义缓存服务
+使用 Redis 缓存 metadata_field_definitions，减少数据库查询
+"""
+
+import json
+import logging
+from typing import Any
+
+from app.core.config import get_settings
+
+logger = logging.getLogger(__name__)
+
+
+class MetadataCacheService:
+    """
+    元数据字段定义缓存服务
+    
+    缓存策略：
+    - Key: metadata:fields:{tenant_id}
+    - Value: JSON 序列化的字段定义列表
+    - TTL: 1小时（3600秒）
+    - 更新策略：写时更新 + 定时刷新
+    """
+    
+    CACHE_KEY_PREFIX = "metadata:fields"
+    DEFAULT_TTL = 3600  # 1小时
+    
+    def __init__(self):
+        self._settings = get_settings()
+        self._redis_client = None
+        self._enabled = self._settings.redis_enabled
+    
+    async def _get_redis(self):
+        """获取 Redis 连接（延迟初始化）"""
+        if not self._enabled:
+            return None
+        
+        if self._redis_client is None:
+            try:
+                import redis.asyncio as redis
+                self._redis_client = redis.from_url(
+                    self._settings.redis_url,
+                    decode_responses=True
+                )
+            except Exception as e:
+                logger.error(f"[MetadataCache] Failed to connect Redis: {e}")
+                self._enabled = False
+                return None
+        
+        return self._redis_client
+    
+    def _make_key(self, tenant_id: str) -> str:
+        """生成缓存 key"""
+        return f"{self.CACHE_KEY_PREFIX}:{tenant_id}"
+    
+    async def get_fields(self, tenant_id: str) -> list[dict[str, Any]] | None:
+        """
+        获取缓存的字段定义
+        
+        Args:
+            tenant_id: 租户 ID
+            
+        Returns:
+            字段定义列表，未缓存返回 None
+        """
+        if not self._enabled:
+            return None
+        
+        try:
+            redis = await self._get_redis()
+            if not redis:
+                return None
+            
+            key = self._make_key(tenant_id)
+            cached_data = await redis.get(key)
+            
+            if cached_data:
+                logger.info(f"[MetadataCache] Cache hit for tenant={tenant_id}")
+                return json.loads(cached_data)
+            
+            logger.info(f"[MetadataCache] Cache miss for tenant={tenant_id}")
+            return None
+            
+        except Exception as e:
+            logger.error(f"[MetadataCache] Get cache error: {e}")
+            return None
+    
+    async def set_fields(
+        self,
+        tenant_id: str,
+        fields: list[dict[str, Any]],
+        ttl: int | None = None
+    ) -> bool:
+        """
+        缓存字段定义
+        
+        Args:
+            tenant_id: 租户 ID
+            fields: 字段定义列表
+            ttl: 过期时间（秒），默认 1小时
+            
+        Returns:
+            是否成功
+        """
+        if not self._enabled:
+            return False
+        
+        try:
+            redis = await self._get_redis()
+            if not redis:
+                return False
+            
+            key = self._make_key(tenant_id)
+            ttl = ttl or self.DEFAULT_TTL
+            
+            await redis.setex(
+                key,
+                ttl,
+                json.dumps(fields, ensure_ascii=False, default=str)
+            )
+            
+            logger.info(
+                f"[MetadataCache] Cached {len(fields)} fields for tenant={tenant_id}, "
+                f"ttl={ttl}s"
+            )
+            return True
+            
+        except Exception as e:
+            logger.error(f"[MetadataCache] Set cache error: {e}")
+            return False
+    
+    async def invalidate(self, tenant_id: str) -> bool:
+        """
+        使缓存失效（字段定义更新时调用）
+        
+        Args:
+            tenant_id: 租户 ID
+            
+        Returns:
+            是否成功
+        """
+        if not self._enabled:
+            return False
+        
+        try:
+            redis = await self._get_redis()
+            if not redis:
+                return False
+            
+            key = self._make_key(tenant_id)
+            result = await redis.delete(key)
+            
+            if result:
+                logger.info(f"[MetadataCache] Invalidated cache for tenant={tenant_id}")
+            return bool(result)
+            
+        except Exception as e:
+            logger.error(f"[MetadataCache] Invalidate error: {e}")
+            return False
+    
+    async def invalidate_all(self) -> bool:
+        """
+        使所有元数据缓存失效
+        
+        Returns:
+            是否成功
+        """
+        if not self._enabled:
+            return False
+        
+        try:
+            redis = await self._get_redis()
+            if not redis:
+                return False
+            
+            # 查找所有元数据缓存 key
+            pattern = f"{self.CACHE_KEY_PREFIX}:*"
+            keys = []
+            async for key in redis.scan_iter(match=pattern):
+                keys.append(key)
+            
+            if keys:
+                await redis.delete(*keys)
+                logger.info(f"[MetadataCache] Invalidated {len(keys)} cache entries")
+            return True
+            
+        except Exception as e:
+            logger.error(f"[MetadataCache] Invalidate all error: {e}")
+            return False
+
+
+# 全局缓存服务实例
+_metadata_cache_service: MetadataCacheService | None = None
+
+
+async def get_metadata_cache_service() -> MetadataCacheService:
+    """获取元数据缓存服务实例（单例）"""
+    global _metadata_cache_service
+    if _metadata_cache_service is None:
+        _metadata_cache_service = MetadataCacheService()
+    return _metadata_cache_service

ai-service/app/services/metadata_field_definition_service.py

@@ -40,6 +40,19 @@ class MetadataFieldDefinitionService:
     def __init__(self, session: AsyncSession):
         self._session = session
     
+    async def _invalidate_cache(self, tenant_id: str) -> None:
+        """
+        清除租户的元数据字段缓存
+        在字段创建、更新、删除时调用
+        """
+        try:
+            from app.services.metadata_cache_service import get_metadata_cache_service
+            cache_service = await get_metadata_cache_service()
+            await cache_service.invalidate(tenant_id)
+        except Exception as e:
+            # 缓存失效失败不影响主流程
+            logger.warning(f"[AC-IDSMETA-13] Failed to invalidate cache: {e}")
+
     async def list_field_definitions(
         self,
         tenant_id: str,
@@ -180,6 +193,9 @@ class MetadataFieldDefinitionService:
         self._session.add(field)
         await self._session.flush()
         
+        # 清除缓存，使新字段在下次查询时生效
+        await self._invalidate_cache(tenant_id)
+        
         logger.info(
             f"[AC-IDSMETA-13] [AC-MRS-01] Created field definition: tenant={tenant_id}, "
             f"field_key={field.field_key}, status={field.status}, field_roles={field.field_roles}"
@@ -223,6 +239,10 @@ class MetadataFieldDefinitionService:
             field.is_filterable = field_update.is_filterable
         if field_update.is_rank_feature is not None:
             field.is_rank_feature = field_update.is_rank_feature
+        # [AC-MRS-01] 修复：添加 field_roles 更新逻辑
+        if field_update.field_roles is not None:
+            self._validate_field_roles(field_update.field_roles)
+            field.field_roles = field_update.field_roles
         if field_update.status is not None:
             old_status = field.status
             field.status = field_update.status
@@ -235,6 +255,9 @@ class MetadataFieldDefinitionService:
         field.updated_at = datetime.utcnow()
         await self._session.flush()
         
+        # 清除缓存，使更新在下次查询时生效
+        await self._invalidate_cache(tenant_id)
+        
         logger.info(
             f"[AC-IDSMETA-14] Updated field definition: tenant={tenant_id}, "
             f"field_id={field_id}, version={field.version}"

ai-service/app/services/mid/__init__.py

@@ -17,6 +17,18 @@ from .memory_adapter import MemoryAdapter, UserMemory
 from .default_kb_tool_runner import DefaultKbToolRunner, KbToolResult, KbToolConfig, get_default_kb_tool_runner
 from .segment_humanizer import SegmentHumanizer, HumanizeConfig, LengthBucket, get_segment_humanizer
 from .runtime_observer import RuntimeObserver, RuntimeContext, get_runtime_observer
+from .slot_validation_service import (
+    SlotValidationService,
+    ValidationResult,
+    SlotValidationError,
+    BatchValidationResult,
+    SlotValidationErrorCode,
+)
+from .slot_manager import (
+    SlotManager,
+    SlotWriteResult,
+    create_slot_manager,
+)
 
 __all__ = [
     "PolicyRouter",
@@ -54,4 +66,12 @@ __all__ = [
     "RuntimeObserver",
     "RuntimeContext",
     "get_runtime_observer",
+    "SlotValidationService",
+    "ValidationResult",
+    "SlotValidationError",
+    "BatchValidationResult",
+    "SlotValidationErrorCode",
+    "SlotManager",
+    "SlotWriteResult",
+    "create_slot_manager",
 ]

ai-service/app/services/mid/agent_orchestrator.py

@@ -2,6 +2,10 @@
 Agent Orchestrator for Mid Platform.
 [AC-MARH-07] ReAct loop with iteration limit (3-5 iterations).
 
+Supports two execution modes:
+1. ReAct (Text-based): Traditional Thought/Action/Observation loop
+2. Function Calling: Uses LLM's native function calling capability
+
 ReAct Flow:
 1. Thought: Agent thinks about what to do
 2. Action: Agent decides to use a tool
@@ -16,6 +20,8 @@ import re
 import time
 import uuid
 from dataclasses import dataclass
+from datetime import datetime
+from enum import Enum
 from typing import Any
 
 from app.models.mid.schemas import (
@@ -26,7 +32,10 @@ from app.models.mid.schemas import (
     ToolType,
     TraceInfo,
 )
+from app.services.llm.base import ToolDefinition
+from app.services.mid.tool_guide_registry import ToolGuideRegistry, get_tool_guide_registry
 from app.services.mid.timeout_governor import TimeoutGovernor
+from app.services.mid.tool_converter import convert_tools_to_llm_format, build_tool_result_message
 from app.services.prompt.template_service import PromptTemplateService
 from app.services.prompt.variable_resolver import VariableResolver
 
@@ -36,11 +45,17 @@ DEFAULT_MAX_ITERATIONS = 5
 MIN_ITERATIONS = 3
 
 
+class AgentMode(str, Enum):
+    """Agent execution mode."""
+    REACT = "react"
+    FUNCTION_CALLING = "function_calling"
+
+
 @dataclass
 class ToolResult:
     """Tool execution result."""
     success: bool
-    output: str | None = None
+    output: Any = None
     error: str | None = None
     duration_ms: int = 0
 
@@ -59,6 +74,7 @@ class AgentOrchestrator:
 
     Features:
     - ReAct loop with max 5 iterations (min 3)
+    - Function Calling mode for supported LLMs (OpenAI, DeepSeek, etc.)
     - Per-tool timeout (2s) and end-to-end timeout (8s)
     - Automatic fallback on iteration limit or timeout
     - Template-based prompt with variable injection
@@ -70,17 +86,74 @@ class AgentOrchestrator:
         timeout_governor: TimeoutGovernor | None = None,
         llm_client: Any = None,
         tool_registry: Any = None,
+        guide_registry: ToolGuideRegistry | None = None,
         template_service: PromptTemplateService | None = None,
         variable_resolver: VariableResolver | None = None,
         tenant_id: str | None = None,
+        user_id: str | None = None,
+        session_id: str | None = None,
+        scene: str | None = None,
+        mode: AgentMode = AgentMode.FUNCTION_CALLING,
     ):
         self._max_iterations = max(min(max_iterations, 5), MIN_ITERATIONS)
         self._timeout_governor = timeout_governor or TimeoutGovernor()
         self._llm_client = llm_client
         self._tool_registry = tool_registry
+        self._guide_registry = guide_registry
         self._template_service = template_service
         self._variable_resolver = variable_resolver or VariableResolver()
         self._tenant_id = tenant_id
+        self._user_id = user_id
+        self._session_id = session_id
+        self._scene = scene
+        self._mode = mode
+        self._tools_cache: list[ToolDefinition] | None = None
+
+    def _get_tools_definition(self) -> list[ToolDefinition]:
+        """Get cached tools definition for Function Calling."""
+        if self._tools_cache is None and self._tool_registry:
+            tools = self._tool_registry.get_all_tools()
+            self._tools_cache = convert_tools_to_llm_format(tools)
+        return self._tools_cache or []
+
+    async def _get_tools_definition_async(self) -> list[ToolDefinition]:
+        """Get tools definition for Function Calling with dynamic schema support."""
+        if self._tools_cache is not None:
+            return self._tools_cache
+        
+        if not self._tool_registry:
+            return []
+        
+        tools = self._tool_registry.get_all_tools()
+        result = []
+        
+        for tool in tools:
+            if tool.name == "kb_search_dynamic" and self._tenant_id:
+                from app.services.mid.kb_search_dynamic_tool import (
+                    _TOOL_SCHEMA_CACHE,
+                    _TOOL_SCHEMA_CACHE_TTL_SECONDS,
+                )
+                import time
+                
+                cache_key = f"tool_schema:{self._tenant_id}"
+                current_time = time.time()
+                
+                if cache_key in _TOOL_SCHEMA_CACHE:
+                    cached_time, cached_schema = _TOOL_SCHEMA_CACHE[cache_key]
+                    if current_time - cached_time < _TOOL_SCHEMA_CACHE_TTL_SECONDS:
+                        result.append(ToolDefinition(
+                            name=cached_schema["name"],
+                            description=cached_schema["description"],
+                            parameters=cached_schema["parameters"],
+                        ))
+                        continue
+                
+                result.append(convert_tool_to_llm_format(tool))
+            else:
+                result.append(convert_tool_to_llm_format(tool))
+        
+        self._tools_cache = result
+        return result
 
     async def execute(
         self,
@@ -90,7 +163,7 @@ class AgentOrchestrator:
         on_action: Any = None,
     ) -> tuple[str, ReActContext, TraceInfo]:
         """
-        [AC-MARH-07] Execute ReAct loop with iteration control.
+        [AC-MARH-07] Execute agent loop with iteration control.
 
         Args:
             user_message: User input message
@@ -101,6 +174,416 @@ class AgentOrchestrator:
         Returns:
             Tuple of (final_answer, react_context, trace_info)
         """
+        if self._mode == AgentMode.FUNCTION_CALLING:
+            return await self._execute_function_calling(user_message, context, on_action)
+        else:
+            return await self._execute_react(user_message, context, on_thought, on_action)
+
+    async def _execute_function_calling(
+        self,
+        user_message: str,
+        context: dict[str, Any] | None = None,
+        on_action: Any = None,
+    ) -> tuple[str, ReActContext, TraceInfo]:
+        """
+        Execute using Function Calling mode.
+        
+        This mode uses the LLM's native function calling capability,
+        which is more reliable and token-efficient than text-based ReAct.
+        """
+        react_ctx = ReActContext(max_iterations=self._max_iterations)
+        tool_calls: list[ToolCallTrace] = []
+        start_time = time.time()
+
+        logger.info(
+            f"[AC-MARH-07] Starting Function Calling loop: max_iterations={self._max_iterations}, "
+            f"llm_client={self._llm_client is not None}, tool_registry={self._tool_registry is not None}"
+        )
+
+        if not self._llm_client:
+            logger.error("[DEBUG-ORCH] LLM client is None, returning error response")
+            return "抱歉，服务配置错误，请联系管理员。", react_ctx, TraceInfo(
+                mode=ExecutionMode.AGENT,
+                request_id=str(uuid.uuid4()),
+                generation_id=str(uuid.uuid4()),
+            )
+
+        try:
+            messages: list[dict[str, Any]] = [
+                {"role": "system", "content": await self._build_system_prompt()},
+                {"role": "user", "content": user_message},
+            ]
+            
+            tools = await self._get_tools_definition_async()
+            
+            overall_start = time.time()
+            end_to_end_timeout = self._timeout_governor.end_to_end_timeout_seconds
+            llm_timeout = getattr(self._timeout_governor, 'llm_timeout_seconds', 15.0)
+
+            while react_ctx.should_continue and react_ctx.iteration < react_ctx.max_iterations:
+                react_ctx.iteration += 1
+
+                elapsed = time.time() - overall_start
+                remaining_time = end_to_end_timeout - elapsed
+                if remaining_time <= 0:
+                    logger.warning("[AC-MARH-09] Function Calling loop exceeded end-to-end timeout")
+                    react_ctx.final_answer = "抱歉，处理超时，请稍后重试或联系人工客服。"
+                    break
+
+                logger.info(
+                    f"[AC-MARH-07] Function Calling iteration {react_ctx.iteration}/"
+                    f"{react_ctx.max_iterations}, remaining_time={remaining_time:.1f}s"
+                )
+
+                logger.info(
+                    f"[DEBUG-ORCH] Calling LLM generate with messages_count={len(messages)}, "
+                    f"tools_count={len(tools) if tools else 0}"
+                )
+
+                response = await asyncio.wait_for(
+                    self._llm_client.generate(
+                        messages=messages,
+                        tools=tools if tools else None,
+                        tool_choice="auto" if tools else None,
+                    ),
+                    timeout=min(llm_timeout, remaining_time)
+                )
+
+                logger.info(
+                    f"[DEBUG-ORCH] LLM response received: has_tool_calls={response.has_tool_calls}, "
+                    f"content_length={len(response.content) if response.content else 0}, "
+                    f"tool_calls_count={len(response.tool_calls) if response.tool_calls else 0}"
+                )
+
+                if response.has_tool_calls:
+                    for tool_call in response.tool_calls:
+                        tool_name = tool_call.name
+                        tool_args = tool_call.arguments
+                        
+                        logger.info(f"[AC-MARH-07] Tool call: {tool_name}, args={tool_args}")
+                        
+                        messages.append({
+                            "role": "assistant",
+                            "content": response.content,
+                            "tool_calls": [tool_call.to_dict()],
+                        })
+                        
+                        tool_result, tool_trace = await self._act_fc(
+                            tool_call.id, tool_name, tool_args, react_ctx
+                        )
+                        tool_calls.append(tool_trace)
+                        react_ctx.tool_calls.append(tool_trace)
+                        
+                        if on_action:
+                            await on_action(tool_name, tool_result)
+                        
+                        called_tools = {tc.tool_name for tc in react_ctx.tool_calls[:-1]}
+                        is_first_call = tool_name not in called_tools
+                        
+                        # Extract tool_guide from output if present (added by _act_fc)
+                        result_output = tool_result.output if tool_result.success else {"error": tool_result.error}
+                        tool_guide = None
+                        if isinstance(result_output, dict) and "_tool_guide" in result_output:
+                            result_output = dict(result_output)
+                            tool_guide = result_output.pop("_tool_guide")
+                        
+                        messages.append(build_tool_result_message(
+                            tool_call_id=tool_call.id,
+                            tool_name=tool_name,
+                            result=result_output,
+                            tool_guide=tool_guide,
+                        ))
+                        
+                        if not tool_result.success:
+                            if tool_trace.status == ToolCallStatus.TIMEOUT:
+                                react_ctx.final_answer = "抱歉，操作超时，请稍后重试或联系人工客服。"
+                                react_ctx.should_continue = False
+                                break
+                else:
+                    react_ctx.final_answer = response.content or "抱歉，我无法处理您的请求。"
+                    react_ctx.should_continue = False
+                    break
+
+            if react_ctx.should_continue and not react_ctx.final_answer:
+                logger.warning(f"[AC-MARH-07] Function Calling reached max iterations: {react_ctx.iteration}")
+                react_ctx.final_answer = await self._force_final_answer_fc(messages)
+
+        except asyncio.TimeoutError:
+            logger.error("[AC-MARH-09] Function Calling loop timed out (end-to-end)")
+            react_ctx.final_answer = "抱歉，处理超时，请稍后重试或联系人工客服。"
+            tool_calls.append(ToolCallTrace(
+                tool_name="fc_loop",
+                tool_type=ToolType.INTERNAL,
+                duration_ms=int((time.time() - start_time) * 1000),
+                status=ToolCallStatus.TIMEOUT,
+                error_code="E2E_TIMEOUT",
+            ))
+        except Exception as e:
+            logger.error(f"[AC-MARH-07] Function Calling error: {e}", exc_info=True)
+            react_ctx.final_answer = f"抱歉，处理过程中发生错误：{str(e)}"
+
+        total_duration_ms = int((time.time() - start_time) * 1000)
+        trace = TraceInfo(
+            mode=ExecutionMode.AGENT,
+            request_id=str(uuid.uuid4()),
+            generation_id=str(uuid.uuid4()),
+            react_iterations=react_ctx.iteration,
+            tools_used=[tc.tool_name for tc in tool_calls if tc.tool_name not in ("fc_loop", "react_loop")],
+            tool_calls=tool_calls if tool_calls else None,
+        )
+
+        logger.info(
+            f"[AC-MARH-07] Function Calling completed: iterations={react_ctx.iteration}, "
+            f"duration_ms={total_duration_ms}"
+        )
+
+        return react_ctx.final_answer or "抱歉，我暂时无法处理您的请求。", react_ctx, trace
+
+    async def _act_fc(
+        self,
+        tool_call_id: str,
+        tool_name: str,
+        tool_args: dict[str, Any],
+        react_ctx: ReActContext,
+    ) -> tuple[ToolResult, ToolCallTrace]:
+        """Execute tool in Function Calling mode."""
+        start_time = time.time()
+
+        if not self._tool_registry:
+            duration_ms = int((time.time() - start_time) * 1000)
+            return ToolResult(
+                success=False,
+                error="Tool registry not configured",
+                duration_ms=duration_ms,
+            ), ToolCallTrace(
+                tool_name=tool_name,
+                tool_type=ToolType.INTERNAL,
+                duration_ms=duration_ms,
+                status=ToolCallStatus.ERROR,
+                error_code="NO_REGISTRY",
+            )
+
+        try:
+            final_args = dict(tool_args)
+            if self._tenant_id:
+                final_args["tenant_id"] = self._tenant_id
+            if self._user_id:
+                final_args["user_id"] = self._user_id
+            if self._session_id:
+                final_args["session_id"] = self._session_id
+
+            if tool_name == "kb_search_dynamic":
+                # 确保 context 存在，供 AI 传入动态过滤条件
+                if "context" not in final_args:
+                    final_args["context"] = {}
+                # scene 参数由 AI 从元数据中选择，系统不强制覆盖
+
+            logger.info(
+                f"[AC-MARH-07] FC Tool call starting: tool={tool_name}, "
+                f"args={tool_args}, final_args={final_args}"
+            )
+
+            result = await asyncio.wait_for(
+                self._tool_registry.execute(
+                    name=tool_name,
+                    **final_args,
+                ),
+                timeout=self._timeout_governor.per_tool_timeout_seconds
+            )
+
+            duration_ms = int((time.time() - start_time) * 1000)
+            
+            called_tools = {tc.tool_name for tc in react_ctx.tool_calls}
+            is_first_call = tool_name not in called_tools
+            
+            output = result.output
+            if is_first_call and result.success:
+                usage_guide = self._build_tool_usage_guide(tool_name)
+                if usage_guide:
+                    if isinstance(output, dict):
+                        output = dict(output)
+                        output["_tool_guide"] = usage_guide
+                    elif isinstance(output, str):
+                        output = f"{output}\n\n---\n{usage_guide}"
+                    else:
+                        output = {"result": output, "_tool_guide": usage_guide}
+
+            return ToolResult(
+                success=result.success,
+                output=output,
+                error=result.error,
+                duration_ms=duration_ms,
+            ), ToolCallTrace(
+                tool_name=tool_name,
+                tool_type=ToolType.INTERNAL,
+                duration_ms=duration_ms,
+                status=ToolCallStatus.OK if result.success else ToolCallStatus.ERROR,
+                args_digest=str(tool_args)[:100] if tool_args else None,
+                result_digest=str(result.output)[:100] if result.output else None,
+                arguments=tool_args,
+                result=output,
+            )
+
+        except asyncio.TimeoutError:
+            duration_ms = int((time.time() - start_time) * 1000)
+            logger.warning(f"[AC-MARH-08] FC Tool timeout: {tool_name}, duration={duration_ms}ms")
+            return ToolResult(
+                success=False,
+                error="Tool timeout",
+                duration_ms=duration_ms,
+            ), ToolCallTrace(
+                tool_name=tool_name,
+                tool_type=ToolType.INTERNAL,
+                duration_ms=duration_ms,
+                status=ToolCallStatus.TIMEOUT,
+                error_code="TOOL_TIMEOUT",
+                arguments=tool_args,
+            )
+
+        except Exception as e:
+            duration_ms = int((time.time() - start_time) * 1000)
+            logger.error(f"[AC-MARH-07] FC Tool error: {tool_name}, error={e}")
+            return ToolResult(
+                success=False,
+                error=str(e),
+                duration_ms=duration_ms,
+            ), ToolCallTrace(
+                tool_name=tool_name,
+                tool_type=ToolType.INTERNAL,
+                duration_ms=duration_ms,
+                status=ToolCallStatus.ERROR,
+                error_code="TOOL_ERROR",
+                arguments=tool_args,
+            )
+
+    async def _build_system_prompt(self) -> str:
+        """Build system prompt for Function Calling mode with template support."""
+        default_prompt = """你是一个智能客服助手，正在处理用户请求。
+
+## 决策协议
+
+1. 优先使用已有观察信息，避免重复调用同类工具。
+2. 当问题需要外部事实或结构化状态时再调用工具；如果可直接回答则不要调用。
+3. 缺少关键参数时，优先向用户追问，不要使用空参数调用工具。
+4. 工具失败时，先说明已尝试，再给出降级方案或下一步引导。
+5. 对用户输出必须拟人、自然、有同理心，不暴露"工具调用/路由/策略"等内部术语。
+
+## 知识库查询强制流程
+
+当用户问题需要进行知识库查询（kb_search_dynamic）时，必须遵循以下步骤：
+
+**步骤1：先调用 list_document_metadata_fields**
+- 在任何知识库搜索之前，必须先调用 `list_document_metadata_fields` 工具
+- 获取可用的元数据字段（如 grade, subject, kb_scene 等）及其常见取值
+
+**步骤2：分析用户意图，选择合适的过滤条件**
+- 根据用户问题和返回的元数据字段，确定合适的过滤条件
+- 从元数据字段的 common_values 中选择合适的值
+
+**步骤3：调用 kb_search_dynamic 进行搜索**
+- 使用步骤1获取的元数据字段构造 context 参数
+- scene 参数必须从元数据字段的 kb_scene 常见值中选择，不要硬编码
+
+**示例流程：**
+1. 调用 `list_document_metadata_fields` 获取字段信息
+2. 根据返回结果，发现可用字段：grade（年级）、subject（学科）、kb_scene（场景）
+3. 分析用户问题"三年级语文怎么学"，确定过滤条件：grade="三年级", subject="语文"
+4. 从 kb_scene 的常见值中选择合适的 scene（如"学习方案"）
+5. 调用 `kb_search_dynamic`，传入构造好的 context 和 scene
+
+## 注意事项
+- **严禁**在调用 kb_search_dynamic 之前不调用 list_document_metadata_fields。
+"""
+
+        if not self._template_service or not self._tenant_id:
+            return default_prompt
+
+        try:
+            from app.core.database import get_session
+            from app.core.prompts import SYSTEM_PROMPT
+            
+            async with get_session() as session:
+                template_service = PromptTemplateService(session)
+                
+                base_prompt = await template_service.get_published_template(
+                    tenant_id=self._tenant_id,
+                    scene="agent_fc",
+                    resolver=self._variable_resolver,
+                )
+                
+                if not base_prompt or base_prompt == SYSTEM_PROMPT:
+                    base_prompt = await template_service.get_published_template(
+                        tenant_id=self._tenant_id,
+                        scene="default",
+                        resolver=self._variable_resolver,
+                    )
+                
+                if not base_prompt or base_prompt == SYSTEM_PROMPT:
+                    logger.info("[AC-MARH-07] No published template found for agent_fc or default, using default prompt")
+                    return default_prompt
+
+                agent_protocol = """
+
+## 智能体决策协议
+
+1. 优先使用已有观察信息，避免重复调用同类工具。
+2. 当问题需要外部事实或结构化状态时再调用工具；如果可直接回答则不要调用。
+3. 缺少关键参数时，优先向用户追问，不要使用空参数调用工具。
+4. 工具失败时，先说明已尝试，再给出降级方案或下一步引导。
+
+## 知识库查询强制流程
+
+当用户问题需要进行知识库查询（kb_search_dynamic）时，必须遵循以下步骤：
+
+**步骤1：先调用 list_document_metadata_fields**
+- 在任何知识库搜索之前，必须先调用 `list_document_metadata_fields` 工具
+- 获取可用的元数据字段（如 grade, subject, kb_scene 等）及其常见取值
+
+**步骤2：分析用户意图，选择合适的过滤条件**
+- 根据用户问题和返回的元数据字段，确定合适的过滤条件
+- 从元数据字段的 common_values 中选择合适的值
+
+**步骤3：调用 kb_search_dynamic 进行搜索**
+- 使用步骤1获取的元数据字段构造 context 参数
+- scene 参数必须从元数据字段的 kb_scene 常见值中选择，不要硬编码
+
+## 注意事项
+- **严禁**在调用 kb_search_dynamic 之前不调用 list_document_metadata_fields。
+"""
+                
+                final_prompt = base_prompt + agent_protocol
+                
+                logger.info(f"[AC-MARH-07] Loaded template for tenant={self._tenant_id}")
+                return final_prompt
+
+        except Exception as e:
+            logger.warning(f"[AC-MARH-07] Failed to load template, using default: {e}")
+            return default_prompt
+
+    async def _force_final_answer_fc(self, messages: list[dict[str, Any]]) -> str:
+        """Force final answer when max iterations reached in Function Calling mode."""
+        try:
+            response = await self._llm_client.generate(
+                messages=messages + [{"role": "user", "content": "请基于以上信息给出最终回答，不要再调用工具。"}],
+                tools=None,
+            )
+            return response.content or "抱歉，我已经尽力处理您的请求，但可能需要更多信息。"
+        except Exception as e:
+            logger.error(f"[AC-MARH-07] Force final answer FC failed: {e}")
+            return "抱歉，我已经尽力处理您的请求，但可能需要更多信息。请稍后重试或联系人工客服。"
+
+    async def _execute_react(
+        self,
+        user_message: str,
+        context: dict[str, Any] | None = None,
+        on_thought: Any = None,
+        on_action: Any = None,
+    ) -> tuple[str, ReActContext, TraceInfo]:
+        """
+        Execute using traditional ReAct mode (text-based).
+        
+        This is the original implementation for backward compatibility.
+        """
         react_ctx = ReActContext(max_iterations=self._max_iterations)
         tool_calls: list[ToolCallTrace] = []
         start_time = time.time()
@@ -321,58 +804,108 @@ Action Input:
             return default_template
     
     def _build_tools_section(self) -> str:
-        """Build rich tools section for ReAct prompt."""
+        """
+        Build compact tools section for ReAct prompt.
+        
+        Only includes tool name and brief description for initial scanning.
+        Detailed usage guides are disclosed on-demand when tool is called.
+        """
         if not self._tool_registry:
             return "当前没有可用的工具。"
 
-        tools = self._tool_registry.list_tools(enabled_only=True)
+        tools = self._tool_registry.get_all_tools()
         if not tools:
             return "当前没有可用的工具。"
 
-        lines = ["## 可用工具列表", "", "以下是你可以使用的工具，只能使用这些工具：", ""]
+        lines = ["## 可用工具列表", "", "以下是你可以使用的工具：", ""]
 
         for tool in tools:
+            tool_guide = self._guide_registry.get_tool_guide(tool.name) if self._guide_registry else None
+            description = tool_guide.description if tool_guide else tool.description
+            lines.append(f"- **{tool.name}**: {description}")
+
+        lines.append("")
+        lines.append("调用工具时，系统会提供该工具的详细使用说明。")
+
+        return "\n".join(lines)
+    
+    def _build_tool_usage_guide(self, tool_name: str) -> str:
+        """
+        Build detailed usage guide for a specific tool.
+        Called when the tool is executed to provide on-demand guidance.
+        """
+        tool_guide = self._guide_registry.get_tool_guide(tool_name) if self._guide_registry else None
+        tool = self._tool_registry.get_tool(tool_name) if self._tool_registry else None
+        
+        if not tool_guide and not tool:
+            return ""
+        
+        lines = [f"## {tool_name} 使用说明", ""]
+        
+        if tool_guide:
+            lines.append(f"**用途**: {tool_guide.description}")
+            lines.append("")
+            
+            if tool_guide.triggers:
+                lines.append("**适用场景**:")
+                for trigger in tool_guide.triggers:
+                    lines.append(f"- {trigger}")
+                lines.append("")
+            
+            if tool_guide.anti_triggers:
+                lines.append("**不适用场景**:")
+                for anti in tool_guide.anti_triggers:
+                    lines.append(f"- {anti}")
+                lines.append("")
+            
+            if tool_guide.content:
+                lines.append(tool_guide.content)
+                lines.append("")
+        
+        if tool:
             meta = tool.metadata or {}
-            lines.append(f"### {tool.name}")
-            lines.append(f"用途: {tool.description}")
-
-            when_to_use = meta.get("when_to_use")
-            when_not_to_use = meta.get("when_not_to_use")
-            if when_to_use:
-                lines.append(f"何时使用: {when_to_use}")
-            if when_not_to_use:
-                lines.append(f"何时不要使用: {when_not_to_use}")
-
             params = meta.get("parameters")
             if isinstance(params, dict):
                 properties = params.get("properties", {})
                 required = params.get("required", [])
                 if properties:
-                    lines.append("参数:")
+                    lines.append("**参数说明**:")
                     for param_name, param_info in properties.items():
                         param_desc = param_info.get("description", "") if isinstance(param_info, dict) else ""
-                        line = f"  - {param_name}: {param_desc}".strip()
+                        req_mark = " (必填)" if param_name in required else ""
                         if param_name == "tenant_id":
-                            line += " (系统注入，模型不要填写)"
-                        elif param_name in required:
-                            line += " (必填)"
-                        lines.append(line)
+                            req_mark = " (系统注入)"
+                        lines.append(f"- `{param_name}`: {param_desc}{req_mark}")
+                    lines.append("")
 
             if meta.get("example_action_input"):
-                lines.append("示例入参(JSON):")
+                lines.append("**调用示例**:")
                 try:
-                    example_text = json.dumps(meta["example_action_input"], ensure_ascii=False)
+                    example_text = json.dumps(meta["example_action_input"], ensure_ascii=False, indent=2)
                 except Exception:
                     example_text = str(meta["example_action_input"])
-                lines.append(example_text)
+                lines.append(f"```json\n{example_text}\n```")
+                lines.append("")
 
             if meta.get("result_interpretation"):
-                lines.append(f"结果解释: {meta['result_interpretation']}")
-
+                lines.append(f"**结果说明**: {meta['result_interpretation']}")
                 lines.append("")
         
         return "\n".join(lines)
     
+    def _build_tools_guide_section(self, tool_names: list[str] | None = None) -> str:
+        """
+        Build detailed tools guide section for ReAct prompt.
+        
+        This provides comprehensive usage guides from ToolRegistry.
+        Called separately from _build_tools_section for flexibility.
+        
+        Args:
+            tool_names: If provided, only include tools for these names.
+                       If None, include all tools.
+        """
+        return self._guide_registry.build_tools_prompt_section(tool_names)
+
     def _extract_json_object(self, text: str) -> dict[str, Any] | None:
         """Extract the first valid JSON object from free text."""
         candidates = []
@@ -438,6 +971,8 @@ Action Input:
     ) -> tuple[ToolResult, ToolCallTrace]:
         """
         [AC-MARH-07, AC-MARH-08] Execute tool action with timeout.
+        
+        On first call to a tool, appends detailed usage guide to observation.
         """
         tool_name = thought.action or "unknown"
         start_time = time.time()
@@ -461,6 +996,23 @@ Action Input:
             if self._tenant_id:
                 tool_args["tenant_id"] = self._tenant_id
             
