使用C#构建一个同时问多个LLM并总结的小工具

技术分享 9个月前 (05-09) 0 999+

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前言

在AI编程时代，如果自己能够知道一些可行的解决方案，那么描述清楚交给AI，可以有很大的帮助。

但是我们往往不知道真正可行的解决方案是什么？

我自己有过这样的经历，遇到一个需求，我不知道有哪些解决方案，就去问AI，然后AI输出一大堆东西，我一个个去试，然后再换个AI问，又提出了不同的解决方案。

在换AI问与一个个试的过程中好像浪费了很多时间。

突然出现了一个想法，不是可以一下子把问题丢给多个AI，然后再总结一下出现最多的三个方案。那么这三个方案可行的概率会大一点。然后再丢给Cursor或者Cline等AI编程工具帮我们实现一下。

这样做的缺点是比起直接在网页上问，调用API需要耗费Token，但是硅基流动给我赠送了很多额度还没用完，随便玩一下。

实现效果：

实现方案

实现方案也很简单，如下图所示：

先设计一下布局：

<UserControl xmlns="https://github.com/avaloniaui"              xmlns:x="http://schemas.microsoft.com/winfx/2006/xaml"              xmlns:d="http://schemas.microsoft.com/expression/blend/2008"              xmlns:mc="http://schemas.openxmlformats.org/markup-compatibility/2006"              mc:Ignorable="d" d:DesignWidth="800" d:DesignHeight="450" 			 xmlns:vm="using:AIE_Studio.ViewModels" 			 x:DataType="vm:DuoWenViewModel"              x:Class="AIE_Studio.Views.DuoWenView"> 	<StackPanel> 		<TextBox Text="{Binding Question}"></TextBox> 		<Button Content="提问" Command="{Binding DuoWenStreamingParallelCommand}" Margin="5"/> 		<ScrollViewer VerticalScrollBarVisibility="Auto"> 		<Grid> 			<Grid.RowDefinitions> 				<RowDefinition Height="*"/> 				<RowDefinition Height="*"/> 			</Grid.RowDefinitions> 			<Grid.ColumnDefinitions> 				<ColumnDefinition Width="*"/> 				<ColumnDefinition Width="*"/> 				<ColumnDefinition Width="*"/> 			</Grid.ColumnDefinitions> 			<!-- Row 1, Column 1 --> 			<StackPanel Grid.Row="0" Grid.Column="0"> 				<TextBlock Text="{Binding Title1}" Margin="5"/> 				<ScrollViewer VerticalScrollBarVisibility="Auto"> 					<TextBox Text="{Binding Result1}" AcceptsReturn="True" Margin="5" Height="300"/> 				</ScrollViewer> 			</StackPanel> 			<!-- Row 1, Column 2 --> 			<StackPanel Grid.Row="0" Grid.Column="1"> 				<TextBlock Text="{Binding Title2}" Margin="5"/> 				<ScrollViewer VerticalScrollBarVisibility="Auto"> 					<TextBox Text="{Binding Result2}" AcceptsReturn="True" Margin="5" Height="300"/> 				</ScrollViewer> 			</StackPanel> 			<!-- Row 1, Column 3 --> 			<StackPanel Grid.Row="0" Grid.Column="2"> 				<TextBlock Text="{Binding Title3}" Margin="5"/> 				<ScrollViewer VerticalScrollBarVisibility="Auto"> 					<TextBox Text="{Binding Result3}" AcceptsReturn="True" Margin="5" Height="300"/> 				</ScrollViewer> 			</StackPanel> 			<!-- Row 2, Column 1 --> 			<StackPanel Grid.Row="1" Grid.Column="0"> 				<TextBlock Text="{Binding Title4}" Margin="5"/> 				<ScrollViewer VerticalScrollBarVisibility="Auto"> 					<TextBox Text="{Binding Result4}" AcceptsReturn="True" Margin="5" Height="300"/> 				</ScrollViewer> 			</StackPanel> 			<!-- Row 2, Column 2 --> 			<StackPanel Grid.Row="1" Grid.Column="1"> 				<TextBlock Text="{Binding Title5}" Margin="5"/> 				<ScrollViewer VerticalScrollBarVisibility="Auto"> 					<TextBox Text="{Binding Result5}" AcceptsReturn="True" Margin="5" Height="300"/> 				</ScrollViewer> 			</StackPanel> 			<!-- Row 2, Column 3 --> 			<StackPanel Grid.Row="1" Grid.Column="2"> 				<TextBlock Text="{Binding Title6}" Margin="5"/> 				<ScrollViewer VerticalScrollBarVisibility="Auto"> 					<TextBox Text="{Binding Result6}" AcceptsReturn="True" Margin="5" Height="300"/> 				</ScrollViewer> 			</StackPanel> 		</Grid> 		</ScrollViewer> 	</StackPanel> </UserControl>

