Build lightweight AI agent admin
This commit is contained in:
Codex
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"""
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AI 平台数据模型
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"""
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from .provider import LLMProvider
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from .model import LLMModel
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from .app import AIApp
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from .conversation import Conversation, Message
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from .workflow import AIWorkflow, AIWorkflowVersion, AIWorkflowRun
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from .prompt_template import PromptTemplate
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from .agent import Agent, AgentConversation, AgentMessage
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from ai_platform.knowledge.models import KnowledgeBase, KnowledgeDocument, KnowledgeSegment
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__all__ = [
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'LLMProvider',
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'LLMModel',
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'AIApp',
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'Conversation',
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'Message',
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'AIWorkflow',
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'AIWorkflowVersion',
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'AIWorkflowRun',
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'PromptTemplate',
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'Agent',
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'AgentConversation',
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'AgentMessage',
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'KnowledgeBase',
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'KnowledgeDocument',
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'KnowledgeSegment',
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]
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"""
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智能体模型
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"""
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from sqlalchemy import Column, String, Text, Float, Integer, Boolean, JSON, Index
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from app.base_model import BaseModel
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class Agent(BaseModel):
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"""
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智能体定义
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智能体是一个能够自主决策、调用工具、多轮推理的 AI 实体
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支持两种模式：
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- autonomous: 自主规划模式 - Agent 自动拆解任务并执行
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- dialog_flow: 对话流模式 - 按预定义流程与用户交互
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"""
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__tablename__ = "ai_agent"
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application_id = Column(String(21), nullable=True, index=True, comment="所属应用ID（逻辑外键关联core_application）")
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is_global = Column(Boolean, default=False, comment="是否在子应用中可见")
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name = Column(String(100), nullable=False, comment="智能体名称")
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code = Column(String(100), unique=True, nullable=False, comment="智能体编码")
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description = Column(Text, default="", comment="智能体描述")
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avatar = Column(String(500), default="", comment="头像 URL")
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mode = Column(String(20), default="autonomous", comment="运行模式: autonomous/dialog_flow")
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status = Column(String(20), default="draft", comment="状态: draft/published/disabled")
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persona = Column(JSON, default=dict, comment="人设配置")
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system_prompt = Column(Text, default="", comment="系统提示词")
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model_id = Column(String(21), nullable=True, index=True, comment="默认模型ID（逻辑外键关联ai_llm_model）")
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temperature = Column(Float, default=0.7, comment="温度参数（0-2）")
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top_p = Column(Float, default=1.0, comment="top_p 参数")
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max_tokens = Column(Integer, default=4096, comment="最大输出 Token")
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max_iterations = Column(Integer, default=10, comment="最大推理轮数")
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welcome_message = Column(Text, default="", comment="开场白")
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suggested_questions = Column(JSON, default=list, comment="推荐问题列表")
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workflow_id = Column(String(21), nullable=True, index=True, comment="关联工作流ID（逻辑外键关联ai_workflow）")
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enable_memory = Column(Boolean, default=False, comment="是否启用对话记忆")
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memory_window = Column(Integer, default=10, comment="记忆窗口大小（最近 N 轮对话）")
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enable_streaming = Column(Boolean, default=True, comment="是否启用流式输出（自主规划模式）")
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knowledge_base_ids = Column(JSON, default=list, comment="关联的知识库ID列表")
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knowledge_config = Column(JSON, default=dict, comment="知识库检索配置(top_k/score_threshold/retrieval_mode等)")
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is_public = Column(Boolean, default=False, comment="是否公开")
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conversation_count = Column(Integer, default=0, comment="对话数量")
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message_count = Column(Integer, default=0, comment="消息数量")
