Files
wecom_it_smart_desk/backend/app/api/quiz.py
T
Simon 449c6d4875 feat: 2026-07-12~13 全量更新 - AI对话链路改造+H5 v4/v5+坐席端v5+上下文感知诊断+知识库迭代3
## H5 员工端 v4 (2026-07-13 00:48 已部署)
- 人工按钮三态文案统一为"人工坐席"
- 按钮位置移至发送键和语音按钮上方(垂直堆叠)
- 点按钮直接调 store.shakeAgent(),删除 CallAgentModal 弹窗动画
- 截图快捷键提示改为"截图->粘贴:Alt+Shift+A-Ctrl+V ---> Ctrl+V"
- 移动端隐藏截图提示(CSS 媒体查询)
- AI转人工提示改为"已为您呼叫人工坐席,请稍等!"
- 坐席接入提示改为"坐席正在查看您的信息,请等待处理回复!"
- 删除"摇铃呼叫坐席"入口和文案
- 删除孤儿组件 MessageList.vue + shake 动画 CSS

## H5 员工端 v5 (2026-07-13 02:08 已部署)
- RightPanel v2.1:删除"软件安装"和"资源权限"标签页
- 移除标签栏,智能推荐(DynamicRecommend)直接展示
- 删除 SoftwareDownloads/ApprovalLinks 引用和相关 CSS

## AI 对话链路全栈改造 Phase 1-6 (已部署)
- Phase 1: Dify JSON输出 + 后端blocking解析 + 双WS推送 + 错误降级
- Phase 2: 关键词收窄(~25强意图词) + 两级分类Prompt + 删除前端checkApprovalIntent
- Phase 3: WS扩展(ai_thinking+dynamic_recommend) + ai_structured气泡 + RightPanel v2 + 选项回传
- Phase 4: VisionService接入 + 图片消息融合(5秒窗口) + 降级策略
- Phase 5: 坐席端ai_thinking指示器 + ai_structured/byod_card渲染 + handleNewMessage修复
- Phase 6: diagnosis_stage(6值) + response_time_ms计时 + 慢响应告警(>10s)

## 坐席端 v5 (2026-07-13 01:38 已部署)
- ai_structured/byod_card 只读渲染
- AI思考指示器 UI
- handleNewMessage 透传 msg_type/extra_data 修复
- 布局优化v2.0: QuickReplyBar L1+L2悬浮 + ReplyBox左右分区 + 右栏260/560px切换
- 键盘快捷键v2.3: 纯数字路由 + ESC分层撤销 + Shift+Space用event.code

## 上下文感知智能诊断闭环 (2026-07-12 已部署)
- 三层诊断(API→Script→AI) + 三段排队(VIP→info_locked→not locked)
- 答题插队 + 五场景关闭
- 迁移052(6表+6列) + queue_service + quiz_service + closing_service
- H5前端: QueueWaiting + RightPanel双Tab + InputBar三态 + ResolveConfirmCard
- 坐席前端: pending_close结单流程 + 信息锁定(Dify步骤完成+有效回答率≥70%)

## 知识库迭代3 (2026-07-12 已部署)
- 分诊交互(H5+坐席+Dify独立应用)
- 拓扑预览(ECharts只读)
- 代答排除(4种匹配器: keyword/regex/intent/category)
- 迁移051 + 44文件43测试通过

## 后端变更
- 6个Python文件改造(h5_ai_task.py/h5.py/ai_service.py/closing_service.py等)
- funny_phrase_service.py: shake/connected/keyword 默认文案更新
- session_service.py: 企微消息文案同步
- 新增: queue.py/quiz.py/triage.py/exclusion_rules.py 等API端点
- 新增: diagnostic.py/quiz.py/triage_session.py 等模型
- 新增: closing_service/queue_service/quiz_service/triage_service 等服务

## 文档更新
- CHANGELOG.md: 新增 [未发布] 区全部变更记录
- 项目管理主文档 v2.5: 新增v0.7.3版本 + 已完成看板 + 最近搞定
- 版本记录: 新增v0.7.3条目
- AI对话链路实施计划: Phase 1-6 全部标记已实施
- 新增架构图/时序图/类图(mermaid)