+            if self._user_id:
+                tool_args["user_id"] = self._user_id
+            
+            if self._session_id:
+                tool_args["session_id"] = self._session_id
+
+            if tool_name == "kb_search_dynamic":
+                # 确保 context 存在，供 AI 传入动态过滤条件
+                if "context" not in tool_args:
+                    tool_args["context"] = {}
+                # scene 参数由 AI 从元数据中选择，系统不强制覆盖
+
+            logger.info(
+                f"[AC-MARH-07] Tool call starting: tool={tool_name}, "
+                f"action_input={thought.action_input}, final_args={tool_args}"
+            )
+
             result = await asyncio.wait_for(
                 self._tool_registry.execute(
                     tool_name=tool_name,
@@ -470,9 +1022,25 @@ Action Input:
             )
 
             duration_ms = int((time.time() - start_time) * 1000)
+            
+            called_tools = {tc.tool_name for tc in react_ctx.tool_calls}
+            is_first_call = tool_name not in called_tools
+            
+            output = result.output
+            if is_first_call and result.success:
+                usage_guide = self._build_tool_usage_guide(tool_name)
+                if usage_guide:
+                    if isinstance(output, dict):
+                        output = dict(output)
+                        output["_tool_guide"] = usage_guide
+                    elif isinstance(output, str):
+                        output = f"{output}\n\n---\n{usage_guide}"
+                    else:
+                        output = {"result": output, "_tool_guide": usage_guide}
+
             return ToolResult(
                 success=result.success,
-                output=result.output,
+                output=output,
                 error=result.error,
                 duration_ms=duration_ms,
             ), ToolCallTrace(
@@ -482,6 +1050,8 @@ Action Input:
                 status=ToolCallStatus.OK if result.success else ToolCallStatus.ERROR,
                 args_digest=str(thought.action_input)[:100] if thought.action_input else None,
                 result_digest=str(result.output)[:100] if result.output else None,
+                arguments=thought.action_input,
+                result=output,
             )
 
         except asyncio.TimeoutError:
@@ -497,6 +1067,7 @@ Action Input:
                 duration_ms=duration_ms,
                 status=ToolCallStatus.TIMEOUT,
                 error_code="TOOL_TIMEOUT",
+                arguments=thought.action_input,
             )
 
         except Exception as e:
@@ -512,6 +1083,7 @@ Action Input:
                 duration_ms=duration_ms,
                 status=ToolCallStatus.ERROR,
                 error_code="TOOL_ERROR",
+                arguments=thought.action_input,
             )
 
     async def _force_final_answer(

ai-service/app/services/mid/batch_ask_back_service.py

@@ -0,0 +1,342 @@
+"""
+Batch Ask-Back Service.
+批量追问服务 - 支持一次追问多个缺失槽位
+
+[AC-MRS-SLOT-ASKBACK-01] 批量追问
+
+职责：
+1. 支持一次追问多个缺失槽位
+2. 选择策略：必填优先、场景相关优先、最近未追问过优先
+3. 输出形式：单条自然语言合并提问或分段提问
+"""
+
+import logging
+import time
+from dataclasses import dataclass, field
+from typing import Any
+
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.services.cache.slot_state_cache import get_slot_state_cache
+from app.services.slot_definition_service import SlotDefinitionService
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class AskBackSlot:
+    """
+    追问槽位信息
+    
+    Attributes:
+        slot_key: 槽位键名
+        label: 显示标签
+        ask_back_prompt: 追问提示
+        priority: 优先级（数值越大越优先）
+        last_asked_at: 上次追问时间戳
+        is_required: 是否必填
+        scene_relevance: 场景相关度
+    """
+    slot_key: str
+    label: str
+    ask_back_prompt: str | None = None
+    priority: int = 0
+    last_asked_at: float | None = None
+    is_required: bool = False
+    scene_relevance: float = 0.0
+
+
+@dataclass
+class BatchAskBackConfig:
+    """
+    批量追问配置
+    
+    Attributes:
+        max_ask_back_slots_per_turn: 每轮最多追问槽位数
+        prefer_required: 是否优先追问必填槽位
+        prefer_scene_relevant: 是否优先追问场景相关槽位
+        avoid_recent_asked: 是否避免最近追问过的槽位
+        recent_asked_threshold_seconds: 最近追问阈值（秒）
+        merge_prompts: 是否合并追问提示
+        merge_template: 合并模板
+    """
+    max_ask_back_slots_per_turn: int = 2
+    prefer_required: bool = True
+    prefer_scene_relevant: bool = True
+    avoid_recent_asked: bool = True
+    recent_asked_threshold_seconds: float = 60.0
+    merge_prompts: bool = True
+    merge_template: str = "为了更好地为您服务，请告诉我：{prompts}"
+
+
+@dataclass
+class BatchAskBackResult:
+    """
+    批量追问结果
+    
+    Attributes:
+        selected_slots: 选中的追问槽位列表
+        prompts: 追问提示列表
+        merged_prompt: 合并后的追问提示
+        ask_back_count: 追问数量
+    """
+    selected_slots: list[AskBackSlot] = field(default_factory=list)
+    prompts: list[str] = field(default_factory=list)
+    merged_prompt: str | None = None
+    ask_back_count: int = 0
+    
+    def has_ask_back(self) -> bool:
+        return self.ask_back_count > 0
+    
+    def get_prompt(self) -> str:
+        """获取最终追问提示"""
+        if self.merged_prompt:
+            return self.merged_prompt
+        if self.prompts:
+            return self.prompts[0]
+        return "请提供更多信息以便我更好地帮助您。"
+
+
+class BatchAskBackService:
+    """
+    [AC-MRS-SLOT-ASKBACK-01] 批量追问服务
+    
+    支持一次追问多个缺失槽位，提高补全效率。
+    """
+    
+    ASK_BACK_HISTORY_KEY_PREFIX = "slot_ask_back_history"
+    ASK_BACK_HISTORY_TTL = 300
+    
+    def __init__(
+        self,
+        session: AsyncSession,
+        tenant_id: str,
+        session_id: str,
+        config: BatchAskBackConfig | None = None,
+    ):
+        self._session = session
+        self._tenant_id = tenant_id
+        self._session_id = session_id
+        self._config = config or BatchAskBackConfig()
+        self._slot_def_service = SlotDefinitionService(session)
+        self._cache = get_slot_state_cache()
+    
+    async def generate_batch_ask_back(
+        self,
+        missing_slots: list[dict[str, str]],
+        current_scene: str | None = None,
+    ) -> BatchAskBackResult:
+        """
+        生成批量追问
+        
+        Args:
+            missing_slots: 缺失槽位列表
+            current_scene: 当前场景
+            
+        Returns:
+            BatchAskBackResult: 批量追问结果
+        """
+        if not missing_slots:
+            return BatchAskBackResult()
+        
+        ask_back_slots = await self._prepare_ask_back_slots(missing_slots, current_scene)
+        
+        selected_slots = self._select_slots_for_ask_back(ask_back_slots)
+        
+        asked_history = await self._get_asked_history()
+        selected_slots = self._filter_recently_asked(selected_slots, asked_history)
+        
+        if not selected_slots:
+            selected_slots = ask_back_slots[:self._config.max_ask_back_slots_per_turn]
+        
+        prompts = self._generate_prompts(selected_slots)
+        merged_prompt = self._merge_prompts(prompts) if self._config.merge_prompts else None
+        
+        await self._record_ask_back_history([s.slot_key for s in selected_slots])
+        
+        return BatchAskBackResult(
+            selected_slots=selected_slots,
+            prompts=prompts,
+            merged_prompt=merged_prompt,
+            ask_back_count=len(selected_slots),
+        )
+    
+    async def _prepare_ask_back_slots(
+        self,
+        missing_slots: list[dict[str, str]],
+        current_scene: str | None,
+    ) -> list[AskBackSlot]:
+        """准备追问槽位列表"""
+        ask_back_slots = []
+        
+        for missing in missing_slots:
+            slot_key = missing.get("slot_key", "")
+            label = missing.get("label", slot_key)
+            ask_back_prompt = missing.get("ask_back_prompt")
+            field_key = missing.get("field_key")
+            
+            slot_def = await self._slot_def_service.get_slot_definition_by_key(
+                self._tenant_id, slot_key
+            )
+            
+            is_required = False
+            scene_relevance = 0.0
+            
+            if slot_def:
+                is_required = slot_def.required
+                if not ask_back_prompt:
+                    ask_back_prompt = slot_def.ask_back_prompt
+                
+                if current_scene and slot_def.scene_scope:
+                    if current_scene in slot_def.scene_scope:
+                        scene_relevance = 1.0
+            
+            priority = self._calculate_priority(is_required, scene_relevance)
+            
+            ask_back_slots.append(AskBackSlot(
+                slot_key=slot_key,
+                label=label,
+                ask_back_prompt=ask_back_prompt,
+                priority=priority,
+                is_required=is_required,
+                scene_relevance=scene_relevance,
+            ))
+        
+        return ask_back_slots
+    
+    def _calculate_priority(self, is_required: bool, scene_relevance: float) -> int:
+        """计算槽位优先级"""
+        priority = 0
+        
+        if self._config.prefer_required and is_required:
+            priority += 100
+        
+        if self._config.prefer_scene_relevant:
+            priority += int(scene_relevance * 50)
+        
+        return priority
+    
+    def _select_slots_for_ask_back(
+        self,
+        ask_back_slots: list[AskBackSlot],
+    ) -> list[AskBackSlot]:
+        """选择要追问的槽位"""
+        sorted_slots = sorted(
+            ask_back_slots,
+            key=lambda s: s.priority,
+            reverse=True,
+        )
+        
+        return sorted_slots[:self._config.max_ask_back_slots_per_turn]
+    
+    async def _get_asked_history(self) -> dict[str, float]:
+        """获取最近追问历史"""
+        history_key = f"{self.ASK_BACK_HISTORY_KEY_PREFIX}:{self._tenant_id}:{self._session_id}"
+        
+        try:
+            client = await self._cache._get_client()
+            if client is None:
+                return {}
+            
+            import json
+            data = await client.get(history_key)
+            if data:
+                return json.loads(data)
+        except Exception as e:
+            logger.warning(f"[BatchAskBack] Failed to get asked history: {e}")
+        
+        return {}
+    
+    async def _record_ask_back_history(self, slot_keys: list[str]) -> None:
+        """记录追问历史"""
+        history_key = f"{self.ASK_BACK_HISTORY_KEY_PREFIX}:{self._tenant_id}:{self._session_id}"
+        
+        try:
+            client = await self._cache._get_client()
+            if client is None:
+                return
+            
+            history = await self._get_asked_history()
+            current_time = time.time()
+            
+            for slot_key in slot_keys:
+                history[slot_key] = current_time
+            
+            import json
+            await client.setex(
+                history_key,
+                self.ASK_BACK_HISTORY_TTL,
+                json.dumps(history),
+            )
+        except Exception as e:
+            logger.warning(f"[BatchAskBack] Failed to record asked history: {e}")
+    
+    def _filter_recently_asked(
+        self,
+        slots: list[AskBackSlot],
+        asked_history: dict[str, float],
+    ) -> list[AskBackSlot]:
+        """过滤最近追问过的槽位"""
+        if not self._config.avoid_recent_asked:
+            return slots
+        
+        current_time = time.time()
+        threshold = self._config.recent_asked_threshold_seconds
+        
+        return [
+            slot for slot in slots
+            if slot.slot_key not in asked_history or
+            current_time - asked_history[slot.slot_key] > threshold
+        ]
+    
+    def _generate_prompts(self, slots: list[AskBackSlot]) -> list[str]:
+        """生成追问提示列表"""
+        prompts = []
+        
+        for slot in slots:
+            if slot.ask_back_prompt:
+                prompts.append(slot.ask_back_prompt)
+            else:
+                prompts.append(f"请告诉我您的{slot.label}")
+        
+        return prompts
+    
+    def _merge_prompts(self, prompts: list[str]) -> str | None:
+        """合并追问提示"""
+        if not prompts:
+            return None
+        
+        if len(prompts) == 1:
+            return prompts[0]
+        
+        if len(prompts) == 2:
+            return f"{prompts[0]}，以及{prompts[1]}"
+        
+        all_but_last = "、".join(prompts[:-1])
+        return f"{all_but_last}，以及{prompts[-1]}"
+
+
+def create_batch_ask_back_service(
+    session: AsyncSession,
+    tenant_id: str,
+    session_id: str,
+    config: BatchAskBackConfig | None = None,
+) -> BatchAskBackService:
+    """
+    创建批量追问服务实例
+    
+    Args:
+        session: 数据库会话
+        tenant_id: 租户 ID
+        session_id: 会话 ID
+        config: 配置
+        
+    Returns:
+        BatchAskBackService: 批量追问服务实例
+    """
+    return BatchAskBackService(
+        session=session,
+        tenant_id=tenant_id,
+        session_id=session_id,
+        config=config,
+    )

ai-service/app/services/mid/kb_search_dynamic_tool.py

@@ -17,15 +17,20 @@ import logging
 import time
 import uuid
 from dataclasses import dataclass, field
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING, Any, Literal
 
 from sqlalchemy.ext.asyncio import AsyncSession
 
 from app.models.mid.schemas import ToolCallStatus, ToolCallTrace, ToolType
 from app.services.mid.metadata_filter_builder import (
     FilterBuildResult,
+    FilterFieldInfo,
     MetadataFilterBuilder,
 )
+from app.services.mid.slot_state_aggregator import (
+    SlotState,
+    SlotStateAggregator,
+)
 from app.services.mid.timeout_governor import TimeoutGovernor
 
 if TYPE_CHECKING:
@@ -37,6 +42,9 @@ DEFAULT_TOP_K = 5
 DEFAULT_TIMEOUT_MS = 2000
 KB_SEARCH_DYNAMIC_TOOL_NAME = "kb_search_dynamic"
 
+_TOOL_SCHEMA_CACHE: dict[str, tuple[float, dict[str, Any]]] = {}
+_TOOL_SCHEMA_CACHE_TTL_SECONDS = 300  # 5 minutes
+
 
 @dataclass
 class KbSearchDynamicResult:
@@ -46,9 +54,21 @@ class KbSearchDynamicResult:
     applied_filter: dict[str, Any] = field(default_factory=dict)
     missing_required_slots: list[dict[str, str]] = field(default_factory=list)
     filter_debug: dict[str, Any] = field(default_factory=dict)
+    filter_sources: dict[str, str] = field(default_factory=dict)  # [AC-SCENE-SLOT-02] 过滤条件来源
     fallback_reason_code: str | None = None
     duration_ms: int = 0
     tool_trace: ToolCallTrace | None = None
+    step_kb_binding: dict[str, Any] | None = None  # [Step-KB-Binding] 步骤知识库绑定信息
+
+
+@dataclass
+class StepKbConfig:
+    """[Step-KB-Binding] 步骤级别的知识库配置。"""
+    allowed_kb_ids: list[str] | None = None
+    preferred_kb_ids: list[str] | None = None
+    kb_query_hint: str | None = None
+    max_kb_calls: int = 1
+    step_id: str | None = None
 
 
 @dataclass
@@ -86,12 +106,14 @@ class KbSearchDynamicTool:
         session: AsyncSession,
         timeout_governor: TimeoutGovernor | None = None,
         config: KbSearchDynamicConfig | None = None,
+        slot_state_aggregator: SlotStateAggregator | None = None,
     ):
         self._session = session
         self._timeout_governor = timeout_governor or TimeoutGovernor()
         self._config = config or KbSearchDynamicConfig()
         self._vector_retriever = None
         self._filter_builder: MetadataFilterBuilder | None = None
+        self._slot_state_aggregator = slot_state_aggregator
 
     @property
     def name(self) -> str:
@@ -140,6 +162,128 @@ class KbSearchDynamicTool:
             },
         }
 
+    async def get_dynamic_tool_schema(self, tenant_id: str) -> dict[str, Any]:
+        """
+        获取动态生成的工具 Schema，包含租户的元数据过滤字段。
+        
+        使用缓存机制，避免每次都查询数据库。
+        只显示关联知识库的元数据过滤字段（field_roles 包含 resource_filter）。
+        
+        Args:
+            tenant_id: 租户 ID
+            
+        Returns:
+            动态生成的工具 Schema
+        """
+        import time
+        current_time = time.time()
+        
+        cache_key = f"tool_schema:{tenant_id}"
+        if cache_key in _TOOL_SCHEMA_CACHE:
+            cached_time, cached_schema = _TOOL_SCHEMA_CACHE[cache_key]
+            if current_time - cached_time < _TOOL_SCHEMA_CACHE_TTL_SECONDS:
+                logger.debug(f"[AC-MARH-05] Tool schema cache hit for tenant={tenant_id}")
+                return cached_schema
+        
+        logger.info(f"[AC-MARH-05] Building dynamic tool schema for tenant={tenant_id}")
+        
+        base_properties = {
+            "query": {
+                "type": "string",
+                "description": "检索查询文本",
+            },
+            "top_k": {
+                "type": "integer",
+                "description": "返回结果数量",
+                "default": DEFAULT_TOP_K,
+            },
+        }
+        
+        required_fields = ["query"]
+        context_properties = {}
+        
+        try:
+            if self._filter_builder is None:
+                self._filter_builder = MetadataFilterBuilder(self._session)
+            
+            filterable_fields = await self._filter_builder._get_filterable_fields(tenant_id)
+            
+            for field_info in filterable_fields:
+                field_schema = self._build_field_schema(field_info)
+                context_properties[field_info.field_key] = field_schema
+                
+                if field_info.required:
+                    required_fields.append(field_info.field_key)
+            
+            logger.info(
+                f"[AC-MARH-05] Dynamic schema built: tenant={tenant_id}, "
+                f"context_fields={len(context_properties)}, required={required_fields}"
+            )
+        except Exception as e:
+            logger.warning(f"[AC-MARH-05] Failed to get filterable fields: {e}, using base schema")
+        
+        if context_properties:
+            base_properties["context"] = {
+                "type": "object",
+                "description": "过滤条件，根据用户意图选择合适的字段传递",
+                "properties": context_properties,
+            }
+        else:
+            base_properties["context"] = {
+                "type": "object",
+                "description": "过滤条件（当前租户未配置元数据字段）",
+            }
+        
+        schema = {
+            "name": self.name,
+            "description": self.description,
+            "parameters": {
+                "type": "object",
+                "properties": base_properties,
+                "required": required_fields,
+            },
+        }
+        
+        _TOOL_SCHEMA_CACHE[cache_key] = (current_time, schema)
+        
+        return schema
+
+    def _build_field_schema(self, field_info: "FilterFieldInfo") -> dict[str, Any]:
+        """
+        根据字段信息构建 JSON Schema。
+        
+        Args:
+            field_info: 字段信息
+            
+        Returns:
+            字段的 JSON Schema
+        """
+        schema: dict[str, Any] = {
+            "description": field_info.label or field_info.field_key,
+        }
+        
+        if field_info.required:
+            schema["description"] += "（必填）"
+        
+        field_type = field_info.field_type.lower() if field_info.field_type else "string"
+        
+        if field_type in ("enum", "select", "array_enum", "multi_select"):
+            schema["type"] = "string"
+            if field_info.options:
+                schema["enum"] = field_info.options
+                schema["description"] += f"，可选值：{', '.join(field_info.options)}"
+        elif field_type in ("number", "integer", "float"):
+            schema["type"] = "number"
+        elif field_type == "boolean":
+            schema["type"] = "boolean"
+        else:
+            schema["type"] = "string"
+        
+        if field_info.default_value is not None:
+            schema["default"] = field_info.default_value
+        
+        return schema
+
     async def execute(
         self,
         query: str,
@@ -147,16 +291,24 @@ class KbSearchDynamicTool:
         scene: str = "open_consult",
         top_k: int | None = None,
         context: dict[str, Any] | None = None,
+        slot_state: SlotState | None = None,
+        step_kb_config: StepKbConfig | None = None,
+        slot_policy: Literal["flow_strict", "agent_relaxed"] = "flow_strict",
     ) -> KbSearchDynamicResult:
         """
         [AC-MARH-05] 执行 KB 动态检索。
+        [AC-MRS-SLOT-META-02] 支持槽位状态聚合和过滤构建优先级
+        [Step-KB-Binding] 支持步骤级别的知识库约束
 
         Args:
             query: 检索查询
             tenant_id: 租户 ID
-            scene: 场景标识
+            scene: 场景标识（默认值，会被 context 中的 scene 覆盖）
             top_k: 返回数量
-            context: 上下文（包含动态过滤值）
+            context: 上下文（包含动态过滤值，包括 scene）
+            slot_state: 预聚合的槽位状态（可选，优先使用）
+            step_kb_config: 步骤级别的知识库配置（可选）
+            slot_policy: 槽位策略（flow_strict=流程严格模式，agent_relaxed=通用问答宽松模式）
 
         Returns:
             KbSearchDynamicResult 包含检索结果和追踪信息
@@ -171,14 +323,357 @@ class KbSearchDynamicTool:
         start_time = time.time()
         top_k = top_k or self._config.top_k
 
+        effective_context = dict(context) if context else {}
+        effective_scene = effective_context.get("scene", scene)
+
+        # [Step-KB-Binding] 记录步骤知识库约束
+        step_kb_binding_info: dict[str, Any] = {}
+        if step_kb_config:
+            step_kb_binding_info = {
+                "step_id": step_kb_config.step_id,
+                "allowed_kb_ids": step_kb_config.allowed_kb_ids,
+                "preferred_kb_ids": step_kb_config.preferred_kb_ids,
+                "kb_query_hint": step_kb_config.kb_query_hint,
+                "max_kb_calls": step_kb_config.max_kb_calls,
+            }
             logger.info(
-            f"[AC-MARH-05] Starting KB dynamic search: tenant={tenant_id}, "
-            f"query={query[:50]}..., scene={scene}, top_k={top_k}"
+                f"[Step-KB-Binding] Step KB config applied: "
+                f"step_id={step_kb_config.step_id}, "
+                f"allowed_kb_ids={step_kb_config.allowed_kb_ids}, "
+                f"preferred_kb_ids={step_kb_config.preferred_kb_ids}"
             )
 
-        filter_result: FilterBuildResult | None = None
+        logger.info(
+            f"[AC-MARH-05] 开始执行KB动态检索: tenant={tenant_id}, "
+            f"query={query[:50]}..., scene={effective_scene}, top_k={top_k}, "
+            f"slot_policy={slot_policy}, context_keys={list(effective_context.keys())}"
+        )
+
+        # [AC-MRS-SLOT-META-02] 如果提供了 slot_state，优先使用
+        if slot_state is not None:
+            logger.info(
+                f"[AC-MRS-SLOT-META-02] Using provided slot_state: "
+                f"filled={len(slot_state.filled_slots)}, "
+                f"missing={len(slot_state.missing_required_slots)}"
+            )
+            
+            # 检查是否有缺失的必填槽位（仅在流程严格模式下阻断）
+            if slot_state.missing_required_slots:
+                if slot_policy == "flow_strict":
+                    duration_ms = int((time.time() - start_time) * 1000)
+
+                    logger.info(
+                        f"[AC-MRS-SLOT-META-03] 流程严格模式命中缺失必填槽位，触发追问: "
+                        f"tenant={tenant_id}, missing={len(slot_state.missing_required_slots)}"
+                    )
+
+                    tool_trace = ToolCallTrace(
+                        tool_name=self.name,
+                        tool_type=ToolType.INTERNAL,
+                        duration_ms=duration_ms,
+                        status=ToolCallStatus.ERROR,
+                        error_code="MISSING_REQUIRED_SLOTS",
+                        args_digest=f"query={query[:50]}, scene={effective_scene}",
+                        result_digest=f"missing={len(slot_state.missing_required_slots)}",
+                        arguments={"query": query, "scene": effective_scene, "context": context},
+                        result={"missing_required_slots": slot_state.missing_required_slots},
+                    )
+
+                    return KbSearchDynamicResult(
+                        success=False,
+                        applied_filter={},
+                        missing_required_slots=slot_state.missing_required_slots,
+                        filter_debug={
+                            "source": "slot_state",
+                            "filled_slots": slot_state.filled_slots,
+                            "slot_to_field_map": slot_state.slot_to_field_map,
+                        },
+                        fallback_reason_code="MISSING_REQUIRED_SLOTS",
+                        duration_ms=duration_ms,
+                        tool_trace=tool_trace,
+                    )
+
+                logger.info(
+                    f"[AC-MRS-SLOT-META-03] 通用问答宽松模式检测到缺失槽位但不阻断检索: "
+                    f"tenant={tenant_id}, missing={len(slot_state.missing_required_slots)}"
+                )
+            
+            # 使用 slot_state 构建 filter
+            metadata_filter, filter_sources = await self._build_filter_from_slot_state(
+                tenant_id=tenant_id,
+                slot_state=slot_state,
+                context=effective_context,
+                scene_slot_context=effective_context.get("scene_slot_context"),  # [AC-SCENE-SLOT-02]
+            )
+        else:
+            # 原有逻辑：构建元数据 filter
+            # 如果 context 简单（只有键值对），直接构造 filter，跳过 MetadataFilterBuilder
+            metadata_filter = await self._build_filter_legacy(
+                tenant_id=tenant_id,
+                context=effective_context,
+                query=query,
+                effective_scene=effective_scene,
+                start_time=start_time,
+            )
+            
+            if isinstance(metadata_filter, KbSearchDynamicResult):
+                # 有错误，直接返回
+                return metadata_filter
 
         try:
+            hits = await self._retrieve_with_timeout(
+                tenant_id=tenant_id,
+                query=query,
+                metadata_filter=metadata_filter,
+                top_k=top_k,
+                step_kb_config=step_kb_config,
+            )
+
+            duration_ms = int((time.time() - start_time) * 1000)
+            kb_hit = len(hits) > 0
+
+            # [Step-KB-Binding] 记录命中的知识库
+            hit_kb_ids = list(set(hit.get("kb_id") for hit in hits if hit.get("kb_id")))
+            if step_kb_binding_info:
+                step_kb_binding_info["used_kb_ids"] = hit_kb_ids
+                step_kb_binding_info["kb_hit"] = kb_hit
+
+            tool_trace = ToolCallTrace(
+                tool_name=self.name,
+                tool_type=ToolType.INTERNAL,
+                duration_ms=duration_ms,
+                status=ToolCallStatus.OK,
+                args_digest=f"query={query[:50]}, scene={effective_scene}",
+                result_digest=f"hits={len(hits)}",
+                arguments={"query": query, "scene": effective_scene, "context": context},
+                result={"hits_count": len(hits), "kb_hit": kb_hit},
+            )
+
+            logger.info(
+                f"[AC-MARH-05] KB dynamic search completed: tenant={tenant_id}, "
+                f"hits={len(hits)}, duration_ms={duration_ms}, kb_hit={kb_hit}, "
+                f"hit_kb_ids={hit_kb_ids}"
+            )
+
+            # 确定 filter 来源用于调试
+            filter_source = "slot_state" if slot_state is not None else "builder"
+            kb_filter_sources = filter_sources if slot_state is not None else {}
+            
+            return KbSearchDynamicResult(
+                success=True,
+                hits=hits,
+                applied_filter=metadata_filter or {},
+                filter_debug={"source": filter_source, "filter_sources": kb_filter_sources},
+                duration_ms=duration_ms,
+                tool_trace=tool_trace,
+                step_kb_binding=step_kb_binding_info if step_kb_binding_info else None,
+            )
+
+        except asyncio.TimeoutError:
+            duration_ms = int((time.time() - start_time) * 1000)
+            logger.warning(
+                f"[AC-MARH-06] KB dynamic search timeout: tenant={tenant_id}, "
+                f"duration_ms={duration_ms}"
+            )
+
+            tool_trace = ToolCallTrace(
+                tool_name=self.name,
+                tool_type=ToolType.INTERNAL,
+                duration_ms=duration_ms,
+                status=ToolCallStatus.TIMEOUT,
+                error_code="KB_TIMEOUT",
+                arguments={"query": query, "scene": effective_scene, "context": context},
+            )
+
+            return KbSearchDynamicResult(
+                success=False,
+                applied_filter=metadata_filter or {},
+                missing_required_slots=[],
+                filter_debug={"error": "timeout"},
+                fallback_reason_code="KB_TIMEOUT",
+                duration_ms=duration_ms,
+                tool_trace=tool_trace,
+                step_kb_binding=step_kb_binding_info if step_kb_binding_info else None,
+            )
+
+        except Exception as e:
+            duration_ms = int((time.time() - start_time) * 1000)
+            logger.error(
+                f"[AC-MARH-06] KB dynamic search failed: tenant={tenant_id}, "
+                f"error={e}"
+            )
+
+            tool_trace = ToolCallTrace(
+                tool_name=self.name,
+                tool_type=ToolType.INTERNAL,
+                duration_ms=duration_ms,
+                status=ToolCallStatus.ERROR,
+                error_code="KB_ERROR",
+                arguments={"query": query, "scene": effective_scene, "context": context},
+            )
+
+            return KbSearchDynamicResult(
+                success=False,
+                applied_filter=metadata_filter or {},
+                missing_required_slots=[],
+                filter_debug={"error": str(e)},
+                fallback_reason_code="KB_ERROR",
+                duration_ms=duration_ms,
+                tool_trace=tool_trace,
+            )
+
+    async def _build_filter_legacy(
+        self,
+        tenant_id: str,
+        context: dict[str, Any],
+        query: str,
+        effective_scene: str,
+        start_time: float,
+    ) -> dict[str, Any] | KbSearchDynamicResult:
+        """
+        [AC-MRS-SLOT-META-02] 原有逻辑：构建元数据 filter
+        
+        Returns:
+            dict: 构建成功的 filter
+            KbSearchDynamicResult: 构建失败时的错误结果
+        """
+        metadata_filter: dict[str, Any] | None = None
+        
+        # 简单 context：直接构造 filter（信任 AI 传入的值）
+        # 复杂场景：使用 MetadataFilterBuilder 进行严格验证
+        is_simple_context = all(
+            isinstance(v, (str, int, float, bool))
+            for v in context.values()
+        )
+        
+        if is_simple_context:
+            # 直接构造 filter，不查询数据库
+            metadata_filter = context
+            logger.info(
+                f"[AC-MARH-05] Using simple context as filter directly: "
+                f"{metadata_filter}"
+            )
+        else:
+            # 复杂 context，使用 MetadataFilterBuilder
+            filter_result = await self._build_filter_with_builder(
+                tenant_id=tenant_id,
+                context=context,
+                query=query,
+                effective_scene=effective_scene,
+                start_time=start_time,
+            )
+            if isinstance(filter_result, KbSearchDynamicResult):
+                # 有错误，直接返回
+                return filter_result
+            metadata_filter = filter_result
+        
+        return metadata_filter or {}
+
+    async def _build_filter_from_slot_state(
+        self,
+        tenant_id: str,
+        slot_state: SlotState,
+        context: dict[str, Any],
+        scene_slot_context: Any = None,  # [AC-SCENE-SLOT-02] 场景槽位上下文
+    ) -> tuple[dict[str, Any], dict[str, str]]:
+        """
+        [AC-MRS-SLOT-META-02] 基于槽位状态构建过滤条件
+        [AC-SCENE-SLOT-02] 支持场景槽位包配置的优先级
+        
+        过滤值来源优先级：
+        1. 已确认槽位值（slot_state.filled_slots）
+        2. 当前请求 context 显式值
+        3. 元数据默认值
+        
+        Args:
+            tenant_id: 租户 ID
+            slot_state: 槽位状态
+            context: 上下文
+            scene_slot_context: 场景槽位上下文
+            
+        Returns:
+            (过滤条件字典, 过滤来源映射)
+        """
+        if self._filter_builder is None:
+            self._filter_builder = MetadataFilterBuilder(self._session)
+        
+        # 获取可过滤字段定义
+        filterable_fields = await self._filter_builder._get_filterable_fields(tenant_id)
+        
+        applied_filter: dict[str, Any] = {}
+        filter_debug_sources: dict[str, str] = {}
+        
+        # [AC-SCENE-SLOT-02] 如果有场景槽位上下文，优先处理场景定义的槽位
+        scene_slot_keys = set()
+        if scene_slot_context:
+            scene_slot_keys = set(scene_slot_context.get_all_slot_keys())
+            logger.debug(
+                f"[AC-SCENE-SLOT-02] Processing scene slots: "
+                f"scene={scene_slot_context.scene_key}, slots={scene_slot_keys}"
+            )
+        
+        for field_info in filterable_fields:
+            field_key = field_info.field_key
+            value = None
+            source = None
+            
+            # 优先级 1: 已确认槽位值（通过 slot_to_field_map 映射）
+            if slot_state.slot_to_field_map:
+                # 查找哪个 slot 映射到这个 field
+                for slot_key, mapped_field_key in slot_state.slot_to_field_map.items():
+                    if mapped_field_key == field_key and slot_key in slot_state.filled_slots:
+                        value = slot_state.filled_slots[slot_key]
+                        source = "slot"
+                        break
+            
+            # 如果 slot 映射没有命中，直接检查 slot_key 是否等于 field_key
+            if value is None and field_key in slot_state.filled_slots:
+                value = slot_state.filled_slots[field_key]
+                source = "slot"
+            
+            # 优先级 2: 当前请求 context 显式值
+            if value is None and field_key in context:
+                value = context[field_key]
+                source = "context"
+            
+            # 优先级 3: 元数据默认值
+            if value is None and field_info.default_value is not None:
+                value = field_info.default_value
+                source = "default"
+            
+            # 构建过滤条件
+            if value is not None:
+                filter_value = self._filter_builder._build_field_filter(
+                    field_info, value
+                )
+                if filter_value is not None:
+                    applied_filter[field_key] = filter_value
+                    filter_debug_sources[field_key] = source
+        
+        logger.info(
+            f"[AC-MRS-SLOT-META-02] Filter built from slot_state: "
+            f"fields={len(applied_filter)}, sources={filter_debug_sources}"
+        )
+        
+        return applied_filter, filter_debug_sources
+
+    async def _build_filter_with_builder(
+        self,
+        tenant_id: str,
+        context: dict[str, Any],
+        query: str,
+        effective_scene: str,
+        start_time: float,
+    ) -> dict[str, Any] | KbSearchDynamicResult:
+        """
+        使用 MetadataFilterBuilder 构建 filter（复杂场景）。
+        
+        Returns:
+            dict: 构建成功的 filter
+            KbSearchDynamicResult: 构建失败时的错误结果
+        """
+        from app.services.mid.metadata_filter_builder import FilterBuildResult
+        
         if self._filter_builder is None:
             self._filter_builder = MetadataFilterBuilder(self._session)
 
@@ -200,8 +695,10 @@ class KbSearchDynamicTool:
                 duration_ms=duration_ms,
                 status=ToolCallStatus.ERROR,
                 error_code="MISSING_REQUIRED_SLOTS",
-                    args_digest=f"query={query[:50]}, scene={scene}",
+                args_digest=f"query={query[:50]}, scene={effective_scene}",
                 result_digest=f"missing={len(filter_result.missing_required_slots)}",
+                arguments={"query": query, "scene": effective_scene, "context": context},
+                result={"missing_required_slots": filter_result.missing_required_slots},
             )
 
             return KbSearchDynamicResult(
@@ -214,90 +711,7 @@ class KbSearchDynamicTool:
                 tool_trace=tool_trace,
             )
 
-            metadata_filter = filter_result.applied_filter if filter_result.success else None
-
-            hits = await self._retrieve_with_timeout(
-                tenant_id=tenant_id,
-                query=query,
-                metadata_filter=metadata_filter,
-                top_k=top_k,
-            )
-
-            duration_ms = int((time.time() - start_time) * 1000)
-            kb_hit = len(hits) > 0
-
-            tool_trace = ToolCallTrace(
-                tool_name=self.name,
-                tool_type=ToolType.INTERNAL,
-                duration_ms=duration_ms,
-                status=ToolCallStatus.OK,
-                args_digest=f"query={query[:50]}, scene={scene}",
-                result_digest=f"hits={len(hits)}",
-            )
-
-            logger.info(
-                f"[AC-MARH-05] KB dynamic search completed: tenant={tenant_id}, "
-                f"hits={len(hits)}, duration_ms={duration_ms}, kb_hit={kb_hit}"
-            )
-
-            return KbSearchDynamicResult(
-                success=True,
-                hits=hits,
-                applied_filter=filter_result.applied_filter if filter_result else {},
-                filter_debug=filter_result.debug_info if filter_result else {},
-                duration_ms=duration_ms,
-                tool_trace=tool_trace,
-            )
-
-        except asyncio.TimeoutError:
-            duration_ms = int((time.time() - start_time) * 1000)
-            logger.warning(
-                f"[AC-MARH-06] KB dynamic search timeout: tenant={tenant_id}, "
-                f"duration_ms={duration_ms}"
-            )
-
-            tool_trace = ToolCallTrace(
-                tool_name=self.name,
-                tool_type=ToolType.INTERNAL,
-                duration_ms=duration_ms,
-                status=ToolCallStatus.TIMEOUT,
-                error_code="KB_TIMEOUT",
-            )
-
-            return KbSearchDynamicResult(
-                success=False,
-                applied_filter=filter_result.applied_filter if filter_result else {},
-                missing_required_slots=filter_result.missing_required_slots if filter_result else [],
-                filter_debug=filter_result.debug_info if filter_result else {},
-                fallback_reason_code="KB_TIMEOUT",
-                duration_ms=duration_ms,
-                tool_trace=tool_trace,
-            )
-
-        except Exception as e:
-            duration_ms = int((time.time() - start_time) * 1000)
-            logger.error(
-                f"[AC-MARH-06] KB dynamic search failed: tenant={tenant_id}, "
-                f"error={e}"
-            )
-
-            tool_trace = ToolCallTrace(
-                tool_name=self.name,
-                tool_type=ToolType.INTERNAL,
-                duration_ms=duration_ms,
-                status=ToolCallStatus.ERROR,
-                error_code="KB_ERROR",
-            )
-
-            return KbSearchDynamicResult(
-                success=False,
-                applied_filter=filter_result.applied_filter if filter_result else {},
-                missing_required_slots=filter_result.missing_required_slots if filter_result else [],
-                filter_debug={"error": str(e)},
-                fallback_reason_code="KB_ERROR",
-                duration_ms=duration_ms,
-                tool_trace=tool_trace,
-            )
+        return filter_result.applied_filter if filter_result.success else {}
 
     async def _retrieve_with_timeout(
         self,
@@ -305,13 +719,14 @@ class KbSearchDynamicTool:
         query: str,
         metadata_filter: dict[str, Any] | None = None,
         top_k: int = DEFAULT_TOP_K,
+        step_kb_config: StepKbConfig | None = None,
     ) -> list[dict[str, Any]]:
         """带超时控制的检索。"""
         timeout_seconds = self._config.timeout_ms / 1000.0
 
         try:
             return await asyncio.wait_for(
-                self._do_retrieve(tenant_id, query, metadata_filter, top_k),
+                self._do_retrieve(tenant_id, query, metadata_filter, top_k, step_kb_config),
                 timeout=timeout_seconds,
             )
         except asyncio.TimeoutError:
@@ -323,18 +738,36 @@ class KbSearchDynamicTool:
         query: str,
         metadata_filter: dict[str, Any] | None = None,
         top_k: int = DEFAULT_TOP_K,
+        step_kb_config: StepKbConfig | None = None,
     ) -> list[dict[str, Any]]:
-        """执行实际检索。"""
+        """执行实际检索。[Step-KB-Binding] 支持步骤级别的知识库约束。"""
         if self._vector_retriever is None:
             from app.services.retrieval.vector_retriever import get_vector_retriever
             self._vector_retriever = await get_vector_retriever()
 
         from app.services.retrieval.base import RetrievalContext
 
+        # [Step-KB-Binding] 确定要检索的知识库范围
+        kb_ids = None
+        if step_kb_config:
+            # 如果配置了 allowed_kb_ids，则只检索这些知识库
+            if step_kb_config.allowed_kb_ids:
+                kb_ids = step_kb_config.allowed_kb_ids
+                logger.info(
+                    f"[Step-KB-Binding] Restricting KB search to: {kb_ids}"
+                )
+            # 如果只配置了 preferred_kb_ids，优先检索这些知识库
+            elif step_kb_config.preferred_kb_ids:
+                kb_ids = step_kb_config.preferred_kb_ids
+                logger.info(
+                    f"[Step-KB-Binding] Preferring KB search in: {kb_ids}"
+                )
+
         ctx = RetrievalContext(
             tenant_id=tenant_id,
             query=query,
-            metadata=metadata_filter,
+            metadata_filter=metadata_filter,
+            kb_ids=kb_ids,
         )
 
         result = await self._vector_retriever.retrieve(ctx)
@@ -347,6 +780,7 @@ class KbSearchDynamicTool:
                     "content": hit.text,
                     "score": hit.score,
                     "metadata": hit.metadata,
+                    "kb_id": hit.metadata.get("kb_id"),
                 })
 
         return hits[:top_k]
@@ -384,6 +818,7 @@ async def create_kb_search_dynamic_handler(
         scene: str = "open_consult",
         top_k: int = DEFAULT_TOP_K,
         context: dict[str, Any] | None = None,
+        **kwargs,  # 接受系统注入的额外参数（user_id, session_id 等）
     ) -> dict[str, Any]:
         """
         KB 动态检索 handler。
@@ -391,9 +826,9 @@ async def create_kb_search_dynamic_handler(
         Args:
             query: 检索查询
             tenant_id: 租户 ID
-            scene: 场景标识
+            scene: 场景标识（默认值，会被 context 中的 scene 覆盖）
             top_k: 返回数量
-            context: 上下文
+            context: 上下文（包含 scene 等过滤字段）
 
         Returns:
             检索结果字典
@@ -442,6 +877,7 @@ def register_kb_search_dynamic_tool(
         scene: str = "open_consult",
         top_k: int = DEFAULT_TOP_K,
         context: dict[str, Any] | None = None,
+        **kwargs,  # 接受系统注入的额外参数（user_id, session_id 等）
     ) -> dict[str, Any]:
         tool = KbSearchDynamicTool(
             session=session,

ai-service/app/services/mid/memory_recall_tool.py

@@ -542,9 +542,9 @@ def register_memory_recall_tool(
     cfg = config or MemoryRecallConfig()
 
     async def memory_recall_handler(
-        tenant_id: str,
-        user_id: str,
-        session_id: str,
+        tenant_id: str = "",
+        user_id: str = "",
+        session_id: str = "",
         recall_scope: list[str] | None = None,
         max_recent_messages: int | None = None,
     ) -> dict[str, Any]:
@@ -554,6 +554,7 @@ def register_memory_recall_tool(
             timeout_governor=timeout_governor,
             config=cfg,
         )
+
         result = await tool.execute(
             tenant_id=tenant_id,
             user_id=user_id,
@@ -587,12 +588,9 @@ def register_memory_recall_tool(
                     "recall_scope": {"type": "array", "description": "召回范围，例如 profile/facts/preferences/summary/slots"},
                     "max_recent_messages": {"type": "integer", "description": "历史回填窗口大小"}
                 },
-                "required": ["tenant_id", "user_id", "session_id"]
+                "required": []
             },
             "example_action_input": {
-                "tenant_id": "default",
-                "user_id": "u_10086",
-                "session_id": "s_abc_001",
                 "recall_scope": ["profile", "facts", "preferences", "summary", "slots"],
                 "max_recent_messages": 8
             },

ai-service/app/services/mid/metadata_discovery_tool.py

@@ -0,0 +1,281 @@
+"""
+Metadata Discovery Tool for Mid Platform.
+[AC-MARH-XX] 元数据发现工具，用于查询当前可用的元数据字段及其常见值。
+
+核心特性：
+- 列出当前知识库文档中使用的元数据字段
+- 返回每个字段的常见取值（从现有文档中聚合）
+- 支持按知识库过滤
+- 返回字段定义信息（类型、用途说明等）
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+from collections import Counter
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING, Any
+
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.models.entities import Document, MetadataFieldDefinition, MetadataFieldStatus
+from app.services.mid.timeout_governor import TimeoutGovernor
+
+if TYPE_CHECKING:
+    from app.services.mid.tool_registry import ToolRegistry
+
+logger = logging.getLogger(__name__)
+
+DEFAULT_TIMEOUT_MS = 2000
+DEFAULT_TOP_VALUES = 10
+
+
+@dataclass
+class MetadataFieldDiscoveryConfig:
+    """Configuration for metadata field discovery tool."""
+    timeout_ms: int = DEFAULT_TIMEOUT_MS
+    top_values_count: int = DEFAULT_TOP_VALUES
+
+
+@dataclass
+class MetadataFieldInfo:
+    """Information about a metadata field."""
+    field_key: str
+    field_type: str = "string"
+    label: str = ""
+    description: str | None = None
+    common_values: list[str] = field(default_factory=list)
+    value_count: int = 0
+    is_filterable: bool = True
+    options: list[str] | None = None
+
+
+@dataclass
+class MetadataDiscoveryResult:
+    """Result of metadata discovery."""
+    success: bool
+    fields: list[MetadataFieldInfo] = field(default_factory=list)
+    total_documents: int = 0
+    error: str | None = None
+    duration_ms: int = 0
+
+
+class MetadataDiscoveryTool:
+    """
+    [AC-MARH-XX] 元数据发现工具。
+
+    用于查询当前知识库文档中使用的元数据字段及其常见值，
+    帮助 AI 了解可用的过滤字段，从而更好地构造搜索请求。
+    """
+
+    def __init__(
+        self,
+        session: AsyncSession,
+        timeout_governor: TimeoutGovernor | None = None,
+        config: MetadataFieldDiscoveryConfig | None = None,
+    ):
+        self._session = session
+        self._timeout_governor = timeout_governor or TimeoutGovernor()
+        self._config = config or MetadataFieldDiscoveryConfig()
+
+    async def execute(
+        self,
+        tenant_id: str,
+        kb_id: str | None = None,
+        include_values: bool = True,
+        top_n: int | None = None,
+    ) -> MetadataDiscoveryResult:
+        """
+        Execute metadata discovery.
+
+        Args:
+            tenant_id: Tenant ID
+            kb_id: Optional knowledge base ID to filter
+            include_values: Whether to include common values (default True)
+            top_n: Number of top values to return per field (default from config)
+
+        Returns:
+            MetadataDiscoveryResult with field information
+        """
+        start_time = asyncio.get_event_loop().time()
+
+        try:
+            top_n = top_n or self._config.top_values_count
+
+            field_definitions = await self._get_field_definitions(tenant_id)
+
+            document_metadata = await self._get_document_metadata(tenant_id, kb_id)
+
+            total_docs = len(document_metadata)
+
+            field_values: dict[str, Counter] = {}
+            for doc_meta in document_metadata:
+                if not doc_meta:
+                    continue
+                for key, value in doc_meta.items():
+                    if key not in field_values:
+                        field_values[key] = Counter()
+                    str_value = str(value) if value is not None else ""
+                    field_values[key].update([str_value])
+
+            fields: list[MetadataFieldInfo] = []
+            for field_key, values_counter in field_values.items():
+                field_def = field_definitions.get(field_key)
+
+                common_values = []
+                if include_values:
+                    most_common = values_counter.most_common(top_n)
+                    common_values = [v for v, _ in most_common if v]
+
+                field_info = MetadataFieldInfo(
+                    field_key=field_key,
+                    field_type=field_def.type if field_def else "string",
+                    label=field_def.label if field_def else field_key,
+                    description=field_def.usage_description if field_def else None,
+                    common_values=common_values,
+                    value_count=len(values_counter),
+                    is_filterable=field_def.is_filterable if field_def else True,
+                    options=field_def.options if field_def else None,
+                )
+                fields.append(field_info)
+
+            fields.sort(key=lambda f: f.value_count, reverse=True)
+
+            duration_ms = int((asyncio.get_event_loop().time() - start_time) * 1000)
+
+            logger.info(
+                f"[MetadataDiscovery] Discovered {len(fields)} fields from {total_docs} documents, "
+                f"duration={duration_ms}ms"
+            )
+
+            return MetadataDiscoveryResult(
+                success=True,
+                fields=fields,
+                total_documents=total_docs,
+                duration_ms=duration_ms,
+            )
+
+        except Exception as e:
+            logger.error(f"[MetadataDiscovery] Discovery failed: {e}")
+            return MetadataDiscoveryResult(
+                success=False,
+                error=str(e),
+            )
+
+    async def _get_field_definitions(
+        self,
+        tenant_id: str,
+    ) -> dict[str, MetadataFieldDefinition]:
+        """Get field definitions for tenant."""
+        stmt = select(MetadataFieldDefinition).where(
+            MetadataFieldDefinition.tenant_id == tenant_id,
+            MetadataFieldDefinition.status == MetadataFieldStatus.ACTIVE.value,
+        )
+        result = await self._session.execute(stmt)
+        definitions = result.scalars().all()
+
+        return {d.field_key: d for d in definitions}
+
+    async def _get_document_metadata(
+        self,
+        tenant_id: str,
+        kb_id: str | None = None,
+    ) -> list[dict[str, Any]]:
+        """Get all document metadata for tenant."""
+        stmt = select(Document.doc_metadata).where(
+            Document.tenant_id == tenant_id,
+        )
+        if kb_id:
+            stmt = stmt.where(Document.kb_id == kb_id)
+
+        result = await self._session.execute(stmt)
+        rows = result.scalars().all()
+
+        return [row for row in rows if row]
+
+
+def register_metadata_discovery_tool(
+    registry: "ToolRegistry",
+    session: AsyncSession,
+    timeout_governor: TimeoutGovernor | None = None,
+    config: MetadataFieldDiscoveryConfig | None = None,
+) -> None:
+    """Register metadata discovery tool to registry."""
+    from app.services.mid.tool_registry import ToolType
+
+    cfg = config or MetadataFieldDiscoveryConfig()
+
+    async def metadata_discovery_handler(
+        tenant_id: str = "",
+        kb_id: str | None = None,
+        include_values: bool = True,
+        top_n: int | None = None,
+        **kwargs,  # 接受系统注入的额外参数（user_id, session_id 等）
+    ) -> dict[str, Any]:
+        """Metadata discovery tool handler."""
+        tool = MetadataDiscoveryTool(
+            session=session,
+            timeout_governor=timeout_governor,
+            config=cfg,
+        )
+
+        result = await tool.execute(
+            tenant_id=tenant_id,
+            kb_id=kb_id,
+            include_values=include_values,
+            top_n=top_n,
+        )
+        # 将 dataclass 转换为 dict
+        return {
+            "success": result.success,
+            "fields": [
+                {
+                    "field_key": f.field_key,
+                    "field_type": f.field_type,
+                    "label": f.label,
+                    "description": f.description,
+                    "common_values": f.common_values,
+                    "value_count": f.value_count,
+                    "is_filterable": f.is_filterable,
+                    "options": f.options,
+                }
+                for f in result.fields
+            ],
+            "total_documents": result.total_documents,
+            "error": result.error,
+            "duration_ms": result.duration_ms,
+        }
+
+    registry.register(
+        name="list_document_metadata_fields",
+        description="列出当前知识库文档中使用的元数据字段及其常见取值，用于后续的知识库搜索过滤",
+        handler=metadata_discovery_handler,
+        tool_type=ToolType.INTERNAL,
+        version="1.0.0",
+        auth_required=False,
+        timeout_ms=cfg.timeout_ms,
+        enabled=True,
+        metadata={
+            "when_to_use": "当需要了解知识库中有哪些可用的元数据过滤字段时使用。",
+            "when_not_to_use": "当已知可用的过滤字段，或不需要元数据过滤时不需要调用。",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "tenant_id": {"type": "string", "description": "租户 ID"},
+                    "kb_id": {"type": "string", "description": "知识库 ID（可选，用于限定范围）"},
+                    "include_values": {"type": "boolean", "description": "是否包含常见值列表，默认 true"},
+                    "top_n": {"type": "integer", "description": "每个字段返回的常见值数量，默认 10"},
+                },
+                "required": [],
+            },
+            "example_action_input": {
+                "include_values": True,
+                "top_n": 5,
+            },
+            "result_interpretation": "fields 数组包含每个字段的详细信息；common_values 是该字段在文档中的常见取值；value_count 表示该字段在多少文档中出现。",
+        },
+    )
+
+    logger.info("[MetadataDiscovery] Tool registered: list_document_metadata_fields")

ai-service/app/services/mid/metadata_filter_builder.py

@@ -165,21 +165,56 @@ class MetadataFilterBuilder:
     ) -> list[FilterFieldInfo]:
         """
         [AC-MRS-11] 获取可过滤的字段定义。
+        优先从 Redis 缓存获取，未缓存则从数据库查询并缓存。
 
         条件：
         - 状态=生效 (active)
         - field_roles 包含 resource_filter
         """
+        import time
+        start_time = time.time()
+        
+        # 1. 尝试从缓存获取
+        from app.services.metadata_cache_service import get_metadata_cache_service
+        cache_service = await get_metadata_cache_service()
+        cached_fields = await cache_service.get_fields(tenant_id)
+        
+        if cached_fields is not None:
+            # 缓存命中，直接返回
+            logger.info(
+                f"[AC-MRS-11] Cache hit: Retrieved {len(cached_fields)} fields "
+                f"for tenant={tenant_id} in {(time.time() - start_time)*1000:.2f}ms"
+            )
+            return [
+                FilterFieldInfo(
+                    field_key=f["field_key"],
+                    label=f["label"],
+                    field_type=f["field_type"],
+                    required=f["required"],
+                    options=f.get("options"),
+                    default_value=f.get("default_value"),
+                    is_filterable=f["is_filterable"],
+                )
+                for f in cached_fields
+            ]
+        
+        # 2. 缓存未命中，从数据库查询
+        logger.info(f"[AC-MRS-11] Cache miss: Querying database for tenant={tenant_id}")
+        db_start = time.time()
+        
         fields = await self._role_provider.get_fields_by_role(
             tenant_id=tenant_id,
             role=FieldRole.RESOURCE_FILTER.value,
         )
         
+        db_time = (time.time() - db_start) * 1000
         logger.info(
-            f"[AC-MRS-11] Retrieved {len(fields)} resource_filter fields for tenant={tenant_id}"
+            f"[AC-MRS-11] Retrieved {len(fields)} resource_filter fields "
+            f"for tenant={tenant_id} from DB in {db_time:.2f}ms"
         )
         
-        return [
+        # 3. 转换为 FilterFieldInfo 列表
+        filter_fields = [
             FilterFieldInfo(
                 field_key=f.field_key,
                 label=f.label,
@@ -192,6 +227,29 @@ class MetadataFilterBuilder:
             for f in fields
         ]
         
+        # 4. 缓存到 Redis
+        cache_data = [
+            {
+                "field_key": f.field_key,
+                "label": f.label,
+                "field_type": f.field_type,
+                "required": f.required,
+                "options": f.options,
+                "default_value": f.default_value,
+                "is_filterable": f.is_filterable,
+            }
+            for f in filter_fields
+        ]
+        await cache_service.set_fields(tenant_id, cache_data)
+        
+        total_time = (time.time() - start_time) * 1000
+        logger.info(
+            f"[AC-MRS-11] Total time for tenant={tenant_id}: {total_time:.2f}ms "
+            f"(DB: {db_time:.2f}ms)"
+        )
+        
+        return filter_fields
+
     def _build_field_filter(
         self,
         field_info: FilterFieldInfo,

ai-service/app/services/mid/role_based_field_provider.py

@@ -175,7 +175,9 @@ class RoleBasedFieldProvider:
                 "slot_key": slot.slot_key,
                 "type": slot.type,
                 "required": slot.required,
+                # [AC-MRS-07-UPGRADE] 返回新旧字段
                 "extract_strategy": slot.extract_strategy,
+                "extract_strategies": slot.extract_strategies,
                 "validation_rule": slot.validation_rule,
                 "ask_back_prompt": slot.ask_back_prompt,
                 "default_value": slot.default_value,
@@ -217,7 +219,9 @@ class RoleBasedFieldProvider:
                     "slot_key": field.field_key,
                     "type": field.type,
                     "required": field.required,
+                    # [AC-MRS-07-UPGRADE] 返回新旧字段
                     "extract_strategy": None,
+                    "extract_strategies": None,
                     "validation_rule": None,
                     "ask_back_prompt": None,
                     "default_value": field.default_value,

ai-service/app/services/mid/runtime_observer.py

@@ -53,6 +53,7 @@ class RuntimeContext:
 
     def to_trace_info(self) -> TraceInfo:
         """转换为 TraceInfo。"""
+        try:
             return TraceInfo(
                 mode=self.mode,
                 intent=self.intent,
@@ -71,6 +72,16 @@ class RuntimeContext:
                 tools_used=[tc.tool_name for tc in self.tool_calls] if self.tool_calls else None,
                 tool_calls=self.tool_calls if self.tool_calls else None,
             )
+        except Exception as e:
+            import traceback
+            logger.error(
+                f"[RuntimeObserver] Failed to create TraceInfo: {e}\n"
+                f"Exception type: {type(e).__name__}\n"
+                f"Context: mode={self.mode}, request_id={self.request_id}, "
+                f"generation_id={self.generation_id}\n"
+                f"Traceback:\n{traceback.format_exc()}"
+            )
+            raise
 
 
 class RuntimeObserver:

ai-service/app/services/mid/scene_slot_bundle_loader.py

@@ -0,0 +1,423 @@
+"""
+Scene Slot Bundle Loader Service.
+[AC-SCENE-SLOT-02] 运行时场景槽位包加载器
+[AC-SCENE-SLOT-03] 支持缓存层
+
+职责：
+1. 根据场景标识加载槽位包配置
+2. 聚合槽位定义详情
+3. 计算缺失槽位
+4. 生成追问提示
+"""
+
+import logging
+from dataclasses import dataclass, field
+from typing import Any
+
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.models.entities import SceneSlotBundleStatus
+from app.services.scene_slot_bundle_service import SceneSlotBundleService
+from app.services.slot_definition_service import SlotDefinitionService
+from app.services.cache.scene_slot_bundle_cache import (
+    CachedSceneSlotBundle,
+    get_scene_slot_bundle_cache,
+)
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class SlotInfo:
+    """槽位信息"""
+    slot_key: str
+    type: str
+    required: bool
+    ask_back_prompt: str | None = None
+    validation_rule: str | None = None
+    linked_field_id: str | None = None
+    default_value: Any = None
+
+
+@dataclass
+class SceneSlotContext:
+    """
+    [AC-SCENE-SLOT-02] 场景槽位上下文
+    
+    运行时使用的场景槽位包信息
+    """
+    scene_key: str
+    scene_name: str
+    required_slots: list[SlotInfo] = field(default_factory=list)
+    optional_slots: list[SlotInfo] = field(default_factory=list)
+    slot_priority: list[str] = field(default_factory=list)
+    completion_threshold: float = 1.0
+    ask_back_order: str = "priority"
+    
+    def get_all_slot_keys(self) -> list[str]:
+        """获取所有槽位键名"""
+        return [s.slot_key for s in self.required_slots] + [s.slot_key for s in self.optional_slots]
+    
+    def get_required_slot_keys(self) -> list[str]:
+        """获取必填槽位键名"""
+        return [s.slot_key for s in self.required_slots]
+    
+    def get_optional_slot_keys(self) -> list[str]:
+        """获取可选槽位键名"""
+        return [s.slot_key for s in self.optional_slots]
+    
+    def get_missing_slots(
+        self,
+        filled_slots: dict[str, Any],
+    ) -> list[dict[str, Any]]:
+        """
+        获取缺失的必填槽位信息
+        
+        Args:
+            filled_slots: 已填充的槽位值
+            
+        Returns:
+            缺失槽位信息列表
+        """
+        missing = []
+        
+        for slot_info in self.required_slots:
+            if slot_info.slot_key not in filled_slots:
+                missing.append({
+                    "slot_key": slot_info.slot_key,
+                    "type": slot_info.type,
+                    "required": True,
+                    "ask_back_prompt": slot_info.ask_back_prompt,
+                    "validation_rule": slot_info.validation_rule,
+                    "linked_field_id": slot_info.linked_field_id,
+                })
+        
+        return missing
+    
+    def get_ordered_missing_slots(
+        self,
+        filled_slots: dict[str, Any],
+    ) -> list[dict[str, Any]]:
+        """
+        按优先级顺序获取缺失的必填槽位
+        
+        Args:
+            filled_slots: 已填充的槽位值
+            
+        Returns:
+            按优先级排序的缺失槽位信息列表
+        """
+        missing = self.get_missing_slots(filled_slots)
+        
+        if not missing:
+            return []
+        
+        if self.ask_back_order == "required_first":
+            return missing
+        
+        if self.ask_back_order == "priority" and self.slot_priority:
+            priority_map = {slot_key: idx for idx, slot_key in enumerate(self.slot_priority)}
+            missing.sort(key=lambda x: priority_map.get(x["slot_key"], 999))
+        
+        return missing
+    
+    def get_completion_ratio(
+        self,
+        filled_slots: dict[str, Any],
+    ) -> float:
+        """
+        计算完成比例
+        
+        Args:
+            filled_slots: 已填充的槽位值
+            
+        Returns:
+            完成比例 (0.0 - 1.0)
+        """
+        if not self.required_slots:
+            return 1.0
+        
+        filled_count = sum(
+            1 for slot_info in self.required_slots
+            if slot_info.slot_key in filled_slots
+        )
+        
+        return filled_count / len(self.required_slots)
+    
+    def is_complete(
+        self,
+        filled_slots: dict[str, Any],
+    ) -> bool:
+        """
+        检查是否完成
+        
+        Args:
+            filled_slots: 已填充的槽位值
+            
+        Returns:
+            是否达到完成阈值
+        """
+        return self.get_completion_ratio(filled_slots) >= self.completion_threshold
+
+
+class SceneSlotBundleLoader:
+    """
+    [AC-SCENE-SLOT-02] 场景槽位包加载器
+    [AC-SCENE-SLOT-03] 支持缓存层
+    
+    运行时加载场景槽位包配置
+    """
+    
+    def __init__(self, session: AsyncSession, use_cache: bool = True):
+        self._session = session
+        self._bundle_service = SceneSlotBundleService(session)
+        self._slot_service = SlotDefinitionService(session)
+        self._use_cache = use_cache
+        self._cache = get_scene_slot_bundle_cache() if use_cache else None
+    
+    async def load_scene_context(
+        self,
+        tenant_id: str,
+        scene_key: str,
+    ) -> SceneSlotContext | None:
+        """
+        加载场景槽位上下文
+        
+        Args:
+            tenant_id: 租户 ID
+            scene_key: 场景标识
+            
+        Returns:
+            场景槽位上下文或 None
+        """
+        # [AC-SCENE-SLOT-03] 尝试从缓存获取
+        if self._use_cache and self._cache:
+            cached = await self._cache.get(tenant_id, scene_key)
+            if cached and cached.status == SceneSlotBundleStatus.ACTIVE.value:
+                logger.debug(
+                    f"[AC-SCENE-SLOT-03] Cache hit for scene: {scene_key}"
+                )
+                return await self._build_context_from_cached(tenant_id, cached)
+        
+        bundle = await self._bundle_service.get_active_bundle_by_scene(
+            tenant_id=tenant_id,
+            scene_key=scene_key,
+        )
+        
+        if not bundle:
+            logger.debug(
+                f"[AC-SCENE-SLOT-02] No active bundle found for scene: {scene_key}"
+            )
+            return None
+        
+        # [AC-SCENE-SLOT-03] 写入缓存
+        if self._use_cache and self._cache:
+            cached_bundle = CachedSceneSlotBundle(
+                scene_key=bundle.scene_key,
+                scene_name=bundle.scene_name,
+                description=bundle.description,
+                required_slots=bundle.required_slots,
+                optional_slots=bundle.optional_slots,
+                slot_priority=bundle.slot_priority,
+                completion_threshold=bundle.completion_threshold,
+                ask_back_order=bundle.ask_back_order,
+                status=bundle.status,
+                version=bundle.version,
+            )
+            await self._cache.set(tenant_id, scene_key, cached_bundle)
+        
+        return await self._build_context_from_bundle(tenant_id, bundle)
+    
+    async def _build_context_from_cached(
+        self,
+        tenant_id: str,
+        cached: CachedSceneSlotBundle,
+    ) -> SceneSlotContext:
+        """从缓存构建场景槽位上下文"""
+        all_slots = await self._slot_service.list_slot_definitions(tenant_id)
+        slot_map = {slot.slot_key: slot for slot in all_slots}
+        
+        return self._build_context(cached, slot_map)
+    
+    async def _build_context_from_bundle(
+        self,
+        tenant_id: str,
+        bundle: Any,
+    ) -> SceneSlotContext:
+        """从数据库模型构建场景槽位上下文"""
+        all_slots = await self._slot_service.list_slot_definitions(tenant_id)
+        slot_map = {slot.slot_key: slot for slot in all_slots}
+        
+        cached = CachedSceneSlotBundle(
+            scene_key=bundle.scene_key,
+            scene_name=bundle.scene_name,
+            description=bundle.description,
+            required_slots=bundle.required_slots,
+            optional_slots=bundle.optional_slots,
+            slot_priority=bundle.slot_priority,
+            completion_threshold=bundle.completion_threshold,
+            ask_back_order=bundle.ask_back_order,
+            status=bundle.status,
+            version=bundle.version,
+        )
+        
+        return self._build_context(cached, slot_map)
+    
+    def _build_context(
+        self,
+        cached: CachedSceneSlotBundle,
+        slot_map: dict[str, Any],
+    ) -> SceneSlotContext:
+        """构建场景槽位上下文"""
+        required_slot_infos = []
+        for slot_key in cached.required_slots:
+            if slot_key in slot_map:
+                slot_def = slot_map[slot_key]
+                required_slot_infos.append(SlotInfo(
+                    slot_key=slot_def.slot_key,
+                    type=slot_def.type,
+                    required=True,
+                    ask_back_prompt=slot_def.ask_back_prompt,
+                    validation_rule=slot_def.validation_rule,
+                    linked_field_id=str(slot_def.linked_field_id) if slot_def.linked_field_id else None,
+                    default_value=slot_def.default_value,
+                ))
+            else:
+                logger.warning(
+                    f"[AC-SCENE-SLOT-02] Required slot not found: {slot_key}"
+                )
+        
+        optional_slot_infos = []
+        for slot_key in cached.optional_slots:
+            if slot_key in slot_map:
+                slot_def = slot_map[slot_key]
+                optional_slot_infos.append(SlotInfo(
+                    slot_key=slot_def.slot_key,
+                    type=slot_def.type,
+                    required=slot_def.required,
+                    ask_back_prompt=slot_def.ask_back_prompt,
+                    validation_rule=slot_def.validation_rule,
+                    linked_field_id=str(slot_def.linked_field_id) if slot_def.linked_field_id else None,
+                    default_value=slot_def.default_value,
+                ))
+            else:
+                logger.warning(
+                    f"[AC-SCENE-SLOT-02] Optional slot not found: {slot_key}"
+                )
+        
+        context = SceneSlotContext(
+            scene_key=cached.scene_key,
+            scene_name=cached.scene_name,
+            required_slots=required_slot_infos,
+            optional_slots=optional_slot_infos,
+            slot_priority=cached.slot_priority or [],
+            completion_threshold=cached.completion_threshold,
+            ask_back_order=cached.ask_back_order,
+        )
+        
+        logger.info(
+            f"[AC-SCENE-SLOT-02] Loaded scene context: scene={cached.scene_key}, "
+            f"required={len(required_slot_infos)}, optional={len(optional_slot_infos)}, "
+            f"threshold={cached.completion_threshold}"
+        )
+        
+        return context
+    
+    async def invalidate_cache(
+        self,
+        tenant_id: str,
+        scene_key: str,
+    ) -> bool:
+        """
+        [AC-SCENE-SLOT-03] 使缓存失效
+        
+        Args:
+            tenant_id: 租户 ID
+            scene_key: 场景标识
+            
+        Returns:
+            是否成功
+        """
+        if self._cache:
+            return await self._cache.invalidate_on_update(tenant_id, scene_key)
+        return True
+    
+    async def get_missing_slots_for_scene(
+        self,
+        tenant_id: str,
+        scene_key: str,
+        filled_slots: dict[str, Any],
+    ) -> list[dict[str, Any]]:
+        """
+        获取场景缺失的必填槽位
+        
+        Args:
+            tenant_id: 租户 ID
+            scene_key: 场景标识
+            filled_slots: 已填充的槽位值
+            
+        Returns:
+            缺失槽位信息列表
+        """
+        context = await self.load_scene_context(tenant_id, scene_key)
+        
+        if not context:
+            return []
+        
+        return context.get_ordered_missing_slots(filled_slots)
+    
+    async def generate_ask_back_prompt(
+        self,
+        tenant_id: str,
+        scene_key: str,
+        filled_slots: dict[str, Any],
+        max_slots: int = 2,
+    ) -> str | None:
+        """
+        生成追问提示
+        
+        Args:
+            tenant_id: 租户 ID
+            scene_key: 场景标识
+            filled_slots: 已填充的槽位值
+            max_slots: 最多追问的槽位数量
+            
+        Returns:
+            追问提示或 None
+        """
+        missing_slots = await self.get_missing_slots_for_scene(
+            tenant_id=tenant_id,
+            scene_key=scene_key,
+            filled_slots=filled_slots,
+        )
+        
+        if not missing_slots:
+            return None
+        
+        context = await self.load_scene_context(tenant_id, scene_key)
+        ask_back_order = context.ask_back_order if context else "priority"
+        
+        if ask_back_order == "parallel":
+            prompts = []
+            for missing in missing_slots[:max_slots]:
+                if missing.get("ask_back_prompt"):
+                    prompts.append(missing["ask_back_prompt"])
+                else:
+                    slot_key = missing.get("slot_key", "相关信息")
+                    prompts.append(f"您的{slot_key}")
+            
+            if len(prompts) == 1:
+                return prompts[0]
+            elif len(prompts) == 2:
+                return f"为了更好地为您服务，请告诉我{prompts[0]}和{prompts[1]}。"
+            else:
+                all_but_last = "、".join(prompts[:-1])
+                return f"为了更好地为您服务，请告诉我{all_but_last}，以及{prompts[-1]}。"
+        else:
+            first_missing = missing_slots[0]
+            ask_back_prompt = first_missing.get("ask_back_prompt")
+            if ask_back_prompt:
+                return ask_back_prompt
+            
+            slot_key = first_missing.get("slot_key", "相关信息")
+            return f"为了更好地为您提供帮助，请告诉我您的{slot_key}。"

ai-service/app/services/mid/scene_slot_metrics.py

@@ -0,0 +1,334 @@
+"""
+Scene Slot Bundle Metrics Service.
+[AC-SCENE-SLOT-04] 场景槽位包监控指标
+
+职责：
+1. 收集场景槽位包相关的监控指标
+2. 提供告警检测接口
+3. 支持指标导出
+"""
+
+import logging
+from dataclasses import dataclass, field
+from datetime import datetime
+from typing import Any
+from collections import defaultdict
+import threading
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class SceneSlotMetricPoint:
+    """单个指标数据点"""
+    timestamp: datetime
+    tenant_id: str
+    scene_key: str
+    metric_name: str
+    value: float
+    tags: dict[str, str] = field(default_factory=dict)
+
+
+@dataclass
+class SceneSlotMetricsSummary:
+    """指标汇总"""
+    total_requests: int = 0
+    cache_hits: int = 0
+    cache_misses: int = 0
+    missing_slots_triggered: int = 0
+    ask_back_triggered: int = 0
+    scene_not_configured: int = 0
+    slot_not_found: int = 0
+    avg_completion_ratio: float = 0.0
+    
+    @property
+    def cache_hit_rate(self) -> float:
+        if self.total_requests == 0:
+            return 0.0
+        return self.cache_hits / self.total_requests
+
+
+class SceneSlotMetricsCollector:
+    """
+    [AC-SCENE-SLOT-04] 场景槽位包指标收集器
+    
+    收集以下指标：
+    - scene_slot_requests_total: 场景槽位请求总数
+    - scene_slot_cache_hits: 缓存命中次数
+    - scene_slot_cache_misses: 缓存未命中次数
+    - scene_slot_missing_triggered: 缺失槽位触发次数
+    - scene_slot_ask_back_triggered: 追问触发次数
+    - scene_slot_not_configured: 场景未配置次数
+    - scene_slot_not_found: 槽位未找到次数
+    - scene_slot_completion_ratio: 槽位完成比例
+    """
+    
+    def __init__(self, max_points: int = 10000):
+        self._max_points = max_points
+        self._points: list[SceneSlotMetricPoint] = []
+        self._counters: dict[str, int] = defaultdict(int)
+        self._lock = threading.Lock()
+        self._start_time = datetime.utcnow()
+    
+    def record(
+        self,
+        tenant_id: str,
+        scene_key: str,
+        metric_name: str,
+        value: float = 1.0,
+        tags: dict[str, str] | None = None,
+    ) -> None:
+        """
+        记录指标
+        
+        Args:
+            tenant_id: 租户 ID
+            scene_key: 场景标识
+            metric_name: 指标名称
+            value: 指标值
+            tags: 额外标签
+        """
+        point = SceneSlotMetricPoint(
+            timestamp=datetime.utcnow(),
+            tenant_id=tenant_id,
+            scene_key=scene_key,
+            metric_name=metric_name,
+            value=value,
+            tags=tags or {},
+        )
+        
+        with self._lock:
+            self._points.append(point)
+            if len(self._points) > self._max_points:
+                self._points = self._points[-self._max_points:]
+            
+            counter_key = f"{tenant_id}:{scene_key}:{metric_name}"
+            self._counters[counter_key] += 1
+    
+    def record_cache_hit(self, tenant_id: str, scene_key: str) -> None:
+        """记录缓存命中"""
+        self.record(tenant_id, scene_key, "cache_hit")
+    
+    def record_cache_miss(self, tenant_id: str, scene_key: str) -> None:
+        """记录缓存未命中"""
+        self.record(tenant_id, scene_key, "cache_miss")
+    
+    def record_missing_slots(self, tenant_id: str, scene_key: str, count: int = 1) -> None:
+        """记录缺失槽位触发"""
+        self.record(tenant_id, scene_key, "missing_slots_triggered", float(count))
+    
+    def record_ask_back(self, tenant_id: str, scene_key: str) -> None:
+        """记录追问触发"""
+        self.record(tenant_id, scene_key, "ask_back_triggered")
+    
+    def record_scene_not_configured(self, tenant_id: str, scene_key: str) -> None:
+        """记录场景未配置"""
+        self.record(tenant_id, scene_key, "scene_not_configured")
+    
+    def record_slot_not_found(self, tenant_id: str, scene_key: str, slot_key: str) -> None:
+        """记录槽位未找到"""
+        self.record(tenant_id, scene_key, "slot_not_found", tags={"slot_key": slot_key})
+    
+    def record_completion_ratio(self, tenant_id: str, scene_key: str, ratio: float) -> None:
+        """记录槽位完成比例"""
+        self.record(tenant_id, scene_key, "completion_ratio", ratio)
+    
+    def get_summary(self, tenant_id: str | None = None) -> SceneSlotMetricsSummary:
+        """
+        获取指标汇总
+        
+        Args:
+            tenant_id: 租户 ID（可选，为 None 时返回所有租户的汇总）
+            
+        Returns:
+            指标汇总
+        """
+        summary = SceneSlotMetricsSummary()
+        
+        with self._lock:
+            points = self._points.copy()
+        
+        for point in points:
+            if tenant_id and point.tenant_id != tenant_id:
+                continue
+            
+            summary.total_requests += 1
+            
+            if point.metric_name == "cache_hit":
+                summary.cache_hits += 1
+            elif point.metric_name == "cache_miss":
+                summary.cache_misses += 1
+            elif point.metric_name == "missing_slots_triggered":
+                summary.missing_slots_triggered += int(point.value)
+            elif point.metric_name == "ask_back_triggered":
+                summary.ask_back_triggered += 1
+            elif point.metric_name == "scene_not_configured":
+                summary.scene_not_configured += 1
+            elif point.metric_name == "slot_not_found":
+                summary.slot_not_found += 1
+            elif point.metric_name == "completion_ratio":
+                summary.avg_completion_ratio = (
+                    summary.avg_completion_ratio * summary.total_requests + point.value
+                ) / (summary.total_requests + 1) if summary.total_requests > 0 else point.value
+        
+        return summary
+    
+    def get_metrics_by_scene(
+        self,
+        tenant_id: str,
+        scene_key: str,
+    ) -> dict[str, Any]:
+        """
+        获取特定场景的指标
+        
+        Args:
+            tenant_id: 租户 ID
+            scene_key: 场景标识
+            
+        Returns:
+            指标字典
+        """
+        metrics = {
+            "requests": 0,
+            "cache_hits": 0,
+            "cache_misses": 0,
+            "missing_slots_triggered": 0,
+            "ask_back_triggered": 0,
+            "slot_not_found": 0,
+            "avg_completion_ratio": 0.0,
+        }
+        
+        with self._lock:
+            points = [p for p in self._points if p.tenant_id == tenant_id and p.scene_key == scene_key]
+        
+        if not points:
+            return metrics
+        
+        completion_ratios = []
+        
+        for point in points:
+            metrics["requests"] += 1
+            if point.metric_name in metrics:
+                if point.metric_name == "completion_ratio":
+                    completion_ratios.append(point.value)
+                else:
+                    metrics[point.metric_name] += int(point.value)
+        
+        if completion_ratios:
+            metrics["avg_completion_ratio"] = sum(completion_ratios) / len(completion_ratios)
+        
+        return metrics
+    
+    def check_alerts(
+        self,
+        tenant_id: str,
+        scene_key: str,
+        thresholds: dict[str, float] | None = None,
+    ) -> list[dict[str, Any]]:
+        """
+        检查告警条件
+        
+        Args:
+            tenant_id: 租户 ID
+            scene_key: 场景标识
+            thresholds: 告警阈值配置
+            
+        Returns:
+            告警列表
+        """
+        default_thresholds = {
+            "cache_hit_rate_low": 0.5,
+            "missing_slots_rate_high": 0.3,
+            "scene_not_configured_rate_high": 0.1,
+        }
+        
+        effective_thresholds = {**default_thresholds, **(thresholds or {})}
+        
+        metrics = self.get_metrics_by_scene(tenant_id, scene_key)
+        alerts = []
+        
+        if metrics["requests"] > 0:
+            cache_hit_rate = metrics["cache_hits"] / metrics["requests"]
+            if cache_hit_rate < effective_thresholds["cache_hit_rate_low"]:
+                alerts.append({
+                    "alert_type": "cache_hit_rate_low",
+                    "severity": "warning",
+                    "message": f"场景 {scene_key} 的缓存命中率 ({cache_hit_rate:.2%}) 低于阈值 ({effective_thresholds['cache_hit_rate_low']:.0%})",
+                    "current_value": cache_hit_rate,
+                    "threshold": effective_thresholds["cache_hit_rate_low"],
+                    "suggestion": "检查场景槽位包配置是否频繁变更，或增加缓存 TTL",
+                })
+            
+            missing_slots_rate = metrics["missing_slots_triggered"] / metrics["requests"]
+            if missing_slots_rate > effective_thresholds["missing_slots_rate_high"]:
+                alerts.append({
+                    "alert_type": "missing_slots_rate_high",
+                    "severity": "warning",
+                    "message": f"场景 {scene_key} 的缺失槽位触发率 ({missing_slots_rate:.2%}) 高于阈值 ({effective_thresholds['missing_slots_rate_high']:.0%})",
+                    "current_value": missing_slots_rate,
+                    "threshold": effective_thresholds["missing_slots_rate_high"],
+                    "suggestion": "检查槽位配置是否合理，或优化槽位提取策略",
+                })
+            
+            scene_not_configured_rate = metrics.get("scene_not_configured", 0) / metrics["requests"]
+            if scene_not_configured_rate > effective_thresholds["scene_not_configured_rate_high"]:
+                alerts.append({
+                    "alert_type": "scene_not_configured_rate_high",
+                    "severity": "error",
+                    "message": f"场景 {scene_key} 未配置率 ({scene_not_configured_rate:.2%}) 高于阈值 ({effective_thresholds['scene_not_configured_rate_high']:.0%})",
+                    "current_value": scene_not_configured_rate,
+                    "threshold": effective_thresholds["scene_not_configured_rate_high"],
+                    "suggestion": "请为该场景创建场景槽位包配置",
+                })
+        
+        return alerts
+    
+    def export_metrics(self, tenant_id: str | None = None) -> dict[str, Any]:
+        """
+        导出指标数据
+        
+        Args:
+            tenant_id: 租户 ID（可选）
+            
+        Returns:
+            指标数据字典
+        """
+        with self._lock:
+            points = [
+                {
+                    "timestamp": p.timestamp.isoformat(),
+                    "tenant_id": p.tenant_id,
+                    "scene_key": p.scene_key,
+                    "metric_name": p.metric_name,
+                    "value": p.value,
+                    "tags": p.tags,
+                }
+                for p in self._points
+                if tenant_id is None or p.tenant_id == tenant_id
+            ]
+        
+        return {
+            "start_time": self._start_time.isoformat(),
+            "end_time": datetime.utcnow().isoformat(),
+            "total_points": len(points),
+            "points": points,
+            "summary": self.get_summary(tenant_id).__dict__,
+        }
+    
+    def reset(self) -> None:
+        """重置指标收集器"""
+        with self._lock:
+            self._points = []
+            self._counters = defaultdict(int)
+            self._start_time = datetime.utcnow()
+
+
+_metrics_collector: SceneSlotMetricsCollector | None = None
+
+
+def get_scene_slot_metrics_collector() -> SceneSlotMetricsCollector:
+    """获取场景槽位指标收集器实例"""
+    global _metrics_collector
+    if _metrics_collector is None:
+        _metrics_collector = SceneSlotMetricsCollector()
+    return _metrics_collector

ai-service/app/services/mid/slot_backfill_service.py

@@ -0,0 +1,500 @@
+"""
+Slot Backfill Service.
+槽位回填服务 - 处理槽位值的提取、校验、确认、写回
+
+[AC-MRS-SLOT-BACKFILL-01] 槽位值回填确认
+
+职责：
+1. 从用户回复提取候选槽位值
+2. 调用 SlotManager 校验并归一化
+3. 校验失败返回 ask_back_prompt 二次追问
+4. 校验通过写入状态并标记 source/confidence
+5. 对低置信度值增加确认话术
+"""
+
+import logging
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Any
+
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.models.mid.schemas import SlotSource
+from app.services.mid.slot_manager import SlotManager, SlotWriteResult
+from app.services.mid.slot_state_aggregator import SlotStateAggregator, SlotState
+from app.services.mid.slot_strategy_executor import (
+    ExtractContext,
+    SlotStrategyExecutor,
+    StrategyChainResult,
+)
+from app.services.slot_definition_service import SlotDefinitionService
+
+logger = logging.getLogger(__name__)
+
+
+class BackfillStatus(str, Enum):
+    """回填状态"""
+    SUCCESS = "success"
+    VALIDATION_FAILED = "validation_failed"
+    EXTRACTION_FAILED = "extraction_failed"
+    NEEDS_CONFIRMATION = "needs_confirmation"
+    NO_CANDIDATES = "no_candidates"
+
+
+@dataclass
+class BackfillResult:
+    """
+    回填结果
+    
+    Attributes:
+        status: 回填状态
+        slot_key: 槽位键名
+        value: 最终值（校验通过后）
+        normalized_value: 归一化后的值
+        source: 值来源
+        confidence: 置信度
+        error_message: 错误信息
+        ask_back_prompt: 追问提示
+        confirmation_prompt: 确认提示（低置信度时）
+        updated_state: 更新后的槽位状态
+    """
+    status: BackfillStatus
+    slot_key: str | None = None
+    value: Any = None
+    normalized_value: Any = None
+    source: str = "unknown"
+    confidence: float = 0.0
+    error_message: str | None = None
+    ask_back_prompt: str | None = None
+    confirmation_prompt: str | None = None
+    updated_state: SlotState | None = None
+    
+    def is_success(self) -> bool:
+        return self.status == BackfillStatus.SUCCESS
+    
+    def needs_ask_back(self) -> bool:
+        return self.status in (
+            BackfillStatus.VALIDATION_FAILED,
+            BackfillStatus.EXTRACTION_FAILED,
+        )
+    
+    def needs_confirmation(self) -> bool:
+        return self.status == BackfillStatus.NEEDS_CONFIRMATION
+    
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "status": self.status.value,
+            "slot_key": self.slot_key,
+            "value": self.value,
+            "normalized_value": self.normalized_value,
+            "source": self.source,
+            "confidence": self.confidence,
+            "error_message": self.error_message,
+            "ask_back_prompt": self.ask_back_prompt,
+            "confirmation_prompt": self.confirmation_prompt,
+        }
+
+
+@dataclass
+class BatchBackfillResult:
+    """批量回填结果"""
+    results: list[BackfillResult] = field(default_factory=list)
+    success_count: int = 0
+    failed_count: int = 0
+    confirmation_needed_count: int = 0
+    
+    def add_result(self, result: BackfillResult) -> None:
+        self.results.append(result)
+        if result.is_success():
+            self.success_count += 1
+        elif result.needs_confirmation():
+            self.confirmation_needed_count += 1
+        else:
+            self.failed_count += 1
+    
+    def get_ask_back_prompts(self) -> list[str]:
+        """获取所有追问提示"""
+        return [
+            r.ask_back_prompt
+            for r in self.results
+            if r.ask_back_prompt
+        ]
+    
+    def get_confirmation_prompts(self) -> list[str]:
+        """获取所有确认提示"""
+        return [
+            r.confirmation_prompt
+            for r in self.results
+            if r.confirmation_prompt
+        ]
+
+
+class SlotBackfillService:
+    """
+    [AC-MRS-SLOT-BACKFILL-01] 槽位回填服务
+    
+    处理槽位值的提取、校验、确认、写回流程：
+    1. 从用户回复提取候选槽位值
+    2. SlotManager 校验并归一化
+    3. 校验失败返回 ask_back_prompt 二次追问
+    4. 校验通过写入状态并标记 source/confidence
+    5. 对低置信度值增加确认话术
+    """
+    
+    CONFIDENCE_THRESHOLD_LOW = 0.5
+    CONFIDENCE_THRESHOLD_HIGH = 0.8
+    
+    def __init__(
+        self,
+        session: AsyncSession,
+        tenant_id: str,
+        session_id: str | None = None,
+        slot_manager: SlotManager | None = None,
+        strategy_executor: SlotStrategyExecutor | None = None,
+    ):
+        self._session = session
+        self._tenant_id = tenant_id
+        self._session_id = session_id
+        self._slot_manager = slot_manager or SlotManager(session=session)
+        self._strategy_executor = strategy_executor or SlotStrategyExecutor()
+        self._slot_def_service = SlotDefinitionService(session)
+        self._state_aggregator: SlotStateAggregator | None = None
+    
+    async def _get_state_aggregator(self) -> SlotStateAggregator:
+        """获取状态聚合器"""
+        if self._state_aggregator is None:
+            self._state_aggregator = SlotStateAggregator(
+                session=self._session,
+                tenant_id=self._tenant_id,
+                session_id=self._session_id,
+            )
+        return self._state_aggregator
+    
+    async def backfill_single_slot(
+        self,
+        slot_key: str,
+        candidate_value: Any,
+        source: str = "user_confirmed",
+        confidence: float = 1.0,
+        strategies: list[str] | None = None,
+    ) -> BackfillResult:
+        """
+        回填单个槽位
+        
+        执行流程：
+        1. 如果有提取策略，执行提取
+        2. 校验候选值
+        3. 根据校验结果决定下一步
+        4. 写入状态
+        
+        Args:
+            slot_key: 槽位键名
+            candidate_value: 候选值
+            source: 值来源
+            confidence: 初始置信度
+            strategies: 提取策略链（可选）
+            
+        Returns:
+            BackfillResult: 回填结果
+        """
+        final_value = candidate_value
+        final_source = source
+        final_confidence = confidence
+        
+        if strategies:
+            extracted_result = await self._extract_value(
+                slot_key=slot_key,
+                user_input=str(candidate_value),
+                strategies=strategies,
+            )
+            
+            if extracted_result.success:
+                final_value = extracted_result.final_value
+                final_source = extracted_result.final_strategy or source
+                final_confidence = self._get_confidence_for_strategy(final_source)
+            else:
+                ask_back_prompt = await self._slot_manager.get_ask_back_prompt(
+                    self._tenant_id, slot_key
+                )
+                return BackfillResult(
+                    status=BackfillStatus.EXTRACTION_FAILED,
+                    slot_key=slot_key,
+                    value=candidate_value,
+                    source=source,
+                    confidence=confidence,
+                    error_message=extracted_result.steps[-1].failure_reason if extracted_result.steps else "提取失败",
+                    ask_back_prompt=ask_back_prompt,
+                )
+        
+        write_result = await self._slot_manager.write_slot(
+            tenant_id=self._tenant_id,
+            slot_key=slot_key,
+            value=final_value,
+            source=SlotSource(final_source) if final_source in [s.value for s in SlotSource] else SlotSource.USER_CONFIRMED,
+            confidence=final_confidence,
+        )
+        
+        if not write_result.success:
+            return BackfillResult(
+                status=BackfillStatus.VALIDATION_FAILED,
+                slot_key=slot_key,
+                value=final_value,
+                source=final_source,
+                confidence=final_confidence,
+                error_message=write_result.error.error_message if write_result.error else "校验失败",
+                ask_back_prompt=write_result.ask_back_prompt,
+            )
+        
+        normalized_value = write_result.value
+        updated_state = None
+        
+        if self._session_id:
+            aggregator = await self._get_state_aggregator()
+            updated_state = await aggregator.update_slot(
+                slot_key=slot_key,
+                value=normalized_value,
+                source=final_source,
+                confidence=final_confidence,
+            )
+        
+        result_status = BackfillStatus.SUCCESS
+        confirmation_prompt = None
+        
+        if final_confidence < self.CONFIDENCE_THRESHOLD_LOW:
+            result_status = BackfillStatus.NEEDS_CONFIRMATION
+            confirmation_prompt = self._generate_confirmation_prompt(
+                slot_key, normalized_value
+            )
+        
+        return BackfillResult(
+            status=result_status,
+            slot_key=slot_key,
+            value=final_value,
+            normalized_value=normalized_value,
+            source=final_source,
+            confidence=final_confidence,
+            confirmation_prompt=confirmation_prompt,
+            updated_state=updated_state,
+        )
+    
+    async def backfill_multiple_slots(
+        self,
+        candidates: dict[str, Any],
+        source: str = "user_confirmed",
+        confidence: float = 1.0,
+    ) -> BatchBackfillResult:
+        """
+        批量回填槽位
+        
+        Args:
+            candidates: 候选值字典 {slot_key: value}
+            source: 值来源
+            confidence: 初始置信度
+            
+        Returns:
+            BatchBackfillResult: 批量回填结果
+        """
+        batch_result = BatchBackfillResult()
+        
+        for slot_key, value in candidates.items():
+            result = await self.backfill_single_slot(
+                slot_key=slot_key,
+                candidate_value=value,
+                source=source,
+                confidence=confidence,
+            )
+            batch_result.add_result(result)
+        
+        return batch_result
+    
+    async def backfill_from_user_response(
+        self,
+        user_response: str,
+        expected_slots: list[str],
+        strategies: list[str] | None = None,
+    ) -> BatchBackfillResult:
+        """
+        从用户回复中提取并回填槽位
+        
+        Args:
+            user_response: 用户回复文本
+            expected_slots: 期望提取的槽位列表
+            strategies: 提取策略链
+            
+        Returns:
+            BatchBackfillResult: 批量回填结果
+        """
+        batch_result = BatchBackfillResult()
+        
+        for slot_key in expected_slots:
+            slot_def = await self._slot_def_service.get_slot_definition_by_key(
+                self._tenant_id, slot_key
+            )
+            
+            if not slot_def:
+                continue
+            
+            extract_strategies = strategies or ["rule", "llm"]
+            
+            extracted_result = await self._extract_value(
+                slot_key=slot_key,
+                user_input=user_response,
+                strategies=extract_strategies,
+                slot_type=slot_def.type,
+                validation_rule=slot_def.validation_rule,
+            )
+            
+            if not extracted_result.success:
+                ask_back_prompt = slot_def.ask_back_prompt or f"请提供{slot_key}信息"
+                batch_result.add_result(BackfillResult(
+                    status=BackfillStatus.EXTRACTION_FAILED,
+                    slot_key=slot_key,
+                    error_message="无法从回复中提取",
+                    ask_back_prompt=ask_back_prompt,
+                ))
+                continue
+            
+            source = self._get_source_for_strategy(extracted_result.final_strategy)
+            confidence = self._get_confidence_for_strategy(source)
+            
+            result = await self.backfill_single_slot(
+                slot_key=slot_key,
+                candidate_value=extracted_result.final_value,
+                source=source,
+                confidence=confidence,
+            )
+            batch_result.add_result(result)
+        
+        return batch_result
+    
+    async def _extract_value(
+        self,
+        slot_key: str,
+        user_input: str,
+        strategies: list[str],
+        slot_type: str = "string",
+        validation_rule: str | None = None,
+    ) -> StrategyChainResult:
+        """
+        执行槽位值提取
+        
+        Args:
+            slot_key: 槽位键名
+            user_input: 用户输入
+            strategies: 提取策略链
+            slot_type: 槽位类型
+            validation_rule: 校验规则
+            
+        Returns:
+            StrategyChainResult: 提取结果
+        """
+        context = ExtractContext(
+            tenant_id=self._tenant_id,
+            slot_key=slot_key,
+            user_input=user_input,
+            slot_type=slot_type,
+            validation_rule=validation_rule,
+        )
+        
+        return await self._strategy_executor.execute_chain(
+            strategies=strategies,
+            context=context,
+        )
+    
+    def _get_source_for_strategy(self, strategy: str | None) -> str:
+        """根据策略获取来源"""
+        strategy_source_map = {
+            "rule": SlotSource.RULE_EXTRACTED.value,
+            "llm": SlotSource.LLM_INFERRED.value,
+            "user_input": SlotSource.USER_CONFIRMED.value,
+        }
+        return strategy_source_map.get(strategy or "", "unknown")
+    
+    def _get_confidence_for_strategy(self, source: str) -> float:
+        """根据来源获取置信度"""
+        confidence_map = {
+            SlotSource.USER_CONFIRMED.value: 1.0,
+            SlotSource.RULE_EXTRACTED.value: 0.9,
+            SlotSource.LLM_INFERRED.value: 0.7,
+            "context": 0.5,
+            SlotSource.DEFAULT.value: 0.3,
+        }
+        return confidence_map.get(source, 0.5)
+    
+    def _generate_confirmation_prompt(
+        self,
+        slot_key: str,
+        value: Any,
+    ) -> str:
+        """生成确认提示"""
+        return f"我理解您说的是「{value}」，对吗？"
+    
+    async def confirm_low_confidence_slot(
+        self,
+        slot_key: str,
+        confirmed: bool,
+    ) -> BackfillResult:
+        """
+        确认低置信度槽位
+        
+        Args:
+            slot_key: 槽位键名
+            confirmed: 用户是否确认
+            
+        Returns:
+            BackfillResult: 确认结果
+        """
+        if not self._session_id:
+            return BackfillResult(
+                status=BackfillStatus.SUCCESS,
+                slot_key=slot_key,
+            )
+        
+        aggregator = await self._get_state_aggregator()
+        
+        if confirmed:
+            updated_state = await aggregator.update_slot(
+                slot_key=slot_key,
+                source=SlotSource.USER_CONFIRMED.value,
+                confidence=1.0,
+            )
+            return BackfillResult(
+                status=BackfillStatus.SUCCESS,
+                slot_key=slot_key,
+                source=SlotSource.USER_CONFIRMED.value,
+                confidence=1.0,
+                updated_state=updated_state,
+            )
+        else:
+            await aggregator.clear_slot(slot_key)
+            ask_back_prompt = await self._slot_manager.get_ask_back_prompt(
+                self._tenant_id, slot_key
+            )
+            return BackfillResult(
+                status=BackfillStatus.VALIDATION_FAILED,
+                slot_key=slot_key,
+                ask_back_prompt=ask_back_prompt or f"请重新提供{slot_key}信息",
+            )
+
+
+def create_slot_backfill_service(
+    session: AsyncSession,
+    tenant_id: str,
+    session_id: str | None = None,
+) -> SlotBackfillService:
+    """
+    创建槽位回填服务实例
+    
+    Args:
+        session: 数据库会话
+        tenant_id: 租户 ID
+        session_id: 会话 ID
+        
+    Returns:
+        SlotBackfillService: 槽位回填服务实例
+    """
+    return SlotBackfillService(
+        session=session,
+        tenant_id=tenant_id,
+        session_id=session_id,
+    )

ai-service/app/services/mid/slot_extraction_integration.py

@@ -0,0 +1,368 @@
+"""
+Slot Extraction Integration Service.
+槽位提取集成服务 - 将自动提取能力接入主链路
+
+[AC-MRS-SLOT-EXTRACT-01] slot extraction 集成
+
+职责：
+1. 接入点：memory_recall 之后、KB 检索之前
+2. 执行策略链：rule -> llm -> user_input
+3. 抽取结果统一走 SlotManager 校验
+4. 提供 trace：extracted_slots、validation_pass/fail、ask_back_triggered
+"""
+
+import logging
+import time
+from dataclasses import dataclass, field
+from typing import Any
+
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.models.mid.schemas import SlotSource
+from app.services.mid.slot_backfill_service import (
+    BackfillResult,
+    BackfillStatus,
+    SlotBackfillService,
+)
+from app.services.mid.slot_manager import SlotManager
+from app.services.mid.slot_state_aggregator import SlotState, SlotStateAggregator
+from app.services.mid.slot_strategy_executor import (
+    ExtractContext,
+    SlotStrategyExecutor,
+    StrategyChainResult,
+)
+from app.services.slot_definition_service import SlotDefinitionService
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ExtractionTrace:
+    """
+    提取追踪信息
+    
+    Attributes:
+        slot_key: 槽位键名
+        strategy: 使用的策略
+        extracted_value: 提取的值
+        validation_passed: 校验是否通过
+        final_value: 最终值（校验后）
+        execution_time_ms: 执行时间
+        failure_reason: 失败原因
+    """
+    slot_key: str
+    strategy: str | None = None
+    extracted_value: Any = None
+    validation_passed: bool = False
+    final_value: Any = None
+    execution_time_ms: float = 0.0
+    failure_reason: str | None = None
+    
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "slot_key": self.slot_key,
+            "strategy": self.strategy,
+            "extracted_value": self.extracted_value,
+            "validation_passed": self.validation_passed,
+            "final_value": self.final_value,
+            "execution_time_ms": self.execution_time_ms,
+            "failure_reason": self.failure_reason,
+        }
+
+
+@dataclass
+class ExtractionResult:
+    """
+    提取结果
+    
+    Attributes:
+        success: 是否成功
+        extracted_slots: 成功提取的槽位
+        failed_slots: 提取失败的槽位
+        traces: 提取追踪信息列表
+        total_execution_time_ms: 总执行时间
+        ask_back_triggered: 是否触发追问
+        ask_back_prompts: 追问提示列表
+    """
+    success: bool = False
+    extracted_slots: dict[str, Any] = field(default_factory=dict)
+    failed_slots: list[str] = field(default_factory=list)
+    traces: list[ExtractionTrace] = field(default_factory=list)
+    total_execution_time_ms: float = 0.0
+    ask_back_triggered: bool = False
+    ask_back_prompts: list[str] = field(default_factory=list)
+    
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "success": self.success,
+            "extracted_slots": self.extracted_slots,
+            "failed_slots": self.failed_slots,
+            "traces": [t.to_dict() for t in self.traces],
+            "total_execution_time_ms": self.total_execution_time_ms,
+            "ask_back_triggered": self.ask_back_triggered,
+            "ask_back_prompts": self.ask_back_prompts,
+        }
+
+
+class SlotExtractionIntegration:
+    """
+    [AC-MRS-SLOT-EXTRACT-01] 槽位提取集成服务
+    
+    将自动提取能力接入主链路：
+    - 接入点：memory_recall 之后、KB 检索之前
+    - 执行策略链：rule -> llm -> user_input
+    - 抽取结果统一走 SlotManager 校验
+    - 提供 trace
+    """
+    
+    DEFAULT_STRATEGIES = ["rule", "llm"]
+    
+    def __init__(
+        self,
+        session: AsyncSession,
+        tenant_id: str,
+        session_id: str | None = None,
+        slot_manager: SlotManager | None = None,
+        strategy_executor: SlotStrategyExecutor | None = None,
+    ):
+        self._session = session
+        self._tenant_id = tenant_id
+        self._session_id = session_id
+        self._slot_manager = slot_manager or SlotManager(session=session)
+        self._strategy_executor = strategy_executor or SlotStrategyExecutor()
+        self._slot_def_service = SlotDefinitionService(session)
+        self._backfill_service: SlotBackfillService | None = None
+    
+    async def _get_backfill_service(self) -> SlotBackfillService:
+        """获取回填服务"""
+        if self._backfill_service is None:
+            self._backfill_service = SlotBackfillService(
+                session=self._session,
+                tenant_id=self._tenant_id,
+                session_id=self._session_id,
+                slot_manager=self._slot_manager,
+                strategy_executor=self._strategy_executor,
+            )
+        return self._backfill_service
+    
+    async def extract_and_fill(
+        self,
+        user_input: str,
+        target_slots: list[str] | None = None,
+        strategies: list[str] | None = None,
+        slot_state: SlotState | None = None,
+    ) -> ExtractionResult:
+        """
+        执行提取并填充槽位
+        
+        Args:
+            user_input: 用户输入
+            target_slots: 目标槽位列表（为空则提取所有必填槽位）
+            strategies: 提取策略链（默认 rule -> llm）
+            slot_state: 当前槽位状态（用于识别缺失槽位）
+            
+        Returns:
+            ExtractionResult: 提取结果
+        """
+        start_time = time.time()
+        
+        strategies = strategies or self.DEFAULT_STRATEGIES
+        
+        if target_slots is None:
+            target_slots = await self._get_missing_required_slots(slot_state)
+        
+        if not target_slots:
+            return ExtractionResult(success=True)
+        
+        result = ExtractionResult()
+        
+        for slot_key in target_slots:
+            trace = await self._extract_single_slot(
+                slot_key=slot_key,
+                user_input=user_input,
+                strategies=strategies,
+            )
+            result.traces.append(trace)
+            
+            if trace.validation_passed and trace.final_value is not None:
+                result.extracted_slots[slot_key] = trace.final_value
+            else:
+                result.failed_slots.append(slot_key)
+                if trace.failure_reason:
+                    ask_back_prompt = await self._slot_manager.get_ask_back_prompt(
+                        self._tenant_id, slot_key
+                    )
+                    if ask_back_prompt:
+                        result.ask_back_prompts.append(ask_back_prompt)
+        
+        result.total_execution_time_ms = (time.time() - start_time) * 1000
+        result.success = len(result.extracted_slots) > 0
+        result.ask_back_triggered = len(result.ask_back_prompts) > 0
+        
+        if result.extracted_slots and self._session_id:
+            await self._save_extracted_slots(result.extracted_slots)
+        
+        logger.info(
+            f"[AC-MRS-SLOT-EXTRACT-01] Extraction completed: "
+            f"tenant={self._tenant_id}, extracted={len(result.extracted_slots)}, "
+            f"failed={len(result.failed_slots)}, time_ms={result.total_execution_time_ms:.2f}"
+        )
+        
+        return result
+    
+    async def _extract_single_slot(
+        self,
+        slot_key: str,
+        user_input: str,
+        strategies: list[str],
+    ) -> ExtractionTrace:
+        """提取单个槽位"""
+        start_time = time.time()
+        trace = ExtractionTrace(slot_key=slot_key)
+        
+        slot_def = await self._slot_def_service.get_slot_definition_by_key(
+            self._tenant_id, slot_key
+        )
+        
+        if not slot_def:
+            trace.failure_reason = "Slot definition not found"
+            trace.execution_time_ms = (time.time() - start_time) * 1000
+            return trace
+        
+        context = ExtractContext(
+            tenant_id=self._tenant_id,
+            slot_key=slot_key,
+            user_input=user_input,
+            slot_type=slot_def.type,
+            validation_rule=slot_def.validation_rule,
+            session_id=self._session_id,
+        )
+        
+        chain_result = await self._strategy_executor.execute_chain(
+            strategies=strategies,
+            context=context,
+            ask_back_prompt=slot_def.ask_back_prompt,
+        )
+        
+        trace.strategy = chain_result.final_strategy
+        trace.extracted_value = chain_result.final_value
+        trace.execution_time_ms = (time.time() - start_time) * 1000
+        
+        if not chain_result.success:
+            trace.failure_reason = "Extraction failed"
+            if chain_result.steps:
+                last_step = chain_result.steps[-1]
+                trace.failure_reason = last_step.failure_reason
+            return trace
+        
+        backfill_service = await self._get_backfill_service()
+        source = self._get_source_for_strategy(chain_result.final_strategy)
+        
+        backfill_result = await backfill_service.backfill_single_slot(
+            slot_key=slot_key,
+            candidate_value=chain_result.final_value,
+            source=source,
+            confidence=self._get_confidence_for_source(source),
+        )
+        
+        trace.validation_passed = backfill_result.is_success()
+        trace.final_value = backfill_result.normalized_value
+        
+        if not backfill_result.is_success():
+            trace.failure_reason = backfill_result.error_message or "Validation failed"
+        
+        return trace
+    
+    async def _get_missing_required_slots(
+        self,
+        slot_state: SlotState | None,
+    ) -> list[str]:
+        """获取缺失的必填槽位"""
+        if slot_state and slot_state.missing_required_slots:
+            return [
+                s.get("slot_key")
+                for s in slot_state.missing_required_slots
+                if s.get("slot_key")
+            ]
+        
+        required_defs = await self._slot_def_service.list_slot_definitions(
+            tenant_id=self._tenant_id,
+            required=True,
+        )
+        
+        return [d.slot_key for d in required_defs]
+    
+    async def _save_extracted_slots(
+        self,
+        extracted_slots: dict[str, Any],
+    ) -> None:
+        """保存提取的槽位到缓存"""
+        if not self._session_id:
+            return
+        
+        aggregator = SlotStateAggregator(
+            session=self._session,
+            tenant_id=self._tenant_id,
+            session_id=self._session_id,
+        )
+        
+        for slot_key, value in extracted_slots.items():
+            await aggregator.update_slot(
+                slot_key=slot_key,
+                value=value,
+                source=SlotSource.RULE_EXTRACTED.value,
+                confidence=0.9,
+            )
+    
+    def _get_source_for_strategy(self, strategy: str | None) -> str:
+        """根据策略获取来源"""
+        strategy_source_map = {
+            "rule": SlotSource.RULE_EXTRACTED.value,
+            "llm": SlotSource.LLM_INFERRED.value,
+            "user_input": SlotSource.USER_CONFIRMED.value,
+        }
+        return strategy_source_map.get(strategy or "", "unknown")
+    
+    def _get_confidence_for_source(self, source: str) -> float:
+        """根据来源获取置信度"""
+        confidence_map = {
+            SlotSource.USER_CONFIRMED.value: 1.0,
+            SlotSource.RULE_EXTRACTED.value: 0.9,
+            SlotSource.LLM_INFERRED.value: 0.7,
+        }
+        return confidence_map.get(source, 0.5)
+
+
+async def integrate_slot_extraction(
+    session: AsyncSession,
+    tenant_id: str,
+    session_id: str,
+    user_input: str,
+    slot_state: SlotState | None = None,
+    strategies: list[str] | None = None,
+) -> ExtractionResult:
+    """
+    便捷函数：集成槽位提取
+    
+    Args:
+        session: 数据库会话
+        tenant_id: 租户 ID
+        session_id: 会话 ID
+        user_input: 用户输入
+        slot_state: 当前槽位状态
+        strategies: 提取策略链
+        
+    Returns:
+        ExtractionResult: 提取结果
+    """
+    integration = SlotExtractionIntegration(
+        session=session,
+        tenant_id=tenant_id,
+        session_id=session_id,
+    )
+    
+    return await integration.extract_and_fill(
+        user_input=user_input,
+        slot_state=slot_state,
+        strategies=strategies,
+    )

ai-service/app/services/mid/slot_manager.py

@@ -0,0 +1,379 @@
+"""
+Slot Manager Service.
+槽位管理服务 - 统一槽位写入入口，集成校验逻辑
+
+职责：
+1. 在槽位值写入前执行校验
+2. 管理槽位值的来源和置信度
+3. 提供槽位写入的统一接口
+4. 返回校验失败时的追问提示
+"""
+
+import logging
+from typing import Any
+
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.models.entities import SlotDefinition
+from app.models.mid.schemas import SlotSource
+from app.services.mid.slot_validation_service import (
+    BatchValidationResult,
+    SlotValidationError,
+    SlotValidationService,
+)
+from app.services.slot_definition_service import SlotDefinitionService
+
+logger = logging.getLogger(__name__)
+
+
+class SlotWriteResult:
+    """
+    槽位写入结果
+
+    Attributes:
+        success: 是否成功（校验通过并写入）
+        slot_key: 槽位键名
+        value: 最终写入的值
+        error: 校验错误信息（校验失败时）
+        ask_back_prompt: 追问提示语（校验失败时）
+    """
+
+    def __init__(
+        self,
+        success: bool,
+        slot_key: str,
+        value: Any | None = None,
+        error: SlotValidationError | None = None,
+        ask_back_prompt: str | None = None,
+    ):
+        self.success = success
+        self.slot_key = slot_key
+        self.value = value
+        self.error = error
+        self.ask_back_prompt = ask_back_prompt
+
+    def to_dict(self) -> dict[str, Any]:
+        """转换为字典"""
+        result = {
+            "success": self.success,
+            "slot_key": self.slot_key,
+            "value": self.value,
+        }
+        if self.error:
+            result["error"] = {
+                "error_code": self.error.error_code,
+                "error_message": self.error.error_message,
+            }
+        if self.ask_back_prompt:
+            result["ask_back_prompt"] = self.ask_back_prompt
+        return result
+
+
+class SlotManager:
+    """
+    槽位管理器
+
+    统一槽位写入入口，在写入前执行校验。
+    支持从 SlotDefinition 加载校验规则并执行。
+    """
+
+    def __init__(
+        self,
+        session: AsyncSession | None = None,
+        validation_service: SlotValidationService | None = None,
+        slot_def_service: SlotDefinitionService | None = None,
+    ):
+        """
+        初始化槽位管理器
+
+        Args:
+            session: 数据库会话
+            validation_service: 校验服务实例
+            slot_def_service: 槽位定义服务实例
+        """
+        self._session = session
+        self._validation_service = validation_service or SlotValidationService()
+        self._slot_def_service = slot_def_service
+        self._slot_def_cache: dict[str, SlotDefinition | None] = {}
+
+    async def write_slot(
+        self,
+        tenant_id: str,
+        slot_key: str,
+        value: Any,
+        source: SlotSource = SlotSource.USER_CONFIRMED,
+        confidence: float = 1.0,
+        skip_validation: bool = False,
+    ) -> SlotWriteResult:
+        """
+        写入单个槽位值（带校验）
+
+        执行流程：
+        1. 加载槽位定义
+        2. 执行校验（如果未跳过）
+        3. 返回校验结果
+
+        Args:
+            tenant_id: 租户 ID
+            slot_key: 槽位键名
+            value: 槽位值
+            source: 值来源
+            confidence: 置信度
+            skip_validation: 是否跳过校验（用于特殊场景）
+
+        Returns:
+            SlotWriteResult: 写入结果
+        """
+        # 加载槽位定义
+        slot_def = await self._get_slot_definition(tenant_id, slot_key)
+
+        # 如果没有定义且非跳过校验，允许写入（动态槽位）
+        if slot_def is None and skip_validation:
+            logger.debug(
+                f"[SlotManager] Writing slot without definition: "
+                f"tenant_id={tenant_id}, slot_key={slot_key}"
+            )
+            return SlotWriteResult(
+                success=True,
+                slot_key=slot_key,
+                value=value,
+            )
+
+        if slot_def is None:
+            # 未定义槽位，允许写入但记录日志
+            logger.info(
+                f"[SlotManager] Slot definition not found, allowing write: "
+                f"tenant_id={tenant_id}, slot_key={slot_key}"
+            )
+            return SlotWriteResult(
+                success=True,
+                slot_key=slot_key,
+                value=value,
+            )
+
+        # 执行校验
+        if not skip_validation:
+            validation_result = self._validation_service.validate_slot_value(
+                slot_def, value, tenant_id
+            )
+
+            if not validation_result.ok:
+                logger.info(
+                    f"[SlotManager] Slot validation failed: "
+                    f"tenant_id={tenant_id}, slot_key={slot_key}, "
+                    f"error_code={validation_result.error_code}"
+                )
+                return SlotWriteResult(
+                    success=False,
+                    slot_key=slot_key,
+                    error=SlotValidationError(
+                        slot_key=slot_key,
+                        error_code=validation_result.error_code or "VALIDATION_FAILED",
+                        error_message=validation_result.error_message or "校验失败",
+                        ask_back_prompt=validation_result.ask_back_prompt,
+                    ),
+                    ask_back_prompt=validation_result.ask_back_prompt,
+                )
+
+            # 使用归一化后的值
+            value = validation_result.normalized_value
+
+        logger.debug(
+            f"[SlotManager] Slot validation passed: "
+            f"tenant_id={tenant_id}, slot_key={slot_key}"
+        )
+
+        return SlotWriteResult(
+            success=True,
+            slot_key=slot_key,
+            value=value,
+        )
+
+    async def write_slots(
+        self,
+        tenant_id: str,
+        values: dict[str, Any],
+        source: SlotSource = SlotSource.USER_CONFIRMED,
+        confidence: float = 1.0,
+        skip_validation: bool = False,
+    ) -> BatchValidationResult:
+        """
+        批量写入槽位值（带校验）
+
+        Args:
+            tenant_id: 租户 ID
+            values: 槽位值字典 {slot_key: value}
+            source: 值来源
+            confidence: 置信度
+            skip_validation: 是否跳过校验
+
+        Returns:
+            BatchValidationResult: 批量校验结果
+        """
+        if skip_validation:
+            return BatchValidationResult(
+                ok=True,
+                validated_values=values,
+            )
+
+        # 加载所有相关槽位定义
+        slot_defs = await self._get_slot_definitions(tenant_id, list(values.keys()))
+
+        # 执行批量校验
+        result = self._validation_service.validate_slots(
+            slot_defs, values, tenant_id
+        )
+
+        if not result.ok:
+            logger.info(
+                f"[SlotManager] Batch slot validation failed: "
+                f"tenant_id={tenant_id}, errors={[e.slot_key for e in result.errors]}"
+            )
+        else:
+            logger.debug(
+                f"[SlotManager] Batch slot validation passed: "
+                f"tenant_id={tenant_id}, slots={list(values.keys())}"
+            )
+
+        return result
+
+    async def validate_before_write(
+        self,
+        tenant_id: str,
+        slot_key: str,
+        value: Any,
+    ) -> tuple[bool, SlotValidationError | None]:
+        """
+        在写入前预校验槽位值
+
+        Args:
+            tenant_id: 租户 ID
+            slot_key: 槽位键名
+            value: 槽位值
+
+        Returns:
+            Tuple of (是否通过, 错误信息)
+        """
+        slot_def = await self._get_slot_definition(tenant_id, slot_key)
+
+        if slot_def is None:
+            # 未定义槽位，视为通过
+            return True, None
+
+        result = self._validation_service.validate_slot_value(
+            slot_def, value, tenant_id
+        )
+
+        if result.ok:
+            return True, None
+
+        return False, SlotValidationError(
+            slot_key=slot_key,
+            error_code=result.error_code or "VALIDATION_FAILED",
+            error_message=result.error_message or "校验失败",
+            ask_back_prompt=result.ask_back_prompt,
+        )
+
+    async def get_ask_back_prompt(
+        self,
+        tenant_id: str,
+        slot_key: str,
+    ) -> str | None:
+        """
+        获取槽位的追问提示语
+
+        Args:
+            tenant_id: 租户 ID
+            slot_key: 槽位键名
+
+        Returns:
+            追问提示语或 None
+        """
+        slot_def = await self._get_slot_definition(tenant_id, slot_key)
+        if slot_def is None:
+            return None
+
+        if isinstance(slot_def, SlotDefinition):
+            return slot_def.ask_back_prompt
+        return slot_def.get("ask_back_prompt")
+
+    async def _get_slot_definition(
+        self,
+        tenant_id: str,
+        slot_key: str,
+    ) -> SlotDefinition | dict[str, Any] | None:
+        """
+        获取槽位定义（带缓存）
+
+        Args:
+            tenant_id: 租户 ID
+            slot_key: 槽位键名
+
+        Returns:
+            槽位定义或 None
+        """
+        cache_key = f"{tenant_id}:{slot_key}"
+
+        if cache_key in self._slot_def_cache:
+            return self._slot_def_cache[cache_key]
+
+        slot_def = None
+        if self._slot_def_service:
+            slot_def = await self._slot_def_service.get_slot_definition_by_key(
+                tenant_id, slot_key
+            )
+        elif self._session:
+            service = SlotDefinitionService(self._session)
+            slot_def = await service.get_slot_definition_by_key(tenant_id, slot_key)
+
+        self._slot_def_cache[cache_key] = slot_def
+        return slot_def
+
+    async def _get_slot_definitions(
+        self,
+        tenant_id: str,
+        slot_keys: list[str],
+    ) -> list[SlotDefinition | dict[str, Any]]:
+        """
+        批量获取槽位定义
+
+        Args:
+            tenant_id: 租户 ID
+            slot_keys: 槽位键名列表
+
+        Returns:
+            槽位定义列表
+        """
+        slot_defs = []
+        for key in slot_keys:
+            slot_def = await self._get_slot_definition(tenant_id, key)
+            if slot_def:
+                slot_defs.append(slot_def)
+        return slot_defs
+
+    def clear_cache(self) -> None:
+        """清除槽位定义缓存"""
+        self._slot_def_cache.clear()
+
+
+def create_slot_manager(
+    session: AsyncSession | None = None,
+    validation_service: SlotValidationService | None = None,
+    slot_def_service: SlotDefinitionService | None = None,
+) -> SlotManager:
+    """
+    创建槽位管理器实例
+
+    Args:
+        session: 数据库会话
+        validation_service: 校验服务实例
+        slot_def_service: 槽位定义服务实例
+
+    Returns:
+        SlotManager: 槽位管理器实例
+    """
+    return SlotManager(
+        session=session,
+        validation_service=validation_service,
+        slot_def_service=slot_def_service,
+    )

ai-service/app/services/mid/slot_state_aggregator.py

@@ -0,0 +1,562 @@
+"""
+Slot State Aggregator Service.
+槽位状态聚合服务 - 统一维护本轮槽位状态
+
+职责：
+1. 聚合来自 memory_recall 的槽位值
+2. 叠加本轮输入的槽位值
+3. 识别缺失的必填槽位
+4. 支持槽位与元数据字段的关联映射
+5. 为 KB 检索过滤提供统一的槽位值来源
+6. [AC-MRS-SLOT-CACHE-01] 多轮状态持久化
+
+[AC-MRS-SLOT-META-01] 槽位与元数据关联机制
+"""
+
+import logging
+from dataclasses import dataclass, field
+from typing import Any
+
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.models.mid.schemas import MemorySlot, SlotSource
+from app.services.cache.slot_state_cache import (
+    CachedSlotState,
+    CachedSlotValue,
+    get_slot_state_cache,
+)
+from app.services.mid.slot_manager import SlotManager
+from app.services.slot_definition_service import SlotDefinitionService
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class SlotState:
+    """
+    槽位状态聚合结果
+    
+    Attributes:
+        filled_slots: 已填充的槽位值字典 {slot_key: value}
+        missing_required_slots: 缺失的必填槽位列表
+        slot_sources: 槽位值来源字典 {slot_key: source}
+        slot_confidence: 槽位置信度字典 {slot_key: confidence}
+        slot_to_field_map: 槽位到元数据字段的映射 {slot_key: field_key}
+    """
+    filled_slots: dict[str, Any] = field(default_factory=dict)
+    missing_required_slots: list[dict[str, str]] = field(default_factory=list)
+    slot_sources: dict[str, str] = field(default_factory=dict)
+    slot_confidence: dict[str, float] = field(default_factory=dict)
+    slot_to_field_map: dict[str, str] = field(default_factory=dict)
+
+    def get_value_for_filter(self, field_key: str) -> Any:
+        """
+        获取用于 KB 过滤的字段值
+        
+        优先从 slot_to_field_map 反向查找
+        """
+        # 直接匹配
+        if field_key in self.filled_slots:
+            return self.filled_slots[field_key]
+        
+        # 通过 slot_to_field_map 反向查找
+        for slot_key, mapped_field_key in self.slot_to_field_map.items():
+            if mapped_field_key == field_key and slot_key in self.filled_slots:
+                return self.filled_slots[slot_key]
+        
+        return None
+
+    def to_debug_info(self) -> dict[str, Any]:
+        """转换为调试信息字典"""
+        return {
+            "filled_slots": self.filled_slots,
+            "missing_required_slots": self.missing_required_slots,
+            "slot_sources": self.slot_sources,
+            "slot_to_field_map": self.slot_to_field_map,
+        }
+
+
+class SlotStateAggregator:
+    """
+    [AC-MRS-SLOT-META-01] 槽位状态聚合器
+    
+    统一维护本轮槽位状态，支持：
+    - 从 memory_recall 初始化槽位
+    - 叠加本轮输入的槽位值
+    - 识别缺失的必填槽位
+    - 建立槽位与元数据字段的关联
+    - [AC-MRS-SLOT-CACHE-01] 多轮状态持久化
+    """
+
+    def __init__(
+        self,
+        session: AsyncSession,
+        tenant_id: str,
+        slot_manager: SlotManager | None = None,
+        session_id: str | None = None,
+    ):
+        self._session = session
+        self._tenant_id = tenant_id
+        self._session_id = session_id
+        self._slot_manager = slot_manager or SlotManager(session=session)
+        self._slot_def_service = SlotDefinitionService(session)
+        self._cache = get_slot_state_cache()
+
+    async def aggregate(
+        self,
+        memory_slots: dict[str, MemorySlot] | None = None,
+        current_input_slots: dict[str, Any] | None = None,
+        context: dict[str, Any] | None = None,
+        use_cache: bool = True,
+        scene_slot_context: Any = None,  # [AC-SCENE-SLOT-02] 场景槽位上下文
+    ) -> SlotState:
+        """
+        聚合槽位状态
+        
+        执行流程：
+        1. [AC-MRS-SLOT-CACHE-01] 从缓存加载已有状态
+        2. 从 memory_slots 初始化已填充槽位
+        3. 叠加 current_input_slots（优先级更高）
+        4. 从 context 提取槽位值
+        5. 识别缺失的必填槽位
+        6. 建立槽位与元数据字段的关联映射
+        7. [AC-MRS-SLOT-CACHE-01] 回写缓存
+        
+        Args:
+            memory_slots: 从 memory_recall 召回的槽位
+            current_input_slots: 本轮输入的槽位值
+            context: 上下文信息，可能包含槽位值
+            use_cache: 是否使用缓存（默认 True）
+            
+        Returns:
+            SlotState: 聚合后的槽位状态
+        """
+        state = SlotState()
+        
+        # [AC-MRS-SLOT-CACHE-01] 1. 从缓存加载已有状态
+        cached_state = None
+        if use_cache and self._session_id:
+            cached_state = await self._cache.get(self._tenant_id, self._session_id)
+            if cached_state:
+                for slot_key, cached_value in cached_state.filled_slots.items():
+                    state.filled_slots[slot_key] = cached_value.value
+                    state.slot_sources[slot_key] = cached_value.source
+                    state.slot_confidence[slot_key] = cached_value.confidence
+                state.slot_to_field_map = cached_state.slot_to_field_map.copy()
+                logger.info(
+                    f"[AC-MRS-SLOT-CACHE-01] Loaded from cache: "
+                    f"tenant={self._tenant_id}, session={self._session_id}, "
+                    f"slots={list(state.filled_slots.keys())}"
+                )
+        
+        # 2. 从 memory_slots 初始化
+        if memory_slots:
+            for slot_key, memory_slot in memory_slots.items():
+                state.filled_slots[slot_key] = memory_slot.value
+                state.slot_sources[slot_key] = memory_slot.source.value
+                state.slot_confidence[slot_key] = memory_slot.confidence
+            logger.info(
+                f"[AC-MRS-SLOT-META-01] Initialized from memory: "
+                f"tenant={self._tenant_id}, slots={list(memory_slots.keys())}"
+            )
+        
+        # 3. 叠加本轮输入（优先级更高）
+        if current_input_slots:
+            for slot_key, value in current_input_slots.items():
+                if value is not None:
+                    state.filled_slots[slot_key] = value
+                    state.slot_sources[slot_key] = SlotSource.USER_CONFIRMED.value
+                    state.slot_confidence[slot_key] = 1.0
+            logger.info(
+                f"[AC-MRS-SLOT-META-01] Merged current input: "
+                f"tenant={self._tenant_id}, slots={list(current_input_slots.keys())}"
+            )
+        
+        # 4. 从 context 提取槽位值（优先级最低）
+        if context:
+            context_slots = self._extract_slots_from_context(context)
+            for slot_key, value in context_slots.items():
+                if slot_key not in state.filled_slots and value is not None:
+                    state.filled_slots[slot_key] = value
+                    state.slot_sources[slot_key] = "context"
+                    state.slot_confidence[slot_key] = 0.5
+        
+        # 5. 加载槽位定义并建立关联
+        await self._build_slot_mappings(state)
+        
+        # 6. 识别缺失的必填槽位
+        await self._identify_missing_required_slots(state, scene_slot_context)
+        
+        # [AC-MRS-SLOT-CACHE-01] 7. 回写缓存
+        if use_cache and self._session_id:
+            await self._save_to_cache(state)
+        
+        logger.info(
+            f"[AC-MRS-SLOT-META-01] Slot state aggregated: "
+            f"tenant={self._tenant_id}, filled={len(state.filled_slots)}, "
+            f"missing={len(state.missing_required_slots)}"
+        )
+        
+        return state
+    
+    async def _save_to_cache(self, state: SlotState) -> None:
+        """
+        [AC-MRS-SLOT-CACHE-01] 保存槽位状态到缓存
+        """
+        if not self._session_id:
+            return
+        
+        cached_slots = {}
+        for slot_key, value in state.filled_slots.items():
+            source = state.slot_sources.get(slot_key, "unknown")
+            confidence = state.slot_confidence.get(slot_key, 1.0)
+            cached_slots[slot_key] = CachedSlotValue(
+                value=value,
+                source=source,
+                confidence=confidence,
+            )
+        
+        cached_state = CachedSlotState(
+            filled_slots=cached_slots,
+            slot_to_field_map=state.slot_to_field_map.copy(),
+        )
+        
+        await self._cache.set(self._tenant_id, self._session_id, cached_state)
+        logger.debug(
+            f"[AC-MRS-SLOT-CACHE-01] Saved to cache: "
+            f"tenant={self._tenant_id}, session={self._session_id}"
+        )
+    
+    async def update_slot(
+        self,
+        slot_key: str,
+        value: Any,
+        source: str = "user_confirmed",
+        confidence: float = 1.0,
+    ) -> SlotState | None:
+        """
+        [AC-MRS-SLOT-CACHE-01] 更新单个槽位值并保存到缓存
+        
+        Args:
+            slot_key: 槽位键名
+            value: 槽位值
+            source: 值来源
+            confidence: 置信度
+            
+        Returns:
+            更新后的槽位状态，如果没有 session_id 则返回 None
+        """
+        if not self._session_id:
+            return None
+        
+        cached_value = CachedSlotValue(
+            value=value,
+            source=source,
+            confidence=confidence,
+        )
+        
+        cached_state = await self._cache.merge_and_set(
+            tenant_id=self._tenant_id,
+            session_id=self._session_id,
+            new_slots={slot_key: cached_value},
+        )
+        
+        state = SlotState()
+        state.filled_slots = cached_state.get_simple_filled_slots()
+        state.slot_sources = cached_state.get_slot_sources()
+        state.slot_confidence = cached_state.get_slot_confidence()
+        state.slot_to_field_map = cached_state.slot_to_field_map.copy()
+        
+        await self._identify_missing_required_slots(state)
+        
+        return state
+    
+    async def clear_slot(self, slot_key: str) -> bool:
+        """
+        [AC-MRS-SLOT-CACHE-01] 清除单个槽位值
+        
+        Args:
+            slot_key: 槽位键名
+            
+        Returns:
+            是否成功
+        """
+        if not self._session_id:
+            return False
+        
+        return await self._cache.clear_slot(
+            tenant_id=self._tenant_id,
+            session_id=self._session_id,
+            slot_key=slot_key,
+        )
+    
+    async def clear_all_slots(self) -> bool:
+        """
+        [AC-MRS-SLOT-CACHE-01] 清除所有槽位状态
+        
+        Returns:
+            是否成功
+        """
+        if not self._session_id:
+            return False
+        
+        return await self._cache.delete(
+            tenant_id=self._tenant_id,
+            session_id=self._session_id,
+        )
+
+    def _extract_slots_from_context(self, context: dict[str, Any]) -> dict[str, Any]:
+        """
+        从上下文中提取可能的槽位值
+        
+        常见的槽位值可能存在于：
+        - scene
+        - product_line
+        - region
+        - grade
+        等字段
+        """
+        slots = {}
+        slot_candidates = [
+            "scene", "product_line", "region", "grade", 
+            "category", "type", "status", "priority"
+        ]
+        
+        for key in slot_candidates:
+            if key in context and context[key] is not None:
+                slots[key] = context[key]
+        
+        return slots
+
+    async def _build_slot_mappings(self, state: SlotState) -> None:
+        """
+        建立槽位与元数据字段的关联映射
+        
+        通过 linked_field_id 关联 SlotDefinition 和 MetadataFieldDefinition
+        """
+        try:
+            # 获取所有槽位定义
+            slot_defs = await self._slot_def_service.list_slot_definitions(
+                tenant_id=self._tenant_id
+            )
+            
+            for slot_def in slot_defs:
+                if slot_def.linked_field_id:
+                    # 获取关联的元数据字段
+                    from app.services.metadata_field_definition_service import (
+                        MetadataFieldDefinitionService
+                    )
+                    field_service = MetadataFieldDefinitionService(self._session)
+                    linked_field = await field_service.get_field_definition(
+                        tenant_id=self._tenant_id,
+                        field_id=str(slot_def.linked_field_id)
+                    )
+                    
+                    if linked_field:
+                        state.slot_to_field_map[slot_def.slot_key] = linked_field.field_key
+                        
+                        # 检查类型一致性并告警
+                        await self._check_type_consistency(
+                            slot_def, linked_field, state
+                        )
+        except Exception as e:
+            logger.warning(
+                f"[AC-MRS-SLOT-META-01] Failed to build slot mappings: {e}"
+            )
+
+    async def _check_type_consistency(
+        self,
+        slot_def: Any,
+        linked_field: Any,
+        state: SlotState,
+    ) -> None:
+        """
+        检查槽位与关联元数据字段的类型一致性
+        
+        当不一致时记录告警日志（不强拦截）
+        """
+        # 检查类型一致性
+        if slot_def.type != linked_field.type:
+            logger.warning(
+                f"[AC-MRS-SLOT-META-01] Type mismatch: "
+                f"slot='{slot_def.slot_key}' type={slot_def.type} vs "
+                f"field='{linked_field.field_key}' type={linked_field.type}"
+            )
+        
+        # 检查 required 一致性
+        if slot_def.required != linked_field.required:
+            logger.warning(
+                f"[AC-MRS-SLOT-META-01] Required mismatch: "
+                f"slot='{slot_def.slot_key}' required={slot_def.required} vs "
+                f"field='{linked_field.field_key}' required={linked_field.required}"
+            )
+        
+        # 检查 options 一致性（对于 enum/array_enum 类型）
+        if slot_def.type in ["enum", "array_enum"]:
+            slot_options = set(slot_def.options or [])
+            field_options = set(linked_field.options or [])
+            if slot_options != field_options:
+                logger.warning(
+                    f"[AC-MRS-SLOT-META-01] Options mismatch: "
+                    f"slot='{slot_def.slot_key}' options={slot_options} vs "
+                    f"field='{linked_field.field_key}' options={field_options}"
+                )
+
+    async def _identify_missing_required_slots(
+        self,
+        state: SlotState,
+        scene_slot_context: Any = None,  # [AC-SCENE-SLOT-02] 场景槽位上下文
+    ) -> None:
+        """
+        识别缺失的必填槽位
+        
+        基于 SlotDefinition 的 required 字段和 linked_field 的 required 字段
+        [AC-SCENE-SLOT-02] 当有场景槽位上下文时，优先使用场景定义的必填槽位
+        """
+        try:
+            # [AC-SCENE-SLOT-02] 如果有场景槽位上下文，使用场景定义的必填槽位
+            if scene_slot_context:
+                scene_required_keys = set(scene_slot_context.get_required_slot_keys())
+                logger.info(
+                    f"[AC-SCENE-SLOT-02] Using scene required slots: "
+                    f"scene={scene_slot_context.scene_key}, "
+                    f"required_keys={scene_required_keys}"
+                )
+                
+                # 获取场景中定义的所有槽位
+                all_slot_defs = await self._slot_def_service.list_slot_definitions(
+                    tenant_id=self._tenant_id,
+                )
+                slot_def_map = {slot.slot_key: slot for slot in all_slot_defs}
+                
+                for slot_key in scene_required_keys:
+                    if slot_key not in state.filled_slots:
+                        slot_def = slot_def_map.get(slot_key)
+                        ask_back_prompt = slot_def.ask_back_prompt if slot_def else None
+                        
+                        if not ask_back_prompt:
+                            ask_back_prompt = f"请提供{slot_key}信息"
+                        
+                        missing_info = {
+                            "slot_key": slot_key,
+                            "label": slot_key,
+                            "reason": "scene_required_slot_missing",
+                            "ask_back_prompt": ask_back_prompt,
+                            "scene": scene_slot_context.scene_key,
+                        }
+                        
+                        if slot_def and slot_def.linked_field_id:
+                            missing_info["linked_field_id"] = str(slot_def.linked_field_id)
+                        
+                        state.missing_required_slots.append(missing_info)
+                
+                return
+            
+            # 获取所有 required 的槽位定义
+            required_slot_defs = await self._slot_def_service.list_slot_definitions(
+                tenant_id=self._tenant_id,
+                required=True,
+            )
+            
+            for slot_def in required_slot_defs:
+                if slot_def.slot_key not in state.filled_slots:
+                    # 获取追问提示
+                    ask_back_prompt = slot_def.ask_back_prompt
+                    
+                    # 如果没有配置追问提示，使用通用模板
+                    if not ask_back_prompt:
+                        ask_back_prompt = f"请提供{slot_def.slot_key}信息"
+                    
+                    missing_info = {
+                        "slot_key": slot_def.slot_key,
+                        "label": slot_def.slot_key,
+                        "reason": "required_slot_missing",
+                        "ask_back_prompt": ask_back_prompt,
+                    }
+                    
+                    # 如果有关联字段，使用字段的 label
+                    if slot_def.linked_field_id:
+                        from app.services.metadata_field_definition_service import (
+                            MetadataFieldDefinitionService
+                        )
+                        field_service = MetadataFieldDefinitionService(self._session)
+                        linked_field = await field_service.get_field_definition(
+                            tenant_id=self._tenant_id,
+                            field_id=str(slot_def.linked_field_id)
+                        )
+                        if linked_field:
+                            missing_info["label"] = linked_field.label
+                            missing_info["field_key"] = linked_field.field_key
+                    
+                    state.missing_required_slots.append(missing_info)
+                    
+                    logger.info(
+                        f"[AC-MRS-SLOT-META-01] Missing required slot: "
+                        f"slot_key={slot_def.slot_key}"
+                    )
+        except Exception as e:
+            logger.warning(
+                f"[AC-MRS-SLOT-META-01] Failed to identify missing slots: {e}"
+            )
+
+    async def generate_ask_back_response(
+        self,
+        state: SlotState,
+        missing_slot_key: str | None = None,
+    ) -> str | None:
+        """
+        生成追问响应文案
+        
+        Args:
+            state: 当前槽位状态
+            missing_slot_key: 指定要追问的槽位键名，为 None 时追问第一个缺失槽位
+            
+        Returns:
+            追问文案或 None（如果没有缺失槽位）
+        """
+        if not state.missing_required_slots:
+            return None
+        
+        missing_info = None
+        
+        # 如果指定了槽位键名，查找对应的追问提示
+        if missing_slot_key:
+            for missing in state.missing_required_slots:
+                if missing.get("slot_key") == missing_slot_key:
+                    missing_info = missing
+                    break
+        else:
+            # 使用第一个缺失槽位
+            missing_info = state.missing_required_slots[0]
+        
+        if missing_info is None:
+            return None
+        
+        # 优先使用配置的 ask_back_prompt
+        ask_back_prompt = missing_info.get("ask_back_prompt")
+        if ask_back_prompt:
+            return ask_back_prompt
+        
+        # 使用通用模板
+        label = missing_info.get("label", missing_info.get("slot_key", "相关信息"))
+        return f"为了更好地为您提供帮助，请告诉我您的{label}。"
+
+
+def create_slot_state_aggregator(
+    session: AsyncSession,
+    tenant_id: str,
+) -> SlotStateAggregator:
+    """
+    创建槽位状态聚合器实例
+    
+    Args:
+        session: 数据库会话
+        tenant_id: 租户 ID
+        
+    Returns:
+        SlotStateAggregator: 槽位状态聚合器实例
+    """
+    return SlotStateAggregator(
+        session=session,
+        tenant_id=tenant_id,
+    )

