b6aea2e23d
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- 新增数据库迁移脚本 (practice_rooms, practice_room_messages) - 新增后端 API: 房间创建/加入/消息同步/报告生成 - 新增前端页面: 入口页/对练房间/报告页 - 新增 AI 双人评估服务和提示词
324 lines
12 KiB
Python
324 lines
12 KiB
Python
"""
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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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