Build lightweight AI agent admin

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2026-06-08 18:14:59 +08:00
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"""
AI 平台 Schema 定义
"""
from .provider_schema import *
from .model_schema import *
from .app_schema import *
from .chat_schema import *
from .workflow_schema import *
from .agent_schema import *
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"""
智能体 Schema
"""
from typing import Optional, List, Any, Dict
from datetime import datetime
from pydantic import BaseModel, Field, ConfigDict
from app.base_schema import CSTDatetime
# ==================== Agent Schema ====================
class PersonaConfig(BaseModel):
"""人设配置"""
role: str = Field(default="", description="角色定位")
personality: List[str] = Field(default_factory=list, description="性格特点")
skills: List[str] = Field(default_factory=list, description="技能")
constraints: List[str] = Field(default_factory=list, description="约束条件")
background: str = Field(default="", description="背景介绍")
examples: List[Dict[str, str]] = Field(default_factory=list, description="对话示例")
class AgentCreate(BaseModel):
"""创建智能体"""
model_config = ConfigDict(protected_namespaces=())
application_id: Optional[str] = Field(None, description="所属应用ID")
is_global: bool = Field(default=False, description="是否在子应用中可见")
name: str = Field(..., max_length=100, description="智能体名称")
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)")
description: str = Field(default="", description="描述")
avatar: str = Field(default="", description="头像")
mode: str = Field(default="autonomous", description="运行模式")
persona: Dict[str, Any] = Field(default_factory=dict, description="人设配置")
system_prompt: str = Field(default="", description="系统提示词")
model_id: Optional[str] = Field(None, description="模型 ID")
temperature: float = Field(default=0.7, description="温度")
top_p: float = Field(default=1.0, description="top_p")
max_tokens: int = Field(default=4096, description="最大 Token")
max_iterations: int = Field(default=10, description="最大推理轮数")
welcome_message: str = Field(default="", description="开场白")
suggested_questions: List[str] = Field(default_factory=list, description="推荐问题")
workflow_id: Optional[str] = Field(None, description="工作流 ID(对话流模式)")
knowledge_base_ids: List[str] = Field(default_factory=list, description="关联的知识库ID列表")
knowledge_config: Dict[str, Any] = Field(default_factory=dict, description="知识库检索配置")
is_public: bool = Field(default=False, description="是否公开")
class AgentUpdate(BaseModel):
"""更新智能体"""
model_config = ConfigDict(protected_namespaces=())
application_id: Optional[str] = None
is_global: Optional[bool] = None
name: Optional[str] = None
description: Optional[str] = None
avatar: Optional[str] = None
mode: Optional[str] = None
persona: Optional[Dict[str, Any]] = None
system_prompt: Optional[str] = None
model_id: Optional[str] = None
temperature: Optional[float] = None
top_p: Optional[float] = None
max_tokens: Optional[int] = None
max_iterations: Optional[int] = None
welcome_message: Optional[str] = None
suggested_questions: Optional[List[str]] = None
workflow_id: Optional[str] = None
knowledge_base_ids: Optional[List[str]] = None
knowledge_config: Optional[Dict[str, Any]] = None
is_public: Optional[bool] = None
enable_memory: Optional[bool] = None
memory_window: Optional[int] = None
enable_streaming: Optional[bool] = None
class AgentResponse(BaseModel):
"""智能体输出"""
model_config = ConfigDict(from_attributes=True, protected_namespaces=())
id: str
application_id: Optional[str] = None
is_global: bool = False
name: str
code: str
description: str = ""
avatar: str = ""
mode: str = "autonomous"
status: str = "draft"
persona: Dict[str, Any] = Field(default_factory=dict)
system_prompt: str = ""
model_id: Optional[str] = None
model_name: str = ""
temperature: float = 0.7
top_p: float = 1.0
max_tokens: int = 4096
max_iterations: int = 10
welcome_message: str = ""
suggested_questions: List[str] = Field(default_factory=list)
workflow_id: Optional[str] = None
workflow_name: str = ""
workflow_type: str = "general"
is_public: bool = False
enable_memory: bool = False
memory_window: int = 10
enable_streaming: bool = True
