使用C#构建一个同时问多个LLM并总结的小工具

## 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： ```xaml ```  在ViewModel中先来看一下最原始的显示结果的方式： ```csharp [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： ```xaml ``` 窗体的ViewModel： ```csharp 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。 ```csharp IChatClient client7 = new OpenAI.Chat.ChatClient("Qwen/Qwen2.5-72B-Instruct", apiKeyCredential, openAIClientOptions).AsChatClient(); List messages = new List(); 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(); var showWindowViewModel = _serviceProvider.GetRequiredService(); 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。 ```csharp [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>(); // 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 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： ```csharp [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。 ```csharp [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 { 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 messages = new List(); 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(); var showWindowViewModel = _serviceProvider.GetRequiredService(); showWindowViewModel.ReceivedValue = result7.ToString(); showWindow.DataContext = showWindowViewModel; showWindow.Show(); } private async Task ProcessStreamingResponse(string model, ApiKeyCredential apiKeyCredential, OpenAIClientOptions options, Action 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：  Qwen/Qwen3-235B-A22B、THUDM/GLM-Z1-32B-0414、deepseek-ai/DeepSeek-R1有思考过程，返回结果比较慢。 目前Microsoft.Extensions.AI.OpenAI好像还无法获取思考内容。  等待久一会之后，可以看到结果都出来了：  然后总结窗口会显示最终的总结内容：  确定方案之后可以让Cursor或者Cline帮我们写一下试试。