2024年12月10日 星期二

Azure OpenAI AOAI 2.0 : 10 如何產生文字嵌入向量

Azure OpenAI AOAI 2.0 : 10 如何產生文字嵌入向量

內嵌 Embedding 在大語言模型中，扮演著一個重要的角色，這個功能可以讓我們將文字轉換成向量，這個向量可以用來計算文字之間的相似度，這個功能在自然語言處理中，是一個非常重要的功能。

為什麼需要用到內嵌 Embedding呢? 這是因為在自然語言處理中，我們需要將文字轉換成數字，這樣才能夠進行數學運算，而這個轉換過程，就是透過 Embedding 來完成的。而甚麼是 Embedding 呢? Embedding 是一個將文字轉換成向量的過程，這個向量可以用來表示文字的特徵，這樣我們就可以用這個向量來計算文字之間的相似度。

對於擁有了大量的 Embedding 之後，我們就可以用這些 Embedding 來進行文字之間的相似度計算，這樣我們就可以找到相似的文字，這個功能在自然語言處理中，是一個非常重要的功能。

而要進行 Embedding 相似度運算，可以有不同的演算法，不過，在這篇文章僅描述如何將本文文字轉換成 Embedding 向量，這個功能是透過 Azure OpenAI AOAI 2.0 服務來完成的。對於如何比對相似性的應用，將會在其他文章中來說明。

在 AOAI 中，提供兩種生成 Embedding 的模型，分別是 text-embedding-3-large / text-embedding-3-small，這兩個模型的差異在於模型的大小，大模型的 Embedding 向量維度較大，而小模型的 Embedding 向量維度較小，這樣的設計是為了讓使用者可以根據自己的需求來選擇適合的模型。

對於 text-embedding-3-large 模型，可以產生出最大 3072 維度的 Embedding 向量，而 text-embedding-3-small 模型，可以產生出 1536 維度的 Embedding 向量，對於要輸入最大 Token 數量，則皆為 8191 ，這樣的設計是為了讓使用者可以根據自己的需求來選擇適合的模型。

建立測試專案

請依照底下的操作，建立起這篇文章需要用到的練習專案

  • 打開 Visual Studio 2022 IDE 應用程式
  • 從 [Visual Studio 2022] 對話窗中，點選右下方的 [建立新的專案] 按鈕
  • 在 [建立新專案] 對話窗右半部
    • 切換 [所有語言 (L)] 下拉選單控制項為 [C#]
    • 切換 [所有專案類型 (T)] 下拉選單控制項為 [主控台]
  • 在中間的專案範本清單中，找到並且點選 [主控台應用程式] 專案範本選項

    專案，用於建立可在 Windows、Linux 及 macOS 於 .NET 執行的命令列應用程式

  • 點選右下角的 [下一步] 按鈕
  • 在 [設定新的專案] 對話窗
  • 找到 [專案名稱] 欄位，輸入 csEmbeddings 作為專案名稱
  • 在剛剛輸入的 [專案名稱] 欄位下方，確認沒有勾選 [將解決方案與專案至於相同目錄中] 這個檢查盒控制項
  • 點選右下角的 [下一步] 按鈕
  • 現在將會看到 [其他資訊] 對話窗
  • 在 [架構] 欄位中，請選擇最新的開發框架，這裡選擇的 [架構] 是 : .NET 8.0 (長期支援)
  • 在這個練習中，需要去勾選 [不要使用最上層陳述式(T)] 這個檢查盒控制項

    這裡的這個操作，可以由讀者自行決定是否要勾選這個檢查盒控制項

  • 請點選右下角的 [建立] 按鈕

稍微等候一下，這個 背景工作服務 專案將會建立完成

安裝要用到的 NuGet 開發套件

因為開發此專案時會用到這些 NuGet 套件，請依照底下說明，將需要用到的 NuGet 套件安裝起來。

安裝 Azure.AI.OpenAI 套件

請依照底下說明操作步驟，將這個套件安裝到專案內

  • 滑鼠右擊 [方案總管] 視窗內的 [專案節點] 下方的 [相依性] 節點
  • 從彈出功能表清單中，點選 [管理 NuGet 套件] 這個功能選項清單
  • 此時，將會看到 [NuGet: csEmbeddings] 視窗
  • 切換此視窗的標籤頁次到名稱為 [瀏覽] 這個標籤頁次
  • 在左上方找到一個搜尋文字輸入盒，在此輸入 Azure.AI.OpenAI
  • 在視窗右方，將會看到該套件詳細說明的內容，其中，右上方有的 [安裝] 按鈕