knowledge_base_ids: List[str] = Field(default_factory=list)
knowledge_config: Dict[str, Any] = Field(default_factory=dict)
conversation_count: int = 0
message_count: int = 0
total_tokens: int = 0
sort: int = 0
sys_create_datetime: Optional[CSTDatetime] = None
sys_update_datetime: Optional[CSTDatetime] = None
class AgentListResponse(BaseModel):
"""智能体列表输出"""
model_config = ConfigDict(from_attributes=True, protected_namespaces=())
id: str
application_id: Optional[str] = None
application_name: str = ""
is_global: bool = False
name: str
code: str
description: str = ""
avatar: str = ""
mode: str = "autonomous"
status: str = "draft"
model_name: str = ""
is_public: bool = False
conversation_count: int = 0
message_count: int = 0
has_menu: bool = False
sys_create_datetime: Optional[CSTDatetime] = None
# ==================== 导入/导出 Schema ====================
class AgentImportCheckIn(BaseModel):
"""智能体导入预检查请求"""
code: str = Field(..., description="智能体编码")
class AgentImportCheckOut(BaseModel):
"""智能体导入预检查结果"""
code_exists: bool = Field(..., description="智能体编码是否已存在")
can_import: bool = Field(..., description="是否可以直接导入(编码不冲突)")
class AgentImportIn(BaseModel):
"""智能体配置导入"""
application_id: Optional[str] = Field(None, description="所属应用ID")
is_global: bool = Field(default=False, description="是否在子应用中可见")
name: str = Field(..., description="智能体名称")
code: str = Field(..., description="智能体编码")
description: str = Field(default="", description="描述")
avatar: str = Field(default="", description="头像")
mode: str = Field(default="autonomous", description="运行模式")
persona: Dict[str, Any] = Field(default_factory=dict, description="人设配置")
system_prompt: str = Field(default="", description="系统提示词")
model_name: str = Field(default="", description="模型名称(API model_name")
temperature: float = Field(default=0.7, description="温度")
top_p: float = Field(default=1.0, description="top_p")
max_tokens: int = Field(default=4096, description="最大 Token")
max_iterations: int = Field(default=10, description="最大推理轮数")
welcome_message: str = Field(default="", description="开场白")
suggested_questions: List[str] = Field(default_factory=list, description="推荐问题")
workflow_code: str = Field(default="", description="关联工作流编码")
knowledge_base_codes: List[str] = Field(default_factory=list, description="知识库编码列表")
knowledge_config: Dict[str, Any] = Field(default_factory=dict, description="知识库检索配置")
enable_memory: bool = Field(default=False, description="是否启用对话记忆")
memory_window: int = Field(default=10, description="记忆窗口大小")
enable_streaming: bool = Field(default=True, description="是否启用流式输出")
is_public: bool = Field(default=False, description="是否公开")
# ==================== Conversation Schema ====================
class AgentConversationCreate(BaseModel):
"""创建对话"""
title: str = Field(default="", description="对话标题")
class AgentConversationResponse(BaseModel):
"""对话输出"""
id: str
agent_id: str
agent_name: str = ""
title: str = ""
summary: str = ""
message_count: int = 0
total_tokens: int = 0
sys_create_datetime: Optional[CSTDatetime] = None
sys_update_datetime: Optional[CSTDatetime] = None
model_config = ConfigDict(from_attributes=True)
class AgentConversationListResponse(BaseModel):
"""对话列表输出"""
id: str
agent_id: str
agent_name: str = ""
title: str = ""
message_count: int = 0
sys_create_datetime: Optional[CSTDatetime] = None
model_config = ConfigDict(from_attributes=True)
# ==================== Message Schema ====================
class ReasoningStep(BaseModel):
"""推理步骤"""
type: str = Field(..., description="步骤类型: thought/action/observation")
content: str = Field(default="", description="内容")
tool: str = Field(default="", description="工具名称(action 类型)")
params: Dict[str, Any] = Field(default_factory=dict, description="工具参数(action 类型)")
timestamp: str = Field(default="", description="时间戳")
class ToolCallRecord(BaseModel):
"""工具调用记录"""
tool: str = Field(..., description="工具名称")
params: Dict[str, Any] = Field(default_factory=dict, description="参数")
result: Any = Field(default=None, description="结果")
elapsed_time: int = Field(default=0, description="耗时(毫秒)")
status: str = Field(default="success", description="状态")
class AttachmentResponse(BaseModel):
"""附件输出
