$ dotnet new console --name ChatApp
$ dotnet add package Microsoft.SemanticKernel

using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.ChatCompletion;
using System.Text;
var apikey = Environment.GetEnvironmentVariable("azureopenaiapikey")!;
// 初始化Semantic Kernel
var kernel = Kernel.CreateBuilder()
    .AddAzureOpenAIChatCompletion(
        "gpt-4", 
        "https://qingy-m2e0gbl3-eastus.openai.azure.com", 
        apikey)
    .Build();
// 創(chuàng)建一個(gè)對(duì)話(huà)完成服務(wù)以及對(duì)話(huà)歷史對(duì)象，用來(lái)保存對(duì)話(huà)歷史，以便后續(xù)為大模型
// 提供對(duì)話(huà)上下文信息。
var chatCompletionService = kernel.GetRequiredService<IChatCompletionService>();
var chat = new ChatHistory("你是一個(gè)AI助手，幫助人們查找信息和回答問(wèn)題");
StringBuilder chatResponse = new();
while (true)
{
    Console.Write("請(qǐng)輸入問(wèn)題>> ");
    // 將用戶(hù)輸入的問(wèn)題添加到對(duì)話(huà)中
    chat.AddUserMessage(Console.ReadLine()!);
    chatResponse.Clear();
    // 獲取大語(yǔ)言模型的反饋，并將結(jié)果逐字輸出
    await foreach (var message in
                   chatCompletionService.GetStreamingChatMessageContentsAsync(chat))
    {
        // 輸出當(dāng)前獲取的結(jié)果字符串
        Console.Write(message);
        // 將輸出內(nèi)容添加到臨時(shí)變量中
        chatResponse.Append(message.Content);
    }
    Console.WriteLine();
    // 在進(jìn)入下一次問(wèn)答之前，將當(dāng)前回答結(jié)果添加到對(duì)話(huà)歷史中，為大語(yǔ)言模型提供問(wèn)答上下文
    chat.AddAssistantMessage(chatResponse.ToString());
    Console.WriteLine();
}

using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.ChatCompletion;
using System.Text;
using Microsoft.SemanticKernel.Connectors.AzureOpenAI;
using Microsoft.SemanticKernel.Memory;
using Microsoft.SemanticKernel.Text;
#pragma warning disable SKEXP0010, SKEXP0001, SKEXP0050
const string CollectionName = "LatestNews";
var apikey = Environment.GetEnvironmentVariable("azureopenaiapikey")!;
// 初始化Semantic Kernel
var kernel = Kernel.CreateBuilder()
    .AddAzureOpenAIChatCompletion(
        "gpt-4", 
        "https://qingy-m2e0gbl3-eastus.openai.azure.com", 
        apikey)
    .Build();
// 創(chuàng)建文本向量生成服務(wù)
var textEmbeddingGenerationService = new AzureOpenAITextEmbeddingGenerationService(
    "text-embedding-3-small",
    "https://qingy-m2e0gbl3-eastus.openai.azure.com",
    apikey);
// 創(chuàng)建用于保存文本向量的內(nèi)存向量數(shù)據(jù)庫(kù)
var memory = new MemoryBuilder()
    .WithMemoryStore(new VolatileMemoryStore())
    .WithTextEmbeddingGeneration(textEmbeddingGenerationService)
    .Build();
// 從外部文件以Markdown格式讀入內(nèi)容，然后根據(jù)語(yǔ)義產(chǎn)生多個(gè)段落
var markdownContent = await File.ReadAllTextAsync(@"input.md");
var paragraphs =
    TextChunker.SplitMarkdownParagraphs(
        TextChunker.SplitMarkDownLines(markdownContent.Replace("\r\n", " "), 128),
        64);
// 將各個(gè)段落進(jìn)行量化并保存到向量數(shù)據(jù)庫(kù)
for (var i = 0; i < paragraphs.Count; i++)
{
    await memory.SaveInformationAsync(CollectionName, paragraphs[i], $"paragraph{i}");
}
// 創(chuàng)建一個(gè)對(duì)話(huà)完成服務(wù)以及對(duì)話(huà)歷史對(duì)象，用來(lái)保存對(duì)話(huà)歷史，以便后續(xù)為大模型
// 提供對(duì)話(huà)上下文信息。
var chatCompletionService = kernel.GetRequiredService<IChatCompletionService>();
var chat = new ChatHistory("你是一個(gè)AI助手，幫助人們查找信息和回答問(wèn)題");
StringBuilder additionalInfo = new();
StringBuilder chatResponse = new();
while (true)
{
    Console.Write("請(qǐng)輸入問(wèn)題>> ");
    var question = Console.ReadLine()!;
    additionalInfo.Clear();
    // 從向量數(shù)據(jù)庫(kù)中找到跟提問(wèn)最為相近的3條信息，將其添加到對(duì)話(huà)歷史中
    await foreach (var hit in memory.SearchAsync(CollectionName, question, limit: 3))
    {
        additionalInfo.AppendLine(hit.Metadata.Text);
    }
    var contextLinesToRemove = -1;
    if (additionalInfo.Length != 0)
    {
        additionalInfo.Insert(0, "以下是一些附加信息：");
        contextLinesToRemove = chat.Count;
        chat.AddUserMessage(additionalInfo.ToString());
    }
    // 將用戶(hù)輸入的問(wèn)題添加到對(duì)話(huà)中
    chat.AddUserMessage(question);
    chatResponse.Clear();
    // 獲取大語(yǔ)言模型的反饋，并將結(jié)果逐字輸出
    await foreach (var message in
                   chatCompletionService.GetStreamingChatMessageContentsAsync(chat))
    {
        // 輸出當(dāng)前獲取的結(jié)果字符串
        Console.Write(message);
        // 將輸出內(nèi)容添加到臨時(shí)變量中
        chatResponse.Append(message.Content);
    }
    Console.WriteLine();
    // 在進(jìn)入下一次問(wèn)答之前，將當(dāng)前回答結(jié)果添加到對(duì)話(huà)歷史中，為大語(yǔ)言模型提供問(wèn)答上下文
    chat.AddAssistantMessage(chatResponse.ToString());
    // 將當(dāng)次問(wèn)題相關(guān)的內(nèi)容從對(duì)話(huà)歷史中移除
    if (contextLinesToRemove >= 0) chat.RemoveAt(contextLinesToRemove);
    Console.WriteLine();
}