    請確認有取消 Pre-release 這個選項，與選擇 2.0 正式版

  • 點選這個 [安裝] 按鈕，將這個套件安裝到專案內

修改 Program.cs 類別內容

在這篇文章中，將會把會用到的新類別與程式碼，都寫入到 [Program.cs] 這個檔案中，請依照底下的操作，修改 [Program.cs] 這個檔案的內容

  • 在專案中找到並且打開 [Program.cs] 檔案
  • 將底下的程式碼取代掉 Program.cs 檔案中內容
using Azure.AI.OpenAI;
using OpenAI.Chat;
using OpenAI.Embeddings;
using System.ClientModel;

namespace csEmbeddings;

internal class Program
{
    static void Main(string[] args)
    {
        string apiKey = System.Environment.GetEnvironmentVariable("AOAILabKey");
        AzureOpenAIClient azureClient = new(
            new Uri("https://gpt4tw.openai.azure.com/"),
            new System.ClientModel.ApiKeyCredential(apiKey));
        //EmbeddingClient embeddingClient = azureClient.GetEmbeddingClient("text-embedding-3-large");
        EmbeddingClient embeddingClient = azureClient.GetEmbeddingClient("text-embedding-3-small");

        string userPrompt = "[客戶服務] 如何聯絡蝦皮客服？\n" +
        "您可以在以下位置找到蝦皮客服團隊(請使用App並登入)：蝦皮購物App右下角『我的』➜ 滑至下方，即可看到客服選項。\n\n" +
        "🔹網路電話客服（語音）：請點選【透過免費網路電話聯繫客服】（下圖一）\n" +
        " 真人客服服務時間：週一至週五 09:00 - 18:00\n\n" +
        "🔹即時交談客服（文字）：請點選【與客服即時交談】（下圖二）\n\n" +
        "真人客服服務時間：週一至週五 09:00 - 21:00\n" +
        "* 特殊服務時間變動請參考置頂公告";

        Console.WriteLine($"{DateTime.Now}  [User]: {userPrompt}");

        ClientResult<OpenAIEmbedding> embedding = 
            embeddingClient.GenerateEmbedding(userPrompt);

        ReadOnlyMemory<float> vector = embedding.Value.ToFloats();

        foreach (var value in vector.Span)
        {
            Console.Write($"{value},");
        }

        Console.WriteLine();
    }
}

由於在這裡需要將傳入的 Prompt 文字，生成出 Embedding 向量，在這個範例中，所要使用的模型名稱，將會設定到變數 embeddingClient 中，這裡使用的是 text-embedding-3-small 模型，這個模型可以生成出 1536 維度的 Embedding 向量。而該物件的型別則是 EmbeddingClient，這個物件是由 AzureOpenAIClient 這個物件所產生出來的。

在此要計算的本文將會儲存在 userPrompt 物件內，這裡的內容將會從網路上隨機抓取一段文字，這段文字將會用來計算 Embedding 向量。

若要取得該文字的 Embedding 向量，將會呼叫 embeddingClient.GenerateEmbedding(userPrompt) 方法，這個方法將會回傳一個 ClientResult<OpenAIEmbedding> 物件，這個物件將會包含了該文字的 Embedding 向量。

透過敘述 ReadOnlyMemory<float> vector = embedding.Value.ToFloats(); 這個敘述，將會取得該 Embedding 向量的數值，這個數值將會是一個 ReadOnlyMemory<float> 物件，這個物件將會包含了該 Embedding 向量的數值。

最後，透過 foreach 迴圈，將會將該 Embedding 向量的數值 vector.Span，逐一印出到螢幕上。

執行測試專案

  • 按下 F5 開始執行專案
  • 將會看到輸出結果

這裡將會使採用 [text-embedding-3-small] 模型產生的 1536 個向量

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若改成 [text-embedding-3-large] 模型，將會得到 3072 維度更好的結果。

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2024年12月3日 星期二

Azure OpenAI AOAI 2.0 : 9 與圖片檔案進行聊天

Azure OpenAI AOAI 2.0 : 9 與圖片檔案進行聊天

在這篇文章之前，當要使用 Azure OpenAI 進行聊天程式設計時，我們都是使用文字輸入的方式，來與 Azure OpenAI 進行對話。但是，有些時候，取得的內容可能是一張圖片，此時，想要能夠讓 LLM 能夠看懂這張圖片，也就是，當我們要使用圖片檔案並且與之聊天時，該如何進行呢？

