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263 lines
9.5 KiB
263 lines
9.5 KiB
"""监控模块路由。
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提供员工监控功能，包括员工列表查询、个人数据看板、AI 辅助的员工交互分析等。
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支持基于数据权限范围（all/subordinate_only/self_only）的访问控制。
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"""
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import uuid
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import json
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from datetime import datetime
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from fastapi import APIRouter, Depends, HTTPException, Request
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from sqlalchemy import select, func
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from sqlalchemy.ext.asyncio import AsyncSession
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from database import get_db
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from models import User, ChatSession, ChatMessage
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from modules.org.router import _get_subordinate_ids, _user_to_out
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from schemas import EmployeeAnalysis, UserOut
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router = APIRouter(prefix="/api/monitor", tags=["monitor"]) # 监控模块路由前缀
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@router.get("/employees", response_model=list[UserOut])
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async def get_monitor_employees(request: Request, db: AsyncSession = Depends(get_db)):
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"""获取可监控的员工列表。
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根据当前用户的数据权限范围返回不同的员工列表：
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- all：返回所有活跃员工
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- subordinate_only：返回当前用户及其所有下级员工
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- self_only：仅返回当前用户
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Args:
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request: HTTP 请求对象，包含当前用户上下文。
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db: 异步数据库会话。
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Returns:
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list[UserOut]: 员工信息列表。
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"""
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user_ctx = request.state.user
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cur_id = uuid.UUID(user_ctx["id"])
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if user_ctx["data_scope"] == "all":
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# 数据权限为全部：返回所有活跃员工
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result = await db.execute(select(User).where(User.status == "active"))
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return [await _user_to_out(db, u) for u in result.scalars().all()]
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elif user_ctx["data_scope"] == "subordinate_only":
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# 数据权限为下级：返回当前用户及其所有下级
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sub_ids = await _get_subordinate_ids(db, cur_id)
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sub_ids.add(cur_id)
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result = await db.execute(select(User).where(User.id.in_(sub_ids)))
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return [await _user_to_out(db, u) for u in result.scalars().all()]
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else:
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# 数据权限为仅自己：仅返回当前用户
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result = await db.execute(select(User).where(User.id == cur_id))
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return [await _user_to_out(db, u) for u in result.scalars().all()]
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@router.get("/employee/{emp_id}/dashboard")
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async def get_employee_dashboard(
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emp_id: uuid.UUID, request: Request, db: AsyncSession = Depends(get_db)
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):
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"""获取指定员工的个人数据看板。
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包括员工基本信息、消息统计、会话统计、活跃天数、消息分类和最近交互记录。
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需要进行数据权限校验。
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Args:
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emp_id: 员工唯一标识 ID。
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request: HTTP 请求对象，包含当前用户上下文。
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db: 异步数据库会话。
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Returns:
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dict: 包含员工信息和统计数据的看板数据。
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Raises:
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HTTPException: 无权查看或员工不存在时抛出异常。
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"""
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user_ctx = request.state.user
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cur_id = uuid.UUID(user_ctx["id"])
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# 数据权限校验
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if user_ctx["data_scope"] != "all":
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if user_ctx["data_scope"] == "self_only" and str(emp_id) != user_ctx["id"]:
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raise HTTPException(403, "无权查看此员工数据")
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elif user_ctx["data_scope"] == "subordinate_only":
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sub_ids = await _get_subordinate_ids(db, cur_id)
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sub_ids.add(cur_id)
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if emp_id not in sub_ids:
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raise HTTPException(403, "无权查看此员工数据")
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# 查询员工信息
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emp_result = await db.execute(select(User).where(User.id == emp_id))
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emp = emp_result.scalar_one_or_none()
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if not emp:
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raise HTTPException(404, "员工不存在")
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# 统计总消息数
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total_msgs_result = await db.execute(
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select(func.count(ChatMessage.id)).where(ChatMessage.user_id == emp_id)
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)
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total_messages = total_msgs_result.scalar() or 0
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# 统计总会话数
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session_result = await db.execute(
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select(func.count(ChatSession.id)).where(ChatSession.user_id == emp_id)
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)
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total_sessions = session_result.scalar() or 0
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# 查询最近 50 条消息
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recent_msgs_result = await db.execute(
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select(ChatMessage)
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.where(ChatMessage.user_id == emp_id)
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.order_by(ChatMessage.created_at.desc())
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.limit(50)
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)
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recent = recent_msgs_result.scalars().all()
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# 统计话题分布和活跃天数
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topics = {}
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active_days = set()
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for msg in recent:
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if msg.created_at:
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active_days.add(msg.created_at.strftime("%Y-%m-%d")) # 记录活跃日期
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role = msg.role
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topics[role] = topics.get(role, 0) + 1 # 按角色统计消息数
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return {
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"code": 200,
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"data": {
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"employee": {
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"id": str(emp.id),
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"name": emp.display_name,
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"department": str(emp.department_id) if emp.department_id else "",
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"position": emp.position or "",
