目前SK已支持OpenAI，Azure OpenAI，Gemini，HuggingFace，MistralAI等LLM，相信之后会越来越丰富。
首先要引入所对应的LLM包，具体项目文件如下：
<Project Sdk="Microsoft.NET.Sdk"><PropertyGroup><OutputType>Exe</OutputType><TargetFramework>net9.0</TargetFramework><RootNamespace>Demo01_Kernel</RootNamespace><ImplicitUsings>enable</ImplicitUsings><Nullable>enable</Nullable></PropertyGroup><ItemGroup><PackageReference Include="Microsoft.Extensions.Logging" Version="9.0.0-preview.4.24266.19" /><PackageReference Include="Microsoft.Extensions.Logging.Console" Version="9.0.0-preview.4.24266.19" /><PackageReference Include="Microsoft.SemanticKernel" Version="1.14.1" /><PackageReference Include="Microsoft.SemanticKernel.Connectors.Google" Version="1.14.1-alpha" /><PackageReference Include="Microsoft.SemanticKernel.Connectors.HuggingFace" Version="1.14.1-preview" /><PackageReference Include="Microsoft.SemanticKernel.Connectors.MistralAI" Version="1.14.1-alpha" /></ItemGroup></Project>
SK作为装LLM的SDK，主要是通过Kernel展开的，这点和asp.net core类似，首先通过Kernel来创建一个Builder，然后给Bulder的Services添加各种服务，最后Build获取到Kernel，然后执行使用各种功能，实现操作。
using Microsoft.Extensions.Configuration;using Microsoft.Extensions.DependencyInjection;using Microsoft.Extensions.Logging;using Microsoft.Extensions.Logging.Console;using Microsoft.Extensions.Options;using Microsoft.SemanticKernel;using Microsoft.SemanticKernel.ChatCompletion;using Microsoft.SemanticKernel.Connectors.OpenAI;using Microsoft.SemanticKernel.Services;using System.Diagnostics.CodeAnalysis;using System.Net.NetworkInformation;var chatModelId = "gpt-4o";var key = File.ReadAllText(@"C:\GPT\key.txt");var endpoint = "";#pragma warning disable SKEXP0010#pragma warning disable SKEXP0070var builder = Kernel.CreateBuilder().AddOpenAIChatCompletion(chatModelId, key);//.AddAzureOpenAIChatCompletion(chatModelId, endpoint, key)//.AddGoogleAIGeminiChatCompletion(chatModelId, key)//.AddHuggingFaceChatCompletion(chatModelId, apiKey: key)//.AddMistralChatCompletion(chatModelId, key)//builder.Services.AddSingleton<IAIServiceSelector>(new GptAIServiceSelector());//添加ServiceSelectorbuilder.Services.AddScoped<IAIServiceSelector, MyAIServiceSelector >();//添回复自定义服务builder.Services.AddScoped<IMyService, MyService>();//添加日志服务builder.Services.AddLogging(c => c.AddConsole()//.AddJsonConsole().SetMinimumLevel(LogLevel.Information));Kernel kernel = builder.Build();var logger = kernel.LoggerFactory.CreateLogger("logger");var prompt = "你好，你能帮我做什么";var result = await kernel.InvokePromptAsync(prompt);var message = @$"返回信息：{result.GetValue<string>()}";logger.LogInformation(message);class MyAIServiceSelector : IAIServiceSelector{private readonly IMyService _myService;private readonly ILogger<GptAIServiceSelector> _logger;public GptAIServiceSelector(IMyService myService, ILogger<GptAIServiceSelector> logger){_myService = myService;_logger = logger;}public bool TrySelectAIService<T>(Kernel kernel, KernelFunction function, KernelArguments arguments,[NotNullWhen(true)] out T? service, out PromptExecutionSettings? serviceSettings) where T : class, IAIService{_myService.Print();foreach (var serviceToCheck in kernel.GetAllServices<T>()){var serviceModelId = serviceToCheck.GetModelId();var endpoint = serviceToCheck.GetEndpoint();if (!string.IsNullOrEmpty(serviceModelId)){_logger.LogInformation($"使用的模型: {serviceModelId} {endpoint}");_logger.LogInformation($"服务类型: {serviceToCheck.GetType().Name}");service = serviceToCheck;serviceSettings = new OpenAIPromptExecutionSettings();return true;}}service = null;serviceSettings = null;return false;}}//添加自定义服务interface IMyService{void Print();}class MyService : IMyService{readonly ILogger<MyService> _logger;public MyService(ILogger<MyService> logger){_logger = logger;logger.LogInformation("MyService实例化");}public void Print(){_logger.LogWarning("开始报警");}}
