快速入門：使用文字、文件和交談摘要

發行項
05/21/2024

重要

我們的預覽區域 (瑞典中部) 展示我們以 GPT 模型為基礎、最新且不斷進化的 LLM 微調技術。歡迎在瑞典中部區域透過語言資源進行試用。

對話摘要只能使用：

REST API
Python
C#

參考文件 | 更多樣本 | 套件 (NuGet) | 程式庫原始程式碼

使用此快速入門，透過適用於 .NET 的用戶端程式庫來建立文字摘要應用程式。在下列範例中，您將建立 C# 應用程式，以摘要文件或文字為基礎的客戶服務交談。

提示

您可以使用 Language Studio 嘗試進行文字摘要，無須撰寫程式碼。

必要條件

Azure 訂用帳戶 - 建立免費帳戶
Visual Studio IDE
擁有 Azure 訂用帳戶之後，在 Azure 入口網站中建立語言資源，以取得您的金鑰與端點。在其部署後，選取 [前往資源]。
- 您將需要來自所建立資源的金鑰與端點以將應用程式連線至該 API。您稍後會在快速入門中將金鑰和端點貼到程式碼中。
- 您可以使用免費定價層 (Free F0) 來試用服務，之後可升級至付費層以用於實際執行環境。
若要使用分析功能，您將需要具有標準定價層 (S) 的語言資源。

設定

建立環境變數

您的應用程式必須經過驗證後，才能傳送 API 要求。在生產環境中，請運用安全的方式來儲存和存取您的登入資訊。在此範例中，您會在執行應用程式的本機電腦上將認證寫入環境變數。

提示

請勿在程式碼中直接包含索引碼，且切勿公開張貼索引碼。如需更多驗證選項 (例如 Azure Key Vault)，請參閱 Azure AI 服務安全性文章。

若要設定語言資源金鑰的環境變數，請開啟主控台視窗，並遵循作業系統和開發環境的指示進行。

若要設定 LANGUAGE_KEY 環境變數，請將 your-key 取代為您資源的其中一個金鑰。
若要設定 LANGUAGE_ENDPOINT 環境變數，請將 your-endpoint 取代為您資源的端點。

setx LANGUAGE_KEY your-key

setx LANGUAGE_ENDPOINT your-endpoint

注意

如果您只需要存取目前執行中主控台的環境變數，您可以使用 set (而不是 setx) 來設定環境變數。

新增環境變數之後，您可能需要重新啟動任何需要讀取環境變數的執行中程式，包括主控台視窗。例如，如果您使用 Visual Studio 做為編輯器，請在執行範例前重新啟動 Visual Studio。

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

新增環境變數之後，請從主控台視窗執行 source ~/.bashrc，讓變更生效。

Bash

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

新增環境變數之後，請從主控台視窗執行 source ~/.bash_profile，讓變更生效。

Xcode

如果是 iOS 或 macOS 開發，請在 Xcode 中設定環境變數。例如，遵循下列步驟，在 Xcode 13.4.1 中設定環境變數。

選取 [產品] > [配置] > [編輯配置]
在 [執行 (偵錯執行)] 頁面上，選取 [引數]
在 [環境變數] 下，選取加號 (+)，以新增環境變數。
針對 [名稱] 輸入 LANGUAGE_KEY，然後輸入 [值] 的語言資源金鑰。
針對您的資源端點執行這些步驟。為新的環境變數 LANGUAGE_ENDPOINT 命名。

如需更多設定選項，請參閱 Xcode 文件。

建立新的 .NET Core 應用程式

使用 Visual Studio IDE，建立新的 .NET Core 主控台應用程式。這會建立 "Hello World" 專案，內含單一 C# 來源檔案：program.cs。

以滑鼠右鍵按一下 [方案總管] 中的解決方案，然後選取 [管理 NuGet 套件]，以安裝用戶端程式庫。在開啟的封裝管理員中，選取 [瀏覽] 並搜尋 Azure.AI.TextAnalytics。請確定已核取 [包含發行前版本]。選取版本 5.3.0，然後安裝。您也可以使用套件管理員主控台。

程式碼範例

將下列程式碼複製到 program.cs 檔案中。然後執行程式碼。

重要

前往 Azure 入口網站。如果您在 [必要條件] 小節中建立的語言資源已成功部署，請按一下 [後續步驟] 底下的 [前往資源] 按鈕。您可以在 [資源管理] 底下瀏覽資源的 [金鑰和端點] 頁面，以找到金鑰和端點。

重要

完成時，請記得從程式碼中移除金鑰，且不要公開張貼金鑰。在生產環境中，請使用安全的方式來儲存和存取您的認證，例如 Azure Key Vault。如需詳細資訊，請參閱 Azure AI 服務安全性一文。

using Azure;
using System;
using Azure.AI.TextAnalytics;
using System.Threading.Tasks;
using System.Collections.Generic;

namespace Example
{
    class Program
    {
        // This example requires environment variables named "LANGUAGE_KEY" and "LANGUAGE_ENDPOINT"
        static string languageKey = Environment.GetEnvironmentVariable("LANGUAGE_KEY");
        static string languageEndpoint = Environment.GetEnvironmentVariable("LANGUAGE_ENDPOINT");

        private static readonly AzureKeyCredential credentials = new AzureKeyCredential(languageKey);
        private static readonly Uri endpoint = new Uri(languageEndpoint);

        // Example method for summarizing text
        static async Task TextSummarizationExample(TextAnalyticsClient client)
        {
            string document = @"The extractive summarization feature uses natural language processing techniques to locate key sentences in an unstructured text document. 
                These sentences collectively convey the main idea of the document. This feature is provided as an API for developers. 
                They can use it to build intelligent solutions based on the relevant information extracted to support various use cases. 
                Extractive summarization supports several languages. It is based on pretrained multilingual transformer models, part of our quest for holistic representations. 
                It draws its strength from transfer learning across monolingual and harness the shared nature of languages to produce models of improved quality and efficiency." ;
        
            // Prepare analyze operation input. You can add multiple documents to this list and perform the same
            // operation to all of them.
            var batchInput = new List<string>
            {
                document
            };
        
            TextAnalyticsActions actions = new TextAnalyticsActions()
            {
                ExtractiveSummarizeActions = new List<ExtractiveSummarizeAction>() { new ExtractiveSummarizeAction() }
            };
        
            // Start analysis process.
            AnalyzeActionsOperation operation = await client.StartAnalyzeActionsAsync(batchInput, actions);
            await operation.WaitForCompletionAsync();
            // View operation status.
            Console.WriteLine($"AnalyzeActions operation has completed");
            Console.WriteLine();
        
            Console.WriteLine($"Created On   : {operation.CreatedOn}");
            Console.WriteLine($"Expires On   : {operation.ExpiresOn}");
            Console.WriteLine($"Id           : {operation.Id}");
            Console.WriteLine($"Status       : {operation.Status}");
        
            Console.WriteLine();
            // View operation results.
            await foreach (AnalyzeActionsResult documentsInPage in operation.Value)
            {
                IReadOnlyCollection<ExtractiveSummarizeActionResult> summaryResults = documentsInPage.ExtractiveSummarizeResults;
        
                foreach (ExtractiveSummarizeActionResult summaryActionResults in summaryResults)
                {
                    if (summaryActionResults.HasError)
                    {
                        Console.WriteLine($"  Error!");
                        Console.WriteLine($"  Action error code: {summaryActionResults.Error.ErrorCode}.");
                        Console.WriteLine($"  Message: {summaryActionResults.Error.Message}");
                        continue;
                    }
        
                    foreach (ExtractiveSummarizeResult documentResults in summaryActionResults.DocumentsResults)
                    {
                        if (documentResults.HasError)
                        {
                            Console.WriteLine($"  Error!");
                            Console.WriteLine($"  Document error code: {documentResults.Error.ErrorCode}.");
                            Console.WriteLine($"  Message: {documentResults.Error.Message}");
                            continue;
                        }
        
                        Console.WriteLine($"  Extracted the following {documentResults.Sentences.Count} sentence(s):");
                        Console.WriteLine();
        
                        foreach (ExtractiveSummarySentence sentence in documentResults.Sentences)
                        {
                            Console.WriteLine($"  Sentence: {sentence.Text}");
                            Console.WriteLine();
                        }
                    }
                }
            }
        }

        static async Task Main(string[] args)
        {
            var client = new TextAnalyticsClient(endpoint, credentials);
            await TextSummarizationExample(client);
        }
    }
}

輸出

AnalyzeActions operation has completed

Created On   : 9/16/2021 8:04:27 PM +00:00
Expires On   : 9/17/2021 8:04:27 PM +00:00
Id           : 2e63fa58-fbaa-4be9-a700-080cff098f91
Status       : succeeded

Extracted the following 3 sentence(s):

Sentence: The extractive summarization feature in uses natural language processing techniques to locate key sentences in an unstructured text document.

