.NET程序员AI开发基座：Microsoft.Extensions.AI

大家好，我是Edison。

微软在2024年11月就发布了新的AI核心库Microsoft.Extensions.AI，虽然目前还是一个预览版，但其可以大大简化我们的AI集成和开发工作。

Microsoft.Extensions.AI介绍

Microsoft.Extensions.AI 是一组核心 .NET 库，是在与整个 .NET 生态系统（包括语义内核）的开发人员协作中创建的。 这些库提供统一的 C# 抽象层，用于与 AI 服务交互，例如小型和大型语言模型（SLA 和 LLM）、嵌入和中间件。

Microsoft.Extensions.AI 提供可由各种服务实现的抽象，所有这些概念都遵循相同的核心概念。 此库不旨在提供针对任何特定提供商服务定制的 API。

Microsoft.Extensions.AI 目标是在 .NET 生态系统中充当一个统一层，使开发人员能够选择他们的首选框架和库，同时确保整个生态系统之间的无缝集成和协作。

画外音>开发者可以节省时间下来专注自己的应用程序的业务逻辑实现，从而不必花过多时间去做AI服务的集成调试，点个大大的赞！

我能使用哪些服务实现？

Microsoft.Extensions.AI 通过 NuGet 包提供了以下服务的实现：

• OpenAI
• Azure OpenAI
• Azure AI Inference
• Ollama

将来，这些抽象的服务实现都将会是客户端库的一部分。

基本使用

安装NuGet包：

Microsoft.Extensions.AI (9.1.0-preview)
Microsoft.Extensions.AI.OpenAI (9.1.0-preivew)

这里我们使用SiliconCloud提供的 DeepSeek-R1-Distill-Llama-8B 模型，这是一个使用DeepSeek-R1开发的基于Llama-3.1-8B的蒸馏模型，免费好用。

注册SiliconCloud：https://cloud.siliconflow.cn/i/DomqCefW

简单对话：

var openAIClientOptions = new OpenAIClientOptions();
 openAIClientOptions.Endpoint = new Uri("https://api.siliconflow.cn/v1");

 var client = new OpenAIClient(new ApiKeyCredential("sk-xxxxxxxxxx"),  openAIClientOptions);
 var chatClient = client.AsChatClient("deepseek-ai/DeepSeek-R1-Distill-Llama-8B");
 var response = await chatClient.CompleteAsync("Who are you?");
 Console.WriteLine(response.Message);

封装的IChatClient对象可以十分方便地屏蔽差异，用起来十分方便。

函数调用

要想实现函数调用（Function Calling），则需要调整一下：

var openAIClientOptions = new OpenAIClientOptions();
 openAIClientOptions.Endpoint = new Uri("https://api.siliconflow.cn/v1");

[Description("Get the current time")]
string GetCurrentTime() => DateTime.Now.ToLocalTime().ToString();

var client = new ChatClientBuilder()
      .UseFunctionInvocation()
      .Use(new OpenAIClient(new ApiKeyCredential("sk-xxxxxxxxx"), openAIClientOptions)
      .AsChatClient("deepseek-ai/DeepSeek-R1-Distill-Llama-8B"));
var response = await client.CompleteAsync(
      "What's the time now?",
      new() { Tools = [AIFunctionFactory.Create(GetCurrentTime)] });
Console.Write(response);

可以看到，需要主动使用 UseFunctionInvocation 方法 及 提供 Tools 注册列表，就能使用我们封装的 Tools 了。

多模型使用

很多时候，我们希望Chat入口用一个模型，业务处理则用另一个模型，我们完全可以对其进行独立配置。

例如，这里参考mingupupu大佬的PaperAssistant，我也实现了一个。

在配置文件中，配置多AI模型：

{
  // For Paper Smmary
  "PaperSummaryModel": {
    "ModelId": "deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
    "ApiKey": "sk-xxxxxxxxxx",
    "EndPoint": "https://api.siliconflow.cn"
  },
  // For Main Chat
  "MainChatModel": {
    "ModelId": "Qwen/Qwen2.5-7B-Instruct",
    "ApiKey": "sk-xxxxxxxxxx",
    "EndPoint": "https://api.siliconflow.cn"
  }
}