using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.ChatCompletion;
using System.Text;
using Microsoft.SemanticKernel.Connectors.AzureOpenAI;
using Microsoft.SemanticKernel.Memory;
using Microsoft.SemanticKernel.Text;
#pragma warning disable SKEXP0010, SKEXP0001, SKEXP0050
const string CollectionName = "LatestNews";
var apikey = Environment.GetEnvironmentVariable("azureopenaiapikey")!;
// 初始化Semantic Kernel
var kernel = Kernel.CreateBuilder()
    .AddAzureOpenAIChatCompletion(
        "gpt-4", 
        "https://qingy-m2e0gbl3-eastus.openai.azure.com", 
        apikey)
    .Build();
// 創(chuàng)建文本向量生成服務(wù)
var textEmbeddingGenerationService = new AzureOpenAITextEmbeddingGenerationService(
    "text-embedding-3-small",
    "https://qingy-m2e0gbl3-eastus.openai.azure.com",
    apikey);
// 創(chuàng)建用于保存文本向量的內(nèi)存向量數(shù)據(jù)庫(kù)
var memory = new MemoryBuilder()
    .WithMemoryStore(new VolatileMemoryStore())
    .WithTextEmbeddingGeneration(textEmbeddingGenerationService)
    .Build();
// 從外部文件以Markdown格式讀入內(nèi)容，然后根據(jù)語(yǔ)義產(chǎn)生多個(gè)段落
var markdownContent = await File.ReadAllTextAsync(@"input.md");
var paragraphs =
    TextChunker.SplitMarkdownParagraphs(
        TextChunker.SplitMarkDownLines(markdownContent.Replace("\r\n", " "), 128),
        64);
// 將各個(gè)段落進(jìn)行量化并保存到向量數(shù)據(jù)庫(kù)
for (var i = 0; i < paragraphs.Count; i++)
{
    await memory.SaveInformationAsync(CollectionName, paragraphs[i], $"paragraph{i}");
}
// 創(chuàng)建一個(gè)對(duì)話(huà)完成服務(wù)以及對(duì)話(huà)歷史對(duì)象，用來(lái)保存對(duì)話(huà)歷史，以便后續(xù)為大模型
// 提供對(duì)話(huà)上下文信息。
var chatCompletionService = kernel.GetRequiredService<IChatCompletionService>();
var chat = new ChatHistory("你是一個(gè)AI助手，幫助人們查找信息和回答問(wèn)題");
StringBuilder additionalInfo = new();
StringBuilder chatResponse = new();
while (true)
{
    Console.Write("請(qǐng)輸入問(wèn)題>> ");
    var question = Console.ReadLine()!;
    additionalInfo.Clear();
    // 從向量數(shù)據(jù)庫(kù)中找到跟提問(wèn)最為相近的3條信息，將其添加到對(duì)話(huà)歷史中
    await foreach (var hit in memory.SearchAsync(CollectionName, question, limit: 3))
    {
        additionalInfo.AppendLine(hit.Metadata.Text);
    }
    var contextLinesToRemove = -1;
    if (additionalInfo.Length != 0)
    {
        additionalInfo.Insert(0, "以下是一些附加信息：");
        contextLinesToRemove = chat.Count;
        chat.AddUserMessage(additionalInfo.ToString());
    }
    // 將用戶(hù)輸入的問(wèn)題添加到對(duì)話(huà)中
    chat.AddUserMessage(question);
    chatResponse.Clear();
    // 獲取大語(yǔ)言模型的反饋，并將結(jié)果逐字輸出
    await foreach (var message in
                   chatCompletionService.GetStreamingChatMessageContentsAsync(chat))
    {
        // 輸出當(dāng)前獲取的結(jié)果字符串
        Console.Write(message);
        // 將輸出內(nèi)容添加到臨時(shí)變量中
        chatResponse.Append(message.Content);
    }
    Console.WriteLine();
    // 在進(jìn)入下一次問(wèn)答之前，將當(dāng)前回答結(jié)果添加到對(duì)話(huà)歷史中，為大語(yǔ)言模型提供問(wèn)答上下文
    chat.AddAssistantMessage(chatResponse.ToString());
    // 將當(dāng)次問(wèn)題相關(guān)的內(nèi)容從對(duì)話(huà)歷史中移除
    if (contextLinesToRemove >= 0) chat.RemoveAt(contextLinesToRemove);
    Console.WriteLine();
}

.WithMemoryStore(new VolatileMemoryStore())

.WithMemoryStore(await SqliteMemoryStore.ConnectAsync("vectors.db"))

// 初始化Semantic Kernel
var kernel = Kernel.CreateBuilder()
    .AddOllamaChatCompletion(
        "llama3.2:3b", 
        new Uri("http://localhost:11434"))
    .Build();
// 創(chuàng)建文本向量生成服務(wù)
var textEmbeddingGenerationService = new OllamaTextEmbeddingGenerationService(
    "mxbai-embed-large:latest", 
    new Uri("http://localhost:11434"));