利用LoggingChatClient中间件来记录针对LLM的调用如果将LoggingChatClient这个中间件至于连接LLM的IChatClient之前那么针对后者对LLM的调用情况会以日志的形式记录下来。我们可以通过设置不同的日志级别来控制输出的详细程度。在如下的演示程序中我们利用创建了一个基于OpenAIClient的IChatClient对象。在调用AsBuilder扩展方法将ChatClientBuilder构建出来后通过调用UseLogging方法来注册LoggingChatClient中间件并且传入一个ILoggerFactory对象来控制日志的输出。由于我们在创建ILoggerFactory对象的时候设置了日志级别为Debug。using Azure; using dotenv.net; using Microsoft.Agents.AI; using Microsoft.Extensions.AI; using Microsoft.Extensions.DependencyInjection; using Microsoft.Extensions.Logging; using OpenAI; DotEnv.Load(); var model Environment.GetEnvironmentVariable(MODEL)!; var apiKey Environment.GetEnvironmentVariable(API_KEY)!; var endpoint Environment.GetEnvironmentVariable(OPENAI_URL)!; var loggerFactory new ServiceCollection() .AddLogging(logginglogging .SetMinimumLevel(LogLevel.Trace) .AddConsole()) .BuildServiceProvider() .GetRequiredServiceILoggerFactory(); var client new OpenAIClient( credential: new AzureKeyCredential(apiKey), options: new OpenAIClientOptions { Endpoint new Uri(endpoint) }) .GetChatClient(model:model) .AsIChatClient() .AsBuilder() .UseLogging(loggerFactory: loggerFactory) .Build(); await client.GetResponseAsync(What is Azure OpenAI?); Console.ReadLine();LoggingChatClient的GetResponseAsync方法会在调用前输出一条日志表示正在调用LLM并且会记录调用的输入内容在调用完成后会输出另一条日志表示调用已经完成并且会记录调用的输出内容。通过这些日志我们可以清楚地看到每次调用的输入和输出以及调用的时间戳等信息。dbug: Microsoft.Extensions.AI.LoggingChatClient[1723383095] GetResponseAsync invoked. dbug: Microsoft.Extensions.AI.LoggingChatClient[1553703230] GetResponseAsync completed.如果我们将日志等级设置为更低的Trace级别那么LoggingChatClient还会输出更详细的日志信息包括调用的输入内容和输出内容等。var loggerFactory new ServiceCollection() .AddLogging(logginglogging .SetMinimumLevel(LogLevel.Trace) .AddConsole()) .BuildServiceProvider() .GetRequiredServiceILoggerFactory();输出trce: Microsoft.Extensions.AI.LoggingChatClient[805843669] GetResponseAsync invoked: [ { role: user, contents: [ { $type: text, text: What is Azure OpenAI? } ] } ]. Options: null. Metadata: { providerName: openai, providerUri: https://eap2410.cognitiveservices.azure.com/openai/v1, defaultModelId: gpt-5.2-chat }. trce: Microsoft.Extensions.AI.LoggingChatClient[384896670] GetResponseAsync completed: { messages: [ { createdAt: 2026-05-22T01:28:4200:00, role: assistant, contents: [ { $type: text, text: **Azure OpenAI** is Microsoft’s cloud-based service that provides access to advanced AI models (like OpenAI’s GPT, GPT‑4, and image generation models) through the **Microsoft Azure** platform.\n\nIn simple terms, it lets businesses and developers use powerful AI models within Microsoft’s secure cloud environment.\n\n### Key Features:\n- **Access to OpenAI models** (GPT‑4, GPT‑4o, embeddings, image generation, etc.)\n- **Enterprise-grade security and compliance**\n- **Data privacy** — your data isn’t used to train the base models\n- **Integration with Azure services** (Azure AI Search, Azure Functions, Power BI, etc.)