对于某个业务处理，将其封装为Plugin，并使用 DeepSeek-R1-Distill-Llama-8B 模型：

public sealed class PaperAssistantPlugins
{
    public PaperAssistantPlugins(IConfiguration config)
    {
        var apiKeyCredential = new ApiKeyCredential(config["PaperSummaryModel:ApiKey"]);
        var aiClientOptions = new OpenAIClientOptions();
        aiClientOptions.Endpoint = new Uri(config["PaperSummaryModel:EndPoint"]);
        var aiClient = new OpenAIClient(apiKeyCredential, aiClientOptions)
            .AsChatClient(config["PaperSummaryModel:ModelId"]);
        ChatClient = new ChatClientBuilder(aiClient)
            .UseFunctionInvocation()
            .Build();
    }

    public IChatClient ChatClient { get; }

    [Description("Read the PDF content from the file path")]
    [return: Description("PDF content")]
    public string ExtractPdfContent(string filePath)
    {
        Console.WriteLine($"[Tool] Now executing {nameof(ExtractPdfContent)}, params: {filePath}");
        var pdfContentBuilder = new StringBuilder();
        using (var document = PdfDocument.Open(filePath))
        {
            foreach (var page in document.GetPages())
                pdfContentBuilder.Append(page.Text);
        }
        return pdfContentBuilder.ToString();
    }

    [Description("Create a markdown note file by file path")]
    public void SaveMarkDownFile([Description("The file path to save")] string filePath, [Description("The content of markdown note")] string content)
    {
        Console.WriteLine($"[Tool] Now executing {nameof(SaveMarkDownFile)}, params: {filePath}, {content}");
        try
        {
            if (!File.Exists(filePath))
                File.WriteAllText(filePath, content);
            else
                File.WriteAllText(filePath, content);
        }
        catch (Exception ex)
        {
            Console.WriteLine($"[Error] An error occurred: {ex.Message}");
        }
    }

    [Description("Generate one summary of one paper and save the summary to a local file by file path")]
    public async Task GeneratePaperSummary(string sourceFilePath, string destFilePath)
    {
        var pdfContent = this.ExtractPdfContent(sourceFilePath);
        var prompt = """
                     You're one smart agent for reading the content of a PDF paper and summarizing it into a markdown note.
                     User will provide the path of the paper and the path to create the note.
                     Please make sure the file path is in the following format:
                     "D:\Documents\xxx.pdf"
                     "D:\Documents\xxx.md"
                     Please summarize the abstract, introduction, literature review, main points, research methods, results, and conclusion of the paper.
                     The tile should be 《[Title]》, Authour should be [Author] and published in [Year].
                     Please make sure the summary should include the following:
                     (1) Abstrat
                     (2) Introduction
                     (3) Literature Review
                     (4) Main Research Questions and Background
                     (5) Research Methods and Techniques Used
                     (6) Main Results and Findings
                     (7) Conclusion and Future Research Directions
                     """;
        var history = new List<ChatMessage>
        {
            new ChatMessage(ChatRole.System, prompt),
            new ChatMessage(ChatRole.User, pdfContent)
        };
        var result = await ChatClient.CompleteAsync(history);
        this.SaveMarkDownFile(destFilePath, result.ToString());
    }
}

