LangBot监控和日志系统

摘要

监控和日志系统是现代软件应用稳定性运维的重要保障。LangBot作为一个企业级聊天机器人平台，内置了完善的监控和日志系统，能够实时跟踪应用状态、性能指标和错误信息。本文将深入探讨LangBot监控和日志系统的架构设计、核心组件、数据收集机制以及最佳实践，帮助开发者和运维人员更好地理解和使用这一重要功能。

正文

1. 监控和日志系统概述

LangBot的监控和日志系统具有以下特点：

• 多层次监控：涵盖应用层、系统层、业务层等多个维度
• 实时日志：支持实时日志收集、分析和查询
• 性能指标：收集关键性能指标(KPI)用于性能分析
• 错误追踪：完整的错误追踪和报警机制
• 可扩展性：支持集成第三方监控和日志系统
• 可视化展示：提供直观的监控面板和报表

2. 系统架构

LangBot监控和日志系统的架构如下图所示：

3. 核心组件

3.1 日志管理器

import logging
import asyncio
import json
import traceback
from datetime import datetime
from typing import Optional, Dict, Any
import aiofiles

class LogManager:
    """日志管理器"""
    
    def __init__(self, ap: "app.Application"):
        self.ap = ap
        self.logger = logging.getLogger("langbot")
        self.log_cache = []
        self.cache_size = 1000
        self.log_file = "logs/langbot.log"
        self.log_level = "INFO"
    
    async def initialize(self):
        """初始化日志管理器"""
        # 配置日志级别
        log_config = self.ap.instance_config.data.get("logging", {})
        self.log_level = log_config.get("level", "INFO")
        
        # 设置日志格式
        log_format = log_config.get("format", "standard")
        if log_format == "json":
            formatter = JsonFormatter()
        else:
            formatter = StandardFormatter()
        
        # 配置控制台处理器
        console_handler = logging.StreamHandler()
        console_handler.setFormatter(formatter)
        console_handler.setLevel(getattr(logging, self.log_level))
        
        # 配置文件处理器
        file_handler = logging.FileHandler(self.log_file)
        file_handler.setFormatter(formatter)
        file_handler.setLevel(getattr(logging, self.log_level))
        
        # 添加处理器到logger
        self.logger.addHandler(console_handler)
        self.logger.addHandler(file_handler)
        self.logger.setLevel(getattr(logging, self.log_level))
        
        # 启动日志写入任务
        self.ap.task_mgr.create_task(
            self._log_writer_worker(),
            name="log-writer"
        )
    
    def debug(self, message: str, extra: Optional[Dict[str, Any]] = None):
        """记录调试日志"""
        self._log(logging.DEBUG, message, extra)
    
    def info(self, message: str, extra: Optional[Dict[str, Any]] = None):
        """记录信息日志"""
        self._log(logging.INFO, message, extra)
    
    def warning(self, message: str, extra: Optional[Dict[str, Any]] = None):
        """记录警告日志"""
        self._log(logging.WARNING, message, extra)
    
    def error(self, message: str, extra: Optional[Dict[str, Any]] = None):
        """记录错误日志"""
        self._log(logging.ERROR, message, extra)
    
    def critical(self, message: str, extra: Optional[Dict[str, Any]] = None):
        """记录严重错误日志"""
        self._log(logging.CRITICAL, message, extra)
    
    def _log(self, level: int, message: str, extra: Optional[Dict[str, Any]] = None):
        """记录日志"""
        # 添加时间戳和额外信息
        log_entry = {
            "timestamp": datetime.now().isoformat(),
            "level": logging.getLevelName(level),
            "message": message,
            "extra": extra or {}
        }
        
        # 添加到缓存
        self.log_cache.append(log_entry)
        if len(self.log_cache) > self.cache_size:
            self.log_cache.pop(0)
        
        # 记录到标准日志系统
        self.logger.log(level, message, extra=extra)
    
    async def _log_writer_worker(self):
        """日志写入工作者"""
        while True:
            try:
                if self.log_cache:
                    # 批量写入日志
                    logs_to_write = self.log_cache.copy()
                    self.log_cache.clear()
                    
                    async with aiofiles.open(self.log_file, mode='a') as f:
                        for log_entry in logs_to_write:
                            await f.write(json.dumps(log_entry, ensure_ascii=False) + '\n')
                
