WIP-CHECKPOINT[auth-refactor]: 固化工程师崩溃前部分成果 + 同树其他未提交WIP（仅源码，不含密钥/二进制）-- 待重激活工程师续作
This commit is contained in:
Simon
@@ -22,6 +22,21 @@ from app.models.config_change_log import ConfigChangeLog
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from app.models.role import Role
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from app.models.user_role import UserRole
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from app.models.role_mapping_rule import RoleMappingRule
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from app.models.conversation_evaluation import ConversationEvaluation
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from app.models.audit_log import AuditLog
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from app.models.conversation_annotation import ConversationAnnotation # P2-10 会话标注
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from app.models.knowledge_suggestion import KnowledgeSuggestion # P2-13 知识库自动迭代
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from app.models.knowledge_base import KnowledgeBase
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# 阶段5 自动化闭环模型
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from app.models.automation import (
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AutoSession,
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AutoAction,
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ApprovalTicket,
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ScenarioConfig,
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RuleVersion,
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ActionLog,
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MappingCache,
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)
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# 所有模型类的列表，方便遍历
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__all__ = [
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"Conversation",
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@@ -40,4 +55,16 @@ __all__ = [
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"Role",
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"UserRole",
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"RoleMappingRule",
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"ConversationEvaluation",
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"AuditLog",
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"ConversationAnnotation",
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"KnowledgeSuggestion",
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"KnowledgeBase",
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"AutoSession",
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"AutoAction",
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"ApprovalTicket",
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"ScenarioConfig",
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"RuleVersion",
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"ActionLog",
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"MappingCache",
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]
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@@ -0,0 +1,313 @@
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# =============================================================================
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# 企微IT智能服务台 — 阶段5 自动化闭环 数据模型
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# =============================================================================
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# 说明：自动化引擎相关表，表名统一前缀 auto_。
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# 模型清单：
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# 1. AutoSession — 自动化处置会话（生命周期/状态机）
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# 2. AutoAction — 单个处置动作（与场景动作计划对应）
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# 3. ApprovalTicket — 审批单（高危动作 / 员工二次确认）
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# 4. ScenarioConfig — 场景配置（开关 + 触发条件 + 动作 + 审批策略）
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# 5. RuleVersion — 规则版本（P1 灰度发布）
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# 6. ActionLog — 外部调用审计日志（出入参全记录）
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# 7. MappingCache — 终端/员工映射缓存（联软>eHR，TTL）
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#
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# 风格：沿用项目既有模型写法（SQLAlchemy 2.0 Mapped / mapped_column + 注释）。
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# 关联：AutoSession 与工单(conversation)为「弱关联」（仅存 conversation_id，
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# 不建外键约束，便于阶段5 独立演进）。
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# =============================================================================
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import uuid
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from datetime import datetime
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from typing import Optional
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from sqlalchemy import JSON, Boolean, DateTime, Float, Integer, String, Text
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from sqlalchemy.orm import Mapped, mapped_column
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from app.database import Base
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def _uuid() -> str:
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"""生成 UUID 字符串主键（兼容 PostgreSQL 与 SQLite）。"""
