gardel/Mofox-Core
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

v0.5.10 在根目录生成统计信息

Browse Source
This commit is contained in:
SengokuCola
2025-03-07 20:41:12 +08:00
parent d2f348ae2c
commit f249f50994
4 changed files with 338 additions and 4 deletions
Download Patch File Download Diff File Expand all files Collapse all files

74
llm_statistics.txt Normal file
View File

@@ -0,0 +1,74 @@
LLM请求统计报告 (生成时间: 2025-03-07 20:38:57)
==================================================
所有时间统计
======
总请求数: 858
总Token数: 285415
总花费: ¥0.3309
按模型统计:
- Pro/Qwen/Qwen2-VL-7B-Instruct: 67次 (花费: ¥0.0272)
- Pro/Qwen/Qwen2.5-7B-Instruct: 646次 (花费: ¥0.0718)
- Pro/deepseek-ai/DeepSeek-V3: 9次 (花费: ¥0.0193)
- Qwen/QwQ-32B: 29次 (花费: ¥0.1246)
- Qwen/Qwen2.5-32B-Instruct: 55次 (花费: ¥0.0771)
- deepseek-ai/DeepSeek-R1-Distill-Qwen-32B: 3次 (花费: ¥0.0067)
- deepseek-ai/DeepSeek-V2.5: 49次 (花费: ¥0.0043)
按请求类型统计:
- chat: 858次 (花费: ¥0.3309)
最近7天统计
======
总请求数: 858
总Token数: 285415
总花费: ¥0.3309
按模型统计:
- Pro/Qwen/Qwen2-VL-7B-Instruct: 67次 (花费: ¥0.0272)
- Pro/Qwen/Qwen2.5-7B-Instruct: 646次 (花费: ¥0.0718)
- Pro/deepseek-ai/DeepSeek-V3: 9次 (花费: ¥0.0193)
- Qwen/QwQ-32B: 29次 (花费: ¥0.1246)
- Qwen/Qwen2.5-32B-Instruct: 55次 (花费: ¥0.0771)
- deepseek-ai/DeepSeek-R1-Distill-Qwen-32B: 3次 (花费: ¥0.0067)
- deepseek-ai/DeepSeek-V2.5: 49次 (花费: ¥0.0043)
按请求类型统计:
- chat: 858次 (花费: ¥0.3309)
最近24小时统计
========
总请求数: 858
总Token数: 285415
总花费: ¥0.3309
按模型统计:
- Pro/Qwen/Qwen2-VL-7B-Instruct: 67次 (花费: ¥0.0272)
- Pro/Qwen/Qwen2.5-7B-Instruct: 646次 (花费: ¥0.0718)
- Pro/deepseek-ai/DeepSeek-V3: 9次 (花费: ¥0.0193)
- Qwen/QwQ-32B: 29次 (花费: ¥0.1246)
- Qwen/Qwen2.5-32B-Instruct: 55次 (花费: ¥0.0771)
- deepseek-ai/DeepSeek-R1-Distill-Qwen-32B: 3次 (花费: ¥0.0067)
- deepseek-ai/DeepSeek-V2.5: 49次 (花费: ¥0.0043)
按请求类型统计:
- chat: 858次 (花费: ¥0.3309)
最近1小时统计
=======
总请求数: 858
总Token数: 285415
总花费: ¥0.3309
按模型统计:
- Pro/Qwen/Qwen2-VL-7B-Instruct: 67次 (花费: ¥0.0272)
- Pro/Qwen/Qwen2.5-7B-Instruct: 646次 (花费: ¥0.0718)
- Pro/deepseek-ai/DeepSeek-V3: 9次 (花费: ¥0.0193)
- Qwen/QwQ-32B: 29次 (花费: ¥0.1246)
- Qwen/Qwen2.5-32B-Instruct: 55次 (花费: ¥0.0771)
- deepseek-ai/DeepSeek-R1-Distill-Qwen-32B: 3次 (花费: ¥0.0067)
- deepseek-ai/DeepSeek-V2.5: 49次 (花费: ¥0.0043)
按请求类型统计:
- chat: 858次 (花费: ¥0.3309)

8
src/plugins/chat/__init__.py
View File

@@ -14,6 +14,10 @@ from nonebot.rule import to_me
from .bot import chat_bot
from .emoji_manager import emoji_manager
import time
from ..utils.statistic import LLMStatistics
# 创建LLM统计实例
llm_stats = LLMStatistics("llm_statistics.txt")
# 添加标志变量
_message_manager_started = False
@@ -57,6 +61,10 @@ scheduler = require("nonebot_plugin_apscheduler").scheduler
@driver.on_startup
async def start_background_tasks():
"""启动后台任务"""
# 启动LLM统计
llm_stats.start()
print("\033[1;32m[初始化]\033[0m LLM统计功能已启动")
# 只启动表情包管理任务
asyncio.create_task(emoji_manager.start_periodic_check(interval_MINS=global_config.EMOJI_CHECK_INTERVAL))
await bot_schedule.initialize()