                # 每秒检查一次
                await asyncio.sleep(1)
            except Exception as e:
                # 避免日志写入错误影响主程序
                print(f"日志写入错误: {e}")

# 日志格式化器
class StandardFormatter(logging.Formatter):
    """标准日志格式化器"""
    
    def format(self, record):
        timestamp = datetime.fromtimestamp(record.created).strftime('%Y-%m-%d %H:%M:%S')
        return f"[{timestamp}] [{record.levelname}] {record.name}: {record.getMessage()}"

class JsonFormatter(logging.Formatter):
    """JSON日志格式化器"""
    
    def format(self, record):
        log_entry = {
            "timestamp": datetime.fromtimestamp(record.created).isoformat(),
            "level": record.levelname,
            "logger": record.name,
            "message": record.getMessage(),
            "module": record.module,
            "function": record.funcName,
            "line": record.lineno
        }
        
        # 添加额外信息
        if hasattr(record, 'extra') and record.extra:
            log_entry["extra"] = record.extra
        
        return json.dumps(log_entry, ensure_ascii=False)

3.2 指标收集器

import time
import asyncio
from collections import defaultdict
from typing import Dict, List, Any
from dataclasses import dataclass, asdict

@dataclass
class Metric:
    """指标数据类"""
    name: str
    value: float
    timestamp: float
    tags: Dict[str, str]
    type: str  # counter, gauge, histogram, summary

class MetricsCollector:
    """指标收集器"""
    
    def __init__(self, ap: "app.Application"):
        self.ap = ap
        self.metrics: Dict[str, List[Metric]] = defaultdict(list)
        self.max_metrics_per_name = 1000
        self.collect_interval = 60  # 60秒收集一次
    
    async def initialize(self):
        """初始化指标收集器"""
        # 启动指标收集任务
        self.ap.task_mgr.create_task(
            self._metrics_collector_worker(),
            name="metrics-collector"
        )
    
    def increment_counter(self, name: str, value: float = 1, tags: Dict[str, str] = None):
        """
        增加计数器
        
        Args:
            name: 指标名称
            value: 增加值
            tags: 标签
        """
        metric = Metric(
            name=name,
            value=value,
            timestamp=time.time(),
            tags=tags or {},
            type="counter"
        )
        self._store_metric(metric)
    
    def set_gauge(self, name: str, value: float, tags: Dict[str, str] = None):
        """
        设置仪表值
        
        Args:
            name: 指标名称
            value: 仪表值
            tags: 标签
        """
        metric = Metric(
            name=name,
            value=value,
            timestamp=time.time(),
            tags=tags or {},
            type="gauge"
        )
        self._store_metric(metric)
    
    def observe_histogram(self, name: str, value: float, tags: Dict[str, str] = None):
        """
        观察直方图值
        
        Args:
            name: 指标名称
            value: 观察值
            tags: 标签
        """
        metric = Metric(
            name=name,
            value=value,
            timestamp=time.time(),
            tags=tags or {},
            type="histogram"
        )
        self._store_metric(metric)
    
    def _store_metric(self, metric: Metric):
        """存储指标"""
        self.metrics[metric.name].append(metric)
        
        # 限制每个指标的存储数量
        if len(self.metrics[metric.name]) > self.max_metrics_per_name:
            # 保留最新的指标
            self.metrics[metric.name] = self.metrics[metric.name][-self.max_metrics_per_name:]
    
    async def _metrics_collector_worker(self):
        """指标收集工作者"""
        while True:
            try:
                # 收集系统指标
                await self._collect_system_metrics()
                
                # 收集应用指标
                await self._collect_application_metrics()
                
                # 等待下次收集
                await asyncio.sleep(self.collect_interval)
            except Exception as e:
                self.ap.logger.error(f"指标收集错误: {e}")
    
    async def _collect_system_metrics(self):
        """收集系统指标"""
        try:
            import psutil
            
            # CPU使用率
            cpu_percent = psutil.cpu_percent(interval=1)
            self.set_gauge("system.cpu.percent", cpu_percent)
            
            # 内存使用率
            memory = psutil.virtual_memory()
            self.set_gauge("system.memory.percent", memory.percent)
            self.set_gauge("system.memory.available", memory.available)
            
