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319 lines
11 KiB
Python
319 lines
11 KiB
Python
"""
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陪练功能相关Schema定义
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"""
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from typing import Optional, List
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from datetime import datetime
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from pydantic import BaseModel, Field, field_validator
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# ==================== 枚举类型 ====================
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class SceneType:
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"""场景类型枚举"""
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PHONE = "phone" # 电话销售
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FACE = "face" # 面对面销售
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COMPLAINT = "complaint" # 客户投诉
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AFTER_SALES = "after-sales" # 售后服务
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PRODUCT_INTRO = "product-intro" # 产品介绍
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class Difficulty:
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"""难度等级枚举"""
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BEGINNER = "beginner" # 入门
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JUNIOR = "junior" # 初级
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INTERMEDIATE = "intermediate" # 中级
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SENIOR = "senior" # 高级
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EXPERT = "expert" # 专家
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class SceneStatus:
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"""场景状态枚举"""
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ACTIVE = "active" # 启用
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INACTIVE = "inactive" # 禁用
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# ==================== 场景Schema ====================
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class PracticeSceneBase(BaseModel):
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"""陪练场景基础Schema"""
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name: str = Field(..., max_length=200, description="场景名称")
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description: Optional[str] = Field(None, description="场景描述")
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type: str = Field(..., description="场景类型: phone/face/complaint/after-sales/product-intro")
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difficulty: str = Field(..., description="难度等级: beginner/junior/intermediate/senior/expert")
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status: str = Field(default="active", description="状态: active/inactive")
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background: str = Field(..., description="场景背景设定")
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ai_role: str = Field(..., description="AI角色描述")
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objectives: List[str] = Field(..., description="练习目标数组")
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keywords: Optional[List[str]] = Field(default=None, description="关键词数组")
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duration: int = Field(default=10, ge=1, le=120, description="预计时长（分钟）")
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@field_validator('type')
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@classmethod
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def validate_type(cls, v):
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"""验证场景类型"""
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valid_types = ['phone', 'face', 'complaint', 'after-sales', 'product-intro']
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if v not in valid_types:
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raise ValueError(f"场景类型必须是: {', '.join(valid_types)}")
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return v
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@field_validator('difficulty')
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@classmethod
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def validate_difficulty(cls, v):
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"""验证难度等级"""
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valid_difficulties = ['beginner', 'junior', 'intermediate', 'senior', 'expert']
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if v not in valid_difficulties:
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raise ValueError(f"难度等级必须是: {', '.join(valid_difficulties)}")
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return v
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@field_validator('status')
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@classmethod
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def validate_status(cls, v):
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"""验证状态"""
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valid_statuses = ['active', 'inactive']
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if v not in valid_statuses:
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raise ValueError(f"状态必须是: {', '.join(valid_statuses)}")
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return v
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@field_validator('objectives')
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@classmethod
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def validate_objectives(cls, v):
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"""验证练习目标"""
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if not v or len(v) < 1:
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raise ValueError("至少需要1个练习目标")
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if len(v) > 10:
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raise ValueError("练习目标不能超过10个")
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return v
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class PracticeSceneCreate(PracticeSceneBase):
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"""创建陪练场景Schema"""
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pass
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class PracticeSceneUpdate(BaseModel):
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"""更新陪练场景Schema（所有字段可选）"""
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name: Optional[str] = Field(None, max_length=200, description="场景名称")
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description: Optional[str] = Field(None, description="场景描述")
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type: Optional[str] = Field(None, description="场景类型")
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difficulty: Optional[str] = Field(None, description="难度等级")
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status: Optional[str] = Field(None, description="状态")
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background: Optional[str] = Field(None, description="场景背景设定")
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ai_role: Optional[str] = Field(None, description="AI角色描述")
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objectives: Optional[List[str]] = Field(None, description="练习目标数组")
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keywords: Optional[List[str]] = Field(None, description="关键词数组")
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duration: Optional[int] = Field(None, ge=1, le=120, description="预计时长（分钟）")
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class PracticeSceneResponse(PracticeSceneBase):
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"""陪练场景响应Schema"""
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id: int
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usage_count: int
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rating: float
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created_by: Optional[int] = None
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updated_by: Optional[int] = None
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created_at: datetime
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updated_at: datetime
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class Config:
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from_attributes = True
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# ==================== 对话Schema ====================
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class StartPracticeRequest(BaseModel):
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"""开始陪练对话请求Schema"""
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# 场景信息（首次消息必填，后续消息可选）
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scene_id: Optional[int] = Field(None, description="场景ID（可选）")
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scene_name: Optional[str] = Field(None, description="场景名称")
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scene_description: Optional[str] = Field(None, description="场景描述")
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scene_background: Optional[str] = Field(None, description="场景背景")
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scene_ai_role: Optional[str] = Field(None, description="AI角色")
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scene_objectives: Optional[List[str]] = Field(None, description="练习目标")
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scene_keywords: Optional[List[str]] = Field(None, description="关键词")
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# 对话信息
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user_message: str = Field(..., description="用户消息")
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conversation_id: Optional[str] = Field(None, description="对话ID（续接对话时必填）")
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is_first: bool = Field(..., description="是否首次消息")
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@field_validator('scene_name')
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@classmethod
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def validate_scene_name_for_first(cls, v, info):
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"""首次消息时场景名称必填"""
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if info.data.get('is_first') and not v:
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raise ValueError("首次消息时场景名称必填")
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return v
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@field_validator('scene_background')
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@classmethod
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def validate_scene_background_for_first(cls, v, info):
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"""首次消息时场景背景必填"""
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if info.data.get('is_first') and not v:
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raise ValueError("首次消息时场景背景必填")
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return v
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@field_validator('scene_ai_role')
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@classmethod
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def validate_scene_ai_role_for_first(cls, v, info):
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"""首次消息时AI角色必填"""
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if info.data.get('is_first') and not v:
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raise ValueError("首次消息时AI角色必填")
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return v
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@field_validator('scene_objectives')
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@classmethod
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def validate_scene_objectives_for_first(cls, v, info):
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"""首次消息时练习目标必填"""
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if info.data.get('is_first') and (not v or len(v) == 0):
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raise ValueError("首次消息时练习目标必填")
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return v
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class InterruptPracticeRequest(BaseModel):
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"""中断对话请求Schema"""
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conversation_id: str = Field(..., description="对话ID")
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chat_id: str = Field(..., description="聊天ID")
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class ConversationInfo(BaseModel):
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"""对话信息Schema"""
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id: str = Field(..., description="对话ID")
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name: str = Field(..., description="对话名称")
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created_at: int = Field(..., description="创建时间（时间戳）")
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class ConversationsResponse(BaseModel):
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"""对话列表响应Schema"""
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items: List[ConversationInfo]
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has_more: bool
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page: int
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size: int
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# ==================== 场景提取Schema ====================
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class ExtractSceneRequest(BaseModel):
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"""提取场景请求Schema"""
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course_id: int = Field(..., description="课程ID")
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class ExtractedSceneData(BaseModel):
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"""提取的场景数据Schema"""
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name: str = Field(..., description="场景名称")
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description: str = Field(..., description="场景描述")
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type: str = Field(..., description="场景类型")
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difficulty: str = Field(..., description="难度等级")
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background: str = Field(..., description="场景背景")
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ai_role: str = Field(..., description="AI角色描述")
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objectives: List[str] = Field(..., description="练习目标数组")
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keywords: Optional[List[str]] = Field(default=[], description="关键词数组")
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class ExtractSceneResponse(BaseModel):
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"""提取场景响应Schema"""
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scene: ExtractedSceneData = Field(..., description="场景数据")
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workflow_run_id: str = Field(..., description="工作流运行ID")
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task_id: str = Field(..., description="任务ID")
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# ==================== 陪练会话Schema ====================
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class PracticeSessionCreate(BaseModel):
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"""创建陪练会话请求Schema"""
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scene_id: Optional[int] = Field(None, description="场景ID")
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scene_name: str = Field(..., description="场景名称")
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scene_type: Optional[str] = Field(None, description="场景类型")
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conversation_id: Optional[str] = Field(None, description="Coze对话ID")
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class PracticeSessionResponse(BaseModel):
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"""陪练会话响应Schema"""
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id: int
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session_id: str
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user_id: int
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scene_id: Optional[int]
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scene_name: str
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scene_type: Optional[str]
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conversation_id: Optional[str]
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start_time: datetime
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end_time: Optional[datetime]
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duration_seconds: int
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turns: int
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status: str
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created_at: datetime
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class Config:
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from_attributes = True
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class SaveDialogueRequest(BaseModel):
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"""保存对话记录请求Schema"""
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session_id: str = Field(..., description="会话ID")
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speaker: str = Field(..., description="说话人: user/ai")
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content: str = Field(..., description="对话内容")
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sequence: int = Field(..., ge=1, description="顺序号（从1开始）")
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class PracticeDialogueResponse(BaseModel):
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"""对话记录响应Schema"""
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id: int
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session_id: str
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speaker: str
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content: str
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timestamp: datetime
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sequence: int
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class Config:
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from_attributes = True
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# ==================== 分析报告Schema ====================
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class ScoreBreakdownItem(BaseModel):
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"""分数细分项"""
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name: str
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score: int = Field(..., ge=0, le=100)
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description: str
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class AbilityDimensionItem(BaseModel):
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"""能力维度项"""
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name: str
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score: int = Field(..., ge=0, le=100)
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feedback: str
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class DialogueReviewItem(BaseModel):
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"""对话复盘项"""
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speaker: str
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time: str
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content: str
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tags: List[str] = Field(default_factory=list)
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comment: str = Field(default="")
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class SuggestionItem(BaseModel):
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"""改进建议项"""
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title: str
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content: str
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example: Optional[str] = None
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class PracticeAnalysisResult(BaseModel):
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"""陪练分析结果Schema"""
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total_score: int = Field(..., ge=0, le=100, description="综合得分")
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score_breakdown: List[ScoreBreakdownItem] = Field(..., description="分数细分")
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ability_dimensions: List[AbilityDimensionItem] = Field(..., description="能力维度")
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dialogue_review: List[DialogueReviewItem] = Field(..., description="对话复盘")
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suggestions: List[SuggestionItem] = Field(..., description="改进建议")
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class PracticeReportResponse(BaseModel):
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"""陪练报告响应Schema"""
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session_info: PracticeSessionResponse
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analysis: PracticeAnalysisResult
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class Config:
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from_attributes = True
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