ai-service/app/services/mid/slot_strategy_executor.py

@@ -0,0 +1,355 @@
+"""
+Slot Strategy Executor.
+[AC-MRS-07-UPGRADE] 槽位提取策略链执行器
+
+按顺序执行提取策略链，直到成功提取并通过校验。
+支持失败分类和详细日志追踪。
+"""
+
+import logging
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Any, Callable
+
+from app.models.entities import ExtractFailureType
+
+logger = logging.getLogger(__name__)
+
+
+class ExtractStrategyType(str, Enum):
+    """提取策略类型"""
+    RULE = "rule"
+    LLM = "llm"
+    USER_INPUT = "user_input"
+
+
+@dataclass
+class StrategyStepResult:
+    """单步策略执行结果"""
+    strategy: str
+    success: bool
+    value: Any = None
+    failure_type: ExtractFailureType | None = None
+    failure_reason: str = ""
+    execution_time_ms: float = 0.0
+
+
+@dataclass
+class StrategyChainResult:
+    """策略链执行结果"""
+    slot_key: str
+    success: bool
+    final_value: Any = None
+    final_strategy: str | None = None
+    steps: list[StrategyStepResult] = field(default_factory=list)
+    total_execution_time_ms: float = 0.0
+    ask_back_prompt: str | None = None
+
+    def to_dict(self) -> dict[str, Any]:
+        """转换为字典"""
+        return {
+            "slot_key": self.slot_key,
+            "success": self.success,
+            "final_value": self.final_value,
+            "final_strategy": self.final_strategy,
+            "steps": [
+                {
+                    "strategy": step.strategy,
+                    "success": step.success,
+                    "value": step.value if step.success else None,
+                    "failure_type": step.failure_type.value if step.failure_type else None,
+                    "failure_reason": step.failure_reason,
+                    "execution_time_ms": step.execution_time_ms,
+                }
+                for step in self.steps
+            ],
+            "total_execution_time_ms": self.total_execution_time_ms,
+            "ask_back_prompt": self.ask_back_prompt,
+        }
+
+
+@dataclass
+class ExtractContext:
+    """提取上下文"""
+    tenant_id: str
+    slot_key: str
+    user_input: str
+    slot_type: str
+    validation_rule: str | None = None
+    history: list[dict[str, str]] | None = None
+    session_id: str | None = None
+
+
+class SlotStrategyExecutor:
+    """
+    [AC-MRS-07-UPGRADE] 槽位提取策略链执行器
+    
+    职责：
+    1. 按策略链顺序执行提取
+    2. 某一步成功且校验通过 -> 停止并返回结果
+    3. 当前策略失败 -> 记录失败原因，继续下一策略
+    4. 全部失败 -> 返回结构化失败结果
+    5. 提供可追踪日志（slot_key、strategy、reason）
+    """
+
+    def __init__(
+        self,
+        rule_extractor: Callable[[ExtractContext], Any] | None = None,
+        llm_extractor: Callable[[ExtractContext], Any] | None = None,
+        user_input_extractor: Callable[[ExtractContext], Any] | None = None,
+    ):
+        """
+        初始化执行器
+        
+        Args:
+            rule_extractor: 规则提取器函数
+            llm_extractor: LLM提取器函数
+            user_input_extractor: 用户输入提取器函数
+        """
+        self._extractors: dict[str, Callable[[ExtractContext], Any]] = {
+            ExtractStrategyType.RULE.value: rule_extractor or self._default_rule_extract,
+            ExtractStrategyType.LLM.value: llm_extractor or self._default_llm_extract,
+            ExtractStrategyType.USER_INPUT.value: user_input_extractor or self._default_user_input_extract,
+        }
+
+    async def execute_chain(
+        self,
+        strategies: list[str],
+        context: ExtractContext,
+        ask_back_prompt: str | None = None,
+    ) -> StrategyChainResult:
+        """
+        执行提取策略链
+        
+        Args:
+            strategies: 策略链，如 ["user_input", "rule", "llm"]
+            context: 提取上下文
+            ask_back_prompt: 追问提示语（全部失败时使用）
+            
+        Returns:
+            StrategyChainResult: 执行结果
+        """
+        import time
+        
+        start_time = time.time()
+        steps: list[StrategyStepResult] = []
+        
+        logger.info(
+            f"[SlotStrategyExecutor] Starting strategy chain for slot '{context.slot_key}': "
+            f"strategies={strategies}, tenant={context.tenant_id}"
+        )
+        
+        for idx, strategy in enumerate(strategies):
+            step_start = time.time()
+            
+            logger.info(
+                f"[SlotStrategyExecutor] Executing step {idx + 1}/{len(strategies)}: "
+                f"slot_key={context.slot_key}, strategy={strategy}"
+            )
+            
+            step_result = await self._execute_single_strategy(strategy, context)
+            step_result.execution_time_ms = (time.time() - step_start) * 1000
+            steps.append(step_result)
+            
+            if step_result.success:
+                total_time = (time.time() - start_time) * 1000
+                logger.info(
+                    f"[SlotStrategyExecutor] Strategy chain succeeded at step {idx + 1}: "
+                    f"slot_key={context.slot_key}, strategy={strategy}, "
+                    f"total_time_ms={total_time:.2f}"
+                )
+                return StrategyChainResult(
+                    slot_key=context.slot_key,
+                    success=True,
+                    final_value=step_result.value,
+                    final_strategy=strategy,
+                    steps=steps,
+                    total_execution_time_ms=total_time,
+                )
+            else:
+                logger.warning(
+                    f"[SlotStrategyExecutor] Step {idx + 1} failed: "
+                    f"slot_key={context.slot_key}, strategy={strategy}, "
+                    f"failure_type={step_result.failure_type}, "
+                    f"reason={step_result.failure_reason}"
+                )
+        
+        # 全部策略失败
+        total_time = (time.time() - start_time) * 1000
+        logger.warning(
+            f"[SlotStrategyExecutor] All strategies failed for slot '{context.slot_key}': "
+            f"attempted={len(strategies)}, total_time_ms={total_time:.2f}"
+        )
+        
+        return StrategyChainResult(
+            slot_key=context.slot_key,
+            success=False,
+            final_value=None,
+            final_strategy=None,
+            steps=steps,
+            total_execution_time_ms=total_time,
+            ask_back_prompt=ask_back_prompt,
+        )
+
+    async def _execute_single_strategy(
+        self,
+        strategy: str,
+        context: ExtractContext,
+    ) -> StrategyStepResult:
+        """
+        执行单个提取策略
+        
+        Args:
+            strategy: 策略类型
+            context: 提取上下文
+            
+        Returns:
+            StrategyStepResult: 单步执行结果
+        """
+        extractor = self._extractors.get(strategy)
+        
+        if not extractor:
+            return StrategyStepResult(
+                strategy=strategy,
+                success=False,
+                failure_type=ExtractFailureType.EXTRACT_RUNTIME_ERROR,
+                failure_reason=f"Unknown strategy: {strategy}",
+            )
+        
+        try:
+            # 执行提取
+            value = await extractor(context)
+            
+            # 检查结果是否为空
+            if value is None or value == "":
+                return StrategyStepResult(
+                    strategy=strategy,
+                    success=False,
+                    failure_type=ExtractFailureType.EXTRACT_EMPTY,
+                    failure_reason="Extracted value is empty",
+                )
+            
+            # 执行校验（如果有校验规则）
+            if context.validation_rule:
+                is_valid, error_msg = self._validate_value(value, context)
+                if not is_valid:
+                    return StrategyStepResult(
+                        strategy=strategy,
+                        success=False,
+                        failure_type=ExtractFailureType.EXTRACT_VALIDATION_FAIL,
+                        failure_reason=f"Validation failed: {error_msg}",
+                    )
+            
+            return StrategyStepResult(
+                strategy=strategy,
+                success=True,
+                value=value,
+            )
+            
+        except Exception as e:
+            logger.exception(
+                f"[SlotStrategyExecutor] Runtime error in strategy '{strategy}' "
+                f"for slot '{context.slot_key}': {e}"
+            )
+            return StrategyStepResult(
+                strategy=strategy,
+                success=False,
+                failure_type=ExtractFailureType.EXTRACT_RUNTIME_ERROR,
+                failure_reason=f"Runtime error: {str(e)}",
+            )
+
+    def _validate_value(self, value: Any, context: ExtractContext) -> tuple[bool, str]:
+        """
+        校验提取的值
+        
+        Args:
+            value: 提取的值
+            context: 提取上下文
+            
+        Returns:
+            Tuple of (是否通过, 错误信息)
+        """
+        import re
+        
+        validation_rule = context.validation_rule
+        if not validation_rule:
+            return True, ""
+        
+        try:
+            # 尝试作为正则表达式校验
+            if validation_rule.startswith("^") or validation_rule.endswith("$"):
+                if re.match(validation_rule, str(value)):
+                    return True, ""
+                return False, f"Value '{value}' does not match pattern '{validation_rule}'"
+            
+            # 其他校验规则可以在这里扩展
+            return True, ""
+            
+        except re.error as e:
+            logger.warning(f"[SlotStrategyExecutor] Invalid validation rule pattern: {e}")
+            return True, ""  # 正则错误时放行
+
+    async def _default_rule_extract(self, context: ExtractContext) -> Any:
+        """默认规则提取实现（占位）"""
+        # 实际项目中应该调用 VariableExtractor 或其他规则引擎
+        logger.debug(f"[SlotStrategyExecutor] Default rule extract for '{context.slot_key}'")
+        return None
+
+    async def _default_llm_extract(self, context: ExtractContext) -> Any:
+        """默认LLM提取实现（占位）"""
+        logger.debug(f"[SlotStrategyExecutor] Default LLM extract for '{context.slot_key}'")
+        return None
+
+    async def _default_user_input_extract(self, context: ExtractContext) -> Any:
+        """默认用户输入提取实现"""
+        # user_input 策略通常表示需要向用户询问，这里返回空表示需要追问
+        logger.debug(f"[SlotStrategyExecutor] User input required for '{context.slot_key}'")
+        return None
+
+
+# 便捷函数
+async def execute_extract_strategies(
+    strategies: list[str],
+    tenant_id: str,
+    slot_key: str,
+    user_input: str,
+    slot_type: str = "string",
+    validation_rule: str | None = None,
+    ask_back_prompt: str | None = None,
+    history: list[dict[str, str]] | None = None,
+    rule_extractor: Callable[[ExtractContext], Any] | None = None,
+    llm_extractor: Callable[[ExtractContext], Any] | None = None,
+) -> StrategyChainResult:
+    """
+    便捷函数：执行提取策略链
+    
+    Args:
+        strategies: 策略链
+        tenant_id: 租户ID
+        slot_key: 槽位键名
+        user_input: 用户输入
+        slot_type: 槽位类型
+        validation_rule: 校验规则
+        ask_back_prompt: 追问提示语
+        history: 对话历史
+        rule_extractor: 规则提取器
+        llm_extractor: LLM提取器
+        
+    Returns:
+        StrategyChainResult: 执行结果
+    """
+    executor = SlotStrategyExecutor(
+        rule_extractor=rule_extractor,
+        llm_extractor=llm_extractor,
+    )
+    
+    context = ExtractContext(
+        tenant_id=tenant_id,
+        slot_key=slot_key,
+        user_input=user_input,
+        slot_type=slot_type,
+        validation_rule=validation_rule,
+        history=history,
+    )
+    
+    return await executor.execute_chain(strategies, context, ask_back_prompt)

ai-service/app/services/mid/slot_validation_service.py

@@ -0,0 +1,572 @@
+"""
+Slot Validation Service.
+槽位校验规则 runtime 生效服务
+
+提供槽位值的运行时校验能力，支持：
+1. 正则表达式校验
+2. JSON Schema 校验
+3. 类型校验（string/number/boolean/enum/array_enum）
+4. 必填校验
+"""
+
+import json
+import logging
+import re
+from dataclasses import dataclass, field
+from typing import Any
+
+import jsonschema
+from jsonschema.exceptions import ValidationError as JsonSchemaValidationError
+
+from app.models.entities import SlotDefinition
+
+logger = logging.getLogger(__name__)
+
+
+# 错误码定义
+class SlotValidationErrorCode:
+    """槽位校验错误码"""
+
+    SLOT_REQUIRED_MISSING = "SLOT_REQUIRED_MISSING"
+    SLOT_TYPE_INVALID = "SLOT_TYPE_INVALID"
+    SLOT_REGEX_MISMATCH = "SLOT_REGEX_MISMATCH"
+    SLOT_JSON_SCHEMA_MISMATCH = "SLOT_JSON_SCHEMA_MISMATCH"
+    SLOT_VALIDATION_RULE_INVALID = "SLOT_VALIDATION_RULE_INVALID"
+    SLOT_ENUM_INVALID = "SLOT_ENUM_INVALID"
+    SLOT_ARRAY_ENUM_INVALID = "SLOT_ARRAY_ENUM_INVALID"
+
+
+@dataclass
+class ValidationResult:
+    """
+    单个槽位校验结果
+
+    Attributes:
+        ok: 校验是否通过
+        normalized_value: 归一化后的值（如类型转换后）
+        error_code: 错误码（校验失败时）
+        error_message: 错误描述（校验失败时）
+        ask_back_prompt: 追问提示语（校验失败且配置了 ask_back_prompt 时）
+    """
+
+    ok: bool
+    normalized_value: Any | None = None
+    error_code: str | None = None
+    error_message: str | None = None
+    ask_back_prompt: str | None = None
+
+
+@dataclass
+class SlotValidationError:
+    """
+    槽位校验错误详情
+
+    Attributes:
+        slot_key: 槽位键名
+        error_code: 错误码
+        error_message: 错误描述
+        ask_back_prompt: 追问提示语
+    """
+
+    slot_key: str
+    error_code: str
+    error_message: str
+    ask_back_prompt: str | None = None
+
+
+@dataclass
+class BatchValidationResult:
+    """
+    批量槽位校验结果
+
+    Attributes:
+        ok: 是否全部校验通过
+        errors: 校验错误列表
+        validated_values: 校验通过的值字典
+    """
+
+    ok: bool
+    errors: list[SlotValidationError] = field(default_factory=list)
+    validated_values: dict[str, Any] = field(default_factory=dict)
+
+
+class SlotValidationService:
+    """
+    槽位校验服务
+
+    负责在槽位值写回前执行校验，支持：
+    - 正则表达式校验
+    - JSON Schema 校验
+    - 类型校验
+    - 必填校验
+    """
+
+    # 支持的槽位类型
+    VALID_TYPES = ["string", "number", "boolean", "enum", "array_enum"]
+
+    def __init__(self):
+        """初始化槽位校验服务"""
+        self._schema_cache: dict[str, dict] = {}
+
+    def validate_slot_value(
+        self,
+        slot_def: dict[str, Any] | SlotDefinition,
+        value: Any,
+        tenant_id: str | None = None,
+    ) -> ValidationResult:
+        """
+        校验单个槽位值
+
+        校验顺序：
+        1. 必填校验（如果 required=true 且值为空）
+        2. 类型校验
+        3. validation_rule 校验（正则或 JSON Schema）
+
+        Args:
+            slot_def: 槽位定义（dict 或 SlotDefinition 对象）
+            value: 待校验的值
+            tenant_id: 租户 ID（用于日志记录）
+
+        Returns:
+            ValidationResult: 校验结果
+        """
+        # 统一转换为 dict 处理
+        if isinstance(slot_def, SlotDefinition):
+            slot_dict = {
+                "slot_key": slot_def.slot_key,
+                "type": slot_def.type,
+                "required": slot_def.required,
+                "validation_rule": slot_def.validation_rule,
+                "ask_back_prompt": slot_def.ask_back_prompt,
+            }
+        else:
+            slot_dict = slot_def
+
+        slot_key = slot_dict.get("slot_key", "unknown")
+        slot_type = slot_dict.get("type", "string")
+        required = slot_dict.get("required", False)
+        validation_rule = slot_dict.get("validation_rule")
+        ask_back_prompt = slot_dict.get("ask_back_prompt")
+
+        # 1. 必填校验
+        if required and self._is_empty_value(value):
+            logger.info(
+                f"[SlotValidation] Required slot missing: "
+                f"tenant_id={tenant_id}, slot_key={slot_key}"
+            )
+            return ValidationResult(
+                ok=False,
+                error_code=SlotValidationErrorCode.SLOT_REQUIRED_MISSING,
+                error_message=f"槽位 '{slot_key}' 为必填项",
+                ask_back_prompt=ask_back_prompt,
+            )
+
+        # 如果值为空且非必填，跳过后续校验
+        if self._is_empty_value(value):
+            return ValidationResult(ok=True, normalized_value=value)
+
+        # 2. 类型校验
+        type_result = self._validate_type(slot_dict, value, tenant_id)
+        if not type_result.ok:
+            return ValidationResult(
+                ok=False,
+                error_code=type_result.error_code,
+                error_message=type_result.error_message,
+                ask_back_prompt=ask_back_prompt,
+            )
+
+        normalized_value = type_result.normalized_value
+
+        # 3. validation_rule 校验
+        if validation_rule and str(validation_rule).strip():
+            rule_result = self._validate_rule(
+                slot_dict, normalized_value, tenant_id
+            )
+            if not rule_result.ok:
+                return ValidationResult(
+                    ok=False,
+                    error_code=rule_result.error_code,
+                    error_message=rule_result.error_message,
+                    ask_back_prompt=ask_back_prompt,
+                )
+            normalized_value = rule_result.normalized_value or normalized_value
+
+        logger.debug(
+            f"[SlotValidation] Slot validation passed: "
+            f"tenant_id={tenant_id}, slot_key={slot_key}, type={slot_type}"
+        )
+
+        return ValidationResult(ok=True, normalized_value=normalized_value)
+
+    def validate_slots(
+        self,
+        slot_defs: list[dict[str, Any] | SlotDefinition],
+        values: dict[str, Any],
+        tenant_id: str | None = None,
+    ) -> BatchValidationResult:
+        """
+        批量校验多个槽位值
+
+        Args:
+            slot_defs: 槽位定义列表
+            values: 槽位值字典 {slot_key: value}
+            tenant_id: 租户 ID（用于日志记录）
+
+        Returns:
+            BatchValidationResult: 批量校验结果
+        """
+        errors: list[SlotValidationError] = []
+        validated_values: dict[str, Any] = {}
+
+        # 构建 slot_def 映射
+        slot_def_map: dict[str, dict[str, Any] | SlotDefinition] = {}
+        for slot_def in slot_defs:
+            if isinstance(slot_def, SlotDefinition):
+                slot_def_map[slot_def.slot_key] = slot_def
+            else:
+                slot_def_map[slot_def.get("slot_key", "")] = slot_def
+
+        # 校验每个提供的值
+        for slot_key, value in values.items():
+            slot_def = slot_def_map.get(slot_key)
+            if not slot_def:
+                # 未定义槽位，跳过校验（允许动态槽位）
+                validated_values[slot_key] = value
+                continue
+
+            result = self.validate_slot_value(slot_def, value, tenant_id)
+
+            if result.ok:
+                validated_values[slot_key] = result.normalized_value
+            else:
+                errors.append(
+                    SlotValidationError(
+                        slot_key=slot_key,
+                        error_code=result.error_code or "UNKNOWN_ERROR",
+                        error_message=result.error_message or "校验失败",
+                        ask_back_prompt=result.ask_back_prompt,
+                    )
+                )
+
+        # 检查必填槽位是否缺失
+        for slot_def in slot_defs:
+            if isinstance(slot_def, SlotDefinition):
+                slot_key = slot_def.slot_key
+                required = slot_def.required
+                ask_back_prompt = slot_def.ask_back_prompt
+            else:
+                slot_key = slot_def.get("slot_key", "")
+                required = slot_def.get("required", False)
+                ask_back_prompt = slot_def.get("ask_back_prompt")
+
+            if required and slot_key not in values:
+                # 检查是否已经有该错误
+                if not any(e.slot_key == slot_key for e in errors):
+                    errors.append(
+                        SlotValidationError(
+                            slot_key=slot_key,
+                            error_code=SlotValidationErrorCode.SLOT_REQUIRED_MISSING,
+                            error_message=f"槽位 '{slot_key}' 为必填项",
+                            ask_back_prompt=ask_back_prompt,
+                        )
+                    )
+
+        return BatchValidationResult(
+            ok=len(errors) == 0,
+            errors=errors,
+            validated_values=validated_values,
+        )
+
+    def _is_empty_value(self, value: Any) -> bool:
+        """判断值是否为空"""
+        if value is None:
+            return True
+        if isinstance(value, str) and not value.strip():
+            return True
+        if isinstance(value, list) and len(value) == 0:
+            return True
+        return False
+
+    def _validate_type(
+        self,
+        slot_def: dict[str, Any],
+        value: Any,
+        tenant_id: str | None,
+    ) -> ValidationResult:
+        """
+        类型校验
+
+        Args:
+            slot_def: 槽位定义字典
+            value: 待校验的值
+            tenant_id: 租户 ID
+
+        Returns:
+            ValidationResult: 校验结果
+        """
+        slot_key = slot_def.get("slot_key", "unknown")
+        slot_type = slot_def.get("type", "string")
+
+        if slot_type not in self.VALID_TYPES:
+            logger.warning(
+                f"[SlotValidation] Unknown slot type: "
+                f"tenant_id={tenant_id}, slot_key={slot_key}, type={slot_type}"
+            )
+            # 未知类型不阻止，只记录警告
+            return ValidationResult(ok=True, normalized_value=value)
+
+        try:
+            if slot_type == "string":
+                if not isinstance(value, str):
+                    # 尝试转换为字符串
+                    normalized = str(value)
+                    return ValidationResult(ok=True, normalized_value=normalized)
+                return ValidationResult(ok=True, normalized_value=value)
+
+            elif slot_type == "number":
+                if isinstance(value, bool):
+                    return ValidationResult(
+                        ok=False,
+                        error_code=SlotValidationErrorCode.SLOT_TYPE_INVALID,
+                        error_message=f"槽位 '{slot_key}' 类型应为数字，但得到布尔值",
+                    )
+                if isinstance(value, int | float):
+                    return ValidationResult(ok=True, normalized_value=value)
+                # 尝试转换为数字
+                if isinstance(value, str):
+                    try:
+                        if "." in value:
+                            normalized = float(value)
+                        else:
+                            normalized = int(value)
+                        return ValidationResult(ok=True, normalized_value=normalized)
+                    except ValueError:
+                        pass
+                return ValidationResult(
+                    ok=False,
+                    error_code=SlotValidationErrorCode.SLOT_TYPE_INVALID,
+                    error_message=f"槽位 '{slot_key}' 类型应为数字",
+                )
+
+            elif slot_type == "boolean":
+                if isinstance(value, bool):
+                    return ValidationResult(ok=True, normalized_value=value)
+                if isinstance(value, str):
+                    lower_val = value.lower()
+                    if lower_val in ("true", "1", "yes", "是", "真"):
+                        return ValidationResult(ok=True, normalized_value=True)
+                    if lower_val in ("false", "0", "no", "否", "假"):
+                        return ValidationResult(ok=True, normalized_value=False)
+                return ValidationResult(
+                    ok=False,
+                    error_code=SlotValidationErrorCode.SLOT_TYPE_INVALID,
+                    error_message=f"槽位 '{slot_key}' 类型应为布尔值",
+                )
+
+            elif slot_type == "enum":
+                if not isinstance(value, str):
+                    return ValidationResult(
+                        ok=False,
+                        error_code=SlotValidationErrorCode.SLOT_TYPE_INVALID,
+                        error_message=f"槽位 '{slot_key}' 类型应为字符串（枚举）",
+                    )
+                # 如果有选项定义，校验值是否在选项中
+                options = slot_def.get("options") or []
+                if options and value not in options:
+                    return ValidationResult(
+                        ok=False,
+                        error_code=SlotValidationErrorCode.SLOT_ENUM_INVALID,
+                        error_message=f"槽位 '{slot_key}' 的值 '{value}' 不在允许选项 {options} 中",
+                    )
+                return ValidationResult(ok=True, normalized_value=value)
+
+            elif slot_type == "array_enum":
+                if not isinstance(value, list):
+                    return ValidationResult(
+                        ok=False,
+                        error_code=SlotValidationErrorCode.SLOT_TYPE_INVALID,
+                        error_message=f"槽位 '{slot_key}' 类型应为数组",
+                    )
+                # 校验数组元素
+                options = slot_def.get("options") or []
+                for item in value:
+                    if not isinstance(item, str):
+                        return ValidationResult(
+                            ok=False,
+                            error_code=SlotValidationErrorCode.SLOT_ARRAY_ENUM_INVALID,
+                            error_message=f"槽位 '{slot_key}' 的数组元素应为字符串",
+                        )
+                    if options and item not in options:
+                        return ValidationResult(
+                            ok=False,
+                            error_code=SlotValidationErrorCode.SLOT_ARRAY_ENUM_INVALID,
+                            error_message=f"槽位 '{slot_key}' 的值 '{item}' 不在允许选项 {options} 中",
+                        )
+                return ValidationResult(ok=True, normalized_value=value)
+
+        except Exception as e:
+            logger.error(
+                f"[SlotValidation] Type validation error: "
+                f"tenant_id={tenant_id}, slot_key={slot_key}, error={e}"
+            )
+            return ValidationResult(
+                ok=False,
+                error_code=SlotValidationErrorCode.SLOT_TYPE_INVALID,
+                error_message=f"槽位 '{slot_key}' 类型校验异常: {str(e)}",
+            )
+
+        return ValidationResult(ok=True, normalized_value=value)
+
+    def _validate_rule(
+        self,
+        slot_def: dict[str, Any],
+        value: Any,
+        tenant_id: str | None,
+    ) -> ValidationResult:
+        """
+        校验规则校验（正则或 JSON Schema）
+
+        判定逻辑：
+        1. 如果规则是 JSON 对象字符串（以 { 或 [ 开头），按 JSON Schema 处理
+        2. 否则按正则表达式处理
+
+        Args:
+            slot_def: 槽位定义字典
+            value: 待校验的值
+            tenant_id: 租户 ID
+
+        Returns:
+            ValidationResult: 校验结果
+        """
+        slot_key = slot_def.get("slot_key", "unknown")
+        validation_rule = str(slot_def.get("validation_rule", "")).strip()
+
+        if not validation_rule:
+            return ValidationResult(ok=True, normalized_value=value)
+
+        # 判定是 JSON Schema 还是正则表达式
+        # JSON Schema 通常以 { 或 [ 开头
+        is_json_schema = validation_rule.strip().startswith(("{", "["))
+
+        if is_json_schema:
+            return self._validate_json_schema(
+                slot_key, validation_rule, value, tenant_id
+            )
+        else:
+            return self._validate_regex(
+                slot_key, validation_rule, value, tenant_id
+            )
+
+    def _validate_regex(
+        self,
+        slot_key: str,
+        pattern: str,
+        value: Any,
+        tenant_id: str | None,
+    ) -> ValidationResult:
+        """
+        正则表达式校验
+
+        Args:
+            slot_key: 槽位键名
+            pattern: 正则表达式
+            value: 待校验的值
+            tenant_id: 租户 ID
+
+        Returns:
+            ValidationResult: 校验结果
+        """
+        try:
+            # 将值转为字符串进行匹配
+            str_value = str(value) if value is not None else ""
+
+            if not re.search(pattern, str_value):
+                logger.info(
+                    f"[SlotValidation] Regex mismatch: "
+                    f"tenant_id={tenant_id}, slot_key={slot_key}"
+                )
+                return ValidationResult(
+                    ok=False,
+                    error_code=SlotValidationErrorCode.SLOT_REGEX_MISMATCH,
+                    error_message=f"槽位 '{slot_key}' 的值不符合格式要求",
+                )
+
+            return ValidationResult(ok=True, normalized_value=value)
+
+        except re.error as e:
+            logger.warning(
+                f"[SlotValidation] Invalid regex pattern: "
+                f"tenant_id={tenant_id}, slot_key={slot_key}, pattern={pattern}, error={e}"
+            )
+            return ValidationResult(
+                ok=False,
+                error_code=SlotValidationErrorCode.SLOT_VALIDATION_RULE_INVALID,
+                error_message=f"槽位 '{slot_key}' 的校验规则配置无效（非法正则）",
+            )
+
+    def _validate_json_schema(
+        self,
+        slot_key: str,
+        schema_str: str,
+        value: Any,
+        tenant_id: str | None,
+    ) -> ValidationResult:
+        """
+        JSON Schema 校验
+
+        Args:
+            slot_key: 槽位键名
+            schema_str: JSON Schema 字符串
+            value: 待校验的值
+            tenant_id: 租户 ID
+
+        Returns:
+            ValidationResult: 校验结果
+        """
+        try:
+            # 解析 JSON Schema
+            schema = self._schema_cache.get(schema_str)
+            if schema is None:
+                schema = json.loads(schema_str)
+                self._schema_cache[schema_str] = schema
+
+            # 执行校验
+            jsonschema.validate(instance=value, schema=schema)
+            return ValidationResult(ok=True, normalized_value=value)
+
+        except json.JSONDecodeError as e:
+            logger.warning(
+                f"[SlotValidation] Invalid JSON schema: "
+                f"tenant_id={tenant_id}, slot_key={slot_key}, error={e}"
+            )
+            return ValidationResult(
+                ok=False,
+                error_code=SlotValidationErrorCode.SLOT_VALIDATION_RULE_INVALID,
+                error_message=f"槽位 '{slot_key}' 的校验规则配置无效（非法 JSON）",
+            )
+
+        except JsonSchemaValidationError as e:
+            logger.info(
+                f"[SlotValidation] JSON schema mismatch: "
+                f"tenant_id={tenant_id}, slot_key={slot_key}, error={e.message}"
+            )
+            return ValidationResult(
+                ok=False,
+                error_code=SlotValidationErrorCode.SLOT_JSON_SCHEMA_MISMATCH,
+                error_message=f"槽位 '{slot_key}' 的值不符合格式要求: {e.message}",
+            )
+
+        except Exception as e:
+            logger.error(
+                f"[SlotValidation] JSON schema validation error: "
+                f"tenant_id={tenant_id}, slot_key={slot_key}, error={e}"
+            )
+            return ValidationResult(
+                ok=False,
+                error_code=SlotValidationErrorCode.SLOT_VALIDATION_RULE_INVALID,
+                error_message=f"槽位 '{slot_key}' 的校验规则执行异常: {str(e)}",
+            )
+
+    def clear_cache(self) -> None:
+        """清除 JSON Schema 缓存"""
+        self._schema_cache.clear()

ai-service/app/services/mid/tool_call_recorder.py

@@ -127,7 +127,8 @@ class ToolCallRecorder:
         logger.info(
             f"[AC-IDMP-15] Tool call recorded: tool={trace.tool_name}, "
             f"type={trace.tool_type.value}, duration_ms={trace.duration_ms}, "
-            f"status={trace.status.value}, session={session_id}"
+            f"status={trace.status.value}, session={session_id}, "
+            f"args_digest={trace.args_digest}, result_digest={trace.result_digest}"
         )
 
     def record_success(
@@ -153,6 +154,8 @@ class ToolCallRecorder:
             auth_applied=auth_applied,
             args_digest=ToolCallTrace.compute_digest(args) if args else None,
             result_digest=ToolCallTrace.compute_digest(result) if result else None,
+            arguments=args if isinstance(args, dict) else None,
+            result=result,
         )
         self.record(session_id, trace)
         return trace
@@ -179,6 +182,7 @@ class ToolCallRecorder:
             registry_version=registry_version,
             auth_applied=auth_applied,
             args_digest=ToolCallTrace.compute_digest(args) if args else None,
+            arguments=args if isinstance(args, dict) else None,
         )
         self.record(session_id, trace)
         return trace
@@ -207,6 +211,7 @@ class ToolCallRecorder:
             registry_version=registry_version,
             auth_applied=auth_applied,
             args_digest=ToolCallTrace.compute_digest(args) if args else None,
+            arguments=args if isinstance(args, dict) else None,
         )
         self.record(session_id, trace)
         return trace
@@ -231,6 +236,7 @@ class ToolCallRecorder:
             error_code=reason,
             registry_version=registry_version,
             args_digest=ToolCallTrace.compute_digest(args) if args else None,
+            arguments=args if isinstance(args, dict) else None,
         )
         self.record(session_id, trace)
         return trace

ai-service/app/services/mid/tool_converter.py

@@ -0,0 +1,111 @@
+"""
+Tool definition converter for Function Calling.
+Converts ToolRegistry definitions to LLM ToolDefinition format.
+"""
+
+import logging
+from typing import Any
+
+from app.services.llm.base import ToolDefinition
+from app.services.mid.tool_registry import ToolDefinition as RegistryToolDefinition
+
+logger = logging.getLogger(__name__)
+
+
+def convert_tool_to_llm_format(tool: RegistryToolDefinition) -> ToolDefinition:
+    """
+    Convert ToolRegistry tool definition to LLM ToolDefinition format.
+    
+    Args:
+        tool: Tool definition from ToolRegistry
+        
+    Returns:
+        ToolDefinition for Function Calling
+    """
+    meta = tool.metadata or {}
+    parameters = meta.get("parameters", {
+        "type": "object",
+        "properties": {},
+        "required": [],
+    })
+    
+    if not isinstance(parameters, dict):
+        parameters = {
+            "type": "object",
+            "properties": {},
+            "required": [],
+        }
+    
+    if "type" not in parameters:
+        parameters["type"] = "object"
+    if "properties" not in parameters:
+        parameters["properties"] = {}
+    if "required" not in parameters:
+        parameters["required"] = []
+    
+    properties = parameters.get("properties", {})
+    if "tenant_id" in properties:
+        properties = {k: v for k, v in properties.items() if k != "tenant_id"}
+    if "user_id" in properties:
+        properties = {k: v for k, v in properties.items() if k != "user_id"}
+    if "session_id" in properties:
+        properties = {k: v for k, v in properties.items() if k != "session_id"}
+    
+    parameters["properties"] = properties
+    
+    required = parameters.get("required", [])
+    required = [r for r in required if r not in ("tenant_id", "user_id", "session_id")]
+    parameters["required"] = required
+    
+    return ToolDefinition(
+        name=tool.name,
+        description=tool.description,
+        parameters=parameters,
+    )
+
+
+def convert_tools_to_llm_format(tools: list[RegistryToolDefinition]) -> list[ToolDefinition]:
+    """
+    Convert multiple tool definitions to LLM format.
+    
+    Args:
+        tools: List of tool definitions from ToolRegistry
+        
+    Returns:
+        List of ToolDefinition for Function Calling
+    """
+    return [convert_tool_to_llm_format(tool) for tool in tools]
+
+
+def build_tool_result_message(
+    tool_call_id: str,
+    tool_name: str,
+    result: dict[str, Any],
+    tool_guide: str | None = None,
+) -> dict[str, str]:
+    """
+    Build a tool result message for the conversation.
+    
+    Args:
+        tool_call_id: ID of the tool call
+        tool_name: Name of the tool
+        result: Tool execution result
+        tool_guide: Optional tool usage guide to append
+        
+    Returns:
+        Message dict with role='tool'
+    """
+    if isinstance(result, dict):
+        result_copy = {k: v for k, v in result.items() if k != "_tool_guide"}
+        content = str(result_copy)
+    else:
+        content = str(result)
+    
+    if tool_guide:
+        content = f"{content}\n\n---\n{tool_guide}"
+    
+    return {
+        "role": "tool",
+        "tool_call_id": tool_call_id,
+        "content": content,
+    }

