WebUI后端整体重构
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墨梓柒
319
src/webui/routers/statistics.py
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319
src/webui/routers/statistics.py
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"""统计数据 API 路由"""
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from fastapi import APIRouter, HTTPException, Depends, Cookie, Header
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from pydantic import BaseModel, Field
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from typing import Dict, Any, List, Optional
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from datetime import datetime, timedelta
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from peewee import fn
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from src.common.logger import get_logger
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from src.common.database.database_model import LLMUsage, OnlineTime, Messages
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from src.webui.core import verify_auth_token_from_cookie_or_header
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logger = get_logger("webui.statistics")
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router = APIRouter(prefix="/statistics", tags=["statistics"])
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def require_auth(
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maibot_session: Optional[str] = Cookie(None),
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authorization: Optional[str] = Header(None),
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) -> bool:
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"""认证依赖：验证用户是否已登录"""
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return verify_auth_token_from_cookie_or_header(maibot_session, authorization)
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class StatisticsSummary(BaseModel):
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"""统计数据摘要"""
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total_requests: int = Field(0, description="总请求数")
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total_cost: float = Field(0.0, description="总花费")
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total_tokens: int = Field(0, description="总token数")
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online_time: float = Field(0.0, description="在线时间（秒）")
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total_messages: int = Field(0, description="总消息数")
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total_replies: int = Field(0, description="总回复数")
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avg_response_time: float = Field(0.0, description="平均响应时间")
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cost_per_hour: float = Field(0.0, description="每小时花费")
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tokens_per_hour: float = Field(0.0, description="每小时token数")
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class ModelStatistics(BaseModel):
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"""模型统计"""
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model_name: str
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request_count: int
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total_cost: float
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total_tokens: int
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avg_response_time: float
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class TimeSeriesData(BaseModel):
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"""时间序列数据"""
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timestamp: str
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requests: int = 0
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cost: float = 0.0
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tokens: int = 0
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class DashboardData(BaseModel):
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"""仪表盘数据"""
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summary: StatisticsSummary
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model_stats: List[ModelStatistics]
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hourly_data: List[TimeSeriesData]
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daily_data: List[TimeSeriesData]
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recent_activity: List[Dict[str, Any]]
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@router.get("/dashboard", response_model=DashboardData)
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async def get_dashboard_data(hours: int = 24, _auth: bool = Depends(require_auth)):
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"""
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获取仪表盘统计数据
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Args:
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hours: 统计时间范围（小时），默认24小时
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Returns:
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仪表盘数据
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"""
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try:
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now = datetime.now()
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start_time = now - timedelta(hours=hours)
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# 获取摘要数据
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summary = await _get_summary_statistics(start_time, now)
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# 获取模型统计
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model_stats = await _get_model_statistics(start_time)
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# 获取小时级时间序列数据
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hourly_data = await _get_hourly_statistics(start_time, now)
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# 获取日级时间序列数据（最近7天）
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daily_start = now - timedelta(days=7)
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daily_data = await _get_daily_statistics(daily_start, now)
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# 获取最近活动
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recent_activity = await _get_recent_activity(limit=10)
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return DashboardData(
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summary=summary,
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model_stats=model_stats,
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hourly_data=hourly_data,
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daily_data=daily_data,
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recent_activity=recent_activity,
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)
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except Exception as e:
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logger.error(f"获取仪表盘数据失败: {e}")
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raise HTTPException(status_code=500, detail=f"获取统计数据失败: {str(e)}") from e
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async def _get_summary_statistics(start_time: datetime, end_time: datetime) -> StatisticsSummary:
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"""获取摘要统计数据（优化：使用数据库聚合）"""
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summary = StatisticsSummary()
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# 使用聚合查询替代全量加载
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query = LLMUsage.select(
