Quickstart: using the Language Detection client library and REST API

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Use this quickstart to create a language detection application with the client library for .NET. In the following example, you create a C# application that can identify the language a text sample was written in.

• Azure subscription - Create one for free
• The Visual Studio IDE

To use the code sample below, you'll need to deploy an Azure resource. This resource will contain a key and endpoint you'll use to authenticate the API calls you send to the Language service.

1. Use the following link to create a language resource using the Azure portal. You will need to sign in using your Azure subscription.

2. On the Select additional features screen that appears, select Continue to create your resource.

   

3. In the Create language screen, provide the following information:

   Expand table

   Detail	Description
   Subscription	The subscription account that your resource will be associated with. Select your Azure subscription from the drop-down menu.
   Resource group	A resource group is a container that stores the resources you create. Select Create new to create a new resource group.
   Region	The location of your Language resource. Different regions may introduce latency depending on your physical location, but have no impact on the runtime availability of your resource. For this quickstart, either select an available region near you, or choose East US.
   Name	The name for your Language resource. This name will also be used to create an endpoint URL that your applications will use to send API requests.
   Pricing tier	The pricing tier for your Language resource. You can use the Free F0 tier to try the service and upgrade later to a paid tier for production.

   

4. Make sure the Responsible AI Notice checkbox is checked.

5. Select Review + Create at the bottom of the page.

6. In the screen that appears, make sure the validation has passed, and that you entered your information correctly. Then select Create.

Next you will need the key and endpoint from the resource to connect your application to the API. You'll paste your key and endpoint into the code later in the quickstart.

1. After the Language resource deploys successfully, click the Go to Resource button under Next Steps.

   

2. On the screen for your resource, select Keys and endpoint on the left pane. You will use one of your keys and your endpoint in the steps below.

   

Your application must be authenticated to send API requests. For production, use a secure way of storing and accessing your credentials. In this example, you will write your credentials to environment variables on the local machine running the application.

To set the environment variable for your Language resource key, open a console window, and follow the instructions for your operating system and development environment.

• To set the LANGUAGE_KEY environment variable, replace your-key with one of the keys for your resource.
• To set the LANGUAGE_ENDPOINT environment variable, replace your-endpoint with the endpoint for your resource.

setx LANGUAGE_KEY your-key

setx LANGUAGE_ENDPOINT your-endpoint

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

Using the Visual Studio IDE, create a new .NET Core console app. This creates a "Hello World" project with a single C# source file: program.cs.

Install the client library by right-clicking on the solution in the Solution Explorer and selecting Manage NuGet Packages. In the package manager that opens select Browse and search for Azure.AI.TextAnalytics. Select version 5.2.0, and then Install. You can also use the Package Manager Console.

Copy the following code into your program.cs file. Then run the code.

using Azure;
using System;
using Azure.AI.TextAnalytics;

namespace LanguageDetectionExample
{
    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 detecting the language of text
        static void LanguageDetectionExample(TextAnalyticsClient client)
        {
            DetectedLanguage detectedLanguage = client.DetectLanguage("Ce document est rédigé en Français.");
            Console.WriteLine("Language:");
            Console.WriteLine($"\t{detectedLanguage.Name},\tISO-6391: {detectedLanguage.Iso6391Name}\n");
        }

        static void Main(string[] args)
        {
            var client = new TextAnalyticsClient(languageEndpoint, languageKey);
            LanguageDetectionExample(client);

            Console.Write("Press any key to exit.");
            Console.ReadKey();
        }

    }
}

Language:
    French, ISO-6391: fr

Reference documentation | More samples | Package (Maven) | Library source code

Use this quickstart to create a language detection application with the client library for Java. In the following example, you create a Java application that can identify the language a text sample was written in.

• Azure subscription - Create one for free
• Java Development Kit (JDK) with version 8 or above

To use the code sample below, you'll need to deploy an Azure resource. This resource will contain a key and endpoint you'll use to authenticate the API calls you send to the Language service.

1. Use the following link to create a language resource using the Azure portal. You will need to sign in using your Azure subscription.