Sentence: This feature is provided as an API for developers.

Sentence: They can use it to build intelligent solutions based on the relevant information extracted to support various use cases.

using System;
using Azure;
using Azure.Core;
using Azure.AI.Language.Conversations;
using System.Text.Json;
using System.Threading.Tasks;

namespace Example
{
    class Program
    {
        private static readonly AzureKeyCredential credentials = new AzureKeyCredential("replace-with-your-key-here");
        private static readonly Uri endpoint = new Uri("replace-with-your-endpoint-here");

        static async Task Main(string[] args)
        {
            ConversationAnalysisClient client = new ConversationAnalysisClient(endpoint, credentials);
            await Summarization(client);
        }

        static async Task Summarization(ConversationAnalysisClient client)
        {
            var data = new
            {
                analysisInput = new
                {
                    conversations = new[]
                    {
                        new
                        {
                            conversationItems = new[]
                            {
                                new
                                {
                                    text = "Hello, you’re chatting with Rene. How may I help you?",
                                    id = "1",
                                    role = "Agent",
                                    participantId = "Agent",
                                },
                                new
                                {
                                    text = "Hi, I tried to set up wifi connection for Smart Brew 300 coffee machine, but it didn’t work.",
                                    id = "2",
                                    role = "Customer",
                                    participantId = "Customer",
                                },
                                new
                                {
                                    text = "I’m sorry to hear that. Let’s see what we can do to fix this issue. Could you please try the following steps for me? First, could you push the wifi connection button, hold for 3 seconds, then let me know if the power light is slowly blinking on and off every second?",
                                    id = "3",
                                    role = "Agent",
                                    participantId = "Agent",
                                },
                                new
                                {
                                    text = "Yes, I pushed the wifi connection button, and now the power light is slowly blinking?",
                                    id = "4",
                                    role = "Customer",
                                    participantId = "Customer",
                                },
                                new
                                {
                                    text = "Great. Thank you! Now, please check in your Contoso Coffee app. Does it prompt to ask you to connect with the machine?",
                                    id = "5",
                                    role = "Agent",
                                    participantId = "Agent",
                                },
                                new
                                {
                                    text = "No. Nothing happened.",
                                    id = "6",
                                    role = "Customer",
                                    participantId = "Customer",
                                },
                                new
                                {
                                    text = "I’m very sorry to hear that. Let me see if there’s another way to fix the issue. Please hold on for a minute.",
                                    id = "7",
                                    role = "Agent",
                                    participantId = "Agent",
                                }
                            },
                            id = "1",
                            language = "en",
                            modality = "text",
                        },
                    }
                },
                tasks = new[]
                {
                    new
                    {
                        parameters = new
                        {
                            summaryAspects = new[]
                            {
                                "issue",
                                "resolution",
                            }
                        },
                        kind = "ConversationalSummarizationTask",
                        taskName = "1",
                    },
                },
            };

            Operation<BinaryData> analyzeConversationOperation = await client.AnalyzeConversationsAsync(WaitUntil.Started, RequestContent.Create(data));
            analyzeConversationOperation.WaitForCompletion();

            using JsonDocument result = JsonDocument.Parse(analyzeConversationOperation.Value.ToStream());
            JsonElement jobResults = result.RootElement;
            foreach (JsonElement task in jobResults.GetProperty("tasks").GetProperty("items").EnumerateArray())
            {
                JsonElement results = task.GetProperty("results");

                Console.WriteLine("Conversations:");
                foreach (JsonElement conversation in results.GetProperty("conversations").EnumerateArray())
                {
                    Console.WriteLine($"Conversation: #{conversation.GetProperty("id").GetString()}");
                    Console.WriteLine("Summaries:");
                    foreach (JsonElement summary in conversation.GetProperty("summaries").EnumerateArray())
                    {
                        Console.WriteLine($"Text: {summary.GetProperty("text").GetString()}");
                        Console.WriteLine($"Aspect: {summary.GetProperty("aspect").GetString()}");
                    }
                    Console.WriteLine();
                }
            }
        }
    }
}

輸出

Conversations:
Conversation: #1
Summaries:
Text: Customer is unable to set up wifi connection for Smart Brew 300 coffee machine.
Aspect: issue
Text: The agent instructed the customer to press the wifi connection button, hold for 3 seconds, and check if the power light blinks. The agent also asked the customer to check if the Contoso Coffee app prompts to connect with the machine, but the issue persisted. The agent is looking for another way to fix the issue.
Aspect: resolution

using Azure;
using System;
using Azure.AI.TextAnalytics;
using System.Threading.Tasks;
using System.Collections.Generic;

namespace Example
{
    class Program
    {
        // This example requires environment variables named "LANGUAGE_KEY" and "LANGUAGE_ENDPOINT"
        static string languageKey = Environment.GetEnvironmentVariable("LANGUAGE_KEY");
        static string languageEndpoint = Environment.GetEnvironmentVariable("LANGUAGE_ENDPOINT");

        private static readonly AzureKeyCredential credentials = new AzureKeyCredential(languageKey);
        private static readonly Uri endpoint = new Uri(languageEndpoint);

        // Example method for summarizing text
        static async Task TextSummarizationExample(TextAnalyticsClient client)
        {
            string document = @"The extractive summarization feature uses natural language processing techniques to locate key sentences in an unstructured text document. 
                These sentences collectively convey the main idea of the document. This feature is provided as an API for developers. 
                They can use it to build intelligent solutions based on the relevant information extracted to support various use cases. 
                Extractive summarization supports several languages. It is based on pretrained multilingual transformer models, part of our quest for holistic representations. 
                It draws its strength from transfer learning across monolingual and harness the shared nature of languages to produce models of improved quality and efficiency." ;
        
            // Prepare analyze operation input. You can add multiple documents to this list and perform the same
            // operation to all of them.
            var batchInput = new List<string>
            {
                document
            };
        
            TextAnalyticsActions actions = new TextAnalyticsActions()
            {
                ExtractiveSummarizeActions = new List<ExtractiveSummarizeAction>() { new ExtractiveSummarizeAction() }
            };
        
            // Start analysis process.
            AnalyzeActionsOperation operation = await client.StartAnalyzeActionsAsync(batchInput, actions);
            await operation.WaitForCompletionAsync();
            // View operation status.
            Console.WriteLine($"AnalyzeActions operation has completed");
            Console.WriteLine();
        
            Console.WriteLine($"Created On   : {operation.CreatedOn}");
            Console.WriteLine($"Expires On   : {operation.ExpiresOn}");
            Console.WriteLine($"Id           : {operation.Id}");
            Console.WriteLine($"Status       : {operation.Status}");
        
            Console.WriteLine();
            // View operation results.
            await foreach (AnalyzeActionsResult documentsInPage in operation.Value)
            {
                IReadOnlyCollection<ExtractiveSummarizeActionResult> summaryResults = documentsInPage.ExtractiveSummarizeResults;
        
                foreach (ExtractiveSummarizeActionResult summaryActionResults in summaryResults)
                {
                    if (summaryActionResults.HasError)
                    {
                        Console.WriteLine($"  Error!");
                        Console.WriteLine($"  Action error code: {summaryActionResults.Error.ErrorCode}.");
                        Console.WriteLine($"  Message: {summaryActionResults.Error.Message}");
                        continue;
                    }
        
                    foreach (ExtractiveSummarizeResult documentResults in summaryActionResults.DocumentsResults)
                    {
                        if (documentResults.HasError)
                        {
                            Console.WriteLine($"  Error!");
                            Console.WriteLine($"  Document error code: {documentResults.Error.ErrorCode}.");
                            Console.WriteLine($"  Message: {documentResults.Error.Message}");
                            continue;
                        }
        
                        Console.WriteLine($"  Extracted the following {documentResults.Sentences.Count} sentence(s):");
                        Console.WriteLine();
        
                        foreach (ExtractiveSummarySentence sentence in documentResults.Sentences)
                        {
                            Console.WriteLine($"  Sentence: {sentence.Text}");
                            Console.WriteLine();
                        }
                    }
                }
            }
        }

        static async Task Main(string[] args)
        {
            var client = new TextAnalyticsClient(endpoint, credentials);
            await TextSummarizationExample(client);
        }
    }
}

輸出

AnalyzeActions operation has completed

Created On   : 9/16/2021 8:04:27 PM +00:00
Expires On   : 9/17/2021 8:04:27 PM +00:00
Id           : 2e63fa58-fbaa-4be9-a700-080cff098f91
Status       : succeeded

Extracted the following 3 sentence(s):

Sentence: The extractive summarization feature in uses natural language processing techniques to locate key sentences in an unstructured text document.

Sentence: This feature is provided as an API for developers.

Sentence: They can use it to build intelligent solutions based on the relevant information extracted to support various use cases.

參考文件 | 更多樣本 | 套件 (Maven) | 程式庫原始程式碼

使用此快速入門，透過適用於 JAVA 的用戶端程式庫來建立文字摘要應用程式。在下列範例中，您將建立可摘要文件的 JAVA 應用程式。

提示

您可以使用 Language Studio 嘗試進行文字摘要，無須撰寫程式碼。

必要條件

Azure 訂用帳戶 - 建立免費帳戶
Java 開發套件 (JDK) 含第 8 版或更新版本
擁有 Azure 訂用帳戶之後，在 Azure 入口網站中建立語言資源，以取得您的金鑰與端點。在其部署後，選取 [前往資源]。
- 您將需要來自所建立資源的金鑰與端點以將應用程式連線至該 API。您稍後會在快速入門中將金鑰和端點貼到下列程式碼中。
- 您可以使用免費定價層 (Free F0) 來試用服務，之後可升級至付費層以用於實際執行環境。
若要使用分析功能，您將需要具有標準定價層 (S) 的語言資源。

設定

新增用戶端程式庫

在您慣用的 IDE 或開發環境中建立 Maven 專案。然後，在專案的 pom.xml 檔案中新增下列相依性。您可以在線上找到其他建置工具的實作語法。

<dependencies>
     <dependency>
        <groupId>com.azure</groupId>
        <artifactId>azure-ai-textanalytics</artifactId>
        <version>5.3.0</version>
    </dependency>
</dependencies>

建立環境變數

提示

請勿在程式碼中直接包含索引碼，且切勿公開張貼索引碼。如需更多驗證選項 (例如 Azure Key Vault)，請參閱 Azure AI 服務安全性文章。

若要設定語言資源金鑰的環境變數，請開啟主控台視窗，並遵循作業系統和開發環境的指示進行。

若要設定 LANGUAGE_KEY 環境變數，請將 your-key 取代為您資源的其中一個金鑰。
若要設定 LANGUAGE_ENDPOINT 環境變數，請將 your-endpoint 取代為您資源的端點。

setx LANGUAGE_KEY your-key

setx LANGUAGE_ENDPOINT your-endpoint

注意

如果您只需要存取目前執行中主控台的環境變數，您可以使用 set (而不是 setx) 來設定環境變數。

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

新增環境變數之後，請從主控台視窗執行 source ~/.bashrc，讓變更生效。

Bash

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

新增環境變數之後，請從主控台視窗執行 source ~/.bash_profile，讓變更生效。

Xcode

如果是 iOS 或 macOS 開發，請在 Xcode 中設定環境變數。例如，遵循下列步驟，在 Xcode 13.4.1 中設定環境變數。

選取 [產品] > [配置] > [編輯配置]
在 [執行 (偵錯執行)] 頁面上，選取 [引數]
在 [環境變數] 下，選取加號 (+)，以新增環境變數。
針對 [名稱] 輸入 LANGUAGE_KEY，然後輸入 [值] 的語言資源金鑰。
針對您的資源端點執行這些步驟。為新的環境變數 LANGUAGE_ENDPOINT 命名。

如需更多設定選項，請參閱 Xcode 文件。

程式碼範例

建立名為 Example.java 的 Java 檔案。開啟檔案，並複製下列程式碼。然後執行程式碼。

重要

import com.azure.core.credential.AzureKeyCredential;
import com.azure.ai.textanalytics.models.*;
import com.azure.ai.textanalytics.TextAnalyticsClientBuilder;
import com.azure.ai.textanalytics.TextAnalyticsClient;
import java.util.ArrayList;
import java.util.List;
import com.azure.core.util.polling.SyncPoller;
import com.azure.ai.textanalytics.util.*;

public class Example {

    // This example requires environment variables named "LANGUAGE_KEY" and "LANGUAGE_ENDPOINT"
    private static String languageKey = System.getenv("LANGUAGE_KEY");
    private static String languageEndpoint = System.getenv("LANGUAGE_ENDPOINT");

    public static void main(String[] args) {
        TextAnalyticsClient client = authenticateClient(languageKey, languageEndpoint);
        summarizationExample(client);
    }
    // Method to authenticate the client object with your key and endpoint
    static TextAnalyticsClient authenticateClient(String key, String endpoint) {
        return new TextAnalyticsClientBuilder()
                .credential(new AzureKeyCredential(key))
                .endpoint(endpoint)
                .buildClient();
    }
    // Example method for summarizing text
    static void summarizationExample(TextAnalyticsClient client) {
        List<String> documents = new ArrayList<>();
        documents.add(
                "The extractive summarization feature uses natural language processing techniques "
                + "to locate key sentences in an unstructured text document. "
                + "These sentences collectively convey the main idea of the document. This feature is provided as an API for developers. "
                + "They can use it to build intelligent solutions based on the relevant information extracted to support various use cases. "
                + "Extractive summarization supports several languages. "
                + "It is based on pretrained multilingual transformer models, part of our quest for holistic representations. "
                + "It draws its strength from transfer learning across monolingual and harness the shared nature of languages "
                + "to produce models of improved quality and efficiency.");
    
        SyncPoller<AnalyzeActionsOperationDetail, AnalyzeActionsResultPagedIterable> syncPoller =
                client.beginAnalyzeActions(documents,
                        new TextAnalyticsActions().setDisplayName("{tasks_display_name}")
                                .setExtractSummaryActions(
                                        new ExtractSummaryAction()),
                        "en",
                        new AnalyzeActionsOptions());
    
        syncPoller.waitForCompletion();
    
        syncPoller.getFinalResult().forEach(actionsResult -> {
            System.out.println("Extractive Summarization action results:");
            for (ExtractSummaryActionResult actionResult : actionsResult.getExtractSummaryResults()) {
                if (!actionResult.isError()) {
                    for (ExtractSummaryResult documentResult : actionResult.getDocumentsResults()) {
                        if (!documentResult.isError()) {
                            System.out.println("\tExtracted summary sentences:");
                            for (SummarySentence summarySentence : documentResult.getSentences()) {
                                System.out.printf(
                                        "\t\t Sentence text: %s, length: %d, offset: %d, rank score: %f.%n",
                                        summarySentence.getText(), summarySentence.getLength(),
                                        summarySentence.getOffset(), summarySentence.getRankScore());
                            }
                        } else {
                            System.out.printf("\tCannot extract summary sentences. Error: %s%n",
                                    documentResult.getError().getMessage());
                        }
                    }
                } else {
                    System.out.printf("\tCannot execute Extractive Summarization action. Error: %s%n",
                            actionResult.getError().getMessage());
                }
            }
        });
    }
}

輸出

Extractive Summarization action results:
	Extracted summary sentences:
		 Sentence text: The extractive summarization feature uses natural language processing techniques to locate key sentences in an unstructured text document., length: 138, offset: 0, rank score: 1.000000.
		 Sentence text: This feature is provided as an API for developers., length: 50, offset: 206, rank score: 0.510000.
		 Sentence text: Extractive summarization supports several languages., length: 52, offset: 378, rank score: 0.410000.

參考文件 | 其他範例 | 套件 (npm) | 程式庫原始程式碼

使用此快速入門，透過適用於 JAVA 的用戶端程式庫來建立文字摘要應用程式。在下列範例中，您將建立可摘要文件的 JavaScript 應用程式。

提示

您可以使用 Language Studio 嘗試進行文字摘要，無須撰寫程式碼。

必要條件

Azure 訂用帳戶 - 建立免費帳戶
Node.js v16 LTS
擁有 Azure 訂用帳戶之後，在 Azure 入口網站中建立語言資源，以取得您的金鑰與端點。在其部署後，選取 [前往資源]。
- 您將需要來自所建立資源的金鑰與端點以將應用程式連線至該 API。您稍後會在快速入門中將金鑰和端點貼到下列程式碼中。
- 您可以使用免費定價層 (Free F0) 來試用服務，之後可升級至付費層以用於實際執行環境。
若要使用分析功能，您將需要具有標準定價層 (S) 的語言資源。

設定

建立環境變數

提示

請勿在程式碼中直接包含索引碼，且切勿公開張貼索引碼。如需更多驗證選項 (例如 Azure Key Vault)，請參閱 Azure AI 服務安全性文章。

若要設定語言資源金鑰的環境變數，請開啟主控台視窗，並遵循作業系統和開發環境的指示進行。

若要設定 LANGUAGE_KEY 環境變數，請將 your-key 取代為您資源的其中一個金鑰。
若要設定 LANGUAGE_ENDPOINT 環境變數，請將 your-endpoint 取代為您資源的端點。

setx LANGUAGE_KEY your-key

setx LANGUAGE_ENDPOINT your-endpoint

注意

如果您只需要存取目前執行中主控台的環境變數，您可以使用 set (而不是 setx) 來設定環境變數。

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

新增環境變數之後，請從主控台視窗執行 source ~/.bashrc，讓變更生效。

Bash

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

新增環境變數之後，請從主控台視窗執行 source ~/.bash_profile，讓變更生效。

Xcode

如果是 iOS 或 macOS 開發，請在 Xcode 中設定環境變數。例如，遵循下列步驟，在 Xcode 13.4.1 中設定環境變數。

選取 [產品] > [配置] > [編輯配置]
在 [執行 (偵錯執行)] 頁面上，選取 [引數]
在 [環境變數] 下，選取加號 (+)，以新增環境變數。
針對 [名稱] 輸入 LANGUAGE_KEY，然後輸入 [值] 的語言資源金鑰。
針對您的資源端點執行這些步驟。為新的環境變數 LANGUAGE_ENDPOINT 命名。

如需更多設定選項，請參閱 Xcode 文件。

建立新的 Node.js 應用程式

在主控台視窗 (例如 cmd、PowerShell 或 Bash) 中，為您的應用程式建立新的目錄，並瀏覽至該目錄。

mkdir myapp 

cd myapp

執行命令 npm init，以使用 package.json 檔案建立節點應用程式。

npm init

安裝用戶端程式庫

安裝 npm 套件：

npm install --save @azure/ai-language-text@1.1.0

程式碼範例

開啟檔案，並複製下列程式碼。然後執行程式碼。

重要

/**
 * This sample program extracts a summary of two sentences at max from an article.
 * For more information, see the feature documentation: {@link https://learn.microsoft.com/azure/ai-services/language-service/summarization/overview}
 *
 * @summary extracts a summary from an article
 */

const { AzureKeyCredential, TextAnalysisClient } = require("@azure/ai-language-text");

// Load the .env file if it exists
require("dotenv").config();

// This example requires environment variables named "LANGUAGE_KEY" and "LANGUAGE_ENDPOINT"
const endpoint = process.env.LANGUAGE_ENDPOINT;
const apiKey = process.env.LANGUAGE_KEY;

const documents = [
  `
           Windows 365 was in the works before COVID-19 sent companies around the world on a scramble to secure solutions to support employees suddenly forced to work from home, but “what really put the firecracker behind it was the pandemic, it accelerated everything,” McKelvey said. She explained that customers were asking, “’How do we create an experience for people that makes them still feel connected to the company without the physical presence of being there?”
           In this new world of Windows 365, remote workers flip the lid on their laptop, bootup the family workstation or clip a keyboard onto a tablet, launch a native app or modern web browser and login to their Windows 365 account. From there, their Cloud PC appears with their background, apps, settings and content just as they left it when they last were last there – in the office, at home or a coffee shop.
           “And then, when you’re done, you’re done. You won’t have any issues around security because you’re not saving anything on your device,” McKelvey said, noting that all the data is stored in the cloud.
           The ability to login to a Cloud PC from anywhere on any device is part of Microsoft’s larger strategy around tailoring products such as Microsoft Teams and Microsoft 365 for the post-pandemic hybrid workforce of the future, she added. It enables employees accustomed to working from home to continue working from home; it enables companies to hire interns from halfway around the world; it allows startups to scale without requiring IT expertise.
           “I think this will be interesting for those organizations who, for whatever reason, have shied away from virtualization. This is giving them an opportunity to try it in a way that their regular, everyday endpoint admin could manage,” McKelvey said.
           The simplicity of Windows 365 won over Dean Wells, the corporate chief information officer for the Government of Nunavut. His team previously attempted to deploy a traditional virtual desktop infrastructure and found it inefficient and unsustainable given the limitations of low-bandwidth satellite internet and the constant need for IT staff to manage the network and infrastructure.
           We didn’t run it for very long,” he said. “It didn’t turn out the way we had hoped. So, we actually had terminated the project and rolled back out to just regular PCs.”
           He re-evaluated this decision after the Government of Nunavut was hit by a ransomware attack in November 2019 that took down everything from the phone system to the government’s servers. Microsoft helped rebuild the system, moving the government to Teams, SharePoint, OneDrive and Microsoft 365. Manchester’s team recruited the Government of Nunavut to pilot Windows 365. Wells was intrigued, especially by the ability to manage the elastic workforce securely and seamlessly.
           “The impact that I believe we are finding, and the impact that we’re going to find going forward, is being able to access specialists from outside the territory and organizations outside the territory to come in and help us with our projects, being able to get people on staff with us to help us deliver the day-to-day expertise that we need to run the government,” he said.
           “Being able to improve healthcare, being able to improve education, economic development is going to improve the quality of life in the communities.”`,
];

async function main() {
  console.log("== Extractive Summarization Sample ==");

  const client = new TextAnalysisClient(endpoint, new AzureKeyCredential(apiKey));
  const actions = [
    {
      kind: "ExtractiveSummarization",
      maxSentenceCount: 2,
    },
  ];
  const poller = await client.beginAnalyzeBatch(actions, documents, "en");

  poller.onProgress(() => {
    console.log(
      `Last time the operation was updated was on: ${poller.getOperationState().modifiedOn}`
    );
  });
  console.log(`The operation was created on ${poller.getOperationState().createdOn}`);
  console.log(`The operation results will expire on ${poller.getOperationState().expiresOn}`);

  const results = await poller.pollUntilDone();

  for await (const actionResult of results) {
    if (actionResult.kind !== "ExtractiveSummarization") {
      throw new Error(`Expected extractive summarization results but got: ${actionResult.kind}`);
    }
    if (actionResult.error) {
      const { code, message } = actionResult.error;
      throw new Error(`Unexpected error (${code}): ${message}`);
    }
    for (const result of actionResult.results) {
      console.log(`- Document ${result.id}`);
      if (result.error) {
        const { code, message } = result.error;
        throw new Error(`Unexpected error (${code}): ${message}`);
      }
      console.log("Summary:");
      console.log(result.sentences.map((sentence) => sentence.text).join("\n"));
    }
  }
}

main().catch((err) => {
  console.error("The sample encountered an error:", err);
});

module.exports = { main };

