使用PocketFlowSharp创建一个Human_Evaluation示例

效果

实践

有时候AI生成的结果我们并不满意在进入下一步之前，我们需要对AI生成的结果进行人工审核，同意了才能进入下一个流程。

Human_Evaluation就是人工判断的一个简单示例。

 internal class Program  {      static async Task Main(string[] args)      {          // Load .env file          DotEnv.Load();           // Get environment variables from .env file          var envVars = DotEnv.Read();           string ModelName = envVars["ModelName"];          string EndPoint = envVars["EndPoint"];          string ApiKey = envVars["ApiKey"];           Utils.ModelName = ModelName;          Utils.EndPoint = EndPoint;          Utils.ApiKey = ApiKey;           // 创建共享数据字典          var shared = new Dictionary<string, object>();           // 创建并运行流程          var humanEvalFlow = CreateFlow();          Console.WriteLine("n欢迎使用人工判断示例！");          Console.WriteLine("------------------------");          await humanEvalFlow.RunAsync(shared);          Console.WriteLine("n感谢使用人工判断示例！");      }       static AsyncFlow CreateFlow()      {          // 创建节点实例          var inputNode = new TaskInputNode();          var aiResponseNode = new AIResponseNode();          var humanApprovalNode = new HumanApprovalNode();          var endNode = new NoOpNode();           // 创建从输入节点开始的流程          var flow = new AsyncFlow(inputNode);           // 连接节点          _ = inputNode - "generate" - aiResponseNode;          _ = aiResponseNode - "approve" - humanApprovalNode;          _ = humanApprovalNode - "retry" - aiResponseNode;     // 不接受时重新生成          _ = humanApprovalNode - "accept" - endNode;          // 接受时结束流程           return flow;      }  } 

看一下整体的流程图：

graph TD A[输入节点] -->|"generate"| B[AI回复节点] B -->|"approve"| C[人工审核节点] C -->|"approve"| D[结束节点] C -->|"retry"| B

输入节点：

 public class TaskInputNode : AsyncNode  {      protected override async Task<object> PrepAsync(Dictionary<string, object> shared)      {          Console.WriteLine("n请输入需要AI处理的任务：");          string task = Console.ReadLine();          return task;      }       protected override async Task<object> ExecAsync(object prepResult)      {          string task = (string)prepResult;          Console.WriteLine($"n已收到任务：{task}");          return task;      }       protected override async Task<object> PostAsync(Dictionary<string, object> shared, object prepResult, object execResult)      {          string task = (string)execResult;          shared["task"] = task;          return "generate";      }  } 

AI回复节点：

public class AIResponseNode : AsyncNode {     private static int attemptCount = 0;      protected override async Task<object> PrepAsync(Dictionary<string, object> shared)     {         return shared["task"];     }      protected override async Task<object> ExecAsync(object prepResult)     {         string task = (string)prepResult;         attemptCount++;                  Console.WriteLine("AI正在生成回复...n");         Console.WriteLine($"任务：{task}n");         Console.WriteLine($"这是第 {attemptCount} 次生成的AI回复：n");         var result = await Utils.CallLLMStreamingAsync(task);          string response="";         Console.ForegroundColor = ConsoleColor.Green;         await foreach (StreamingChatCompletionUpdate completionUpdate in result)         {             if (completionUpdate.ContentUpdate.Count > 0)             {                 Console.Write(completionUpdate.ContentUpdate[0].Text);                 response += completionUpdate.ContentUpdate[0].Text.ToString();             }         }         Console.ForegroundColor = ConsoleColor.White;          return response;     }      protected override async Task<object> PostAsync(Dictionary<string, object> shared, object prepResult, object execResult)     {         string response = (string)execResult;         shared["response"] = response;         return "approve";     } } 

人工审核节点：

 public class HumanApprovalNode : AsyncNode  {      protected override async Task<object> PrepAsync(Dictionary<string, object> shared)      {          return shared["response"];      }       protected override async Task<object> ExecAsync(object prepResult)      {          Console.Write("n您接受这个AI回复吗？(y/n): ");          string answer = Console.ReadLine()?.ToLower() ?? "n";          return answer;      }       protected override async Task<object> PostAsync(Dictionary<string, object> shared, object prepResult, object execResult)      {          string answer = (string)execResult;           if (answer == "y")          {              Console.WriteLine($"已接受的回复：n{shared["response"]}");              return "accept";          }          else          {              Console.WriteLine("n好的，让AI重新生成回复...");              return "retry";          }      }  } 

结束节点：

 public class NoOpNode : AsyncNode  {      protected override async Task<object> PrepAsync(Dictionary<string, object> shared) => null;      protected override async Task<object> ExecAsync(object prepResult) => null;      protected override async Task<object> PostAsync(Dictionary<string, object> shared, object prepResult, object execResult) => null;  } 

帮助类：

 public static class Utils  {      public static string ModelName { get; set; }      public static string EndPoint { get; set; }      public static string ApiKey { get; set; }       public static async Task<string> CallLLMAsync(string prompt)      {          ApiKeyCredential apiKeyCredential = new ApiKeyCredential(ApiKey);           OpenAIClientOptions openAIClientOptions = new OpenAIClientOptions();          openAIClientOptions.Endpoint = new Uri(EndPoint);           ChatClient client = new(model: ModelName, apiKeyCredential, openAIClientOptions);           ChatCompletion completion = await client.CompleteChatAsync(prompt);           return completion.Content[0].Text;      }       public static async Task<AsyncCollectionResult<StreamingChatCompletionUpdate>> CallLLMStreamingAsync(string prompt)      {          ApiKeyCredential apiKeyCredential = new ApiKeyCredential(ApiKey);           OpenAIClientOptions openAIClientOptions = new OpenAIClientOptions();          openAIClientOptions.Endpoint = new Uri(EndPoint);           ChatClient client = new(model: ModelName, apiKeyCredential, openAIClientOptions);           var completion = client.CompleteChatStreamingAsync(prompt);                   return completion;      }  } 

全部代码在：https://github.com/Ming-jiayou/PocketFlowSharp/tree/main/PocketFlowSharpSamples.Console/Human_Evaluation