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。
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。
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。
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
。
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:
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()); } } }
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; } } }
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。
通过本文的介绍,我们了解了如何在GraphRag.Net中集成国产模型和本地模型。希望大家能够根据这些示例,开发出更多适合自己需求的功能。更多精彩内容,欢迎关注我的公众号,并发送进群加入我们的GraphRag.Net交流群,与社区小伙伴们一起交流学习!
感谢阅读,我们下期再见!