Início Rápido: usar resumo de texto, documento e conversa

Documentação de referência | Mais amostras | Pacote (NuGet) | Código-fonte da biblioteca

Documentação de referência | Mais amostras | Pacote (NuGet) | Código-fonte da biblioteca

Documentação de referência | Mais amostras | Pacote (NuGet) | Código-fonte da biblioteca

setx LANGUAGE_KEY your-key

setx LANGUAGE_ENDPOINT your-endpoint

Após adicionar as variáveis de ambiente, é aconselhável reiniciar todos os programas em execução que precisarem ler as variáveis de ambiente, incluindo a janela do console. Por exemplo, se estiver usando o Visual Studio como seu editor, reinicie-o antes de executar o exemplo.

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

Depois de adicionar as variáveis de ambiente, execute source ~/.bashrc na janela do console para que as alterações entrem em vigor.

Bash

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

Depois de adicionar as variáveis de ambiente, execute source ~/.bash_profile na janela do console para que as alterações entrem em vigor.

Xcode

Para desenvolvimento em iOS e macOS, defina as variáveis de ambiente no Xcode. Por exemplo, siga estas etapas para definir a variável de ambiente no Xcode 13.4.1.

1. Selecione Produto>Esquema>Editar esquema
2. Selecione Argumentos na página Executar (Execução de Depuração)
3. Em Variáveis de Ambiente, selecione o sinal de adição (+) para adicionar uma nova variável de ambiente.
4. Insira LANGUAGE_KEY para o Nome e insira a chave de recurso de linguagem para o Valor.
5. Realize essas etapas para o ponto de extremidade de recurso. Nomear a nova variável de ambiente LANGUAGE_ENDPOINT.

Para obter mais opções de configuração, consulte a documentação do Xcode.

Instale a biblioteca de cliente clicando com o botão direito do mouse na solução no Gerenciador de Soluções e selecionando Gerenciar Pacotes do NuGet. No gerenciador de pacotes que é aberto, selecione Procurar e pesquise por Azure.AI.TextAnalytics. Verifique se a opção Incluir pré-lançamento está marcada. Selecione a versão 5.3.0 e, em seguida, Instalar. Você também pode usar o Console do Gerenciador de Pacotes.

Instale a biblioteca de cliente clicando com o botão direito do mouse na solução no Gerenciador de Soluções e selecionando Gerenciar Pacotes do NuGet. No gerenciador de pacotes que é aberto, selecione Procurar e pesquise por Azure.AI.Language.Conversations. Verifique se a opção Incluir pré-lançamento está marcada. Selecione a versão 1.1.0 e, em seguida, Instalar. Você também pode usar o Console do Gerenciador de Pacotes.

Instale a biblioteca de cliente clicando com o botão direito do mouse na solução no Gerenciador de Soluções e selecionando Gerenciar Pacotes do NuGet. No gerenciador de pacotes que é aberto, selecione Procurar e pesquise por Azure.AI.TextAnalytics. Verifique se a opção Incluir pré-lançamento está marcada. Selecione a versão 5.3.0 e, em seguida, Instalar. Você também pode usar o Console do Gerenciador de Pacotes.

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);
        }
    }
}

Saída

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();
                }
            }
        }
    }
}

Saída

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);
        }
    }
}

Saída

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.

setx LANGUAGE_KEY your-key

setx LANGUAGE_ENDPOINT your-endpoint

Após adicionar as variáveis de ambiente, é aconselhável reiniciar todos os programas em execução que precisarem ler as variáveis de ambiente, incluindo a janela do console. Por exemplo, se estiver usando o Visual Studio como seu editor, reinicie-o antes de executar o exemplo.

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

Depois de adicionar as variáveis de ambiente, execute source ~/.bashrc na janela do console para que as alterações entrem em vigor.

Bash

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

Depois de adicionar as variáveis de ambiente, execute source ~/.bash_profile na janela do console para que as alterações entrem em vigor.

Xcode

Para desenvolvimento em iOS e macOS, defina as variáveis de ambiente no Xcode. Por exemplo, siga estas etapas para definir a variável de ambiente no Xcode 13.4.1.