            # 磁盘使用率
            disk = psutil.disk_usage("/")
            self.set_gauge("system.disk.percent", disk.percent)
            self.set_gauge("system.disk.free", disk.free)
            
        except ImportError:
            self.ap.logger.warning("psutil未安装，无法收集系统指标")
        except Exception as e:
            self.ap.logger.error(f"收集系统指标失败: {e}")
    
    async def _collect_application_metrics(self):
        """收集应用指标"""
        # 活跃任务数
        active_tasks = len(self.ap.task_mgr.tasks)
        self.set_gauge("app.tasks.active", active_tasks)
        
        # 数据库连接数
        # 这里需要根据实际的数据库连接池实现来获取
        # db_connections = self.ap.database_mgr.get_active_connections()
        # self.set_gauge("app.db.connections", db_connections)
        
        # 在线机器人数量
        # active_bots = len(self.ap.platform_mgr.adapters)
        # self.set_gauge("app.bots.active", active_bots)

# 使用装饰器收集函数执行时间
def timer(metric_name: str, tags: Dict[str, str] = None):
    """
    计时装饰器
    
    Args:
        metric_name: 指标名称
        tags: 标签
    """
    def decorator(func):
        async def wrapper(*args, **kwargs):
            start_time = time.time()
            try:
                result = await func(*args, **kwargs)
                return result
            finally:
                duration = time.time() - start_time
                metrics_collector.observe_histogram(
                    metric_name, 
                    duration, 
                    tags
                )
        return wrapper
    return decorator

3.3 错误追踪器

import traceback
import uuid
from datetime import datetime
from typing import Optional, Dict, Any

class ErrorTracker:
    """错误追踪器"""
    
    def __init__(self, ap: "app.Application"):
        self.ap = ap
        self.errors = []
        self.max_errors = 1000
    
    async def initialize(self):
        """初始化错误追踪器"""
        pass
    
    def track_exception(self, exception: Exception, context: Optional[Dict[str, Any]] = None):
        """
        追踪异常
        
        Args:
            exception: 异常对象
            context: 上下文信息
        """
        error_id = str(uuid.uuid4())
        error_info = {
            "id": error_id,
            "timestamp": datetime.now().isoformat(),
            "type": type(exception).__name__,
            "message": str(exception),
            "traceback": traceback.format_exc(),
            "context": context or {}
        }
        
        # 添加到错误列表
        self.errors.append(error_info)
        if len(self.errors) > self.max_errors:
            self.errors.pop(0)
        
        # 记录错误日志
        self.ap.logger.error(
            f"错误ID: {error_id}, 类型: {type(exception).__name__}, 消息: {str(exception)}",
            extra={"error_id": error_id, "context": context}
        )
        
        # 增加错误计数器
        self.ap.metrics_collector.increment_counter("app.errors.total")
        
        # 如果是严重错误，发送报警
        if isinstance(exception, (SystemExit, KeyboardInterrupt)):
            self._send_alert(error_info)
    
    def get_errors(self, limit: int = 50) -> list:
        """
        获取错误列表
        
        Args:
            limit: 限制数量
            
        Returns:
            错误列表
        """
        return self.errors[-limit:]
    
    def get_error_by_id(self, error_id: str) -> Optional[Dict[str, Any]]:
        """
        根据ID获取错误详情
        
        Args:
            error_id: 错误ID
            
        Returns:
            错误详情
        """
        for error in self.errors:
            if error["id"] == error_id:
                return error
        return None
    
    def _send_alert(self, error_info: Dict[str, Any]):
        """
        发送报警
        
        Args:
            error_info: 错误信息
        """
        # 这里可以集成邮件、短信、微信等报警方式
        self.ap.logger.critical(f"严重错误报警: {error_info['message']}")
        
        # 如果配置了报警webhook，发送通知
        alert_config = self.ap.instance_config.data.get("alerting", {})
        webhook_url = alert_config.get("webhook_url")
        if webhook_url:
            self._send_webhook_alert(webhook_url, error_info)
    
    async def _send_webhook_alert(self, webhook_url: str, error_info: Dict[str, Any]):
        """
        发送Webhook报警
        
        Args:
            webhook_url: Webhook URL
            error_info: 错误信息
        """
        try:
            import aiohttp
            
            async with aiohttp.ClientSession() as session:
                alert_data = {
                    "error_id": error_info["id"],
                    "timestamp": error_info["timestamp"],
                    "type": error_info["type"],
                    "message": error_info["message"],
                    "context": error_info["context"]
                }
                
                await session.post(webhook_url, json=alert_data)
        except Exception as e:
            self.ap.logger.error(f"发送Webhook报警失败: {e}")

