大模型应用开发初探 : 手搓一个简易Agent

news/2024/9/19 9:01:53/文章来源:https://www.cnblogs.com/edisonchou/p/-/quick-start-on-ai-agent-by-sk

大家好，我是Edison。

今天是中秋节前最后一个工作日，加油挺住，马上就放假了！

近期我一直在学习和了解LLM的相关知识，听到大家都在谈论AI Agent，说它是接下来几年大模型应用开发的新范式，那么什么是AI Agent，如何快速开发一个AI Agent呢？

AI Agent：可以帮你执行任务的助手

学术界和工业界对术语“AI Agent”提出了各种定义。其中，OpenAI将AI Agent定义为“以大语言模型为大脑驱动的系统，具备自主理解、感知、规划、记忆和使用工具的能力，能够自动化执行完成复杂任务的系统。”

说人话就是：大多数时候你给它一个最终你想要达成的目标，它能直接交付结果，过程你啥都不用管。

NOTE：如果说人和动物的区别是人会使用各种工具，那么Agent和大模型的区别亦然。

我们可以把Agent与LLM形象地比作生物体与其大脑，Agent有手有脚，可以自己干活自己执行，而LLM呢，就是它的大脑。比如，如果你使用LLM大模型，它可能只能给你输出一份食谱，告诉你需要哪些食材和步骤来制作。但如果你使用Agent，它可能就是不仅提供食谱和步骤，还会根据你的需求，帮你选择合适的食材甚至自动下单购买，监控烹饪过程，确保食物口感，最终为你呈上一份佳肴。

AI Agent如何工作？

AI Agent的架构是其智能行为的基础，它通常包括感知、规划、记忆、工具使用和行动等关键组件，这些组件协同工作以实现高效的智能行为。

AI Agent的工作流程其实就是一个连续的循环过程。

它从感知环境开始，经过信息处理、规划和决策，然后执行行动。最后，根据执行结果和环境反馈进行调整，以优化未来的行动和决策。

通过这种结构化和层次化的方式，AI Agent能够有效地处理信息，做出决策，并在复杂环境中执行任务。

如何开发AI Agent？

目前业界开发AI Agent主要有两种模式：

一种是基于Python或C#等编程语言，结合LangChain或Semantic Kernel等大模型应用开发框架，集成某个大模型API 和企业内部的业务API能力，来完成具体领域的Agent。

另一种是基于Coze、Dify、AutoGen等Agent开发管理平台，拖过拖拉拽的方式快速生成一个Agent，与其说是开发，不如说是Workflow一样的配置。当然，也需要给这些平台注册封装好的企业内部API平台提供的能力供配置好的Agent去实现工具调用。

使用Semantic Kernel开发AI Agent

这里我们快速使用Semantic Kernel开发一个简易的WorkOrder Agent（MES工单助手），重点关注如何给LLM添加Function Calling能力，直观了解Agent规划任务和执行任务的效果，而至于其他更加具体的，等待后续了解深入后再交流，这里我们就先来个感性认识即可。

以终为始，先看效果吧：

（1）没有实现Function Calling的效果，它就只是个Chatbot

（2）实现了Function Calling的效果，它就可以称为Agent

可以看到，我的需求其实包含两个步骤：第一步是更新工单的Quantity，第二步是查询更新后的工单信息。而这两个步骤我们假设其实都是需要去调用MES WorkOrderService API才能获得的，而这就需要我们给LLM加入Function Calling的能力，当然LLM自己得知道如何规划执行的步骤，哪个步骤先执行，哪个后执行。

示例代码的结构如下所示：

关键部分代码：

（1）Shared

OpenAiConfiguration.cs

public class OpenAiConfiguration
{public string Provider { get; set; }public string ModelId { get; set; }public string EndPoint { get; set; }public string ApiKey { get; set; }public OpenAiConfiguration(string modelId, string endPoint, string apiKey){Provider = ConfigConstants.LLMProviders.OpenAI; // Default OpenAI-Compatible LLM API ProviderModelId = modelId;EndPoint = endPoint;ApiKey = apiKey;}public OpenAiConfiguration(string provider, string modelId, string endPoint, string apiKey){Provider = provider;ModelId = modelId;EndPoint = endPoint;ApiKey = apiKey;}
}

CustomLLMApiHandler.cs

public class CustomLLMApiHandler : HttpClientHandler
{private readonly string _openAiProvider;private readonly string _openAiBaseAddress;public CustomLLMApiHandler(string openAiProvider, string openAiBaseAddress){_openAiProvider = openAiProvider;_openAiBaseAddress = openAiBaseAddress;}protected override async Task<HttpResponseMessage> SendAsync(HttpRequestMessage request, CancellationToken cancellationToken){UriBuilder uriBuilder;Uri uri = new Uri(_openAiBaseAddress);switch (request.RequestUri?.LocalPath){case "/v1/chat/completions":switch (_openAiProvider){case ConfigConstants.LLMProviders.ZhiPuAI:uriBuilder = new UriBuilder(request.RequestUri){Scheme = "https",Host = uri.Host,Path = ConfigConstants.LLMApiPaths.ZhiPuAIChatCompletions,};request.RequestUri = uriBuilder.Uri;break;default: // Default: OpenAI-Compatible API ProvidersuriBuilder = new UriBuilder(request.RequestUri){Scheme = "https",Host = uri.Host,Path = ConfigConstants.LLMApiPaths.OpenAIChatCompletions,};request.RequestUri = uriBuilder.Uri;break;}break;}HttpResponseMessage response = await base.SendAsync(request, cancellationToken);return response;}
}

