P1-2: 统一Dify调用点 - 删除detect-intent死链路(approval/byod) + routing_service共享client修复连接泄漏 + 前端死代码清理
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Simon
@@ -8,7 +8,6 @@
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# =============================================================================
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import asyncio
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import json
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import logging
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import os
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from typing import Optional
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@@ -332,34 +331,6 @@ class ApprovalUrgeRequest(BaseModel):
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sp_no: str
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class ApprovalDetectIntentRequest(BaseModel):
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"""审批意图检测请求"""
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text: str
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employee_id: Optional[str] = None
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class ApprovalDetectIntentResponse(BaseModel):
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"""审批意图检测响应
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Attributes:
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is_approval_request: 是否为审批请求（原字段，语义不变）
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confidence: 置信度（0.0~1.0）（原字段，语义不变）
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approval_type: 审批类型（原字段，语义不变）
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source: 结果来源 — dify(Dify识别) / keyword_prefilter(关键词预过滤未命中) / fallback(降级兜底)
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intent_type: 意图大类（新增）— approval/it_consult/non_it_routing/chitchat
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business_category: 非IT业务类别（新增）— 行政/人力资源/财务/法务/行政-物业，仅 non_it_routing 时有值
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routing_confidence: 路由置信度（新增）— 0.0~1.0，≥0.7 触发名片推荐
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"""
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is_approval_request: bool
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confidence: float
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approval_type: Optional[str] = None
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source: str # "dify" | "keyword_prefilter" | "fallback"
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# 以下为 v3 统一意图识别新增字段（向后兼容：原审批逻辑只读取前4个字段）
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intent_type: str = "chitchat"
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business_category: Optional[str] = None
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routing_confidence: float = 0.0
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# =============================================================================
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# 企微API调用辅助函数
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# =============================================================================
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@@ -973,179 +944,3 @@ async def get_approval_keywords():
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return success_response(data=keywords)
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# =============================================================================
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# 审批意图识别（Dify + 关键词预过滤 + 降级兜底）
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# =============================================================================
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def _keyword_prefilter(text: str) -> bool:
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"""关键词预过滤：检查文本是否包含审批相关关键词（v2.0 收窄版）。
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v2.0 变更（2026-07-13）：
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- 不再合并 APPROVAL_TEMPLATES 的 keywords（包含"借用""升级""外联"等泛化词）
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- 仅使用 APPROVAL_PREFILTER_KEYWORDS（强意图词 + 复合专有词）
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- 模板 keywords 仍保留在 KEYWORD_TO_APPROVAL_TYPE 中，仅用于 Dify 不可用时的降级兜底
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Args:
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text: 用户消息文本
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Returns:
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bool: 是否包含审批关键词
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"""
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if not text:
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return False
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lower_text = text.lower()
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# v2.0: 仅使用预过滤关键词列表，不合并模板 keywords
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return any(kw.lower() in lower_text for kw in APPROVAL_PREFILTER_KEYWORDS)
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def _fallback_detect(text: str) -> tuple[bool, float, Optional[str]]:
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"""关键词兜底：Dify 不可用时通过关键词匹配判断审批意图。
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遍历 KEYWORD_TO_APPROVAL_TYPE 映射，命中第一个关键词即返回对应审批类型。
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置信度取 0.6（略低于阈值，但预过滤已通过说明有审批关键词）。
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Args:
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text: 用户消息文本
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Returns:
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tuple: (is_approval_request, confidence, approval_type)
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"""
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lower_text = (text or "").lower()
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approval_type: Optional[str] = None
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for kw, atype in KEYWORD_TO_APPROVAL_TYPE.items():
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if kw.lower() in lower_text:
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approval_type = atype
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break
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# 预过滤已通过（说明有审批关键词），兜底返回 is_approval_request=True
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return True, 0.6, approval_type
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async def _call_dify_approval_intent(text: str, employee_id: str = "") -> dict:
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"""调用 Dify 审批意图识别应用（Dify 原生 API）。
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直接调用 Dify 原生 /v1/chat-messages 接口，绕过 Dify2OpenAI 代理。
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Dify2OpenAI 代理会将 JSON 响应序列化为 "[object Object]" 字符串，
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导致后端无法解析。使用原生 API 可获得正确的 JSON 响应。
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Dify 应用的 System Prompt 已在 Dify 后台配置好，后端只需把用户消息传过去。
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返回 JSON: {"is_approval_request": bool, "confidence": float, "approval_type": str|null}
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Args:
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text: 用户消息文本
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employee_id: 员工 ID（可选，传给 Dify 的 user 字段）
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Returns:
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dict: {"is_approval_request": bool, "confidence": float, "approval_type": str|None}
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Raises:
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Exception: Dify 调用失败或响应解析失败
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"""
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base_url = settings.approval_dify_base_url
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api_key = settings.approval_dify_api_key
