前ChatGPT让人惊叹的是，它好像有了真人的思维逻辑，能记住上下文，还能很融洽地和你聊天，并且回答问题让你满意。但如果你问他一些自己身边事，或者公司最新产品的事，ChatGPT的回复就天马行空了。怎么才能让他成为自己的问答机器人呢？下面给出了一个简单的事例，一起看一下吧。.
后端代码：
using Microsoft.SemanticKernel;using Microsoft.SemanticKernel.Connectors.Memory.Sqlite;using Microsoft.SemanticKernel.Orchestration;using Microsoft.SemanticKernel.SkillDefinition;var builder = WebApplication.CreateBuilder(args);await builder.AddEmband();var app = builder.Build();app.UseStaticFiles();app.MapGet("/bot", async (IKernel kernel, SKContext context, ISKFunction semanticFunction, string ask,CancellationToken token) =>{var facts = kernel.Memory.SearchAsync("gsw", ask, limit: 10, withEmbeddings: true,cancellationToken:token);var fact = await facts.FirstOrDefaultAsync(cancellationToken: token);context["fact"] = fact?.Metadata?.Text!;context["ask"] = ask;var resultContext = await semanticFunction.InvokeAsync(context);return resultContext.Result;});app.Run();public static class BuilderExt{public static async Task AddEmband(this WebApplicationBuilder builder){var key = File.ReadAllText(@"C:\\GPT\key.txt");var store = Directory.GetCurrentDirectory() + "/db.sqlite";var kernel = Kernel.Builder.WithOpenAITextCompletionService("text-davinci-003", key, serviceId: "gsw").WithOpenAITextEmbeddingGenerationService("text-embedding-ada-002", key, serviceId: "gsw").WithMemoryStorage(await SqliteMemoryStore.ConnectAsync(store)).Build();const string MemoryCollectionName = "gsw";await kernel.Memory.SaveInformationAsync(MemoryCollectionName, id: "info0", text: "名字叫桂素伟");await kernel.Memory.SaveInformationAsync(MemoryCollectionName, id: "info1", text: "性别男，身高171cm，\r\n体重75千克");await kernel.Memory.SaveInformationAsync(MemoryCollectionName, id: "info2", text: "职业是农民，他擅长种茄子");await kernel.Memory.SaveInformationAsync(MemoryCollectionName, id: "info3", text: "有20年的种地经验");await kernel.Memory.SaveInformationAsync(MemoryCollectionName, id: "info4", text: "现在住在五十亩村");await kernel.Memory.SaveInformationAsync(MemoryCollectionName, id: "info5", text: "祖籍山西长治市省黎城县西井镇五十亩村");await kernel.Memory.SaveInformationAsync(MemoryCollectionName, id: "info6", text: "老家山西长治市省黎城县西井镇五十亩村");await kernel.Memory.SaveInformationAsync(MemoryCollectionName, id: "info7", text: "来自山西长治市省黎城县西井镇五十亩村");var prompt = """给出答案或者不知道答案时说“非常抱歉，我没有找到你要的问题！”对话中的关于桂素伟的信息:{{ $fact }}用户: {{ $ask }}机器人:""";var semanticFunction = kernel.CreateSemanticFunction(prompt, temperature: 0.7, topP: 0.5);var context = kernel.CreateNewContext();builder.Services.AddSingleton(kernel);builder.Services.AddSingleton(semanticFunction);builder.Services.AddSingleton(context);}}
本例用到OpenAITextCompletion和OpenAITextEmbeddingGeneration两个服务，前者是用来补全词语，后者是用来本地存储自己的问题，本例是用sqlite的方式来持久化。基本原理是，当你提问一个问题，首先会从本地存储的问题向量中找到得分最高的答案，然后一起提交给OpenAI，进行回复优化汇总，然后给出结果。
前端代码：
<!DOCTYPE html><html><head><title>机器人</title><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bootstrap@5.2.3/dist/css/bootstrap.min.css"></head><body><div class="container"><div class="row"><h3 class="display-4">机器人</h3></div><div class="row"><div class="input-group mb-3"><input type="text" id="ask" class="form-control" placeholder="请输入问题" aria-label="请输入问题" aria-describedby="chat"><button class="btn btn-outline-secondary" type="button" id="bot">开始</button></div></div><div id="messagesdiv" class="row"></div></div><script src="https://code.jquery.com/jquery-3.6.0.min.js"></script><script src="https://cdn.jsdelivr.net/npm/bootstrap@5.2.3/dist/js/bootstrap.bundle.min.js"></script><script>$(function () {$("#bot").click(function () {var askDiv = $("<div class='alert alert-primary'>");askDiv.text("【您】" + $("#ask").val());var answerDiv = $("<div class='alert alert-warning'>");answerDiv.text("……");$("#messagesdiv").append(askDiv);$("#messagesdiv").append(answerDiv);$.ajax({url: '/bot',type: 'GET',dataType: 'text',data: { ask: $("#ask").val() },success: function (data) {answerDiv.removeClass("alert-warning")answerDiv.addClass("alert-success")answerDiv.text(data)$("#ask").val("")},error: function (xhr, status, error) {answerDiv.text(error)}});})});</script></body></html>
前端代码相对简单，把问题提交后端，等结果就ok
运行效果：