參考文件 | 更多樣本 | 套件 (PyPi) | 程式庫原始程式碼

使用此快速入門，透過適用於 Python 的用戶端程式庫來建立文字摘要應用程式。在下列範例中，您將建立 Python 應用程式，摘要以文件或文字為基礎的客戶服務交談。

提示

您可以使用 Language Studio 嘗試進行文字摘要，無須撰寫程式碼。

必要條件

Azure 訂用帳戶 - 建立免費帳戶
Python 3.x
擁有 Azure 訂用帳戶之後，在 Azure 入口網站中建立語言資源，以取得您的金鑰與端點。在其部署後，選取 [前往資源]。
- 您將需要來自所建立資源的金鑰與端點以將應用程式連線至該 API。您稍後會在快速入門中將金鑰和端點貼到下列程式碼中。
- 您可以使用免費定價層 (Free F0) 來試用服務，之後可升級至付費層以用於實際執行環境。
若要使用分析功能，您將需要具有標準定價層 (S) 的語言資源。

設定

建立環境變數

提示

請勿在程式碼中直接包含索引碼，且切勿公開張貼索引碼。如需更多驗證選項 (例如 Azure Key Vault)，請參閱 Azure AI 服務安全性文章。

若要設定語言資源金鑰的環境變數，請開啟主控台視窗，並遵循作業系統和開發環境的指示進行。

若要設定 LANGUAGE_KEY 環境變數，請將 your-key 取代為您資源的其中一個金鑰。
若要設定 LANGUAGE_ENDPOINT 環境變數，請將 your-endpoint 取代為您資源的端點。

setx LANGUAGE_KEY your-key

setx LANGUAGE_ENDPOINT your-endpoint

注意

如果您只需要存取目前執行中主控台的環境變數，您可以使用 set (而不是 setx) 來設定環境變數。

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

新增環境變數之後，請從主控台視窗執行 source ~/.bashrc，讓變更生效。

Bash

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

新增環境變數之後，請從主控台視窗執行 source ~/.bash_profile，讓變更生效。

Xcode

如果是 iOS 或 macOS 開發，請在 Xcode 中設定環境變數。例如，遵循下列步驟，在 Xcode 13.4.1 中設定環境變數。

選取 [產品] > [配置] > [編輯配置]
在 [執行 (偵錯執行)] 頁面上，選取 [引數]
在 [環境變數] 下，選取加號 (+)，以新增環境變數。
針對 [名稱] 輸入 LANGUAGE_KEY，然後輸入 [值] 的語言資源金鑰。
針對您的資源端點執行這些步驟。為新的環境變數 LANGUAGE_ENDPOINT 命名。

如需更多設定選項，請參閱 Xcode 文件。

安裝用戶端程式庫

安裝 Python 之後，您可以透過以下項目安裝用戶端程式庫：

pip install azure-ai-textanalytics==5.3.0

pip install azure-ai-language-conversations==1.1.0

pip install azure-ai-textanalytics==5.3.0

程式碼範例

建立新的 Python 檔案，並複製下列程式碼。然後執行程式碼。

重要

# This example requires environment variables named "LANGUAGE_KEY" and "LANGUAGE_ENDPOINT"
key = os.environ.get('LANGUAGE_KEY')
endpoint = os.environ.get('LANGUAGE_ENDPOINT')

from azure.ai.textanalytics import TextAnalyticsClient
from azure.core.credentials import AzureKeyCredential

# Authenticate the client using your key and endpoint 
def authenticate_client():
    ta_credential = AzureKeyCredential(key)
    text_analytics_client = TextAnalyticsClient(
            endpoint=endpoint, 
            credential=ta_credential)
    return text_analytics_client

client = authenticate_client()

# Example method for summarizing text
def sample_extractive_summarization(client):
    from azure.core.credentials import AzureKeyCredential
    from azure.ai.textanalytics import (
        TextAnalyticsClient,
        ExtractiveSummaryAction
    ) 

    document = [
        "The extractive summarization feature uses natural language processing techniques to locate key sentences in an unstructured text document. "
        "These sentences collectively convey the main idea of the document. This feature is provided as an API for developers. " 
        "They can use it to build intelligent solutions based on the relevant information extracted to support various use cases. "
        "Extractive summarization supports several languages. It is based on pretrained multilingual transformer models, part of our quest for holistic representations. "
        "It draws its strength from transfer learning across monolingual and harness the shared nature of languages to produce models of improved quality and efficiency. "
    ]

    poller = client.begin_analyze_actions(
        document,
        actions=[
            ExtractiveSummaryAction(max_sentence_count=4)
        ],
    )

    document_results = poller.result()
    for result in document_results:
        extract_summary_result = result[0]  # first document, first result
        if extract_summary_result.is_error:
            print("...Is an error with code '{}' and message '{}'".format(
                extract_summary_result.code, extract_summary_result.message
            ))
        else:
            print("Summary extracted: \n{}".format(
                " ".join([sentence.text for sentence in extract_summary_result.sentences]))
            )

sample_extractive_summarization(client)

輸出

Summary extracted: 
The extractive summarization feature uses natural language processing techniques to locate key sentences in an unstructured text document. This feature is provided as an API for developers. They can use it to build intelligent solutions based on the relevant information extracted to support various use cases.


key = "paste-your-key-here"
endpoint = "paste-your-endpoint-here"

import os
from azure.core.credentials import AzureKeyCredential
from azure.ai.language.conversations import ConversationAnalysisClient

client = ConversationAnalysisClient(endpoint, AzureKeyCredential(key))
with client:
    poller = client.begin_conversation_analysis(
        task={
            "displayName": "Analyze conversations from xxx",
            "analysisInput": {
                "conversations": [
                    {
                        "conversationItems": [
                            {
                                "text": "Hello, you’re chatting with Rene. How may I help you?",
                                "id": "1",
                                "role": "Agent",
                                "participantId": "Agent_1",
                            },
                            {
                                "text": "Hi, I tried to set up wifi connection for Smart Brew 300 coffee machine, but it didn’t work.",
                                "id": "2",
                                "role": "Customer",
                                "participantId": "Customer_1",
                            },
                            {
                                "text": "I’m sorry to hear that. Let’s see what we can do to fix this issue. Could you please try the following steps for me? First, could you push the wifi connection button, hold for 3 seconds, then let me know if the power light is slowly blinking on and off every second?",
                                "id": "3",
                                "role": "Agent",
                                "participantId": "Agent_1",
                            },
                            {
                                "text": "Yes, I pushed the wifi connection button, and now the power light is slowly blinking.",
                                "id": "4",
                                "role": "Customer",
                                "participantId": "Customer_1",
                            },
                            {
                                "text": "Great. Thank you! Now, please check in your Contoso Coffee app. Does it prompt to ask you to connect with the machine?",
                                "id": "5",
                                "role": "Agent",
                                "participantId": "Agent_1",
                            },
                            {
                                "text": "No. Nothing happened.",
                                "id": "6",
                                "role": "Customer",
                                "participantId": "Customer_1",
                            },
                            {
                                "text": "I’m very sorry to hear that. Let me see if there’s another way to fix the issue. Please hold on for a minute.",
                                "id": "7",
                                "role": "Agent",
                                "participantId": "Agent_1",
                            },
                        ],
                        "modality": "text",
                        "id": "conversation1",
                        "language": "en",
                    },
                ]
            },
            "tasks": [
                {
                    "taskName": "Issue task",
                    "kind": "ConversationalSummarizationTask",
                    "parameters": {"summaryAspects": ["issue"]},
                },
                {
                    "taskName": "Resolution task",
                    "kind": "ConversationalSummarizationTask",
                    "parameters": {"summaryAspects": ["resolution"]},
                },
            ],
        }
    )

    # view result
    result = poller.result()
    task_results = result["tasks"]["items"]
    for task in task_results:
        print(f"\n{task['taskName']} status: {task['status']}")
        task_result = task["results"]
        if task_result["errors"]:
            print("... errors occurred ...")
            for error in task_result["errors"]:
                print(error)
        else:
            conversation_result = task_result["conversations"][0]
            if conversation_result["warnings"]:
                print("... view warnings ...")
                for warning in conversation_result["warnings"]:
                    print(warning)
            else:
                summaries = conversation_result["summaries"]
                for summary in summaries:
                    print(f"{summary['aspect']}: {summary['text']}")

輸出

Issue task status: succeeded
issue: Customer tried to set up wifi connection for Smart Brew 300 coffee machine but it didn't work. No error message.

