Semantic Kernel 通过 LocalAI 集成本地模型

本文是基于 LLama 2是由Meta 开源的大语言模型，通过LocalAI 来集成LLama2 来演示Semantic kernel（简称SK） 和 本地大模型的集成示例。

上面我们做好了所有的准备工作，接下来就是要把所有的组件组装起来，让它们协同工作。因此打开Visual studio code 创建一个c# 项目sk-csharp-hello-world，其中Program.cs 内容如下：

using System.Reflection;

using config;

using Microsoft.Extensions.DependencyInjection;

using Microsoft.Extensions.Logging;

using Microsoft.SemanticKernel;

using Microsoft.SemanticKernel.ChatCompletion;

using Microsoft.SemanticKernel.Connectors.OpenAI;

using Microsoft.SemanticKernel.PromptTemplates.Handlebars;

using Plugins;

var kernelSettings = KernelSettings.LoadSettings();

var handler = new OpenAIHttpclientHandler(kernelSettings);

IKernelBuilder builder = Kernel.CreateBuilder();

builder.Services.AddLogging(c => c.SetMinimumLevel(LogLevel.Information).AddDebug());

builder.AddChatCompletionService(kernelSettings,handler);

builder.Plugins.AddFromType<LightPlugin>();

Kernel kernel = builder.Build();

// Load prompt from resource

using StreamReader reader = new(Assembly.GetExecutingAssembly().GetManifestResourceStream("prompts.Chat.yaml")!);

KernelFunction prompt = kernel.CreateFunctionFromPromptYaml(

    reader.ReadToEnd(),

    promptTemplateFactory: new HandlebarsPromptTemplateFactory()

);

// Create the chat history

ChatHistory chatMessages = [];

// Loop till we are cancelled

while (true)

{

    // Get user input

    System.Console.Write("User > ");

    chatMessages.AddUserMessage(Console.ReadLine()!);

    // Get the chat completions

    OpenAIPromptExecutionSettings openAIPromptExecutionSettings = new()

    {

    };

    var result = kernel.InvokeStreamingAsync<StreamingChatMessageContent>(

        prompt,

        arguments: new KernelArguments(openAIPromptExecutionSettings) {

            { "messages", chatMessages }

        });

    // Print the chat completions

    ChatMessageContent? chatMessageContent = null;

    await foreach (var content in result)

    {

        System.Console.Write(content);

        if (chatMessageContent == null)

        {

            System.Console.Write("Assistant > ");

            chatMessageContent = new ChatMessageContent(

                content.Role ?? AuthorRole.Assistant,

                content.ModelId!,

                content.Content!,

                content.InnerContent,

                content.Encoding,

                content.Metadata);

        }

        else

        {

            chatMessageContent.Content += content;

        }

    }

    System.Console.WriteLine();

    chatMessages.Add(chatMessageContent!);

}

首先，我们做的第一件事是导入一堆必要的命名空间，使一切正常（第 1 行到第 9 行）。

然后，我们创建一个内核构建器的实例（通过模式，而不是因为它是构造函数），这将有助于塑造我们的内核。

IKernelBuilder builder = Kernel.CreateBuilder();

你需要知道每时每刻都在发生什么吗？答案是肯定的！让我们在内核中添加一个日志。我们在第14行添加了日志的支持。

我们想使用Azure，OpenAI中使用Microsoft的AI模型，以及我们LocalAI 集成的本地大模型，我们可以将它们包含在我们的内核中。正如我们在15行看到的那样：

internal static class ServiceCollectionExtensions
{
     /// <summary>
     /// Adds a chat completion service to the list. It can be either an OpenAI or Azure OpenAI backend service.
     /// </summary>
     /// <param name="kernelBuilder"></param>
     /// <param name="kernelSettings"></param>
     /// <exception cref="ArgumentException"></exception>
     internal static IKernelBuilder AddChatCompletionService(this IKernelBuilder kernelBuilder, KernelSettings kernelSettings, HttpClientHandler handler)
     {
       
         switch (kernelSettings.ServiceType.ToUpperInvariant())
         {
             case ServiceTypes.AzureOpenAI:
                 kernelBuilder = kernelBuilder.AddAzureOpenAIChatCompletion(kernelSettings.DeploymentId,  endpoint: kernelSettings.Endpoint, apiKey: kernelSettings.ApiKey, serviceId: kernelSettings.ServiceId, kernelSettings.ModelId);
                 break;

            case ServiceTypes.OpenAI:
                 kernelBuilder = kernelBuilder.AddOpenAIChatCompletion(modelId: kernelSettings.ModelId, apiKey: kernelSettings.ApiKey, orgId: kernelSettings.OrgId, serviceId: kernelSettings.ServiceId);
                 break;

            case ServiceTypes.HunyuanAI:               
                 kernelBuilder = kernelBuilder.AddOpenAIChatCompletion(modelId: kernelSettings.ModelId, apiKey: kernelSettings.ApiKey, httpClient: new HttpClient(handler));
                 break;
             case ServiceTypes.LocalAI:
                 kernelBuilder = kernelBuilder.AddOpenAIChatCompletion(modelId: kernelSettings.ModelId, apiKey: kernelSettings.ApiKey, httpClient: new HttpClient(handler));
                 break;
             default:
                 throw new ArgumentException($"Invalid service type value: {kernelSettings.ServiceType}");
         }

        return kernelBuilder;
     }
}

接下来开启一个聊天循环，使用SK的流式传输 InvokeStreamingAsync，如第42行到46行代码所示，运行起来就可以体验下列的效果：

本文示例源代码：https://github.com/geffzhang/sk-csharp-hello-world

参考文章：

• Docker部署LocalAI 实现本地私有化 文本转语音（TTS） 语音转文本 GPT功能 | Mr.Pu 个站博客 (putianhui.cn)
• LocalAI 自托管、社区驱动的本地 OpenAI API 兼容替代方案