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
- 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();
}
- 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。
- NuGet包安装
dotnet add package Microsoft.SemanticKernel
- 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 = builder1.Build();
- 聊天机器人开发
var chatCompletionService = kernel1.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
