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# Schemas 模块
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## 作用
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定义 API 与工作流的数据模型，统一请求与响应结构。
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## 文件
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- `agent_input.py` / `agent_output.py`：Agent 入参与出参模型
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- `tool_input.py` / `tool_output.py`：工具调用模型
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- `chat_message_request.py` / `chat_message_response.py`：聊天接口模型，其中 `ChatMessageRequestDTO` 对齐前端 `query / inputs / response_mode / user / conversation_id / files` DTO
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- `stream_input.py`：流式接口输入模型
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"""数据模型层"""
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from .agent_input import AgentInput
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from .agent_output import AgentOutput
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from .tool_input import ToolInput
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from .tool_output import ToolOutput
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from .chat_message_request import ChatMessageRequestDTO, ChatMessageFileDTO
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from .chat_message_response import ChatMessageResponseDTO
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from .message_feedback_request import MessageFeedbackRequestDTO
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from .messages import MessagesDTO
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__all__ = [
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"AgentInput",
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"AgentOutput",
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"ToolInput",
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"ToolOutput",
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"ChatMessageRequestDTO",
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"ChatMessageFileDTO",
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"ChatMessageResponseDTO",
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"MessageFeedbackRequestDTO",
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"MessagesDTO",
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]
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from typing import Any, Dict, Optional
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from pydantic import BaseModel, ConfigDict, Field
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from schemas.chat_message_request import ChatMessageRequestDTO
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class AgentInput(BaseModel):
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"""统一工作流输入模型（新版本 DTO）"""
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class AgentInput(ChatMessageRequestDTO):
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"""兼容旧名称，实际与 ChatMessageRequestDTO 使用同一套前端请求模型。"""
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model_config = ConfigDict(extra="forbid")
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query: str
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conversation_id: Optional[str] = None
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workflow_type: str = "conversation"
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response_mode: str = "blocking"
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user: Optional[str] = None
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inputs: Dict[str, Any] = Field(default_factory=dict)
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from typing import Any, Dict, List, Optional
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from typing import Any, List, Optional
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from pydantic import BaseModel, ConfigDict, Field
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class ChatMessageFileDTO(BaseModel):
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"""文件输入模型（预留）"""
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"""文件输入模型（保持与前端 DTO 兼容，允许附带任意文件元数据）。"""
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model_config = ConfigDict(extra="allow")
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class ChatMessageRequestDTO(BaseModel):
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"""/api/workflows/stream 请求模型（对齐标准 chat message 请求）"""
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"""/api/workflows/stream 请求模型（对齐前端 ChatMessageRequestDTO）。"""
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model_config = ConfigDict(extra="forbid")
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query: str
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inputs: Dict[str, Any] = Field(default_factory=dict)
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response_mode: str = "streaming"
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user: Optional[str] = None
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conversation_id: Optional[str] = None
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files: List[ChatMessageFileDTO] = Field(default_factory=list)
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query: Optional[str] = Field(default=None, description="用户输入/提问内容。")
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inputs: Any = Field(default_factory=dict, description="App 定义的变量值，默认 {}。")
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response_mode: Optional[str] = Field(default=None, description="streaming 或 blocking。")
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user: Optional[str] = Field(default=None, description="用户唯一标识。")
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conversation_id: Optional[str] = Field(default=None, description="可选会话 ID。")
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files: List[ChatMessageFileDTO] = Field(default_factory=list, description="可选文件列表。")
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auto_generate_name: Optional[bool] = Field(default=None, description="兼容旧前端字段，当前后端忽略。")
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from typing import Literal, Optional
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from pydantic import BaseModel, Field, model_validator
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class MessageFeedbackRequestDTO(BaseModel):
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"""消息反馈请求模型"""
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message_id: str = Field(..., description="消息 ID")
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feedback: Literal["like", "dislike"] = Field(..., description="反馈类型: like/dislike")
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feedback_content: Optional[str] = Field(default=None, description="点踩反馈内容")
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@model_validator(mode="after")
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def validate_feedback_content(self):
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if self.feedback == "dislike" and not (self.feedback_content or "").strip():
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raise ValueError("feedback_content is required when feedback=dislike")
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return self
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from typing import Any, Dict, Optional
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from pydantic import BaseModel, Field
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class MessagesDTO(BaseModel):
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"""messages 表字段契约，对齐 Java Messages 实体。"""
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message_id: str = Field(..., description="消息 ID")
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conversation_id: str = Field(..., description="会话 ID")
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user: Optional[str] = Field(default=None, description="用户")
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query: str = Field(..., description="用户输入 / 提问内容")
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answer: Optional[str] = Field(default=None, description="回答消息内容")
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feedback: Optional[str] = Field(default=None, description="点赞 like / 点踩 dislike")
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feedback_content: Optional[str] = Field(default=None, description="点踩内容")
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created_at: int = Field(..., description="创建时间(毫秒时间戳)")
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updated_at: int = Field(..., description="更新时间(毫秒时间戳)")
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log: Dict[str, Any] = Field(default_factory=dict, description="当前对话日志(JSON)")
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from schemas.agent_input import AgentInput
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from schemas.chat_message_request import ChatMessageRequestDTO
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class StreamInputDTO(AgentInput):
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"""流式输入 DTO（与统一 AgentInput 保持一致）"""
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response_mode: str = "streaming"
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class StreamInputDTO(ChatMessageRequestDTO):
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"""流式输入 DTO（与统一 ChatMessageRequestDTO 保持一致）。"""
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@@ -1,37 +0,0 @@
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from typing import Any, Dict, Optional
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from pydantic import BaseModel, ConfigDict, Field
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class SuperAgentRequest(BaseModel):
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"""Super Agent 请求模型"""
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model_config = ConfigDict(extra="forbid")
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query: str = Field(..., desc ription="用户查询")
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conversation_id: Optional[str] = Field(None, description="会话ID")
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user_id: Optional[str] = Field(None, description="用户ID")
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workflow_type: str = Field(default="conversation", description="工作流类型")
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context: Dict[str, str] = Field(default_factory=dict, description="上下文信息")
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timeout_seconds: int = Field(default=30, description="超时时间(秒)")
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class SuperAgentResponse(BaseModel):
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"""Super Agent 响应模型"""
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conversation_id: str = Field(..., description="会话ID")
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workflow_type: str = Field(..., description="工作流类型")
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status: str = Field(default="success", description="状态: success/error")
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sql: Optional[str] = Field(None, description="生成的SQL")
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result: Optional[str] = Field(None, description="查询结果")
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error: Optional[str] = Field(None, description="错误信息")
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metadata: Dict[str, str] = Field(default_factory=dict, description="元数据")
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class SuperAgentStreamEvent(BaseModel):
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"""Super Agent 流式响应事件"""
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conversation_id: str = Field(..., description="会话ID")
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event: str = Field(..., description="事件类型: sql_generated/sql_executing/result/error/done")
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data: str = Field(..., description="事件数据")
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timestamp: int = Field(..., description="时间戳(毫秒)")
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