1. Selecione Produto>Esquema>Editar esquema
2. Selecione Argumentos na página Executar (Execução de Depuração)
3. Em Variáveis de Ambiente, selecione o sinal de adição (+) para adicionar uma nova variável de ambiente.
4. Insira LANGUAGE_KEY para o Nome e insira a chave de recurso de linguagem para o Valor.
5. Realize essas etapas para o ponto de extremidade de recurso. Nomear a nova variável de ambiente LANGUAGE_ENDPOINT.

Para obter mais opções de configuração, consulte a documentação do Xcode.

setx LANGUAGE_KEY your-key

setx LANGUAGE_ENDPOINT your-endpoint

Após adicionar as variáveis de ambiente, é aconselhável reiniciar todos os programas em execução que precisarem ler as variáveis de ambiente, incluindo a janela do console. Por exemplo, se estiver usando o Visual Studio como seu editor, reinicie-o antes de executar o exemplo.

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

Depois de adicionar as variáveis de ambiente, execute source ~/.bashrc na janela do console para que as alterações entrem em vigor.

Bash

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

Depois de adicionar as variáveis de ambiente, execute source ~/.bash_profile na janela do console para que as alterações entrem em vigor.

Xcode

Para desenvolvimento em iOS e macOS, defina as variáveis de ambiente no Xcode. Por exemplo, siga estas etapas para definir a variável de ambiente no Xcode 13.4.1.

1. Selecione Produto>Esquema>Editar esquema
2. Selecione Argumentos na página Executar (Execução de Depuração)
3. Em Variáveis de Ambiente, selecione o sinal de adição (+) para adicionar uma nova variável de ambiente.
4. Insira LANGUAGE_KEY para o Nome e insira a chave de recurso de linguagem para o Valor.
5. Realize essas etapas para o ponto de extremidade de recurso. Nomear a nova variável de ambiente LANGUAGE_ENDPOINT.

Para obter mais opções de configuração, consulte a documentação do Xcode.

Documentação de referência | Mais amostras | Pacote (PyPi) | Código-fonte da biblioteca

Documentação de referência | Mais amostras | Pacote (PyPi) | Código-fonte da biblioteca

Documentação de referência | Mais amostras | Pacote (PyPi) | Código-fonte da biblioteca

setx LANGUAGE_KEY your-key

setx LANGUAGE_ENDPOINT your-endpoint

Após adicionar as variáveis de ambiente, é aconselhável reiniciar todos os programas em execução que precisarem ler as variáveis de ambiente, incluindo a janela do console. Por exemplo, se estiver usando o Visual Studio como seu editor, reinicie-o antes de executar o exemplo.

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

Depois de adicionar as variáveis de ambiente, execute source ~/.bashrc na janela do console para que as alterações entrem em vigor.

Bash

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

Depois de adicionar as variáveis de ambiente, execute source ~/.bash_profile na janela do console para que as alterações entrem em vigor.

Xcode

Para desenvolvimento em iOS e macOS, defina as variáveis de ambiente no Xcode. Por exemplo, siga estas etapas para definir a variável de ambiente no Xcode 13.4.1.

1. Selecione Produto>Esquema>Editar esquema
2. Selecione Argumentos na página Executar (Execução de Depuração)
3. Em Variáveis de Ambiente, selecione o sinal de adição (+) para adicionar uma nova variável de ambiente.
4. Insira LANGUAGE_KEY para o Nome e insira a chave de recurso de linguagem para o Valor.
5. Realize essas etapas para o ponto de extremidade de recurso. Nomear a nova variável de ambiente LANGUAGE_ENDPOINT.

Para obter mais opções de configuração, consulte a documentação do Xcode.

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

# 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)

Saída

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']}")

Saída

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)

Saída

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.

setx LANGUAGE_KEY your-key

setx LANGUAGE_ENDPOINT your-endpoint

Após adicionar as variáveis de ambiente, é aconselhável reiniciar todos os programas em execução que precisarem ler as variáveis de ambiente, incluindo a janela do console. Por exemplo, se estiver usando o Visual Studio como seu editor, reinicie-o antes de executar o exemplo.

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

Depois de adicionar as variáveis de ambiente, execute source ~/.bashrc na janela do console para que as alterações entrem em vigor.

Bash

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

Depois de adicionar as variáveis de ambiente, execute source ~/.bash_profile na janela do console para que as alterações entrem em vigor.