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fn.COUNT(LLMUsage.id).alias("total_requests"),
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fn.COALESCE(fn.SUM(LLMUsage.cost), 0).alias("total_cost"),
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fn.COALESCE(fn.SUM(LLMUsage.prompt_tokens + LLMUsage.completion_tokens), 0).alias("total_tokens"),
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fn.COALESCE(fn.AVG(LLMUsage.time_cost), 0).alias("avg_response_time"),
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).where((LLMUsage.timestamp >= start_time) & (LLMUsage.timestamp <= end_time))
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result = query.dicts().get()
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summary.total_requests = result["total_requests"]
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summary.total_cost = result["total_cost"]
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summary.total_tokens = result["total_tokens"]
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summary.avg_response_time = result["avg_response_time"] or 0.0
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# 查询在线时间 - 这个数据量通常不大，保留原逻辑
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online_records = list(
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OnlineTime.select().where((OnlineTime.start_timestamp >= start_time) | (OnlineTime.end_timestamp >= start_time))
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)
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for record in online_records:
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start = max(record.start_timestamp, start_time)
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end = min(record.end_timestamp, end_time)
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if end > start:
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summary.online_time += (end - start).total_seconds()
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# 查询消息数量 - 使用聚合优化
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messages_query = Messages.select(fn.COUNT(Messages.id).alias("total")).where(
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(Messages.time >= start_time.timestamp()) & (Messages.time <= end_time.timestamp())
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)
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summary.total_messages = messages_query.scalar() or 0
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# 统计回复数量
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replies_query = Messages.select(fn.COUNT(Messages.id).alias("total")).where(
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(Messages.time >= start_time.timestamp())
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& (Messages.time <= end_time.timestamp())
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& (Messages.reply_to.is_null(False))
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)
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summary.total_replies = replies_query.scalar() or 0
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# 计算派生指标
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if summary.online_time > 0:
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online_hours = summary.online_time / 3600.0
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summary.cost_per_hour = summary.total_cost / online_hours
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summary.tokens_per_hour = summary.total_tokens / online_hours
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return summary
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async def _get_model_statistics(start_time: datetime) -> List[ModelStatistics]:
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"""获取模型统计数据（优化：使用数据库聚合和分组）"""
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# 使用GROUP BY聚合，避免全量加载
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query = (
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LLMUsage.select(
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fn.COALESCE(LLMUsage.model_assign_name, LLMUsage.model_name, "unknown").alias("model_name"),
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fn.COUNT(LLMUsage.id).alias("request_count"),
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fn.COALESCE(fn.SUM(LLMUsage.cost), 0).alias("total_cost"),
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fn.COALESCE(fn.SUM(LLMUsage.prompt_tokens + LLMUsage.completion_tokens), 0).alias("total_tokens"),
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fn.COALESCE(fn.AVG(LLMUsage.time_cost), 0).alias("avg_response_time"),
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)
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.where(LLMUsage.timestamp >= start_time)
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.group_by(fn.COALESCE(LLMUsage.model_assign_name, LLMUsage.model_name, "unknown"))
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.order_by(fn.COUNT(LLMUsage.id).desc())
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.limit(10) # 只取前10个
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)
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result = []
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for row in query.dicts():
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result.append(
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ModelStatistics(
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model_name=row["model_name"],
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request_count=row["request_count"],
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total_cost=row["total_cost"],
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total_tokens=row["total_tokens"],
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avg_response_time=row["avg_response_time"] or 0.0,
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)
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)
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return result
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async def _get_hourly_statistics(start_time: datetime, end_time: datetime) -> List[TimeSeriesData]:
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"""获取小时级统计数据（优化：使用数据库聚合）"""
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# SQLite的日期时间函数进行小时分组
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# 使用strftime将timestamp格式化为小时级别
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query = (
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LLMUsage.select(
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fn.strftime("%Y-%m-%dT%H:00:00", LLMUsage.timestamp).alias("hour"),
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fn.COUNT(LLMUsage.id).alias("requests"),
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fn.COALESCE(fn.SUM(LLMUsage.cost), 0).alias("cost"),
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fn.COALESCE(fn.SUM(LLMUsage.prompt_tokens + LLMUsage.completion_tokens), 0).alias("tokens"),
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)
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.where((LLMUsage.timestamp >= start_time) & (LLMUsage.timestamp <= end_time))
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.group_by(fn.strftime("%Y-%m-%dT%H:00:00", LLMUsage.timestamp))
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)
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# 转换为字典以快速查找
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data_dict = {row["hour"]: row for row in query.dicts()}
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# 填充所有小时（包括没有数据的）
