解锁GraphRag.Net的无限可能：手把手教你集成国产模型和本地模型-数字星空

在上次的文章中，我们已经详细介绍了GraphRag的基本功能和使用方式。如果你还不熟悉，建议先阅读前面的文章

通过前两篇文章，相信你已经了解到GraphRag.Net目前只支持OpenAI规范的接口，但许多小伙伴在社区中提议，希望能增加对本地模型（例如：ollama等）的支持。所以这次，我们将探讨如何在GraphRag.Net中使用自定义模型和本地模型。

为什么选择GraphRag.Net？

GraphRag.Net采用了Semantic Kernel作为基础，让我们能够非常简洁地抽象出会话与向量接口。因此，用户可以非常方便地实现自己定制的解决方案。接下来，我们会通过一个具体的例子，展示如何将本地模型和国产模型集成到GraphRag.Net中。

默认配置方法

首先，我们来看看如何进行默认配置：

// OpenAI配置
builder.Configuration.GetSection("OpenAI").Get<OpenAIOption>();
// 文档切片配置
builder.Configuration.GetSection("TextChunker").Get<TextChunkerOption>();
// 配置数据库连接
builder.Configuration.GetSection("GraphDBConnection").Get<GraphDBConnectionOption>();

// 注意，需要先注入配置文件，然后再注入GraphRag.Net
builder.Services.AddGraphRagNet();

这里，我们将在默认配置中注入OpenAI的配置、文本切片的配置和数据库连接的配置。然后，依次注入这些配置文件和GraphRag.Net的服务。

自定义配置方法

如果需要自定义模型或本地模型，可能需要实现一些额外的服务接口，下面是自定义配置的示例：

var kernelBuild = Kernel.CreateBuilder();
kernelBuild.Services.AddKeyedSingleton<ITextGenerationService>("mock-text", new MockTextCompletion());
kernelBuild.Services.AddKeyedSingleton<IChatCompletionService>("mock-chat", new MockChatCompletion());
kernelBuild.Services.AddSingleton<ITextEmbeddingGenerationService>(new MockTextEmbeddingGeneratorService());
kernelBuild.Services.AddKeyedSingleton("mock-embedding", new MockTextEmbeddingGeneratorService());

builder.Services.AddGraphRagNet(kernelBuild.Build());

在这个自定义配置示例中，我们引入了三个自定义服务接口：ITextGenerationService、IChatCompletionService和ITextEmbeddingGenerationService。

实现自定义服务接口

接下来，我们需要为每个服务接口提供具体的实现。以下是三个接口的具体实现：

实现`IChatCompletionService`

  public class MockChatCompletion : IChatCompletionService
  {
      private readonly Dictionary<string, object?> _attributes = new();
      private string _chatId;


      private static readonly JsonSerializerOptions _jsonSerializerOptions = new()
      {
          NumberHandling = JsonNumberHandling.AllowReadingFromString,
          Encoder = JavaScriptEncoder.Create(UnicodeRanges.All)
      };

      public IReadOnlyDictionary<string, object?> Attributes => _attributes;

      public MockChatCompletion()
      {

      }

      public async Task<IReadOnlyList<ChatMessageContent>> GetChatMessageContentsAsync(ChatHistory chatHistory, PromptExecutionSettings? executionSettings = null, Kernel? kernel = null, [EnumeratorCancellation] CancellationToken cancellationToken = default)
      {
          StringBuilder sb = new();
          string result = $"这是一条Mock数据，便于聊天测试，你的消息是：{chatHistory.LastOrDefault().ToString()}";
          return [new(AuthorRole.Assistant, result.ToString())];
      }

      public async IAsyncEnumerable<StreamingChatMessageContent> GetStreamingChatMessageContentsAsync(ChatHistory chatHistory, PromptExecutionSettings? executionSettings = null, Kernel? kernel = null, [EnumeratorCancellation] CancellationToken cancellationToken = default)
      {
          StringBuilder sb = new();
          string result = $"这是一条Mock数据，便于聊天测试，你的消息是：{chatHistory.LastOrDefault().ToString()}";
          foreach (var c in result)
          {
              yield return new StreamingChatMessageContent(AuthorRole.Assistant, c.ToString());
          }
      }
  }

实现`ITextGenerationService`

 public class MockTextCompletion : ITextGenerationService, IAIService
 {
     private readonly Dictionary<string, object?> _attributes = new();
     private string _chatId;

     private static readonly JsonSerializerOptions _jsonSerializerOptions = new()
     {
         NumberHandling = JsonNumberHandling.AllowReadingFromString,
         Encoder = JavaScriptEncoder.Create(UnicodeRanges.All)
     };

     public IReadOnlyDictionary<string, object?> Attributes => _attributes;

     public MockTextCompletion()
     {

     }

     public async Task<IReadOnlyList<TextContent>> GetTextContentsAsync(string prompt, PromptExecutionSettings? executionSettings = null, Kernel? kernel = null, CancellationToken cancellationToken = default)
     {
         StringBuilder sb = new();
         string result = $"这是一条Mock数据，便于聊天测试，你的消息是：{prompt}";
         return [new(result.ToString())];
     }

     public async IAsyncEnumerable<StreamingTextContent> GetStreamingTextContentsAsync(string prompt, PromptExecutionSettings? executionSettings = null, Kernel? kernel = null, CancellationToken cancellationToken = default)
     {
         StringBuilder sb = new();
         string result = $"这是一条Mock数据，便于聊天测试，你的消息是：{prompt}";
         foreach (var c in result)
         {
             var streamingTextContent = new StreamingTextContent(c.ToString(), modelId: "mock");

             yield return streamingTextContent;
         }
     }
 }

实现`ITextEmbeddingGenerationService`

  public sealed class MockTextEmbeddingGeneratorService : ITextEmbeddingGenerationService
  {
      private Dictionary<string, object?> AttributesInternal { get; } = [];
      public IReadOnlyDictionary<string, object?> Attributes => this.AttributesInternal;
      public MockTextEmbeddingGeneratorService()
      {

      }
      public async Task<IList<ReadOnlyMemory<float>>> GenerateEmbeddingsAsync(
        IList<string> data,
        Kernel? kernel = null,
        CancellationToken cancellationToken = default)
      {
          IList<ReadOnlyMemory<float>> results = new List<ReadOnlyMemory<float>>();

          float[] array1 = { 1.0f, 2.0f, 3.0f };
          float[] array2 = { 4.0f, 5.0f, 6.0f };
          float[] array3 = { 7.0f, 8.0f, 9.0f };

          // 将数组包装为ReadOnlyMemory<float>并添加到列表中
          results.Add(new ReadOnlyMemory<float>(array1));
          results.Add(new ReadOnlyMemory<float>(array2));
          results.Add(new ReadOnlyMemory<float>(array3));

          return results;
      }

      public void Dispose()
      {

      }
  }

看到这里，你可能已经发现，集成自定义模型和本地模型非常简单。只需按照上述步骤，实现相应的接口并注入配置，你就可以在GraphRag.Net中使用这些自定义的功能。