133 lines
4.5 KiB
Python
133 lines
4.5 KiB
Python
# File: backend/app/models/pydantic_models.py (Update Read models, add Message models)
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# Description: Pydantic 模型定义 API 数据结构
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from pydantic import BaseModel, Field
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from typing import Dict, Optional, List
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import uuid
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from datetime import datetime # Use datetime directly
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# --- Assistant Models ---
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class AssistantBase(BaseModel):
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"""助手的基础模型，包含通用字段"""
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name: str = Field(..., min_length=1, max_length=50, description="助手名称")
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description: Optional[str] = Field(None, max_length=200, description="助手描述")
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avatar: Optional[str] = Field(None, max_length=5, description="头像 Emoji 或字符")
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system_prompt: str = Field(..., min_length=1, max_length=4000, description="系统提示")
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model: str = Field(..., description="使用的 LLM 模型")
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temperature: float = Field(0.7, ge=0.0, le=1.0, description="温度参数 (0.0-1.0)")
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# 可以添加 top_p, max_tokens 等
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class AssistantCreate(AssistantBase):
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"""创建助手时使用的模型 (不需要 ID)"""
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pass
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class AssistantUpdate(BaseModel):
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"""更新助手时使用的模型 (所有字段可选)"""
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name: Optional[str] = Field(None, min_length=1, max_length=50)
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description: Optional[str] = Field(None, max_length=200)
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avatar: Optional[str] = Field(None, max_length=5)
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system_prompt: Optional[str] = Field(None, min_length=1, max_length=4000)
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model: Optional[str] = None
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temperature: Optional[float] = Field(None, ge=0.0, le=1.0)
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class AssistantRead(AssistantBase):
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"""读取助手信息时返回的模型 (包含 ID)"""
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id: str = Field(..., description="助手唯一 ID")
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created_at: datetime # Add timestamps
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updated_at: Optional[datetime] = None
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class Config:
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from_attributes = True # Pydantic v2: orm_mode = True
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# --- Chat Models (更新) ---
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class ChatRequest(BaseModel):
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"""聊天请求模型 (添加 sessionId 和 assistantId)"""
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message: str
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session_id: str = Field(..., description="当前会话 ID (可以是 'temp-new-chat')")
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assistant_id: str = Field(..., description="当前使用的助手 ID")
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class ChatResponse(BaseModel):
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"""聊天响应模型"""
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reply: str
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session_id: Optional[str] = None # (可选) 如果创建了新会话，返回新 ID
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session_title: Optional[str] = None # (可选) 如果创建了新会话，返回新标题
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# --- Session Models ---
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class SessionCreateRequest(BaseModel):
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"""创建会话请求模型"""
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assistant_id: str
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first_message: str # 用户的第一条消息，用于生成标题
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class SessionCreateResponse(BaseModel):
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"""创建会话响应模型"""
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id: str
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title: str
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assistant_id: str
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created_at: str # 返回 ISO 格式时间字符串
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class SessionRead(BaseModel):
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"""读取会话信息模型"""
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id: str
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title: str
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assistant_id: str
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created_at: datetime # Use datetime
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updated_at: Optional[datetime] = None
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class Config:
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from_attributes = True
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# --- Message Models (New) ---
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class MessageBase(BaseModel):
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sender: str # 'user' or 'ai'
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text: str
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class MessageRead(MessageBase):
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id: str
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session_id: str
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order: int
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created_at: datetime
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class Config:
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from_attributes = True
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# --- Workflow Node/Edge Models (for API request/response) ---
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# Mirrors React Flow structure loosely
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class NodeData(BaseModel):
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# Define common fields or use Dict[str, Any]
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label: Optional[str] = None
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text: Optional[str] = None # For ChatInput
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displayText: Optional[str] = None # For ChatOutput
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model: Optional[str] = None # For LLMNode
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temperature: Optional[float] = None # For LLMNode
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systemPrompt: Optional[str] = None # For LLMNode
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# Add other potential data fields from your nodes
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# Use Extra.allow for flexibility if needed:
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# class Config:
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# extra = 'allow'
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class NodeModel(BaseModel):
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id: str
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type: str # e.g., 'chatInputNode', 'llmNode'
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position: Dict[str, float] # { x: number, y: number }
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data: NodeData # Use the specific data model
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class EdgeModel(BaseModel):
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id: str
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source: str
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target: str
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sourceHandle: Optional[str] = None
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targetHandle: Optional[str] = None
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# --- Workflow Execution Models ---
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class WorkflowRunRequest(BaseModel):
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nodes: List[NodeModel]
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edges: List[EdgeModel]
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class WorkflowRunResponse(BaseModel):
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success: bool
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message: Optional[str] = None
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output: Optional[str] = None # The final output text
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output_node_id: Optional[str] = None # ID of the node that produced the output
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