在ViewModel中先来看一下最原始的显示结果的方式：

 [RelayCommand]  private async Task DuoWen()  {      ApiKeyCredential apiKeyCredential = new ApiKeyCredential("your api key");       OpenAIClientOptions openAIClientOptions = new OpenAIClientOptions();      openAIClientOptions.Endpoint = new Uri("https://api.siliconflow.cn/v1");          IChatClient client1 =      new OpenAI.Chat.ChatClient("Qwen/Qwen2.5-72B-Instruct", apiKeyCredential, openAIClientOptions).AsChatClient();       var result1 = await client1.GetResponseAsync(Question);       Result1 = result1.ToString();       IChatClient client2 =      new OpenAI.Chat.ChatClient("Qwen/Qwen3-235B-A22B", apiKeyCredential, openAIClientOptions).AsChatClient();       var result2 = await client2.GetResponseAsync(Question);       Result2 = result2.ToString();       IChatClient client3 =      new OpenAI.Chat.ChatClient("THUDM/GLM-Z1-32B-0414", apiKeyCredential, openAIClientOptions).AsChatClient();       var result3 = await client3.GetResponseAsync(Question);       Result3 = result3.ToString();       IChatClient client4 =      new OpenAI.Chat.ChatClient("THUDM/GLM-4-32B-0414", apiKeyCredential, openAIClientOptions).AsChatClient();       var result4 = await client4.GetResponseAsync(Question);       Result4 = result4.ToString();       IChatClient client5 =     new OpenAI.Chat.ChatClient("deepseek-ai/DeepSeek-R1", apiKeyCredential, openAIClientOptions).AsChatClient();       var result5 = await client5.GetResponseAsync(Question);       Result5 = result5.ToString();       IChatClient client6 =      new OpenAI.Chat.ChatClient("deepseek-ai/DeepSeek-V3", apiKeyCredential, openAIClientOptions).AsChatClient();       var result6 = await client6.GetResponseAsync(Question);       Result6 = result6.ToString();

这种最简单的方式是非流式的并且也不是并行的，你会发现一个结束了才会继续向下一个提问。

但至少已经成功显示结果了，现在想要实现的是有一个窗体进行总结。

窗体设计：

<Window xmlns="https://github.com/avaloniaui"         xmlns:x="http://schemas.microsoft.com/winfx/2006/xaml"         xmlns:d="http://schemas.microsoft.com/expression/blend/2008"         xmlns:mc="http://schemas.openxmlformats.org/markup-compatibility/2006"         mc:Ignorable="d" d:DesignWidth="600" d:DesignHeight="450" 		xmlns:vm="using:AIE_Studio.ViewModels"         x:Class="AIE_Studio.Views.ShowResultWindow" 		x:DataType="vm:ShowResultWindowViewModel"         Title="ShowResultWindow"> 	<StackPanel> 		<TextBlock Text="最终结果：" Margin="5" /> 		<ScrollViewer VerticalScrollBarVisibility="Auto"> 			<TextBox Text="{Binding ReceivedValue}" AcceptsReturn="True" Margin="5" Height="400"/> 		</ScrollViewer> 	</StackPanel> </Window>

窗体的ViewModel：

public partial class ShowResultWindowViewModel : ViewModelBase {     [ObservableProperty]     private string? receivedValue;       }

然后只要在全部都有结果之后，再进行一下总结即可。

IChatClient client7 = new OpenAI.Chat.ChatClient("Qwen/Qwen2.5-72B-Instruct", apiKeyCredential, openAIClientOptions).AsChatClient(); List<Microsoft.Extensions.AI.ChatMessage> messages = new List<Microsoft.Extensions.AI.ChatMessage>();  string prompt = $"""       请分析以下各个助手给出的方案，选择其中提到最多的3种方案。       助手1：{result1}       助手2：{result2}       助手3：{result3}       助手4：{result4}       助手5：{result5}       助手6：{result6}       """; messages.Add(new Microsoft.Extensions.AI.ChatMessage(ChatRole.User, prompt)); var result7 = await client7.GetResponseAsync(messages);  var showWindow = _serviceProvider.GetRequiredService<ShowResultWindow>(); var showWindowViewModel = _serviceProvider.GetRequiredService<ShowResultWindowViewModel>(); showWindowViewModel.ReceivedValue = result7.ToString(); showWindow.DataContext = showWindowViewModel; showWindow.Show();