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total_tokens = Column(Integer, default=0, comment="总 Token 消耗")
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class AgentConversation(BaseModel):
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"""
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智能体对话
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"""
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__tablename__ = "ai_agent_conversation"
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agent_id = Column(String(21), nullable=False, index=True, comment="智能体ID（逻辑外键关联ai_agent）")
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user_id = Column(String(21), nullable=True, index=True, comment="用户ID（逻辑外键关联core_user）")
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title = Column(String(200), default="", comment="对话标题")
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summary = Column(Text, default="", comment="对话摘要")
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workflow_run_id = Column(String(21), nullable=True, comment="工作流运行实例ID（逻辑外键关联ai_workflow_run）")
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waiting_node_id = Column(String(100), default="", comment="等待输入的节点 ID")
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extra_data = Column(JSON, default=dict, comment="元数据")
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message_count = Column(Integer, default=0, comment="消息数量")
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total_tokens = Column(Integer, default=0, comment="总 Token 消耗")
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__table_args__ = (
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Index("ix_ai_agent_conversation_agent_user", "agent_id", "user_id"),
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)
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class AgentMessage(BaseModel):
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"""
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智能体消息
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记录对话中的每条消息，包括用户消息、助手回复、工具调用等
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"""
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__tablename__ = "ai_agent_message"
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conversation_id = Column(String(21), nullable=False, index=True, comment="对话ID（逻辑外键关联ai_agent_conversation）")
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role = Column(String(20), nullable=False, comment="角色: user/assistant/tool/system")
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content = Column(Text, default="", comment="消息内容")
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attachments = Column(JSON, default=list, comment="附件列表 [{id, type, name, url, mime_type, size}]")
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status = Column(String(20), default="completed", comment="状态: pending/completed/failed")
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reasoning_steps = Column(JSON, default=list, comment="推理步骤")
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tool_calls = Column(JSON, default=list, comment="工具调用记录")
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prompt_tokens = Column(Integer, default=0, comment="提示 Token")
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completion_tokens = Column(Integer, default=0, comment="生成 Token")
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total_tokens = Column(Integer, default=0, comment="总 Token")
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elapsed_time = Column(Integer, default=0, comment="耗时（毫秒）")
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error_message = Column(Text, default="", comment="错误信息")
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feedback = Column(String(20), default="", comment="用户反馈（like/dislike）")
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feedback_content = Column(Text, default="", comment="反馈内容")
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__table_args__ = (
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Index("ix_ai_agent_message_conversation_role", "conversation_id", "role"),
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)
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"""
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AI 应用模型
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"""
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from sqlalchemy import Column, String, Text, Float, Integer, Boolean, JSON
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from app.base_model import BaseModel
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class AIApp(BaseModel):
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"""
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AI 应用
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支持的应用类型：
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- chat: 聊天助手
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- completion: 文本生成
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- workflow: 工作流应用
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- agent: Agent 应用（预留）
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"""
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__tablename__ = "ai_app"
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name = Column(String(100), nullable=False, comment="应用名称")
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code = Column(String(100), unique=True, nullable=False, comment="应用编码")
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description = Column(Text, default="", comment="应用描述")
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icon = Column(String(100), default="", comment="应用图标")
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app_type = Column(String(20), default="chat", comment="应用类型: chat/completion/workflow/agent")
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status = Column(String(20), default="draft", comment="状态: draft/published/disabled")
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model_id = Column(String(21), nullable=True, index=True, comment="默认模型ID（逻辑外键关联ai_llm_model）")
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system_prompt = Column(Text, default="", comment="系统提示词")
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temperature = Column(Float, default=0.7, comment="温度参数")
