本文是基於 LLama 2是由Meta 開源的大語言模型,通過LocalAI 來集成LLama2 來演示Semantic kernel(簡稱SK) 和 本地大模型的集成示例。
SK 可以支持各種大模型,在官方示例中多是OpenAI 和 Azure OpenAI service 的GPT 3.5+。今天我們就來看一看如何把SK 和 本地部署的開源大模型集成起來。我們使用MIT協議的開源項目“LocalAI“:https://github.com/go-skynet/LocalAI。
LocalAI 是一個本地推理框架,提供了 RESTFul API,與 OpenAI API 規範兼容。它允許你在消費級硬件上本地或者在自有服務器上運行 LLM(和其他模型),支持與 ggml 格式兼容的多種模型家族。不需要 GPU。LocalAI 使用 C++ 綁定來優化速度。 它基於用於音頻轉錄的 llama.cpp、gpt4all、rwkv.cpp、ggml、whisper.cpp 和用於嵌入的 bert.cpp。
可參考官方 Getting Started 進行部署,通過LocalAI我們將本地部署的大模型轉換爲OpenAI的格式,通過SK 的OpenAI 的Connector 訪問,這裏需要做的是把openai的Endpoint 指向 LocalAI,這個我們可以通過一個自定義的HttpClient來完成這項工作,例如下面的這個示例:
internal class OpenAIHttpclientHandler : HttpClientHandler
{
private KernelSettings _kernelSettings;
public OpenAIHttpclientHandler(KernelSettings settings)
{
this._kernelSettings = settings;
}
protected override async Task<HttpResponseMessage> SendAsync(HttpRequestMessage request, CancellationToken cancellationToken)
{
if (request.RequestUri.LocalPath == "/v1/chat/completions")
{
UriBuilder uriBuilder = new UriBuilder(request.RequestUri)
{
Scheme = this._kernelSettings.Scheme,
Host = this._kernelSettings.Host,
Port = this._kernelSettings.Port
};
request.RequestUri = uriBuilder.Uri;
}
return await base.SendAsync(request, cancellationToken);
}
}
上面我們做好了所有的準備工作,接下來就是要把所有的組件組裝起來,讓它們協同工作。因此打開Visual studio code 創建一個c# 項目sk-csharp-hello-world,其中Program.cs 內容如下:
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,我們做的第一件事是導入一堆必要的命名空間,使一切正常(第 1 行到第 9 行)。
然後,我們創建一個內核構建器的實例(通過模式,而不是因爲它是構造函數),這將有助於塑造我們的內核。
IKernelBuilder builder = Kernel.CreateBuilder();
你需要知道每時每刻都在發生什麼嗎?答案是肯定的!讓我們在內核中添加一個日誌。我們在第14行添加了日誌的支持。
我們想使用Azure,OpenAI中使用Microsoft的AI模型,以及我們LocalAI 集成的本地大模型,我們可以將它們包含在我們的內核中。正如我們在15行看到的那樣:
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;
}
}
接下來開啓一個聊天循環,使用SK的流式傳輸 InvokeStreamingAsync,如第42行到46行代碼所示,運行起來就可以體驗下列的效果:
本文示例源代碼:https://github.com/geffzhang/sk-csharp-hello-world
參考文章: