1. 课程学习进度追踪
- 新增 UserCourseProgress 和 UserMaterialProgress 模型
- 新增 /api/v1/progress/* 进度追踪 API
- 更新 admin.py 使用真实课程完成率数据
2. 路由权限检查完善
- 新增前端 permissionChecker.ts 权限检查工具
- 更新 router/guard.ts 实现团队和课程权限验证
- 新增后端 permission_service.py
3. AI 陪练音频转文本
- 新增 speech_recognition.py 语音识别服务
- 新增 /api/v1/speech/* API
- 更新 ai-practice-coze.vue 支持语音输入
4. 双人对练报告生成
- 更新 practice_room_service.py 添加报告生成功能
- 新增 /rooms/{room_code}/report API
- 更新 duo-practice-report.vue 调用真实 API
5. 学习提醒推送
- 新增 notification_service.py 通知服务
- 新增 scheduler_service.py 定时任务服务
- 支持钉钉、企微、站内消息推送
6. 智能学习推荐
- 新增 recommendation_service.py 推荐服务
- 新增 /api/v1/recommendations/* API
- 支持错题、能力、进度、热门多维度推荐
7. 安全问题修复
- DEBUG 默认值改为 False
- 添加 SECRET_KEY 安全警告
- 新增 check_security_settings() 检查函数
8. 证书 PDF 生成
- 更新 certificate_service.py 添加 PDF 生成
- 添加 weasyprint、Pillow、qrcode 依赖
- 更新下载 API 支持 PDF 和 PNG 格式
This commit is contained in:
yuliang_guo
@@ -1,323 +1,323 @@
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"""
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双人对练分析服务
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功能：
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- 分析双人对练对话
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- 生成双方评估报告
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- 对话标注和建议
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"""
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import json
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import logging
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from dataclasses import dataclass, field
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from typing import Any, Dict, List, Optional
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from app.services.ai.ai_service import AIService
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from app.services.ai.prompts.duo_practice_prompts import SYSTEM_PROMPT, USER_PROMPT
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logger = logging.getLogger(__name__)
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@dataclass
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class UserEvaluation:
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"""用户评估结果"""
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user_name: str
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role_name: str
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total_score: int
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dimensions: Dict[str, Dict[str, Any]]
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highlights: List[str]
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improvements: List[Dict[str, str]]
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@dataclass
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class DuoPracticeAnalysisResult:
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"""双人对练分析结果"""
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# 整体评估
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interaction_quality: int = 0
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scene_restoration: int = 0
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overall_comment: str = ""
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# 用户A评估
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user_a_evaluation: Optional[UserEvaluation] = None
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# 用户B评估
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user_b_evaluation: Optional[UserEvaluation] = None
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# 对话标注
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dialogue_annotations: List[Dict[str, Any]] = field(default_factory=list)
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# AI 元数据
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raw_response: str = ""
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ai_provider: str = ""
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ai_model: str = ""
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ai_latency_ms: int = 0
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class DuoPracticeAnalysisService:
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"""
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双人对练分析服务
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使用示例：
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```python
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service = DuoPracticeAnalysisService()
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result = await service.analyze(
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scene_name="销售场景",
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scene_background="客户咨询产品",
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role_a_name="销售顾问",
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role_b_name="顾客",
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user_a_name="张三",
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user_b_name="李四",
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dialogue_history=dialogue_list,
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duration_seconds=300,
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total_turns=20
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)
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```
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"""
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MODULE_CODE = "duo_practice_analysis"
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async def analyze(
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self,
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scene_name: str,
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scene_background: str,
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role_a_name: str,
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role_b_name: str,
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role_a_description: str,
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role_b_description: str,
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user_a_name: str,
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user_b_name: str,
