【OpenAI&SK】：实现自己的问答机器人

　　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>