在 LLM 會有多模態（Multimodal）這個名詞，這是指AI系統能夠處理和理解多種不同類型的輸入數據，例如文字、圖像、音頻、視頻等。在大型語言模型（LLM）的應用中，多模態具有重要的價值和意義：

  1. 全面感知與理解 多模態AI可以像人類一樣，整合來自不同感官的信息。例如，不僅僅侷限於文字描述，還能同時理解圖像中的場景、物體、顏色和空間關係。這種全面的感知能力使AI的理解更加豐富和立體。

  2. 更自然的人機交互 傳統的AI交互主要依賴文字，而多模態技術使得人機交互更加自然和直觀。用戶可以通過語音、圖像、手勢等多種方式與AI系統溝通，大大提升了交互的靈活性和用戶體驗。

  3. 複雜任務解決 多模態AI可以處理更加複雜的任務，如：

  • 醫學影像診斷：結合醫學圖像和文字報告
  • 自動駕駛：整合攝像頭、雷達和傳感器數據
  • 教育：根據學生的表情和行為調整教學內容
  • 翻譯：理解語境和非語言交流信號

  1. 跨模態推理 多模態AI能夠在不同模態之間進行推理和轉換。例如，從圖像生成文字描述，或從文字描述重建圖像，這種能力極大地擴展了AI的應用邊界。

  2. 更精確的上下文理解 通過整合多種模態的信息，AI可以獲得更豐富的上下文，從而做出更準確和有洞察力的判斷。比如在社交媒體分析中，不僅看文字，還可以分析圖像和視頻。

  3. 無障礙與包容性技術 多模態技術可以幫助殘疾人更好地與技術交互。例如，為視障人士提供圖像描述，或為聽障人士提供語音轉文字服務。

  4. 創新應用場景

  • 藝術創作：AI可以根據文字提示生成圖像
  • 內容推薦：基於用戶的多模態行為數據
  • 虛擬助手：更智能、更人性化的交互體驗
  1. 研究與發展前沿 多模態AI代表了人工智能向更接近人類認知和理解方式的重要發展方向，是未來AI技術的重要研究領域。

總的來說，多模態技術正在徹底改變我們與AI交互的方式，使AI系統變得更加智能、靈活和貼近人類的感知模式。它不僅是一種技術創新，更是邁向更智能、更自然的人工智能的重要一步。

因此，在這篇文章中，將會介紹如何使用 Azure OpenAI 進行圖片檔案的聊天，也就是，當我們有一張圖片檔案時，該如何透過 Azure OpenAI 進行聊天。

建立測試專案

請依照底下的操作，建立起這篇文章需要用到的練習專案

  • 打開 Visual Studio 2022 IDE 應用程式
  • 從 [Visual Studio 2022] 對話窗中，點選右下方的 [建立新的專案] 按鈕
  • 在 [建立新專案] 對話窗右半部
    • 切換 [所有語言 (L)] 下拉選單控制項為 [C#]
    • 切換 [所有專案類型 (T)] 下拉選單控制項為 [主控台]
  • 在中間的專案範本清單中，找到並且點選 [主控台應用程式] 專案範本選項

    專案，用於建立可在 Windows、Linux 及 macOS 於 .NET 執行的命令列應用程式

  • 點選右下角的 [下一步] 按鈕
  • 在 [設定新的專案] 對話窗
  • 找到 [專案名稱] 欄位，輸入 csChatImage 作為專案名稱
  • 在剛剛輸入的 [專案名稱] 欄位下方，確認沒有勾選 [將解決方案與專案至於相同目錄中] 這個檢查盒控制項
  • 點選右下角的 [下一步] 按鈕
  • 現在將會看到 [其他資訊] 對話窗
  • 在 [架構] 欄位中，請選擇最新的開發框架，這裡選擇的 [架構] 是 : .NET 8.0 (長期支援)
  • 在這個練習中，需要去勾選 [不要使用最上層陳述式(T)] 這個檢查盒控制項