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return str(uuid.uuid4())
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# =============================================================================
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# 1. 自动化处置会话
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# =============================================================================
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class AutoSession(Base):
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"""自动化处置会话 — 对应 auto_sessions 表。
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一个员工的一次自动化处置诉求对应一个会话，贯穿「意图识别→动作编排→
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执行/审批→处置成功→静默关单」全过程。
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Attributes:
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id: 会话唯一ID
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conversation_id: 关联工单ID（弱关联，可空）
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employee_id: 发起员工企微 UserID
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agent_id: 接管坐席ID（转人工后填入）
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scenario_key: 命中场景（password_reset/software_install/virus_dispose/terminal_locate）
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status: 会话状态机（created/running/paused/resolved/closed/handoff/error）
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mode: 执行模式（plan_only/real_exec）
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confidence: 意图识别置信度（0-1）
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intent: 意图识别原始结果（JSON）
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current_action_id: 当前正在执行的动作ID
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title: 会话标题（展示用）
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auto_close_at: 静默关单触发时间（写入 Redis TTL 同步）
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resolved_at: 处置成功时间
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closed_by: 关单/接管操作人
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meta: 扩展上下文（原始消息、映射结果等）
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"""
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__tablename__ = "auto_sessions"
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id: Mapped[str] = mapped_column(String(36), primary_key=True, default=_uuid)
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conversation_id: Mapped[Optional[str]] = mapped_column(String(36), nullable=True, index=True)
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employee_id: Mapped[str] = mapped_column(String(64), nullable=False, index=True)
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agent_id: Mapped[Optional[str]] = mapped_column(String(64), nullable=True, index=True)
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scenario_key: Mapped[Optional[str]] = mapped_column(String(64), nullable=True, index=True)
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status: Mapped[str] = mapped_column(String(20), nullable=False, default="created", index=True)
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mode: Mapped[str] = mapped_column(String(20), nullable=False, default="real_exec")
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confidence: Mapped[float] = mapped_column(Float, nullable=False, default=0.0)
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intent: Mapped[Optional[dict]] = mapped_column(JSON, nullable=True)
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current_action_id: Mapped[Optional[str]] = mapped_column(String(36), nullable=True)
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title: Mapped[str] = mapped_column(String(256), nullable=False, default="")
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auto_close_at: Mapped[Optional[datetime]] = mapped_column(DateTime(timezone=True), nullable=True)
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resolved_at: Mapped[Optional[datetime]] = mapped_column(DateTime(timezone=True), nullable=True)
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closed_by: Mapped[Optional[str]] = mapped_column(String(64), nullable=True)
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meta: Mapped[Optional[dict]] = mapped_column(JSON, nullable=True)
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created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), nullable=False, default=datetime.now)
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updated_at: Mapped[datetime] = mapped_column(
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DateTime(timezone=True), nullable=False, default=datetime.now, onupdate=datetime.now
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)
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def __repr__(self) -> str:
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return f"<AutoSession(id={self.id}, scenario={self.scenario_key}, status={self.status})>"
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# =============================================================================
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# 2. 处置动作
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# =============================================================================
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class AutoAction(Base):
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"""单个处置动作 — 对应 auto_actions 表。
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每个动作对应场景动作计划中的一步，由 action_registry 的适配器真正执行。