4. 监控面板

4.1 实时监控API

from fastapi import APIRouter, WebSocket
from typing import Dict, Any
import json
import asyncio

router = APIRouter(prefix="/api/v1/monitoring", tags=["monitoring"])

@router.get("/metrics")
async def get_metrics():
    """获取指标数据"""
    metrics_data = {}
    for name, metrics in app.metrics_collector.metrics.items():
        # 获取最新的指标值
        if metrics:
            latest_metric = metrics[-1]
            metrics_data[name] = {
                "value": latest_metric.value,
                "timestamp": latest_metric.timestamp,
                "tags": latest_metric.tags
            }
    
    return {
        "success": True,
        "data": metrics_data
    }

@router.get("/logs")
async def get_logs(limit: int = 100):
    """获取日志数据"""
    logs = app.log_manager.log_cache[-limit:] if app.log_manager.log_cache else []
    return {
        "success": True,
        "data": logs
    }

@router.get("/errors")
async def get_errors(limit: int = 50):
    """获取错误数据"""
    errors = app.error_tracker.get_errors(limit)
    return {
        "success": True,
        "data": errors
    }

@router.websocket("/ws")
async def websocket_endpoint(websocket: WebSocket):
    """WebSocket实时监控"""
    await websocket.accept()
    
    try:
        while True:
            # 收集实时数据
            data = {
                "timestamp": datetime.now().isoformat(),
                "metrics": {},
                "logs": app.log_manager.log_cache[-10:] if app.log_manager.log_cache else [],
                "errors": app.error_tracker.get_errors(5)
            }
            
            # 添加指标数据
            for name, metrics in app.metrics_collector.metrics.items():
                if metrics:
                    latest_metric = metrics[-1]
                    data["metrics"][name] = {
                        "value": latest_metric.value,
                        "type": latest_metric.type
                    }
            
            # 发送数据
            await websocket.send_text(json.dumps(data, ensure_ascii=False))
            
            # 每2秒发送一次更新
            await asyncio.sleep(2)
    except Exception as e:
        app.logger.error(f"WebSocket连接错误: {e}")
    finally:
        await websocket.close()

4.2 前端监控面板

// React监控面板组件
import React, { useState, useEffect } from 'react';
import { useWebSocket } from '../hooks/useWebSocket';

interface MetricData {
  value: number;
  type: string;
}

interface LogEntry {
  timestamp: string;
  level: string;
  message: string;
  extra?: Record<string, any>;
}

interface ErrorEntry {
  id: string;
  timestamp: string;
  type: string;
  message: string;
  context: Record<string, any>;
}

export function MonitoringDashboard() {
  const [metrics, setMetrics] = useState<Record<string, MetricData>>({});
  const [logs, setLogs] = useState<LogEntry[]>([]);
  const [errors, setErrors] = useState<ErrorEntry[]>([]);
  const [connected, setConnected] = useState(false);
  
  const wsUrl = `${window.location.protocol === 'https:' ? 'wss:' : 'ws:'}//${window.location.host}/api/v1/monitoring/ws`;
  const { data, error, connect } = useWebSocket(wsUrl);
  
  useEffect(() => {
    connect();
  }, [connect]);
  
  useEffect(() => {
    if (data) {
      setConnected(true);
      const parsedData = JSON.parse(data);
      
      setMetrics(parsedData.metrics || {});
      setLogs(prev => {
        const newLogs = [...prev, ...(parsedData.logs || [])];
        return newLogs.slice(-100); // 保留最近100条日志
      });
      setErrors(parsedData.errors || []);
    }
  }, [data]);
  
  useEffect(() => {
    if (error) {
      setConnected(false);
      console.error('WebSocket错误:', error);
    }
  }, [error]);
  
  return (
    <div className="monitoring-dashboard">
      <header>
        <h1>系统监控面板</h1>
        <div className={`connection-status ${connected ? 'connected' : 'disconnected'}`}>
          {connected ? '已连接' : '连接断开'}
        </div>
      </header>
      
      <div className="dashboard-grid">
        {/* 指标面板 */}
        <div className="panel metrics-panel">
          <h2>系统指标</h2>
          <div className="metrics-grid">
            {Object.entries(metrics).map(([name, metric]) => (
              <div key={name} className="metric-card">
                <div className="metric-name">{name}</div>
                <div className="metric-value">{metric.value.toFixed(2)}</div>
                <div className="metric-type">{metric.type}</div>
              </div>
            ))}
          </div>
        </div>
        