附件通过 file_id 关联文件管理系统(core_file_manager)
前端通过 file_id 调用文件管理 API 获取访问 URL
"""
file_id: str = Field(..., description="文件管理系统中的文件 ID")
type: str = Field(default="file", description="附件类型: image/file/audio/video")
name: str = Field(default="", description="文件名")
mime_type: str = Field(default="", description="MIME 类型")
size: int = Field(default=0, description="文件大小(字节)")
class AgentMessageResponse(BaseModel):
"""消息输出"""
id: str
role: str
content: str = ""
attachments: List[AttachmentResponse] = Field(default_factory=list)
status: str = "completed"
reasoning_steps: List[ReasoningStep] = Field(default_factory=list)
tool_calls: List[ToolCallRecord] = Field(default_factory=list)
prompt_tokens: int = 0
completion_tokens: int = 0
total_tokens: int = 0
elapsed_time: int = 0
error_message: str = ""
feedback: str = ""
sys_create_datetime: Optional[CSTDatetime] = None
model_config = ConfigDict(from_attributes=True)
class AttachmentInput(BaseModel):
"""附件输入
附件通过 file_id 关联文件管理系统(core_file_manager)
"""
file_id: str = Field(..., description="文件管理系统中的文件 ID")
type: str = Field(default="file", description="附件类型: image/file/audio/video")
class ChatInput(BaseModel):
"""对话输入"""
message: str = Field(..., description="用户消息")
conversation_id: Optional[str] = Field(None, description="对话 ID(续聊时提供)")
application_id: Optional[str] = Field(None, description="子应用 ID(在子应用下自动注入)")
form_code: Optional[str] = Field(None, description="表单编码(从表单列表调用时自动注入)")
attachments: Optional[List[AttachmentInput]] = Field(None, description="附件列表")
class ChatStreamEvent(BaseModel):
"""对话流式事件"""
type: str = Field(..., description="事件类型")
content: str = Field(default="", description="内容")
tool: str = Field(default="", description="工具名称")
params: Dict[str, Any] = Field(default_factory=dict, description="工具参数")
message_id: str = Field(default="", description="消息 ID")
conversation_id: str = Field(default="", description="对话 ID")
tokens_used: int = Field(default=0, description="Token 使用量")
elapsed_time: int = Field(default=0, description="耗时")
# ==================== Feedback Schema ====================
class MessageFeedback(BaseModel):
"""消息反馈"""
feedback: str = Field(..., description="反馈类型: like/dislike")
content: str = Field(default="", description="反馈内容")
# ==================== Publish Schema ====================
class AgentPublishInput(BaseModel):
"""发布智能体到菜单"""
menu_name: str = Field(..., description="菜单名称")
menu_parent_id: Optional[str] = Field(None, description="上级菜单 ID")
menu_icon: str = Field(default="lucide:bot", description="菜单图标")
menu_order: int = Field(default=0, description="菜单排序")
@@ -0,0 +1,92 @@
"""
AI 应用 Schema
"""
from typing import Optional, List, Any
from datetime import datetime
from pydantic import BaseModel, Field, ConfigDict
from app.base_schema import CSTDatetime
class AppCreate(BaseModel):
"""创建应用"""
model_config = ConfigDict(protected_namespaces=())
name: str = Field(..., max_length=100, description="应用名称")
code: str = Field(..., max_length=100, description="应用编码")
description: str = Field(default="", description="描述")
icon: str = Field(default="", description="图标")
app_type: str = Field(default="chat", description="应用类型")
model_id: Optional[str] = Field(None, description="默认模型 ID")
system_prompt: str = Field(default="", description="系统提示词")
temperature: float = Field(default=0.7, description="温度")
top_p: float = Field(default=1.0, description="top_p")
max_tokens: int = Field(default=2048, description="最大 Token")
opening_statement: str = Field(default="", description="开场白")
suggested_questions: List[str] = Field(default_factory=list, description="建议问题")
is_public: bool = Field(default=False, description="是否公开")
class AppUpdate(BaseModel):
"""更新应用"""
model_config = ConfigDict(protected_namespaces=())
name: Optional[str] = None
description: Optional[str] = None
icon: Optional[str] = None
model_id: Optional[str] = None
system_prompt: Optional[str] = None
temperature: Optional[float] = None
top_p: Optional[float] = None
max_tokens: Optional[int] = None
opening_statement: Optional[str] = None
suggested_questions: Optional[List[str]] = None
is_public: Optional[bool] = None
workflow_definition: Optional[dict] = None
class AppResponse(BaseModel):