## 部署路径修正
- 服务器项目根路径: /opt/wecom-it-desk/
- 所有前端dist均为ro bind mount,只能在宿主机源路径操作
- 服务器nginx /h5/ 是静态文件服务(非proxy_pass)
- elFinder上传二进制不可靠(MD5不匹配),改用base64分块上传
2026-07-13 02:17:03 +08:00

142 lines
4.7 KiB
Python

# =============================================================================
# 企微IT智能服务台 — 答题系统 API
# =============================================================================
# 说明:排队等待期间的答题+积分API
#
# 路由:
# GET /api/h5/quiz/question — 获取下一道题(双模式自动选择)
# POST /api/h5/quiz/answer — 提交答案(正误+积分+插队+下一题)
# GET /api/h5/quiz/history — 答题历史记录和积分
# =============================================================================
import logging
from typing import Optional
from fastapi import APIRouter, Depends, Query
from pydantic import BaseModel
from sqlalchemy import select
from sqlalchemy.ext.asyncio import AsyncSession
from app.database import get_db
from app.models.conversation import Conversation
from app.services.quiz_service import get_quiz_service
from app.utils.response import AppException, success_response
logger = logging.getLogger(__name__)
router = APIRouter(tags=["quiz"])
# =============================================================================
# 请求体定义
# =============================================================================
class AnswerRequest(BaseModel):
"""提交答案请求体。"""
employee_id: str
question_id: str
selected_index: int
conversation_id: Optional[str] = None
# =============================================================================
# GET /api/h5/quiz/question — 获取下一道题
# =============================================================================
@router.get("/h5/quiz/question")
async def get_quiz_question(
employee_id: str = Query(..., description="员工ID"),
conversation_id: Optional[str] = Query(None, description="当前会话ID(排队时传入)"),
db: AsyncSession = Depends(get_db),
):
"""获取下一道题(双模式自动选择)。
模式选择:
- 排队中且 info_locked=false → 诊断题(答案附加到会话上下文)
- 排队中且 info_locked=true → IT知识题
- 非排队 → IT知识题
Args:
employee_id: 员工ID
conversation_id: 当前会话ID(可选)
Returns:
题目数据
"""
quiz_service = get_quiz_service()
# 查找当前会话
conversation = None
if conversation_id:
result = await db.execute(
select(Conversation).where(Conversation.id == conversation_id)
)
conversation = result.scalar_one_or_none()
question = await quiz_service.get_next_question(db, employee_id, conversation)
return success_response(data=question)
# =============================================================================
# POST /api/h5/quiz/answer — 提交答案
# =============================================================================
@router.post("/h5/quiz/answer")
async def submit_quiz_answer(
body: AnswerRequest,
db: AsyncSession = Depends(get_db),
):
"""提交答案,返回正误+积分变化+插队效果+下一题。
处理流程:
1. 判定正误
2. 记录答题
3. 更新积分(答对+10分,跨会话累积)
4. 更新 queue_priority(每答3题前移1位,上限2)
5. 如果是诊断题且答对,答案附加到会话上下文
6. 返回下一道题
Args:
body: 答案请求体
Returns:
答题结果+下一题
"""
quiz_service = get_quiz_service()
# 查找当前会话
conversation = None
if body.conversation_id:
result = await db.execute(
select(Conversation).where(Conversation.id == body.conversation_id)
)
conversation = result.scalar_one_or_none()
result = await quiz_service.submit_answer(
db, body.employee_id, body.question_id, body.selected_index, conversation
)
return success_response(data=result)
# =============================================================================
# GET /api/h5/quiz/history — 答题历史记录和积分
# =============================================================================
@router.get("/h5/quiz/history")
async def get_quiz_history(
employee_id: str = Query(..., description="员工ID"),
page: int = Query(1, ge=1, description="页码"),
page_size: int = Query(20, ge=1, le=100, description="每页数量"),
db: AsyncSession = Depends(get_db),
):
"""获取答题历史记录和积分信息。
Args:
employee_id: 员工ID
page: 页码
page_size: 每页数量
Returns:
答题历史+积分信息
"""
quiz_service = get_quiz_service()
history = await quiz_service.get_quiz_history(db, employee_id, page, page_size)
return success_response(data=history)