以上就成功实现了。

但是还是有可以改进的地方，首先是并行，一个一个问不如同时问。

 [RelayCommand]  private async Task DuoWenParallel()  {      ApiKeyCredential apiKeyCredential = new ApiKeyCredential("your api key");       OpenAIClientOptions openAIClientOptions = new OpenAIClientOptions();      openAIClientOptions.Endpoint = new Uri("https://api.siliconflow.cn/v1");       // 创建一个列表来存储所有的任务      var tasks = new List<Task<string>>();       // 向每个助手发送请求并将任务添加到列表中      tasks.Add(GetResponseFromClient("Qwen/Qwen2.5-72B-Instruct", apiKeyCredential, openAIClientOptions));      tasks.Add(GetResponseFromClient("Qwen/Qwen3-235B-A22B", apiKeyCredential, openAIClientOptions));      tasks.Add(GetResponseFromClient("THUDM/GLM-Z1-32B-0414", apiKeyCredential, openAIClientOptions));      tasks.Add(GetResponseFromClient("THUDM/GLM-4-32B-0414", apiKeyCredential, openAIClientOptions));      tasks.Add(GetResponseFromClient("deepseek-ai/DeepSeek-R1", apiKeyCredential, openAIClientOptions));      tasks.Add(GetResponseFromClient("deepseek-ai/DeepSeek-V3", apiKeyCredential, openAIClientOptions));       // 等待所有任务完成      var results = await Task.WhenAll(tasks);       // 将结果分配给相应的属性      Result1 = results[0];      Result2 = results[1];      Result3 = results[2];      Result4 = results[3];      Result5 = results[4];      Result6 = results[5];  }    private async Task<string> GetResponseFromClient(string model, ApiKeyCredential apiKeyCredential, OpenAIClientOptions options)   {       IChatClient client = new OpenAI.Chat.ChatClient(model, apiKeyCredential, options).AsChatClient();       var result = await client.GetResponseAsync(Question);       return result.ToString();   }

现在虽然是并行了，但是只有等到所有助手都回答了之后，才会统一显示，用户体验也不好。

改成流式：

[RelayCommand] private async Task DuoWenStreaming() {     ApiKeyCredential apiKeyCredential = new ApiKeyCredential("your api key");      OpenAIClientOptions openAIClientOptions = new OpenAIClientOptions();     openAIClientOptions.Endpoint = new Uri("https://api.siliconflow.cn/v1");      //string question = "C#如何获取鼠标滑动选中的值？请告诉我一些可能的方案，每个方案只需用一句话描述即可，不用展开说明。";      IChatClient client1 =     new OpenAI.Chat.ChatClient("Qwen/Qwen2.5-72B-Instruct", apiKeyCredential, openAIClientOptions).AsChatClient();      await foreach (var item in client1.GetStreamingResponseAsync(Question))     {        Result1 += item.ToString();     }           }

现在查看效果：

最后再改造成流式+并行就好了。

 [RelayCommand]  private async Task DuoWenStreamingParallel()  {      ApiKeyCredential apiKeyCredential = new ApiKeyCredential("your api key");       OpenAIClientOptions openAIClientOptions = new OpenAIClientOptions();      openAIClientOptions.Endpoint = new Uri("https://api.siliconflow.cn/v1");       // Clear previous results      Result1 = Result2 = Result3 = Result4 = Result5 = Result6 = string.Empty;       // Create a list of tasks for parallel processing      var tasks = new List<Task>      {          ProcessStreamingResponse("Qwen/Qwen2.5-72B-Instruct", apiKeyCredential, openAIClientOptions, (text) => Result1 += text),          ProcessStreamingResponse("Qwen/Qwen3-235B-A22B", apiKeyCredential, openAIClientOptions, (text) => Result2 += text),          ProcessStreamingResponse("THUDM/GLM-Z1-32B-0414", apiKeyCredential, openAIClientOptions, (text) => Result3 += text),          ProcessStreamingResponse("THUDM/GLM-4-32B-0414", apiKeyCredential, openAIClientOptions, (text) => Result4 += text),          ProcessStreamingResponse("deepseek-ai/DeepSeek-R1", apiKeyCredential, openAIClientOptions, (text) => Result5 += text),          ProcessStreamingResponse("deepseek-ai/DeepSeek-V3", apiKeyCredential, openAIClientOptions, (text) => Result6 += text)      };       // Wait for all streaming responses to complete      await Task.WhenAll(tasks);       IChatClient client7 =      new OpenAI.Chat.ChatClient("Qwen/Qwen2.5-72B-Instruct", apiKeyCredential, openAIClientOptions).AsChatClient();      List<Microsoft.Extensions.AI.ChatMessage> messages = new List<Microsoft.Extensions.AI.ChatMessage>();       string prompt = $"""            请分析以下各个助手给出的方案，选择其中提到最多的3种方案。            助手1：{Result1}            助手2：{Result2}            助手3：{Result3}            助手4：{Result4}            助手5：{Result5}            助手6：{Result6}            """;      messages.Add(new Microsoft.Extensions.AI.ChatMessage(ChatRole.User, prompt));      var result7 = await client7.GetResponseAsync(messages);       var showWindow = _serviceProvider.GetRequiredService<ShowResultWindow>();      var showWindowViewModel = _serviceProvider.GetRequiredService<ShowResultWindowViewModel>();      showWindowViewModel.ReceivedValue = result7.ToString();      showWindow.DataContext = showWindowViewModel;      showWindow.Show();  }  private async Task ProcessStreamingResponse(string model, ApiKeyCredential apiKeyCredential, OpenAIClientOptions options, Action<string> updateResult) {     IChatClient client = new OpenAI.Chat.ChatClient(model, apiKeyCredential, options).AsChatClient();          await foreach (var item in client.GetStreamingResponseAsync(Question))     {         updateResult(item.ToString());     } }

这里使用了一个带有一个参数的委托来更新每个助手回复的结果。

现在再查看效果：