引言：AI时代下的聊天机器人开发范式演进

在生成式AI技术爆发的当下，基于大语言模型（LLM）的聊天机器人开发已形成标准化技术链路。本文将结合DeepSeek API与微软Semantic Kernel框架，以C#语言实战演示从基础API调用到高级框架集成的完整开发流程。

环境准备与基础配置

• .NET 9 SDK
• Visual Studio 2022或VSCode
• DeepSeek API密钥 官网申请

DeepSeek API基础调用

DeepSeek API的Endpoint地址为：https://api.deepseek.com/chat/completions，相关文档可查看官方文档

1. 单轮对话实现
   代码示例

public async Task<ResponseBody> GetChatMessageContentsAsync(CancellationToken cancellationToken = new CancellationToken())
{
    var client = new HttpClient();
    var request = new HttpRequestMessage(HttpMethod.Post, _builder.Endpoint);
    request.Headers.Add("Accept", "application/json");
    request.Headers.Add("Authorization", $"Bearer {_builder.ApiKey}");

    _body.Stream = false;
    var content = new StringContent(_body.SerializeObject(), null, "application/json");
    request.Content = content;
    var response = await client.SendAsync(request, cancellationToken);
    var responseBody = await response.Content.ReadAsStringAsync(cancellationToken);
    return JsonConvert.DeserializeObject<ResponseBody>(responseBody) ?? new ResponseBody();
}

2. 流式响应处理
   代码示例

public async IAsyncEnumerable<ResponseBody> GetStreamingChatMessageContentsAsync([EnumeratorCancellation] CancellationToken cancellationToken = new CancellationToken())
{
    var client = new HttpClient();
    var request = new HttpRequestMessage(HttpMethod.Post, _builder.Endpoint);
    request.Headers.Add("Accept", "application/json");
    request.Headers.Add("Authorization", $"Bearer {_builder.ApiKey}");
    
    _body.Stream = true;
    var content = new StringContent(_body.SerializeObject(), null, "application/json");
    request.Content = content;
    var response = await client.SendAsync(request, cancellationToken);
    var stream = await response.Content.ReadAsStreamAsync(cancellationToken);
    var reader = new StreamReader(stream);
    while (!reader.EndOfStream)
    {
        var line = await reader.ReadLineAsync(cancellationToken);
        if (string.IsNullOrEmpty(line) || line.StartsWith(":")) continue;
        if (line.StartsWith("data: "))
        {
            var jsonData = line["data: ".Length ..];
            if (jsonData == "[DONE]") break;
            yield return JsonConvert.DeserializeObject<ResponseBody>(jsonData) ?? new ResponseBody();
        }
    }
}

Semantic Kernel框架集成

Semantic Kernel是一种轻型开源开发工具包，可用于轻松生成 AI 代理并将最新的 AI 模型集成到 C#、Python 或 Java 代码库中。 它充当一个高效的中间件，可实现企业级解决方案的快速交付。
DeepSeek API与Semantic Kernel框架集成，可快速实现基于大语言模型的聊天机器人开发。由于DeepSeek API与OpenAI API的兼容性，因此DeepSeek API与Semantic Kernel框架的集成非常简单。只需使用OpenAI的连接器，即可快速实现DeepSeek API与Semantic Kernel框架的集成。

1. NuGet包安装

dotnet add package Microsoft.SemanticKernel

2. Semantic Kernel初始化

var openAIClientCredential = new ApiKeyCredential(apiKey);
var openAIClientOption = new OpenAIClientOptions
{
    Endpoint = new Uri("https://api.deepseek.com"),

};
var builder = Kernel.CreateBuilder()
    .AddOpenAIChatCompletion(modelId, new OpenAIClient(openAIClientCredential, openAIClientOption));

var kernel = builder.Build();

3. 聊天机器人开发

var chatCompletionService = kernel.GetRequiredService<IChatCompletionService>();

Console.WriteLine("😀User >> "+ chatHistory.Last().Content);
var result = chatCompletionService.GetStreamingChatMessageContentsAsync(
    chatHistory
);
Console.Write("👨Assistant >> ");
await foreach (var item in result)
{
    Thread.Sleep(200);
    Console.Write(item.Content);
}

代码示例