\n- **Scalable infrastructure** for production workloads\n\n### What It’s Used For:\n- Chatbots and virtual assistants \n- Document summarization \n- Code generation \n- Data analysis \n- Image generation \n- Semantic search and embeddings \n\n### How It’s Different from OpenAI’s public API:\n- Runs within the **Azure ecosystem**\n- Offers enterprise security controls\n- Regional data hosting options\n- Integrated billing through Azure\n\nIn short: \n**Azure OpenAI OpenAI models Microsoft Azure’s enterprise cloud platform.** } ], messageId: chatcmpl-Di8yoRfX62nycHbngYbn11qNFWvJk } ], responseId: chatcmpl-Di8yoRfX62nycHbngYbn11qNFWvJk, modelId: gpt-5.2-chat-latest, createdAt: 2026-05-22T01:28:4200:00, finishReason: stop, usage: { inputTokenCount: 12, outputTokenCount: 252, totalTokenCount: 264, cachedInputTokenCount: 0, reasoningTokenCount: 0, additionalCounts: { InputTokenDetails.AudioTokenCount: 0, OutputTokenDetails.AudioTokenCount: 0, OutputTokenDetails.AcceptedPredictionTokenCount: 0, OutputTokenDetails.RejectedPredictionTokenCount: 0 } } }.2. LoggingChatClientLoggingChatClient直接继承自DelegatingChatClient是一个非常简单的中间件实现它直接利用构造函数传入的ILogger对象来输出日志信息。DelegatingChatClient在没有出错的情况下只会输出等级分别为Debug和Trace的日志信息如果最低日志等级设置为Debug那么就只会输出调用前和调用后的日志如果最低日志等级设置为Trace那么就会输出更详细的日志信息包括调用的输入内容和输出内容等。Trace等级的日志的内容以JSON形式输出所以它提供了一个JsonSerializerOptions属性来控制日志中输入输出内容的序列化方式。我们可以通过设置这个属性来控制日志中输入输出内容的格式比如是否使用驼峰命名、是否忽略空值等。public partial class LoggingChatClient : DelegatingChatClient { public LoggingChatClient(IChatClient innerClient, ILogger logger); public JsonSerializerOptions JsonSerializerOptions { get; set; } public override async TaskChatResponse GetResponseAsync( IEnumerableChatMessage messages, ChatOptions? options null, CancellationToken cancellationToken default); public override async IAsyncEnumerableChatResponseUpdate GetStreamingResponseAsync( IEnumerableChatMessage messages, ChatOptions? options null, CancellationToken cancellationToken default); }针对GetResponseAsync的日志输出采用如下的逻辑在调用innerClient的GetResponseAsync方法之前输出一条Debug/Trace等级的日志表示正在调用LLM并且会记录调用的输入内容在成功调用并得到响应之后输出另一条Debug/Trace等级的日志表示调用已经完成并且会记录调用的输出内容如果调用过程中发生了异常那么会输出一条Error等级的日志表示调用失败并且会记录异常信息针对GetStreamingResponseAsync的日志输出采用如下的逻辑在调用innerClient的GetStreamingResponseAsync方法之前输出一条Debug/Trace等级的日志表示正在调用LLM并且会记录调用的输入内容如果调用失败那么会输出一条Error等级的日志表示调用失败并且会记录异常信息GetStreamingResponseAsync会对返回的IAsyncEnumerableChatResponseUpdate进行迭代对于每一次迭代如果成功获取到一个ChatResponseUpdate并且最低日志等级设置为Trace那么会输出一条Trace等级的日志表示获取到了一个更新并且会记录这个更新的内容如果在迭代过程中发生了异常那么会输出一条Error等级的日志表示迭代失败并且会记录异常信息在迭代完成之后输出一条Debug等级的日志表示调用已经完成对于我们前面演示的例子如果我们将日志等级设置为Trace那么在调用GetStreamingResponseAsync方法时我们就可以看到每一次迭代获取到的ChatResponseUpdate的内容都被记录在日志中了这对于调试和监控Agent的行为非常有用。由于这种情况下输出内容容量可能会非常大所以当我们将日志等级设置为Trace时得评估一下日志对性能带来得影响。