在C#程序中调用大模型的API接口

当前，以ChatGPT、DeepSeek为代表的各种大语言模型层出不穷，让我们切实感受到人工智能的强大。如果能在程序中集成大语言模型的问答能力，那么将能实现一些非常有用和非常有趣的需求。

本文简单介绍在C#应用中调用大模型接口的几种方法，其中基于HttpClient的原生方法、OpenAPI包是两种通用方法，前者依赖最少推荐。

以下示例借助DeepSeek实现语言翻译功能。

基于HttpClient的原生方法

以下是访问自部署的大模型接口：

private async Task OKAsync() {
    var apiKey = "sk-XXX";
    var apiUrl = "http://XXX.XXX.XXX.XXX:XXX/v1/chat/completions";

    if (string.IsNullOrWhiteSpace(Input)) return;

    var requestContent = new {
        model = "crcc-qwen3",
        messages = new[]
        {
            new { role = "system", content = "你是一个语言翻译家，擅长将中文翻译成英文！请只输出翻译后的文本，不要输出其他不相关的文字。" },
            new { role = "user", content = $"请翻译'{Input}'为英语!" }
        },
        Stream = false,
    };

    var json = JsonSerializer.Serialize(requestContent, new JsonSerializerOptions { PropertyNamingPolicy = JsonNamingPolicy.CamelCase });
    var httpContent = new StringContent(json, Encoding.UTF8, "application/json");

    using var httpClient = new HttpClient();
    httpClient.DefaultRequestHeaders.Clear();
    httpClient.DefaultRequestHeaders.Add("Authorization", $"Bearer {apiKey}");

    try {
        var response = await httpClient.PostAsync(apiUrl, httpContent);
        response.EnsureSuccessStatusCode();

        var responseBody = await response.Content.ReadAsStringAsync();
        using var jsonDocument = JsonDocument.Parse(responseBody);
        var root = jsonDocument.RootElement;

        if (root.TryGetProperty("choices", out var choices) && choices.GetArrayLength() > 0) {
            var content = choices[0].GetProperty("message").GetProperty("content").GetString();
            Output = content;
        } else {
            Output = "No result returned.";
        }
    } catch (HttpRequestException ex) {
        Output = $"HTTP Error: {ex.Message}";
    } catch (Exception ex) {
        Output = $"Error: {ex.Message}";
    }
}

如果是访问DeepSeek的官方接口：

var apiKey = "sk-XXX";
var apiUrl = "https://api.deepseek.com/v1/chat/completions";
var requestContent = new {
    model = "deepseek-chat",
    messages = new[]
    {
        new { role = "system", content = "你是一个语言翻译家，擅长将中文翻译成英文！请只输出翻译后的文本，不要输出其他不相关的文字。" },
        new { role = "user", content = $"请翻译'{Input}'为英语!" }
    },
    // 不使用流模式，即整体输出最终结果
    Stream = false,
};

使用OpenAPI包访问

安装以下NuGet包：

使用方法如下，非常简洁，以下是访问自部署的大模型接口：

static async Task Main() {
    var client = new ChatClient(
        "crcc-qwen3", 
        new ApiKeyCredential("sk-XXX"), 
        new OpenAIClientOptions() {
            Endpoint = new Uri("http://XXX.XXX.XXX.XXX:54000/v1")
        }
    );
    var res =await client.CompleteChatAsync(
        ChatMessage.CreateSystemMessage("你是一个语言翻译家，擅长将中文翻译成英文！请只输出翻译后的文本，不要输出其他不相关的文字。"),
        ChatMessage.CreateUserMessage($"请翻译'这是一个挑战！'为英语!"),
    );
    Console.WriteLine( res.Value.Content.FirstOrDefault().Text );
}

如果是访问DeepSeek的官方接口：

static async Task Main() {
    var client = new ChatClient(
        "deepseek-chat", 
        new ApiKeyCredential("sk-XXX"), 
        new OpenAIClientOptions() {
            Endpoint = new Uri("https://api.deepseek.com/v1")
        }
    );
  // ......
}

但是最终程序会依赖很多类库，较为冗余：

DeepSeek.Core库

该库仅支持访问官方DeepSeek大模型接口，只需要传递APIKey和消息。

private async Task OKAsync() {
    if (string.IsNullOrWhiteSpace(Input)) return;

    var apiKey = "sk-XXX";
    var client = new DeepSeekClient(apiKey);
    var request = new ChatRequest {
        Messages = [
            Message.NewSystemMessage("你是一个语言翻译家，擅长将中文翻译成英文！请只输出翻译后的文本，不要输出其他不相关的文字。"),
            Message.NewUserMessage($"请翻译'{Input}'为英语!")
        ],
        Model = DeepSeekModels.ChatModel
    };

    var chatResponse = await client.ChatAsync(request, CancellationToken.None);
    Output = chatResponse?.Choices.First().Message?.Content;
}

访问Ollama上部署的大模型

using System.Text;
using System.Text.Json;

namespace OllamaClient {
    public class OllamaRequest {
        public string Model { get; set; } = string.Empty;
        public string Prompt { get; set; } = string.Empty;
        public bool Stream { get; set; } = false;
    }

    public class OllamaResponse {
        public string Model { get; set; } = string.Empty;
        public string Response { get; set; } = string.Empty;
        public bool Done { get; set; }
    }

    internal class Program {
        private static readonly HttpClient client = new HttpClient();
        private const string OllamaApiUrl = "http://XXX.XXX.XXX.XXX:XXX/api/generate";

        static async Task Main(string[] args) {
            var request = new OllamaRequest {
                Model = "gpt-oss:20b",
                Prompt = "你是一个语言翻译家，擅长将中文翻译成英文！请只输出翻译后的文本，不要输出其他不相关的文字。请翻译`翻译家`这句话。",
                Stream = false,
             };

            try {
                var response = await CallOllamaAsync(request);
                Console.WriteLine("\n模型回复：\n");
                Console.WriteLine(response.Response);
            } catch (Exception ex) {
                Console.WriteLine($"调用失败: {ex.Message}");
            }

            Console.WriteLine("\n按任意键退出...");
            Console.ReadKey();
        }

        static async Task<OllamaResponse> CallOllamaAsync(OllamaRequest request) {
            var options = new JsonSerializerOptions {
                PropertyNamingPolicy = JsonNamingPolicy.CamelCase,
            };
            var json = JsonSerializer.Serialize(request, options);
            var content = new StringContent(json, Encoding.UTF8, "application/json");

            var httpResponse = await client.PostAsync(OllamaApiUrl, content);
            httpResponse.EnsureSuccessStatusCode();

            var jsonResponse = await httpResponse.Content.ReadAsStringAsync();
            var result = JsonSerializer.Deserialize<OllamaResponse>(jsonResponse, options);

            if (result == null)
                throw new Exception("反序列化失败");

            return result;
        }
    }
}

效果