对于对话主入口，则使用 Qwen2.5-7B-Instruct 模型即可：

Console.WriteLine("Now loading the configuration...");
var config = new ConfigurationBuilder()
    .AddJsonFile($"appsettings.json")
    .Build();
Console.WriteLine("Now loading the chat client...");
var apiKeyCredential = new ApiKeyCredential(config["MainChatModel:ApiKey"]);
var aiClientOptions = new OpenAIClientOptions();
aiClientOptions.Endpoint = new Uri(config["MainChatModel:EndPoint"]);
var aiClient = new OpenAIClient(apiKeyCredential, aiClientOptions)
    .AsChatClient(config["MainChatModel:ModelId"]);
var chatClient = new ChatClientBuilder(aiClient)
    .UseFunctionInvocation()
    .Build();
Console.WriteLine("Now loading the plugins...");
var plugins = new PaperAssistantPlugins(config);
var chatOptions = new ChatOptions()
{
    Tools =
    [
      AIFunctionFactory.Create(plugins.ExtractPdfContent),
      AIFunctionFactory.Create(plugins.SaveMarkDownFile),
      AIFunctionFactory.Create(plugins.GeneratePaperSummary)
    ]
};
Console.WriteLine("Now starting chatting...");
var prompt = """
             You're one smart agent for reading the content of a PDF paper and summarizing it into a markdown note.
             User will provide the path of the paper and the path to create the note.
             Please make sure the file path is in the following format:
             "D:\Documents\xxx.pdf"
             "D:\Documents\xxx.md"
             Please summarize the abstract, introduction, literature review, main points, research methods, results, and conclusion of the paper.
             The tile should be 《[Title]》, Authour should be [Author] and published in [Year].
             Please make sure the summary should include the following:
             (1) Abstrat
             (2) Introduction
             (3) Literature Review
             (4) Main Research Questions and Background
             (5) Research Methods and Techniques Used
             (6) Main Results and Findings
             (7) Conclusion and Future Research Directions
             """;
var history = new List<ChatMessage>
{
    new ChatMessage(ChatRole.System, prompt)
};
bool isComplete = false;
Console.WriteLine("AI> I'm Ready! What can I do for you?");
do
{
    Console.Write("You> ");
    string? input = Console.ReadLine();
    if (string.IsNullOrWhiteSpace(input))
        continue;

    if (input.Trim().Equals("EXIT", StringComparison.OrdinalIgnoreCase))
    {
        isComplete = true;
        break;
    }

    if (input.Trim().Equals("Clear", StringComparison.OrdinalIgnoreCase))
    {
        history.Clear();
        Console.WriteLine("Cleared our chatting history successfully!");
        continue;
    }

    history.Add(new ChatMessage(ChatRole.User, input));
    Console.WriteLine();
    var result = await chatClient.CompleteAsync(input, chatOptions);
    Console.WriteLine(result.ToString());
    history.Add(new ChatMessage(ChatRole.Assistant, result.ToString() ?? string.Empty));
} while (!isComplete);

这里测试一下，我让它帮我总结一个pdf并将总结内容生成到一个md文件中输出到我指定的目录下保存。

可以看出，它成功地调用了Plugin完成了PDF读取、内容提取总结 和 生成Markdown文件。

eShopSupport

ShopSupport 是一个开源的AI示例应用程序，客户可以使用它来与AI客户对话查询产品，实现网站系统的“智能客服”的场景。

这个开源项目就使用了 Microsoft.Extensions.AI 作为和AI服务集成的抽象层，值得我们参考学习。

值得一提的是，它并没有说全部统一.NET技术栈，而是保留了Python作为机器学习模型训练和推理的，展示了技术异构在这个场景下的融合。

此外，基于Aspire来生成可观察和可靠的云原生应用也是这个项目带来的一个亮点，可以学习下。

小结

本文介绍了Microsoft.Extensions.AI的基本概念 和 基本使用，如果你也是.NET程序员希望参与AI应用的开发，那就快快了解和使用起来吧。

示例源码

GitHub：https://github.com/Coder-EdisonZhou/EDT.Agent.Demos

参考内容

mingupupu的文章：https://www.cnblogs.com/mingupupu/p/18651932

更多

Microsoft Learn: https://learn.microsoft.com/zh-cn/dotnet/ai/ai-extensions

eShopSupport:  https://github.com/dotnet/eShopSupport

devblogs: https://devblogs.microsoft.com/dotnet/e-shop-infused-with-ai-comprehensive-intelligent-dotnet-app-sample

作者：周旭龙

出处：https://edisonchou.cnblogs.com

本文版权归作者和博客园共有，欢迎转载，但未经作者同意必须保留此段声明，且在文章页面明显位置给出原文链接。