# =============================================================================
# 企微IT智能服务台 — 分诊会话模型
# =============================================================================
# 说明：对应数据库 triage_sessions 表
# 存储 AI 分诊的完整会话记录，包括分诊步骤、收集的上下文、路由结果等。
# =============================================================================

import uuid
from datetime import datetime
from typing import Any, Dict, List, Optional

from sqlalchemy import DateTime, Float, Index, Integer, JSON, String, Text
from sqlalchemy.orm import Mapped, mapped_column

from app.database import Base


class TriageSession(Base):
    """分诊会话模型 — 对应 triage_sessions 表。

    存储员工发起的 AI 分诊全流程数据，从发起分诊到最终路由。

    Attributes:
        id: 分诊会话ID（UUID）
        conversation_id: 关联的企微会话ID
        user_id: 员工企微UserID
        user_name: 员工姓名
        user_dept: 员工部门
        user_level: 员工IT技能等级
        device_info: 设备信息
        request_title: 问题标题
        request_content: 问题原文
        source: 来源渠道（wecom_h5 / api / other）
        problem_type: AI识别的问题类型（硬件/软件/网络/安全/账号/其他）
        problem_category: AI识别的问题分类
        confidence: AI置信度（0.0-1.0）
        urgency: 紧急度（high/medium/low）
        suggested_route: AI建议路由（ai_self/human/auto_approval）
        matched_knowledge: 匹配到的知识条目
        match_score: 知识匹配分数
        context_tags: 上下文标签列表（JSON数组）
        triage_steps: 分诊步骤数据（JSON数组）
        collected_context: 已收集的上下文列表（JSON数组）
        status: 分诊状态（pending/triaging/routed/skipped/timeout）
        route_action: 最终路由动作（ai_self/human/auto_approval/skip）
        route_note: 路由备注
        operator_id: 操作坐席ID
        operated_at: 操作时间
        created_at: 创建时间
        updated_at: 更新时间
    """

    __tablename__ = "triage_sessions"

    # 主键
    id: Mapped[str] = mapped_column(
        String(36),
        primary_key=True,
        default=lambda: str(uuid.uuid4()),
    )

    # 会话关联
    conversation_id: Mapped[str] = mapped_column(
        String(36),
        nullable=False,
        comment="关联的企微会话ID",
    )

    # 用户信息
    user_id: Mapped[str] = mapped_column(
        String(100),
        nullable=False,
        comment="员工企微UserID",
    )
    user_name: Mapped[Optional[str]] = mapped_column(
        String(100),
        nullable=True,
        comment="员工姓名",
    )
    user_dept: Mapped[Optional[str]] = mapped_column(
        String(100),
        nullable=True,
        comment="员工部门",
    )
    user_level: Mapped[Optional[str]] = mapped_column(
        String(20),
        nullable=True,
        comment="员工IT技能等级",
    )
    device_info: Mapped[Optional[str]] = mapped_column(
        String(200),
        nullable=True,
        comment="设备信息",
    )

    # 问题描述
    request_title: Mapped[str] = mapped_column(
        String(200),
        nullable=False,
        comment="问题标题",
    )
    request_content: Mapped[str] = mapped_column(
        Text,
        nullable=False,
        comment="问题原文",
    )
    source: Mapped[str] = mapped_column(
        String(50),
        nullable=False,
        default="wecom_h5",
        comment="来源渠道",
    )

    # AI 分诊分析结果
    problem_type: Mapped[Optional[str]] = mapped_column(
        String(50),
        nullable=True,
        comment="问题类型：硬件/软件/网络/安全/账号/其他",
    )
    problem_category: Mapped[Optional[str]] = mapped_column(
        String(100),
        nullable=True,
        comment="问题分类",
    )
    confidence: Mapped[Optional[float]] = mapped_column(
        Float,
        nullable=True,
        comment="AI置信度（0.0-1.0）",
    )
    urgency: Mapped[str] = mapped_column(
        String(20),
        nullable=False,
        default="medium",
        comment="紧急度：high/medium/low",
    )
    suggested_route: Mapped[Optional[str]] = mapped_column(
        String(50),
        nullable=True,
        comment="AI建议路由：ai_self/human/auto_approval",
    )
    matched_knowledge: Mapped[Optional[str]] = mapped_column(
        String(500),
        nullable=True,
        comment="匹配到的知识条目",
    )
    match_score: Mapped[Optional[float]] = mapped_column(
        Float,
        nullable=True,
        comment="知识匹配分数",
    )
    context_tags: Mapped[List[str]] = mapped_column(
        JSON,
        nullable=False,
        default=list,
        comment="上下文标签列表",
    )

    # 分诊步骤数据
    triage_steps: Mapped[List[Dict[str, Any]]] = mapped_column(
        JSON,
        nullable=False,
        default=list,
        comment="分诊步骤数据：[{question, options:[{label, probability}]}]",
    )
    collected_context: Mapped[List[str]] = mapped_column(
        JSON,
        nullable=False,
        default=list,
        comment="已收集的上下文列表",
    )

    # 状态与路由
    status: Mapped[str] = mapped_column(
        String(30),
        nullable=False,
        default="pending",
        comment="分诊状态：pending/triaging/routed/skipped/timeout",
    )
    route_action: Mapped[Optional[str]] = mapped_column(
        String(50),
        nullable=True,
        comment="最终路由动作：ai_self/human/auto_approval/skip",
    )
    route_note: Mapped[Optional[str]] = mapped_column(
        Text,
        nullable=True,
        comment="路由备注",
    )
    operator_id: Mapped[Optional[str]] = mapped_column(
        String(100),
        nullable=True,
        comment="操作坐席ID",
    )
    operated_at: Mapped[Optional[datetime]] = mapped_column(
        DateTime(timezone=True),
        nullable=True,
        comment="操作时间",
    )

    # 时间戳
    created_at: Mapped[datetime] = mapped_column(
        DateTime(timezone=True),
        nullable=False,
        default=datetime.now,
        comment="创建时间",
    )
    updated_at: Mapped[datetime] = mapped_column(
        DateTime(timezone=True),
        nullable=False,
        default=datetime.now,
        onupdate=datetime.now,
        comment="更新时间",
    )

    # 索引
    __table_args__ = (
        Index("idx_triage_status", "status"),
        Index("idx_triage_urgency", "urgency"),
        Index("idx_triage_conversation", "conversation_id"),
        Index("idx_triage_created", "created_at"),
        Index("idx_triage_user", "user_id"),
    )

    def __repr__(self) -> str:
        """分诊会话对象的字符串表示。"""
        return (
            f"<TriageSession(id={self.id}, user={self.user_id}, "
            f"status={self.status}, urgency={self.urgency})>"
        )