"""
知识条目模型 — Agent 执行经验的结构化沉淀
"""
import uuid
from datetime import datetime
from sqlalchemy import Column, String, Text, Integer, DateTime, Boolean, JSON, Float, Index
from app.core.database import Base


class KnowledgeEntry(Base):
    """从 Agent 执行日志中提取的可复用知识条目"""
    __tablename__ = "knowledge_entries"

    id = Column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()))
    title = Column(String(500), nullable=False, comment="知识标题（一句话概括）")
    category = Column(String(30), nullable=False, index=True,
                      comment="类别: bug_fix/best_practice/workaround/optimization/insight")
    tags = Column(JSON, nullable=True, comment="标签列表: ['mysql','deadlock','retry']")

    # 知识内容
    situation = Column(Text, nullable=True, comment="适用场景")
    solution = Column(Text, nullable=True, comment="解决方案")
    caveats = Column(Text, nullable=True, comment="注意事项/踩坑记录")

    # 来源追溯
    source_execution_ids = Column(JSON, nullable=True, comment="原始执行日志ID列表")
    source_agent_name = Column(String(200), nullable=True, comment="来源 Agent 名称")
    source_model = Column(String(100), nullable=True, comment="来源模型")

    # RAG 检索
    embedding_text = Column(Text, nullable=True, comment="用于生成 embedding 的合并文本")
    embedding = Column(Text, nullable=True, comment="JSON 序列化的 embedding 向量")

    # 效果度量
    retrieval_count = Column(Integer, default=0, comment="被检索次数")
    success_rate = Column(Float, nullable=True, comment="应用成功率")

    # 提取信息
    extracted_by = Column(String(100), nullable=True, comment="提取方式: llm_auto/manual/reviewed")
    confidence = Column(Float, default=0.5, comment="提取置信度(0-1)")

    is_active = Column(Boolean, default=True, comment="是否启用")
    created_at = Column(DateTime, default=datetime.now, comment="创建时间")
    updated_at = Column(DateTime, default=datetime.now, onupdate=datetime.now, comment="更新时间")

    __table_args__ = (
        Index("ix_knowledge_entries_category", "category"),
        Index("ix_knowledge_entries_active", "is_active"),
    )

    def __repr__(self):
        return f"<KnowledgeEntry(id={self.id}, title={self.title}, category={self.category})>"

    def to_dict(self) -> dict:
        return {
            "id": self.id,
            "title": self.title,
            "category": self.category,
            "tags": self.tags or [],
            "situation": self.situation,
            "solution": self.solution,
            "caveats": self.caveats,
            "source_agent_name": self.source_agent_name,
            "retrieval_count": self.retrieval_count,
            "confidence": self.confidence,
            "created_at": self.created_at.isoformat() if self.created_at else None,
        }