"""应用输出"""
model_config = ConfigDict(from_attributes=True, protected_namespaces=())
id: str
name: str
code: str
description: str = ""
icon: str = ""
app_type: str = "chat"
status: str = "draft"
model_id: Optional[str] = None
model_name: str = ""
system_prompt: str = ""
temperature: float = 0.7
top_p: float = 1.0
max_tokens: int = 2048
opening_statement: str = ""
suggested_questions: List[Any] = Field(default_factory=list)
workflow_definition: dict = Field(default_factory=dict)
is_public: bool = False
conversation_count: int = 0
message_count: int = 0
sort: int = 0
sys_create_datetime: Optional[CSTDatetime] = None
sys_update_datetime: Optional[CSTDatetime] = None
class AppListResponse(BaseModel):
"""应用列表输出"""
model_config = ConfigDict(from_attributes=True, protected_namespaces=())
id: str
name: str
code: str
description: str = ""
icon: str = ""
app_type: str = "chat"
status: str = "draft"
model_name: str = ""
is_public: bool = False
conversation_count: int = 0
message_count: int = 0
sys_create_datetime: Optional[CSTDatetime] = None
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"""
对话 Schema
"""
from typing import Optional
from datetime import datetime
from pydantic import BaseModel, Field, ConfigDict
from app.base_schema import CSTDatetime
class ConversationCreate(BaseModel):
"""创建对话"""
app_id: str = Field(..., description="应用 ID")
title: str = Field(default="", description="对话标题")
class ConversationUpdate(BaseModel):
"""更新对话"""
title: Optional[str] = None
is_pinned: Optional[bool] = None
class ConversationResponse(BaseModel):
"""对话输出"""
id: str
app_id: str
app_name: str = ""
title: str = ""
message_count: int = 0
total_tokens: int = 0
is_pinned: bool = False
sort: int = 0
sys_create_datetime: Optional[CSTDatetime] = None
sys_update_datetime: Optional[CSTDatetime] = None
model_config = ConfigDict(from_attributes=True)
class ConversationListResponse(BaseModel):
"""对话列表输出"""
id: str
title: str = ""
message_count: int = 0
is_pinned: bool = False
sys_update_datetime: Optional[CSTDatetime] = None
model_config = ConfigDict(from_attributes=True)
class MessageResponse(BaseModel):
"""消息输出"""
model_config = ConfigDict(from_attributes=True, protected_namespaces=())
id: str
role: str
content: str = ""
status: str = "completed"
prompt_tokens: int = 0
completion_tokens: int = 0
total_tokens: int = 0
model_name: str = ""
latency: int = 0
error_message: str = ""
feedback: str = ""
sys_create_datetime: Optional[CSTDatetime] = None
class SendMessageInput(BaseModel):
"""发送消息"""
content: str = Field(..., description="消息内容")
class MessageFeedbackInput(BaseModel):
"""消息反馈"""
feedback: str = Field(..., description="反馈: like/dislike")
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"""
LLM 模型 Schema
"""
from typing import Optional
from datetime import datetime
from decimal import Decimal
from pydantic import BaseModel, Field, ConfigDict
from app.base_schema import CSTDatetime
class ModelCreate(BaseModel):
"""创建模型"""
model_config = ConfigDict(protected_namespaces=())
provider_id: str = Field(..., description="提供商 ID")
model_name: str = Field(..., max_length=100, description="模型名称")
display_name: str = Field(..., max_length=100, description="显示名称")
model_type: str = Field(default="chat", description="模型类型")
max_tokens: int = Field(default=4096, description="最大 Token")
context_window: int = Field(default=4096, description="上下文窗口")
default_temperature: float = Field(default=0.7, description="默认温度")
default_top_p: float = Field(default=1.0, description="默认 top_p")
input_price: Decimal = Field(default=0, description="输入价格")
output_price: Decimal = Field(default=0, description="输出价格")
supports_vision: bool = Field(default=False, description="支持视觉")
supports_function_call: bool = Field(default=False, description="支持函数调用")
supports_streaming: bool = Field(default=True, description="支持流式")
is_active: bool = Field(default=True, description="是否启用")
class ModelUpdate(BaseModel):
"""更新模型"""
model_config = ConfigDict(protected_namespaces=())
display_name: Optional[str] = None
model_type: Optional[str] = None
max_tokens: Optional[int] = None
context_window: Optional[int] = None
default_temperature: Optional[float] = None