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Attributes:
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id: 动作ID
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session_id: 所属会话
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action_index: 动作在计划中的顺序
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action_type: 语义动作类型（password_reset_guide/software_install/virus_quarantine/terminal_locate...）
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adapter: 执行适配器（huorong/lianruan/ehr/atrust/...）
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risk_level: 风险等级（read/low/high）
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title/description: 展示信息
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status: 动作状态机
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payload: 动作入参
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result: 动作执行结果
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error: 错误信息
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approved_by/approved_at: 审批人/时间
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"""
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__tablename__ = "auto_actions"
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id: Mapped[str] = mapped_column(String(36), primary_key=True, default=_uuid)
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session_id: Mapped[str] = mapped_column(String(36), nullable=False, index=True)
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action_index: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
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action_type: Mapped[str] = mapped_column(String(64), nullable=False, default="")
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adapter: Mapped[str] = mapped_column(String(32), nullable=False, default="")
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risk_level: Mapped[str] = mapped_column(String(16), nullable=False, default="read")
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title: Mapped[str] = mapped_column(String(256), nullable=False, default="")
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description: Mapped[str] = mapped_column(Text, nullable=False, default="")
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status: Mapped[str] = mapped_column(String(20), nullable=False, default="pending", index=True)
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payload: Mapped[Optional[dict]] = mapped_column(JSON, nullable=True)
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result: Mapped[Optional[dict]] = mapped_column(JSON, nullable=True)
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error: Mapped[Optional[str]] = mapped_column(Text, nullable=True)
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approved_by: Mapped[Optional[str]] = mapped_column(String(64), nullable=True)
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approved_at: Mapped[Optional[datetime]] = mapped_column(DateTime(timezone=True), nullable=True)
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created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), nullable=False, default=datetime.now)
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updated_at: Mapped[datetime] = mapped_column(
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DateTime(timezone=True), nullable=False, default=datetime.now, onupdate=datetime.now
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)
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def __repr__(self) -> str:
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return f"<AutoAction(id={self.id}, type={self.action_type}, status={self.status})>"
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# =============================================================================
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# 3. 审批单
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# =============================================================================
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class ApprovalTicket(Base):
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"""审批单 — 对应 auto_approval_tickets 表。
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高危动作需坐席审批，或写操作需员工 H5 二次确认时生成审批单。
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Attributes:
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id: 审批单ID
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action_id: 关联动作
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session_id: 关联会话
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approver_id: 审批人（坐席/员工）
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channel: 审批渠道（agent/h5）
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status: 审批状态（pending/approved/rejected/expired）
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reason: 申请理由
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decision_note: 审批意见
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decided_at: 决定时间
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"""
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__tablename__ = "auto_approval_tickets"
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id: Mapped[str] = mapped_column(String(36), primary_key=True, default=_uuid)
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action_id: Mapped[str] = mapped_column(String(36), nullable=False, index=True)
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session_id: Mapped[str] = mapped_column(String(36), nullable=False, index=True)