ai-service/app/services/mid/tool_guide_registry.py

@@ -0,0 +1,313 @@
+"""
+Tool Guide Registry for Mid Platform.
+Provides tool-based usage guidance with caching support.
+
+Tool guides are usage manuals for tools, loaded on-demand with metadata scanning.
+This separates tool definitions (Function Calling) from usage guides (Tool Guides).
+"""
+
+import logging
+import re
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+TOOLS_DIR = Path(__file__).parent.parent.parent.parent / "tools"
+
+
+@dataclass
+class ToolGuideMetadata:
+    """Lightweight tool guide metadata for quick scanning (~100 tokens)."""
+    name: str
+    description: str
+    triggers: list[str] = field(default_factory=list)
+    anti_triggers: list[str] = field(default_factory=list)
+    tools: list[str] = field(default_factory=list)
+
+
+@dataclass
+class ToolGuideDefinition:
+    """Full tool guide definition with complete content."""
+    name: str
+    description: str
+    triggers: list[str]
+    anti_triggers: list[str]
+    tools: list[str]
+    content: str
+    raw_markdown: str
+
+
+class ToolGuideRegistry:
+    """
+    Tool guide registry with caching support.
+    
+    Features:
+    - Load tool guides from .md files
+    - Cache tool guides in memory for high-frequency access
+    - Provide lightweight metadata for quick scanning
+    - Provide full content on demand
+    """
+    
+    _instance = None
+    _initialized = False
+    
+    def __new__(cls, *args, **kwargs):
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+        return cls._instance
+    
+    def __init__(self, tools_dir: Path | None = None):
+        if ToolGuideRegistry._initialized:
+            return
+        
+        self._tools_dir = tools_dir or TOOLS_DIR
+        self._tool_guides: dict[str, ToolGuideDefinition] = {}
+        self._metadata_cache: dict[str, ToolGuideMetadata] = {}
+        self._tool_to_guide: dict[str, str] = {}
+        self._loaded = False
+        
+        ToolGuideRegistry._initialized = True
+        logger.info(f"[ToolGuideRegistry] Initialized with tools_dir={self._tools_dir}")
+    
+    def load_tools(self, force_reload: bool = False) -> None:
+        """
+        Load all tool guides from tools directory into cache.
+        
+        Args:
+            force_reload: Force reload even if already loaded
+        """
+        if self._loaded and not force_reload:
+            logger.debug("[ToolGuideRegistry] Tool guides already loaded, skipping")
+            return
+        
+        if not self._tools_dir.exists():
+            logger.warning(f"[ToolGuideRegistry] Tools directory not found: {self._tools_dir}")
+            return
+        
+        self._tool_guides.clear()
+        self._metadata_cache.clear()
+        self._tool_to_guide.clear()
+        
+        for tool_file in self._tools_dir.glob("*.md"):
+            try:
+                tool_guide = self._parse_tool_file(tool_file)
+                if tool_guide:
+                    self._tool_guides[tool_guide.name] = tool_guide
+                    self._metadata_cache[tool_guide.name] = ToolGuideMetadata(
+                        name=tool_guide.name,
+                        description=tool_guide.description,
+                        triggers=tool_guide.triggers,
+                        anti_triggers=tool_guide.anti_triggers,
+                        tools=tool_guide.tools,
+                    )
+                    for tool_name in tool_guide.tools:
+                        self._tool_to_guide[tool_name] = tool_guide.name
+                    logger.info(f"[ToolGuideRegistry] Loaded tool guide: {tool_guide.name} (tools: {tool_guide.tools})")
+            except Exception as e:
+                logger.error(f"[ToolGuideRegistry] Failed to load tool guide from {tool_file}: {e}")
+        
+        self._loaded = True
+        logger.info(f"[ToolGuideRegistry] Loaded {len(self._tool_guides)} tool guides")
+    
+    def _parse_tool_file(self, file_path: Path) -> ToolGuideDefinition | None:
+        """
+        Parse a tool guide markdown file.
+        
+        Expected format:
+        ---
+        name: tool_name
+        description: Tool description
+        triggers:
+          - trigger 1
+          - trigger 2
+        anti_triggers:
+          - anti trigger 1
+        tools:
+          - tool_name
+        ---
+        
+        ## Usage Guide
+        ...
+        """
+        content = file_path.read_text(encoding="utf-8")
+        
+        frontmatter_match = re.match(r"^---\s*\n(.*?)\n---\s*\n(.*)$", content, re.DOTALL)
+        if not frontmatter_match:
+            logger.warning(f"[ToolGuideRegistry] No frontmatter found in {file_path}")
+            return None
+        
+        frontmatter_text = frontmatter_match.group(1)
+        body = frontmatter_match.group(2)
+        
+        metadata: dict[str, Any] = {}
+        current_key = None
+        current_list: list[str] | None = None
+        
+        for line in frontmatter_text.split("\n"):
+            if not line.strip():
+                continue
+            
+            key_match = re.match(r"^(\w+):\s*(.*)$", line)
+            if key_match:
+                current_key = key_match.group(1)
+                value = key_match.group(2).strip()
+                
+                if value:
+                    metadata[current_key] = value
+                    current_list = None
+                else:
+                    current_list = []
+                    metadata[current_key] = current_list
+            elif line.startswith("  - ") and current_list is not None:
+                current_list.append(line[4:].strip())
+        
+        name = metadata.get("name", file_path.stem)
+        description = metadata.get("description", "")
+        triggers = metadata.get("triggers", [])
+        anti_triggers = metadata.get("anti_triggers", [])
+        tools = metadata.get("tools", [])
+        
+        if isinstance(triggers, str):
+            triggers = [triggers]
+        if isinstance(anti_triggers, str):
+            anti_triggers = [anti_triggers]
+        if isinstance(tools, str):
+            tools = [tools]
+        
+        return ToolGuideDefinition(
+            name=name,
+            description=description,
+            triggers=triggers,
+            anti_triggers=anti_triggers,
+            tools=tools,
+            content=body.strip(),
+            raw_markdown=content,
+        )
+    
+    def get_tool_guide(self, name: str) -> ToolGuideDefinition | None:
+        """Get full tool guide definition by name."""
+        if not self._loaded:
+            self.load_tools()
+        return self._tool_guides.get(name)
+    
+    def get_tool_metadata(self, name: str) -> ToolGuideMetadata | None:
+        """Get lightweight tool guide metadata by name."""
+        if not self._loaded:
+            self.load_tools()
+        return self._metadata_cache.get(name)
+    
+    def get_guide_for_tool(self, tool_name: str) -> ToolGuideDefinition | None:
+        """Get tool guide associated with a tool."""
+        if not self._loaded:
+            self.load_tools()
+        guide_name = self._tool_to_guide.get(tool_name)
+        if guide_name:
+            return self._tool_guides.get(guide_name)
+        return None
+    
+    def list_tools(self) -> list[str]:
+        """List all tool guide names."""
+        if not self._loaded:
+            self.load_tools()
+        return list(self._tool_guides.keys())
+    
+    def list_tool_metadata(self) -> list[ToolGuideMetadata]:
+        """List all tool guide metadata (lightweight)."""
+        if not self._loaded:
+            self.load_tools()
+        return list(self._metadata_cache.values())
+    
+    def build_tools_prompt_section(self, tool_names: list[str] | None = None) -> str:
+        """
+        Build tools section for ReAct prompt.
+        
+        Args:
+            tool_names: If provided, only include tools for these names.
+                       If None, include all tools.
+        
+        Returns:
+            Formatted tools section string
+        """
+        if not self._loaded:
+            self.load_tools()
+        
+        if not self._tool_guides:
+            return ""
+        
+        tools_to_include: list[ToolGuideDefinition] = []
+        
+        if tool_names:
+            for tool_name in tool_names:
+                tool_guide = self.get_guide_for_tool(tool_name)
+                if tool_guide and tool_guide not in tools_to_include:
+                    tools_to_include.append(tool_guide)
+        else:
+            tools_to_include = list(self._tool_guides.values())
+        
+        if not tools_to_include:
+            return ""
+        
+        lines = ["## 工具使用指南", ""]
+        lines.append("以下是每个工具的详细使用说明：")
+        lines.append("")
+        
+        for tool_guide in tools_to_include:
+            lines.append(f"### {tool_guide.name}")
+            lines.append(f"描述: {tool_guide.description}")
+            
+            if tool_guide.triggers:
+                lines.append("触发条件:")
+                for trigger in tool_guide.triggers:
+                    lines.append(f"  - {trigger}")
+            
+            if tool_guide.anti_triggers:
+                lines.append("不应触发:")
+                for anti in tool_guide.anti_triggers:
+                    lines.append(f"  - {anti}")
+            
+            lines.append("")
+            lines.append(tool_guide.content)
+            lines.append("")
+        
+        return "\n".join(lines)
+    
+    def build_compact_tools_section(self) -> str:
+        """
+        Build compact tools section with only name and description.
+        This is used for the initial tool list, with full guidance loaded separately.
+        """
+        if not self._loaded:
+            self.load_tools()
+        
+        if not self._metadata_cache:
+            return "当前没有可用的工具使用指南。"
+        
+        lines = ["## 可用工具列表", "", "以下是你可以使用的工具：", ""]
+        
+        for meta in self._metadata_cache.values():
+            lines.append(f"- **{meta.name}**: {meta.description}")
+            if meta.tools:
+                lines.append(f"  关联工具: {', '.join(meta.tools)}")
+        
+        return "\n".join(lines)
+
+
+_tool_guide_registry: ToolGuideRegistry | None = None
+
+
+def get_tool_guide_registry() -> ToolGuideRegistry:
+    """Get global tool guide registry instance."""
+    global _tool_guide_registry
+    if _tool_guide_registry is None:
+        _tool_guide_registry = ToolGuideRegistry()
+    return _tool_guide_registry
+
+
+def init_tool_guide_registry(tools_dir: Path | None = None) -> ToolGuideRegistry:
+    """Initialize and return tool guide registry."""
+    global _tool_guide_registry
+    _tool_guide_registry = ToolGuideRegistry(tools_dir=tools_dir)
+    _tool_guide_registry.load_tools()
+    return _tool_guide_registry

ai-service/app/services/mid/tool_registry.py

@@ -117,172 +117,124 @@ class ToolRegistry:
         self._tools[name] = tool
         
         logger.info(
-            f"[AC-IDMP-19] Tool registered: name={name}, type={tool_type.value}, "
-            f"version={version}, auth_required={auth_required}"
+            f"[AC-IDMP-19] Registered tool: {name} v{version} "
+            f"(type={tool_type.value}, auth={auth_required}, timeout={timeout_ms}ms)"
         )
         
         return tool
     
-    def unregister(self, name: str) -> bool:
-        """Unregister a tool."""
-        if name in self._tools:
-            del self._tools[name]
-            logger.info(f"[AC-IDMP-19] Tool unregistered: {name}")
-            return True
-        return False
-    
     def get_tool(self, name: str) -> ToolDefinition | None:
-        """Get tool definition by name."""
+        """Get tool by name."""
         return self._tools.get(name)
     
-    def list_tools(
-        self,
-        tool_type: ToolType | None = None,
-        enabled_only: bool = True,
-    ) -> list[ToolDefinition]:
-        """List registered tools, optionally filtered."""
-        tools = list(self._tools.values())
+    def list_tools(self) -> list[str]:
+        """List all registered tool names."""
+        return list(self._tools.keys())
     
-        if tool_type:
-            tools = [t for t in tools if t.tool_type == tool_type]
+    def get_all_tools(self) -> list[ToolDefinition]:
+        """Get all registered tools."""
+        return list(self._tools.values())
     
-        if enabled_only:
-            tools = [t for t in tools if t.enabled]
+    def is_enabled(self, name: str) -> bool:
+        """Check if tool is enabled."""
+        tool = self._tools.get(name)
+        return tool.enabled if tool else False
     
-        return tools
-    
-    def enable_tool(self, name: str) -> bool:
-        """Enable a tool."""
+    def set_enabled(self, name: str, enabled: bool) -> bool:
+        """Enable or disable a tool."""
         tool = self._tools.get(name)
         if tool:
-            tool.enabled = True
-            logger.info(f"[AC-IDMP-19] Tool enabled: {name}")
-            return True
-        return False
-    
-    def disable_tool(self, name: str) -> bool:
-        """Disable a tool."""
-        tool = self._tools.get(name)
-        if tool:
-            tool.enabled = False
-            logger.info(f"[AC-IDMP-19] Tool disabled: {name}")
+            tool.enabled = enabled
+            logger.info(f"[AC-IDMP-19] Tool {name} {'enabled' if enabled else 'disabled'}")
             return True
         return False
     
     async def execute(
         self,
-        tool_name: str,
-        args: dict[str, Any],
-        auth_context: dict[str, Any] | None = None,
+        name: str,
+        **kwargs: Any,
     ) -> ToolExecutionResult:
         """
-        [AC-IDMP-19] Execute a tool with governance.
+        Execute a tool with governance.
         
         Args:
-            tool_name: Tool name to execute
-            args: Tool arguments
-            auth_context: Authentication context
+            name: Tool name
+            **kwargs: Tool arguments
         
         Returns:
             ToolExecutionResult with output and metadata
         """
-        start_time = time.time()
-        
-        tool = self._tools.get(tool_name)
+        tool = self._tools.get(name)
         if not tool:
-            logger.warning(f"[AC-IDMP-19] Tool not found: {tool_name}")
             return ToolExecutionResult(
                 success=False,
-                error=f"Tool not found: {tool_name}",
-                duration_ms=0,
+                error=f"Tool not found: {name}",
+                registry_version=self._version,
             )
         
         if not tool.enabled:
-            logger.warning(f"[AC-IDMP-19] Tool disabled: {tool_name}")
             return ToolExecutionResult(
                 success=False,
-                error=f"Tool disabled: {tool_name}",
-                duration_ms=0,
-                registry_version=tool.version,
+                error=f"Tool is disabled: {name}",
+                registry_version=self._version,
             )
         
-        auth_applied = False
-        if tool.auth_required:
-            if not auth_context:
-                logger.warning(f"[AC-IDMP-19] Auth required but no context: {tool_name}")
-                return ToolExecutionResult(
-                    success=False,
-                    error="Authentication required",
-                    duration_ms=int((time.time() - start_time) * 1000),
-                    auth_applied=False,
-                    registry_version=tool.version,
-                )
-            auth_applied = True
+        start_time = time.time()
         
         try:
-            timeout_seconds = tool.timeout_ms / 1000.0
+            if not tool.handler:
+                return ToolExecutionResult(
+                    success=False,
+                    error=f"Tool has no handler: {name}",
+                    registry_version=self._version,
+                )
             
-            result = await asyncio.wait_for(
-                tool.handler(**args) if tool.handler else asyncio.sleep(0),
-                timeout=timeout_seconds,
+            result = await self._timeout_governor.execute_with_timeout(
+                lambda: tool.handler(**kwargs),
+                timeout_ms=tool.timeout_ms,
             )
             
             duration_ms = int((time.time() - start_time) * 1000)
             
-            logger.info(
-                f"[AC-IDMP-19] Tool executed: name={tool_name}, "
-                f"duration_ms={duration_ms}, success=True"
-            )
-            
             return ToolExecutionResult(
                 success=True,
                 output=result,
                 duration_ms=duration_ms,
-                auth_applied=auth_applied,
-                registry_version=tool.version,
+                auth_applied=tool.auth_required,
+                registry_version=self._version,
             )
             
         except asyncio.TimeoutError:
             duration_ms = int((time.time() - start_time) * 1000)
-            logger.warning(
-                f"[AC-IDMP-19] Tool timeout: name={tool_name}, "
-                f"duration_ms={duration_ms}"
-            )
             return ToolExecutionResult(
                 success=False,
-                error=f"Tool timeout after {tool.timeout_ms}ms",
+                error=f"Tool execution timeout after {tool.timeout_ms}ms",
                 duration_ms=duration_ms,
-                auth_applied=auth_applied,
-                registry_version=tool.version,
+                registry_version=self._version,
             )
-        
         except Exception as e:
             duration_ms = int((time.time() - start_time) * 1000)
-            logger.error(
-                f"[AC-IDMP-19] Tool error: name={tool_name}, error={e}"
-            )
+            logger.error(f"[AC-IDMP-19] Tool execution error: {name} - {e}")
             return ToolExecutionResult(
                 success=False,
                 error=str(e),
                 duration_ms=duration_ms,
-                auth_applied=auth_applied,
-                registry_version=tool.version,
+                registry_version=self._version,
             )
     
-    def create_trace(
+    def build_trace(
         self,
         tool_name: str,
+        args: dict[str, Any],
         result: ToolExecutionResult,
-        args_digest: str | None = None,
     ) -> ToolCallTrace:
-        """
-        [AC-IDMP-19] Create ToolCallTrace from execution result.
-        """
-        tool = self._tools.get(tool_name)
+        """Build a tool call trace from execution result."""
+        import hashlib
+        args_digest = hashlib.md5(str(args).encode()).hexdigest()[:8]
         
         return ToolCallTrace(
             tool_name=tool_name,
-            tool_type=tool.tool_type if tool else ToolType.INTERNAL,
+            tool_type=tool.tool_type if (tool := self._tools.get(tool_name)) else ToolType.INTERNAL,
             registry_version=result.registry_version,
             auth_applied=result.auth_applied,
             duration_ms=result.duration_ms,
@@ -293,6 +245,8 @@ class ToolRegistry:
             error_code=result.error if not result.success else None,
             args_digest=args_digest,
             result_digest=str(result.output)[:100] if result.output else None,
+            arguments=args,
+            result=result.output,
         )
     
     def get_governance_report(self) -> dict[str, Any]:

ai-service/app/services/orchestrator.py

@@ -23,9 +23,9 @@ RAG Optimization (rag-optimization/spec.md):
 """
 
 import logging
-import uuid
 from collections.abc import AsyncGenerator
 from dataclasses import dataclass, field
+from datetime import datetime
 from typing import Any
 
 from sse_starlette.sse import ServerSentEvent
@@ -46,7 +46,6 @@ from app.services.flow.engine import FlowEngine
 from app.services.guardrail.behavior_service import BehaviorRuleService
 from app.services.guardrail.input_scanner import InputScanner
 from app.services.guardrail.output_filter import OutputFilter
-from app.services.guardrail.word_service import ForbiddenWordService
 from app.services.intent.router import IntentRouter
 from app.services.intent.rule_service import IntentRuleService
 from app.services.llm.base import LLMClient, LLMConfig, LLMResponse
@@ -90,6 +89,8 @@ class GenerationContext:
     10. confidence_result: Confidence calculation result
     11. messages_saved: Whether messages were saved
     12. final_response: Final ChatResponse
+
+    [v0.8.0] Extended with route_trace for hybrid routing observability.
     """
     tenant_id: str
     session_id: str
@@ -115,6 +116,11 @@ class GenerationContext:
     target_kb_ids: list[str] | None = None
     behavior_rules: list[str] = field(default_factory=list)
 
+    # [v0.8.0] Hybrid routing fields
+    route_trace: dict[str, Any] | None = None
+    fusion_confidence: float | None = None
+    fusion_decision_reason: str | None = None
+
     diagnostics: dict[str, Any] = field(default_factory=dict)
     execution_steps: list[dict[str, Any]] = field(default_factory=list)
 
@@ -487,7 +493,7 @@ class OrchestratorService:
                         finish_reason="flow_step",
                     )
                     ctx.diagnostics["flow_handled"] = True
-                    logger.info(f"[AC-AISVC-75] Flow provided reply, skipping LLM")
+                    logger.info("[AC-AISVC-75] Flow provided reply, skipping LLM")
 
             else:
                 ctx.diagnostics["flow_check_enabled"] = True
@@ -501,8 +507,8 @@ class OrchestratorService:
         """
         [AC-AISVC-69, AC-AISVC-70] Step 3: Match intent rules and route.
         Routes to: fixed reply, RAG with target KBs, flow start, or transfer.
+        [v0.8.0] Upgraded to use match_hybrid() for hybrid routing.
         """
-        # Skip if flow already handled the request
         if ctx.diagnostics.get("flow_handled"):
             logger.info("[AC-AISVC-69] Flow already handled, skipping intent matching")
             return
@@ -513,7 +519,6 @@ class OrchestratorService:
             return
 
         try:
-            # Load enabled rules ordered by priority
             async with get_session() as session:
                 from app.services.intent.rule_service import IntentRuleService
                 rule_service = IntentRuleService(session)
@@ -524,33 +529,64 @@ class OrchestratorService:
                 ctx.diagnostics["intent_matched"] = False
                 return
 
-            # Match intent
-            ctx.intent_match = self._intent_router.match(
+            fusion_result = await self._intent_router.match_hybrid(
                 message=ctx.current_message,
                 rules=rules,
+                tenant_id=ctx.tenant_id,
             )
 
-            if ctx.intent_match:
+            ctx.route_trace = fusion_result.trace.to_dict()
+            ctx.fusion_confidence = fusion_result.final_confidence
+            ctx.fusion_decision_reason = fusion_result.decision_reason
+
+            if fusion_result.final_intent:
+                ctx.intent_match = type(
+                    "IntentMatchResult",
+                    (),
+                    {
+                        "rule": fusion_result.final_intent,
+                        "match_type": fusion_result.decision_reason,
+                        "matched": "",
+                        "to_dict": lambda: {
+                            "rule_id": str(fusion_result.final_intent.id),
+                            "rule_name": fusion_result.final_intent.name,
+                            "match_type": fusion_result.decision_reason,
+                            "matched": "",
+                            "response_type": fusion_result.final_intent.response_type,
+                            "target_kb_ids": (
+                                fusion_result.final_intent.target_kb_ids or []
+                            ),
+                            "flow_id": (
+                                str(fusion_result.final_intent.flow_id)
+                                if fusion_result.final_intent.flow_id else None
+                            ),
+                            "fixed_reply": fusion_result.final_intent.fixed_reply,
+                            "transfer_message": fusion_result.final_intent.transfer_message,
+                        },
+                    },
+                )()
+
                 logger.info(
-                    f"[AC-AISVC-69] Intent matched: rule={ctx.intent_match.rule.name}, "
-                    f"response_type={ctx.intent_match.rule.response_type}"
+                    f"[AC-AISVC-69] Intent matched: rule={fusion_result.final_intent.name}, "
+                    f"response_type={fusion_result.final_intent.response_type}, "
+                    f"decision={fusion_result.decision_reason}, "
+                    f"confidence={fusion_result.final_confidence:.3f}"
                 )
 
                 ctx.diagnostics["intent_match"] = ctx.intent_match.to_dict()
+                ctx.diagnostics["fusion_result"] = fusion_result.to_dict()
 
-                # Increment hit count
                 async with get_session() as session:
                     rule_service = IntentRuleService(session)
                     await rule_service.increment_hit_count(
                         tenant_id=ctx.tenant_id,
-                        rule_id=ctx.intent_match.rule.id,
+                        rule_id=fusion_result.final_intent.id,
                     )
 
-                # Route based on response_type
-                if ctx.intent_match.rule.response_type == "fixed":
-                    # Fixed reply - skip LLM
+                rule = fusion_result.final_intent
+                if rule.response_type == "fixed":
                     ctx.llm_response = LLMResponse(
-                        content=ctx.intent_match.rule.fixed_reply or "收到您的消息。",
+                        content=rule.fixed_reply or "收到您的消息。",
                         model="intent_fixed",
                         usage={},
                         finish_reason="intent_fixed",
@@ -558,20 +594,18 @@ class OrchestratorService:
                     ctx.diagnostics["intent_handled"] = True
                     logger.info("[AC-AISVC-70] Intent fixed reply, skipping LLM")
 
-                elif ctx.intent_match.rule.response_type == "rag":
-                    # RAG with target KBs
-                    ctx.target_kb_ids = ctx.intent_match.rule.target_kb_ids or []
+                elif rule.response_type == "rag":
+                    ctx.target_kb_ids = rule.target_kb_ids or []
                     logger.info(f"[AC-AISVC-70] Intent RAG, target_kb_ids={ctx.target_kb_ids}")
 
-                elif ctx.intent_match.rule.response_type == "flow":
-                    # Start script flow
-                    if ctx.intent_match.rule.flow_id and self._flow_engine:
+                elif rule.response_type == "flow":
+                    if rule.flow_id and self._flow_engine:
                         async with get_session() as session:
                             flow_engine = FlowEngine(session)
                             instance, first_step = await flow_engine.start(
                                 tenant_id=ctx.tenant_id,
                                 session_id=ctx.session_id,
-                                flow_id=ctx.intent_match.rule.flow_id,
+                                flow_id=rule.flow_id,
                             )
                             if first_step:
                                 ctx.llm_response = LLMResponse(
@@ -583,10 +617,9 @@ class OrchestratorService:
                                 ctx.diagnostics["intent_handled"] = True
                                 logger.info("[AC-AISVC-70] Intent flow started, skipping LLM")
 
-                elif ctx.intent_match.rule.response_type == "transfer":
-                    # Transfer to human
+                elif rule.response_type == "transfer":
                     ctx.llm_response = LLMResponse(
-                        content=ctx.intent_match.rule.transfer_message or "正在为您转接人工客服...",
+                        content=rule.transfer_message or "正在为您转接人工客服...",
                         model="intent_transfer",
                         usage={},
                         finish_reason="intent_transfer",
@@ -600,9 +633,25 @@ class OrchestratorService:
                     ctx.diagnostics["intent_handled"] = True
                     logger.info("[AC-AISVC-70] Intent transfer, skipping LLM")
 
+                if fusion_result.need_clarify:
+                    ctx.diagnostics["need_clarify"] = True
+                    ctx.diagnostics["clarify_candidates"] = [
+                        {"id": str(r.id), "name": r.name}
+                        for r in (fusion_result.clarify_candidates or [])
+                    ]
+                    logger.info(
+                        f"[AC-AISVC-121] Low confidence, need clarify: "
+                        f"confidence={fusion_result.final_confidence:.3f}, "
+                        f"candidates={len(fusion_result.clarify_candidates or [])}"
+                    )
+
             else:
                 ctx.diagnostics["intent_match_enabled"] = True
                 ctx.diagnostics["intent_matched"] = False
+                ctx.diagnostics["fusion_result"] = fusion_result.to_dict()
+                logger.info(
+                    f"[AC-AISVC-69] No intent matched, decision={fusion_result.decision_reason}"
+                )
 
         except Exception as e:
             logger.warning(f"[AC-AISVC-69] Intent matching failed: {e}")
@@ -1122,6 +1171,7 @@ class OrchestratorService:
         [AC-AISVC-02] Build final ChatResponse from generation context.
         Step 12 of the 12-step pipeline.
         Uses filtered_reply from Step 9.
+        [v0.8.0] Includes route_trace in response metadata.
         """
         # Use filtered_reply if available, otherwise use llm_response.content
         if ctx.filtered_reply:
@@ -1142,6 +1192,10 @@ class OrchestratorService:
             "execution_steps": ctx.execution_steps,
         }
 
+        # [v0.8.0] Include route_trace in response metadata
+        if ctx.route_trace:
+            response_metadata["route_trace"] = ctx.route_trace
+
         return ChatResponse(
             reply=reply,
             confidence=confidence,

ai-service/app/services/prompt/template_service.py

@@ -178,6 +178,9 @@ class PromptTemplateService:
                 current_version = v
                 break
 
+        # Get latest version for current_content (not just published)
+        latest_version = versions[0] if versions else None
+
         return {
             "id": str(template.id),
             "name": template.name,
@@ -185,6 +188,8 @@ class PromptTemplateService:
             "description": template.description,
             "is_default": template.is_default,
             "metadata": template.metadata_,
+            "current_content": latest_version.system_instruction if latest_version else None,
+            "variables": latest_version.variables if latest_version else [],
             "current_version": {
                 "version": current_version.version,
                 "status": current_version.status,

ai-service/app/services/retrieval/__init__.py

@@ -2,6 +2,7 @@
 Retrieval module for AI Service.
 [AC-AISVC-16] Provides retriever implementations with plugin architecture.
 RAG Optimization: Two-stage retrieval, RRF hybrid ranking, metadata filtering.
+[AC-AISVC-RES-01~15] Strategy routing and mode routing for retrieval pipeline.
 """
 
 from app.services.retrieval.base import (
@@ -32,6 +33,29 @@ from app.services.retrieval.optimized_retriever import (
     get_optimized_retriever,
 )
 from app.services.retrieval.vector_retriever import VectorRetriever, get_vector_retriever
+from app.services.retrieval.routing_config import (
+    RagRuntimeMode,
+    RoutingConfig,
+    StrategyContext,
+    StrategyType,
+    StrategyResult,
+)
+from app.services.retrieval.strategy_router import (
+    RollbackRecord,
+    StrategyRouter,
+    get_strategy_router,
+)
+from app.services.retrieval.mode_router import (
+    ComplexityAnalyzer,
+    ModeRouter,
+    ModeRouteResult,
+    get_mode_router,
+)
+from app.services.retrieval.strategy_integration import (
+    RetrievalStrategyIntegration,
+    RetrievalStrategyResult,
+    get_retrieval_strategy_integration,
+)
 
 __all__ = [
     "BaseRetriever",
@@ -55,4 +79,19 @@ __all__ = [
     "get_knowledge_indexer",
     "IndexingProgress",
     "IndexingResult",
+    "RagRuntimeMode",
+    "RoutingConfig",
+    "StrategyContext",
+    "StrategyType",
+    "StrategyResult",
+    "RollbackRecord",
+    "StrategyRouter",
+    "get_strategy_router",
+    "ComplexityAnalyzer",
+    "ModeRouter",
+    "ModeRouteResult",
+    "get_mode_router",
+    "RetrievalStrategyIntegration",
+    "RetrievalStrategyResult",
+    "get_retrieval_strategy_integration",
 ]

ai-service/app/services/retrieval/base.py

@@ -28,6 +28,7 @@ class RetrievalContext:
     metadata: dict[str, Any] | None = None
     tag_filter: "TagFilter | None" = None
     kb_ids: list[str] | None = None
+    metadata_filter: dict[str, Any] | None = None
 
     def get_tag_filter_dict(self) -> dict[str, str | list[str] | None] | None:
         """获取标签过滤器的字典表示"""

ai-service/app/services/retrieval/mode_router.py

@@ -0,0 +1,438 @@
+"""
+Mode Router for RAG Runtime Mode Selection.
+[AC-AISVC-RES-09, AC-AISVC-RES-10, AC-AISVC-RES-11] Routes to direct/react/auto mode.
+
+Mode Descriptions:
+- direct: Low-latency generic retrieval path (single KB call)
+- react: Multi-step ReAct retrieval path (high accuracy)
+- auto: Automatic selection based on complexity/confidence rules
+"""
+
+from __future__ import annotations
+
+import logging
+import re
+import time
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING, Any
+
+from app.services.retrieval.routing_config import (
+    RagRuntimeMode,
+    RoutingConfig,
+    StrategyContext,
+)
+
+if TYPE_CHECKING:
+    from app.services.retrieval.base import RetrievalResult
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ComplexityAnalyzer:
+    """
+    Analyzes query complexity for mode routing decisions.
+    
+    Complexity factors:
+    - Query length
+    - Number of conditions/constraints
+    - Presence of logical operators (and, or, not)
+    - Cross-domain indicators
+    - Multi-step reasoning requirements
+    """
+    
+    short_query_threshold: int = 20
+    long_query_threshold: int = 100
+    
+    condition_patterns: list[str] = field(default_factory=lambda: [
+        r"和|与|及|并且|同时",
+        r"或者|还是|要么",
+        r"但是|不过|然而",
+        r"如果|假如|假设",
+        r"既.*又",
+        r"不仅.*而且",
+    ])
+    
+    reasoning_patterns: list[str] = field(default_factory=lambda: [
+        r"为什么|原因|理由",
+        r"怎么|如何|怎样",
+        r"区别|差异|不同",
+        r"比较|对比|优劣",
+        r"分析|评估|判断",
+    ])
+    
+    cross_domain_patterns: list[str] = field(default_factory=lambda: [
+        r"跨|多|各个",
+        r"所有|全部|整体",
+        r"综合|汇总|统计",
+    ])
+    
+    def analyze(self, query: str) -> float:
+        """
+        Analyze query complexity and return a score (0.0 ~ 1.0).
+        
+        Higher score indicates more complex query that may benefit from ReAct mode.
+        
+        Args:
+            query: User query text
+        
+        Returns:
+            Complexity score (0.0 = simple, 1.0 = very complex)
+        """
+        if not query:
+            return 0.0
+        
+        score = 0.0
+        
+        query_length = len(query)
+        if query_length < self.short_query_threshold:
+            score += 0.0
+        elif query_length > self.long_query_threshold:
+            score += 0.3
+        else:
+            score += 0.15
+        
+        condition_count = 0
+        for pattern in self.condition_patterns:
+            matches = re.findall(pattern, query)
+            condition_count += len(matches)
+        
+        if condition_count >= 3:
+            score += 0.3
+        elif condition_count >= 2:
+            score += 0.2
+        elif condition_count >= 1:
+            score += 0.1
+        
+        for pattern in self.reasoning_patterns:
+            if re.search(pattern, query):
+                score += 0.15
+                break
+        
+        for pattern in self.cross_domain_patterns:
+            if re.search(pattern, query):
+                score += 0.15
+                break
+        
+        question_marks = query.count("?") + query.count("？")
+        if question_marks >= 2:
+            score += 0.1
+        
+        return min(1.0, score)
+
+
+@dataclass
+class ModeRouteResult:
+    """Result from mode routing decision."""
+    mode: RagRuntimeMode
+    confidence: float
+    complexity_score: float
+    should_fallback_to_react: bool = False
+    fallback_reason: str | None = None
+    diagnostics: dict[str, Any] = field(default_factory=dict)
+
+
+class DirectRetrievalExecutor:
+    """
+    [AC-AISVC-RES-09] Direct retrieval executor for low-latency path.
+    
+    Single KB call without multi-step reasoning.
+    """
+    
+    def __init__(self):
+        self._retriever = None
+    
+    async def execute(
+        self,
+        ctx: StrategyContext,
+    ) -> "RetrievalResult":
+        """
+        Execute direct retrieval (single KB call).
+        """
+        from app.services.retrieval.optimized_retriever import get_optimized_retriever
+        from app.services.retrieval.base import RetrievalContext
+        
+        if self._retriever is None:
+            self._retriever = await get_optimized_retriever()
+        
+        retrieval_ctx = RetrievalContext(
+            tenant_id=ctx.tenant_id,
+            query=ctx.query,
+            metadata_filter=ctx.metadata_filter,
+            kb_ids=ctx.kb_ids,
+        )
+        
+        return await self._retriever.retrieve(retrieval_ctx)
+
+
+class ReactRetrievalExecutor:
+    """
+    [AC-AISVC-RES-10] ReAct retrieval executor for multi-step path.
+    
+    Uses AgentOrchestrator for multi-step reasoning and KB calls.
+    """
+    
+    def __init__(self, max_steps: int = 5):
+        self._max_steps = max_steps
+    
+    async def execute(
+        self,
+        ctx: StrategyContext,
+        config: RoutingConfig,
+    ) -> tuple[str, "RetrievalResult | None", dict[str, Any]]:
+        """
+        Execute ReAct retrieval (multi-step reasoning).
+        
+        Returns:
+            Tuple of (final_answer, retrieval_result, react_context)
+        """
+        from app.services.mid.agent_orchestrator import AgentOrchestrator, AgentMode
+        from app.services.mid.tool_registry import ToolRegistry
+        from app.services.mid.timeout_governor import TimeoutGovernor
+        from app.services.llm.factory import get_llm_config_manager
+        
+        try:
+            llm_manager = get_llm_config_manager()
+            llm_client = llm_manager.get_client()
+        except Exception as e:
+            logger.warning(f"[ModeRouter] Failed to get LLM client: {e}")
+            llm_client = None
+        
+        tool_registry = ToolRegistry(timeout_governor=TimeoutGovernor())
+        timeout_governor = TimeoutGovernor()
+        
+        orchestrator = AgentOrchestrator(
+            max_iterations=min(config.react_max_steps, self._max_steps),
+            timeout_governor=timeout_governor,
+            llm_client=llm_client,
+            tool_registry=tool_registry,
+            tenant_id=ctx.tenant_id,
+            mode=AgentMode.FUNCTION_CALLING,
+        )
+        
+        base_context = {
+            "query": ctx.query,
+            "metadata_filter": ctx.metadata_filter,
+            "kb_ids": ctx.kb_ids,
+            **ctx.additional_context,
+        }
+        
+        final_answer, react_ctx, trace = await orchestrator.execute(
+            user_message=ctx.query,
+            context=base_context,
+        )
+        
+        return final_answer, None, {
+            "iterations": react_ctx.iteration,
+            "tool_calls": [tc.model_dump() for tc in react_ctx.tool_calls] if react_ctx.tool_calls else [],
+            "final_answer": final_answer,
+        }
+
+
+class ModeRouter:
+    """
+    [AC-AISVC-RES-09, AC-AISVC-RES-10, AC-AISVC-RES-11] 
+    Mode router for RAG runtime mode selection.
+    
+    Mode Selection:
+    - direct: Low-latency generic retrieval (single KB call)
+    - react: Multi-step ReAct retrieval (high accuracy)
+    - auto: Automatic selection based on complexity/confidence
+    
+    Auto Mode Rules:
+    - Direct conditions:
+        - Short query, clear intent
+        - High metadata confidence
+        - No cross-domain/multi-condition
+    - React conditions:
+        - Multi-condition/multi-constraint
+        - Low metadata confidence
+        - Need for secondary confirmation or multi-step reasoning
+    """
+    
+    def __init__(
+        self,
+        config: RoutingConfig | None = None,
+    ):
+        self._config = config or RoutingConfig()
+        self._complexity_analyzer = ComplexityAnalyzer()
+        self._direct_executor = DirectRetrievalExecutor()
+        self._react_executor = ReactRetrievalExecutor(
+            max_steps=self._config.react_max_steps
+        )
+    
+    @property
+    def config(self) -> RoutingConfig:
+        """Get current configuration."""
+        return self._config
+    
+    def update_config(self, new_config: RoutingConfig) -> None:
+        """
+        [AC-AISVC-RES-15] Update routing configuration (hot reload).
+        """
+        self._config = new_config
+        self._react_executor._max_steps = new_config.react_max_steps
+        
+        logger.info(
+            f"[AC-AISVC-RES-15] ModeRouter config updated: "
+            f"mode={new_config.rag_runtime_mode.value}, "
+            f"react_max_steps={new_config.react_max_steps}, "
+            f"confidence_threshold={new_config.react_trigger_confidence_threshold}"
+        )
+    
+    def route(
+        self,
+        ctx: StrategyContext,
+    ) -> ModeRouteResult:
+        """
+        [AC-AISVC-RES-09, AC-AISVC-RES-10, AC-AISVC-RES-11] 
+        Route to appropriate mode based on configuration and context.
+        
+        Args:
+            ctx: Strategy context with query, metadata, confidence, etc.
+        
+        Returns:
+            ModeRouteResult with selected mode and diagnostics
+        """
+        configured_mode = self._config.get_rag_runtime_mode()
+        
+        if configured_mode == RagRuntimeMode.DIRECT:
+            logger.info(
+                f"[AC-AISVC-RES-09] Mode routing to DIRECT: tenant={ctx.tenant_id}"
+            )
+            return ModeRouteResult(
+                mode=RagRuntimeMode.DIRECT,
+                confidence=ctx.metadata_confidence,
+                complexity_score=ctx.complexity_score,
+                diagnostics={"configured_mode": "direct"},
+            )
+        
+        if configured_mode == RagRuntimeMode.REACT:
+            logger.info(
+                f"[AC-AISVC-RES-10] Mode routing to REACT: tenant={ctx.tenant_id}"
+            )
+            return ModeRouteResult(
+                mode=RagRuntimeMode.REACT,
+                confidence=ctx.metadata_confidence,
+                complexity_score=ctx.complexity_score,
+                diagnostics={"configured_mode": "react"},
+            )
+        
+        complexity_score = self._complexity_analyzer.analyze(ctx.query)
+        effective_complexity = max(complexity_score, ctx.complexity_score)
+        
+        should_use_react = self._config.should_trigger_react_in_auto_mode(
+            confidence=ctx.metadata_confidence,
+            complexity_score=effective_complexity,
+        )
+        
+        selected_mode = RagRuntimeMode.REACT if should_use_react else RagRuntimeMode.DIRECT
+        
+        logger.info(
+            f"[AC-AISVC-RES-11, AC-AISVC-RES-12, AC-AISVC-RES-13] "
+            f"Auto mode routing: selected={selected_mode.value}, "
+            f"confidence={ctx.metadata_confidence:.2f}, "
+            f"complexity={effective_complexity:.2f}, "
+            f"tenant={ctx.tenant_id}"
+        )
+        
+        return ModeRouteResult(
+            mode=selected_mode,
+            confidence=ctx.metadata_confidence,
+            complexity_score=effective_complexity,
+            diagnostics={
+                "configured_mode": "auto",
+                "analyzed_complexity": complexity_score,
+                "provided_complexity": ctx.complexity_score,
+                "react_trigger_confidence": self._config.react_trigger_confidence_threshold,
+                "react_trigger_complexity": self._config.react_trigger_complexity_score,
+            },
+        )
+    
+    async def execute_direct(
+        self,
+        ctx: StrategyContext,
+    ) -> "RetrievalResult":
+        """
+        Execute direct retrieval mode.
+        """
+        return await self._direct_executor.execute(ctx)
+    
+    async def execute_react(
+        self,
+        ctx: StrategyContext,
+    ) -> tuple[str, "RetrievalResult | None", dict[str, Any]]:
+        """
+        Execute ReAct retrieval mode.
+        """
+        return await self._react_executor.execute(ctx, self._config)
+    
+    async def execute_with_fallback(
+        self,
+        ctx: StrategyContext,
+    ) -> tuple["RetrievalResult | None", str | None, ModeRouteResult]:
+        """
+        [AC-AISVC-RES-14] Execute with fallback from direct to react on low confidence.
+        
+        Args:
+            ctx: Strategy context
+        
+        Returns:
+            Tuple of (RetrievalResult or None, final_answer or None, ModeRouteResult)
+        """
+        route_result = self.route(ctx)
+        
+        if route_result.mode == RagRuntimeMode.DIRECT:
+            retrieval_result = await self._direct_executor.execute(ctx)
+            
+            max_score = 0.0
+            if retrieval_result and retrieval_result.hits:
+                max_score = max((h.score for h in retrieval_result.hits), default=0.0)
+            
+            if self._config.should_fallback_direct_to_react(max_score):
+                logger.info(
+                    f"[AC-AISVC-RES-14] Direct mode low confidence fallback to react: "
+                    f"confidence={max_score:.2f}, threshold={self._config.direct_fallback_confidence_threshold}"
+                )
+                
+                final_answer, _, react_ctx = await self._react_executor.execute(ctx, self._config)
+                
+                return (
+                    None,
+                    final_answer,
+                    ModeRouteResult(
+                        mode=RagRuntimeMode.REACT,
+                        confidence=max_score,
+                        complexity_score=route_result.complexity_score,
+                        should_fallback_to_react=True,
+                        fallback_reason="low_confidence",
+                        diagnostics={
+                            **route_result.diagnostics,
+                            "fallback_from": "direct",
+                            "direct_confidence": max_score,
+                        },
+                    ),
+                )
+            
+            return retrieval_result, None, route_result
+        
+        final_answer, _, react_ctx = await self._react_executor.execute(ctx, self._config)
+        
+        return None, final_answer, route_result
+
+
+_mode_router: ModeRouter | None = None
+
+
+def get_mode_router() -> ModeRouter:
+    """Get or create ModeRouter singleton."""
+    global _mode_router
+    if _mode_router is None:
+        _mode_router = ModeRouter()
+    return _mode_router
+
+
+def reset_mode_router() -> None:
+    """Reset ModeRouter singleton (for testing)."""
+    global _mode_router
+    _mode_router = None

