v0.5.10 在根目录生成统计信息

src/plugins/chat/__init__.py

@@ -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()

src/plugins/models/utils_model.py

@@ -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
+

src/plugins/utils/statistic.py

@@ -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)