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top_p = Column(Float, default=1.0, comment="top_p 参数")
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max_tokens = Column(Integer, default=2048, comment="最大输出 Token")
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workflow_definition = Column(JSON, default=dict, comment="工作流定义")
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opening_statement = Column(Text, default="", comment="开场白")
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suggested_questions = Column(JSON, default=list, comment="建议问题列表")
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is_public = Column(Boolean, default=False, comment="是否公开")
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conversation_count = Column(Integer, default=0, comment="对话数量")
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message_count = Column(Integer, default=0, comment="消息数量")
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"""
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对话模型
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"""
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from sqlalchemy import Column, String, Text, Float, Integer, Boolean, Index
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from app.base_model import BaseModel
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class Conversation(BaseModel):
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"""
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对话
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"""
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__tablename__ = "ai_conversation"
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app_id = Column(String(21), nullable=False, index=True, comment="所属应用ID（逻辑外键关联ai_app）")
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user_id = Column(String(21), nullable=False, index=True, comment="用户ID（逻辑外键关联core_user）")
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title = Column(String(200), default="", comment="对话标题")
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model_override_id = Column(String(21), nullable=True, comment="覆盖模型ID（逻辑外键关联ai_llm_model）")
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temperature_override = Column(Float, nullable=True, comment="覆盖温度参数")
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message_count = Column(Integer, default=0, comment="消息数量")
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total_tokens = Column(Integer, default=0, comment="总 Token 数")
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is_pinned = Column(Boolean, default=False, comment="是否置顶")
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__table_args__ = (
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Index("ix_ai_conversation_user_app", "user_id", "app_id"),
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)
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class Message(BaseModel):
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"""
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消息
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"""
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__tablename__ = "ai_message"
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conversation_id = Column(String(21), nullable=False, index=True, comment="所属对话ID（逻辑外键关联ai_conversation）")
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role = Column(String(20), nullable=False, comment="角色: system/user/assistant")
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content = Column(Text, nullable=False, comment="消息内容")
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status = Column(String(20), default="completed", comment="状态: pending/completed/failed/stopped")
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prompt_tokens = Column(Integer, default=0, comment="提示 Token 数")
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completion_tokens = Column(Integer, default=0, comment="补全 Token 数")
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total_tokens = Column(Integer, default=0, comment="总 Token 数")
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model_name = Column(String(100), default="", comment="使用的模型名称")
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latency = Column(Integer, default=0, comment="响应耗时（毫秒）")
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error_message = Column(Text, default="", comment="错误信息")
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parent_message_id = Column(String(21), nullable=True, comment="父消息ID（逻辑外键关联自身）")
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feedback = Column(String(20), default="", comment="用户反馈: like/dislike")
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__table_args__ = (
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Index("ix_ai_message_conversation_created", "conversation_id", "sys_create_datetime"),
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)
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"""
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LLM 模型配置
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"""
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from sqlalchemy import Column, String, Integer, Float, Boolean, Numeric
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from app.base_model import BaseModel
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class LLMModel(BaseModel):
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"""
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LLM 模型配置
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每个提供商可以配置多个模型
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"""
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__tablename__ = "ai_llm_model"
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provider_id = Column(String(21), nullable=False, index=True, comment="所属提供商ID（逻辑外键关联ai_llm_provider）")
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model_name = Column(String(100), nullable=False, comment="模型名称（API 调用时使用）")
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display_name = Column(String(100), nullable=False, comment="显示名称")
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model_type = Column(String(20), default="chat", comment="模型类型: chat/completion/embedding/rerank")
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max_tokens = Column(Integer, default=4096, comment="最大 Token 数")