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approver_id: Mapped[Optional[str]] = mapped_column(String(64), nullable=True)
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channel: Mapped[str] = mapped_column(String(16), nullable=False, default="agent")
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status: Mapped[str] = mapped_column(String(20), nullable=False, default="pending", index=True)
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reason: Mapped[Optional[str]] = mapped_column(Text, nullable=True)
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decision_note: Mapped[Optional[str]] = mapped_column(Text, nullable=True)
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decided_at: Mapped[Optional[datetime]] = mapped_column(DateTime(timezone=True), nullable=True)
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created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), nullable=False, default=datetime.now)
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def __repr__(self) -> str:
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return f"<ApprovalTicket(id={self.id}, action={self.action_id}, status={self.status})>"
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# =============================================================================
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# 4. 场景配置
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# =============================================================================
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class ScenarioConfig(Base):
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"""场景配置 — 对应 auto_scenario_configs 表。
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本期（Q5）管理后台结构化简易配置：场景开关 + 触发条件 + 动作 + 审批策略。
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编排引擎列为 P2，本期动作计划为静态模板。
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Attributes:
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scenario_key: 场景键（唯一，password_reset/software_install/virus_dispose/terminal_locate）
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name/description: 展示信息
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enabled: 是否启用
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trigger_conditions: 触发条件（{"intents":[],"keywords":[]}）
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actions: 动作计划（有序列表，每项 {action_type, adapter, risk_level, params}）
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approval_strategy: 审批策略（{"read":"auto","low":"auto","high":"approval"}）
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current_version_id: 当前生效版本ID
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"""
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__tablename__ = "auto_scenario_configs"
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id: Mapped[str] = mapped_column(String(36), primary_key=True, default=_uuid)
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scenario_key: Mapped[str] = mapped_column(String(64), nullable=False, unique=True, index=True)
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name: Mapped[str] = mapped_column(String(128), nullable=False, default="")
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description: Mapped[str] = mapped_column(Text, nullable=False, default="")
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enabled: Mapped[bool] = mapped_column(Boolean, nullable=False, default=True)
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trigger_conditions: Mapped[Optional[dict]] = mapped_column(JSON, nullable=True)
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actions: Mapped[Optional[list]] = mapped_column(JSON, nullable=True)
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approval_strategy: Mapped[Optional[dict]] = mapped_column(JSON, nullable=True)
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current_version_id: Mapped[Optional[str]] = mapped_column(String(36), nullable=True)
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created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), nullable=False, default=datetime.now)
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updated_at: Mapped[datetime] = mapped_column(
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DateTime(timezone=True), nullable=False, default=datetime.now, onupdate=datetime.now
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)
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def __repr__(self) -> str:
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return f"<ScenarioConfig(key={self.scenario_key}, enabled={self.enabled})>"
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# =============================================================================
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# 5. 规则版本（P1 灰度）
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# =============================================================================
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class RuleVersion(Base):
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"""规则版本 — 对应 auto_rule_versions 表。
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支持场景配置的版本化与灰度发布（P1）：每次新建/修改生成快照，
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canary_percent 控制灰度比例，status 控制 draft/published/archived。
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Attributes:
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scenario_key: 所属场景
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version: 版本号（同场景内递增）