        {/* 日志面板 */}
        <div className="panel logs-panel">
          <h2>实时日志</h2>
          <div className="logs-list">
            {logs.map((log, index) => (
              <div key={index} className={`log-entry level-${log.level.toLowerCase()}`}>
                <span className="timestamp">[{log.timestamp}]</span>
                <span className="level">[{log.level}]</span>
                <span className="message">{log.message}</span>
                {log.extra && (
                  <span className="extra">额外信息: {JSON.stringify(log.extra)}</span>
                )}
              </div>
            ))}
          </div>
        </div>
        
        {/* 错误面板 */}
        <div className="panel errors-panel">
          <h2>最近错误</h2>
          <div className="errors-list">
            {errors.map(error => (
              <div key={error.id} className="error-entry">
                <div className="error-header">
                  <span className="error-id">ID: {error.id}</span>
                  <span className="error-time">{error.timestamp}</span>
                </div>
                <div className="error-type">类型: {error.type}</div>
                <div className="error-message">消息: {error.message}</div>
                {Object.keys(error.context).length > 0 && (
                  <div className="error-context">
                    上下文: {JSON.stringify(error.context)}
                  </div>
                )}
              </div>
            ))}
          </div>
        </div>
      </div>
    </div>
  );
}

// WebSocket Hook
export function useWebSocket(url: string) {
  const [data, setData] = useState<string | null>(null);
  const [error, setError] = useState<string | null>(null);
  const [ws, setWs] = useState<WebSocket | null>(null);
  
  const connect = useCallback(() => {
    try {
      const websocket = new WebSocket(url);
      
      websocket.onopen = () => {
        console.log('WebSocket连接已建立');
      };
      
      websocket.onmessage = (event) => {
        setData(event.data);
      };
      
      websocket.onerror = (event) => {
        setError('WebSocket连接错误');
      };
      
      websocket.onclose = () => {
        setError('WebSocket连接已关闭');
      };
      
      setWs(websocket);
    } catch (err) {
      setError('WebSocket连接失败');
    }
  }, [url]);
  
  const disconnect = useCallback(() => {
    if (ws) {
      ws.close();
      setWs(null);
    }
  }, [ws]);
  
  useEffect(() => {
    return () => {
      disconnect();
    };
  }, [disconnect]);
  
  return { data, error, connect, disconnect };
}

5. 第三方集成

5.1 Prometheus集成

# Prometheus指标导出
from prometheus_client import Counter, Gauge, Histogram, generate_latest
from fastapi import Response

# 定义Prometheus指标
REQUEST_COUNT = Counter('http_requests_total', 'Total HTTP Requests', ['method', 'endpoint', 'status'])
REQUEST_DURATION = Histogram('http_request_duration_seconds', 'HTTP request duration in seconds', ['method', 'endpoint'])
ACTIVE_CONNECTIONS = Gauge('active_connections', 'Number of active connections')
CPU_USAGE = Gauge('cpu_usage_percent', 'CPU usage percentage')
MEMORY_USAGE = Gauge('memory_usage_percent', 'Memory usage percentage')

@router.get("/metrics/prometheus")
async def prometheus_metrics():
    """Prometheus指标端点"""
    # 更新系统指标
    try:
        import psutil
        CPU_USAGE.set(psutil.cpu_percent())
        MEMORY_USAGE.set(psutil.virtual_memory().percent)
    except Exception:
        pass
    
    # 返回Prometheus格式的指标
    return Response(generate_latest(), media_type="text/plain")

5.2 日志收集集成

# ELK集成示例
import logging
from logging.handlers import HTTPHandler

class ELKHandler(HTTPHandler):
    """ELK日志处理器"""
    
    def __init__(self, host, url, method="POST"):
        super().__init__(host, url, method)
    
    def mapLogRecord(self, record):
        """映射日志记录到ELK格式"""
        return {
            "@timestamp": datetime.fromtimestamp(record.created).isoformat(),
            "level": record.levelname,
            "logger": record.name,
            "message": record.getMessage(),
            "module": record.module,
            "function": record.funcName,
            "line": record.lineno,
            "extra": getattr(record, 'extra', {})
        }