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result = []
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current = start_time.replace(minute=0, second=0, microsecond=0)
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while current <= end_time:
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hour_str = current.strftime("%Y-%m-%dT%H:00:00")
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if hour_str in data_dict:
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row = data_dict[hour_str]
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result.append(
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TimeSeriesData(timestamp=hour_str, requests=row["requests"], cost=row["cost"], tokens=row["tokens"])
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)
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else:
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result.append(TimeSeriesData(timestamp=hour_str, requests=0, cost=0.0, tokens=0))
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current += timedelta(hours=1)
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return result
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async def _get_daily_statistics(start_time: datetime, end_time: datetime) -> List[TimeSeriesData]:
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"""获取日级统计数据（优化：使用数据库聚合）"""
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# 使用strftime按日期分组
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query = (
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LLMUsage.select(
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fn.strftime("%Y-%m-%dT00:00:00", LLMUsage.timestamp).alias("day"),
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fn.COUNT(LLMUsage.id).alias("requests"),
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fn.COALESCE(fn.SUM(LLMUsage.cost), 0).alias("cost"),
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fn.COALESCE(fn.SUM(LLMUsage.prompt_tokens + LLMUsage.completion_tokens), 0).alias("tokens"),
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)
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.where((LLMUsage.timestamp >= start_time) & (LLMUsage.timestamp <= end_time))
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.group_by(fn.strftime("%Y-%m-%dT00:00:00", LLMUsage.timestamp))
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)
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# 转换为字典
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data_dict = {row["day"]: row for row in query.dicts()}
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# 填充所有天
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result = []
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current = start_time.replace(hour=0, minute=0, second=0, microsecond=0)
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while current <= end_time:
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day_str = current.strftime("%Y-%m-%dT00:00:00")
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if day_str in data_dict:
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row = data_dict[day_str]
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result.append(
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TimeSeriesData(timestamp=day_str, requests=row["requests"], cost=row["cost"], tokens=row["tokens"])
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)
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else:
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result.append(TimeSeriesData(timestamp=day_str, requests=0, cost=0.0, tokens=0))
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current += timedelta(days=1)
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return result
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async def _get_recent_activity(limit: int = 10) -> List[Dict[str, Any]]:
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"""获取最近活动"""
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records = list(LLMUsage.select().order_by(LLMUsage.timestamp.desc()).limit(limit))
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activities = []
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for record in records:
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activities.append(
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{
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"timestamp": record.timestamp.isoformat(),
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"model": record.model_assign_name or record.model_name,
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"request_type": record.request_type,
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"tokens": (record.prompt_tokens or 0) + (record.completion_tokens or 0),
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"cost": record.cost or 0.0,
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"time_cost": record.time_cost or 0.0,
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"status": record.status,
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}
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)
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return activities
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@router.get("/summary")
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async def get_summary(hours: int = 24, _auth: bool = Depends(require_auth)):
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"""
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获取统计摘要
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Args:
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hours: 统计时间范围（小时）
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"""
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try:
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now = datetime.now()
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start_time = now - timedelta(hours=hours)
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summary = await _get_summary_statistics(start_time, now)
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return summary
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except Exception as e:
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logger.error(f"获取统计摘要失败: {e}")
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raise HTTPException(status_code=500, detail=str(e)) from e
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@router.get("/models")
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async def get_model_stats(hours: int = 24, _auth: bool = Depends(require_auth)):
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"""
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获取模型统计
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Args:
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hours: 统计时间范围（小时）
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"""
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try:
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now = datetime.now()
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start_time = now - timedelta(hours=hours)
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stats = await _get_model_statistics(start_time)
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return stats
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except Exception as e:
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logger.error(f"获取模型统计失败: {e}")
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raise HTTPException(status_code=500, detail=str(e)) from e
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