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content: 配置快照（actions + approval_strategy + trigger_conditions）
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status: 版本状态（draft/published/archived）
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canary_percent: 灰度比例（0-100，100 表示全量）
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created_by: 创建人
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remark: 版本说明
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"""
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__tablename__ = "auto_rule_versions"
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id: Mapped[str] = mapped_column(String(36), primary_key=True, default=_uuid)
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scenario_key: Mapped[str] = mapped_column(String(64), nullable=False, index=True)
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version: Mapped[int] = mapped_column(Integer, nullable=False, default=1)
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content: Mapped[Optional[dict]] = mapped_column(JSON, nullable=True)
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status: Mapped[str] = mapped_column(String(20), nullable=False, default="draft", index=True)
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canary_percent: Mapped[int] = mapped_column(Integer, nullable=False, default=100)
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created_by: Mapped[Optional[str]] = mapped_column(String(64), nullable=True)
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remark: Mapped[str] = mapped_column(Text, nullable=False, default="")
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created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), nullable=False, default=datetime.now)
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def __repr__(self) -> str:
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return f"<RuleVersion(key={self.scenario_key}, v={self.version}, status={self.status})>"
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# =============================================================================
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# 6. 外部调用审计日志
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# =============================================================================
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class ActionLog(Base):
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"""外部调用审计日志 — 对应 auto_action_logs 表。
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所有外部系统调用的出入参全量落表，满足审计与排障需求。
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Attributes:
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session_id: 关联会话
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action_id: 关联动作（可选）
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employee_id: 关联员工（可选）
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event: 事件名（如 huorong.isolate / dify.intent / lianruan.query）
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direction: 方向（in/out）
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system: 外部系统（huorong/lianruan/dify/ragflow/ehr/internal）
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request/response: 出入参（脱敏后）
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status: 状态（success/error/http_xxx）
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latency_ms: 耗时
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error: 错误
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"""
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__tablename__ = "auto_action_logs"
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id: Mapped[str] = mapped_column(String(36), primary_key=True, default=_uuid)
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session_id: Mapped[Optional[str]] = mapped_column(String(36), nullable=True, index=True)
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action_id: Mapped[Optional[str]] = mapped_column(String(36), nullable=True, index=True)
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employee_id: Mapped[Optional[str]] = mapped_column(String(64), nullable=True)
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event: Mapped[str] = mapped_column(String(128), nullable=False, default="")
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direction: Mapped[str] = mapped_column(String(8), nullable=False, default="out")
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system: Mapped[str] = mapped_column(String(32), nullable=False, default="internal")
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request: Mapped[Optional[dict]] = mapped_column(JSON, nullable=True)
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response: Mapped[Optional[dict]] = mapped_column(JSON, nullable=True)
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status: Mapped[str] = mapped_column(String(32), nullable=False, default="")
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latency_ms: Mapped[Optional[int]] = mapped_column(Integer, nullable=True)
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error: Mapped[Optional[str]] = mapped_column(Text, nullable=True)
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created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), nullable=False, default=datetime.now)
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def __repr__(self) -> str:
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return f"<ActionLog(event={self.event}, system={self.system}, status={self.status})>"