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timeout = settings.approval_dify_timeout
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if not base_url or not api_key:
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raise ValueError("Dify 审批意图识别应用未配置（APPROVAL_DIFY_BASE_URL / APPROVAL_DIFY_API_KEY）")
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# 构建请求 URL：base_url + /v1/chat-messages（Dify 原生 API）
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url = f"{base_url.rstrip('/')}/v1/chat-messages"
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body = {
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"inputs": {}, # Dify 应用的输入变量（无自定义变量时为空）
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"query": text, # 用户消息文本
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"response_mode": "blocking", # 阻塞模式，等待完整响应
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"user": employee_id or "approval_detection", # 用户标识
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}
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headers = {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json",
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}
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async with httpx.AsyncClient(timeout=httpx.Timeout(timeout)) as client:
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response = await client.post(url, json=body, headers=headers)
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response.raise_for_status()
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data = response.json()
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# 解析 Dify 原生响应：answer 字段包含 AI 返回的文本（JSON 字符串）
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answer = data.get("answer", "")
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parsed = json.loads(answer)
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return {
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"is_approval_request": bool(parsed.get("is_approval_request", False)),
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"confidence": float(parsed.get("confidence", 0.0)),
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"approval_type": parsed.get("approval_type"),
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# v3 统一意图识别新增字段（向后兼容：旧 Prompt 无这些字段时取默认值）
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"intent_type": str(parsed.get("intent_type", "chitchat")),
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"business_category": parsed.get("business_category"),
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"routing_confidence": float(parsed.get("routing_confidence", 0.0)),
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}
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@router.post("/approval/detect-intent")
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async def detect_approval_intent(request: ApprovalDetectIntentRequest):
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"""审批意图检测端点。
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流程：
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1. 关键词预过滤 — 未命中直接返回 false（避免每条消息都调 Dify）
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2. 命中关键词 → 调用 Dify 审批意图识别应用
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3. Dify 调用失败 → 降级为关键词匹配（兜底）
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Args:
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request: 包含 text（用户消息）和可选的 employee_id
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Returns:
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ApprovalDetectIntentResponse: 检测结果
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"""
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text = request.text or ""
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# 1. 关键词预过滤
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if not _keyword_prefilter(text):
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return success_response(data=ApprovalDetectIntentResponse(
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is_approval_request=False,
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confidence=0.0,
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approval_type=None,
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source="keyword_prefilter",
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intent_type="chitchat",
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business_category=None,
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routing_confidence=0.0,
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))
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# 2. 调用 Dify 意图识别
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try:
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result = await _call_dify_approval_intent(text, request.employee_id or "")
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# 检查置信度阈值
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threshold = settings.approval_confidence_threshold
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is_approval = result["is_approval_request"] and result["confidence"] >= threshold
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logger.info(
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f"审批意图检测(Dify): is_approval={is_approval}, "
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f"confidence={result['confidence']}, type={result.get('approval_type')}, "
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f"intent_type={result.get('intent_type')}, "
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f"business_category={result.get('business_category')}, "
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f"routing_confidence={result.get('routing_confidence')}"
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)
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return success_response(data=ApprovalDetectIntentResponse(
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is_approval_request=is_approval,
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confidence=result["confidence"],
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approval_type=result.get("approval_type"),
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source="dify",
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intent_type=result.get("intent_type", "chitchat"),
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business_category=result.get("business_category"),
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routing_confidence=result.get("routing_confidence", 0.0),
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))
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except Exception as e:
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logger.warning(f"Dify 审批意图识别失败，降级为关键词匹配: {e}")
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# 3. 降级为关键词匹配
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is_approval, confidence, approval_type = _fallback_detect(text)
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logger.info(
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f"审批意图检测(兜底): is_approval={is_approval}, "
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f"confidence={confidence}, type={approval_type}"
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)
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return success_response(data=ApprovalDetectIntentResponse(
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is_approval_request=is_approval,
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confidence=confidence,
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approval_type=approval_type,
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source="fallback",
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intent_type="approval" if is_approval else "chitchat",
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business_category=None,
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routing_confidence=0.0,
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))
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