docker run -p 6333:6333 -p 6334:6334 \
-v $(pwd)/qdrant_storage:/qdrant/storage \
qdrant/qdrant

ollama pull bge-m3

public sealed class TextSnippet<TKey>
{
    [VectorStoreRecordKey]
    public required TKey Key { get; set; }

    [VectorStoreRecordData]
    public string? Text { get; set; }

    [VectorStoreRecordData]
    public string? ReferenceDescription { get; set; }

    [VectorStoreRecordData]
    public string? ReferenceLink { get; set; }

    [VectorStoreRecordVector(Dimensions: 1024)]
    public ReadOnlyMemory<float> TextEmbedding { get; set; }
}

public class TextSearchResult
{
    public string  Value { get; set; }
    public string? Link { get; set; }
    public double? Score { get; set; }
}

public sealed class RawContent
{
    public string? Text { get; init; }

    public int PageNumber { get; init; }
}

public sealed class PdfDataLoader<TKey> where TKey : notnull
{
    private readonly IVectorStoreRecordCollection<TKey, TextSnippet<TKey>> _vectorStoreRecordCollection;
    private readonly UniqueKeyGenerator<TKey> _uniqueKeyGenerator;
    private readonly IEmbeddingGenerator<string, Embedding<float>> _embeddingGenerator;

    public PdfDataLoader(
        UniqueKeyGenerator<TKey> uniqueKeyGenerator,
        IVectorStoreRecordCollection<TKey, TextSnippet<TKey>> vectorStoreRecordCollection,
        IEmbeddingGenerator<string, Embedding<float>> embeddingGenerator)
    {
        _vectorStoreRecordCollection = vectorStoreRecordCollection;
        _uniqueKeyGenerator = uniqueKeyGenerator;
        _embeddingGenerator = embeddingGenerator;
    }

    public async Task LoadPdf(string pdfPath, int batchSize, int betweenBatchDelayInMs)
    {
        // Create the collection if it doesn't exist.
        await _vectorStoreRecordCollection.CreateCollectionIfNotExistsAsync();

        // Load the text and images from the PDF file and split them into batches.
        var sections = LoadAllTexts(pdfPath);
        var batches = sections.Chunk(batchSize);

        // Process each batch of content items.
        foreach (var batch in batches)
        {
            // Get text contents
            var textContentTasks = batch.Select(async content =>
            {
                if (content.Text != null)
                    return content;

                return new RawContent { Text = string.Empty, PageNumber = content.PageNumber };
            });
            var textContent = (await Task.WhenAll(textContentTasks))
                .Where(c => !string.IsNullOrEmpty(c.Text))
                .ToList();

            // Map each paragraph to a TextSnippet and generate an embedding for it.
            var recordTasks = textContent.Select(async content => new TextSnippet<TKey>
            {
                Key = _uniqueKeyGenerator.GenerateKey(),
                Text = content.Text,
                ReferenceDescription = $"{new FileInfo(pdfPath).Name}#page={content.PageNumber}",
                ReferenceLink = $"{new Uri(new FileInfo(pdfPath).FullName).AbsoluteUri}#page={content.PageNumber}",
                TextEmbedding = await _embeddingGenerator.GenerateEmbeddingVectorAsync(content.Text!)
            });

            // Upsert the records into the vector store.
            var records = await Task.WhenAll(recordTasks);
            var upsertedKeys = _vectorStoreRecordCollection.UpsertBatchAsync(records);
            await foreach (var key in upsertedKeys)
            {
                Console.WriteLine($"Upserted record '{key}' into VectorDB");
            }

            await Task.Delay(betweenBatchDelayInMs);
        }
    }

    private static IEnumerable<RawContent> LoadAllTexts(string pdfPath)
    {
        using (PdfDocument document = PdfDocument.Open(pdfPath))
        {
            foreach (Page page in document.GetPages())
            {
                var blocks = DefaultPageSegmenter.Instance.GetBlocks(page.GetWords());
                foreach (var block in blocks)
                    yield return new RawContent { Text = block.Text, PageNumber = page.Number };
            }
        }
    }
}