Xcode

Para desenvolvimento em iOS e macOS, defina as variáveis de ambiente no Xcode. Por exemplo, siga estas etapas para definir a variável de ambiente no Xcode 13.4.1.

1. Selecione Produto>Esquema>Editar esquema
2. Selecione Argumentos na página Executar (Execução de Depuração)
3. Em Variáveis de Ambiente, selecione o sinal de adição (+) para adicionar uma nova variável de ambiente.
4. Insira LANGUAGE_KEY para o Nome e insira a chave de recurso de linguagem para o Valor.
5. Realize essas etapas para o ponto de extremidade de recurso. Nomear a nova variável de ambiente LANGUAGE_ENDPOINT.

Para obter mais opções de configuração, consulte a documentação do Xcode.

Os comandos cURL a seguir são executados por meio de um shell BASH. Edite esses comandos com seus próprios valores JSON.

Resumo de texto

Exemplo de resumo extrativo de texto

O exemplo a seguir o ajudará a começar a usar o resumo extrativo de texto:

1. Copie o comando abaixo em um editor de texto. O exemplo de BASH usa o caractere de continuação de linha \. Se o console ou o terminal usar um caractere de continuação de linha diferente, use esse caractere.

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
      }
    }
  ]
}
'

2. Abra uma janela do prompt de comando (por exemplo: BASH).

3. Cole o comando do editor de texto na janela do prompt de comando e, em seguida, execute-o.

4. Obtenha operation-location do cabeçalho de resposta. O valor será parecido com a seguinte URL:

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

5. Para obter os resultados da solicitação, use o comando cURL a seguir. Substitua <my-job-id> pelo valor de ID numérica recebido do cabeçalho de resposta operation-location anterior:

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"

Resposta JSON de exemplo de resumo extrativo de texto

{
    "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"
                }
            }
        ]
    }
}

Sumarização de problemas e resolução de conversas

O seguinte exemplo mostra conceitos básicos da sumarização de problemas e resolução de conversas:

1. Copie o comando abaixo em um editor de texto. O exemplo de BASH usa o caractere de continuação de linha \. Se o console ou o terminal usar um caractere de continuação de linha diferente, use esse caractere.

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
      }
    }
  ]
}
'

Somente o aspecto resolution dá suporte ao sentenceCount. Se você não especificar o parâmetro sentenceCount, o modelo determinará o tamanho do resumo. Observe que sentenceCount é apenas a aproximação da contagem de frases do resumo de saída, intervalo de 1 a 7.

2. Abra uma janela do prompt de comando (por exemplo: BASH).

3. Cole o comando do editor de texto na janela do prompt de comando e, em seguida, execute-o.

4. Obtenha operation-location do cabeçalho de resposta. O valor será parecido com a seguinte URL:

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

5. Para obter os resultados da solicitação, use o comando cURL a seguir. Substitua <my-job-id> pelo valor de ID numérica recebido do cabeçalho de resposta operation-location anterior:

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"

Resposta JSON de exemplo da sumarização de problemas e resolução de conversas

{
  "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"
        }
      }
    ]
  }
}

Documento de exemplo de resumo

Neste projeto, você precisa de um documento de origem carregado no contêiner de origem. Você pode baixar nosso documento de exemplo do Microsoft Word ou Adobe PDF para este início rápido. O idioma de origem é o inglês.

Compilar a solicitação POST

1. Usando seu editor ou IDE favorito, crie um novo diretório para seu aplicativo chamado native-document.

2. Crie um novo arquivo json chamado document-summarization.json no diretório native-document.

3. Copie e cole a amostra de solicitação da Sumarização de Documentos em seu arquivo document-summarization.json. Substitua {your-source-container-SAS-URL} e {your-target-container-SAS-URL} pelos valores da instância de contêineres da conta de Armazenamento do portal do Azure:

Amostra de solicitação

{
 "kind": "ExtractiveSummarization",
 "parameters": {
      "sentenceCount": 6
  },
 "analysisInput":{
      "documents":[
          {
        "source":{
          "location":"{your-source-blob-SAS-URL}"
        },
        "targets":
          {
            "location":"{your-target-container-SAS-URL}",
          }
          }
      ]
  }
}

Executar a solicitação POST

Antes de executar a solicitação POST, substitua {your-language-resource-endpoint} e {your-key} pelo valor do ponto de extremidade da instância de recurso de linguagem do portal do Azure.