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# =============================================================================
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# 7. 终端/员工映射缓存
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# =============================================================================
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class MappingCache(Base):
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"""映射缓存 — 对应 auto_mapping_cache 表。
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联软(主)解析员工→终端结果缓存，TTL 过期后回退 EHR 兜底并刷新。
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避免每次处置都打联软，降低外部依赖压力。
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Attributes:
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employee_id: 员工ID
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source: 映射源（lianruan/ehr）
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mapped_data: 映射结果（终端列表/部门/资产等）
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expires_at: 过期时间
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"""
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__tablename__ = "auto_mapping_cache"
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id: Mapped[str] = mapped_column(String(36), primary_key=True, default=_uuid)
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employee_id: Mapped[str] = mapped_column(String(64), nullable=False, index=True)
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source: Mapped[str] = mapped_column(String(32), nullable=False, default="lianruan")
|
||||
mapped_data: Mapped[Optional[dict]] = mapped_column(JSON, nullable=True)
|
||||
expires_at: Mapped[Optional[datetime]] = mapped_column(DateTime(timezone=True), nullable=True)
|
||||
created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), nullable=False, default=datetime.now)
|
||||
|
||||
def __repr__(self) -> str:
|
||||
return f"<MappingCache(employee={self.employee_id}, source={self.source})>"
|
||||
@@ -0,0 +1,100 @@
|
||||
# =============================================================================
|
||||
# 企微IT智能服务台 — 会话标注模型
|
||||
# =============================================================================
|
||||
# 说明：对应数据库 conversation_annotations 表
|
||||
# 存储坐席对AI回复的标注数据，用于模型优化
|
||||
# =============================================================================
|
||||
|
||||
import uuid
|
||||
from datetime import datetime
|
||||
|
||||
from sqlalchemy import DateTime, Index, Integer, String, Text
|
||||
from sqlalchemy.orm import Mapped, mapped_column
|
||||
|
||||
from app.database import Base
|
||||
|
||||
|
||||
class ConversationAnnotation(Base):
|
||||
"""会话标注模型 — 对应 conversation_annotations 表。
|
||||
|
||||
存储坐席对AI回复的标注数据，用于持续优化AI能力。
|
||||
|
||||
Attributes:
|
||||
id: 标注ID（UUID）
|
||||
conversation_id: 会话ID
|
||||
agent_id: 坐席ID
|
||||
message_id: 被标注的消息ID（AI回复）
|
||||
feedback: 反馈类型（useful=有用/useless=无用）
|
||||
comment: 备注（可选）
|
||||
created_at: 创建时间
|
||||
"""
|
||||
|
||||
# 表名
|
||||
__tablename__ = "conversation_annotations"
|
||||
|
||||
# --------------------------------------------------------------------------
|
||||
# 字段定义
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
# 主键
|
||||
id: Mapped[str] = mapped_column(
|
||||
String(36),
|
||||
primary_key=True,
|
||||
default=lambda: str(uuid.uuid4()),
|
||||
)
|
||||
|
||||
# 会话ID
|
||||
conversation_id: Mapped[str] = mapped_column(
|
||||
String(36),
|
||||
nullable=False,
|
||||
index=True,
|
||||
comment="会话ID",
|
||||
)
|
||||
|
||||
# 坐席ID
|
||||
agent_id: Mapped[str] = mapped_column(
|
||||
String(36),
|
||||
nullable=False,
|
||||
comment="坐席ID",
|
||||
)
|
||||
|
||||
# 被标注的消息ID
|
||||
message_id: Mapped[str] = mapped_column(
|
||||
String(36),
|
||||
nullable=False,
|
||||
comment="被标注的消息ID",
|
||||
)
|
||||
|
||||
# 反馈类型
|
||||
feedback: Mapped[str] = mapped_column(
|
||||
String(20),
|
||||
nullable=False,
|
||||
comment="useful=有用/useless=无用",
|
||||
)
|
||||
|
||||
# 备注
|
||||
comment: Mapped[str] = mapped_column(
|
||||
Text,
|
||||
nullable=True,
|
||||
comment="备注",
|
||||
)
|
||||
|
||||
# 创建时间
|
||||
created_at: Mapped[datetime] = mapped_column(
|
||||
DateTime(timezone=True),
|
||||
nullable=False,
|
||||
default=datetime.now,
|
||||
comment="创建时间",
|
||||
)
|
||||
|
||||
# --------------------------------------------------------------------------
|
||||
# 索引定义
|
||||
# --------------------------------------------------------------------------
|
||||
__table_args__ = (
|
||||
Index("idx_annotation_conversation", "conversation_id"),
|
||||
Index("idx_annotation_message", "message_id"),
|
||||
)
|
||||
|
||||
def __repr__(self) -> str:
|
||||
"""标注对象的字符串表示。"""
|
||||
return f"<ConversationAnnotation(id={self.id}, feedback={self.feedback})>"
|
||||
@@ -0,0 +1,122 @@
|
||||
# =============================================================================
|
||||
# 企微IT智能服务台 — 满意度评价模型
|
||||
# =============================================================================
|
||||
# 说明：对应数据库 conversation_evaluations 表，存储会话满意度评价数据
|
||||
# 核心概念：每个会话结束后，员工对本次服务进行满意度评价
|
||||
# 评价要素：星级（1-5）、表情（满意/一般/不满意）、文字反馈
|
||||
# =============================================================================
|
||||
|
||||
import uuid
|
||||
from datetime import datetime
|
||||
from typing import Optional
|
||||
|
||||
from sqlalchemy import DateTime, Index, Integer, String, Text
|
||||
from sqlalchemy.orm import Mapped, mapped_column
|
||||
|
||||
