mirror of https://github.com/Mai-with-u/MaiBot.git
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
墨梓柒
parent
2eceee7e08
commit
d7fccfe634
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@ -76,6 +76,22 @@ class EmojiDeleteResponse(BaseModel):
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message: str
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class BatchDeleteRequest(BaseModel):
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"""批量删除请求"""
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emoji_ids: List[int]
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class BatchDeleteResponse(BaseModel):
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"""批量删除响应"""
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success: bool
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message: str
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deleted_count: int
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failed_count: int
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failed_ids: List[int] = []
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def verify_auth_token(authorization: Optional[str]) -> bool:
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"""验证认证 Token"""
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if not authorization or not authorization.startswith("Bearer "):
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@ -503,3 +519,59 @@ async def get_emoji_thumbnail(
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except Exception as e:
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logger.exception(f"获取表情包缩略图失败: {e}")
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raise HTTPException(status_code=500, detail=f"获取表情包缩略图失败: {str(e)}") from e
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@router.post("/batch/delete", response_model=BatchDeleteResponse)
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async def batch_delete_emojis(request: BatchDeleteRequest, authorization: Optional[str] = Header(None)):
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"""
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批量删除表情包
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Args:
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request: 包含emoji_ids列表的请求
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authorization: Authorization header
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Returns:
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批量删除结果
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"""
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try:
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verify_auth_token(authorization)
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if not request.emoji_ids:
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raise HTTPException(status_code=400, detail="未提供要删除的表情包ID")
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deleted_count = 0
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failed_count = 0
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failed_ids = []
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for emoji_id in request.emoji_ids:
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try:
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emoji = Emoji.get_or_none(Emoji.id == emoji_id)
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if emoji:
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emoji.delete_instance()
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deleted_count += 1
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logger.info(f"批量删除表情包: {emoji_id}")
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else:
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failed_count += 1
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failed_ids.append(emoji_id)
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except Exception as e:
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logger.error(f"删除表情包 {emoji_id} 失败: {e}")
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failed_count += 1
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failed_ids.append(emoji_id)
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message = f"成功删除 {deleted_count} 个表情包"
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if failed_count > 0:
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message += f"，{failed_count} 个失败"
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return BatchDeleteResponse(
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success=True,
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message=message,
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deleted_count=deleted_count,
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failed_count=failed_count,
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failed_ids=failed_ids,
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)
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except HTTPException:
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raise
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except Exception as e:
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logger.exception(f"批量删除表情包失败: {e}")
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raise HTTPException(status_code=500, detail=f"批量删除失败: {str(e)}") from e
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@ -338,6 +338,53 @@ async def delete_expression(expression_id: int, authorization: Optional[str] = H
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raise HTTPException(status_code=500, detail=f"删除表达方式失败: {str(e)}") from e
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class BatchDeleteRequest(BaseModel):
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"""批量删除请求"""
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ids: List[int]
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@router.post("/batch/delete", response_model=ExpressionDeleteResponse)
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async def batch_delete_expressions(request: BatchDeleteRequest, authorization: Optional[str] = Header(None)):
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"""
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批量删除表达方式
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Args:
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request: 包含要删除的ID列表的请求
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authorization: Authorization header
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Returns:
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删除结果
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"""
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try:
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verify_auth_token(authorization)
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if not request.ids:
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raise HTTPException(status_code=400, detail="未提供要删除的表达方式ID")
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# 查找所有要删除的表达方式
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expressions = Expression.select().where(Expression.id.in_(request.ids))
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found_ids = [expr.id for expr in expressions]
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# 检查是否有未找到的ID
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not_found_ids = set(request.ids) - set(found_ids)
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if not_found_ids:
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logger.warning(f"部分表达方式未找到: {not_found_ids}")
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# 执行批量删除
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deleted_count = Expression.delete().where(Expression.id.in_(found_ids)).execute()
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logger.info(f"批量删除了 {deleted_count} 个表达方式")
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return ExpressionDeleteResponse(success=True, message=f"成功删除 {deleted_count} 个表达方式")