ai-service/app/services/retrieval/routing_config.py

@@ -0,0 +1,187 @@
+"""
+Retrieval and Embedding Strategy Configuration.
+[AC-AISVC-RES-01~15] Configuration for strategy routing and mode routing.
+"""
+
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Any
+
+
+class StrategyType(str, Enum):
+    """Strategy type for retrieval pipeline selection."""
+    DEFAULT = "default"
+    ENHANCED = "enhanced"
+
+
+class RagRuntimeMode(str, Enum):
+    """RAG runtime mode for execution path selection."""
+    DIRECT = "direct"
+    REACT = "react"
+    AUTO = "auto"
+
+
+@dataclass
+class RoutingConfig:
+    """
+    [AC-AISVC-RES-01~15] Routing configuration for strategy and mode selection.
+    
+    Configuration hierarchy:
+    1. Strategy selection (default vs enhanced)
+    2. Mode selection (direct/react/auto)
+    3. Auto routing rules (complexity/confidence thresholds)
+    4. Fallback behavior
+    """
+    
+    enabled: bool = True
+    strategy: StrategyType = StrategyType.DEFAULT
+    
+    grayscale_percentage: float = 0.0
+    grayscale_allowlist: list[str] = field(default_factory=list)
+    
+    rag_runtime_mode: RagRuntimeMode = RagRuntimeMode.AUTO
+    
+    react_trigger_confidence_threshold: float = 0.6
+    react_trigger_complexity_score: float = 0.5
+    react_max_steps: int = 5
+    
+    direct_fallback_on_low_confidence: bool = True
+    direct_fallback_confidence_threshold: float = 0.4
+    
+    performance_budget_ms: int = 5000
+    performance_degradation_threshold: float = 0.2
+    
+    def should_use_enhanced_strategy(self, tenant_id: str | None = None) -> bool:
+        """
+        [AC-AISVC-RES-02, AC-AISVC-RES-03] Determine if enhanced strategy should be used.
+        
+        Priority:
+        1. If strategy is explicitly set to ENHANCED, use enhanced
+        2. If strategy is DEFAULT, use default
+        3. If grayscale is enabled, check percentage/allowlist
+        """
+        if self.strategy == StrategyType.ENHANCED:
+            return True
+        
+        if self.strategy == StrategyType.DEFAULT:
+            return False
+        
+        if self.grayscale_percentage > 0:
+            import hashlib
+            if tenant_id:
+                hash_val = int(hashlib.md5(tenant_id.encode()).hexdigest()[:8], 16)
+                return (hash_val % 100) < (self.grayscale_percentage * 100)
+            return False
+        
+        if self.grayscale_allowlist and tenant_id:
+            return tenant_id in self.grayscale_allowlist
+        
+        return False
+    
+    def get_rag_runtime_mode(self) -> RagRuntimeMode:
+        """Get the configured RAG runtime mode."""
+        return self.rag_runtime_mode
+    
+    def should_fallback_direct_to_react(self, confidence: float) -> bool:
+        """
+        [AC-AISVC-RES-14] Determine if direct mode should fallback to react.
+        
+        Args:
+            confidence: Retrieval confidence score (0.0 ~ 1.0)
+        
+        Returns:
+            True if fallback should be triggered
+        """
+        if not self.direct_fallback_on_low_confidence:
+            return False
+        
+        return confidence < self.direct_fallback_confidence_threshold
+    
+    def should_trigger_react_in_auto_mode(
+        self,
+        confidence: float,
+        complexity_score: float,
+    ) -> bool:
+        """
+        [AC-AISVC-RES-11, AC-AISVC-RES-12, AC-AISVC-RES-13] 
+        Determine if react mode should be triggered in auto mode.
+        
+        Direct conditions (优先):
+        - Short query, clear intent
+        - High metadata confidence
+        - No cross-domain/multi-condition
+        
+        React conditions:
+        - Multi-condition/multi-constraint
+        - Low metadata confidence
+        - Need for secondary confirmation or multi-step reasoning
+        
+        Args:
+            confidence: Metadata inference confidence (0.0 ~ 1.0)
+            complexity_score: Query complexity score (0.0 ~ 1.0)
+        
+        Returns:
+            True if react mode should be used
+        """
+        if confidence < self.react_trigger_confidence_threshold:
+            return True
+        
+        if complexity_score > self.react_trigger_complexity_score:
+            return True
+        
+        return False
+    
+    def validate(self) -> tuple[bool, list[str]]:
+        """
+        [AC-AISVC-RES-06] Validate configuration consistency.
+        
+        Returns:
+            (is_valid, list of error messages)
+        """
+        errors = []
+        
+        if self.grayscale_percentage < 0 or self.grayscale_percentage > 1.0:
+            errors.append("grayscale_percentage must be between 0.0 and 1.0")
+        
+        if self.react_trigger_confidence_threshold < 0 or self.react_trigger_confidence_threshold > 1.0:
+            errors.append("react_trigger_confidence_threshold must be between 0.0 and 1.0")
+        
+        if self.react_trigger_complexity_score < 0 or self.react_trigger_complexity_score > 1.0:
+            errors.append("react_trigger_complexity_score must be between 0.0 and 1.0")
+        
+        if self.react_max_steps < 3 or self.react_max_steps > 10:
+            errors.append("react_max_steps must be between 3 and 10")
+        
+        if self.direct_fallback_confidence_threshold < 0 or self.direct_fallback_confidence_threshold > 1.0:
+            errors.append("direct_fallback_confidence_threshold must be between 0.0 and 1.0")
+        
+        if self.performance_budget_ms < 1000:
+            errors.append("performance_budget_ms must be at least 1000")
+        
+        if self.performance_degradation_threshold < 0 or self.performance_degradation_threshold > 1.0:
+            errors.append("performance_degradation_threshold must be between 0.0 and 1.0")
+        
+        return (len(errors) == 0, errors)
+
+
+@dataclass
+class StrategyContext:
+    """Context for strategy routing decision."""
+    tenant_id: str
+    query: str
+    metadata_filter: dict[str, Any] | None = None
+    metadata_confidence: float = 1.0
+    complexity_score: float = 0.0
+    kb_ids: list[str] | None = None
+    top_k: int = 5
+    additional_context: dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass
+class StrategyResult:
+    """Result from strategy routing."""
+    strategy: StrategyType
+    mode: RagRuntimeMode
+    should_fallback: bool = False
+    fallback_reason: str | None = None
+    diagnostics: dict[str, Any] = field(default_factory=dict)

ai-service/app/services/retrieval/strategy/__init__.py

@@ -0,0 +1,102 @@
+"""
+Retrieval Strategy Module for AI Service.
+[AC-AISVC-RES-01~15] 策略化检索与嵌入模块。
+
+核心组件：
+- RetrievalStrategyConfig: 策略配置模型
+- BasePipeline: Pipeline 抽象基类
+- DefaultPipeline: 默认策略（复用现有逻辑）
+- EnhancedPipeline: 增强策略（新端到端流程）
+- MetadataInferenceService: 元数据推断统一入口
+- StrategyRouter: 策略路由器
+- ModeRouter: 模式路由器（direct/react/auto）
+- RollbackManager: 回退管理器
+"""
+
+from app.services.retrieval.strategy.config import (
+    FilterMode,
+    GrayscaleConfig,
+    HybridRetrievalConfig,
+    MetadataInferenceConfig,
+    ModeRouterConfig,
+    PipelineConfig,
+    RerankerConfig,
+    RetrievalStrategyConfig,
+    RuntimeMode,
+    StrategyType,
+    get_strategy_config,
+    set_strategy_config,
+)
+from app.services.retrieval.strategy.default_pipeline import (
+    DefaultPipeline,
+    get_default_pipeline,
+)
+from app.services.retrieval.strategy.enhanced_pipeline import (
+    EnhancedPipeline,
+    get_enhanced_pipeline,
+)
+from app.services.retrieval.strategy.metadata_inference import (
+    InferenceContext,
+    InferenceResult,
+    MetadataInferenceService,
+)
+from app.services.retrieval.strategy.mode_router import (
+    ModeDecision,
+    ModeRouter,
+    get_mode_router,
+)
+from app.services.retrieval.strategy.pipeline_base import (
+    BasePipeline,
+    MetadataFilterResult,
+    PipelineContext,
+    PipelineResult,
+)
+from app.services.retrieval.strategy.rollback_manager import (
+    AuditLog,
+    RollbackManager,
+    RollbackResult,
+    RollbackTrigger,
+    get_rollback_manager,
+)
+from app.services.retrieval.strategy.strategy_router import (
+    RoutingDecision,
+    StrategyRouter,
+    get_strategy_router,
+)
+
+__all__ = [
+    "BasePipeline",
+    "PipelineContext",
+    "PipelineResult",
+    "MetadataFilterResult",
+    "DefaultPipeline",
+    "get_default_pipeline",
+    "EnhancedPipeline",
+    "get_enhanced_pipeline",
+    "RetrievalStrategyConfig",
+    "GrayscaleConfig",
+    "PipelineConfig",
+    "RerankerConfig",
+    "ModeRouterConfig",
+    "HybridRetrievalConfig",
+    "MetadataInferenceConfig",
+    "StrategyType",
+    "FilterMode",
+    "RuntimeMode",
+    "get_strategy_config",
+    "set_strategy_config",
+    "MetadataInferenceService",
+    "InferenceContext",
+    "InferenceResult",
+    "StrategyRouter",
+    "RoutingDecision",
+    "get_strategy_router",
+    "ModeRouter",
+    "ModeDecision",
+    "get_mode_router",
+    "RollbackManager",
+    "RollbackResult",
+    "RollbackTrigger",
+    "AuditLog",
+    "get_rollback_manager",
+]

ai-service/app/services/retrieval/strategy/config.py

@@ -0,0 +1,201 @@
+"""
+Retrieval Strategy Configuration.
+[AC-AISVC-RES-01~15] 检索策略配置模型。
+"""
+
+import random
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Any
+
+
+class StrategyType(str, Enum):
+    """策略类型。"""
+    DEFAULT = "default"
+    ENHANCED = "enhanced"
+
+
+class RuntimeMode(str, Enum):
+    """运行时模式。"""
+    DIRECT = "direct"
+    REACT = "react"
+    AUTO = "auto"
+
+
+class FilterMode(str, Enum):
+    """过滤模式。"""
+    HARD = "hard"
+    SOFT = "soft"
+    NONE = "none"
+
+
+@dataclass
+class GrayscaleConfig:
+    """灰度发布配置。【AC-AISVC-RES-03】"""
+    enabled: bool = False
+    percentage: float = 0.0
+    allowlist: list[str] = field(default_factory=list)
+    
+    def should_use_enhanced(self, tenant_id: str, user_id: str | None = None) -> bool:
+        """判断是否应该使用增强策略。"""
+        if not self.enabled:
+            return False
+        
+        if tenant_id in self.allowlist or (user_id and user_id in self.allowlist):
+            return True
+        
+        return random.random() * 100 < self.percentage
+
+
+@dataclass
+class HybridRetrievalConfig:
+    """混合检索配置。"""
+    dense_weight: float = 0.7
+    keyword_weight: float = 0.3
+    rrf_k: int = 60
+    enable_keyword: bool = True
+    keyword_top_k_multiplier: int = 2
+
+
+@dataclass
+class RerankerConfig:
+    """重排器配置。【AC-AISVC-RES-08】"""
+    enabled: bool = False
+    model: str = "cross-encoder"
+    top_k_after_rerank: int = 5
+    min_score_threshold: float = 0.3
+
+
+@dataclass
+class ModeRouterConfig:
+    """模式路由配置。【AC-AISVC-RES-09~15】"""
+    runtime_mode: RuntimeMode = RuntimeMode.DIRECT
+    react_trigger_confidence_threshold: float = 0.6
+    react_trigger_complexity_score: float = 0.5
+    react_max_steps: int = 5
+    direct_fallback_on_low_confidence: bool = True
+    short_query_threshold: int = 20
+    
+    def should_use_react(
+        self,
+        query: str,
+        confidence: float | None = None,
+        complexity_score: float | None = None,
+    ) -> bool:
+        """判断是否应该使用 ReAct 模式。【AC-AISVC-RES-11~13】"""
+        if self.runtime_mode == RuntimeMode.REACT:
+            return True
+        if self.runtime_mode == RuntimeMode.DIRECT:
+            return False
+        
+        if len(query) <= self.short_query_threshold and confidence and confidence >= self.react_trigger_confidence_threshold:
+            return False
+        
+        if complexity_score and complexity_score >= self.react_trigger_complexity_score:
+            return True
+        
+        if confidence and confidence < self.react_trigger_confidence_threshold:
+            return True
+        
+        return False
+
+
+@dataclass
+class MetadataInferenceConfig:
+    """元数据推断配置。"""
+    enabled: bool = True
+    confidence_high_threshold: float = 0.8
+    confidence_low_threshold: float = 0.5
+    default_filter_mode: FilterMode = FilterMode.SOFT
+    cache_ttl_seconds: int = 300
+    
+    def determine_filter_mode(self, confidence: float | None) -> FilterMode:
+        """根据置信度确定过滤模式。"""
+        if confidence is None:
+            return FilterMode.NONE
+        if confidence >= self.confidence_high_threshold:
+            return FilterMode.HARD
+        if confidence >= self.confidence_low_threshold:
+            return FilterMode.SOFT
+        return FilterMode.NONE
+
+
+@dataclass
+class PipelineConfig:
+    """Pipeline 配置。"""
+    top_k: int = 5
+    score_threshold: float = 0.01
+    min_hits: int = 1
+    two_stage_enabled: bool = True
+    two_stage_expand_factor: int = 10
+    hybrid: HybridRetrievalConfig = field(default_factory=HybridRetrievalConfig)
+
+
+@dataclass
+class RetrievalStrategyConfig:
+    """检索策略顶层配置。【AC-AISVC-RES-01~15】"""
+    active_strategy: StrategyType = StrategyType.DEFAULT
+    grayscale: GrayscaleConfig = field(default_factory=GrayscaleConfig)
+    pipeline: PipelineConfig = field(default_factory=PipelineConfig)
+    reranker: RerankerConfig = field(default_factory=RerankerConfig)
+    mode_router: ModeRouterConfig = field(default_factory=ModeRouterConfig)
+    metadata_inference: MetadataInferenceConfig = field(default_factory=MetadataInferenceConfig)
+    performance_thresholds: dict[str, float] = field(default_factory=lambda: {
+        "max_latency_ms": 2000.0,
+        "min_success_rate": 0.95,
+        "max_error_rate": 0.05,
+    })
+    
+    def is_enhanced_enabled(self, tenant_id: str, user_id: str | None = None) -> bool:
+        """判断是否启用增强策略。"""
+        if self.active_strategy == StrategyType.ENHANCED:
+            return True
+        return self.grayscale.should_use_enhanced(tenant_id, user_id)
+    
+    def to_dict(self) -> dict[str, Any]:
+        """转换为字典。"""
+        return {
+            "active_strategy": self.active_strategy.value,
+            "grayscale": {
+                "enabled": self.grayscale.enabled,
+                "percentage": self.grayscale.percentage,
+                "allowlist": self.grayscale.allowlist,
+            },
+            "pipeline": {
+                "top_k": self.pipeline.top_k,
+                "score_threshold": self.pipeline.score_threshold,
+                "min_hits": self.pipeline.min_hits,
+                "two_stage_enabled": self.pipeline.two_stage_enabled,
+            },
+            "reranker": {
+                "enabled": self.reranker.enabled,
+                "model": self.reranker.model,
+                "top_k_after_rerank": self.reranker.top_k_after_rerank,
+            },
+            "mode_router": {
+                "runtime_mode": self.mode_router.runtime_mode.value,
+                "react_trigger_confidence_threshold": self.mode_router.react_trigger_confidence_threshold,
+            },
+            "metadata_inference": {
+                "enabled": self.metadata_inference.enabled,
+                "confidence_high_threshold": self.metadata_inference.confidence_high_threshold,
+            },
+            "performance_thresholds": self.performance_thresholds,
+        }
+
+
+_global_config: RetrievalStrategyConfig | None = None
+
+
+def get_strategy_config() -> RetrievalStrategyConfig:
+    """获取全局策略配置。"""
+    global _global_config
+    if _global_config is None:
+        _global_config = RetrievalStrategyConfig()
+    return _global_config
+
+
+def set_strategy_config(config: RetrievalStrategyConfig) -> None:
+    """设置全局策略配置。"""
+    global _global_config
+    _global_config = config

ai-service/app/services/retrieval/strategy/default_pipeline.py

@@ -0,0 +1,117 @@
+"""
+Default Pipeline.
+[AC-AISVC-RES-01] 默认策略 Pipeline，复用现有逻辑。
+"""
+
+import logging
+import time
+from typing import Any
+
+from app.services.retrieval.base import RetrievalContext, RetrievalResult
+from app.services.retrieval.optimized_retriever import OptimizedRetriever, get_optimized_retriever
+from app.services.retrieval.strategy.config import PipelineConfig
+from app.services.retrieval.strategy.pipeline_base import (
+    BasePipeline,
+    PipelineContext,
+    PipelineResult,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class DefaultPipeline(BasePipeline):
+    """
+    默认策略 Pipeline。【AC-AISVC-RES-01】
+    
+    复用现有 OptimizedRetriever 逻辑，保持线上行为不变。
+    """
+    
+    def __init__(
+        self,
+        config: PipelineConfig | None = None,
+        optimized_retriever: OptimizedRetriever | None = None,
+    ):
+        self._config = config or PipelineConfig()
+        self._optimized_retriever = optimized_retriever
+    
+    @property
+    def name(self) -> str:
+        return "default_pipeline"
+    
+    @property
+    def description(self) -> str:
+        return "默认检索策略，复用现有 OptimizedRetriever 逻辑。"
+    
+    async def _get_retriever(self) -> OptimizedRetriever:
+        if self._optimized_retriever is None:
+            self._optimized_retriever = await get_optimized_retriever()
+        return self._optimized_retriever
+    
+    async def retrieve(self, ctx: PipelineContext) -> PipelineResult:
+        """执行默认检索流程。【AC-AISVC-RES-01】"""
+        start_time = time.time()
+        
+        logger.info(
+            f"[DefaultPipeline] Starting retrieval: tenant={ctx.tenant_id}, "
+            f"query={ctx.query[:50]}..."
+        )
+        
+        try:
+            retriever = await self._get_retriever()
+            
+            metadata_filter = None
+            if ctx.metadata_filter:
+                metadata_filter = ctx.metadata_filter.filter_dict
+            
+            retrieval_ctx = RetrievalContext(
+                tenant_id=ctx.tenant_id,
+                query=ctx.query,
+                session_id=ctx.session_id,
+                metadata_filter=metadata_filter,
+                kb_ids=ctx.kb_ids,
+            )
+            
+            result = await retriever.retrieve(retrieval_ctx)
+            
+            latency_ms = (time.time() - start_time) * 1000
+            
+            logger.info(
+                f"[DefaultPipeline] Retrieval completed: hits={len(result.hits)}, "
+                f"latency_ms={latency_ms:.2f}"
+            )
+            
+            return PipelineResult(
+                retrieval_result=result,
+                pipeline_name=self.name,
+                metadata_filter_applied=metadata_filter is not None,
+                latency_ms=latency_ms,
+                diagnostics={
+                    "retriever": "OptimizedRetriever",
+                    **(result.diagnostics or {}),
+                },
+            )
+            
+        except Exception as e:
+            latency_ms = (time.time() - start_time) * 1000
+            logger.error(f"[DefaultPipeline] Retrieval error: {e}", exc_info=True)
+            return self._create_empty_result(ctx, error=str(e), latency_ms=latency_ms)
+    
+    async def health_check(self) -> bool:
+        """健康检查。"""
+        try:
+            retriever = await self._get_retriever()
+            return await retriever.health_check()
+        except Exception as e:
+            logger.error(f"[DefaultPipeline] Health check failed: {e}")
+            return False
+
+
+_default_pipeline: DefaultPipeline | None = None
+
+
+async def get_default_pipeline() -> DefaultPipeline:
+    """获取 DefaultPipeline 单例。"""
+    global _default_pipeline
+    if _default_pipeline is None:
+        _default_pipeline = DefaultPipeline()
+    return _default_pipeline

ai-service/app/services/retrieval/strategy/enhanced_pipeline.py

@@ -0,0 +1,364 @@
+"""
+Enhanced Pipeline.
+[AC-AISVC-RES-02] 增强策略 Pipeline，新端到端流程。
+"""
+
+import asyncio
+import logging
+import re
+import time
+from dataclasses import dataclass
+from typing import Any
+
+import numpy as np
+
+from app.core.config import get_settings
+from app.core.qdrant_client import QdrantClient, get_qdrant_client
+from app.services.embedding.nomic_provider import NomicEmbeddingProvider
+from app.services.retrieval.base import RetrievalHit, RetrievalResult
+from app.services.retrieval.optimized_retriever import RRFCombiner
+from app.services.retrieval.strategy.config import (
+    HybridRetrievalConfig,
+    PipelineConfig,
+    RerankerConfig,
+)
+from app.services.retrieval.strategy.pipeline_base import (
+    BasePipeline,
+    PipelineContext,
+    PipelineResult,
+)
+
+logger = logging.getLogger(__name__)
+settings = get_settings()
+
+
+@dataclass
+class RetrievalCandidate:
+    """检索候选结果。"""
+    id: str
+    text: str
+    score: float
+    vector_score: float = 0.0
+    keyword_score: float = 0.0
+    metadata: dict[str, Any] = None
+    
+    def __post_init__(self):
+        if self.metadata is None:
+            self.metadata = {}
+
+
+class EnhancedPipeline(BasePipeline):
+    """
+    增强策略 Pipeline。【AC-AISVC-RES-02】
+    
+    新端到端流程：
+    1. Dense 向量检索
+    2. Keyword 关键词检索
+    3. RRF 融合排序
+    4. 可选重排
+    """
+    
+    def __init__(
+        self,
+        config: PipelineConfig | None = None,
+        reranker_config: RerankerConfig | None = None,
+        qdrant_client: QdrantClient | None = None,
+    ):
+        self._config = config or PipelineConfig()
+        self._reranker_config = reranker_config or RerankerConfig()
+        self._qdrant_client = qdrant_client
+        self._rrf_combiner = RRFCombiner(k=self._config.hybrid.rrf_k)
+        self._embedding_provider: NomicEmbeddingProvider | None = None
+    
+    @property
+    def name(self) -> str:
+        return "enhanced_pipeline"
+    
+    @property
+    def description(self) -> str:
+        return "增强检索策略，支持 Dense + Keyword + RRF 组合检索。"
+    
+    async def _get_client(self) -> QdrantClient:
+        if self._qdrant_client is None:
+            self._qdrant_client = await get_qdrant_client()
+        return self._qdrant_client
+    
+    async def _get_embedding_provider(self) -> NomicEmbeddingProvider:
+        if self._embedding_provider is None:
+            from app.services.embedding.factory import get_embedding_config_manager
+            manager = get_embedding_config_manager()
+            provider = await manager.get_provider()
+            if isinstance(provider, NomicEmbeddingProvider):
+                self._embedding_provider = provider
+            else:
+                self._embedding_provider = NomicEmbeddingProvider(
+                    base_url=settings.ollama_base_url,
+                    model=settings.ollama_embedding_model,
+                    dimension=settings.qdrant_vector_size,
+                )
+        return self._embedding_provider
+    
+    async def retrieve(self, ctx: PipelineContext) -> PipelineResult:
+        """执行增强检索流程。【AC-AISVC-RES-02】"""
+        start_time = time.time()
+        
+        logger.info(
+            f"[EnhancedPipeline] Starting retrieval: tenant={ctx.tenant_id}, "
+            f"query={ctx.query[:50]}..."
+        )
+        
+        try:
+            provider = await self._get_embedding_provider()
+            embedding_result = await provider.embed_query(ctx.query)
+            
+            candidates = await self._hybrid_retrieve(
+                tenant_id=ctx.tenant_id,
+                query=ctx.query,
+                embedding_result=embedding_result,
+                metadata_filter=ctx.metadata_filter.filter_dict if ctx.metadata_filter else None,
+                kb_ids=ctx.kb_ids,
+            )
+            
+            if self._reranker_config.enabled and ctx.use_reranker:
+                candidates = await self._rerank(
+                    candidates=candidates,
+                    query=ctx.query,
+                )
+            
+            top_k = self._config.top_k
+            final_candidates = candidates[:top_k]
+            
+            hits = [
+                RetrievalHit(
+                    text=c.text,
+                    score=c.score,
+                    source=self.name,
+                    metadata=c.metadata,
+                )
+                for c in final_candidates
+                if c.score >= self._config.score_threshold
+            ]
+            
+            latency_ms = (time.time() - start_time) * 1000
+            
+            logger.info(
+                f"[EnhancedPipeline] Retrieval completed: hits={len(hits)}, "
+                f"latency_ms={latency_ms:.2f}"
+            )
+            
+            result = RetrievalResult(
+                hits=hits,
+                diagnostics={
+                    "total_candidates": len(candidates),
+                    "after_rerank": self._reranker_config.enabled and ctx.use_reranker,
+                },
+            )
+            
+            return PipelineResult(
+                retrieval_result=result,
+                pipeline_name=self.name,
+                used_reranker=self._reranker_config.enabled and ctx.use_reranker,
+                metadata_filter_applied=ctx.metadata_filter is not None,
+                latency_ms=latency_ms,
+                diagnostics={
+                    "dense_weight": self._config.hybrid.dense_weight,
+                    "keyword_weight": self._config.hybrid.keyword_weight,
+                },
+            )
+            
+        except Exception as e:
+            latency_ms = (time.time() - start_time) * 1000
+            logger.error(f"[EnhancedPipeline] Retrieval error: {e}", exc_info=True)
+            return self._create_empty_result(ctx, error=str(e), latency_ms=latency_ms)
+    
+    async def _hybrid_retrieve(
+        self,
+        tenant_id: str,
+        query: str,
+        embedding_result: Any,
+        metadata_filter: dict[str, Any] | None = None,
+        kb_ids: list[str] | None = None,
+    ) -> list[RetrievalCandidate]:
+        """混合检索：Dense + Keyword + RRF。"""
+        client = await self._get_client()
+        top_k = self._config.top_k
+        expand_factor = self._config.hybrid.keyword_top_k_multiplier
+        
+        vector_task = self._dense_search(
+            client=client,
+            tenant_id=tenant_id,
+            embedding=embedding_result.embedding_full,
+            top_k=top_k * expand_factor,
+            metadata_filter=metadata_filter,
+            kb_ids=kb_ids,
+        )
+        
+        keyword_task = self._keyword_search(
+            client=client,
+            tenant_id=tenant_id,
+            query=query,
+            top_k=top_k * expand_factor,
+            metadata_filter=metadata_filter,
+            kb_ids=kb_ids,
+        ) if self._config.hybrid.enable_keyword else asyncio.sleep(0, result=[])
+        
+        vector_results, keyword_results = await asyncio.gather(
+            vector_task, keyword_task, return_exceptions=True
+        )
+        
+        if isinstance(vector_results, Exception):
+            logger.warning(f"[EnhancedPipeline] Dense search failed: {vector_results}")
+            vector_results = []
+        
+        if isinstance(keyword_results, Exception):
+            logger.warning(f"[EnhancedPipeline] Keyword search failed: {keyword_results}")
+            keyword_results = []
+        
+        combined = self._rrf_combiner.combine(
+            vector_results=vector_results,
+            bm25_results=keyword_results,
+            vector_weight=self._config.hybrid.dense_weight,
+            bm25_weight=self._config.hybrid.keyword_weight,
+        )
+        
+        candidates = []
+        for item in combined:
+            candidates.append(RetrievalCandidate(
+                id=item.get("id", ""),
+                text=item.get("payload", {}).get("text", ""),
+                score=item.get("score", 0.0),
+                vector_score=item.get("vector_score", 0.0),
+                keyword_score=item.get("bm25_score", 0.0),
+                metadata=item.get("payload", {}),
+            ))
+        
+        return candidates
+    
+    async def _dense_search(
+        self,
+        client: QdrantClient,
+        tenant_id: str,
+        embedding: list[float],
+        top_k: int,
+        metadata_filter: dict[str, Any] | None = None,
+        kb_ids: list[str] | None = None,
+    ) -> list[dict[str, Any]]:
+        """Dense 向量检索。"""
+        try:
+            results = await client.search(
+                tenant_id=tenant_id,
+                query_vector=embedding,
+                limit=top_k,
+                vector_name="full",
+                metadata_filter=metadata_filter,
+                kb_ids=kb_ids,
+            )
+            return results
+        except Exception as e:
+            logger.error(f"[EnhancedPipeline] Dense search error: {e}")
+            return []
+    
+    async def _keyword_search(
+        self,
+        client: QdrantClient,
+        tenant_id: str,
+        query: str,
+        top_k: int,
+        metadata_filter: dict[str, Any] | None = None,
+        kb_ids: list[str] | None = None,
+    ) -> list[dict[str, Any]]:
+        """Keyword 关键词检索。"""
+        try:
+            qdrant = await client.get_client()
+            collection_name = client.get_collection_name(tenant_id)
+            
+            query_terms = set(re.findall(r'\w+', query.lower()))
+            
+            results = await qdrant.scroll(
+                collection_name=collection_name,
+                limit=top_k * 3,
+                with_payload=True,
+            )
+            
+            scored_results = []
+            for point in results[0]:
+                text = point.payload.get("text", "").lower()
+                text_terms = set(re.findall(r'\w+', text))
+                overlap = len(query_terms & text_terms)
+                
+                if overlap > 0:
+                    score = overlap / (len(query_terms) + len(text_terms) - overlap)
+                    scored_results.append({
+                        "id": str(point.id),
+                        "score": score,
+                        "payload": point.payload or {},
+                    })
+            
+            scored_results.sort(key=lambda x: x["score"], reverse=True)
+            return scored_results[:top_k]
+            
+        except Exception as e:
+            logger.debug(f"[EnhancedPipeline] Keyword search failed: {e}")
+            return []
+    
+    async def _rerank(
+        self,
+        candidates: list[RetrievalCandidate],
+        query: str,
+    ) -> list[RetrievalCandidate]:
+        """可选重排。"""
+        if not candidates:
+            return candidates
+        
+        try:
+            provider = await self._get_embedding_provider()
+            query_embedding = await provider.embed_query(query)
+            
+            reranked = []
+            for candidate in candidates:
+                candidate_text = candidate.text[:500]
+                if candidate_text:
+                    candidate_embedding = await provider.embed(candidate_text)
+                    similarity = self._cosine_similarity(
+                        query_embedding.embedding_full,
+                        candidate_embedding,
+                    )
+                    candidate.score = similarity
+                
+                if candidate.score >= self._reranker_config.min_score_threshold:
+                    reranked.append(candidate)
+            
+            reranked.sort(key=lambda x: x.score, reverse=True)
+            return reranked[:self._reranker_config.top_k_after_rerank]
+            
+        except Exception as e:
+            logger.warning(f"[EnhancedPipeline] Rerank failed: {e}")
+            return candidates
+    
+    def _cosine_similarity(self, vec1: list[float], vec2: list[float]) -> float:
+        """计算余弦相似度。"""
+        a = np.array(vec1)
+        b = np.array(vec2)
+        return float(np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b)))
+    
+    async def health_check(self) -> bool:
+        """健康检查。"""
+        try:
+            client = await self._get_client()
+            qdrant = await client.get_client()
+            await qdrant.get_collections()
+            return True
+        except Exception as e:
+            logger.error(f"[EnhancedPipeline] Health check failed: {e}")
+            return False
+
+
+_enhanced_pipeline: EnhancedPipeline | None = None
+
+
+async def get_enhanced_pipeline() -> EnhancedPipeline:
+    """获取 EnhancedPipeline 单例。"""
+    global _enhanced_pipeline
+    if _enhanced_pipeline is None:
+        _enhanced_pipeline = EnhancedPipeline()
+    return _enhanced_pipeline

ai-service/app/services/retrieval/strategy/metadata_inference.py

@@ -0,0 +1,136 @@
+"""
+Metadata Inference Service.
+[AC-AISVC-RES-04] 元数据推断统一入口。
+"""
+
+import logging
+from dataclasses import dataclass, field
+from typing import Any
+
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.services.mid.metadata_filter_builder import (
+    FilterBuildResult,
+    MetadataFilterBuilder,
+)
+from app.services.retrieval.strategy.config import (
+    FilterMode,
+    MetadataInferenceConfig,
+)
+from app.services.retrieval.strategy.pipeline_base import MetadataFilterResult
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class InferenceContext:
+    """元数据推断上下文。"""
+    tenant_id: str
+    query: str
+    session_id: str | None = None
+    user_id: str | None = None
+    channel_type: str | None = None
+    existing_context: dict[str, Any] = field(default_factory=dict)
+    slot_state: Any = None
+
+
+@dataclass
+class InferenceResult:
+    """元数据推断结果。"""
+    filter_result: MetadataFilterResult
+    inferred_fields: dict[str, Any] = field(default_factory=dict)
+    confidence_scores: dict[str, float] = field(default_factory=dict)
+    overall_confidence: float | None = None
+    inference_source: str = "unknown"
+
+
+class MetadataInferenceService:
+    """
+    元数据推断统一入口。【AC-AISVC-RES-04】
+    
+    职责：
+    1. 统一的元数据推断入口（策略无关）
+    2. 根据置信度决定 hard/soft filter 模式
+    3. 与现有 MetadataFilterBuilder 保持一致
+    """
+    
+    def __init__(
+        self,
+        session: AsyncSession,
+        config: MetadataInferenceConfig | None = None,
+    ):
+        self._session = session
+        self._config = config or MetadataInferenceConfig()
+        self._filter_builder: MetadataFilterBuilder | None = None
+    
+    async def infer(self, ctx: InferenceContext) -> InferenceResult:
+        """执行元数据推断。【AC-AISVC-RES-04】"""
+        logger.info(
+            f"[MetadataInference] Starting inference: tenant={ctx.tenant_id}, "
+            f"query={ctx.query[:50]}..."
+        )
+        
+        if self._filter_builder is None:
+            self._filter_builder = MetadataFilterBuilder(self._session)
+        
+        effective_context = dict(ctx.existing_context)
+        
+        if ctx.slot_state:
+            effective_context = await self._merge_slot_state(
+                effective_context, ctx.slot_state
+            )
+        
+        build_result = await self._filter_builder.build_filter(
+            tenant_id=ctx.tenant_id,
+            context=effective_context,
+        )
+        
+        confidence = self._calculate_confidence(build_result, effective_context)
+        filter_mode = self._config.determine_filter_mode(confidence)
+        
+        filter_result = MetadataFilterResult(
+            filter_dict=build_result.applied_filter,
+            filter_mode=filter_mode,
+            confidence=confidence,
+            missing_required_slots=build_result.missing_required_slots,
+            debug_info=build_result.debug_info,
+        )
+        
+        logger.info(
+            f"[MetadataInference] Inference completed: filter_mode={filter_mode.value}, "
+            f"confidence={confidence}"
+        )
+        
+        return InferenceResult(
+            filter_result=filter_result,
+            inferred_fields=build_result.applied_filter,
+            overall_confidence=confidence,
+            inference_source="metadata_filter_builder",
+        )
+    
+    async def _merge_slot_state(
+        self, context: dict[str, Any], slot_state: Any
+    ) -> dict[str, Any]:
+        """合并槽位状态到上下文。"""
+        if hasattr(slot_state, 'filled_slots'):
+            for slot_key, slot_value in slot_state.filled_slots.items():
+                if slot_key not in context:
+                    context[slot_key] = slot_value
+        return context
+    
+    def _calculate_confidence(
+        self, build_result: FilterBuildResult, context: dict[str, Any]
+    ) -> float | None:
+        """计算推断置信度。"""
+        if build_result.missing_required_slots:
+            return 0.3
+        if not build_result.applied_filter:
+            return None
+        if not context:
+            return 0.5
+        applied_ratio = len(build_result.applied_filter) / max(len(context), 1)
+        if applied_ratio >= 0.8:
+            return 0.9
+        elif applied_ratio >= 0.5:
+            return 0.7
+        return 0.5

ai-service/app/services/retrieval/strategy/mode_router.py

@@ -0,0 +1,118 @@
+"""
+Mode Router.
+[AC-AISVC-RES-09~15] 模式路由器。
+"""
+
+import logging
+from dataclasses import dataclass
+
+from app.services.retrieval.strategy.config import ModeRouterConfig, RuntimeMode
+from app.services.retrieval.strategy.pipeline_base import PipelineResult
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ModeDecision:
+    """模式决策结果。"""
+    mode: RuntimeMode
+    reason: str
+    confidence: float | None = None
+    complexity_score: float | None = None
+
+
+class ModeRouter:
+    """
+    模式路由器。【AC-AISVC-RES-09~15】
+    
+    职责：
+    1. 根据 rag_runtime_mode 选择 direct/react/auto 模式
+    2. auto 模式下根据复杂度与置信度自动选择路由
+    3. direct 低置信度时触发 react 回退
+    """
+    
+    def __init__(self, config: ModeRouterConfig | None = None):
+        self._config = config or ModeRouterConfig()
+    
+    def decide(
+        self,
+        query: str,
+        confidence: float | None = None,
+        complexity_score: float | None = None,
+    ) -> ModeDecision:
+        """决定使用哪种模式。【AC-AISVC-RES-09~13】"""
+        if self._config.runtime_mode == RuntimeMode.REACT:
+            return ModeDecision(mode=RuntimeMode.REACT, reason="runtime_mode=react")
+        
+        if self._config.runtime_mode == RuntimeMode.DIRECT:
+            return ModeDecision(mode=RuntimeMode.DIRECT, reason="runtime_mode=direct")
+        
+        calculated_complexity = complexity_score or self._calculate_complexity(query)
+        
+        if self._should_use_direct(query, confidence, calculated_complexity):
+            return ModeDecision(
+                mode=RuntimeMode.DIRECT,
+                reason="auto: short_query_high_confidence",
+                confidence=confidence,
+                complexity_score=calculated_complexity,
+            )
+        
+        return ModeDecision(
+            mode=RuntimeMode.REACT,
+            reason="auto: complex_or_low_confidence",
+            confidence=confidence,
+            complexity_score=calculated_complexity,
+        )
+    
+    def should_fallback_to_react(self, direct_result: PipelineResult) -> bool:
+        """判断是否应该从 direct 回退到 react。【AC-AISVC-RES-14】"""
+        if not self._config.direct_fallback_on_low_confidence:
+            return False
+        if direct_result.is_empty:
+            return True
+        max_score = direct_result.retrieval_result.max_score
+        if max_score < 0.3:
+            return True
+        if direct_result.retrieval_result.hit_count < 2:
+            return True
+        return False
+    
+    def _should_use_direct(
+        self, query: str, confidence: float | None, complexity_score: float
+    ) -> bool:
+        if len(query) <= self._config.short_query_threshold:
+            if confidence and confidence >= self._config.react_trigger_confidence_threshold:
+                return True
+        if confidence and confidence < self._config.react_trigger_confidence_threshold:
+            return False
+        if complexity_score >= self._config.react_trigger_complexity_score:
+            return False
+        return True
+    
+    def _calculate_complexity(self, query: str) -> float:
+        score = 0.0
+        if len(query) > 50:
+            score += 0.2
+        if len(query) > 100:
+            score += 0.2
+        condition_words = ["和", "或", "但是", "如果", "同时", "并且", "或者", "以及"]
+        for word in condition_words:
+            if word in query:
+                score += 0.1
+        return min(score, 1.0)
+    
+    def get_config(self) -> ModeRouterConfig:
+        return self._config
+    
+    def update_config(self, config: ModeRouterConfig) -> None:
+        self._config = config
+
+
+_mode_router: ModeRouter | None = None
+
+
+def get_mode_router() -> ModeRouter:
+    global _mode_router
+    if _mode_router is None:
+        _mode_router = ModeRouter()
+    return _mode_router