from app.database import Base
|
||||
|
||||
|
||||
class ConversationEvaluation(Base):
|
||||
"""满意度评价模型 — 对应 conversation_evaluations 表。
|
||||
|
||||
在会话结束后，员工对本次IT服务进行满意度评价。
|
||||
评价数据关联会话ID，用于服务质量分析和改进。
|
||||
|
||||
Attributes:
|
||||
id: 评价记录唯一标识（UUID，数据库自动生成）
|
||||
conversation_id: 关联的会话ID（关联 conversations 表）
|
||||
employee_id: 评价员工UserID
|
||||
employee_name: 评价员工姓名（冗余存储）
|
||||
star_rating: 星级评分（1-5）
|
||||
emoji: 表情评价（satisfied:满意/neutral:一般/dissatisfied:不满意）
|
||||
feedback_text: 文字反馈（可选，限200字）
|
||||
created_at: 评价时间
|
||||
"""
|
||||
|
||||
# 表名
|
||||
__tablename__ = "conversation_evaluations"
|
||||
|
||||
# --------------------------------------------------------------------------
|
||||
# 字段定义
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
# 主键：UUID，Python端生成
|
||||
id: Mapped[str] = mapped_column(
|
||||
String(36),
|
||||
primary_key=True,
|
||||
default=lambda: str(uuid.uuid4()),
|
||||
)
|
||||
|
||||
# 关联的会话ID（关联 conversations 表）
|
||||
conversation_id: Mapped[str] = mapped_column(
|
||||
String(36),
|
||||
nullable=False,
|
||||
comment="关联的会话ID",
|
||||
)
|
||||
|
||||
# 评价员工UserID
|
||||
employee_id: Mapped[str] = mapped_column(
|
||||
String(64),
|
||||
nullable=False,
|
||||
comment="评价员工UserID",
|
||||
)
|
||||
|
||||
# 评价员工姓名（冗余存储）
|
||||
employee_name: Mapped[str] = mapped_column(
|
||||
String(128),
|
||||
nullable=False,
|
||||
default="",
|
||||
comment="评价员工姓名",
|
||||
)
|
||||
|
||||
# 星级评分（1-5）
|
||||
star_rating: Mapped[int] = mapped_column(
|
||||
Integer,
|
||||
nullable=False,
|
||||
comment="星级评分（1-5）",
|
||||
)
|
||||
|
||||
# 表情评价
|
||||
# satisfied: 满意 😀
|
||||
# neutral: 一般 😐
|
||||
# dissatisfied: 不满意 😞
|
||||
emoji: Mapped[str] = mapped_column(
|
||||
String(20),
|
||||
nullable=False,
|
||||
comment="表情评价（satisfied/neutral/dissatisfied）",
|
||||
)
|
||||
|
||||
# 文字反馈（可选，限200字）
|
||||
feedback_text: Mapped[Optional[str]] = mapped_column(
|
||||
Text,
|
||||
nullable=True,
|
||||
comment="文字反馈（可选，限200字）",
|
||||
)
|
||||
|
||||
# 评价时间
|
||||
created_at: Mapped[datetime] = mapped_column(
|
||||
DateTime(timezone=True),
|
||||
nullable=False,
|
||||
default=datetime.now,
|
||||
comment="评价时间",
|
||||
)
|
||||
|
||||
# --------------------------------------------------------------------------
|
||||
# 索引定义
|
||||
# --------------------------------------------------------------------------
|
||||
__table_args__ = (
|
||||
# 按会话ID查询（获取某会话的评价）
|
||||
Index("idx_evaluations_conversation_id", "conversation_id"),
|
||||
# 按员工ID查询（查询某员工的评价历史）
|
||||
Index("idx_evaluations_employee_id", "employee_id"),
|
||||
# 按创建时间倒序查询
|
||||
Index("idx_evaluations_created_at", "created_at"),
|
||||
)
|
||||
|
||||
def __repr__(self) -> str:
|
||||
"""评价对象的字符串表示。"""
|
||||
return (
|
||||
f"<ConversationEvaluation(id={self.id}, "
|
||||
f"conversation_id={self.conversation_id}, "
|
||||
f"star_rating={self.star_rating}, emoji={self.emoji})>"
|
||||
)
|
||||
@@ -0,0 +1,123 @@
|
||||
# =============================================================================
|
||||
# 企微IT智能服务台 — 知识库模型
|
||||
# =============================================================================
|
||||
# 说明：对应数据库 knowledge_base 表，存储IT知识库FAQ
|
||||
# 分类：按问题类型（硬件/软件/网络/安全/账号/其他）
|
||||
# 支持标签、命中统计
|
||||
# =============================================================================
|
||||
|
||||
import uuid
|
||||
from datetime import datetime
|
||||
from typing import List
|
||||
|
||||
from sqlalchemy import DateTime, Index, Integer, JSON, String, Text
|
||||
from sqlalchemy.orm import Mapped, mapped_column
|
||||
|
||||
from app.database import Base
|
||||
|
||||
|
||||
class KnowledgeBase(Base):
|
||||
"""知识库FAQ模型 — 对应 knowledge_base 表。
|
||||
|
||||
存储IT知识库的问答对，支持分类、标签、命中统计。
|
||||
|
||||
Attributes:
|
||||
id: 知识ID（UUID）
|
||||
category: 分类（硬件/软件/网络/安全/账号/其他）
|
||||
title: 问题标题
|
||||
content: 答案内容（支持富文本）
|
||||
tags: 标签列表（JSON数组）
|
||||
view_count: 查看次数
|
||||
use_count: 使用次数（坐席引用次数）
|
||||
created_at: 创建时间
|
||||
updated_at: 更新时间
|
||||
"""
|
||||
|
||||
# 表名
|
||||
__tablename__ = "knowledge_base"
|
||||
|
||||
# --------------------------------------------------------------------------
|
||||
# 字段定义
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
# 主键
|
||||
id: Mapped[str] = mapped_column(
|
||||
String(36),
|
||||
primary_key=True,
|
||||
default=lambda: str(uuid.uuid4()),
|
||||
)
|
||||
|
||||
# 分类
|
||||
category: Mapped[str] = mapped_column(
|
||||
String(64),
|
||||
nullable=False,
|
||||
default="其他",
|
||||
comment="分类：硬件/软件/网络/安全/账号/其他",
|
||||
)
|
||||
|
||||
# 问题标题
|
||||
title: Mapped[str] = mapped_column(
|
||||
String(256),
|
||||
nullable=False,
|
||||
comment="问题标题",
|
||||
)
|
||||
|
||||
# 答案内容
|
||||
content: Mapped[str] = mapped_column(
|
||||
Text,
|
||||
nullable=False,
|
||||
comment="答案内容",
|
||||
)
|
||||
|
||||
# 标签列表
|
||||
tags: Mapped[List[str]] = mapped_column(
|
||||
JSON,
|
||||
nullable=False,
|
||||
default=list,
|
||||
comment="标签列表",
|
||||
)
|
||||
|
||||
# 查看次数
|
||||
view_count: Mapped[int] = mapped_column(
|
||||
Integer,
|
||||
nullable=False,
|
||||
default=0,
|
||||
comment="查看次数",
|
||||
)
|
||||
|
||||
# 使用次数（坐席引用次数）
|
||||
use_count: Mapped[int] = mapped_column(
|
||||
Integer,
|
||||
nullable=False,
|
||||
default=0,
|
||||
comment="使用次数",
|
||||
)
|
||||
|
||||
# 创建时间
|
||||
created_at: Mapped[datetime] = mapped_column(
|
||||
DateTime(timezone=True),
|
||||
nullable=False,
|
||||
default=datetime.now,
|
||||
comment="创建时间",
|
||||
)
|
||||
|
||||
# 更新时间
|
||||