98
src/plugins/models/utils_model.py
View File

@@ -8,6 +8,8 @@ from nonebot import get_driver
from loguru import logger
from ..chat.config import global_config
from ..chat.utils_image import compress_base64_image_by_scale
from datetime import datetime
from ...common.database import Database
driver = get_driver()
config = driver.config
@@ -24,6 +26,75 @@ class LLM_request:
raise ValueError(f"配置错误：找不到对应的配置项 - {str(e)}") from e
self.model_name = model["name"]
self.params = kwargs
self.pri_in = model.get("pri_in", 0)
self.pri_out = model.get("pri_out", 0)
# 获取数据库实例
self.db = Database.get_instance()
self._init_database()
def _init_database(self):
"""初始化数据库集合"""
try:
# 创建llm_usage集合的索引
self.db.db.llm_usage.create_index([("timestamp", 1)])
self.db.db.llm_usage.create_index([("model_name", 1)])
self.db.db.llm_usage.create_index([("user_id", 1)])
self.db.db.llm_usage.create_index([("request_type", 1)])
except Exception as e:
logger.error(f"创建数据库索引失败: {e}")
def _record_usage(self, prompt_tokens: int, completion_tokens: int, total_tokens: int,
user_id: str = "system", request_type: str = "chat",
endpoint: str = "/chat/completions"):
"""记录模型使用情况到数据库
Args:
prompt_tokens: 输入token数
completion_tokens: 输出token数
total_tokens: 总token数
user_id: 用户ID，默认为system
request_type: 请求类型(chat/embedding/image等)
endpoint: API端点
"""
try:
usage_data = {
"model_name": self.model_name,
"user_id": user_id,
"request_type": request_type,
"endpoint": endpoint,
"prompt_tokens": prompt_tokens,
"completion_tokens": completion_tokens,
"total_tokens": total_tokens,
"cost": self._calculate_cost(prompt_tokens, completion_tokens),
"status": "success",
"timestamp": datetime.now()
}
self.db.db.llm_usage.insert_one(usage_data)
logger.info(
f"Token使用情况 - 模型: {self.model_name}, "
f"用户: {user_id}, 类型: {request_type}, "
f"提示词: {prompt_tokens}, 完成: {completion_tokens}, "
f"总计: {total_tokens}"
)
except Exception as e:
logger.error(f"记录token使用情况失败: {e}")
def _calculate_cost(self, prompt_tokens: int, completion_tokens: int) -> float:
"""计算API调用成本
使用模型的pri_in和pri_out价格计算输入和输出的成本
Args:
prompt_tokens: 输入token数量
completion_tokens: 输出token数量
Returns:
float: 总成本（元）
"""
# 使用模型的pri_in和pri_out计算成本
input_cost = (prompt_tokens / 1000000) * self.pri_in
output_cost = (completion_tokens / 1000000) * self.pri_out
return round(input_cost + output_cost, 6)
async def _execute_request(
self,
@@ -33,6 +104,8 @@ class LLM_request:
payload: dict = None,
retry_policy: dict = None,
response_handler: callable = None,
user_id: str = "system",
request_type: str = "chat"
):
"""统一请求执行入口
Args:
@@ -40,10 +113,10 @@ class LLM_request:
prompt: prompt文本
image_base64: 图片的base64编码
payload: 请求体数据
is_async: 是否异步
retry_policy: 自定义重试策略
(示例: {"max_retries":3, "base_wait":15, "retry_codes":[429,500]})
response_handler: 自定义响应处理器
user_id: 用户ID
request_type: 请求类型
"""
# 合并重试策略
default_retry = {
@@ -105,7 +178,7 @@ class LLM_request:
result = await response.json()
# 使用自定义处理器或默认处理
return response_handler(result) if response_handler else self._default_response_handler(result)
return response_handler(result) if response_handler else self._default_response_handler(result, user_id, request_type, endpoint)
except Exception as e:
if retry < policy["max_retries"] - 1:
@@ -145,7 +218,8 @@ class LLM_request:
**self.params
}
def _default_response_handler(self, result: dict) -> Tuple:
def _default_response_handler(self, result: dict, user_id: str = "system",
request_type: str = "chat", endpoint: str = "/chat/completions") -> Tuple:
"""默认响应解析"""
if "choices" in result and result["choices"]:
message = result["choices"][0]["message"]
@@ -157,6 +231,21 @@ class LLM_request:
if not reasoning_content:
reasoning_content = reasoning
# 记录token使用情况
usage = result.get("usage", {})
if usage:
prompt_tokens = usage.get("prompt_tokens", 0)
completion_tokens = usage.get("completion_tokens", 0)
total_tokens = usage.get("total_tokens", 0)
self._record_usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=total_tokens,
user_id=user_id,
request_type=request_type,
endpoint=endpoint
)
return content, reasoning_content
return "没有返回结果", ""
@@ -244,3 +333,4 @@ class LLM_request:
response_handler=embedding_handler
)
return embedding