# 配置ELK集成
def setup_elk_logging():
    """设置ELK日志集成"""
    elk_config = app.instance_config.data.get("logging", {}).get("elk", {})
    if elk_config.get("enabled", False):
        elk_handler = ELKHandler(
            elk_config.get("host", "localhost:9200"),
            elk_config.get("url", "/logs/_doc")
        )
        elk_handler.setLevel(getattr(logging, elk_config.get("level", "INFO")))
        
        logger = logging.getLogger("langbot")
        logger.addHandler(elk_handler)

6. 性能优化

6.1 日志采样

class SamplingLogManager(LogManager):
    """采样日志管理器"""
    
    def __init__(self, ap: "app.Application"):
        super().__init__(ap)
        self.sampling_rate = 0.1  # 10%采样率
        self.debug_sampling_rate = 0.01  # 调试日志1%采样率
    
    def debug(self, message: str, extra: Optional[Dict[str, Any]] = None):
        """采样记录调试日志"""
        if random.random() < self.debug_sampling_rate:
            super().debug(message, extra)
    
    def info(self, message: str, extra: Optional[Dict[str, Any]] = None):
        """采样记录信息日志"""
        if random.random() < self.sampling_rate:
            super().info(message, extra)

6.2 异步日志写入

class AsyncLogManager(LogManager):
    """异步日志管理器"""
    
    def __init__(self, ap: "app.Application"):
        super().__init__(ap)
        self.log_queue = asyncio.Queue(maxsize=10000)
        self.batch_size = 100
        self.flush_interval = 5  # 5秒刷新一次
    
    async def initialize(self):
        """初始化异步日志管理器"""
        await super().initialize()
        
        # 启动异步写入任务
        self.ap.task_mgr.create_task(
            self._async_log_writer(),
            name="async-log-writer"
        )
    
    def _log(self, level: int, message: str, extra: Optional[Dict[str, Any]] = None):
        """异步记录日志"""
        log_entry = {
            "timestamp": datetime.now().isoformat(),
            "level": logging.getLevelName(level),
            "message": message,
            "extra": extra or {}
        }
        
        # 尝试添加到队列，如果队列满则丢弃
        try:
            self.log_queue.put_nowait(log_entry)
        except asyncio.QueueFull:
            # 队列满时丢弃日志，避免阻塞主程序
            pass
    
    async def _async_log_writer(self):
        """异步日志写入工作者"""
        while True:
            try:
                logs_batch = []
                
                # 收集一批日志
                try:
                    for _ in range(self.batch_size):
                        log_entry = self.log_queue.get_nowait()
                        logs_batch.append(log_entry)
                except asyncio.QueueEmpty:
                    pass
                
                # 写入日志
                if logs_batch:
                    async with aiofiles.open(self.log_file, mode='a') as f:
                        for log_entry in logs_batch:
                            await f.write(json.dumps(log_entry, ensure_ascii=False) + '\n')
                
                # 等待刷新间隔
                await asyncio.sleep(self.flush_interval)
            except Exception as e:
                print(f"异步日志写入错误: {e}")

总结

LangBot的监控和日志系统为应用的稳定运行提供了全面的保障。通过多层次的监控、实时日志收集、错误追踪和可视化展示，运维人员可以及时发现和解决问题，确保系统的高可用性。

关键要点包括：

1. 完整架构：涵盖日志收集、指标监控、错误追踪等多个维度
2. 实时性：支持实时数据收集和展示
3. 可扩展性：支持集成第三方监控和日志系统
4. 性能优化：通过采样、异步写入等技术优化性能
5. 可视化：提供直观的监控面板和实时数据展示

在实际应用中，建议遵循以下最佳实践：

1. 合理配置：根据实际需求配置日志级别和采样率
2. 性能监控：持续监控系统性能指标
3. 错误追踪：建立完善的错误追踪和报警机制
4. 日志轮转：实施日志轮转策略避免磁盘空间不足
5. 第三方集成：集成Prometheus、ELK等专业监控工具
6. 安全考虑：避免在日志中记录敏感信息

通过合理使用LangBot的监控和日志系统，可以显著提高应用的可维护性和稳定性，为用户提供更好的服务体验。

参考资料

1. Prometheus监控系统
2. ELK Stack官方文档
3. Python日志模块文档
4. LangBot监控文档
5. 系统性能监控最佳实践