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dialogue_history: List[Dict[str, Any]],
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duration_seconds: int,
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total_turns: int,
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db: Any = None
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) -> DuoPracticeAnalysisResult:
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"""
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分析双人对练
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Args:
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scene_name: 场景名称
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scene_background: 场景背景
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role_a_name: 角色A名称
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role_b_name: 角色B名称
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role_a_description: 角色A描述
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role_b_description: 角色B描述
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user_a_name: 用户A名称
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user_b_name: 用户B名称
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dialogue_history: 对话历史列表
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duration_seconds: 对练时长（秒）
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total_turns: 总对话轮次
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db: 数据库会话
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Returns:
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DuoPracticeAnalysisResult: 分析结果
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"""
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try:
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logger.info(f"开始双人对练分析: {scene_name}, 轮次={total_turns}")
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# 格式化对话历史
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dialogue_text = self._format_dialogue_history(dialogue_history)
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# 创建 AI 服务
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ai_service = AIService(module_code=self.MODULE_CODE, db_session=db)
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# 构建用户提示词
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user_prompt = USER_PROMPT.format(
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scene_name=scene_name,
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scene_background=scene_background or "未设置",
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role_a_name=role_a_name,
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role_b_name=role_b_name,
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role_a_description=role_a_description or f"扮演{role_a_name}角色",
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role_b_description=role_b_description or f"扮演{role_b_name}角色",
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user_a_name=user_a_name,
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user_b_name=user_b_name,
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dialogue_history=dialogue_text,
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duration_seconds=duration_seconds,
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total_turns=total_turns
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)
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# 调用 AI
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messages = [
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": user_prompt}
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]
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ai_response = await ai_service.chat(
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messages=messages,
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model="gemini-3-flash-preview", # 使用快速模型
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temperature=0.3,
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prompt_name="duo_practice_analysis"
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)
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logger.info(f"AI 分析完成: provider={ai_response.provider}, latency={ai_response.latency_ms}ms")
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# 解析 AI 输出
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result = self._parse_analysis_result(
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ai_response.content,
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user_a_name=user_a_name,
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user_b_name=user_b_name,
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role_a_name=role_a_name,
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role_b_name=role_b_name
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)
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# 填充 AI 元数据
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result.raw_response = ai_response.content
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result.ai_provider = ai_response.provider
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result.ai_model = ai_response.model
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result.ai_latency_ms = ai_response.latency_ms
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return result
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except Exception as e:
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logger.error(f"双人对练分析失败: {e}", exc_info=True)
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# 返回空结果
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return DuoPracticeAnalysisResult(
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overall_comment=f"分析失败: {str(e)}"
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)
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def _format_dialogue_history(self, dialogues: List[Dict[str, Any]]) -> str:
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"""格式化对话历史"""
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lines = []
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for d in dialogues:
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speaker = d.get("role_name") or d.get("speaker", "未知")