default_top_p: Optional[float] = None
input_price: Optional[Decimal] = None
output_price: Optional[Decimal] = None
supports_vision: Optional[bool] = None
supports_function_call: Optional[bool] = None
supports_streaming: Optional[bool] = None
is_active: Optional[bool] = None
class ModelResponse(BaseModel):
"""模型输出"""
model_config = ConfigDict(from_attributes=True, protected_namespaces=())
id: str
provider_id: str
provider_name: str = ""
model_name: str
display_name: str
model_type: str = "chat"
max_tokens: int = 4096
context_window: int = 4096
default_temperature: float = 0.7
default_top_p: float = 1.0
input_price: Decimal = Decimal(0)
output_price: Decimal = Decimal(0)
supports_vision: bool = False
supports_function_call: bool = False
supports_streaming: bool = True
is_active: bool = True
sort: int = 0
sys_create_datetime: Optional[CSTDatetime] = None
sys_update_datetime: Optional[CSTDatetime] = None
class ModelListResponse(BaseModel):
"""模型列表输出"""
model_config = ConfigDict(from_attributes=True, protected_namespaces=())
id: str
provider_id: str
provider_name: str = ""
model_name: str
display_name: str
model_type: str = "chat"
is_active: bool = True
class DefaultModelResponse(BaseModel):
"""默认模型信息"""
model_config = ConfigDict(protected_namespaces=())
model_name: str
display_name: str
model_type: str = "chat"
max_tokens: int = 4096
context_window: int = 4096
supports_vision: bool = False
supports_function_call: bool = False
input_price: float = 0
output_price: float = 0
@@ -0,0 +1,70 @@
"""
LLM 提供商 Schema
"""
from typing import Optional
from datetime import datetime
from pydantic import BaseModel, Field, ConfigDict
from app.base_schema import CSTDatetime
class ProviderCreate(BaseModel):
"""创建提供商"""
name: str = Field(..., max_length=100, description="提供商名称")
provider_type: str = Field(..., description="提供商类型")
api_key: str = Field(default="", description="API Key")
api_base: str = Field(default="", description="API 地址")
api_version: str = Field(default="", description="API 版本")
ollama_host: str = Field(default="http://localhost:11434", description="Ollama 地址")
description: str = Field(default="", description="描述")
is_active: bool = Field(default=True, description="是否启用")
class ProviderUpdate(BaseModel):
"""更新提供商"""
name: Optional[str] = Field(None, max_length=100)
api_key: Optional[str] = None
api_base: Optional[str] = None
api_version: Optional[str] = None
ollama_host: Optional[str] = None
description: Optional[str] = None
is_active: Optional[bool] = None
class ProviderResponse(BaseModel):
"""提供商输出"""
id: str
name: str
provider_type: str
api_key_masked: str = ""
api_base: str = ""
api_version: str = ""
ollama_host: str = ""
description: str = ""
is_active: bool = True
quota_limit: int = 0
quota_used: int = 0
sort: int = 0
sys_create_datetime: Optional[CSTDatetime] = None
sys_update_datetime: Optional[CSTDatetime] = None
model_config = ConfigDict(from_attributes=True)
class ProviderListResponse(BaseModel):
"""提供商列表输出"""
id: str
name: str
provider_type: str
is_active: bool = True
description: str = ""
sys_create_datetime: Optional[CSTDatetime] = None
model_config = ConfigDict(from_attributes=True)
class ProviderTypeResponse(BaseModel):
"""提供商类型"""
type: str
name: str
@@ -0,0 +1,170 @@
"""
AI 工作流 Schema
"""
from typing import Optional, List, Any
from datetime import datetime
from pydantic import BaseModel, Field, ConfigDict
from app.base_schema import CSTDatetime
class WorkflowCreate(BaseModel):
"""创建工作流"""
application_id: Optional[str] = Field(None, description="所属应用ID")
is_global: bool = Field(default=False, description="是否在子应用中可见")
name: str = Field(..., max_length=100, description="工作流名称")
code: str = Field(..., max_length=100, pattern=r"^[a-zA-Z][a-zA-Z0-9_]*$", description="工作流编码(字母开头,只能包含字母、数字和下划线)")
workflow_type: str = Field(default="general", description="工作流类型: general/application/form/report/data_process/automation")
description: str = Field(default="", description="描述")
definition: dict = Field(default_factory=dict, description="工作流定义")
input_variables: List[dict] = Field(default_factory=list, description="输入变量")
output_variables: List[dict] = Field(default_factory=list, description="输出变量")