    這裡的這個操作，可以由讀者自行決定是否要勾選這個檢查盒控制項

  • 請點選右下角的 [建立] 按鈕

稍微等候一下，這個 背景工作服務 專案將會建立完成

安裝要用到的 NuGet 開發套件

因為開發此專案時會用到這些 NuGet 套件，請依照底下說明，將需要用到的 NuGet 套件安裝起來。

安裝 Azure.AI.OpenAI 套件

請依照底下說明操作步驟，將這個套件安裝到專案內

  • 滑鼠右擊 [方案總管] 視窗內的 [專案節點] 下方的 [相依性] 節點
  • 從彈出功能表清單中，點選 [管理 NuGet 套件] 這個功能選項清單
  • 此時，將會看到 [NuGet: csChatImage] 視窗
  • 切換此視窗的標籤頁次到名稱為 [瀏覽] 這個標籤頁次
  • 在左上方找到一個搜尋文字輸入盒，在此輸入 Azure.AI.OpenAI
  • 在視窗右方，將會看到該套件詳細說明的內容，其中，右上方有的 [安裝] 按鈕

    請確認有取消 Pre-release 這個選項，與選擇 2.0 正式版

  • 點選這個 [安裝] 按鈕，將這個套件安裝到專案內

修改 Program.cs 類別內容

在這篇文章中，將會把會用到的新類別與程式碼，都寫入到 [Program.cs] 這個檔案中，請依照底下的操作，修改 [Program.cs] 這個檔案的內容

  • 在專案中找到並且打開 [Program.cs] 檔案
  • 將底下的程式碼取代掉 Program.cs 檔案中內容
using Azure.AI.OpenAI;
using OpenAI.Chat;
using System.Reflection.PortableExecutable;
using System.Text;

namespace csChatImage;

internal class Program
{
    static void Main(string[] args)
    {
        // 讀取環境變數 AOAILabKey 的 API Key
        string apiKey = System.Environment.GetEnvironmentVariable("AOAILabKey");
        AzureOpenAIClient azureClient = new(
            new Uri("https://gpt4tw.openai.azure.com/"),
            new System.ClientModel.ApiKeyCredential(apiKey));
        ChatClient chatClient = azureClient.GetChatClient("gpt-4o");

        ChatImage(chatClient);
    }
    private static void ChatImage(ChatClient chatClient)
    {
        #region 提供圖片並與其聊天
        List<ChatMessage> prompts;
        ChatCompletion completion;
        string userPrompt1 = $"從這張圖片中，請分析該名運動員的狀態與給出適當建議";

        var imageFilePath = "sample.png";
        using Stream imageStream = File.OpenRead(imageFilePath);
        var imageBytes = BinaryData.FromStream(imageStream);

        prompts = new List<ChatMessage>
            {
                new UserChatMessage(new List<ChatMessageContentPart>
                {
                    ChatMessageContentPart.CreateTextPart(userPrompt1),
                    ChatMessageContentPart.CreateImagePart(imageBytes, "image/png")
                })
            };

        foreach (var message in prompts)
        {
            string roleName = message is SystemChatMessage ? "System" :
                message is UserChatMessage ? "User" :
                "Assistant";
            Console.WriteLine($"{DateTime.Now}  [{roleName}]: {message.Content[0].Text}");
        }

        completion = chatClient.CompleteChat(prompts);
        Console.WriteLine($"{DateTime.Now}  [Assistant]");
        foreach (var message in completion.Content)
        {
            Console.WriteLine($"{DateTime.Now} {message.Text}");
        }

        Console.WriteLine($"");
        Console.WriteLine($"Role : {completion.Role}");
        Console.WriteLine($"InputTokenCount : {completion.Usage.InputTokenCount}");
        Console.WriteLine($"OutputTokenCount : {completion.Usage.OutputTokenCount}");
        Console.WriteLine($"ReasoningTokenCount : {completion.Usage.OutputTokenDetails?.ReasoningTokenCount}");
        Console.WriteLine($"TotalTokenCount : {completion.Usage.TotalTokenCount}");
        Console.WriteLine($"");
        Console.WriteLine($"");
        #endregion
    }
}

首先，同樣的是要取得 Azure OpenAI 的 API Key，這裡使用環境變數 AOAILabKey 來存放這個 API Key，這樣可以避免直接寫在程式碼中，這樣可以增加安全性。