162
src/plugins/utils/statistic.py Normal file
View File

@@ -0,0 +1,162 @@
from typing import Dict, List, Any
import time
import threading
import json
from datetime import datetime, timedelta
from collections import defaultdict
from ...common.database import Database
class LLMStatistics:
def __init__(self, output_file: str = "llm_statistics.txt"):
"""初始化LLM统计类
Args:
output_file: 统计结果输出文件路径
"""
self.db = Database.get_instance()
self.output_file = output_file
self.running = False
self.stats_thread = None
def start(self):
"""启动统计线程"""
if not self.running:
self.running = True
self.stats_thread = threading.Thread(target=self._stats_loop)
self.stats_thread.daemon = True
self.stats_thread.start()
def stop(self):
"""停止统计线程"""
self.running = False
if self.stats_thread:
self.stats_thread.join()
def _collect_statistics_for_period(self, start_time: datetime) -> Dict[str, Any]:
"""收集指定时间段的LLM请求统计数据
Args:
start_time: 统计开始时间
"""
stats = {
"total_requests": 0,
"requests_by_type": defaultdict(int),
"requests_by_user": defaultdict(int),
"requests_by_model": defaultdict(int),
"average_tokens": 0,
"total_tokens": 0,
"total_cost": 0.0,
"costs_by_user": defaultdict(float),
"costs_by_type": defaultdict(float),
"costs_by_model": defaultdict(float)
}
cursor = self.db.db.llm_usage.find({
"timestamp": {"$gte": start_time}
})
total_requests = 0
for doc in cursor:
stats["total_requests"] += 1
request_type = doc.get("request_type", "unknown")
user_id = str(doc.get("user_id", "unknown"))
model_name = doc.get("model_name", "unknown")
stats["requests_by_type"][request_type] += 1
stats["requests_by_user"][user_id] += 1
stats["requests_by_model"][model_name] += 1
prompt_tokens = doc.get("prompt_tokens", 0)
completion_tokens = doc.get("completion_tokens", 0)
stats["total_tokens"] += prompt_tokens + completion_tokens
cost = doc.get("cost", 0.0)
stats["total_cost"] += cost
stats["costs_by_user"][user_id] += cost
stats["costs_by_type"][request_type] += cost
stats["costs_by_model"][model_name] += cost
total_requests += 1
if total_requests > 0:
stats["average_tokens"] = stats["total_tokens"] / total_requests
return stats
def _collect_all_statistics(self) -> Dict[str, Dict[str, Any]]:
"""收集所有时间范围的统计数据"""
now = datetime.now()
return {
"all_time": self._collect_statistics_for_period(datetime.min),
"last_7_days": self._collect_statistics_for_period(now - timedelta(days=7)),
"last_24_hours": self._collect_statistics_for_period(now - timedelta(days=1)),
"last_hour": self._collect_statistics_for_period(now - timedelta(hours=1))
}
def _format_stats_section(self, stats: Dict[str, Any], title: str) -> str:
"""格式化统计部分的输出
Args:
stats: 统计数据
title: 部分标题
"""
output = []
output.append(f"\n{title}")
output.append("=" * len(title))
output.append(f"总请求数: {stats['total_requests']}")
if stats['total_requests'] > 0:
output.append(f"总Token数: {stats['total_tokens']}")
output.append(f"总花费: ¥{stats['total_cost']:.4f}")
output.append("\n按模型统计:")
for model_name, count in sorted(stats["requests_by_model"].items()):
cost = stats["costs_by_model"][model_name]
output.append(f"- {model_name}: {count}次 (花费: ¥{cost:.4f})")
output.append("\n按请求类型统计:")
for req_type, count in sorted(stats["requests_by_type"].items()):
cost = stats["costs_by_type"][req_type]
output.append(f"- {req_type}: {count}次 (花费: ¥{cost:.4f})")
return "\n".join(output)
def _save_statistics(self, all_stats: Dict[str, Dict[str, Any]]):
"""将统计结果保存到文件"""
current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
output = []
output.append(f"LLM请求统计报告 (生成时间: {current_time})")
output.append("=" * 50)
# 添加各个时间段的统计
sections = [
("所有时间统计", "all_time"),
("最近7天统计", "last_7_days"),
("最近24小时统计", "last_24_hours"),
("最近1小时统计", "last_hour")
]
for title, key in sections:
output.append(self._format_stats_section(all_stats[key], title))
# 写入文件
with open(self.output_file, "w", encoding="utf-8") as f:
f.write("\n".join(output))
def _stats_loop(self):
"""统计循环，每1分钟运行一次"""
while self.running:
try:
all_stats = self._collect_all_statistics()
self._save_statistics(all_stats)
except Exception as e:
print(f"\033[1;31m[错误]\033[0m 统计数据处理失败: {e}")
# 等待1分钟
for _ in range(60):
if not self.running:
break
time.sleep(1)