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context_window = Column(Integer, default=4096, comment="上下文窗口大小")
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default_temperature = Column(Float, default=0.7, comment="默认温度参数")
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default_top_p = Column(Float, default=1.0, comment="默认 top_p 参数")
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input_price = Column(Numeric(10, 6), default=0, comment="输入价格（每 1K tokens）")
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output_price = Column(Numeric(10, 6), default=0, comment="输出价格（每 1K tokens）")
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is_active = Column(Boolean, default=True, comment="是否启用")
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supports_vision = Column(Boolean, default=False, comment="是否支持视觉（图片输入）")
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supports_function_call = Column(Boolean, default=False, comment="是否支持函数调用")
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supports_streaming = Column(Boolean, default=True, comment="是否支持流式输出")
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@@ -0,0 +1,40 @@
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"""
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Prompt 模板模型
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"""
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from sqlalchemy import Column, String, Text, Integer, Boolean, JSON
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from app.base_model import BaseModel
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class PromptTemplate(BaseModel):
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"""
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Prompt 模板
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用于管理和复用 Prompt
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"""
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__tablename__ = "ai_prompt_template"
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name = Column(String(100), nullable=False, comment="模板名称")
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code = Column(String(100), unique=True, nullable=False, comment="模板编码")
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category = Column(String(20), default="system", comment="分类: system/user/assistant/few_shot")
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description = Column(Text, default="", comment="描述")
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content = Column(Text, nullable=False, comment="模板内容")
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variables = Column(JSON, default=list, comment="变量定义列表")
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tags = Column(JSON, default=list, comment="标签列表")
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is_public = Column(Boolean, default=False, comment="是否公开")
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usage_count = Column(Integer, default=0, comment="使用次数")
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def render(self, variables: dict) -> str:
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"""
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渲染模板
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Args:
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variables: 变量字典
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Returns:
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渲染后的内容
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"""
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content = self.content or ""
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for key, value in variables.items():
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content = content.replace(f'{{{{{key}}}}}', str(value))
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return content
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@@ -0,0 +1,42 @@
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"""
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LLM 提供商模型
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"""
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from sqlalchemy import Column, String, Text, Boolean, Integer
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from app.base_model import BaseModel
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class LLMProvider(BaseModel):
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"""
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LLM 提供商配置
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支持的提供商类型：
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- openai: OpenAI (GPT-3.5, GPT-4, etc.)
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- claude: Anthropic Claude
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- qwen: 阿里通义千问
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- ollama: 本地 Ollama
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- azure_openai: Azure OpenAI
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- zhipu: 智谱 AI
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- moonshot: Moonshot (Kimi)
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- deepseek: DeepSeek
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"""
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__tablename__ = "ai_llm_provider"
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name = Column(String(100), nullable=False, comment="提供商名称")
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provider_type = Column(String(50), nullable=False, comment="提供商类型")
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api_key = Column(Text, default="", comment="API Key（加密存储）")
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api_base = Column(String(500), default="", comment="API 地址（可选，用于自定义端点）")
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api_version = Column(String(50), default="", comment="API 版本（Azure OpenAI 专用）")
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ollama_host = Column(String(200), default="http://localhost:11434", comment="Ollama 服务地址")
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is_active = Column(Boolean, default=True, comment="是否启用")
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description = Column(Text, default="", comment="描述")
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quota_limit = Column(Integer, default=0, comment="配额限制（0 表示无限制）")