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content = d.get("content", "")
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seq = d.get("sequence", 0)
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lines.append(f"[{seq}] {speaker}：{content}")
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return "\n".join(lines)
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def _parse_analysis_result(
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self,
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ai_output: str,
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user_a_name: str,
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user_b_name: str,
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role_a_name: str,
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role_b_name: str
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) -> DuoPracticeAnalysisResult:
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"""解析 AI 输出"""
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result = DuoPracticeAnalysisResult()
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try:
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# 尝试提取 JSON
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json_str = ai_output
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# 如果输出包含 markdown 代码块，提取其中的 JSON
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if "```json" in ai_output:
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start = ai_output.find("```json") + 7
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end = ai_output.find("```", start)
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json_str = ai_output[start:end].strip()
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elif "```" in ai_output:
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start = ai_output.find("```") + 3
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end = ai_output.find("```", start)
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json_str = ai_output[start:end].strip()
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data = json.loads(json_str)
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# 解析整体评估
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overall = data.get("overall_evaluation", {})
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result.interaction_quality = overall.get("interaction_quality", 0)
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result.scene_restoration = overall.get("scene_restoration", 0)
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result.overall_comment = overall.get("overall_comment", "")
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# 解析用户A评估
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user_a_data = data.get("user_a_evaluation", {})
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if user_a_data:
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result.user_a_evaluation = UserEvaluation(
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user_name=user_a_data.get("user_name", user_a_name),
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role_name=user_a_data.get("role_name", role_a_name),
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total_score=user_a_data.get("total_score", 0),
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dimensions=user_a_data.get("dimensions", {}),
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highlights=user_a_data.get("highlights", []),
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improvements=user_a_data.get("improvements", [])
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)
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# 解析用户B评估
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user_b_data = data.get("user_b_evaluation", {})
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if user_b_data:
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result.user_b_evaluation = UserEvaluation(
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user_name=user_b_data.get("user_name", user_b_name),
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role_name=user_b_data.get("role_name", role_b_name),
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total_score=user_b_data.get("total_score", 0),
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dimensions=user_b_data.get("dimensions", {}),
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highlights=user_b_data.get("highlights", []),
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improvements=user_b_data.get("improvements", [])
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)
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# 解析对话标注
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result.dialogue_annotations = data.get("dialogue_annotations", [])
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except json.JSONDecodeError as e:
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logger.warning(f"JSON 解析失败: {e}")
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result.overall_comment = "AI 输出格式异常，请重试"
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except Exception as e:
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logger.error(f"解析分析结果失败: {e}")
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result.overall_comment = f"解析失败: {str(e)}"
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return result
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def result_to_dict(self, result: DuoPracticeAnalysisResult) -> Dict[str, Any]:
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"""将结果转换为字典（用于 API 响应）"""
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return {
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"overall_evaluation": {
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"interaction_quality": result.interaction_quality,
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"scene_restoration": result.scene_restoration,
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"overall_comment": result.overall_comment
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},
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"user_a_evaluation": {
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"user_name": result.user_a_evaluation.user_name,