using Azure; using dotenv.net; using Microsoft.Agents.AI; using Microsoft.Extensions.AI; using Microsoft.Extensions.DependencyInjection; using Microsoft.Extensions.Logging; using OpenAI; DotEnv.Load(); var model Environment.GetEnvironmentVariable(MODEL)!; var apiKey Environment.GetEnvironmentVariable(API_KEY)!; var endpoint Environment.GetEnvironmentVariable(OPENAI_URL)!; var loggerFactory new ServiceCollection() .AddLogging(logginglogging .SetMinimumLevel(LogLevel.Trace) .AddConsole()) .BuildServiceProvider() .GetRequiredServiceILoggerFactory(); var client new OpenAIClient( credential: new AzureKeyCredential(apiKey), options: new OpenAIClientOptions { Endpoint new Uri(endpoint) }) .GetChatClient(model:model) .AsIChatClient() .AsBuilder() .UseLogging(loggerFactory: loggerFactory) .Build(); await foreach(var update in client.GetStreamingResponseAsync(世界上最深的淡水湖是哪个在10字内作答)) { }输出trce: Microsoft.Extensions.AI.LoggingChatClient[805843669] GetStreamingResponseAsync invoked: [ { role: user, contents: [ { $type: text, text: 世界上最深的淡水湖是哪个在10字内作答 } ] } ]. Options: null. Metadata: { providerName: openai, providerUri: https://eap2410.cognitiveservices.azure.com/openai/v1, defaultModelId: gpt-5.2-chat }. trce: Microsoft.Extensions.AI.LoggingChatClient[1513570378] GetStreamingResponseAsync received update: { contents: [], responseId: , messageId: , createdAt: 1970-01-01T00:00:0000:00, modelId: } trce: Microsoft.Extensions.AI.LoggingChatClient[1513570378] GetStreamingResponseAsync received update: { role: assistant, contents: [ { $type: text, text: } ], responseId: chatcmpl-Di9WdprY6ZgpHqiLY25t8Y23kmsRa, messageId: chatcmpl-Di9WdprY6ZgpHqiLY25t8Y23kmsRa, createdAt: 2026-05-22T02:03:3900:00, modelId: } trce: Microsoft.Extensions.AI.LoggingChatClient[1513570378] GetStreamingResponseAsync received update: { role: assistant, contents: [ { $type: text, text: 贝 } ], responseId: chatcmpl-Di9WdprY6ZgpHqiLY25t8Y23kmsRa, messageId: chatcmpl-Di9WdprY6ZgpHqiLY25t8Y23kmsRa, createdAt: 2026-05-22T02:03:3900:00, modelId: } trce: Microsoft.Extensions.AI.LoggingChatClient[1513570378] GetStreamingResponseAsync received update: { role: assistant, contents: [ { $type: text, text: 加 } ], responseId: chatcmpl-Di9WdprY6ZgpHqiLY25t8Y23kmsRa, messageId: chatcmpl-Di9WdprY6ZgpHqiLY25t8Y23kmsRa, createdAt: 2026-05-22T02:03:3900:00, modelId: } trce: Microsoft.Extensions.AI.LoggingChatClient[1513570378] GetStreamingResponseAsync received update: { role: assistant, contents: [ { $type: text, text: 尔 } ], responseId: chatcmpl-Di9WdprY6ZgpHqiLY25t8Y23kmsRa, messageId: chatcmpl-Di9WdprY6ZgpHqiLY25t8Y23kmsRa, createdAt: 2026-05-22T02:03:3900:00, modelId: } trce: Microsoft.Extensions.AI.LoggingChatClient[1513570378] GetStreamingResponseAsync received update: { role: assistant, contents: [ { $type: text, text: 湖 } ], responseId: chatcmpl-Di9WdprY6ZgpHqiLY25t8Y23kmsRa, messageId: chatcmpl-Di9WdprY6ZgpHqiLY25t8Y23kmsRa, createdAt: 2026-05-22T02:03:3900:00, modelId: } trce: Microsoft.Extensions.AI.LoggingChatClient[1513570378] GetStreamingResponseAsync received update: { role: assistant, contents: [], responseId: chatcmpl-Di9WdprY6ZgpHqiLY25t8Y23kmsRa, messageId: chatcmpl-Di9WdprY6ZgpHqiLY25t8Y23kmsRa, createdAt: 2026-05-22T02:03:3900:00, finishReason: stop, modelId: } trce: Microsoft.Extensions.AI.LoggingChatClient[1513570378] GetStreamingResponseAsync received update: { role: assistant, contents: [ { $type: usage, details: { inputTokenCount: 24, outputTokenCount: 78, totalTokenCount: 102, cachedInputTokenCount: 0, reasoningTokenCount: 64, additionalCounts: { InputTokenDetails.AudioTokenCount: 0, OutputTokenDetails.AudioTokenCount: 0, OutputTokenDetails.AcceptedPredictionTokenCount: 0, OutputTokenDetails.RejectedPredictionTokenCount: 0 } } } ], responseId: chatcmpl-Di9WdprY6ZgpHqiLY25t8Y23kmsRa, messageId: chatcmpl-Di9WdprY6ZgpHqiLY25t8Y23kmsRa, createdAt: 2026-05-22T02:03:3900:00, finishReason: stop, modelId: } dbug: Microsoft.Extensions.AI.LoggingChatClient[1553703230] GetStreamingResponseAsync completed.3. 利用Source Generator生成日志输出代码日志是典型得高频操作尤其是当我们将日志等级设置得很低得时候更是如此所以针对日志输出的每一个微小的细节都会高倍放大比如字符串拼接和值类型转换成引用类型导致的装箱等。在此方面Source Generator就能派上用场了。我们可以利用Source Generator来生成日志输出的代码从而避免手写日志输出代码可能带来的性能问题。Microsoft.Extensions.Logging库已经提供了一个名为LoggerMessageAttribute的Source Generator我们可以利用它来生成日志输出的代码。LoggingChatClient涉及的日志输出被定义成对应的方法并在这些方法上使用LoggerMessageAttribute特性来标记日志的级别和消息模板。LoggerMessageAttribute特性会告诉Source Generator生成对应的日志输出代码从而避免了手写日志输出代码可能带来的性能问题。这也是LoggingChatClient被定义成partial类的原因。public partial class LoggingChatClient : DelegatingChatClient { [LoggerMessage(LogLevel.Debug, {MethodName} invoked.)] private partial void LogInvoked(string methodName); [LoggerMessage(LogLevel.Trace, {MethodName} invoked: {Messages}. Options: {ChatOptions}. Metadata: {ChatClientMetadata}.)] private partial void LogInvokedSensitive(string methodName, string messages, string chatOptions, string chatClientMetadata); [LoggerMessage(LogLevel.Debug, {MethodName} completed.)] private partial void LogCompleted(string methodName); [LoggerMessage(LogLevel.Trace, {MethodName} completed: {ChatResponse}.)] private partial void LogCompletedSensitive(string methodName, string chatResponse); [LoggerMessage(LogLevel.Trace, GetStreamingResponseAsync received update: {ChatResponseUpdate})] private partial void LogStreamingUpdateSensitive(string chatResponseUpdate); [LoggerMessage(LogLevel.Debug, {MethodName} canceled.)] private partial void LogInvocationCanceled(string methodName); [LoggerMessage(LogLevel.Error, {MethodName} failed.)] private partial void LogInvocationFailed(string methodName, Exception error); }4. UseLogging扩展方法UseLogging是一个ChatClientBuilder的扩展方法它提供了一种简便的方式来注册LoggingChatClient中间件。我们只需要在构建IChatClient对象的时候调用UseLogging方法并传入一个ILoggerFactory对象来控制日志的输出就可以轻松地将LoggingChatClient中间件添加到我们的IChatClient对象中了。除此之外UseLogging方法还提供了一个可选的configure参数它允许我们在注册LoggingChatClient中间件的时候对其进行一些额外的配置比如设置JsonSerializerOptions属性来控制日志中输入输出内容的序列化方式等。public static class LoggingChatClientBuilderExtensions { public static ChatClientBuilder UseLogging( this ChatClientBuilder builder, ILoggerFactory? loggerFactory null, ActionLoggingChatClient? configure null); }标签: Agent, AI, IChatClient, Logging, MAF免责声明本内容来自平台创作者博客园系信息发布平台仅提供信息存储空间服务。好文要顶 关注我 收藏该文 微信分享Artech粉丝 - 10569 关注 - 0推荐博客加关注60« 上一篇 [MAF的Agent管道详解-07]利用AIAgent中间件构建Agent管道» 下一篇 [MAF预定义ChatClient中间件-02]FunctionInvokingChatClient——实现ReAct循环和人机交互的大功臣posted 2026-06-03 08:53 Artech 阅读(215) 评论(1) 收藏 举报