Resolution task status: succeeded
resolution: Asked customer to check if the Contoso Coffee app prompts to connect with the machine. Customer ended the chat.

# This example requires environment variables named "LANGUAGE_KEY" and "LANGUAGE_ENDPOINT"
key = os.environ.get('LANGUAGE_KEY')
endpoint = os.environ.get('LANGUAGE_ENDPOINT')

from azure.ai.textanalytics import TextAnalyticsClient
from azure.core.credentials import AzureKeyCredential

# Authenticate the client using your key and endpoint 
def authenticate_client():
    ta_credential = AzureKeyCredential(key)
    text_analytics_client = TextAnalyticsClient(
            endpoint=endpoint, 
            credential=ta_credential)
    return text_analytics_client

client = authenticate_client()

# Example method for summarizing text
def sample_extractive_summarization(client):
    from azure.core.credentials import AzureKeyCredential
    from azure.ai.textanalytics import (
        TextAnalyticsClient,
        ExtractiveSummaryAction
    ) 

    document = [
        "The extractive summarization feature uses natural language processing techniques to locate key sentences in an unstructured text document. "
        "These sentences collectively convey the main idea of the document. This feature is provided as an API for developers. " 
        "They can use it to build intelligent solutions based on the relevant information extracted to support various use cases. "
        "Extractive summarization supports several languages. It is based on pretrained multilingual transformer models, part of our quest for holistic representations. "
        "It draws its strength from transfer learning across monolingual and harness the shared nature of languages to produce models of improved quality and efficiency. "
    ]

    poller = client.begin_analyze_actions(
        document,
        actions=[
            ExtractiveSummaryAction(max_sentence_count=4)
        ],
    )

    document_results = poller.result()
    for result in document_results:
        extract_summary_result = result[0]  # first document, first result
        if extract_summary_result.is_error:
            print("...Is an error with code '{}' and message '{}'".format(
                extract_summary_result.code, extract_summary_result.message
            ))
        else:
            print("Summary extracted: \n{}".format(
                " ".join([sentence.text for sentence in extract_summary_result.sentences]))
            )

sample_extractive_summarization(client)

輸出

Summary extracted: 
The extractive summarization feature uses natural language processing techniques to locate key sentences in an unstructured text document. This feature is provided as an API for developers. They can use it to build intelligent solutions based on the relevant information extracted to support various use cases.

使用本快速入門，使用 REST API 傳送文字摘要要求。在下列範例中，您將使用 cURL，摘要以文件或文字為基礎的客戶服務交談。

提示

您可以使用 Language Studio 嘗試進行文字摘要，無須撰寫程式碼。

必要條件

最新版的 cURL。
擁有 Azure 訂用帳戶之後，在 Azure 入口網站中建立語言資源，以取得您的金鑰與端點。在其部署後，選取 [前往資源]。
- 您將需要來自所建立資源的金鑰與端點以將應用程式連線至該 API。您稍後會在快速入門中將金鑰和端點貼到下列程式碼中。
- 您可以使用免費定價層 (Free F0) 來試用服務，之後可升級至付費層以用於實際執行環境。

設定

建立環境變數

提示

請勿在程式碼中直接包含索引碼，且切勿公開張貼索引碼。如需更多驗證選項 (例如 Azure Key Vault)，請參閱 Azure AI 服務安全性文章。

若要設定語言資源金鑰的環境變數，請開啟主控台視窗，並遵循作業系統和開發環境的指示進行。

若要設定 LANGUAGE_KEY 環境變數，請將 your-key 取代為您資源的其中一個金鑰。
若要設定 LANGUAGE_ENDPOINT 環境變數，請將 your-endpoint 取代為您資源的端點。

setx LANGUAGE_KEY your-key

setx LANGUAGE_ENDPOINT your-endpoint

注意

如果您只需要存取目前執行中主控台的環境變數，您可以使用 set (而不是 setx) 來設定環境變數。

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

新增環境變數之後，請從主控台視窗執行 source ~/.bashrc，讓變更生效。

Bash

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

新增環境變數之後，請從主控台視窗執行 source ~/.bash_profile，讓變更生效。

Xcode

如果是 iOS 或 macOS 開發，請在 Xcode 中設定環境變數。例如，遵循下列步驟，在 Xcode 13.4.1 中設定環境變數。

選取 [產品] > [配置] > [編輯配置]
在 [執行 (偵錯執行)] 頁面上，選取 [引數]
在 [環境變數] 下，選取加號 (+)，以新增環境變數。
針對 [名稱] 輸入 LANGUAGE_KEY，然後輸入 [值] 的語言資源金鑰。
針對您的資源端點執行這些步驟。為新的環境變數 LANGUAGE_ENDPOINT 命名。

如需更多設定選項，請參閱 Xcode 文件。

範例要求

注意

下列 BASH 範例會使用 \ 行接續字元。如果您的主控台或終端機使用不同的行接續字元，請使用該字元。
您可以在 GitHub 上找到語言特定的範例。若要呼叫 API，您需要下列資訊：

選擇您想要執行的摘要類型，然後選取下列其中一個索引標籤，以查看範例 API 呼叫：

功能	描述
文字摘要	使用擷取式文字摘要來產生文件中重要或相關資訊的摘要。
交談摘要	使用抽象式文字摘要，從客戶服務專員和客戶之間的文字記錄中產生問題和解決方案的摘要。

parameter	描述
`-X POST <endpoint>`	指定用於存取 API 的端點。
`-H Content-Type: application/json`	用於傳送 JSON 資料的內容類型。
`-H "Ocp-Apim-Subscription-Key:<key>`	指定用於存取 API 的金鑰。
`-d <documents>`	JSON，其中包含您想要傳送的文件。

下列 cURL 命令是從 BASH 殼層執行。用自己的 JSON 值編輯這些命令。

文字摘要

文件擷取式摘要範例

下列範例將讓您開始使用文字擷取式摘要：

將以下命令複製到文字編輯器。 BASH 範例會使用 \ 行接續字元。如果您的主控台或終端使用不同的行接續字元，請改為使用該字元。

curl -i -X POST $LANGUAGE_ENDPOINT/language/analyze-text/jobs?api-version=2023-04-01 \
-H "Content-Type: application/json" \
-H "Ocp-Apim-Subscription-Key: $LANGUAGE_KEY" \
-d \
' 
{
  "displayName": "Text ext Summarization Task Example",
  "analysisInput": {
    "documents": [
      {
        "id": "1",
        "language": "en",
        "text": "At Microsoft, we have been on a quest to advance AI beyond existing techniques, by taking a more holistic, human-centric approach to learning and understanding. As Chief Technology Officer of Azure AI services, I have been working with a team of amazing scientists and engineers to turn this quest into a reality. In my role, I enjoy a unique perspective in viewing the relationship among three attributes of human cognition: monolingual text (X), audio or visual sensory signals, (Y) and multilingual (Z). At the intersection of all three, there’s magic—what we call XYZ-code as illustrated in Figure 1—a joint representation to create more powerful AI that can speak, hear, see, and understand humans better. We believe XYZ-code will enable us to fulfill our long-term vision: cross-domain transfer learning, spanning modalities and languages. The goal is to have pre-trained models that can jointly learn representations to support a broad range of downstream AI tasks, much in the way humans do today. Over the past five years, we have achieved human performance on benchmarks in conversational speech recognition, machine translation, conversational question answering, machine reading comprehension, and image captioning. These five breakthroughs provided us with strong signals toward our more ambitious aspiration to produce a leap in AI capabilities, achieving multi-sensory and multilingual learning that is closer in line with how humans learn and understand. I believe the joint XYZ-code is a foundational component of this aspiration, if grounded with external knowledge sources in the downstream AI tasks."
      }
    ]
  },
  "tasks": [
    {
      "kind": "ExtractiveSummarization",
      "taskName": "Text Extractive Summarization Task 1",
      "parameters": {
        "sentenceCount": 6
      }
    }
  ]
}
'

