Simon
bea288e414
== 已部署上线 (9项) == - 代办事项真实数据源集成 (企微审批API 8bug修复链) - H5/坐席端 Logo样式统一+绿色背景 - 视频引导页修复 (localStorage key v2) - 坐席端 v9 Vue版本修复 (ElMessage._context) - 截图按钮 v10 修复 (getDisplayMedia user gesture) - 扫码样式恢复+H5扫码登录跳转修复 - H5截图快捷键提示 == 代码完成待部署 (3项) == - 知识迭代3Bug修复 (#8 POST端点/#7 MERGE幂等/#6 过期检查) - 会议室预定-小鱼易联终端 (40文件, 40/40测试通过) - IT资产升级审批推送 (asset_service.py) == 需求文档 (2项) == - 坐席端AI辅助消息框-PRD (4项新功能确认) - 坐席端布局优化建议 v2.0 (7天计划) == 新增文档 == - 日报-2026-07-11.md - 知识迭代Bug修复报告-20260711.md - 会议室预定-部署指南.md - CHANGELOG.md 更新 == 测试 == - test_todo_integration.py: 40/40 - test_meetingroom.py: 40/40 - test_bugfix_ki_suggestions.py: 21/21
474 lines
19 KiB
Python
474 lines
19 KiB
Python
# =============================================================================
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# 企微IT智能服务台 — H5 员工端 AI 回复后台任务
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# =============================================================================
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# 背景：原 h5_send_message 在同步 HTTP 请求内 await AI 推理（Dify 3~15s），
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# 整条请求被阻塞，前端表现为"发送中"长时间卡顿。
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# 本模块将 AI 推理移出请求，改为 asyncio 后台任务，结果经 WebSocket
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# 流式推回（ai_reply_chunk / ai_reply），发送瞬时完成。
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#
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# 关键约束（详见 docs/02-需求分析/技术架构演进/员工端消息发送延时改造方案.md）：
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# 1. 必须单 worker 运行（docker-compose --workers 1）：
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# ws_manager 是进程内单例，多 worker 时后台任务与员工 WS 连接可能不在
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# 同进程，broadcast 会静默丢失（约 50%）。
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# 2. 使用独立 DB session（_get_session_factory），不可复用请求的 db
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# （请求返回后该 session 会被关闭）。
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# =============================================================================
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import logging
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from datetime import datetime
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from app.api.byod import _byod_keyword_prefilter
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from app.database import _get_session_factory
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from app.dependencies import get_shared_ai_handler
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from app.models.conversation import Conversation
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from app.models.message import Message
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from app.services.routing_service import (
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routing_keyword_prefilter,
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detect_routing_intent,
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get_contact_by_category,
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send_contact_card,
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record_routing_event,
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_keyword_fallback_category,
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)
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from app.services.ws_manager import manager as ws_manager
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logger = logging.getLogger(__name__)
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async def _persist_and_push(
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db,
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conversation: Conversation,
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employee_id: str,
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content: str,
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is_guidance: bool,
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should_count: bool,
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should_transfer: bool,
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dify_conversation_id,
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):
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"""持久化 AI 回复并推送给员工端 + 广播坐席端。
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做什么：
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1. 存 AI 消息到 DB
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2. 更新会话状态（dify 上下文 / 计数 / 转人工）
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3. 经 WS 向员工推 ai_reply 终态（前端据此替换打字机气泡）
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4. 经 WS 向坐席端广播 new_message + conversation_updated
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为什么：把"落库 + 推送"封装为单点，供同步路径与流式路径复用。
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"""
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# 1. 存 AI 消息
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ai_message = Message(
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conversation_id=conversation.id,
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sender_type="ai",
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sender_id="ai_bot",
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sender_name="Duckula（达寇拉）",
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content=content,
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msg_type="text",
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is_read=True,
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)
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db.add(ai_message)
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await db.flush()
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# 2. 更新会话状态
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if dify_conversation_id:
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conversation.dify_conversation_id = dify_conversation_id
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if should_count:
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conversation.ai_substantive_reply_count += 1
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if should_transfer:
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conversation.status = "queued"
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conversation.updated_at = datetime.now()
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db.add(conversation)
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await db.flush()
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await db.commit()
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# 3. 推 ai_reply 终态给员工（前端替换打字机气泡）
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await ws_manager.broadcast_to_employees([employee_id], {
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"type": "ai_reply",
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"data": {
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"message_id": str(ai_message.id),
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"conversation_id": str(conversation.id),
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"sender_type": "ai",
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"sender_id": "ai_bot",
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"sender_name": "Duckula（达寇拉）",
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"content": content,
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"msg_type": "text",
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"is_guidance": is_guidance,
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"ai_reply_count": conversation.ai_substantive_reply_count,
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"can_call_agent": conversation.ai_substantive_reply_count >= 3,