updated_at: Mapped[datetime] = mapped_column(
|
||||
DateTime(timezone=True),
|
||||
nullable=False,
|
||||
default=datetime.now,
|
||||
onupdate=datetime.now,
|
||||
comment="更新时间",
|
||||
)
|
||||
|
||||
# --------------------------------------------------------------------------
|
||||
# 索引定义
|
||||
# --------------------------------------------------------------------------
|
||||
__table_args__ = (
|
||||
Index("idx_kb_category", "category"),
|
||||
# Index("idx_kb_tags", "tags"), # JSON 字段不能用 btree，需要 GIN 索引或注释
|
||||
)
|
||||
|
||||
def __repr__(self) -> str:
|
||||
"""知识库对象的字符串表示。"""
|
||||
return f"<KnowledgeBase(id={self.id}, category={self.category}, title={self.title})>"
|
||||
@@ -0,0 +1,173 @@
|
||||
# =============================================================================
|
||||
# 企微IT智能服务台 — 知识库优化建议模型
|
||||
# =============================================================================
|
||||
# 说明：对应数据库 knowledge_suggestions 表
|
||||
# 存储AI分析生成的优化建议，用于知识库迭代
|
||||
# 分析维度：错误标注高频问题、未命中知识库的会话、AI不确定回复
|
||||
# =============================================================================
|
||||
|
||||
import uuid
|
||||
from datetime import datetime
|
||||
from typing import List, Optional
|
||||
|
||||
from sqlalchemy import DateTime, Index, Integer, JSON, String, Text
|
||||
from sqlalchemy.orm import Mapped, mapped_column
|
||||
|
||||
from app.database import Base
|
||||
|
||||
|
||||
class KnowledgeSuggestion(Base):
|
||||
"""知识库优化建议模型 — 对应 knowledge_suggestions 表。
|
||||
|
||||
存储AI自动分析生成的优化建议，用于知识库持续迭代。
|
||||
|
||||
Attributes:
|
||||
id: 建议ID（UUID）
|
||||
suggestion_type: 建议类型（new_faq=新增FAQ/update=更新/outdated=标记过时）
|
||||
status: 状态（pending=待审核/approved=已通过/rejected=已拒绝/applied=已应用）
|
||||
title: 建议标题（新增/更新的FAQ标题）
|
||||
content: 建议内容（答案内容）
|
||||
category: 分类
|
||||
tags: 标签列表（JSON数组）
|
||||
source_type: 分析来源（annotation=标注数据/conversation=会话数据/ai_uncertain=AI不确定）
|
||||
source_data: 来源数据（JSON，存储相关会话ID或标注ID列表）
|
||||
reason: 生成理由（AI分析的理由）
|
||||
reject_reason: 拒绝理由（审核拒绝时填写）
|
||||
reviewer_id: 审核人ID
|
||||
reviewed_at: 审核时间
|
||||
created_at: 创建时间
|
||||
updated_at: 更新时间
|
||||
"""
|
||||
|
||||
# 表名
|
||||
__tablename__ = "knowledge_suggestions"
|
||||
|
||||
# --------------------------------------------------------------------------
|
||||
# 字段定义
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
# 主键
|
||||
id: Mapped[str] = mapped_column(
|
||||
String(36),
|
||||
primary_key=True,
|
||||
default=lambda: str(uuid.uuid4()),
|
||||
)
|
||||
|
||||
# 建议类型
|
||||
suggestion_type: Mapped[str] = mapped_column(
|
||||
String(20),
|
||||
nullable=False,
|
||||
default="new_faq",
|
||||
comment="new_faq=新增FAQ/update=更新/outdated=标记过时",
|
||||
)
|
||||
|
||||
# 状态
|
||||
status: Mapped[str] = mapped_column(
|
||||
String(20),
|
||||
nullable=False,
|
||||
default="pending",
|
||||
index=True,
|
||||
comment="pending=待审核/approved=已通过/rejected=已拒绝/applied=已应用",
|
||||
)
|
||||
|
||||
# 建议标题
|
||||
title: Mapped[str] = mapped_column(
|
||||
String(256),
|
||||
nullable=False,
|
||||
comment="新增/更新的FAQ标题",
|
||||
)
|
||||
|
||||
# 建议内容
|
||||
content: Mapped[str] = mapped_column(
|
||||
Text,
|
||||
nullable=False,
|
||||
comment="答案内容",
|
||||
)
|
||||
|
||||
# 分类
|
||||
category: Mapped[str] = mapped_column(
|
||||
String(64),
|
||||
nullable=False,
|
||||
default="其他",
|
||||
comment="分类：硬件/软件/网络/安全/账号/其他",
|
||||
)
|
||||
|
||||
# 标签列表
|
||||
tags: Mapped[List[str]] = mapped_column(
|
||||
JSON,
|
||||
nullable=False,
|
||||
default=list,
|
||||
comment="标签列表",
|
||||
)
|
||||
|
||||
# 分析来源
|
||||
source_type: Mapped[str] = mapped_column(
|
||||
String(30),
|
||||
nullable=False,
|
||||
comment="annotation=标注数据/conversation=会话数据/ai_uncertain=AI不确定",
|
||||
)
|
||||
|
||||
# 来源数据（JSON）
|
||||
source_data: Mapped[Optional[List[str]]] = mapped_column(
|
||||
JSON,
|
||||
nullable=True,
|
||||
comment="相关会话ID或标注ID列表",
|
||||
)
|
||||
|
||||
# 生成理由
|
||||
reason: Mapped[Optional[str]] = mapped_column(
|
||||
Text,
|
||||
nullable=True,
|
||||
comment="AI分析的理由",
|
||||
)
|
||||
|
||||
# 拒绝理由
|
||||
reject_reason: Mapped[Optional[str]] = mapped_column(
|
||||
Text,
|
||||
nullable=True,
|
||||
comment="审核拒绝时填写",
|
||||
)
|
||||
|
||||
# 审核人ID
|
||||
reviewer_id: Mapped[Optional[str]] = mapped_column(
|
||||
String(36),
|
||||
nullable=True,
|
||||
comment="审核人ID",
|
||||
)
|
||||
|
||||
# 审核时间
|
||||
reviewed_at: Mapped[Optional[datetime]] = mapped_column(
|
||||
DateTime(timezone=True),
|
||||
nullable=True,
|
||||
comment="审核时间",
|
||||
)
|
||||
|
||||
# 创建时间
|
||||
created_at: Mapped[datetime] = mapped_column(
|
||||
DateTime(timezone=True),
|
||||
nullable=False,
|
||||
default=datetime.now,
|
||||
comment="创建时间",
|
||||
)
|
||||
|
||||
# 更新时间
|
||||
updated_at: Mapped[datetime] = mapped_column(
|
||||
DateTime(timezone=True),
|
||||
nullable=False,
|
||||
default=datetime.now,
|
||||
onupdate=datetime.now,
|
||||
comment="更新时间",
|
||||
)
|
||||
|
||||
# --------------------------------------------------------------------------
|
||||
# 索引定义
|
||||
# --------------------------------------------------------------------------
|
||||
__table_args__ = (
|
||||
Index("idx_suggestion_status", "status"),
|
||||
Index("idx_suggestion_type", "suggestion_type"),
|
||||
Index("idx_suggestion_created", "created_at"),
|
||||
)
|
||||
|
||||
def __repr__(self) -> str:
|
||||
"""建议对象的字符串表示。"""
|
||||
return f"<KnowledgeSuggestion(id={self.id}, type={self.suggestion_type}, status={self.status})>"
|
||||
Reference in New Issue
Block a user