開啟命令提示字元視窗 (例如：BASH)。
將文字編輯器中的命令貼到命令提示字元視窗中，然後執行該命令。
從回應標頭取得 operation-location。數值會如以下 URL 所示：

https://<your-language-resource-endpoint>/language/analyze-text/jobs/12345678-1234-1234-1234-12345678?api-version=2023-04-01

若要取得要求的結果，請使用下列 cURL 命令。請務必將 <my-job-id> 取代為您從先前的 operation-location 回應標頭收到的數字識別碼數值：

curl -X GET $LANGUAGE_ENDPOINT/language/analyze-text/jobs/<my-job-id>?api-version=2023-04-01 \
-H "Content-Type: application/json" \
-H "Ocp-Apim-Subscription-Key: $LANGUAGE_KEY"

文件擷取式摘要範例 JSON 回應

{
    "jobId": "56e43bcf-70d8-44d2-a7a7-131f3dff069f",
    "lastUpdateDateTime": "2022-09-28T19:33:43Z",
    "createdDateTime": "2022-09-28T19:33:42Z",
    "expirationDateTime": "2022-09-29T19:33:42Z",
    "status": "succeeded",
    "errors": [],
    "displayName": "Text ext Summarization Task Example",
    "tasks": {
        "completed": 1,
        "failed": 0,
        "inProgress": 0,
        "total": 1,
        "items": [
            {
                "kind": "ExtractiveSummarizationLROResults",
                "taskName": "Text Extractive Summarization Task 1",
                "lastUpdateDateTime": "2022-09-28T19:33:43.6712507Z",
                "status": "succeeded",
                "results": {
                    "documents": [
                        {
                            "id": "1",
                            "sentences": [
                                {
                                    "text": "At Microsoft, we have been on a quest to advance AI beyond existing techniques, by taking a more holistic, human-centric approach to learning and understanding.",
                                    "rankScore": 0.69,
                                    "offset": 0,
                                    "length": 160
                                },
                                {
                                    "text": "In my role, I enjoy a unique perspective in viewing the relationship among three attributes of human cognition: monolingual text (X), audio or visual sensory signals, (Y) and multilingual (Z).",
                                    "rankScore": 0.66,
                                    "offset": 324,
                                    "length": 192
                                },
                                {
                                    "text": "At the intersection of all three, there’s magic—what we call XYZ-code as illustrated in Figure 1—a joint representation to create more powerful AI that can speak, hear, see, and understand humans better.",
                                    "rankScore": 0.63,
                                    "offset": 517,
                                    "length": 203
                                },
                                {
                                    "text": "We believe XYZ-code will enable us to fulfill our long-term vision: cross-domain transfer learning, spanning modalities and languages.",
                                    "rankScore": 1.0,
                                    "offset": 721,
                                    "length": 134
                                },
                                {
                                    "text": "The goal is to have pre-trained models that can jointly learn representations to support a broad range of downstream AI tasks, much in the way humans do today.",
                                    "rankScore": 0.74,
                                    "offset": 856,
                                    "length": 159
                                },
                                {
                                    "text": "I believe the joint XYZ-code is a foundational component of this aspiration, if grounded with external knowledge sources in the downstream AI tasks.",
                                    "rankScore": 0.49,
                                    "offset": 1481,
                                    "length": 148
                                }
                            ],
                            "warnings": []
                        }
                    ],
                    "errors": [],
                    "modelVersion": "latest"
                }
            }
        ]
    }
}

交談問題和解決方法摘要

下列範例將讓您開始使用交談問題和解決方法摘要：

將以下命令複製到文字編輯器。 BASH 範例會使用 \ 行接續字元。如果您的主控台或終端使用不同的行接續字元，請改為使用該字元。

curl -i -X POST $LANGUAGE_ENDPOINT/language/analyze-conversations/jobs?api-version=2023-04-01 \
-H "Content-Type: application/json" \
-H "Ocp-Apim-Subscription-Key: $LANGUAGE_KEY" \
-d \
' 
{
  "displayName": "Conversation Task Example",
  "analysisInput": {
    "conversations": [
      {
        "conversationItems": [
          {
            "text": "Hello, you’re chatting with Rene. How may I help you?",
            "id": "1",
            "role": "Agent",
            "participantId": "Agent_1"
          },
          {
            "text": "Hi, I tried to set up wifi connection for Smart Brew 300 espresso machine, but it didn’t work.",
            "id": "2",
            "role": "Customer",
            "participantId": "Customer_1"
          },
          {
            "text": "I’m sorry to hear that. Let’s see what we can do to fix this issue. Could you please try the following steps for me? First, could you push the wifi connection button, hold for 3 seconds, then let me know if the power light is slowly blinking on and off every second?",
            "id": "3",
            "role": "Agent",
            "participantId": "Agent_1"
          },
          {
            "text": "Yes, I pushed the wifi connection button, and now the power light is slowly blinking.",
            "id": "4",
            "role": "Customer",
            "participantId": "Customer_1"
          },
          {
            "text": "Great. Thank you! Now, please check in your Contoso Coffee app. Does it prompt to ask you to connect with the machine? ",
            "id": "5",
            "role": "Agent",
            "participantId": "Agent_1"
          },
          {
            "text": "No. Nothing happened.",
            "id": "6",
            "role": "Customer",
            "participantId": "Customer_1"
          },
          {
            "text": "I’m very sorry to hear that. Let me see if there’s another way to fix the issue. Please hold on for a minute.",
            "id": "7",
            "role": "Agent",
            "participantId": "Agent_1"
          }
        ],
        "modality": "text",
        "id": "conversation1",
        "language": "en"
      }
    ]
  },
  "tasks": [
    {
      "taskName": "Conversation Task 1",
      "kind": "ConversationalSummarizationTask",
      "parameters": {
        "summaryAspects": ["issue"]
      }
    },
    {
      "taskName": "Conversation Task 2",
      "kind": "ConversationalSummarizationTask",
      "parameters": {
        "summaryAspects": ["resolution"],
        "sentenceCount": 1
      }
    }
  ]
}
'

只有 resolution 層面支援 sentenceCount。如果您未指定 sentenceCount 參數，模型會決定摘要的長度。請注意，sentenceCount 只是輸出摘要的句子計數近似值，範圍 1 到 7。

開啟命令提示字元視窗 (例如：BASH)。
將文字編輯器中的命令貼到命令提示字元視窗中，然後執行該命令。
從回應標頭取得 operation-location。數值會如以下 URL 所示：

https://<your-language-resource-endpoint>/language/analyze-conversations/jobs/12345678-1234-1234-1234-12345678?api-version=2023-04-01

若要取得要求的結果，請使用下列 cURL 命令。請務必將 <my-job-id> 取代為您從先前的 operation-location 回應標頭收到的數字識別碼數值：

curl -X GET $LANGUAGE_ENDPOINT/language/analyze-conversations/jobs/<my-job-id>?api-version=2023-04-01 \
-H "Content-Type: application/json" \
-H "Ocp-Apim-Subscription-Key: $LANGUAGE_KEY"