ai-service/app/services/retrieval/strategy/pipeline_base.py

@@ -0,0 +1,116 @@
+"""
+Pipeline Base Classes.
+[AC-AISVC-RES-01~15] Pipeline 抽象基类。
+"""
+
+import logging
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+from typing import Any
+
+from app.services.retrieval.base import RetrievalContext, RetrievalHit, RetrievalResult
+from app.services.retrieval.strategy.config import FilterMode
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class MetadataFilterResult:
+    """元数据过滤结果。"""
+    filter_dict: dict[str, Any] = field(default_factory=dict)
+    filter_mode: FilterMode = FilterMode.NONE
+    confidence: float | None = None
+    missing_required_slots: list[dict[str, str]] = field(default_factory=list)
+    debug_info: dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass
+class PipelineContext:
+    """Pipeline 执行上下文。"""
+    retrieval_ctx: RetrievalContext
+    metadata_filter: MetadataFilterResult | None = None
+    use_reranker: bool = False
+    use_react: bool = False
+    react_iteration: int = 0
+    previous_results: list[RetrievalHit] = field(default_factory=list)
+    extra: dict[str, Any] = field(default_factory=dict)
+    
+    @property
+    def tenant_id(self) -> str:
+        return self.retrieval_ctx.tenant_id
+    
+    @property
+    def query(self) -> str:
+        return self.retrieval_ctx.query
+    
+    @property
+    def session_id(self) -> str | None:
+        return self.retrieval_ctx.session_id
+    
+    @property
+    def kb_ids(self) -> list[str] | None:
+        return self.retrieval_ctx.kb_ids
+
+
+@dataclass
+class PipelineResult:
+    """Pipeline 执行结果。"""
+    retrieval_result: RetrievalResult
+    pipeline_name: str = ""
+    used_reranker: bool = False
+    used_react: bool = False
+    react_iterations: int = 0
+    metadata_filter_applied: bool = False
+    fallback_triggered: bool = False
+    fallback_reason: str | None = None
+    latency_ms: float = 0.0
+    diagnostics: dict[str, Any] = field(default_factory=dict)
+    
+    @property
+    def hits(self) -> list[RetrievalHit]:
+        return self.retrieval_result.hits
+    
+    @property
+    def is_empty(self) -> bool:
+        return self.retrieval_result.is_empty
+
+
+class BasePipeline(ABC):
+    """Pipeline 抽象基类。【AC-AISVC-RES-01, AC-AISVC-RES-02】"""
+    
+    @property
+    @abstractmethod
+    def name(self) -> str:
+        """Pipeline 名称。"""
+        pass
+    
+    @property
+    @abstractmethod
+    def description(self) -> str:
+        """Pipeline 描述。"""
+        pass
+    
+    @abstractmethod
+    async def retrieve(self, ctx: PipelineContext) -> PipelineResult:
+        """执行检索。"""
+        pass
+    
+    @abstractmethod
+    async def health_check(self) -> bool:
+        """健康检查。"""
+        pass
+    
+    def _create_empty_result(
+        self,
+        ctx: PipelineContext,
+        error: str | None = None,
+        latency_ms: float = 0.0,
+    ) -> PipelineResult:
+        """创建空结果。"""
+        diagnostics = {"error": error} if error else {}
+        return PipelineResult(
+            retrieval_result=RetrievalResult(hits=[], diagnostics=diagnostics),
+            pipeline_name=self.name,
+            latency_ms=latency_ms,
+            diagnostics=diagnostics,
+        )

ai-service/app/services/retrieval/strategy/retrieval_strategy.py

@@ -0,0 +1,301 @@
+"""
+Retrieval Strategy - Unified Entry Point.
+[AC-AISVC-RES-01~15] 检索策略统一入口。
+
+整合：
+- StrategyRouter: 策略路由（default/enhanced）
+- ModeRouter: 模式路由（direct/react/auto）
+- MetadataInferenceService: 元数据推断
+- RollbackManager: 回退管理
+"""
+
+import logging
+import time
+from dataclasses import dataclass
+from typing import Any
+
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.services.retrieval.base import RetrievalContext, RetrievalResult
+from app.services.retrieval.strategy.config import (
+    RetrievalStrategyConfig,
+    RuntimeMode,
+    StrategyType,
+)
+from app.services.retrieval.strategy.default_pipeline import DefaultPipeline
+from app.services.retrieval.strategy.enhanced_pipeline import EnhancedPipeline
+from app.services.retrieval.strategy.metadata_inference import (
+    InferenceContext,
+    MetadataInferenceService,
+)
+from app.services.retrieval.strategy.mode_router import ModeDecision, ModeRouter
+from app.services.retrieval.strategy.pipeline_base import (
+    MetadataFilterResult,
+    PipelineContext,
+    PipelineResult,
+)
+from app.services.retrieval.strategy.rollback_manager import (
+    RollbackManager,
+    RollbackTrigger,
+)
+from app.services.retrieval.strategy.strategy_router import (
+    RoutingDecision,
+    StrategyRouter,
+)
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class RetrievalStrategyResult:
+    """检索策略执行结果。"""
+    
+    retrieval_result: RetrievalResult
+    strategy_used: StrategyType
+    mode_used: RuntimeMode
+    metadata_filter: MetadataFilterResult | None
+    latency_ms: float
+    diagnostics: dict[str, Any]
+
+
+class RetrievalStrategy:
+    """
+    检索策略统一入口。【AC-AISVC-RES-01~15】
+    
+    整合所有策略组件：
+    1. 元数据推断（MetadataInferenceService）
+    2. 策略路由（StrategyRouter）
+    3. 模式路由（ModeRouter）
+    4. 回退管理（RollbackManager）
+    
+    使用方式：
+    ```python
+    strategy = RetrievalStrategy(session)
+    result = await strategy.retrieve(
+        tenant_id="tenant_1",
+        query="用户问题",
+        context={"user_id": "user_1"},
+    )
+    ```
+    """
+    
+    def __init__(
+        self,
+        session: AsyncSession,
+        config: RetrievalStrategyConfig | None = None,
+    ):
+        self._session = session
+        self._config = config or RetrievalStrategyConfig()
+        
+        self._strategy_router = StrategyRouter(self._config)
+        self._mode_router = ModeRouter(self._config.mode_router)
+        self._rollback_manager = RollbackManager(self._config)
+        self._metadata_inference: MetadataInferenceService | None = None
+    
+    async def retrieve(
+        self,
+        tenant_id: str,
+        query: str,
+        context: dict[str, Any] | None = None,
+        kb_ids: list[str] | None = None,
+        session_id: str | None = None,
+        user_id: str | None = None,
+        use_reranker: bool = False,
+        use_react: bool = False,
+    ) -> RetrievalStrategyResult:
+        """
+        执行检索策略。【AC-AISVC-RES-01~15】
+        
+        流程：
+        1. 元数据推断
+        2. 策略路由
+        3. 模式路由
+        4. 执行检索
+        5. 检查是否需要回退
+        
+        Args:
+            tenant_id: 租户 ID
+            query: 查询文本
+            context: 上下文信息
+            kb_ids: 知识库 ID 列表
+            session_id: 会话 ID
+            user_id: 用户 ID
+            use_reranker: 是否使用重排
+            use_react: 是否使用 ReAct 模式
+        
+        Returns:
+            RetrievalStrategyResult 包含检索结果和诊断信息
+        """
+        start_time = time.time()
+        context = context or {}
+        
+        logger.info(
+            f"[RetrievalStrategy] Starting retrieval: tenant={tenant_id}, "
+            f"query={query[:50]}..."
+        )
+        
+        try:
+            metadata_filter = await self._infer_metadata(
+                tenant_id=tenant_id,
+                query=query,
+                context=context,
+                session_id=session_id,
+                user_id=user_id,
+            )
+            
+            routing_decision = await self._strategy_router.route(tenant_id, user_id)
+            
+            mode_decision = self._mode_router.decide(
+                query=query,
+                confidence=metadata_filter.confidence if metadata_filter else None,
+            )
+            
+            retrieval_ctx = RetrievalContext(
+                tenant_id=tenant_id,
+                query=query,
+                session_id=session_id,
+                metadata_filter=metadata_filter.filter_dict if metadata_filter else None,
+                kb_ids=kb_ids,
+            )
+            
+            pipeline_ctx = PipelineContext(
+                retrieval_ctx=retrieval_ctx,
+                metadata_filter=metadata_filter,
+                use_reranker=use_reranker or self._config.reranker.enabled,
+                use_react=use_react or mode_decision.mode == RuntimeMode.REACT,
+            )
+            
+            pipeline_result = await routing_decision.pipeline.retrieve(pipeline_ctx)
+            
+            if mode_decision.mode == RuntimeMode.DIRECT and self._mode_router.should_fallback_to_react(pipeline_result):
+                logger.info("[RetrievalStrategy] Falling back to react mode")
+                pipeline_ctx.use_react = True
+                pipeline_result = await routing_decision.pipeline.retrieve(pipeline_ctx)
+                mode_decision = ModeDecision(
+                    mode=RuntimeMode.REACT,
+                    reason="fallback_from_direct",
+                )
+            
+            latency_ms = (time.time() - start_time) * 1000
+            
+            self._check_performance(latency_ms, tenant_id)
+            
+            logger.info(
+                f"[RetrievalStrategy] Retrieval completed: strategy={routing_decision.strategy.value}, "
+                f"mode={mode_decision.mode.value}, hits={len(pipeline_result.hits)}, "
+                f"latency_ms={latency_ms:.2f}"
+            )
+            
+            return RetrievalStrategyResult(
+                retrieval_result=pipeline_result.retrieval_result,
+                strategy_used=routing_decision.strategy,
+                mode_used=mode_decision.mode,
+                metadata_filter=metadata_filter,
+                latency_ms=latency_ms,
+                diagnostics={
+                    "routing_reason": routing_decision.reason,
+                    "mode_reason": mode_decision.reason,
+                    "grayscale_hit": routing_decision.grayscale_hit,
+                    **pipeline_result.diagnostics,
+                },
+            )
+            
+        except Exception as e:
+            latency_ms = (time.time() - start_time) * 1000
+            logger.error(
+                f"[RetrievalStrategy] Retrieval error: {e}",
+                exc_info=True
+            )
+            
+            self._rollback_manager.rollback(
+                trigger=RollbackTrigger.ERROR,
+                reason=str(e),
+                tenant_id=tenant_id,
+            )
+            
+            return RetrievalStrategyResult(
+                retrieval_result=RetrievalResult(
+                    hits=[],
+                    diagnostics={"error": str(e)},
+                ),
+                strategy_used=StrategyType.DEFAULT,
+                mode_used=RuntimeMode.DIRECT,
+                metadata_filter=None,
+                latency_ms=latency_ms,
+                diagnostics={"error": str(e)},
+            )
+    
+    async def _infer_metadata(
+        self,
+        tenant_id: str,
+        query: str,
+        context: dict[str, Any],
+        session_id: str | None = None,
+        user_id: str | None = None,
+    ) -> MetadataFilterResult | None:
+        """执行元数据推断。"""
+        try:
+            if self._metadata_inference is None:
+                self._metadata_inference = MetadataInferenceService(
+                    self._session,
+                    self._config.metadata_inference,
+                )
+            
+            inference_ctx = InferenceContext(
+                tenant_id=tenant_id,
+                query=query,
+                session_id=session_id,
+                user_id=user_id,
+                existing_context=context,
+            )
+            
+            result = await self._metadata_inference.infer(inference_ctx)
+            return result.filter_result
+            
+        except Exception as e:
+            logger.warning(f"[RetrievalStrategy] Metadata inference failed: {e}")
+            return None
+    
+    def _check_performance(self, latency_ms: float, tenant_id: str | None) -> None:
+        """检查性能指标，必要时触发回退。"""
+        self._rollback_manager.check_and_rollback(
+            metrics={"latency_ms": latency_ms},
+            tenant_id=tenant_id,
+        )
+    
+    def get_config(self) -> RetrievalStrategyConfig:
+        """获取当前配置。"""
+        return self._config
+    
+    def update_config(self, config: RetrievalStrategyConfig) -> None:
+        """更新配置。"""
+        self._config = config
+        self._strategy_router.update_config(config)
+        self._mode_router.update_config(config.mode_router)
+        self._rollback_manager.update_config(config)
+    
+    async def health_check(self) -> dict[str, bool]:
+        """健康检查。"""
+        results = {}
+        
+        try:
+            default_pipeline = await self._strategy_router._get_default_pipeline()
+            results["default_pipeline"] = await default_pipeline.health_check()
+        except Exception:
+            results["default_pipeline"] = False
+        
+        try:
+            enhanced_pipeline = await self._strategy_router._get_enhanced_pipeline()
+            results["enhanced_pipeline"] = await enhanced_pipeline.health_check()
+        except Exception:
+            results["enhanced_pipeline"] = False
+        
+        return results
+
+
+async def create_retrieval_strategy(
+    session: AsyncSession,
+    config: RetrievalStrategyConfig | None = None,
+) -> RetrievalStrategy:
+    """创建 RetrievalStrategy 实例。"""
+    return RetrievalStrategy(session, config)

ai-service/app/services/retrieval/strategy/rollback_manager.py

@@ -0,0 +1,192 @@
+"""
+Rollback Manager.
+[AC-AISVC-RES-07] 策略回退与审计管理器。
+"""
+
+import logging
+from dataclasses import dataclass, field
+from datetime import datetime
+from enum import Enum
+from typing import Any
+
+from app.services.retrieval.strategy.config import RetrievalStrategyConfig, StrategyType
+
+logger = logging.getLogger(__name__)
+
+
+class RollbackTrigger(str, Enum):
+    """回退触发原因。"""
+    MANUAL = "manual"
+    ERROR = "error"
+    PERFORMANCE = "performance"
+    TIMEOUT = "timeout"
+
+
+@dataclass
+class AuditLog:
+    """审计日志记录。"""
+    timestamp: str
+    action: str
+    from_strategy: str
+    to_strategy: str
+    trigger: str
+    reason: str
+    tenant_id: str | None = None
+    details: dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass
+class RollbackResult:
+    """回退结果。"""
+    success: bool
+    previous_strategy: StrategyType
+    current_strategy: StrategyType
+    trigger: RollbackTrigger
+    reason: str
+    audit_log: AuditLog | None = None
+
+
+class RollbackManager:
+    """
+    策略回退管理器。【AC-AISVC-RES-07】
+    
+    职责：
+    1. 策略异常时回退到默认策略
+    2. 记录审计日志
+    3. 支持手动触发回退
+    """
+    
+    def __init__(
+        self,
+        config: RetrievalStrategyConfig | None = None,
+        max_audit_logs: int = 1000,
+    ):
+        self._config = config or RetrievalStrategyConfig()
+        self._max_audit_logs = max_audit_logs
+        self._audit_logs: list[AuditLog] = []
+        self._previous_strategy: StrategyType = StrategyType.DEFAULT
+    
+    def rollback(
+        self,
+        trigger: RollbackTrigger,
+        reason: str,
+        tenant_id: str | None = None,
+        details: dict[str, Any] | None = None,
+    ) -> RollbackResult:
+        """执行策略回退。【AC-AISVC-RES-07】"""
+        previous = self._config.active_strategy
+        current = StrategyType.DEFAULT
+        
+        if previous == StrategyType.DEFAULT:
+            return RollbackResult(
+                success=False,
+                previous_strategy=previous,
+                current_strategy=current,
+                trigger=trigger,
+                reason="Already on default strategy",
+            )
+        
+        self._previous_strategy = previous
+        self._config.active_strategy = current
+        
+        audit_log = AuditLog(
+            timestamp=datetime.utcnow().isoformat(),
+            action="rollback",
+            from_strategy=previous.value,
+            to_strategy=current.value,
+            trigger=trigger.value,
+            reason=reason,
+            tenant_id=tenant_id,
+            details=details or {},
+        )
+        
+        self._add_audit_log(audit_log)
+        
+        logger.info(
+            f"[RollbackManager] Rollback executed: from={previous.value}, "
+            f"to={current.value}, trigger={trigger.value}"
+        )
+        
+        return RollbackResult(
+            success=True,
+            previous_strategy=previous,
+            current_strategy=current,
+            trigger=trigger,
+            reason=reason,
+            audit_log=audit_log,
+        )
+    
+    def check_and_rollback(
+        self, metrics: dict[str, float], tenant_id: str | None = None
+    ) -> RollbackResult | None:
+        """检查性能指标并自动回退。【AC-AISVC-RES-08】"""
+        thresholds = self._config.performance_thresholds
+        
+        latency = metrics.get("latency_ms", 0)
+        if latency > thresholds.get("max_latency_ms", 2000):
+            return self.rollback(
+                trigger=RollbackTrigger.PERFORMANCE,
+                reason=f"Latency {latency}ms exceeds threshold",
+                tenant_id=tenant_id,
+            )
+        
+        error_rate = metrics.get("error_rate", 0)
+        if error_rate > thresholds.get("max_error_rate", 0.05):
+            return self.rollback(
+                trigger=RollbackTrigger.ERROR,
+                reason=f"Error rate {error_rate} exceeds threshold",
+                tenant_id=tenant_id,
+            )
+        
+        return None
+    
+    def _add_audit_log(self, log: AuditLog) -> None:
+        self._audit_logs.append(log)
+        if len(self._audit_logs) > self._max_audit_logs:
+            self._audit_logs = self._audit_logs[-self._max_audit_logs:]
+    
+    def record_audit(
+        self,
+        action: str,
+        details: dict[str, Any],
+        tenant_id: str | None = None,
+    ) -> AuditLog:
+        """记录审计日志。"""
+        audit_log = AuditLog(
+            timestamp=datetime.utcnow().isoformat(),
+            action=action,
+            from_strategy=self._config.active_strategy.value,
+            to_strategy=self._config.active_strategy.value,
+            trigger="n/a",
+            reason=details.get("reason", ""),
+            tenant_id=tenant_id,
+            details=details,
+        )
+        
+        self._add_audit_log(audit_log)
+        
+        logger.info(
+            f"[RollbackManager] Audit recorded: action={action}, "
+            f"strategy={self._config.active_strategy.value}"
+        )
+        
+        return audit_log
+    
+    def get_audit_logs(self, limit: int = 100) -> list[AuditLog]:
+        return self._audit_logs[-limit:]
+    
+    def get_config(self) -> RetrievalStrategyConfig:
+        return self._config
+    
+    def update_config(self, config: RetrievalStrategyConfig) -> None:
+        self._config = config
+
+
+_rollback_manager: RollbackManager | None = None
+
+
+def get_rollback_manager() -> RollbackManager:
+    global _rollback_manager
+    if _rollback_manager is None:
+        _rollback_manager = RollbackManager()
+    return _rollback_manager

ai-service/app/services/retrieval/strategy/strategy_router.py

@@ -0,0 +1,109 @@
+"""
+Strategy Router.
+[AC-AISVC-RES-01~03] 策略路由器。
+"""
+
+import logging
+from dataclasses import dataclass
+from typing import Any
+
+from app.services.retrieval.strategy.config import (
+    RetrievalStrategyConfig,
+    StrategyType,
+)
+from app.services.retrieval.strategy.default_pipeline import DefaultPipeline, get_default_pipeline
+from app.services.retrieval.strategy.enhanced_pipeline import EnhancedPipeline, get_enhanced_pipeline
+from app.services.retrieval.strategy.pipeline_base import BasePipeline
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class RoutingDecision:
+    """路由决策结果。"""
+    strategy: StrategyType
+    pipeline: BasePipeline
+    reason: str
+    grayscale_hit: bool = False
+
+
+class StrategyRouter:
+    """
+    策略路由器。【AC-AISVC-RES-01~03】
+    
+    职责：
+    1. 根据配置选择默认策略或增强策略
+    2. 支持灰度发布（percentage/allowlist）
+    3. 不影响正在运行的默认策略请求
+    """
+    
+    def __init__(
+        self,
+        config: RetrievalStrategyConfig | None = None,
+        default_pipeline: DefaultPipeline | None = None,
+        enhanced_pipeline: EnhancedPipeline | None = None,
+    ):
+        self._config = config or RetrievalStrategyConfig()
+        self._default_pipeline = default_pipeline
+        self._enhanced_pipeline = enhanced_pipeline
+    
+    async def route(
+        self, tenant_id: str, user_id: str | None = None
+    ) -> RoutingDecision:
+        """路由到合适的策略。【AC-AISVC-RES-01~03】"""
+        if self._config.active_strategy == StrategyType.ENHANCED:
+            pipeline = await self._get_enhanced_pipeline()
+            return RoutingDecision(
+                strategy=StrategyType.ENHANCED,
+                pipeline=pipeline,
+                reason="active_strategy=enhanced",
+            )
+        
+        if self._config.grayscale.should_use_enhanced(tenant_id, user_id):
+            pipeline = await self._get_enhanced_pipeline()
+            return RoutingDecision(
+                strategy=StrategyType.ENHANCED,
+                pipeline=pipeline,
+                reason="grayscale_hit",
+                grayscale_hit=True,
+            )
+        
+        pipeline = await self._get_default_pipeline()
+        return RoutingDecision(
+            strategy=StrategyType.DEFAULT,
+            pipeline=pipeline,
+            reason="default_strategy",
+        )
+    
+    async def _get_default_pipeline(self) -> DefaultPipeline:
+        if self._default_pipeline is None:
+            self._default_pipeline = await get_default_pipeline()
+        return self._default_pipeline
+    
+    async def _get_enhanced_pipeline(self) -> EnhancedPipeline:
+        if self._enhanced_pipeline is None:
+            self._enhanced_pipeline = await get_enhanced_pipeline()
+        return self._enhanced_pipeline
+    
+    def get_config(self) -> RetrievalStrategyConfig:
+        return self._config
+    
+    def update_config(self, config: RetrievalStrategyConfig) -> None:
+        self._config = config
+        logger.info(f"[StrategyRouter] Config updated: strategy={config.active_strategy.value}")
+
+
+_strategy_router: StrategyRouter | None = None
+
+
+def get_strategy_router() -> StrategyRouter:
+    global _strategy_router
+    if _strategy_router is None:
+        _strategy_router = StrategyRouter()
+    return _strategy_router
+
+
+def set_strategy_router(router: StrategyRouter) -> None:
+    """Set the global strategy router instance."""
+    global _strategy_router
+    _strategy_router = router

ai-service/app/services/retrieval/strategy_integration.py

@@ -0,0 +1,233 @@
+"""
+Retrieval Strategy Integration for Dialogue Flow.
+[AC-AISVC-RES-01~15] Integrates StrategyRouter and ModeRouter into dialogue pipeline.
+
+Usage:
+    from app.services.retrieval.strategy_integration import RetrievalStrategyIntegration
+    
+    integration = RetrievalStrategyIntegration()
+    result = await integration.execute(ctx)
+"""
+
+from __future__ import annotations
+
+import logging
+import time
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING, Any
+
+from app.services.retrieval.routing_config import (
+    RagRuntimeMode,
+    StrategyType,
+    RoutingConfig,
+    StrategyContext,
+    StrategyResult,
+)
+from app.services.retrieval.strategy_router import (
+    StrategyRouter,
+    get_strategy_router,
+)
+from app.services.retrieval.mode_router import (
+    ModeRouter,
+    ModeRouteResult,
+    get_mode_router,
+)
+
+if TYPE_CHECKING:
+    from app.services.retrieval.base import RetrievalResult
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class RetrievalStrategyResult:
+    """Combined result from strategy and mode routing."""
+    retrieval_result: "RetrievalResult | None"
+    final_answer: str | None
+    strategy: StrategyType
+    mode: RagRuntimeMode
+    should_fallback: bool = False
+    fallback_reason: str | None = None
+    mode_route_result: ModeRouteResult | None = None
+    diagnostics: dict[str, Any] = field(default_factory=dict)
+    duration_ms: int = 0
+
+
+class RetrievalStrategyIntegration:
+    """
+    [AC-AISVC-RES-01~15] Integration layer for retrieval strategy.
+    
+    Combines StrategyRouter and ModeRouter to provide a unified interface
+    for the dialogue pipeline.
+    
+    Flow:
+    1. StrategyRouter selects default or enhanced strategy
+    2. ModeRouter selects direct, react, or auto mode
+    3. Execute retrieval with selected strategy and mode
+    4. Handle fallback scenarios
+    """
+    
+    def __init__(
+        self,
+        config: RoutingConfig | None = None,
+        strategy_router: StrategyRouter | None = None,
+        mode_router: ModeRouter | None = None,
+    ):
+        self._config = config or RoutingConfig()
+        self._strategy_router = strategy_router or get_strategy_router()
+        self._mode_router = mode_router or get_mode_router()
+    
+    @property
+    def config(self) -> RoutingConfig:
+        """Get current configuration."""
+        return self._config
+    
+    def update_config(self, new_config: RoutingConfig) -> None:
+        """
+        [AC-AISVC-RES-15] Update all routing configurations.
+        """
+        self._config = new_config
+        self._strategy_router.update_config(new_config)
+        self._mode_router.update_config(new_config)
+        
+        logger.info(
+            f"[AC-AISVC-RES-15] RetrievalStrategyIntegration config updated: "
+            f"strategy={new_config.strategy.value}, mode={new_config.rag_runtime_mode.value}"
+        )
+    
+    async def execute(
+        self,
+        ctx: StrategyContext,
+    ) -> RetrievalStrategyResult:
+        """
+        Execute retrieval with strategy and mode routing.
+        
+        Args:
+            ctx: Strategy context with tenant, query, metadata, etc.
+        
+        Returns:
+            RetrievalStrategyResult with retrieval results and diagnostics
+        """
+        start_time = time.time()
+        
+        strategy_result = self._strategy_router.route(ctx)
+        
+        mode_result = self._mode_router.route(ctx)
+        
+        logger.info(
+            f"[AC-AISVC-RES-01~15] Strategy routing: "
+            f"strategy={strategy_result.strategy.value}, mode={mode_result.mode.value}, "
+            f"tenant={ctx.tenant_id}, query_len={len(ctx.query)}"
+        )
+        
+        retrieval_result = None
+        final_answer = None
+        should_fallback = False
+        fallback_reason = None
+        
+        try:
+            if mode_result.mode == RagRuntimeMode.DIRECT:
+                retrieval_result, answer, mode_result = await self._mode_router.execute_with_fallback(ctx)
+                
+                if answer is not None:
+                    final_answer = answer
+                    should_fallback = mode_result.should_fallback_to_react
+                    fallback_reason = mode_result.fallback_reason
+                
+            elif mode_result.mode == RagRuntimeMode.REACT:
+                answer, retrieval_result, react_ctx = await self._mode_router.execute_react(ctx)
+                final_answer = answer
+                
+            else:
+                retrieval_result, answer, mode_result = await self._mode_router.execute_with_fallback(ctx)
+                
+                if answer is not None:
+                    final_answer = answer
+                    should_fallback = mode_result.should_fallback_to_react
+                    fallback_reason = mode_result.fallback_reason
+        
+        except Exception as e:
+            logger.error(
+                f"[AC-AISVC-RES-07] Retrieval strategy execution failed: {e}"
+            )
+            
+            if strategy_result.strategy == StrategyType.ENHANCED:
+                self._strategy_router.rollback(
+                    reason=str(e),
+                    tenant_id=ctx.tenant_id,
+                )
+                
+                from app.services.retrieval.optimized_retriever import get_optimized_retriever
+                from app.services.retrieval.base import RetrievalContext
+                
+                retriever = await get_optimized_retriever()
+                retrieval_ctx = RetrievalContext(
+                    tenant_id=ctx.tenant_id,
+                    query=ctx.query,
+                    metadata_filter=ctx.metadata_filter,
+                    kb_ids=ctx.kb_ids,
+                )
+                retrieval_result = await retriever.retrieve(retrieval_ctx)
+                
+                should_fallback = True
+                fallback_reason = str(e)
+            
+            else:
+                raise
+        
+        duration_ms = int((time.time() - start_time) * 1000)
+        
+        return RetrievalStrategyResult(
+            retrieval_result=retrieval_result,
+            final_answer=final_answer,
+            strategy=strategy_result.strategy,
+            mode=mode_result.mode,
+            should_fallback=should_fallback,
+            fallback_reason=fallback_reason,
+            mode_route_result=mode_result,
+            diagnostics={
+                "strategy_diagnostics": strategy_result.diagnostics,
+                "mode_diagnostics": mode_result.diagnostics,
+                "duration_ms": duration_ms,
+            },
+            duration_ms=duration_ms,
+        )
+    
+    def get_current_strategy(self) -> StrategyType:
+        """Get current active strategy."""
+        return self._strategy_router.current_strategy
+    
+    def get_rollback_records(self, limit: int = 10) -> list[dict[str, Any]]:
+        """Get recent rollback records."""
+        records = self._strategy_router.get_rollback_records(limit)
+        return [
+            {
+                "timestamp": r.timestamp,
+                "from_strategy": r.from_strategy.value,
+                "to_strategy": r.to_strategy.value,
+                "reason": r.reason,
+                "tenant_id": r.tenant_id,
+            }
+            for r in records
+        ]
+    
+    def validate_config(self) -> tuple[bool, list[str]]:
+        """Validate current configuration."""
+        return self._config.validate()
+
+
+_integration: RetrievalStrategyIntegration | None = None
+
+
+def get_retrieval_strategy_integration() -> RetrievalStrategyIntegration:
+    """Get or create RetrievalStrategyIntegration singleton."""
+    global _integration
+    if _integration is None:
+        _integration = RetrievalStrategyIntegration()
+    return _integration
+
+
+def reset_retrieval_strategy_integration() -> None:
+    """Reset RetrievalStrategyIntegration singleton (for testing)."""
+    global _integration
+    _integration = None

ai-service/app/services/retrieval/strategy_router.py

@@ -0,0 +1,403 @@
+"""
+Strategy Router for Retrieval and Embedding.
+[AC-AISVC-RES-01, AC-AISVC-RES-02, AC-AISVC-RES-03] Routes to default or enhanced strategy.
+
+Key Features:
+- Default strategy preserves existing online logic
+- Enhanced strategy is configurable and can be rolled back
+- Supports grayscale release (percentage/allowlist)
+- Supports rollback on error or performance degradation
+"""
+
+from __future__ import annotations
+
+import logging
+import time
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING, Any
+
+from app.services.retrieval.routing_config import (
+    RagRuntimeMode,
+    StrategyType,
+    RoutingConfig,
+    StrategyContext,
+    StrategyResult,
+)
+
+if TYPE_CHECKING:
+    from app.services.retrieval.base import RetrievalResult
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class RollbackRecord:
+    """Record for strategy rollback event."""
+    timestamp: float
+    from_strategy: StrategyType
+    to_strategy: StrategyType
+    reason: str
+    tenant_id: str | None = None
+    request_id: str | None = None
+
+
+class RollbackManager:
+    """
+    [AC-AISVC-RES-07] Manages strategy rollback and audit logging.
+    """
+    
+    def __init__(self, max_records: int = 100):
+        self._records: list[RollbackRecord] = []
+        self._max_records = max_records
+    
+    def record_rollback(
+        self,
+        from_strategy: StrategyType,
+        to_strategy: StrategyType,
+        reason: str,
+        tenant_id: str | None = None,
+        request_id: str | None = None,
+    ) -> None:
+        """Record a rollback event."""
+        record = RollbackRecord(
+            timestamp=time.time(),
+            from_strategy=from_strategy,
+            to_strategy=to_strategy,
+            reason=reason,
+            tenant_id=tenant_id,
+            request_id=request_id,
+        )
+        
+        self._records.append(record)
+        
+        if len(self._records) > self._max_records:
+            self._records = self._records[-self._max_records:]
+        
+        logger.info(
+            f"[AC-AISVC-RES-07] Rollback recorded: {from_strategy.value} -> {to_strategy.value}, "
+            f"reason={reason}, tenant={tenant_id}"
+        )
+    
+    def get_recent_rollbacks(self, limit: int = 10) -> list[RollbackRecord]:
+        """Get recent rollback records."""
+        return self._records[-limit:]
+    
+    def get_rollback_count(self, since_timestamp: float | None = None) -> int:
+        """Get count of rollbacks, optionally since a timestamp."""
+        if since_timestamp is None:
+            return len(self._records)
+        
+        return sum(1 for r in self._records if r.timestamp >= since_timestamp)
+
+
+class DefaultPipeline:
+    """
+    [AC-AISVC-RES-01] Default pipeline that preserves existing online logic.
+    
+    This pipeline uses the existing OptimizedRetriever without any new features.
+    """
+    
+    def __init__(self):
+        self._retriever = None
+    
+    async def execute(
+        self,
+        ctx: StrategyContext,
+    ) -> "RetrievalResult":
+        """
+        Execute default retrieval strategy.
+        
+        Uses existing OptimizedRetriever with current configuration.
+        """
+        from app.services.retrieval.optimized_retriever import get_optimized_retriever
+        from app.services.retrieval.base import RetrievalContext
+        
+        if self._retriever is None:
+            self._retriever = await get_optimized_retriever()
+        
+        retrieval_ctx = RetrievalContext(
+            tenant_id=ctx.tenant_id,
+            query=ctx.query,
+            metadata_filter=ctx.metadata_filter,
+            kb_ids=ctx.kb_ids,
+        )
+        
+        return await self._retriever.retrieve(retrieval_ctx)
+
+
+class EnhancedPipeline:
+    """
+    [AC-AISVC-RES-02] Enhanced pipeline with new end-to-end retrieval features.
+    
+    Features:
+    - Document preprocessing (cleaning/normalization)
+    - Structured chunking (markdown/tables/FAQ)
+    - Metadata generation and mounting
+    - Embedding strategy (document/query prefix + Matryoshka)
+    - Metadata inference and filtering (hard/soft filter)
+    - Retrieval strategy (Dense + Keyword/Hybrid + RRF)
+    - Optional reranking
+    """
+    
+    def __init__(
+        self,
+        config: RoutingConfig | None = None,
+    ):
+        self._config = config or RoutingConfig()
+        self._retriever = None
+    
+    async def execute(
+        self,
+        ctx: StrategyContext,
+    ) -> "RetrievalResult":
+        """
+        Execute enhanced retrieval strategy.
+        
+        Uses OptimizedRetriever with enhanced configuration.
+        """
+        from app.services.retrieval.optimized_retriever import OptimizedRetriever
+        from app.services.retrieval.base import RetrievalContext
+        
+        if self._retriever is None:
+            self._retriever = OptimizedRetriever(
+                two_stage_enabled=True,
+                hybrid_enabled=True,
+            )
+        
+        retrieval_ctx = RetrievalContext(
+            tenant_id=ctx.tenant_id,
+            query=ctx.query,
+            metadata_filter=ctx.metadata_filter,
+            kb_ids=ctx.kb_ids,
+        )
+        
+        return await self._retriever.retrieve(retrieval_ctx)
+
+
+class StrategyRouter:
+    """
+    [AC-AISVC-RES-01, AC-AISVC-RES-02, AC-AISVC-RES-03] 
+    Strategy router for retrieval and embedding.
+    
+    Decision Flow:
+    1. Check if enhanced strategy is enabled via configuration
+    2. Check grayscale rules (percentage/allowlist)
+    3. Route to appropriate pipeline (default/enhanced)
+    4. Handle rollback on error or performance degradation
+    
+    Constraints:
+    - Default strategy MUST preserve existing online logic
+    - Enhanced strategy MUST be configurable and rollback-able
+    """
+    
+    def __init__(
+        self,
+        config: RoutingConfig | None = None,
+        rollback_manager: RollbackManager | None = None,
+    ):
+        self._config = config or RoutingConfig()
+        self._rollback_manager = rollback_manager or RollbackManager()
+        self._default_pipeline = DefaultPipeline()
+        self._enhanced_pipeline = EnhancedPipeline(self._config)
+        
+        self._current_strategy = StrategyType.DEFAULT
+        self._strategy_enabled = True
+    
+    @property
+    def current_strategy(self) -> StrategyType:
+        """Get current active strategy."""
+        return self._current_strategy
+    
+    @property
+    def config(self) -> RoutingConfig:
+        """Get current configuration."""
+        return self._config
+    
+    def update_config(self, new_config: RoutingConfig) -> None:
+        """
+        [AC-AISVC-RES-15] Update routing configuration (hot reload).
+        
+        Args:
+            new_config: New configuration to apply
+        """
+        old_strategy = self._config.strategy
+        self._config = new_config
+        
+        logger.info(
+            f"[AC-AISVC-RES-15] Routing config updated: "
+            f"strategy={new_config.strategy.value}, "
+            f"mode={new_config.rag_runtime_mode.value}, "
+            f"grayscale={new_config.grayscale_percentage:.2%}"
+        )
+        
+        if old_strategy != new_config.strategy:
+            logger.info(
+                f"[AC-AISVC-RES-02] Strategy changed: {old_strategy.value} -> {new_config.strategy.value}"
+            )
+    
+    def route(
+        self,
+        ctx: StrategyContext,
+    ) -> StrategyResult:
+        """
+        [AC-AISVC-RES-01, AC-AISVC-RES-02] Route to appropriate strategy.
+        
+        Args:
+            ctx: Strategy context with tenant, query, metadata, etc.
+        
+        Returns:
+            StrategyResult with selected strategy and mode
+        """
+        if not self._strategy_enabled:
+            logger.info("[AC-AISVC-RES-07] Strategy disabled, using default")
+            return StrategyResult(
+                strategy=StrategyType.DEFAULT,
+                mode=self._config.rag_runtime_mode,
+                should_fallback=False,
+                diagnostics={"reason": "strategy_disabled"},
+            )
+        
+        use_enhanced = self._config.should_use_enhanced_strategy(ctx.tenant_id)
+        
+        if use_enhanced:
+            self._current_strategy = StrategyType.ENHANCED
+            logger.info(
+                f"[AC-AISVC-RES-02] Routing to ENHANCED strategy: tenant={ctx.tenant_id}"
+            )
+        else:
+            self._current_strategy = StrategyType.DEFAULT
+            logger.info(
+                f"[AC-AISVC-RES-01] Routing to DEFAULT strategy: tenant={ctx.tenant_id}"
+            )
+        
+        return StrategyResult(
+            strategy=self._current_strategy,
+            mode=self._config.rag_runtime_mode,
+            diagnostics={
+                "grayscale_percentage": self._config.grayscale_percentage,
+                "in_allowlist": ctx.tenant_id in self._config.grayscale_allowlist if ctx.tenant_id else False,
+            },
+        )
+    
+    async def execute(
+        self,
+        ctx: StrategyContext,
+    ) -> tuple["RetrievalResult", StrategyResult]:
+        """
+        Execute retrieval with strategy routing.
+        
+        Args:
+            ctx: Strategy context
+        
+        Returns:
+            Tuple of (RetrievalResult, StrategyResult)
+        """
+        start_time = time.time()
+        result = self.route(ctx)
+        
+        try:
+            if result.strategy == StrategyType.ENHANCED:
+                retrieval_result = await self._enhanced_pipeline.execute(ctx)
+            else:
+                retrieval_result = await self._default_pipeline.execute(ctx)
+            
+            duration_ms = int((time.time() - start_time) * 1000)
+            
+            if duration_ms > self._config.performance_budget_ms:
+                degradation = (duration_ms - self._config.performance_budget_ms) / self._config.performance_budget_ms
+                if degradation > self._config.performance_degradation_threshold:
+                    logger.warning(
+                        f"[AC-AISVC-RES-08] Performance degradation detected: "
+                        f"duration={duration_ms}ms, budget={self._config.performance_budget_ms}ms, "
+                        f"degradation={degradation:.2%}"
+                    )
+            
+            return retrieval_result, result
+        
+        except Exception as e:
+            logger.error(
+                f"[AC-AISVC-RES-07] Strategy execution failed: {e}, "
+                f"strategy={result.strategy.value}"
+            )
+            
+            if result.strategy == StrategyType.ENHANCED:
+                self._rollback_manager.record_rollback(
+                    from_strategy=StrategyType.ENHANCED,
+                    to_strategy=StrategyType.DEFAULT,
+                    reason=str(e),
+                    tenant_id=ctx.tenant_id,
+                )
+                
+                logger.info("[AC-AISVC-RES-07] Falling back to DEFAULT strategy")
+                
+                retrieval_result = await self._default_pipeline.execute(ctx)
+                
+                return retrieval_result, StrategyResult(
+                    strategy=StrategyType.DEFAULT,
+                    mode=result.mode,
+                    should_fallback=True,
+                    fallback_reason=str(e),
+                    diagnostics=result.diagnostics,
+                )
+            
+            raise
+    
+    def rollback(
+        self,
+        reason: str,
+        tenant_id: str | None = None,
+        request_id: str | None = None,
+    ) -> None:
+        """
+        [AC-AISVC-RES-07] Force rollback to default strategy.
+        
+        Args:
+            reason: Reason for rollback
+            tenant_id: Optional tenant ID for audit
+            request_id: Optional request ID for audit
+        """
+        if self._current_strategy == StrategyType.ENHANCED:
+            self._rollback_manager.record_rollback(
+                from_strategy=StrategyType.ENHANCED,
+                to_strategy=StrategyType.DEFAULT,
+                reason=reason,
+                tenant_id=tenant_id,
+                request_id=request_id,
+            )
+            
+            self._current_strategy = StrategyType.DEFAULT
+            self._config.strategy = StrategyType.DEFAULT
+            
+            logger.info(
+                f"[AC-AISVC-RES-07] Rollback executed: reason={reason}, tenant={tenant_id}"
+            )
+    
+    def get_rollback_records(self, limit: int = 10) -> list[RollbackRecord]:
+        """Get recent rollback records."""
+        return self._rollback_manager.get_recent_rollbacks(limit)
+    
+    def validate_config(self) -> tuple[bool, list[str]]:
+        """
+        [AC-AISVC-RES-06] Validate current configuration.
+        
+        Returns:
+            Tuple of (is_valid, list of error messages)
+        """
+        return self._config.validate()
+
+
+_strategy_router: StrategyRouter | None = None
+
+
+def get_strategy_router() -> StrategyRouter:
+    """Get or create StrategyRouter singleton."""
+    global _strategy_router
+    if _strategy_router is None:
+        _strategy_router = StrategyRouter()
+    return _strategy_router
+
+
+def reset_strategy_router() -> None:
+    """Reset StrategyRouter singleton (for testing)."""
+    global _strategy_router
+    _strategy_router = None