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C#测试调用LM Studio服务接口

news 来源：原创 2025/4/30 4:42:17

使用LM Studio部署本地DeepSeek模型后，除了在LM Studio的聊天界面与大模型对话之外，还可以调用LM Studio提供的服务接口自编程序调用。如下图所示，在开发者界面点击Settings，在弹出的窗口中启动“在网络中提供服务”，同时在标“4”处设置Status为Running，此时就可以调用LM Studio服务接口了。标“5”处显示了服务基础地址，标“6”处显示了可用服务地址。
在这里插入图片描述
本文测试在Winform中调用LM Studio服务接口与大模型对话的基本用法，一开始先测试了是否能够用Ater. DeepSeek.AspNetCore包调用本地模型，结果不行。于是决定使用之前用的Flurl包基于http通信调用服务接口。
主要测试获取模型列表（v1/models）和聊天补全（v1/chat/completions）两种服务，前者不需要输入参数，后者的话LM Studio中给出了输入参数示例，在postman中也能看到返回结果样式，

curl http://localhost:1234/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-r1-distill-qwen-1.5b",
    "messages": [
      { "role": "system", "content": "Always answer in rhymes. Today is Thursday" },
      { "role": "user", "content": "What day is it today?" }
    ],
    "temperature": 0.7,
    "max_tokens": -1,
    "stream": false
}'

在这里插入图片描述
在项目中定义对应的类（对话结果返回类只定义了部分内容，没有定义全），主要代码如下所示：

// 单个模型信息
public class LMStudioModel
{
    public string Id { get; set; } = string.Empty;
    public string Object { get; set; } = string.Empty;

    public string Owned_by { get; set; } = string.Empty;
}

//模型集合
public class LMStudioModels
{
    public string Object{get;set;}=string.Empty;
    public List<LMStudioModel> Data { get; set; } = new List<LMStudioModel>();
}

//问题及回答中的单条消息
public class ChatMessage
{
    public string role { get; set; } = string.Empty;
    public string content { get; set; } = string.Empty;
}

//对话问题
public class ChatQuestion
{
    public string model { get; set; } = string.Empty;
    public bool stream { get; set; } = false;
    public int max_tokens { get; set; } = -1;

    public List<ChatMessage> messages { get; set; } = new List<ChatMessage>();
}

//单个对话结果
public class AnswerChoice
{
    public int Index { get; set; } = 0;
    public string Finish_reason { get; set; } = string.Empty;

    public ChatMessage message { get; set; } = new ChatMessage();
}

//对话结果集合
public class ChatAnswer
{
    public string Id { get; set; } = string.Empty;
    public string Object { get; set; } = string.Empty;
    public long Created { get; set; } = 0;

    public string Model { get; set; } = string.Empty;

    public List<AnswerChoice> Choices { get; set; } = new List<AnswerChoice>();
}

//对话地址
private string m_modelUrl = "http://192.168.11.102:1234/v1/models";
private string m_chatUrl = "http://192.168.11.102:1234/v1/chat/completions";

//获取模型信息
string retResult = m_modelUrl.GetStringAsync().Result;
JsonSerializerOptions options = new JsonSerializerOptions();
options.PropertyNameCaseInsensitive = true;

m_models = JsonSerializer.Deserialize<LMStudioModels>(retResult, options);
foreach (var model in m_models.Data)
{
    comboBox1.Items.Add(model.Id);
}

//对话
ChatQuestion question = new ChatQuestion();
ChatMessage message = new ChatMessage();
message.role = "user";
message.content = txtChat.Text;
question.messages.Add(message);
question.model = m_models.Data[comboBox1.SelectedIndex].Id;

string result = m_chatUrl.PostJsonAsync(question).ReceiveString().Result;
txtResult.Text = result;

JsonSerializerOptions options = new JsonSerializerOptions();
options.PropertyNameCaseInsensitive = true;

ChatAnswer answer = JsonSerializer.Deserialize<ChatAnswer>(result, options);
txtResult.Text = answer?.Choices.FirstOrDefault()?.message.content;