Build lightweight AI agent admin
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"""
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智能体 Schema
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"""
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from typing import Optional, List, Any, Dict
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from datetime import datetime
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from pydantic import BaseModel, Field, ConfigDict
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from app.base_schema import CSTDatetime
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# ==================== Agent Schema ====================
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class PersonaConfig(BaseModel):
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"""人设配置"""
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role: str = Field(default="", description="角色定位")
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personality: List[str] = Field(default_factory=list, description="性格特点")
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skills: List[str] = Field(default_factory=list, description="技能")
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constraints: List[str] = Field(default_factory=list, description="约束条件")
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background: str = Field(default="", description="背景介绍")
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examples: List[Dict[str, str]] = Field(default_factory=list, description="对话示例")
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class AgentCreate(BaseModel):
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"""创建智能体"""
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model_config = ConfigDict(protected_namespaces=())
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application_id: Optional[str] = Field(None, description="所属应用ID")
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is_global: bool = Field(default=False, description="是否在子应用中可见")
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name: str = Field(..., max_length=100, description="智能体名称")
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code: str = Field(..., max_length=100, pattern=r"^[a-zA-Z][a-zA-Z0-9_]*$", description="智能体编码（字母开头，只能包含字母、数字和下划线）")
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description: str = Field(default="", description="描述")
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avatar: str = Field(default="", description="头像")
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mode: str = Field(default="autonomous", description="运行模式")
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persona: Dict[str, Any] = Field(default_factory=dict, description="人设配置")
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system_prompt: str = Field(default="", description="系统提示词")
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model_id: Optional[str] = Field(None, description="模型 ID")
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temperature: float = Field(default=0.7, description="温度")
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top_p: float = Field(default=1.0, description="top_p")
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max_tokens: int = Field(default=4096, description="最大 Token")
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max_iterations: int = Field(default=10, description="最大推理轮数")
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welcome_message: str = Field(default="", description="开场白")
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suggested_questions: List[str] = Field(default_factory=list, description="推荐问题")
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workflow_id: Optional[str] = Field(None, description="工作流 ID（对话流模式）")
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knowledge_base_ids: List[str] = Field(default_factory=list, description="关联的知识库ID列表")
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knowledge_config: Dict[str, Any] = Field(default_factory=dict, description="知识库检索配置")
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is_public: bool = Field(default=False, description="是否公开")
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class AgentUpdate(BaseModel):
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"""更新智能体"""
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model_config = ConfigDict(protected_namespaces=())
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application_id: Optional[str] = None
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is_global: Optional[bool] = None
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name: Optional[str] = None
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description: Optional[str] = None
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avatar: Optional[str] = None
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mode: Optional[str] = None
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persona: Optional[Dict[str, Any]] = None
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system_prompt: Optional[str] = None
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model_id: Optional[str] = None
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temperature: Optional[float] = None
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top_p: Optional[float] = None
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max_tokens: Optional[int] = None
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max_iterations: Optional[int] = None
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welcome_message: Optional[str] = None
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suggested_questions: Optional[List[str]] = None
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workflow_id: Optional[str] = None
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knowledge_base_ids: Optional[List[str]] = None
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knowledge_config: Optional[Dict[str, Any]] = None
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is_public: Optional[bool] = None
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enable_memory: Optional[bool] = None
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memory_window: Optional[int] = None
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enable_streaming: Optional[bool] = None
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class AgentResponse(BaseModel):
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"""智能体输出"""
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model_config = ConfigDict(from_attributes=True, protected_namespaces=())
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id: str
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application_id: Optional[str] = None
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is_global: bool = False
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name: str
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code: str
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description: str = ""
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avatar: str = ""
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mode: str = "autonomous"
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status: str = "draft"
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persona: Dict[str, Any] = Field(default_factory=dict)
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system_prompt: str = ""
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model_id: Optional[str] = None
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model_name: str = ""
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temperature: float = 0.7
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top_p: float = 1.0
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max_tokens: int = 4096
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max_iterations: int = 10
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welcome_message: str = ""