public class VectorDataSearcher<TKey> where TKey : notnull
{
    private readonly IVectorStoreRecordCollection<TKey, TextSnippet<TKey>> _vectorStoreRecordCollection;
    private readonly IEmbeddingGenerator<string, Embedding<float>> _embeddingGenerator;

    public VectorDataSearcher(IVectorStoreRecordCollection<TKey, TextSnippet<TKey>> vectorStoreRecordCollection, IEmbeddingGenerator<string, Embedding<float>> embeddingGenerator)
    {
        _vectorStoreRecordCollection = vectorStoreRecordCollection;
        _embeddingGenerator = embeddingGenerator;
    }

    [Description("Get top N text search results from vector store by user's query (N is 1 by default)")]
    [return: Description("Collection of text search result")]
    public async Task<IEnumerable<TextSearchResult>> GetTextSearchResults(string query, int topN = 1)
    {
        var queryEmbedding = await _embeddingGenerator.GenerateEmbeddingVectorAsync(query);
        // Query from vector data store
        var searchOptions = new VectorSearchOptions()
        {
            Top = topN,
            VectorPropertyName = nameof(TextSnippet<TKey>.TextEmbedding)
        };
        var searchResults = await _vectorStoreRecordCollection.VectorizedSearchAsync(queryEmbedding, searchOptions);
        var responseResults = new List<TextSearchResult>();
        await foreach (var result in searchResults.Results)
        {
            responseResults.Add(new TextSearchResult()
            {
                Value = result.Record.Text ?? string.Empty,
                Link = result.Record.ReferenceLink ?? string.Empty,
                Score = result.Score
            });
        }

        return responseResults;
    }
}

public sealed class UniqueKeyGenerator<TKey>(Func<TKey> generator)
    where TKey : notnull
{
    /// <summary>
    /// Generate a unique key.
    /// </summary>
    /// <returns>The unique key that was generated.</returns>
    public TKey GenerateKey() => generator();
}

Microsoft.Extensions.AI (preview)
Microsoft.Extensions.Ollama (preivew)
Microsoft.Extensions.AI.OpenAI (preivew)
Microsoft.Extensions.VectorData.Abstractions (preivew)
Microsoft.SemanticKernel.Connectors.Qdrant (preivew)
PdfPig (0.1.9)
Microsoft.Extensions.Configuration (8.0.0)
Microsoft.Extensions.Configuration.Json (8.0.0)

{
  "LLM": {
    "EndPoint": "//api.siliconflow.cn",
    "ApiKey": "sk-**********************", // Replace with your ApiKey
    "ModelId": "Qwen/Qwen2.5-7B-Instruct"
  },
  "Embeddings": {
    "Ollama": {
      "EndPoint": "//localhost:11434",
      "ModelId": "bge-m3"
    }
  },
  "VectorStores": {
    "Qdrant": {
      "Host": "edt-dev-server",
      "Port": 6334,
      "ApiKey": "EdisonTalk@2025"
    }
  },
  "RAG": {
    "CollectionName": "oneflower",
    "DataLoadingBatchSize": 10,
    "DataLoadingBetweenBatchDelayInMilliseconds": 1000,
    "PdfFileFolder": "Documents"
  }
}

var config = new ConfigurationBuilder()
    .AddJsonFile($"appsettings.json")
    .Build();

# ChatClient
var apiKeyCredential = new ApiKeyCredential(config["LLM:ApiKey"]);
var aiClientOptions = new OpenAIClientOptions();
aiClientOptions.Endpoint = new Uri(config["LLM:EndPoint"]);
var aiClient = new OpenAIClient(apiKeyCredential, aiClientOptions)
    .AsChatClient(config["LLM:ModelId"]);
var chatClient = new ChatClientBuilder(aiClient)
    .UseFunctionInvocation()
    .Build();
# EmbeddingGenerator
var embedingGenerator =
    new OllamaEmbeddingGenerator(new Uri(config["Embeddings:Ollama:EndPoint"]), config["Embeddings:Ollama:ModelId"]);
# VectorStore
var vectorStore = 
    new QdrantVectorStore(new QdrantClient(host: config["VectorStores:Qdrant:Host"], port: int.Parse(config["VectorStores:Qdrant:Port"]), apiKey: config["VectorStores:Qdrant:ApiKey"]));

