feat: 添加双人对练功能

- 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

backend/app/services/ai/duo_practice_analysis_service.py

@@ -0,0 +1,323 @@
+"""
+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
+"""
+import json
+import logging
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional
+
+from app.services.ai.ai_service import AIService
+from app.services.ai.prompts.duo_practice_prompts import SYSTEM_PROMPT, USER_PROMPT
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class UserEvaluation:
+    """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
+class DuoPracticeAnalysisResult:
+    """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
+    dialogue_annotations: List[Dict[str, Any]] = field(default_factory=list)
+    
+    # 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
+    )