class WorkflowUpdate(BaseModel):
"""更新工作流"""
application_id: Optional[str] = None
is_global: Optional[bool] = None
name: Optional[str] = None
code: Optional[str] = Field(None, max_length=100, pattern=r"^[a-zA-Z][a-zA-Z0-9_]*$", description="工作流编码(字母开头,只能包含字母、数字和下划线)")
workflow_type: Optional[str] = None
description: Optional[str] = None
definition: Optional[dict] = None
input_variables: Optional[List[dict]] = None
output_variables: Optional[List[dict]] = None
class WorkflowResponse(BaseModel):
"""工作流输出"""
id: str
application_id: Optional[str] = None
is_global: bool = False
name: str
code: str
workflow_type: str = "general"
description: str = ""
status: str = "draft"
version: int = 1
published_version: Optional[int] = None
published_at: Optional[CSTDatetime] = None
published_definition: Optional[dict] = None
definition: dict = Field(default_factory=dict)
input_variables: List[dict] = Field(default_factory=list)
output_variables: List[dict] = Field(default_factory=list)
run_count: int = 0
success_count: int = 0
sort: int = 0
sys_create_datetime: Optional[CSTDatetime] = None
sys_update_datetime: Optional[CSTDatetime] = None
model_config = ConfigDict(from_attributes=True)
class WorkflowListResponse(BaseModel):
"""工作流列表输出"""
id: str
application_id: Optional[str] = None
application_name: str = ""
is_global: bool = False
name: str
code: str
workflow_type: str = "general"
description: str = ""
status: str = "draft"
version: int = 1
run_count: int = 0
success_count: int = 0
sys_create_datetime: Optional[CSTDatetime] = None
model_config = ConfigDict(from_attributes=True)
class WorkflowRunInput(BaseModel):
"""运行工作流"""
inputs: dict = Field(default_factory=dict, description="输入数据")
use_draft: bool = Field(default=False, description="是否使用草稿版本(编辑器调试用)")
class WorkflowResumeInput(BaseModel):
"""恢复工作流执行"""
user_input: Any = Field(..., description="用户输入的值")
class WorkflowRunResponse(BaseModel):
"""工作流运行记录输出"""
id: str
workflow_id: str
workflow_name: str = ""
status: str = "pending"
trigger_type: str = "api"
use_draft: bool = False
workflow_version: Optional[int] = None
definition_snapshot: dict = Field(default_factory=dict)
inputs: dict = Field(default_factory=dict)
outputs: dict = Field(default_factory=dict)
execution_log: List[dict] = Field(default_factory=list)
current_node_id: str = ""
waiting_config: dict = Field(default_factory=dict)
error_message: str = ""
total_tokens: int = 0
total_steps: int = 0
elapsed_time: int = 0
started_at: Optional[CSTDatetime] = None
completed_at: Optional[CSTDatetime] = None
sys_create_datetime: Optional[CSTDatetime] = None
model_config = ConfigDict(from_attributes=True)
class WorkflowRunListResponse(BaseModel):
"""工作流运行记录列表输出"""
id: str
workflow_id: str
workflow_name: str = ""
status: str = "pending"
trigger_type: str = "api"
total_steps: int = 0
total_tokens: int = 0
elapsed_time: int = 0
error_message: str = ""
started_at: Optional[CSTDatetime] = None
completed_at: Optional[CSTDatetime] = None
model_config = ConfigDict(from_attributes=True)
class WorkflowImportCheckIn(BaseModel):
"""工作流导入预检查请求"""
code: str = Field(..., description="工作流编码")
class WorkflowImportCheckOut(BaseModel):
"""工作流导入预检查结果"""
code_exists: bool = Field(..., description="工作流编码是否已存在")
can_import: bool = Field(..., description="是否可以直接导入(编码不冲突)")
class WorkflowImportIn(BaseModel):
"""工作流配置导入"""
application_id: Optional[str] = Field(None, description="所属应用ID")
is_global: bool = Field(default=False, description="是否在子应用中可见")
name: str = Field(..., description="工作流名称")
code: str = Field(..., description="工作流编码")
workflow_type: str = Field(default="general", description="工作流类型")
description: str = Field(default="", description="描述")
definition: dict = Field(default_factory=dict, description="工作流定义")
input_variables: List[dict] = Field(default_factory=list, description="输入变量")
output_variables: List[dict] = Field(default_factory=list, description="输出变量")
class NodeSchemaResponse(BaseModel):
"""节点 Schema 输出"""
type: str
name: str
category: str = ""
icon: str = ""
description: str = ""
inputs: List[dict] = Field(default_factory=list)
outputs: List[dict] = Field(default_factory=list)
supports_branches: bool = False