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"conversation_status": conversation.status,
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},
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})
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# 4. 广播坐席端（new_message + conversation_updated）
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try:
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await ws_manager.broadcast({
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"type": "new_message",
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"data": {
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"conversation_id": str(conversation.id),
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"message_id": str(ai_message.id),
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"sender_type": "ai",
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"sender_id": "ai_bot",
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"sender_name": "Duckula（达寇拉）",
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"content": content,
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"msg_type": "text",
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},
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})
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await ws_manager.broadcast({
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"type": "conversation_updated",
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"data": {
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"conversation_id": str(conversation.id),
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"status": conversation.status,
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"assigned_agent_id": str(conversation.assigned_agent_id) if conversation.assigned_agent_id else None,
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},
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})
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except Exception as ws_err:
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# WS 广播失败不阻塞消息存储，只记录 warning
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logger.warning(f"WS 广播 AI 回复给坐席失败（消息已存储）: {ws_err}")
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async def _handle_byod_query(db, conversation, employee_id, content):
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"""处理 BYOD 自备电脑补贴查询。
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在 H5 聊天消息流中拦截 BYOD 关键词后执行资格检查，并以 byod_card
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卡片消息形式推送给员工端（前端 MessageBubble 据 msg_type 渲染
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ByodSubsidyCard）。
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流程：
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1. 通过 WecomService 获取员工岗位（position）
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2. 与 BYOD 资格清单匹配（_match_position）
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3. 创建 byod_card 类型 AI 消息并落库
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4. 经 WS 推送 ai_reply 给员工端（携带 extra_data.byod_result）
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5. 广播 new_message + conversation_updated 给坐席端（与 _persist_and_push 一致）
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Args:
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db: 异步 DB session（process_h5_ai_reply 的 factory session）
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conversation: 当前会话对象（Conversation）
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employee_id: 员工企微 UserID
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content: 用户消息原文（用于日志）
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"""
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# 延迟导入避免循环依赖（byod 模块注册路由时可能引用 app.main）
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from app.api.byod import _match_position, BYOD_APPLICATION_URL, BYOD_NOTES, BYOD_REGISTER_NOTES
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from app.services.wecom_service import WecomService
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# 1. 获取员工岗位（企微通讯录 API）
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position = ""
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try:
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wecom_service = WecomService()
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try:
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user_info = await wecom_service.get_user_info(employee_id)
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position = user_info.get("position", "")
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finally:
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await wecom_service.close()
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except Exception as e:
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logger.error(f"BYOD: 获取员工岗位失败: {e}")
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# 2. 岗位匹配（返回: 是否匹配, 匹配岗位, 匹配类别）
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eligible, matched_pos, matched_category = _match_position(position)
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# 3. 构建 BYOD 结果数据
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# 字段与前端 ByodSubsidyCard.vue props 完全一致：
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# eligible / position / matched_category / application_url / notes / reason
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byod_result = {
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"eligible": eligible,
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"has_subsidy": eligible,
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"position": position,
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"matched_category": matched_category,
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"application_url": BYOD_APPLICATION_URL, # 所有岗位都提供链接
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"notes": BYOD_NOTES if eligible else BYOD_REGISTER_NOTES,
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"reason": (
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"" if eligible
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else f"您的岗位「{position}」不在自备电脑补贴资格清单中，可进行自备电脑登记（无补贴）"
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),
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}
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# 4. 展示文本（AI 气泡的 content，卡片下方不直接展示，但会话列表/坐席端可见）
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if eligible:
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display_text = f"您岗位为「{position}」，符合自备电脑补贴申请资格"
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else:
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display_text = f"您岗位为「{position}」，可进行自备电脑登记（无补贴）"
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# 5. 创建 AI 消息（byod_card 类型，携带 byod_result）
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ai_message = Message(
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conversation_id=conversation.id,
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sender_type="ai",
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sender_id="ai_bot",
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sender_name="Duckula（达寇拉）",
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content=display_text,
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msg_type="byod_card",
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extra_data={"byod_result": byod_result},
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is_read=True,
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)
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db.add(ai_message)
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await db.flush()
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# 6. 更新会话状态（计数 + 时间，BYOD 视为一次实质性 AI 回复）
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conversation.ai_substantive_reply_count += 1