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except HTTPException:
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raise
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except Exception as e:
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logger.exception(f"批量删除表达方式失败: {e}")
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raise HTTPException(status_code=500, detail=f"批量删除表达方式失败: {str(e)}") from e
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@router.get("/stats/summary")
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async def get_expression_stats(authorization: Optional[str] = Header(None)):
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"""
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@ -75,6 +75,22 @@ class PersonDeleteResponse(BaseModel):
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message: str
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class BatchDeleteRequest(BaseModel):
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"""批量删除请求"""
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person_ids: List[str]
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class BatchDeleteResponse(BaseModel):
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"""批量删除响应"""
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success: bool
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message: str
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deleted_count: int
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failed_count: int
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failed_ids: List[str] = []
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def verify_auth_token(authorization: Optional[str]) -> bool:
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"""验证认证 Token"""
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if not authorization or not authorization.startswith("Bearer "):
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@ -334,3 +350,59 @@ async def get_person_stats(authorization: Optional[str] = Header(None)):
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except Exception as e:
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logger.exception(f"获取统计数据失败: {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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@router.post("/batch/delete", response_model=BatchDeleteResponse)
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async def batch_delete_persons(request: BatchDeleteRequest, authorization: Optional[str] = Header(None)):
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"""
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批量删除人物信息
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Args:
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request: 包含person_ids列表的请求
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authorization: Authorization header
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Returns:
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批量删除结果
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"""
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try:
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verify_auth_token(authorization)
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if not request.person_ids:
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raise HTTPException(status_code=400, detail="未提供要删除的人物ID")
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deleted_count = 0
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failed_count = 0
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failed_ids = []
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for person_id in request.person_ids:
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try:
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person = PersonInfo.get_or_none(PersonInfo.person_id == person_id)
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if person:
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person.delete_instance()
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deleted_count += 1
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logger.info(f"批量删除: {person_id}")
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else:
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failed_count += 1
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failed_ids.append(person_id)
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except Exception as e:
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logger.error(f"删除 {person_id} 失败: {e}")
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failed_count += 1
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failed_ids.append(person_id)
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message = f"成功删除 {deleted_count} 个人物"
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if failed_count > 0:
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message += f"，{failed_count} 个失败"
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return BatchDeleteResponse(
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success=True,
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message=message,
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deleted_count=deleted_count,
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failed_count=failed_count,
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failed_ids=failed_ids,
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)
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except HTTPException:
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raise
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except Exception as e:
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logger.exception(f"批量删除人物信息失败: {e}")
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raise HTTPException(status_code=500, detail=f"批量删除失败: {str(e)}") from e
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@ -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"""
|
||||
daily_buckets = defaultdict(lambda: {"requests": 0, "cost": 0.0, "tokens": 0})
|
||||
"""获取日级统计数据（优化：使用数据库聚合）"""
|
||||
# 使用strftime按日期分组
|
||||
query = (
|
||||
LLMUsage.select(
|
||||
fn.strftime("%Y-%m-%dT00:00:00", LLMUsage.timestamp).alias("day"),
|
||||
fn.COUNT(LLMUsage.id).alias("requests"),
|
||||
fn.COALESCE(fn.SUM(LLMUsage.cost), 0).alias("cost"),
|
||||
fn.COALESCE(fn.SUM(LLMUsage.prompt_tokens + LLMUsage.completion_tokens), 0).alias("tokens"),
|
||||
)
|
||||
.where((LLMUsage.timestamp >= start_time) & (LLMUsage.timestamp <= end_time))
|
||||
.group_by(fn.strftime("%Y-%m-%dT00:00:00", LLMUsage.timestamp))
|
||||
)
|
||||
|
||||
records = list(LLMUsage.select().where(LLMUsage.timestamp >= start_time).where(LLMUsage.timestamp <= end_time))
|
||||
|
||||
for record in records:
|
||||
# 获取日期键
|
||||
day_key = record.timestamp.replace(hour=0, minute=0, second=0, microsecond=0)
|
||||
day_str = day_key.isoformat()
|
||||
|
||||
daily_buckets[day_str]["requests"] += 1
|
||||
daily_buckets[day_str]["cost"] += record.cost or 0.0
|
||||
daily_buckets[day_str]["tokens"] += (record.prompt_tokens or 0) + (record.completion_tokens or 0)
|
||||
# 转换为字典
|
||||
data_dict = {row["day"]: row for row in query.dicts()}
|
||||
|
||||
# 填充所有天
|
||||
result = []
|
||||
current = start_time.replace(hour=0, minute=0, second=0, microsecond=0)
|
||||
while current <= end_time:
|
||||
day_str = current.isoformat()
|
||||
data = daily_buckets.get(day_str, {"requests": 0, "cost": 0.0, "tokens": 0})
|
||||
result.append(
|
||||
TimeSeriesData(timestamp=day_str, requests=data["requests"], cost=data["cost"], tokens=data["tokens"])
|
||||
)
|
||||
day_str = current.strftime("%Y-%m-%dT00:00:00")
|
||||
if day_str in data_dict:
|
||||
row = data_dict[day_str]
|
||||
result.append(
|
||||
TimeSeriesData(timestamp=day_str, requests=row["requests"], cost=row["cost"], tokens=row["tokens"])
|
||||
)
|
||||
else:
|
||||
result.append(TimeSeriesData(timestamp=day_str, requests=0, cost=0.0, tokens=0))
|
||||
current += timedelta(days=1)
|
||||
|
||||
return result
|
||||
|
|
|
|||
Loading…
Reference in New Issue