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suggested_questions: List[str] = Field(default_factory=list)
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workflow_id: Optional[str] = None
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workflow_name: str = ""
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workflow_type: str = "general"
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is_public: bool = False
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enable_memory: bool = False
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memory_window: int = 10
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enable_streaming: bool = True
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knowledge_base_ids: List[str] = Field(default_factory=list)
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knowledge_config: Dict[str, Any] = Field(default_factory=dict)
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conversation_count: int = 0
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message_count: int = 0
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total_tokens: int = 0
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sort: int = 0
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sys_create_datetime: Optional[CSTDatetime] = None
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sys_update_datetime: Optional[CSTDatetime] = None
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class AgentListResponse(BaseModel):
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"""智能体列表输出"""
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model_config = ConfigDict(from_attributes=True, protected_namespaces=())
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id: str
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application_id: Optional[str] = None
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application_name: str = ""
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is_global: bool = False
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name: str
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code: str
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description: str = ""
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avatar: str = ""
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mode: str = "autonomous"
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status: str = "draft"
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model_name: str = ""
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is_public: bool = False
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conversation_count: int = 0
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message_count: int = 0
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has_menu: bool = False
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sys_create_datetime: Optional[CSTDatetime] = None
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# ==================== 导入/导出 Schema ====================
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class AgentImportCheckIn(BaseModel):
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"""智能体导入预检查请求"""
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code: str = Field(..., description="智能体编码")
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class AgentImportCheckOut(BaseModel):
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"""智能体导入预检查结果"""
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code_exists: bool = Field(..., description="智能体编码是否已存在")
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can_import: bool = Field(..., description="是否可以直接导入（编码不冲突）")
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class AgentImportIn(BaseModel):
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"""智能体配置导入"""
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application_id: Optional[str] = Field(None, description="所属应用ID")
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is_global: bool = Field(default=False, description="是否在子应用中可见")
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name: str = Field(..., description="智能体名称")
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code: str = Field(..., description="智能体编码")
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description: str = Field(default="", description="描述")
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avatar: str = Field(default="", description="头像")
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mode: str = Field(default="autonomous", description="运行模式")
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persona: Dict[str, Any] = Field(default_factory=dict, description="人设配置")
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system_prompt: str = Field(default="", description="系统提示词")
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model_name: str = Field(default="", description="模型名称（API model_name）")
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temperature: float = Field(default=0.7, description="温度")
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top_p: float = Field(default=1.0, description="top_p")
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max_tokens: int = Field(default=4096, description="最大 Token")
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max_iterations: int = Field(default=10, description="最大推理轮数")
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welcome_message: str = Field(default="", description="开场白")
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suggested_questions: List[str] = Field(default_factory=list, description="推荐问题")
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workflow_code: str = Field(default="", description="关联工作流编码")
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knowledge_base_codes: List[str] = Field(default_factory=list, description="知识库编码列表")
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knowledge_config: Dict[str, Any] = Field(default_factory=dict, description="知识库检索配置")
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enable_memory: bool = Field(default=False, description="是否启用对话记忆")
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memory_window: int = Field(default=10, description="记忆窗口大小")
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enable_streaming: bool = Field(default=True, description="是否启用流式输出")
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is_public: bool = Field(default=False, description="是否公开")
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# ==================== Conversation Schema ====================
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class AgentConversationCreate(BaseModel):
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"""创建对话"""
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title: str = Field(default="", description="对话标题")
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class AgentConversationResponse(BaseModel):
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"""对话输出"""
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id: str
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agent_id: str
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agent_name: str = ""
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title: str = ""
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summary: str = ""
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message_count: int = 0
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total_tokens: int = 0
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sys_create_datetime: Optional[CSTDatetime] = None
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sys_update_datetime: Optional[CSTDatetime] = None
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model_config = ConfigDict(from_attributes=True)
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class AgentConversationListResponse(BaseModel):
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"""对话列表输出"""
