151 lines
3.5 KiB
Markdown
151 lines
3.5 KiB
Markdown
# 统一日志管理系统说明
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## 功能特性
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1. **多级别日志支持**：DEBUG, INFO, WARNING, ERROR, CRITICAL
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2. **结构化日志**：支持JSON格式的结构化日志输出
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3. **文件轮转**：自动按日期和大小分割日志文件
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4. **系统监控**：记录系统状态和性能指标
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5. **请求追踪**：记录API请求和响应信息
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6. **错误追踪**：详细记录异常和错误信息
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## 使用方法
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### 1. 基本日志记录
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```python
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from utils.logger import log_debug, log_info, log_warning, log_error, log_critical
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log_info("服务启动成功")
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log_warning("内存使用率较高")
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log_error("API请求失败")
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```
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### 2. 结构化日志
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```python
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from utils.logger import log_structured
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log_structured(
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"info",
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"用户登录成功",
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user_id="12345",
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ip_address="192.168.1.100",
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timestamp=datetime.now().isoformat()
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)
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```
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### 3. 请求日志
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```python
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from utils.logger import log_request_info, log_response_info
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log_request_info("POST", "/api/chat", "192.168.1.100", "Mozilla/5.0...")
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log_response_info(200, 150.5, "/api/chat", "POST", "192.168.1.100")
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```
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### 4. 错误详情记录
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```python
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from utils.logger import log_error_detail
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try:
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# 可能出错的代码
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pass
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except Exception as e:
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log_error_detail(
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type(e).__name__,
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str(e),
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str(e.__traceback__),
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context={"user_id": "123", "action": "chat_request"}
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)
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```
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### 5. 对话交互记录
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```python
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from utils.logger import log_chat_interaction
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log_chat_interaction(
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user_input="你好，请帮我分析这张图片",
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ai_response="这张图片显示了一座山和一片湖泊",
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model="qwen-vl-plus",
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conversation_id="conv_abc123"
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)
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```
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### 6. 系统状态记录
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```python
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from utils.logger import log_system_status
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log_system_status(
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status="healthy",
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uptime=3600.5,
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cpu_usage=45.2,
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memory_usage=60.8,
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disk_usage=75.1
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)
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```
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## 配置
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### 环境变量配置
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| 环境变量 | 默认值 | 说明 |
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|---------|--------|------|
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| LOG_LEVEL | INFO | 日志级别 |
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| LOG_DIR | logs | 日志文件目录 |
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| LOG_MAX_BYTES | 10485760 | 单个日志文件最大大小 |
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| LOG_BACKUP_COUNT | 5 | 保留的备份日志数量 |
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### 配置文件
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创建 `logging.conf` 文件来配置日志系统：
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```
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LOG_LEVEL=INFO
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LOG_DIR=logs
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LOG_MAX_BYTES=10485760
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LOG_BACKUP_COUNT=5
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```
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## 日志文件组织
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- 日志文件按日期分割：`ai-chat-api_2026-03-03.log`
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- 自动轮转，当日志文件达到指定大小时创建新文件
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- 保留最近5个日志文件，旧文件会被自动删除
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- 日志文件存放在 `logs/` 目录下
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## 集成到现有代码
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在你的 FastAPI 应用中：
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```python
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from utils.logger import setup_global_logger
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# 初始化日志系统
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logger = setup_global_logger()
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@app.middleware("http")
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async def logging_middleware(request, call_next):
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start_time = time.time()
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# 记录请求
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logger.info(f"Request: {request.method} {request.url.path}")
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response = await call_next(request)
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# 记录响应
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process_time = time.time() - start_time
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logger.info(f"Response: {response.status_code} in {process_time:.2f}s")
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return response
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```
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## 注意事项
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1. 使用结构化日志便于日志分析和查询
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2. 避免在日志中记录敏感信息（如密码、token等）
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3. 适当使用日志级别，避免过度记录DEBUG信息
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4. 定期清理旧的日志文件，防止磁盘空间不足 |