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},
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"stats": {
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"total_messages": total_messages, # 总消息数
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"total_sessions": total_sessions, # 总会话数
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"active_days": len(active_days), # 活跃天数
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"message_breakdown": topics, # 消息角色分布
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"recent_interactions": [
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{"role": m.role, "content": m.content[:200], "created_at": str(m.created_at)}
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for m in recent[:10] # 最近 10 条交互记录
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],
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},
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},
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}
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@router.get("/employee/{emp_id}/analysis", response_model=EmployeeAnalysis)
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async def get_employee_analysis(
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emp_id: uuid.UUID, request: Request, db: AsyncSession = Depends(get_db)
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):
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"""获取指定员工的 AI 分析报告。
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基于员工与 AI 的交互记录，使用大语言模型生成员工分析报告，
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包括任务完成率、活跃度、主要话题、效率趋势、优势和改进建议等。
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Args:
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emp_id: 员工唯一标识 ID。
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request: HTTP 请求对象，包含当前用户上下文。
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db: 异步数据库会话。
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Returns:
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EmployeeAnalysis: 员工分析报告。
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Raises:
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HTTPException: 无权查看或员工不存在时抛出异常。
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"""
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user_ctx = request.state.user
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cur_id = uuid.UUID(user_ctx["id"])
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# 数据权限校验
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if user_ctx["data_scope"] != "all":
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if user_ctx["data_scope"] == "self_only" and str(emp_id) != user_ctx["id"]:
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raise HTTPException(403, "无权查看此员工数据")
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elif user_ctx["data_scope"] == "subordinate_only":
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sub_ids = await _get_subordinate_ids(db, cur_id)
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sub_ids.add(cur_id)
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if emp_id not in sub_ids:
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raise HTTPException(403, "无权查看此员工数据")
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# 查询员工信息
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emp_result = await db.execute(select(User).where(User.id == emp_id))
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emp = emp_result.scalar_one_or_none()
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if not emp:
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raise HTTPException(404, "员工不存在")
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from config import settings
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from agentscope.model import OpenAIChatModel
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from agentscope.formatter import OpenAIChatFormatter
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from agentscope.message import Msg
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# 查询最近 100 条消息作为分析数据
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msgs_result = await db.execute(
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select(ChatMessage)
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.where(ChatMessage.user_id == emp_id)
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.order_by(ChatMessage.created_at.desc())
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.limit(100)
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)
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messages = msgs_result.scalars().all()
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# 构建交互记录文本
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interaction_log = "\n".join([
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f"[{m.role}] {m.content[:300]}" for m in messages
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])
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# 初始化 LLM 模型
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model = OpenAIChatModel(
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config_name="analysis_model",
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model_name=settings.LLM_MODEL,
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api_key=settings.LLM_API_KEY,
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api_base=settings.LLM_API_BASE,
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)
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formatter = OpenAIChatFormatter()
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# 构建分析提示词
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prompt = formatter.format([
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Msg("system", f"""你是一个企业管理者分析助手。请根据员工与AI的交互记录，生成一个JSON格式的分析报告。
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要求:
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1. 分析员工的task_completion_rate (0-1的浮点数)
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2. 统计active_days和total_interactions
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3. 提取main_topics (最多5个关键词)
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4. 评估efficiency_trend ("提升" / "稳定" / "下降")
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5. 给出efficiency_detail (一句话说明)
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6. 列出strengths (2-3个优点)
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7. 给出growth_suggestions (2-3条建议)
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8. 总结personality_traits (一句话)
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输出严格JSON格式，不要包含markdown代码块标记。""", "system"),
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Msg("user", f"员工姓名: {emp.display_name}\n交互记录:\n{interaction_log}", "user"),
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])
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try:
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res = await model(prompt) # 调用 LLM 生成分析
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res_text = ""
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if isinstance(res, list):
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res_text = res[0].get_text_content() if hasattr(res[0], 'get_text_content') else str(res[0])
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elif hasattr(res, 'get_text_content'):
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res_text = res.get_text_content()
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else:
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res_text = str(res)
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analysis_data = json.loads(res_text) # 解析 JSON 响应
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except Exception:
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# LLM 调用失败时使用默认分析数据
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analysis_data = {
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"task_completion_rate": 0.7,
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"active_days": 0,
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"total_interactions": len(messages),
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"main_topics": [],
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"efficiency_trend": "稳定",
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"efficiency_detail": "暂无足够数据",
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"strengths": [],
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"growth_suggestions": [],
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"personality_traits": "暂未收集足够人格特征数据",
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}
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return EmployeeAnalysis(
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employee_name=emp.display_name,
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department=str(emp.department_id) if emp.department_id else "",
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period=f"最近数据",
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**analysis_data
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)
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