PowerShell

 cmd /c curl "{your-language-resource-endpoint}/language/analyze-documents/jobs?api-version=2023-11-15-preview" -i -X POST --header "Content-Type: application/json" --header "Ocp-Apim-Subscription-Key: {your-key}" --data "@document-summarization.json"

Terminal/prompt de comando

curl -v -X POST "{your-language-resource-endpoint}/language/analyze-documents/jobs?api-version=2023-11-15-preview" --header "Content-Type: application/json" --header "Ocp-Apim-Subscription-Key: {your-key}" --data "@document-summarization.json"

Veja abaixo um exemplo de resposta:

HTTP/1.1 202 Accepted
Content-Length: 0
operation-location: https://{your-language-resource-endpoint}/language/analyze-documents/jobs/f1cc29ff-9738-42ea-afa5-98d2d3cabf94?api-version=2023-11-15-preview
apim-request-id: e7d6fa0c-0efd-416a-8b1e-1cd9287f5f81
x-ms-region: West US 2
Date: Thu, 25 Jan 2024 15:12:32 GMT

Resposta POST (jobId)

Você recebe uma resposta 202 (Êxito) que inclui um cabeçalho Operation-Location somente leitura. O valor desse cabeçalho contém uma jobId que pode ser consultada para obter o status da operação assíncrona e recuperar os resultados usando uma solicitação GET:

Obter resultados da análise (solicitação GET)

1. Após sua solicitação POST bem-sucedida, faça a sondagem do cabeçalho de local de operação retornado na solicitação POST para exibir os dados processados.

2. Veja a estrutura da solicitação GET:

   GET {cognitive-service-endpoint}/language/analyze-documents/jobs/{jobId}?api-version=2023-11-15-preview
   

3. Antes de executar o comando, faça essas alterações:

   • Substitua {jobId} pelo cabeçalho Operation-Location da resposta POST.

   • Substitua {your-language-resource-endpoint} e {your-key} pelos valores da instância do serviço de idioma no portal do Azure.

Solicitação GET

    cmd /c curl "{your-language-resource-endpoint}/language/analyze-documents/jobs/{jobId}?api-version=2023-11-15-preview" -i -X GET --header "Content-Type: application/json" --header "Ocp-Apim-Subscription-Key: {your-key}"

    curl -v -X GET "{your-language-resource-endpoint}/language/analyze-documents/jobs/{jobId}?api-version=2023-11-15-preview" --header "Content-Type: application/json" --header "Ocp-Apim-Subscription-Key: {your-key}"

Examinar a resposta

Você recebe uma resposta 200 (Êxito) com saída JSON. O campo status indica o resultado da operação. Se a operação não estiver concluída, o valor do status será "em execução" ou "notStarted" e você deverá chamar a API novamente, manualmente ou por meio de um script. Recomendamos dar um intervalo de um segundo ou mais entre chamadas.

Resposta de exemplo

{
  "jobId": "f1cc29ff-9738-42ea-afa5-98d2d3cabf94",
  "lastUpdatedDateTime": "2024-01-24T13:17:58Z",
  "createdDateTime": "2024-01-24T13:17:47Z",
  "expirationDateTime": "2024-01-25T13:17:47Z",
  "status": "succeeded",
  "errors": [],
  "tasks": {
    "completed": 1,
    "failed": 0,
    "inProgress": 0,
    "total": 1,
    "items": [
      {
        "kind": "ExtractiveSummarizationLROResults",
        "lastUpdateDateTime": "2024-01-24T13:17:58.33934Z",
        "status": "succeeded",
        "results": {
          "documents": [
            {
              "id": "doc_0",
              "source": {
                "kind": "AzureBlob",
                "location": "https://myaccount.blob.core.windows.net/sample-input/input.pdf"
              },
              "targets": [
                {
                  "kind": "AzureBlob",
                  "location": "https://myaccount.blob.core.windows.net/sample-output/df6611a3-fe74-44f8-b8d4-58ac7491cb13/ExtractiveSummarization-0001/input.result.json"
                }
              ],
              "warnings": []
            }
          ],
          "errors": [],
          "modelVersion": "2023-02-01-preview"
        }
      }
    ]
  }
}