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"role_name": result.user_a_evaluation.role_name,
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"total_score": result.user_a_evaluation.total_score,
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"dimensions": result.user_a_evaluation.dimensions,
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"highlights": result.user_a_evaluation.highlights,
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"improvements": result.user_a_evaluation.improvements
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} if result.user_a_evaluation else None,
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"user_b_evaluation": {
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"user_name": result.user_b_evaluation.user_name,
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"role_name": result.user_b_evaluation.role_name,
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"total_score": result.user_b_evaluation.total_score,
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"dimensions": result.user_b_evaluation.dimensions,
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"highlights": result.user_b_evaluation.highlights,
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"improvements": result.user_b_evaluation.improvements
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} if result.user_b_evaluation else None,
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"dialogue_annotations": result.dialogue_annotations,
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"ai_metadata": {
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"provider": result.ai_provider,
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"model": result.ai_model,
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"latency_ms": result.ai_latency_ms
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}
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}
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# ==================== 全局实例 ====================
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duo_practice_analysis_service = DuoPracticeAnalysisService()
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# ==================== 便捷函数 ====================
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async def analyze_duo_practice(
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scene_name: str,
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scene_background: str,
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role_a_name: str,
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role_b_name: str,
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role_a_description: str,
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role_b_description: str,
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user_a_name: str,
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user_b_name: str,
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dialogue_history: List[Dict[str, Any]],
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duration_seconds: int,
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total_turns: int,
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db: Any = None
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) -> DuoPracticeAnalysisResult:
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"""便捷函数：分析双人对练"""
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return await duo_practice_analysis_service.analyze(
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scene_name=scene_name,
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scene_background=scene_background,
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role_a_name=role_a_name,
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role_b_name=role_b_name,
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role_a_description=role_a_description,
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role_b_description=role_b_description,
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user_a_name=user_a_name,
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user_b_name=user_b_name,
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dialogue_history=dialogue_history,
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duration_seconds=duration_seconds,
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total_turns=total_turns,
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db=db
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)
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"""
|
||||
双人对练分析服务
|
||||
|
||||
功能：
|
||||
- 分析双人对练对话
|
||||
- 生成双方评估报告
|
||||
- 对话标注和建议
|
||||
"""
|
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import json
|
||||
import logging
|
||||
from dataclasses import dataclass, field
|
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from typing import Any, Dict, List, Optional
|
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|
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from app.services.ai.ai_service import AIService
|
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from app.services.ai.prompts.duo_practice_prompts import SYSTEM_PROMPT, USER_PROMPT
|
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|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
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@dataclass
|
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class UserEvaluation:
|
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"""用户评估结果"""
|
||||
user_name: str
|
||||
role_name: str
|
||||
total_score: int
|
||||
dimensions: Dict[str, Dict[str, Any]]
|
||||
highlights: List[str]
|
||||
improvements: List[Dict[str, str]]
|
||||
|
||||
|
||||
@dataclass
|
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class DuoPracticeAnalysisResult:
|
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"""双人对练分析结果"""
|
||||
# 整体评估
|
||||
interaction_quality: int = 0
|
||||
scene_restoration: int = 0
|
||||
overall_comment: str = ""
|
||||
|
||||
# 用户A评估
|
||||
user_a_evaluation: Optional[UserEvaluation] = None
|
||||
|
||||
# 用户B评估
|
||||
user_b_evaluation: Optional[UserEvaluation] = None