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conversation.updated_at = datetime.now()
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db.add(conversation)
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await db.flush()
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await db.commit()
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# 7. 推送 ai_reply 给员工端（前端据 msg_type="byod_card" 渲染卡片）
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await ws_manager.broadcast_to_employees([employee_id], {
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"type": "ai_reply",
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"data": {
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"message_id": str(ai_message.id),
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"conversation_id": str(conversation.id),
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"sender_type": "ai",
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"sender_id": "ai_bot",
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"sender_name": "Duckula（达寇拉）",
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"content": display_text,
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"msg_type": "byod_card",
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"extra_data": {"byod_result": byod_result},
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"is_guidance": False,
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"ai_reply_count": conversation.ai_substantive_reply_count,
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"can_call_agent": conversation.ai_substantive_reply_count >= 3,
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"conversation_status": conversation.status,
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},
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})
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# 8. 广播坐席端（new_message + conversation_updated，与 _persist_and_push 一致）
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try:
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await ws_manager.broadcast({
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"type": "new_message",
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"data": {
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"conversation_id": str(conversation.id),
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"message_id": str(ai_message.id),
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"sender_type": "ai",
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"sender_id": "ai_bot",
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"sender_name": "Duckula（达寇拉）",
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"content": display_text,
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"msg_type": "byod_card",
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"extra_data": {"byod_result": byod_result},
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},
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})
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await ws_manager.broadcast({
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"type": "conversation_updated",
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"data": {
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"conversation_id": str(conversation.id),
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"status": conversation.status,
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"assigned_agent_id": (
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str(conversation.assigned_agent_id)
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if conversation.assigned_agent_id else None
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),
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},
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})
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except Exception as ws_err:
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logger.warning(f"BYOD: WS 广播给坐席失败: {ws_err}")
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logger.info(
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f"BYOD 查询完成: employee_id={employee_id}, position={position}, "
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f"eligible={eligible}, matched_category={matched_category}"
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)
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async def _handle_routing(
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db,
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conversation: Conversation,
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employee_id: str,
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content: str,
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) -> bool:
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"""处理非IT业务路由推荐。
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在 H5 聊天消息流中拦截路由关键词后调用 Dify 统一意图识别，
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判定为 non_it_routing 且 routing_confidence ≥ 阈值时发送名片三段式消息。
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流程：
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1. 调用 Dify 统一意图识别（detect_routing_intent）
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2. 检查 intent_type == "non_it_routing" && routing_confidence ≥ 阈值
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→ YES: 查询联系人 → 发送名片三段式消息 → 记录路由事件 → 返回 True
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→ NO: 返回 False（继续走正常 AI 流程）
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3. Dify 调用失败 → 关键词降级兜底（按 ROUTING_KEYWORD_TO_CATEGORY 映射）
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Args:
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db: 异步 DB session（process_h5_ai_reply 的 factory session）
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conversation: 当前会话对象（Conversation）
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employee_id: 员工企微 UserID
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content: 用户消息原文
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Returns:
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bool: True 表示已发送路由名片（应 return 中断后续流程），
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False 表示未触发路由（继续走正常 AI 流程）
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"""
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from app.config import settings
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# 1. 调用 Dify 统一意图识别
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try:
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result = await detect_routing_intent(content, employee_id)
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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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# 如果是审批意图，不拦截（让审批流程处理）
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if intent_type == "approval":
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return False
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logger.info(
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f"路由意图检测(Dify): intent_type={intent_type}, "
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f"business_category={business_category}, "
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f"routing_confidence={routing_confidence}"
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)
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# 2. 检查是否触发路由推荐
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threshold = settings.routing_confidence_threshold
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if intent_type != "non_it_routing" or routing_confidence < threshold:
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# 置信度不足或非路由意图，走正常 AI 流程
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return False
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if not business_category:
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logger.warning("路由意图为 non_it_routing 但 business_category 为空，跳过")
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return False
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except Exception as e:
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logger.warning(f"Dify 路由意图识别失败，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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business_category = _keyword_fallback_category(content)
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if not business_category:
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# 关键词也未命中，走正常 AI 流程
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return False
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routing_confidence = 0.75 # 降级兜底给一个略高于阈值的置信度
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logger.info(f"路由意图检测(兜底): business_category={business_category}")
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# 4. 查询联系人
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contact = await get_contact_by_category(db, business_category)
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if not contact:
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logger.warning(f"未找到 {business_category} 类别的联系人，跳过路由推荐")
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return False
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# 5. 构建路由说明文本
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category_display = business_category.replace("行政-物业", "物业")
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reason = (
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f"您的问题属于{category_display}业务范畴，不在IT服务台服务范围内 😊\n\n"
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f"为您推荐{category_display}服务相关联系人，您可以直接点击名片联系TA："
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)
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# 6. 发送名片三段式消息
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await send_contact_card(
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db=db,
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conversation=conversation,
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employee_id=employee_id,
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contact=contact,
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reason=reason,
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business_category=business_category,
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routing_confidence=routing_confidence,
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)
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# 7. 记录路由事件（P1）
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await record_routing_event(
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db=db,
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conversation_id=str(conversation.id),
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employee_id=employee_id,
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message_content=content,
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business_category=business_category,
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routing_confidence=routing_confidence,
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contact=contact,
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)
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return True
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async def process_h5_ai_reply(
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conversation_id: str,
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employee_id: str,
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content: str,
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dify_conversation_id=None,
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):
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"""H5 发送消息后的 AI 回复处理（asyncio.create_task 入口）。
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流程：
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- 本地快判断（打招呼 / 呼叫人工）→ 同步结果，整段推送（不调 Dify）
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- 否则流式调 Dify，逐 chunk 推 ai_reply_chunk，流结束推 ai_reply 终态
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- 任意异常 → 推 ai_reply_failed，不阻塞用户
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"""
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ai_handler = get_shared_ai_handler()
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factory = _get_session_factory()
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async with factory() as db:
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try:
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conversation = await db.get(Conversation, conversation_id)
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if not conversation:
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logger.warning(f"后台 AI 任务：会话不存在 {conversation_id}")
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return
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is_guidance = False
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||
should_count = False
|
||
should_transfer = False
|
||
new_dify_conv_id = dify_conversation_id
|
||
full_parts: list = []
|
||
|
||
# === BYOD 关键词拦截 ===
|
||
# 在打招呼/呼叫人工判断之前，先检查是否为 BYOD（自备电脑补贴）意图。
|
||
# 命中关键词 → 执行 BYOD 资格检查并推送 byod_card 卡片，不走正常 AI 流程。
|
||
if _byod_keyword_prefilter(content):
|
||
await _handle_byod_query(db, conversation, employee_id, content)
|
||
return # BYOD 处理完毕，直接返回
|
||
|
||
# === 业务路由检测（新增）===
|
||
# 在 BYOD 检测之后、打招呼/呼叫人工检测之前，检查是否为非IT业务路由。
|
||
# 命中路由关键词 → 调用 Dify 统一意图识别 → non_it_routing && confidence≥0.7
|
||
# → 发送名片三段式消息（路由文本 + contact_card + 系统提示）
|
||
# 置信度不足或非路由意图 → 继续往下走正常 AI 流程
|
||
if routing_keyword_prefilter(content):
|
||
routed = await _handle_routing(db, conversation, employee_id, content)
|
||
if routed:
|
||
return # 路由名片已发送，直接返回
|
||
|
||
# 本地快判断（不打 Dify）：打招呼 / 呼叫人工 → 同步路径
|
||
if ai_handler.is_greeting(content) or ai_handler.is_call_human(content):
|
||
result = await ai_handler.handle_message(
|
||
content=content,
|
||
dify_conversation_id=dify_conversation_id,
|
||
user_id=employee_id,
|
||
)
|
||
await _persist_and_push(
|
||
db, conversation, employee_id, result.content,
|
||
result.is_guidance, result.should_count,
|
||
result.should_transfer, result.dify_conversation_id,
|
||
)
|
||
return
|
||
|
||
# 流式调 Dify（get_reply_stream 内部已处理真 SSE / 非流式 fallback）
|
||
# 注意：首参是 message（用户文本），不是 content
|
||
async for chunk in ai_handler.ai_service.get_reply_stream(
|
||
message=content,
|
||
conversation_id=dify_conversation_id,
|
||
user_id=employee_id,
|
||
):
|
||
delta = chunk.get("delta", "")
|
||
if delta:
|
||
full_parts.append(delta)
|
||
await ws_manager.broadcast_to_employees([employee_id], {
|
||
"type": "ai_reply_chunk",
|
||
"data": {
|
||
"conversation_id": conversation_id,
|
||
"chunk": delta,
|
||
},
|
||
})
|
||
if chunk.get("finished"):
|
||
new_dify_conv_id = chunk.get("conversation_id") or dify_conversation_id
|
||
hit = chunk.get("hit")
|
||
# 命中 → 计数；未命中 → 转人工
|
||
should_count = bool(hit)
|
||
should_transfer = not bool(hit)
|
||
|
||
content_ai = "".join(full_parts)
|
||
if not content_ai:
|
||
# 流式无内容（极端情况），给降级提示，不转人工
|
||
content_ai = "⚠️ AI 暂时没有返回内容，请输入「IT」转人工。"
|
||
should_count = False
|
||
should_transfer = False
|
||
await _persist_and_push(
|
||
db, conversation, employee_id, content_ai,
|
||
is_guidance, should_count, should_transfer, new_dify_conv_id,
|
||
)
|
||
except Exception as e:
|
||
logger.error(f"后台 AI 任务异常: {e}", exc_info=True)
|
||
try:
|
||
await ws_manager.broadcast_to_employees([employee_id], {
|
||
"type": "ai_reply_failed",
|
||
"data": {
|
||
"conversation_id": conversation_id,
|
||
"message": "⚠️ AI 服务异常，请输入「IT」转人工或稍后重试。",
|
||
},
|
||
})
|
||
except Exception:
|
||
# 推送失败也无所谓，员工端 3 秒轮询兜底
|
||
pass
|