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id: str
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agent_id: str
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agent_name: str = ""
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title: str = ""
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message_count: int = 0
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sys_create_datetime: Optional[CSTDatetime] = None
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model_config = ConfigDict(from_attributes=True)
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# ==================== Message Schema ====================
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class ReasoningStep(BaseModel):
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"""推理步骤"""
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type: str = Field(..., description="步骤类型: thought/action/observation")
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content: str = Field(default="", description="内容")
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tool: str = Field(default="", description="工具名称（action 类型）")
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params: Dict[str, Any] = Field(default_factory=dict, description="工具参数（action 类型）")
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timestamp: str = Field(default="", description="时间戳")
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class ToolCallRecord(BaseModel):
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"""工具调用记录"""
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tool: str = Field(..., description="工具名称")
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params: Dict[str, Any] = Field(default_factory=dict, description="参数")
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result: Any = Field(default=None, description="结果")
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elapsed_time: int = Field(default=0, description="耗时（毫秒）")
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status: str = Field(default="success", description="状态")
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class AttachmentResponse(BaseModel):
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"""附件输出
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附件通过 file_id 关联文件管理系统(core_file_manager)
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前端通过 file_id 调用文件管理 API 获取访问 URL
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"""
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file_id: str = Field(..., description="文件管理系统中的文件 ID")
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type: str = Field(default="file", description="附件类型: image/file/audio/video")
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name: str = Field(default="", description="文件名")
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mime_type: str = Field(default="", description="MIME 类型")
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size: int = Field(default=0, description="文件大小（字节）")
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class AgentMessageResponse(BaseModel):
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"""消息输出"""
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id: str
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role: str
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content: str = ""
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attachments: List[AttachmentResponse] = Field(default_factory=list)
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status: str = "completed"
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reasoning_steps: List[ReasoningStep] = Field(default_factory=list)
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tool_calls: List[ToolCallRecord] = Field(default_factory=list)
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prompt_tokens: int = 0
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completion_tokens: int = 0
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total_tokens: int = 0
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elapsed_time: int = 0
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error_message: str = ""
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feedback: str = ""
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sys_create_datetime: Optional[CSTDatetime] = None
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model_config = ConfigDict(from_attributes=True)
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class AttachmentInput(BaseModel):
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"""附件输入
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附件通过 file_id 关联文件管理系统(core_file_manager)
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"""
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file_id: str = Field(..., description="文件管理系统中的文件 ID")
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type: str = Field(default="file", description="附件类型: image/file/audio/video")
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class ChatInput(BaseModel):
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"""对话输入"""
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message: str = Field(..., description="用户消息")
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conversation_id: Optional[str] = Field(None, description="对话 ID（续聊时提供）")
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application_id: Optional[str] = Field(None, description="子应用 ID（在子应用下自动注入）")
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form_code: Optional[str] = Field(None, description="表单编码（从表单列表调用时自动注入）")
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attachments: Optional[List[AttachmentInput]] = Field(None, description="附件列表")
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class ChatStreamEvent(BaseModel):
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"""对话流式事件"""
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type: str = Field(..., description="事件类型")
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content: str = Field(default="", description="内容")
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tool: str = Field(default="", description="工具名称")
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params: Dict[str, Any] = Field(default_factory=dict, description="工具参数")
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message_id: str = Field(default="", description="消息 ID")
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conversation_id: str = Field(default="", description="对话 ID")
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tokens_used: int = Field(default=0, description="Token 使用量")
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elapsed_time: int = Field(default=0, description="耗时")
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# ==================== Feedback Schema ====================
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class MessageFeedback(BaseModel):
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"""消息反馈"""
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feedback: str = Field(..., description="反馈类型: like/dislike")
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content: str = Field(default="", description="反馈内容")
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# ==================== Publish Schema ====================
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class AgentPublishInput(BaseModel):
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"""发布智能体到菜单"""
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menu_name: str = Field(..., description="菜单名称")
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menu_parent_id: Optional[str] = Field(None, description="上级菜单 ID")
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menu_icon: str = Field(default="lucide:bot", description="菜单图标")
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menu_order: int = Field(default=0, description="菜单排序")
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