接著會呼叫 [ChatImage] 這個方法，在此方法內，將會嘗試進提供的圖片檔案，送至 Azure OpenAI 內，並且進行聊天。

在這個專案內，將會有個 [sample.png] 圖片檔案，其內容如下

首先將會宣告要提問的提示詞 [userPrompt1] ，設定其值為 從這張圖片中，請分析該名運動員的狀態與給出適當建議，接著，將會讀取這個圖片檔案，並且將其轉換為 BinaryData 類別的物件，這樣才能夠將圖片檔案送至 Azure OpenAI 內。

BinaryData 類別是 .NET 中的一個類別，主要用於處理和管理二進位資料。這個類別的目的是提供一種標準、高效且靈活的方式來處理位元組資料。讓我詳細解釋其主要特點和用途：

  1. 統一的二進位資料表示
  • BinaryData 提供了一個一致的方式來表示和處理任意長度的二進位資料
  • 可以從各種來源建立 BinaryData 實例，如位元組陣列、串流或其他二進位資料源
  1. 記憶體效率
  • 類別內部優化了記憶體管理
  • 支援零拷貝作業，可以減少記憶體複製和分配的開銷
  • 對於大型二進位資料，提供了更高效的處理方式
  1. 常見使用場景
  • 檔案處理
  • 網路通訊
  • 資料序列化
  • 圖像和多媒體資料處理
  • 加密和解密作業
  1. 主要功能
  • 資料讀取和寫入
  • 資料轉換
  • 記憶體管理
  • 支援不同資料格式的轉換
  1. 與現代 .NET 整合
  • 設計用於支援新的 .NET API
  • 在 .NET 6 及更新版本中引入
  • 提供更現代和高效的二進位資料處理方式

在 List 提示詞內，將會首先加入要提問的提示詞文字內容，接著，使用 ChatMessageContentPart.CreateImagePart(imageBytes, "image/png") 這個方法，將圖片檔案加入到提示詞內。

最後，呼叫 [CompleteChat] 方法，將這些提示詞送至 Azure OpenAI 內，並且取得回應結果，將會看到回應結果的內容。

執行測試專案

  • 按下 F5 開始執行專案
  • 將會看到輸出結果

2024/12/3 上午 09:02:05  [User]: 從這張圖片中，請分析該名運動員的狀態與給出適當建議
2024/12/3 上午 09:02:17  [Assistant]
2024/12/3 上午 09:02:17 從圖片所提供的數據來看：

1. **內臟脂肪百分位數**：23
2. **皮下脂肪百分位數**：33
3. **代謝失調風險**：7
4. **腰圍百分位數**：40
5. **肌肉健康度**：98
6. **肌力**：90
7. **肌耐力**：96
8. **核心均衡力**：85

### 分析
1. **內臟脂肪百分位數 (23)**：
   - 比較低，表明內臟脂肪水平較低，這是健康的一個標誌。

2. **皮下脂肪百分位數 (33)**：
   - 偏低或中等，表示皮下脂肪量在正常範圍內。

3. **代謝失調風險 (7)**：
   - 非常低，表示運動員代謝健康狀況良好，風險極低。

4. **腰圍百分位數 (40)**：
   - 中等，處於健康範圍，但仍有進步的空間以減少更多腹圍。

5. **肌肉健康度 (98)**：
   - 極高，表示整體肌肉的質量和健康狀況非常好。

6. **肌力 (90)**：
   - 非常好，表示運動員擁有優秀的肌肉力量。

7. **肌耐力 (96)**：
   - 極高，表示運動員的肌耐力非常強。

8. **核心均衡力 (85)**：
   - 優秀，表明核心區域的平衡力和穩定性很好。

### 建議
1. **維持現有的運動和飲食習慣**：
   - 現在的運動和飲食計劃已經達到了很好的效果，應該繼續保持。

2. **針對核心區域進行增強訓練**：
   - 雖然核心均衡力已經很高，但仍可以進一步通過瑜伽或普拉提等訓練來進一步提升。

3. **保持低代謝失調風險**：
   - 繼續保持健康的生活方式，避免高糖和高脂肪飲食，以維持低代謝失調風險。

4. **適當增加腰腹運動**：
   - 可以進行更多針對腰腹的運動，以進一步降低腰圍，進一步提升整體健康狀況。

總的來說，這位運動員的健康狀況非常良好，只需要在現有基礎上進行些微調整即可進一步提升表現與健康。

Role : Assistant
InputTokenCount : 1477
OutputTokenCount : 639
ReasoningTokenCount : 

TotalTokenCount : 2116 




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