.NET程序员AI开发基座：Microsoft.Extensions.AI

j05tTbVxH1Nu · 2025-2-19 11:35:55

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

eShopSupport 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
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