|
||||
|
||||
# 对话标注
|
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dialogue_annotations: List[Dict[str, Any]] = field(default_factory=list)
|
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|
||||
# AI 元数据
|
||||
raw_response: str = ""
|
||||
ai_provider: str = ""
|
||||
ai_model: str = ""
|
||||
ai_latency_ms: int = 0
|
||||
|
||||
|
||||
class DuoPracticeAnalysisService:
|
||||
"""
|
||||
双人对练分析服务
|
||||
|
||||
使用示例：
|
||||
```python
|
||||
service = DuoPracticeAnalysisService()
|
||||
result = await service.analyze(
|
||||
scene_name="销售场景",
|
||||
scene_background="客户咨询产品",
|
||||
role_a_name="销售顾问",
|
||||
role_b_name="顾客",
|
||||
user_a_name="张三",
|
||||
user_b_name="李四",
|
||||
dialogue_history=dialogue_list,
|
||||
duration_seconds=300,
|
||||
total_turns=20
|
||||
)
|
||||
```
|
||||
"""
|
||||
|
||||
MODULE_CODE = "duo_practice_analysis"
|
||||
|
||||
async def analyze(
|
||||
self,
|
||||
scene_name: str,
|
||||
scene_background: str,
|
||||
role_a_name: str,
|
||||
role_b_name: str,
|
||||
role_a_description: str,
|
||||
role_b_description: str,
|
||||
user_a_name: str,
|
||||
user_b_name: str,
|
||||
dialogue_history: List[Dict[str, Any]],
|
||||
duration_seconds: int,
|
||||
total_turns: int,
|
||||
db: Any = None
|
||||
) -> DuoPracticeAnalysisResult:
|
||||
"""
|
||||
分析双人对练
|
||||
|
||||
Args:
|
||||
scene_name: 场景名称
|
||||
scene_background: 场景背景
|
||||
role_a_name: 角色A名称
|
||||
role_b_name: 角色B名称
|
||||
role_a_description: 角色A描述
|
||||
role_b_description: 角色B描述
|
||||
user_a_name: 用户A名称
|
||||
user_b_name: 用户B名称
|
||||
dialogue_history: 对话历史列表
|
||||
duration_seconds: 对练时长（秒）
|
||||
total_turns: 总对话轮次
|
||||
db: 数据库会话
|
||||
|
||||
Returns:
|
||||
DuoPracticeAnalysisResult: 分析结果
|
||||
"""
|
||||
try:
|
||||
logger.info(f"开始双人对练分析: {scene_name}, 轮次={total_turns}")
|
||||
|
||||
# 格式化对话历史
|
||||
dialogue_text = self._format_dialogue_history(dialogue_history)
|
||||
|
||||
# 创建 AI 服务
|
||||
ai_service = AIService(module_code=self.MODULE_CODE, db_session=db)
|
||||
|
||||
# 构建用户提示词
|
||||
user_prompt = USER_PROMPT.format(
|
||||
scene_name=scene_name,
|
||||
scene_background=scene_background or "未设置",
|
||||
role_a_name=role_a_name,
|
||||
role_b_name=role_b_name,
|
||||
role_a_description=role_a_description or f"扮演{role_a_name}角色",
|
||||
role_b_description=role_b_description or f"扮演{role_b_name}角色",
|
||||
user_a_name=user_a_name,
|
||||
user_b_name=user_b_name,
|
||||
dialogue_history=dialogue_text,
|
||||
duration_seconds=duration_seconds,
|
||||
total_turns=total_turns
|
||||
)
|
||||
|
||||
# 调用 AI
|
||||
messages = [
|
||||
{"role": "system", "content": SYSTEM_PROMPT},
|
||||
{"role": "user", "content": user_prompt}
|
||||
]
|
||||
|
||||
ai_response = await ai_service.chat(
|
||||
messages=messages,
|
||||
model="gemini-3-flash-preview", # 使用快速模型
|
||||
temperature=0.3,
|
||||
prompt_name="duo_practice_analysis"
|
||||
)
|
||||
|
||||
logger.info(f"AI 分析完成: provider={ai_response.provider}, latency={ai_response.latency_ms}ms")
|
||||
|
||||
# 解析 AI 输出
|
||||
result = self._parse_analysis_result(
|
||||
ai_response.content,
|
||||
user_a_name=user_a_name,
|
||||
user_b_name=user_b_name,
|
||||
role_a_name=role_a_name,
|
||||
role_b_name=role_b_name
|
||||
)
|
||||
|
||||
# 填充 AI 元数据
|
||||
result.raw_response = ai_response.content
|
||||
result.ai_provider = ai_response.provider
|
||||
result.ai_model = ai_response.model
|
||||
result.ai_latency_ms = ai_response.latency_ms
|
||||
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"双人对练分析失败: {e}", exc_info=True)
|
||||
# 返回空结果
|
||||
return DuoPracticeAnalysisResult(
|
||||
overall_comment=f"分析失败: {str(e)}"
|
||||
)
|
||||
|
||||
def _format_dialogue_history(self, dialogues: List[Dict[str, Any]]) -> str:
|
||||
"""格式化对话历史"""
|
||||
lines = []
|
||||
for d in dialogues:
|
||||
speaker = d.get("role_name") or d.get("speaker", "未知")
|
||||
content = d.get("content", "")
|
||||
seq = d.get("sequence", 0)
|
||||
lines.append(f"[{seq}] {speaker}：{content}")
|
||||
return "\n".join(lines)
|
||||
|
||||
def _parse_analysis_result(
|
||||
self,
|
||||
ai_output: str,
|
||||
user_a_name: str,
|
||||
user_b_name: str,
|
||||
role_a_name: str,
|
||||
role_b_name: str
|
||||
) -> DuoPracticeAnalysisResult:
|
||||
"""解析 AI 输出"""
|
||||
result = DuoPracticeAnalysisResult()
|
||||
|
||||
try:
|
||||
# 尝试提取 JSON
|
||||
json_str = ai_output
|
||||
|
||||
# 如果输出包含 markdown 代码块，提取其中的 JSON
|
||||
if "```json" in ai_output:
|
||||
start = ai_output.find("```json") + 7
|
||||
end = ai_output.find("```", start)
|
||||
json_str = ai_output[start:end].strip()
|
||||
elif "```" in ai_output:
|
||||
start = ai_output.find("```") + 3
|
||||
end = ai_output.find("```", start)
|
||||
json_str = ai_output[start:end].strip()
|
||||
|
||||
data = json.loads(json_str)
|
||||
|
||||
# 解析整体评估
|
||||
overall = data.get("overall_evaluation", {})
|
||||
result.interaction_quality = overall.get("interaction_quality", 0)
|
||||
result.scene_restoration = overall.get("scene_restoration", 0)
|
||||
result.overall_comment = overall.get("overall_comment", "")
|
||||
|
||||
# 解析用户A评估
|
||||
user_a_data = data.get("user_a_evaluation", {})
|
||||
if user_a_data:
|
||||
result.user_a_evaluation = UserEvaluation(
|
||||
user_name=user_a_data.get("user_name", user_a_name),
|
||||
role_name=user_a_data.get("role_name", role_a_name),
|
||||
total_score=user_a_data.get("total_score", 0),
|
||||
dimensions=user_a_data.get("dimensions", {}),
|
||||
highlights=user_a_data.get("highlights", []),
|
||||
improvements=user_a_data.get("improvements", [])
|
||||
)
|
||||
|
||||
# 解析用户B评估
|
||||
user_b_data = data.get("user_b_evaluation", {})
|
||||
if user_b_data:
|
||||
result.user_b_evaluation = UserEvaluation(
|
||||
user_name=user_b_data.get("user_name", user_b_name),
|
||||
role_name=user_b_data.get("role_name", role_b_name),
|
||||
total_score=user_b_data.get("total_score", 0),
|
||||
dimensions=user_b_data.get("dimensions", {}),
|
||||
highlights=user_b_data.get("highlights", []),
|
||||
improvements=user_b_data.get("improvements", [])
|
||||
)
|
||||
|
||||
# 解析对话标注
|
||||
result.dialogue_annotations = data.get("dialogue_annotations", [])
|
||||
|
||||
except json.JSONDecodeError as e:
|
||||
logger.warning(f"JSON 解析失败: {e}")
|
||||
result.overall_comment = "AI 输出格式异常，请重试"
|
||||
except Exception as e:
|
||||
logger.error(f"解析分析结果失败: {e}")
|
||||
result.overall_comment = f"解析失败: {str(e)}"
|
||||
|
||||
return result
|
||||
|
||||
def result_to_dict(self, result: DuoPracticeAnalysisResult) -> Dict[str, Any]:
|
||||
"""将结果转换为字典（用于 API 响应）"""
|
||||
return {
|
||||
"overall_evaluation": {
|
||||
"interaction_quality": result.interaction_quality,
|
||||
"scene_restoration": result.scene_restoration,
|
||||
"overall_comment": result.overall_comment
|
||||
},
|
||||
"user_a_evaluation": {
|
||||
"user_name": result.user_a_evaluation.user_name,
|
||||
"role_name": result.user_a_evaluation.role_name,
|
||||
"total_score": result.user_a_evaluation.total_score,
|
||||
"dimensions": result.user_a_evaluation.dimensions,
|
||||
"highlights": result.user_a_evaluation.highlights,
|
||||
"improvements": result.user_a_evaluation.improvements
|
||||
} if result.user_a_evaluation else None,
|
||||
"user_b_evaluation": {
|
||||
"user_name": result.user_b_evaluation.user_name,
|
||||
"role_name": result.user_b_evaluation.role_name,
|
||||
"total_score": result.user_b_evaluation.total_score,
|
||||
"dimensions": result.user_b_evaluation.dimensions,
|
||||
"highlights": result.user_b_evaluation.highlights,
|
||||
"improvements": result.user_b_evaluation.improvements
|
||||
} if result.user_b_evaluation else None,
|
||||
"dialogue_annotations": result.dialogue_annotations,
|
||||
"ai_metadata": {
|
||||
"provider": result.ai_provider,
|
||||
"model": result.ai_model,
|
||||
"latency_ms": result.ai_latency_ms
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
# ==================== 全局实例 ====================
|
||||
|
||||
duo_practice_analysis_service = DuoPracticeAnalysisService()
|
||||
|
||||
|
||||
# ==================== 便捷函数 ====================
|
||||
|
||||
async def analyze_duo_practice(
|
||||
scene_name: str,
|
||||
scene_background: str,
|
||||
role_a_name: str,
|
||||
role_b_name: str,
|
||||
role_a_description: str,
|
||||
role_b_description: str,
|
||||
user_a_name: str,
|
||||
user_b_name: str,
|
||||
dialogue_history: List[Dict[str, Any]],
|
||||
duration_seconds: int,
|
||||
total_turns: int,
|
||||
db: Any = None
|
||||
) -> DuoPracticeAnalysisResult:
|
||||
"""便捷函数：分析双人对练"""
|
||||
return await duo_practice_analysis_service.analyze(
|
||||
scene_name=scene_name,
|
||||
scene_background=scene_background,
|
||||
role_a_name=role_a_name,
|
||||
role_b_name=role_b_name,
|
||||
role_a_description=role_a_description,
|
||||
role_b_description=role_b_description,
|
||||
user_a_name=user_a_name,
|
||||
user_b_name=user_b_name,
|
||||
dialogue_history=dialogue_history,
|
||||
duration_seconds=duration_seconds,
|
||||
total_turns=total_turns,
|
||||
db=db
|
||||
)
|
||||
|
||||
@@ -1,207 +1,207 @@
|
||||
"""
|
||||
双人对练评估提示词模板
|
||||
|
||||
功能：评估双人角色扮演对练的表现
|
||||
"""
|
||||
|
||||
# ==================== 元数据 ====================
|
||||
|
||||
PROMPT_META = {
|
||||
"name": "duo_practice_analysis",
|
||||
"display_name": "双人对练评估",
|
||||
"description": "评估双人角色扮演对练中双方的表现",
|
||||
"module": "kaopeilian",
|
||||
"variables": [
|
||||
"scene_name", "scene_background",
|
||||
"role_a_name", "role_b_name",
|
||||
"role_a_description", "role_b_description",
|
||||
"user_a_name", "user_b_name",
|
||||
"dialogue_history",
|
||||
"duration_seconds", "total_turns"
|
||||
],
|
||||
"version": "1.0.0",
|
||||
"author": "kaopeilian-team",
|
||||
}
|
||||
|
||||
|
||||
# ==================== 系统提示词 ====================
|
||||
|
||||
SYSTEM_PROMPT = """你是一位资深的销售培训专家和沟通教练，擅长评估角色扮演对练的表现。
|
||||
你需要观察双人对练的对话记录，分别对两位参与者的表现进行专业评估。
|
||||
|
||||
评估原则：
|
||||
1. 客观公正，基于对话内容给出评价
|
||||
2. 突出亮点，指出不足
|
||||
3. 给出具体、可操作的改进建议
|
||||
4. 考虑角色特点，评估角色代入度
|
||||
|
||||
输出格式要求：
|
||||
- 必须返回有效的 JSON 格式
|
||||
- 分数范围 0-100
|
||||
- 建议具体可行"""
|
||||
|
||||
|
||||
# ==================== 用户提示词模板 ====================
|
||||
|
||||
USER_PROMPT = """# 双人对练评估任务
|
||||
|
||||
## 场景信息
|
||||
- **场景名称**：{scene_name}
|
||||
- **场景背景**：{scene_background}
|
||||
|
||||
## 角色设置
|
||||
### {role_a_name}
|
||||
- **扮演者**：{user_a_name}
|
||||
- **角色描述**：{role_a_description}
|
||||
|
||||
### {role_b_name}
|
||||
- **扮演者**：{user_b_name}
|
||||
- **角色描述**：{role_b_description}
|
||||
|
||||
## 对练数据
|
||||
- **对练时长**：{duration_seconds} 秒
|
||||
- **总对话轮次**：{total_turns} 轮
|
||||
|
||||
## 对话记录
|
||||
{dialogue_history}
|
||||
|
||||
---
|
||||
|
||||
## 评估要求
|
||||
|
||||
请按以下 JSON 格式输出评估结果：
|
||||
|
||||
```json
|
||||
{{
|
||||
"overall_evaluation": {{
|
||||
"interaction_quality": 85,
|
||||
"scene_restoration": 80,
|
||||
"overall_comment": "整体评价..."
|
||||
}},
|
||||
"user_a_evaluation": {{
|
||||
"user_name": "{user_a_name}",
|
||||
"role_name": "{role_a_name}",
|
||||
"total_score": 85,
|
||||
"dimensions": {{
|
||||
"role_immersion": {{
|
||||
"score": 85,
|
||||
"comment": "角色代入度评价..."
|
||||
}},
|
||||
"communication": {{
|
||||
"score": 80,
|
||||
"comment": "沟通表达能力评价..."
|
||||
}},
|
||||
"professional_knowledge": {{
|
||||
"score": 75,
|
||||
"comment": "专业知识运用评价..."
|
||||
}},
|
||||
"response_quality": {{
|
||||
"score": 82,
|
||||
"comment": "回应质量评价..."
|
||||
}},
|
||||
"goal_achievement": {{
|
||||
"score": 78,
|
||||
"comment": "目标达成度评价..."
|
||||
}}
|
||||
}},
|
||||
"highlights": [
|
||||
"亮点1...",
|
||||
"亮点2..."
|
||||
],
|
||||
"improvements": [
|
||||
{{
|
||||
"issue": "问题描述",
|
||||
"suggestion": "改进建议",
|
||||
"example": "示例话术"
|
||||
}}
|
||||
]
|
||||
}},
|
||||
"user_b_evaluation": {{
|
||||
"user_name": "{user_b_name}",
|
||||
"role_name": "{role_b_name}",
|
||||
"total_score": 82,
|
||||
"dimensions": {{
|
||||
"role_immersion": {{
|
||||
"score": 80,
|
||||
"comment": "角色代入度评价..."
|
||||
}},
|
||||
"communication": {{
|
||||
"score": 85,
|
||||
"comment": "沟通表达能力评价..."
|
||||
}},
|
||||
"professional_knowledge": {{
|
||||
"score": 78,
|
||||
"comment": "专业知识运用评价..."
|
||||
}},
|
||||
"response_quality": {{
|
||||
"score": 80,
|
||||
"comment": "回应质量评价..."
|
||||
}},
|
||||
"goal_achievement": {{
|
||||
"score": 75,
|
||||
"comment": "目标达成度评价..."
|
||||
}}
|
||||
}},
|
||||
"highlights": [
|
||||
"亮点1...",
|
||||
"亮点2..."
|
||||
],
|
||||
"improvements": [
|
||||
{{
|
||||
"issue": "问题描述",
|
||||
"suggestion": "改进建议",
|
||||
"example": "示例话术"
|
||||
}}
|
||||
]
|
||||
}},
|
||||
"dialogue_annotations": [
|
||||
{{
|
||||
"sequence": 1,
|
||||
"speaker": "{role_a_name}",
|
||||
"tags": ["good_opening"],
|
||||
"comment": "开场白自然得体"
|
||||
}},
|
||||
{{
|
||||
"sequence": 3,
|
||||
"speaker": "{role_b_name}",
|
||||
"tags": ["needs_improvement"],
|
||||
"comment": "可以更主动表达需求"
|
||||
}}
|
||||
]
|
||||
}}
|
||||
```
|
||||
|
||||
请基于对话内容，给出客观、专业的评估。"""
|
||||
|
||||
|
||||
# ==================== 维度说明 ====================
|
||||
|
||||
DIMENSION_DESCRIPTIONS = {
|
||||
"role_immersion": "角色代入度：是否完全进入角色，语言风格、态度是否符合角色设定",
|
||||
"communication": "沟通表达：语言是否清晰、逻辑是否通顺、表达是否得体",
|
||||
"professional_knowledge": "专业知识：是否展现出角色应有的专业素养和知识储备",
|
||||
"response_quality": "回应质量：对对方发言的回应是否及时、恰当、有针对性",
|
||||
"goal_achievement": "目标达成：是否朝着对练目标推进，是否达成预期效果"
|
||||
}
|
||||
|
||||
|
||||
# ==================== 对话标签 ====================
|
||||
|
||||
DIALOGUE_TAGS = {
|
||||
# 正面标签
|
||||
"good_opening": "开场良好",
|
||||
"active_listening": "积极倾听",
|
||||
"empathy": "共情表达",
|
||||
"professional": "专业表现",
|
||||
"good_closing": "结束得体",
|
||||
"creative_response": "创意回应",
|
||||
"problem_solving": "问题解决",
|
||||
|
||||
# 需改进标签
|
||||
"needs_improvement": "需要改进",
|
||||
"off_topic": "偏离主题",
|
||||
"too_passive": "过于被动",
|
||||
"lack_detail": "缺乏细节",
|
||||
"missed_opportunity": "错失机会",
|
||||
"unclear_expression": "表达不清"
|
||||
}
|
||||
"""
|
||||
双人对练评估提示词模板
|
||||
|
||||
功能：评估双人角色扮演对练的表现
|
||||
"""
|
||||
|
||||
# ==================== 元数据 ====================
|
||||
|
||||
PROMPT_META = {
|
||||
"name": "duo_practice_analysis",
|
||||
"display_name": "双人对练评估",
|
||||
"description": "评估双人角色扮演对练中双方的表现",
|
||||
"module": "kaopeilian",
|
||||
"variables": [
|
||||
"scene_name", "scene_background",
|
||||
"role_a_name", "role_b_name",
|
||||
"role_a_description", "role_b_description",
|
||||
"user_a_name", "user_b_name",
|
||||
"dialogue_history",
|
||||
"duration_seconds", "total_turns"
|
||||
],
|
||||
"version": "1.0.0",
|
||||
"author": "kaopeilian-team",
|
||||
}
|
||||
|
||||
|
||||
# ==================== 系统提示词 ====================
|
||||
|
||||
SYSTEM_PROMPT = """你是一位资深的销售培训专家和沟通教练，擅长评估角色扮演对练的表现。
|
||||
你需要观察双人对练的对话记录，分别对两位参与者的表现进行专业评估。
|
||||
|
||||
评估原则：
|
||||
1. 客观公正，基于对话内容给出评价
|
||||
2. 突出亮点，指出不足
|
||||
3. 给出具体、可操作的改进建议
|
||||
4. 考虑角色特点，评估角色代入度
|
||||
|
||||
输出格式要求：
|
||||
- 必须返回有效的 JSON 格式
|
||||
- 分数范围 0-100
|
||||
- 建议具体可行"""
|
||||
|
||||
|
||||
# ==================== 用户提示词模板 ====================
|
||||
|
||||
USER_PROMPT = """# 双人对练评估任务
|
||||
|
||||
## 场景信息
|
||||
- **场景名称**：{scene_name}
|
||||
- **场景背景**：{scene_background}
|
||||
|
||||
## 角色设置
|
||||
### {role_a_name}
|
||||
- **扮演者**：{user_a_name}
|
||||
- **角色描述**：{role_a_description}
|
||||
|
||||
### {role_b_name}
|
||||
- **扮演者**：{user_b_name}
|
||||
- **角色描述**：{role_b_description}
|
||||
|
||||
## 对练数据
|
||||
- **对练时长**：{duration_seconds} 秒
|
||||
- **总对话轮次**：{total_turns} 轮
|
||||
|
||||
## 对话记录
|
||||
{dialogue_history}
|
||||
|
||||
---
|
||||
|
||||
## 评估要求
|
||||
|
||||
请按以下 JSON 格式输出评估结果：
|
||||
|
||||
```json
|
||||
{{
|
||||
"overall_evaluation": {{
|
||||
"interaction_quality": 85,
|
||||
"scene_restoration": 80,
|
||||
"overall_comment": "整体评价..."