var ragConfig = config.GetSection("RAG");
// Get the unique key genrator
var uniqueKeyGenerator = new UniqueKeyGenerator<Guid>(() => Guid.NewGuid());
// Get the collection in qdrant
var ragVectorRecordCollection = vectorStore.GetCollection<Guid, TextSnippet<Guid>>(ragConfig["CollectionName"]);
// Get the PDF loader
var pdfLoader = new PdfDataLoader<Guid>(uniqueKeyGenerator, ragVectorRecordCollection, embedingGenerator);
// Start to load PDF to VectorStore
var pdfFilePath = ragConfig["PdfFileFolder"];
var pdfFiles = Directory.GetFiles(pdfFilePath);
try
{
    foreach (var pdfFile in pdfFiles)
    {
        Console.WriteLine($"[LOG] Start Loading PDF into vector store: {pdfFile}");
        await pdfLoader.LoadPdf(
            pdfFile,
            int.Parse(ragConfig["DataLoadingBatchSize"]),
            int.Parse(ragConfig["DataLoadingBetweenBatchDelayInMilliseconds"]));
        Console.WriteLine($"[LOG] Finished Loading PDF into vector store: {pdfFile}");
    }
    Console.WriteLine($"[LOG] All PDFs loaded into vector store succeed!");
}
catch (Exception ex)
{
    Console.WriteLine($"[ERROR] Failed to load PDFs: {ex.Message}");
    return;
}

Console.WriteLine("[LOG] Now starting the chatting window for you...");
Console.ForegroundColor = ConsoleColor.Green;
var promptTemplate = """
          你是一(yi)個專(zhuan)業的(de)AI聊(liao)天機器(qi)人，為易速(su)鮮花(hua)網站的(de)所(suo)有員工提(ti)(ti)供(gong)信(xin)息(xi)咨詢服務。
          請使用(yong)下面的(de)提(ti)(ti)示使用(yong)工具(ju)從向量數據庫(ku)中獲取(qu)相關信(xin)息(xi)來回答用(yong)戶提(ti)(ti)出的(de)問(wen)題：
          {{#with (SearchPlugin-GetTextSearchResults question)}}  
            {{#each this}}  
              Value: {{Value}}
              Link: {{Link}}
              Score: {{Score}}
              -----------------
             {{/each}}
            {{/with}}
            
            輸出要求：請在回復中引用相(xiang)關(guan)信息的地方包(bao)括對(dui)相(xiang)關(guan)信息的引用。

            用戶問題: {{question}}
            """;
var history = new List<ChatMessage>();
var vectorSearchTool = new VectorDataSearcher<Guid>(ragVectorRecordCollection, embedingGenerator);
var chatOptions = new ChatOptions()
{
    Tools =
    [
      AIFunctionFactory.Create(vectorSearchTool.GetTextSearchResults)
    ]
};
// Prompt the user for a question.
Console.ForegroundColor = ConsoleColor.Green;
Console.WriteLine($"助手> 今天有什么可(ke)以幫到你的?");
while (true)
{
    // Read the user question.
    Console.ForegroundColor = ConsoleColor.White;
    Console.Write("用戶> ");
    var question = Console.ReadLine();
    // Exit the application if the user didn't type anything.
    if (!string.IsNullOrWhiteSpace(question) && question.ToUpper() == "EXIT")
        break;

    var ragPrompt = promptTemplate.Replace("{question}", question);
    history.Add(new ChatMessage(ChatRole.User, ragPrompt));
    Console.ForegroundColor = ConsoleColor.Green;
    Console.Write("助手> ");
    var result = await chatClient.GetResponseAsync(history, chatOptions);
    var response = result.ToString();
    Console.Write(response);
    history.Add(new ChatMessage(ChatRole.Assistant, response));

    Console.WriteLine();
}