feat: 添加批量删除功能至表情包、表达方式和人物信息管理 API
This commit is contained in:
墨梓柒
@@ -4,7 +4,7 @@ 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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from datetime import datetime, timedelta
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from collections import defaultdict
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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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@@ -101,29 +101,24 @@ 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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"""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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# 查询 LLM 使用记录
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llm_records = list(LLMUsage.select().where(LLMUsage.timestamp >= start_time).where(LLMUsage.timestamp <= end_time))
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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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total_time_cost = 0.0
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time_cost_count = 0
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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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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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# 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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@@ -134,14 +129,19 @@ async def _get_summary_statistics(start_time: datetime, end_time: datetime) -> S
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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().where(Messages.time >= start_time.timestamp()).where(Messages.time <= end_time.timestamp())
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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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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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# 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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@@ -153,93 +153,101 @@ async def _get_summary_statistics(start_time: datetime, end_time: datetime) -> S
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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: {"request_count": 0, "total_cost": 0.0, "total_tokens": 0, "time_costs": []})
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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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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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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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# 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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for row in query.dicts():
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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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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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# 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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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"""
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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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"""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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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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# 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.isoformat()
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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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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"""
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daily_buckets = defaultdict(lambda: {"requests": 0, "cost": 0.0, "tokens": 0})
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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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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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# 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.isoformat()
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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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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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