|
||||
}},
|
||||
"user_a_evaluation": {{
|
||||
"user_name": "{user_a_name}",
|
||||
"role_name": "{role_a_name}",
|
||||
"total_score": 85,
|
||||
"dimensions": {{
|
||||
"role_immersion": {{
|
||||
"score": 85,
|
||||
"comment": "角色代入度评价..."
|
||||
}},
|
||||
"communication": {{
|
||||
"score": 80,
|
||||
"comment": "沟通表达能力评价..."
|
||||
}},
|
||||
"professional_knowledge": {{
|
||||
"score": 75,
|
||||
"comment": "专业知识运用评价..."
|
||||
}},
|
||||
"response_quality": {{
|
||||
"score": 82,
|
||||
"comment": "回应质量评价..."
|
||||
}},
|
||||
"goal_achievement": {{
|
||||
"score": 78,
|
||||
"comment": "目标达成度评价..."
|
||||
}}
|
||||
}},
|
||||
"highlights": [
|
||||
"亮点1...",
|
||||
"亮点2..."
|
||||
],
|
||||
"improvements": [
|
||||
{{
|
||||
"issue": "问题描述",
|
||||
"suggestion": "改进建议",
|
||||
"example": "示例话术"
|
||||
}}
|
||||
]
|
||||
}},
|
||||
"user_b_evaluation": {{
|
||||
"user_name": "{user_b_name}",
|
||||
"role_name": "{role_b_name}",
|
||||
"total_score": 82,
|
||||
"dimensions": {{
|
||||
"role_immersion": {{
|
||||
"score": 80,
|
||||
"comment": "角色代入度评价..."
|
||||
}},
|
||||
"communication": {{
|
||||
"score": 85,
|
||||
"comment": "沟通表达能力评价..."
|
||||
}},
|
||||
"professional_knowledge": {{
|
||||
"score": 78,
|
||||
"comment": "专业知识运用评价..."
|
||||
}},
|
||||
"response_quality": {{
|
||||
"score": 80,
|
||||
"comment": "回应质量评价..."
|
||||
}},
|
||||
"goal_achievement": {{
|
||||
"score": 75,
|
||||
"comment": "目标达成度评价..."
|
||||
}}
|
||||
}},
|
||||
"highlights": [
|
||||
"亮点1...",
|
||||
"亮点2..."
|
||||
],
|
||||
"improvements": [
|
||||
{{
|
||||
"issue": "问题描述",
|
||||
"suggestion": "改进建议",
|
||||
"example": "示例话术"
|
||||
}}
|
||||
]
|
||||
}},
|
||||
"dialogue_annotations": [
|
||||
{{
|
||||
"sequence": 1,
|
||||
"speaker": "{role_a_name}",
|
||||
"tags": ["good_opening"],
|
||||
"comment": "开场白自然得体"
|
||||
}},
|
||||
{{
|
||||
"sequence": 3,
|
||||
"speaker": "{role_b_name}",
|
||||
"tags": ["needs_improvement"],
|
||||
"comment": "可以更主动表达需求"
|
||||
}}
|
||||
]
|
||||
}}
|
||||
```
|
||||
|
||||
请基于对话内容，给出客观、专业的评估。"""
|
||||
|
||||
|
||||
# ==================== 维度说明 ====================
|
||||
|
||||
DIMENSION_DESCRIPTIONS = {
|
||||
"role_immersion": "角色代入度：是否完全进入角色，语言风格、态度是否符合角色设定",
|
||||
"communication": "沟通表达：语言是否清晰、逻辑是否通顺、表达是否得体",
|
||||
"professional_knowledge": "专业知识：是否展现出角色应有的专业素养和知识储备",
|
||||
"response_quality": "回应质量：对对方发言的回应是否及时、恰当、有针对性",
|
||||
"goal_achievement": "目标达成：是否朝着对练目标推进，是否达成预期效果"
|
||||
}
|
||||
|
||||
|
||||
# ==================== 对话标签 ====================
|
||||
|
||||
DIALOGUE_TAGS = {
|
||||
# 正面标签
|
||||
"good_opening": "开场良好",
|
||||
"active_listening": "积极倾听",
|
||||
"empathy": "共情表达",
|
||||
"professional": "专业表现",
|
||||
"good_closing": "结束得体",
|
||||
"creative_response": "创意回应",
|
||||
"problem_solving": "问题解决",
|
||||
|
||||
# 需改进标签
|
||||
"needs_improvement": "需要改进",
|
||||
"off_topic": "偏离主题",
|
||||
"too_passive": "过于被动",
|
||||
"lack_detail": "缺乏细节",
|
||||
"missed_opportunity": "错失机会",
|
||||
"unclear_expression": "表达不清"
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user