chore: initial baseline with P0-safety .gitignore
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Simon
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# =============================================================================
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# 企微IT智能服务台 — AI Wingman API 路由
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# =============================================================================
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# 说明：坐席端 AI 智能副驾驶 API，包含 3 个核心端点：
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# 1. POST /api/conversations/{id}/wingman/draft — 生成 AI 草稿回复
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# 2. POST /api/conversations/{id}/wingman/summary — 生成会话自动摘要
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# 3. POST /api/conversations/{id}/wingman/tags — 生成自动标签建议
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#
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# 所有端点需要坐席认证（get_current_agent）
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# =============================================================================
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import logging
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from fastapi import APIRouter, Depends
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.database import get_db
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from app.dependencies import dep_wingman_service
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from app.models.agent import Agent
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from app.models.conversation import Conversation
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from app.models.message import Message
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from app.services.wingman_service import WingmanService
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from app.utils.response import ERR_NOT_FOUND, success_response
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# 复用坐席认证依赖
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from app.api.agents import get_current_agent
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logger = logging.getLogger(__name__)
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# 创建路由器
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router = APIRouter()
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# --------------------------------------------------------------------------
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# 辅助函数
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# --------------------------------------------------------------------------
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async def _validate_conversation(
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conversation_id: str,
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agent: Agent,
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db: AsyncSession,
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) -> Conversation:
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"""验证会话存在性并返回会话对象。
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Args:
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conversation_id: 会话ID
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agent: 当前坐席
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db: 数据库会话
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Returns:
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Conversation: 会话对象
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Raises:
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AppException: 会话不存在
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"""
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stmt = select(Conversation).where(Conversation.id == conversation_id)
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result = await db.execute(stmt)
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conversation = result.scalars().first()
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if not conversation:
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raise ERR_NOT_FOUND
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return conversation
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async def _get_recent_messages(
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conversation_id: str,
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db: AsyncSession,
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limit: int = 20,
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) -> list[dict]:
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"""获取会话最近的消息历史（转换为字典列表）。
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Args:
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conversation_id: 会话ID
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db: 数据库会话
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limit: 获取的消息条数
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Returns:
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list[dict]: 消息字典列表
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"""
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stmt = (
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select(Message)
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.where(Message.conversation_id == conversation_id)
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.order_by(Message.created_at.desc())
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.limit(limit)
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)
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result = await db.execute(stmt)
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messages = list(result.scalars().all())
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# 按时间正序排列（最早的在前）
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messages.reverse()
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# 转换为字典列表
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return [
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{
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"id": msg.id,
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"sender_type": msg.sender_type,
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"sender_name": msg.sender_name,
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"content": msg.content,
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"msg_type": msg.msg_type,
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"created_at": msg.created_at.isoformat() if msg.created_at else "",
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}
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for msg in messages
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]
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# --------------------------------------------------------------------------
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# POST /api/conversations/{conversation_id}/wingman/draft
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# --------------------------------------------------------------------------
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@router.post("/conversations/{conversation_id}/wingman/draft")
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async def generate_draft(
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conversation_id: str,
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agent: Agent = Depends(get_current_agent),
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db: AsyncSession = Depends(get_db),
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wingman_service: WingmanService = Depends(dep_wingman_service),
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):
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"""生成 AI 草稿回复。
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基于当前会话的消息历史，让 Wingman Agent 生成坐席可以采纳的草稿回复。
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Args:
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conversation_id: 会话ID
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agent: 当前坐席（通过认证依赖注入）
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db: 数据库会话
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wingman_service: Wingman 服务实例
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Returns:
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Dict: 统一响应格式，包含草稿内容、置信度和推理说明
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"""
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# 1. 验证坐席身份 + 会话存在性
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await _validate_conversation(conversation_id, agent, db)
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# 2. 从数据库读取该会话的消息历史（最近 20 条）
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messages = await _get_recent_messages(conversation_id, db, limit=20)
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# 3. 调用 WingmanService 生成草稿
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result = await wingman_service.generate_draft(
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conversation_id=conversation_id,
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messages=messages,
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db=db,
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)
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return success_response(data=result)
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# --------------------------------------------------------------------------
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# POST /api/conversations/{conversation_id}/wingman/summary
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# --------------------------------------------------------------------------
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@router.post("/conversations/{conversation_id}/wingman/summary")
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async def generate_summary(
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conversation_id: str,
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agent: Agent = Depends(get_current_agent),
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db: AsyncSession = Depends(get_db),
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wingman_service: WingmanService = Depends(dep_wingman_service),
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):
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"""生成会话自动摘要。
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基于完整对话生成结构化摘要，包含问题、原因、解决方案。
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通常在结单时调用。
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Args:
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conversation_id: 会话ID
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agent: 当前坐席
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db: 数据库会话
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wingman_service: Wingman 服务实例
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Returns:
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Dict: 统一响应格式，包含问题、原因、解决方案
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"""
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# 1. 验证坐席身份 + 会话存在性
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await _validate_conversation(conversation_id, agent, db)
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# 2. 从数据库读取该会话的完整消息历史（最多 50 条）
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messages = await _get_recent_messages(conversation_id, db, limit=50)
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# 3. 调用 WingmanService 生成摘要
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result = await wingman_service.generate_summary(
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conversation_id=conversation_id,
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messages=messages,
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)
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return success_response(data=result)
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# --------------------------------------------------------------------------
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# POST /api/conversations/{conversation_id}/wingman/tags
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# --------------------------------------------------------------------------
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@router.post("/conversations/{conversation_id}/wingman/tags")
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async def suggest_tags(
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conversation_id: str,
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agent: Agent = Depends(get_current_agent),
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db: AsyncSession = Depends(get_db),
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wingman_service: WingmanService = Depends(dep_wingman_service),
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):
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"""生成自动标签建议。
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基于对话内容建议标签分类，包含标签列表、分类和优先级。
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Args:
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conversation_id: 会话ID
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agent: 当前坐席
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db: 数据库会话
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wingman_service: Wingman 服务实例
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Returns:
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Dict: 统一响应格式，包含建议标签、分类和优先级
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"""
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# 1. 验证坐席身份 + 会话存在性
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conversation = await _validate_conversation(conversation_id, agent, db)
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# 2. 从数据库读取该会话的消息历史（最近 20 条）
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messages = await _get_recent_messages(conversation_id, db, limit=20)
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# 3. 获取已有标签（用于避免重复建议）
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existing_tags = {}
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if hasattr(conversation, 'tags') and conversation.tags:
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existing_tags = conversation.tags if isinstance(conversation.tags, dict) else {}
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# 4. 调用 WingmanService 生成标签建议
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result = await wingman_service.suggest_tags(
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conversation_id=conversation_id,
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messages=messages,
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existing_tags=existing_tags,
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)
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return success_response(data=result)
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