交談問題和解決方法摘要範例 JSON 回應

{
  "jobId": "02ec5134-78bf-45da-8f63-d7410291ec40",
  "lastUpdatedDateTime": "2022-09-29T17:43:02Z",
  "createdDateTime": "2022-09-29T17:43:01Z",
  "expirationDateTime": "2022-09-30T17:43:01Z",
  "status": "succeeded",
  "errors": [],
  "displayName": "Conversation Task Example",
  "tasks": {
    "completed": 2,
    "failed": 0,
    "inProgress": 0,
    "total": 2,
    "items": [
      {
        "kind": "conversationalSummarizationResults",
        "taskName": "Conversation Task 1",
        "lastUpdateDateTime": "2022-09-29T17:43:02.3584219Z",
        "status": "succeeded",
        "results": {
          "conversations": [
            {
              "summaries": [
                {
                  "aspect": "issue",
                  "text": "Customer wants to connect their Smart Brew 300 to their Wi-Fi. | The Wi-Fi connection didn't work."
                }
              ],
              "id": "conversation1",
              "warnings": []
            }
          ],
          "errors": [],
          "modelVersion": "latest"
        }
      },
      {
        "kind": "conversationalSummarizationResults",
        "taskName": "Conversation Task 2",
        "lastUpdateDateTime": "2022-09-29T17:43:02.2099663Z",
        "status": "succeeded",
        "results": {
          "conversations": [
            {
              "summaries": [
                {
                  "aspect": "resolution",
                  "text": "Asked customer to check if the power light is blinking on and off every second."
                }
              ],
              "id": "conversation1",
              "warnings": []
            }
          ],
          "errors": [],
          "modelVersion": "latest"
        }
      }
    ]
  }
}

parameter	描述
`-X POST <endpoint>`	指定用於存取 API 的端點。
`-H Content-Type: application/json`	用於傳送 JSON 資料的內容類型。
`-H "Ocp-Apim-Subscription-Key:<key>`	指定用於存取 API 的金鑰。
`-d <documents>`	JSON，其中包含您想要傳送的文件。

下列 cURL 命令是從 BASH 殼層執行。用自己的 JSON 值編輯這些命令。

文件摘要

文件擷取式摘要範例

下列範例將讓您開始使用文件擷取式摘要：

將以下命令複製到文字編輯器。 BASH 範例會使用 \ 行接續字元。如果您的主控台或終端使用不同的行接續字元，請改為使用該字元。

curl -i -X POST $LANGUAGE_ENDPOINT/language/analyze-text/jobs?api-version=2023-04-01 \
-H "Content-Type: application/json" \
-H "Ocp-Apim-Subscription-Key: $LANGUAGE_KEY" \
-d \
' 
{
  "displayName": "Document ext Summarization Task Example",
  "analysisInput": {
    "documents": [
      {
        "id": "1",
        "language": "en",
        "text": "At Microsoft, we have been on a quest to advance AI beyond existing techniques, by taking a more holistic, human-centric approach to learning and understanding. As Chief Technology Officer of Azure AI services, I have been working with a team of amazing scientists and engineers to turn this quest into a reality. In my role, I enjoy a unique perspective in viewing the relationship among three attributes of human cognition: monolingual text (X), audio or visual sensory signals, (Y) and multilingual (Z). At the intersection of all three, there’s magic—what we call XYZ-code as illustrated in Figure 1—a joint representation to create more powerful AI that can speak, hear, see, and understand humans better. We believe XYZ-code will enable us to fulfill our long-term vision: cross-domain transfer learning, spanning modalities and languages. The goal is to have pre-trained models that can jointly learn representations to support a broad range of downstream AI tasks, much in the way humans do today. Over the past five years, we have achieved human performance on benchmarks in conversational speech recognition, machine translation, conversational question answering, machine reading comprehension, and image captioning. These five breakthroughs provided us with strong signals toward our more ambitious aspiration to produce a leap in AI capabilities, achieving multi-sensory and multilingual learning that is closer in line with how humans learn and understand. I believe the joint XYZ-code is a foundational component of this aspiration, if grounded with external knowledge sources in the downstream AI tasks."
      }
    ]
  },
  "tasks": [
    {
      "kind": "ExtractiveSummarization",
      "taskName": "Document Extractive Summarization Task 1",
      "parameters": {
        "sentenceCount": 6
      }
    }
  ]
}
'

開啟命令提示字元視窗 (例如：BASH)。
將文字編輯器中的命令貼到命令提示字元視窗中，然後執行該命令。
從回應標頭取得 operation-location。數值會如以下 URL 所示：

https://<your-language-resource-endpoint>/language/analyze-text/jobs/12345678-1234-1234-1234-12345678?api-version=2023-04-01

若要取得要求的結果，請使用下列 cURL 命令。請務必將 <my-job-id> 取代為您從先前的 operation-location 回應標頭收到的數字識別碼數值：

curl -X GET $LANGUAGE_ENDPOINT/language/analyze-text/jobs/<my-job-id>?api-version=2023-04-01 \
-H "Content-Type: application/json" \
-H "Ocp-Apim-Subscription-Key: $LANGUAGE_KEY"

文件擷取式摘要範例 JSON 回應

{
    "jobId": "56e43bcf-70d8-44d2-a7a7-131f3dff069f",
    "lastUpdateDateTime": "2022-09-28T19:33:43Z",
    "createdDateTime": "2022-09-28T19:33:42Z",
    "expirationDateTime": "2022-09-29T19:33:42Z",
    "status": "succeeded",
    "errors": [],
    "displayName": "Text ext Summarization Task Example",
    "tasks": {
        "completed": 1,
        "failed": 0,
        "inProgress": 0,
        "total": 1,
        "items": [
            {
                "kind": "ExtractiveSummarizationLROResults",
                "taskName": "Text Extractive Summarization Task 1",
                "lastUpdateDateTime": "2022-09-28T19:33:43.6712507Z",
                "status": "succeeded",
                "results": {
                    "documents": [
                        {
                            "id": "1",
                            "sentences": [
                                {
                                    "text": "At Microsoft, we have been on a quest to advance AI beyond existing techniques, by taking a more holistic, human-centric approach to learning and understanding.",
                                    "rankScore": 0.69,
                                    "offset": 0,
                                    "length": 160
                                },
                                {
                                    "text": "In my role, I enjoy a unique perspective in viewing the relationship among three attributes of human cognition: monolingual text (X), audio or visual sensory signals, (Y) and multilingual (Z).",
                                    "rankScore": 0.66,
                                    "offset": 324,
                                    "length": 192
                                },
                                {
                                    "text": "At the intersection of all three, there’s magic—what we call XYZ-code as illustrated in Figure 1—a joint representation to create more powerful AI that can speak, hear, see, and understand humans better.",
                                    "rankScore": 0.63,
                                    "offset": 517,
                                    "length": 203
                                },
                                {
                                    "text": "We believe XYZ-code will enable us to fulfill our long-term vision: cross-domain transfer learning, spanning modalities and languages.",
                                    "rankScore": 1.0,
                                    "offset": 721,
                                    "length": 134
                                },
                                {
                                    "text": "The goal is to have pre-trained models that can jointly learn representations to support a broad range of downstream AI tasks, much in the way humans do today.",
                                    "rankScore": 0.74,
                                    "offset": 856,
                                    "length": 159
                                },
                                {
                                    "text": "I believe the joint XYZ-code is a foundational component of this aspiration, if grounded with external knowledge sources in the downstream AI tasks.",
                                    "rankScore": 0.49,
                                    "offset": 1481,
                                    "length": 148
                                }
                            ],
                            "warnings": []
                        }
                    ],
                    "errors": [],
                    "modelVersion": "latest"
                }
            }
        ]
    }
}

清除資源

如果您想要清除和移除 Azure AI 服務訂用帳戶，則可以刪除資源或資源群組。刪除資源群組也會刪除與其相關聯的任何其他資源。

共用方式為

快速入門：使用文字、文件和交談摘要

必要條件

設定

建立環境變數

建立新的 .NET Core 應用程式

程式碼範例

輸出

必要條件

設定

新增 用戶端程式庫

建立環境變數

程式碼範例

輸出

必要條件

設定

建立環境變數

建立新的 Node.js 應用程式

安裝用戶端程式庫

程式碼範例

必要條件

設定

建立環境變數

安裝用戶端程式庫

程式碼範例

輸出

必要條件

設定

建立環境變數

範例要求

文字摘要

文件擷取式摘要範例

文件擷取式摘要範例 JSON 回應

清除資源

下一步

其他資源

新增用戶端程式庫