（2）WorkOrderService

这里我直接模拟的API的逻辑，你可以使用HttpClient去实际调用某个API。

public class WorkOrderService
{private static List<WorkOrder> workOrders = new List<WorkOrder>{new WorkOrder { WorkOrderName = "9050100", ProductName = "A5E900100", ProductVersion = "001 / AB", Quantity = 100, Status = "Ready" },new WorkOrder { WorkOrderName = "9050101", ProductName = "A5E900101", ProductVersion = "001 / AB", Quantity = 200, Status = "Ready" },new WorkOrder { WorkOrderName = "9050102", ProductName = "A5E900102", ProductVersion = "001 / AB", Quantity = 300, Status = "InProcess" },new WorkOrder { WorkOrderName = "9050103", ProductName = "A5E900103", ProductVersion = "001 / AB", Quantity = 400, Status = "InProcess" },new WorkOrder { WorkOrderName = "9050104", ProductName = "A5E900104", ProductVersion = "001 / AB", Quantity = 500, Status = "Completed" }};public WorkOrder GetWorkOrderInfo(string orderName){return workOrders.Find(o => o.WorkOrderName == orderName);}public string UpdateWorkOrderStatus(string orderName, string newStatus){var workOrder = this.GetWorkOrderInfo(orderName);if (workOrder == null)return "Operate Failed : The work order is not existing!";// Update status if it is validworkOrder.Status = newStatus;return "Operate Succeed!";}public string ReduceWorkOrderQuantity(string orderName, int newQuantity){var workOrder = this.GetWorkOrderInfo(orderName);if (workOrder == null)return "Operate Failed : The work order is not existing!";// Some business checking logic like thisif (workOrder.Status == "Completed")return "Operate Failed : The work order is completed, can not be reduced!";if (newQuantity <= 1 || newQuantity >= workOrder.Quantity)return "Operate Failed : The new quantity is invalid!";// Update quantity if it is validworkOrder.Quantity = newQuantity;return "Operate Succeed!";}
}

（3）Form

appsetting.json

{"LLM_API_PROVIDER": "ZhiPuAI","LLM_API_MODEL": "glm-4","LLM_API_BASE_URL": "https://open.bigmodel.cn","LLM_API_KEY": "***********" // Update this value to yours
}

AgentForm.cs

初始化Kernel

 private Kernel _kernel = null;private OpenAIPromptExecutionSettings _settings = null;private IChatCompletionService _chatCompletion = null;private ChatHistory _chatHistory = null;private void ChatForm_Load(object sender, EventArgs e){var configuration = new ConfigurationBuilder().AddJsonFile($"appsettings.ZhiPu.json");var config = configuration.Build();var openAiConfiguration = new OpenAiConfiguration(config.GetSection("LLM_API_PROVIDER").Value,config.GetSection("LLM_API_MODEL").Value,config.GetSection("LLM_API_BASE_URL").Value,config.GetSection("LLM_API_KEY").Value);var openAiClient = new HttpClient(new CustomLlmApiHandler(openAiConfiguration.Provider, openAiConfiguration.EndPoint));_kernel = Kernel.CreateBuilder().AddOpenAIChatCompletion(openAiConfiguration.ModelId, openAiConfiguration.ApiKey, httpClient: openAiClient).Build();_chatCompletion = _kernel.GetRequiredService<IChatCompletionService>();_chatHistory = new ChatHistory();_chatHistory.AddSystemMessage("You are one WorkOrder Assistant.");}

注册Functions

_kernel.Plugins.Add(KernelPluginFactory.CreateFromFunctions("WorkOrderHelperPlugin",new List<KernelFunction>{_kernel.CreateFunctionFromMethod((string orderName) =>{var workOrderRepository = new WorkOrderService();return workOrderRepository.GetWorkOrderInfo(orderName);}, "GetWorkOrderInfo", "Get WorkOrder's Detail Information"),_kernel.CreateFunctionFromMethod((string orderName, int newQuantity) =>{var workOrderRepository = new WorkOrderService();return workOrderRepository.ReduceWorkOrderQuantity(orderName, newQuantity);}, "ReduceWorkOrderQuantity", "Reduce WorkOrder's Quantity to new Quantity"),_kernel.CreateFunctionFromMethod((string orderName, string newStatus) =>{var workOrderRepository = new WorkOrderService();return workOrderRepository.UpdateWorkOrderStatus(orderName, newStatus);}, "UpdateWorkOrderStatus", "Update WorkOrder's Status to new Status")}
));

开启自动调用Function，告诉大模型可以自行决定调用相关Functions，而且大模型会自行决定根据什么顺序来调用，这就是大模型作为Agent大脑的规划能力。当然，我们还可以通过定制化Planner来增强agent的规划能力，这个就留到后面再分享：

_settings = new OpenAIPromptExecutionSettings
{ToolCallBehavior = ToolCallBehavior.AutoInvokeKernelFunctions
};

发送用户的提示词给大模型：

_chatHistory.AddUserMessage(tbxPrompt.Text);ChatMessageContent chatResponse = null;tbxResponse.Clear();if (cbxUseFunctionCalling.Checked)
{Task.Run(() =>{ShowProcessMessage("AI is handling your request now...");chatResponse = _chatCompletion.GetChatMessageContentAsync(_chatHistory, _settings, _kernel).GetAwaiter().GetResult();UpdateResponseContent(chatResponse.ToString());ShowProcessMessage("AI Response:");});}
else
{Task.Run(() =>{ShowProcessMessage("AI is handling your request now...");chatResponse = _chatCompletion.GetChatMessageContentAsync(_chatHistory, null, _kernel).GetAwaiter().GetResult();UpdateResponseContent(chatResponse.ToString());ShowProcessMessage("AI Response:");});
}