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quota_used = Column(Integer, default=0, comment="已使用配额")
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def get_api_key_masked(self) -> str:
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"""获取脱敏的 API Key"""
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if not self.api_key:
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return ''
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if len(self.api_key) <= 8:
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return '*' * len(self.api_key)
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return self.api_key[:4] + '*' * (len(self.api_key) - 8) + self.api_key[-4:]
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@@ -0,0 +1,83 @@
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"""
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AI 工作流模型
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"""
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from sqlalchemy import Column, String, Text, Integer, Boolean, DateTime, JSON, Index
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from app.base_model import BaseModel
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class AIWorkflow(BaseModel):
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"""
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AI 工作流定义
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独立于 AIApp 的工作流定义，可以被多个应用引用
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"""
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__tablename__ = "ai_workflow"
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application_id = Column(String(21), nullable=True, index=True, comment="所属应用ID（逻辑外键关联core_application）")
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is_global = Column(Boolean, default=False, comment="是否在子应用中可见")
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name = Column(String(100), nullable=False, comment="工作流名称")
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code = Column(String(100), unique=True, nullable=False, comment="工作流编码")
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workflow_type = Column(String(30), default="general", comment="工作流类型: general/application/form/report/data_process/automation")
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description = Column(Text, default="", comment="描述")
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status = Column(String(20), default="draft", comment="状态: draft/published/disabled")
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version = Column(Integer, default=1, comment="当前草稿版本号")
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published_version = Column(Integer, nullable=True, comment="已发布的版本号")
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published_at = Column(DateTime, nullable=True, comment="最后发布时间")
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published_definition = Column(JSON, default=dict, comment="已发布版本的工作流定义")
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definition = Column(JSON, default=dict, comment="工作流定义（草稿）")
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input_variables = Column(JSON, default=list, comment="输入变量定义")
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output_variables = Column(JSON, default=list, comment="输出变量定义")
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run_count = Column(Integer, default=0, comment="运行次数")
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success_count = Column(Integer, default=0, comment="成功次数")
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class AIWorkflowVersion(BaseModel):
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"""
|
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AI 工作流版本历史
|
||||
|
||||
每次发布时创建一条版本记录
|
||||
"""
|
||||
__tablename__ = "ai_workflow_version"
|
||||
|
||||
workflow_id = Column(String(21), nullable=False, index=True, comment="工作流ID（逻辑外键关联ai_workflow）")
|
||||
version = Column(Integer, nullable=False, comment="版本号")
|
||||
definition = Column(JSON, default=dict, comment="该版本的工作流定义")
|
||||
description = Column(Text, default="", comment="版本说明")
|
||||
published_by_id = Column(String(21), nullable=True, comment="发布人ID（逻辑外键关联core_user）")
|
||||
published_at = Column(DateTime, nullable=True, comment="发布时间")
|
||||
run_count = Column(Integer, default=0, comment="运行次数")
|
||||
success_count = Column(Integer, default=0, comment="成功次数")
|
||||
|
||||
|
||||
class AIWorkflowRun(BaseModel):
|
||||
"""
|
||||
AI 工作流运行记录
|
||||
"""
|
||||
__tablename__ = "ai_workflow_run"
|
||||
|
||||
workflow_id = Column(String(21), nullable=False, index=True, comment="工作流ID（逻辑外键关联ai_workflow）")
|
||||
app_id = Column(String(21), nullable=True, index=True, comment="关联应用ID（逻辑外键关联ai_app）")
|
||||
conversation_id = Column(String(21), nullable=True, comment="关联对话ID（逻辑外键关联ai_conversation）")
|
||||
user_id = Column(String(21), nullable=True, index=True, comment="执行用户ID（逻辑外键关联core_user）")
|
||||
status = Column(String(20), default="pending", comment="状态: pending/running/waiting/completed/failed/stopped")
|
||||
trigger_type = Column(String(30), default="api", comment="触发来源: editor_draft/editor_published/agent/api/form_button")
|
||||
use_draft = Column(Boolean, default=False, comment="是否使用草稿定义执行")
|
||||
workflow_version = Column(Integer, nullable=True, comment="执行时发布版本号，草稿运行为空")
|
||||
definition_snapshot = Column(JSON, default=dict, comment="运行开始时的工作流定义快照")
|
||||
inputs = Column(JSON, default=dict, comment="输入数据")
|
||||
outputs = Column(JSON, default=dict, comment="输出数据")
|
||||
execution_log = Column(JSON, default=list, comment="执行日志")
|
||||
current_node_id = Column(String(100), default="", comment="当前节点 ID")
|
||||
waiting_config = Column(JSON, default=dict, comment="等待用户输入的配置")
|
||||
error_message = Column(Text, default="", comment="错误信息")
|
||||
total_tokens = Column(Integer, default=0, comment="总 Token 数")
|
||||
total_steps = Column(Integer, default=0, comment="总步骤数")
|
||||
elapsed_time = Column(Integer, default=0, comment="总耗时（毫秒）")
|
||||
started_at = Column(DateTime, nullable=True, comment="开始时间")
|
||||
completed_at = Column(DateTime, nullable=True, comment="完成时间")
|
||||
|
||||
__table_args__ = (
|
||||
Index("ix_ai_workflow_run_workflow_status", "workflow_id", "status"),
|
||||
Index("ix_ai_workflow_run_user_status", "user_id", "status"),
|
||||
)
|
||||
Reference in New Issue
Block a user