"""
Agent 执行日志模型 — 结构化记录每次 Agent 执行的完整信息
用于知识自进化系统的数据基础
"""
from sqlalchemy import Column, String, Text, Integer, DateTime, JSON, Float, Boolean
from sqlalchemy.dialects.mysql import CHAR
from app.core.database import Base
import uuid
from datetime import datetime


class AgentExecutionLog(Base):
    """Agent 每次执行的完整结构化日志"""
    __tablename__ = "agent_execution_logs"

    id = Column(CHAR(36), primary_key=True, default=lambda: str(uuid.uuid4()), comment="日志ID")
    agent_id = Column(String(36), nullable=True, index=True, comment="Agent ID")
    agent_name = Column(String(200), nullable=True, comment="Agent 名称")
    goal_id = Column(String(36), nullable=True, index=True, comment="关联 Goal ID")
    task_id = Column(String(36), nullable=True, index=True, comment="关联 Task ID")
    user_id = Column(String(36), nullable=True, index=True, comment="用户 ID")
    session_id = Column(String(100), nullable=True, comment="会话标识")

    # 输入/输出
    input_text = Column(Text, nullable=True, comment="用户输入文本")
    output_text = Column(Text, nullable=True, comment="Agent 输出文本")
    output_truncated = Column(Boolean, default=False, comment="输出是否被截断")

    # 执行结果
    success = Column(Boolean, default=True, comment="是否成功")
    error_message = Column(Text, nullable=True, comment="错误信息")

    # 性能指标
    latency_ms = Column(Integer, nullable=True, comment="总耗时(ms)")
    iterations_used = Column(Integer, default=0, comment="ReAct 迭代次数")
    tool_calls_made = Column(Integer, default=0, comment="工具调用总次数")

    # 结构化明细（JSON）
    tool_chain = Column(JSON, nullable=True, comment="工具调用链: [{tool_name, input, output, duration_ms}]")
    llm_calls = Column(JSON, nullable=True, comment="LLM调用明细: [{model, prompt_tokens, completion_tokens, latency_ms}]")
    steps = Column(JSON, nullable=True, comment="执行步骤详情（精简版）")

    # 模型信息
    model = Column(String(100), nullable=True, comment="使用的模型")
    provider = Column(String(50), nullable=True, comment="模型提供商")

    # 用户反馈（后续补充）
    user_rating = Column(Integer, nullable=True, comment="用户评分(1-5)")
    user_feedback = Column(Text, nullable=True, comment="用户反馈文本")

    # 知识提取标记
    knowledge_extracted = Column(Boolean, default=False, comment="是否已提取知识")

    created_at = Column(DateTime, default=datetime.now, comment="创建时间")

    def __repr__(self):
        return f"<AgentExecutionLog(id={self.id}, agent={self.agent_name}, success={self.success})>"