SemanticKernel之LLama3案例

　　之前的篇章都是用SemanticKernel来连接OpenAI的API，当然是需要费用，另外还有使用限制，本篇来说明在SK中使用开源模型LLama3。

　　首先引入Nuget包，这里使用的是LLamaSharp这个三方包，因为没有显卡，只能跑在CPU上，所以也需要引入对应的Cpu包，最后引入SK的LLama版的包。

<ItemGroup>
  <PackageReference Include="LLamaSharp" Version="0.11.2" />
  <PackageReference Include="LLamaSharp.Backend.Cpu" Version="0.11.2" />
  <PackageReference Include="LLamaSharp.semantic-kernel" Version="0.11.2" />
</ItemGroup>

　　接下就是下载最新的LLama3了，扩展名是gguf，如下代码就可以轻松地跑起本地小模型了。

using LLama.Common;
using LLama;
using LLamaSharp.SemanticKernel.ChatCompletion;
using System.Text;
using ChatHistory = LLama.Common.ChatHistory;
using AuthorRole = LLama.Common.AuthorRole;

await SKRunAsync();
async Task SKRunAsync()
{
    var modelPath = @"C:\llama\llama-2-coder-7b.Q8_0.gguf";
    var parameters = new ModelParams(modelPath)
    {
        ContextSize = 1024,
        Seed = 1337,
        GpuLayerCount = 5,
        Encoding = Encoding.UTF8,
    };

    using var model = LLamaWeights.LoadFromFile(parameters);
    var ex = new StatelessExecutor(model, parameters);

    var chatGPT = new LLamaSharpChatCompletion(ex);
    var chatHistory = chatGPT.CreateNewChat(@"这是assistant和user之间的对话。

assistant是一名.net和C#专家，能准确回答user提出的专业问题。");

    Console.WriteLine("开始聊天:");
    Console.WriteLine("------------------------");
    while (true)
    {
        Console.Write("user：");
        var userMessage = Console.ReadLine();
        chatHistory.AddUserMessage(userMessage);
        var first = true;
        var content = "";
        await foreach (var reply in chatGPT.GetStreamingChatMessageContentsAsync(chatHistory))
        {
            if (first)
            {
                first = false;
                Console.Write(reply.Role + "：");
            }
            content += reply.Content;
            Console.Write(reply.Content);
        }
        chatHistory.AddAssistantMessage(content);
    }
}