Ruff fix
This commit is contained in:
墨梓柒
@@ -1,4 +1,5 @@
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"""统计数据 API 路由"""
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from fastapi import APIRouter, HTTPException
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from pydantic import BaseModel, Field
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from typing import Dict, Any, List
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@@ -15,6 +16,7 @@ router = APIRouter(prefix="/statistics", tags=["statistics"])
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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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@@ -28,6 +30,7 @@ class StatisticsSummary(BaseModel):
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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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@@ -37,6 +40,7 @@ class ModelStatistics(BaseModel):
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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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@@ -45,6 +49,7 @@ class TimeSeriesData(BaseModel):
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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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@@ -56,39 +61,39 @@ class DashboardData(BaseModel):
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async def get_dashboard_data(hours: int = 24):
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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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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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@@ -98,100 +103,84 @@ async def get_dashboard_data(hours: int = 24):
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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"""
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summary = StatisticsSummary()
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# 查询 LLM 使用记录
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llm_records = list(
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LLMUsage.select()
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.where(LLMUsage.timestamp >= start_time)
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.where(LLMUsage.timestamp <= end_time)
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)
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llm_records = list(LLMUsage.select().where(LLMUsage.timestamp >= start_time).where(LLMUsage.timestamp <= end_time))
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total_time_cost = 0.0
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time_cost_count = 0
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for record in llm_records:
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summary.total_requests += 1
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summary.total_cost += record.cost or 0.0
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summary.total_tokens += (record.prompt_tokens or 0) + (record.completion_tokens or 0)
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if record.time_cost and record.time_cost > 0:
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total_time_cost += record.time_cost
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time_cost_count += 1
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# 计算平均响应时间
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if time_cost_count > 0:
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summary.avg_response_time = total_time_cost / time_cost_count
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# 查询在线时间
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online_records = list(
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OnlineTime.select()
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.where(
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(OnlineTime.start_timestamp >= start_time) |
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(OnlineTime.end_timestamp >= start_time)
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)
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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
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messages = list(
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Messages.select()
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.where(Messages.time >= start_time.timestamp())
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.where(Messages.time <= end_time.timestamp())
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Messages.select().where(Messages.time >= start_time.timestamp()).where(Messages.time <= end_time.timestamp())
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)
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summary.total_messages = len(messages)
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# 简单统计：如果 reply_to 不为空，则认为是回复
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summary.total_replies = len([m for m in messages if m.reply_to])
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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"""
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model_data = defaultdict(lambda: {
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'request_count': 0,
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'total_cost': 0.0,
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'total_tokens': 0,
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'time_costs': []
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})
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records = list(
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LLMUsage.select()
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.where(LLMUsage.timestamp >= start_time)
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)
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model_data = defaultdict(lambda: {"request_count": 0, "total_cost": 0.0, "total_tokens": 0, "time_costs": []})
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records = list(LLMUsage.select().where(LLMUsage.timestamp >= start_time))
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for record in records:
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model_name = record.model_assign_name or record.model_name or "unknown"
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model_data[model_name]['request_count'] += 1
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model_data[model_name]['total_cost'] += record.cost or 0.0
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model_data[model_name]['total_tokens'] += (record.prompt_tokens or 0) + (record.completion_tokens or 0)
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model_data[model_name]["request_count"] += 1
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model_data[model_name]["total_cost"] += record.cost or 0.0
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model_data[model_name]["total_tokens"] += (record.prompt_tokens or 0) + (record.completion_tokens or 0)
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if record.time_cost and record.time_cost > 0:
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model_data[model_name]['time_costs'].append(record.time_cost)
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model_data[model_name]["time_costs"].append(record.time_cost)
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# 转换为列表并排序
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result = []
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for model_name, data in model_data.items():
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avg_time = sum(data['time_costs']) / len(data['time_costs']) if data['time_costs'] else 0.0
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result.append(ModelStatistics(
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model_name=model_name,
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request_count=data['request_count'],
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total_cost=data['total_cost'],
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total_tokens=data['total_tokens'],
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avg_response_time=avg_time
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))
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avg_time = sum(data["time_costs"]) / len(data["time_costs"]) if data["time_costs"] else 0.0
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result.append(
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ModelStatistics(
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model_name=model_name,
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request_count=data["request_count"],
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total_cost=data["total_cost"],
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total_tokens=data["total_tokens"],
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avg_response_time=avg_time,
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)
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)
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# 按请求数排序
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result.sort(key=lambda x: x.request_count, reverse=True)
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return result[:10] # 返回前10个
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@@ -200,96 +189,80 @@ async def _get_model_statistics(start_time: datetime) -> List[ModelStatistics]:
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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"""
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# 创建小时桶
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hourly_buckets = defaultdict(lambda: {'requests': 0, 'cost': 0.0, 'tokens': 0})
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records = list(
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LLMUsage.select()
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.where(LLMUsage.timestamp >= start_time)
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.where(LLMUsage.timestamp <= end_time)
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)
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hourly_buckets = defaultdict(lambda: {"requests": 0, "cost": 0.0, "tokens": 0})
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records = list(LLMUsage.select().where(LLMUsage.timestamp >= start_time).where(LLMUsage.timestamp <= end_time))
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for record in records:
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# 获取小时键（去掉分钟和秒）
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hour_key = record.timestamp.replace(minute=0, second=0, microsecond=0)
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hour_str = hour_key.isoformat()
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hourly_buckets[hour_str]['requests'] += 1
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hourly_buckets[hour_str]['cost'] += record.cost or 0.0
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hourly_buckets[hour_str]['tokens'] += (record.prompt_tokens or 0) + (record.completion_tokens or 0)
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hourly_buckets[hour_str]["requests"] += 1
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hourly_buckets[hour_str]["cost"] += record.cost or 0.0
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hourly_buckets[hour_str]["tokens"] += (record.prompt_tokens or 0) + (record.completion_tokens or 0)
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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.isoformat()
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data = hourly_buckets.get(hour_str, {'requests': 0, 'cost': 0.0, 'tokens': 0})
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result.append(TimeSeriesData(
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timestamp=hour_str,
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requests=data['requests'],
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cost=data['cost'],
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tokens=data['tokens']
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))
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data = hourly_buckets.get(hour_str, {"requests": 0, "cost": 0.0, "tokens": 0})
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result.append(
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TimeSeriesData(timestamp=hour_str, requests=data["requests"], cost=data["cost"], tokens=data["tokens"])
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)
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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"""
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daily_buckets = defaultdict(lambda: {'requests': 0, 'cost': 0.0, 'tokens': 0})
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records = list(
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LLMUsage.select()
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.where(LLMUsage.timestamp >= start_time)
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.where(LLMUsage.timestamp <= end_time)
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)
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daily_buckets = defaultdict(lambda: {"requests": 0, "cost": 0.0, "tokens": 0})
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records = list(LLMUsage.select().where(LLMUsage.timestamp >= start_time).where(LLMUsage.timestamp <= end_time))
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for record in records:
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# 获取日期键
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day_key = record.timestamp.replace(hour=0, minute=0, second=0, microsecond=0)
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day_str = day_key.isoformat()
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daily_buckets[day_str]['requests'] += 1
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daily_buckets[day_str]['cost'] += record.cost or 0.0
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daily_buckets[day_str]['tokens'] += (record.prompt_tokens or 0) + (record.completion_tokens or 0)
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daily_buckets[day_str]["requests"] += 1
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daily_buckets[day_str]["cost"] += record.cost or 0.0
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daily_buckets[day_str]["tokens"] += (record.prompt_tokens or 0) + (record.completion_tokens or 0)
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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.isoformat()
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data = daily_buckets.get(day_str, {'requests': 0, 'cost': 0.0, 'tokens': 0})
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result.append(TimeSeriesData(
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timestamp=day_str,
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requests=data['requests'],
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cost=data['cost'],
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tokens=data['tokens']
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))
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data = daily_buckets.get(day_str, {"requests": 0, "cost": 0.0, "tokens": 0})
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result.append(
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TimeSeriesData(timestamp=day_str, requests=data["requests"], cost=data["cost"], tokens=data["tokens"])
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)
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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(
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LLMUsage.select()
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.order_by(LLMUsage.timestamp.desc())
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.limit(limit)
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)
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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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'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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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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@@ -297,7 +270,7 @@ async def _get_recent_activity(limit: int = 10) -> List[Dict[str, Any]]:
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async def get_summary(hours: int = 24):
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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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@@ -315,7 +288,7 @@ async def get_summary(hours: int = 24):
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async def get_model_stats(hours: int = 24):
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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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