2. On the Select additional features screen that appears, select Continue to create your resource.

   

3. In the Create language screen, provide the following information:

   Expand table

   Detail	Description
   Subscription	The subscription account that your resource will be associated with. Select your Azure subscription from the drop-down menu.
   Resource group	A resource group is a container that stores the resources you create. Select Create new to create a new resource group.
   Region	The location of your Language resource. Different regions may introduce latency depending on your physical location, but have no impact on the runtime availability of your resource. For this quickstart, either select an available region near you, or choose East US.
   Name	The name for your Language resource. This name will also be used to create an endpoint URL that your applications will use to send API requests.
   Pricing tier	The pricing tier for your Language resource. You can use the Free F0 tier to try the service and upgrade later to a paid tier for production.

   

4. Make sure the Responsible AI Notice checkbox is checked.

5. Select Review + Create at the bottom of the page.

6. In the screen that appears, make sure the validation has passed, and that you entered your information correctly. Then select Create.

Next you will need the key and endpoint from the resource to connect your application to the API. You'll paste your key and endpoint into the code later in the quickstart.

1. After the Language resource deploys successfully, click the Go to Resource button under Next Steps.

   

2. On the screen for your resource, select Keys and endpoint on the left pane. You will use one of your keys and your endpoint in the steps below.

   

Your application must be authenticated to send API requests. For production, use a secure way of storing and accessing your credentials. In this example, you will write your credentials to environment variables on the local machine running the application.

To set the environment variable for your Language resource key, open a console window, and follow the instructions for your operating system and development environment.

• To set the LANGUAGE_KEY environment variable, replace your-key with one of the keys for your resource.
• To set the LANGUAGE_ENDPOINT environment variable, replace your-endpoint with the endpoint for your resource.

setx LANGUAGE_KEY your-key

setx LANGUAGE_ENDPOINT your-endpoint

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

Create a Maven project in your preferred IDE or development environment. Then add the following dependency to your project's pom.xml file. You can find the implementation syntax for other build tools online.

<dependencies>
     <dependency>
        <groupId>com.azure</groupId>
        <artifactId>azure-ai-textanalytics</artifactId>
        <version>5.2.0</version>
    </dependency>
</dependencies>

Create a Java file named Example.java. Open the file and copy the below code. Then run the code.

import com.azure.core.credential.AzureKeyCredential;
import com.azure.ai.textanalytics.models.*;
import com.azure.ai.textanalytics.TextAnalyticsClientBuilder;
import com.azure.ai.textanalytics.TextAnalyticsClient;

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);
        detectLanguageExample(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 detecting the language of text
    static void detectLanguageExample(TextAnalyticsClient client)
    {
        // The text to be analyzed.
        String text = "Ce document est rédigé en Français.";

        DetectedLanguage detectedLanguage = client.detectLanguage(text);
        System.out.printf("Detected primary language: %s, ISO 6391 name: %s, score: %.2f.%n",
                detectedLanguage.getName(),
                detectedLanguage.getIso6391Name(),
                detectedLanguage.getConfidenceScore());
    }
}

Detected primary language: French, ISO 6391 name: fr, score: 1.00.

Reference documentation | More samples | Package (npm) | Library source code

Use this quickstart to create a language detection application with the client library for Node.js. In the following example, you create a JavaScript application that can identify the language a text sample was written in.

• Azure subscription - Create one for free
• Node.js v14 LTS or later

To use the code sample below, you'll need to deploy an Azure resource. This resource will contain a key and endpoint you'll use to authenticate the API calls you send to the Language service.

1. Use the following link to create a language resource using the Azure portal. You will need to sign in using your Azure subscription.

2. On the Select additional features screen that appears, select Continue to create your resource.

   

3. In the Create language screen, provide the following information:

   Expand table

   Detail	Description
   Subscription	The subscription account that your resource will be associated with. Select your Azure subscription from the drop-down menu.
   Resource group	A resource group is a container that stores the resources you create. Select Create new to create a new resource group.
   Region	The location of your Language resource. Different regions may introduce latency depending on your physical location, but have no impact on the runtime availability of your resource. For this quickstart, either select an available region near you, or choose East US.
   Name	The name for your Language resource. This name will also be used to create an endpoint URL that your applications will use to send API requests.
   Pricing tier	The pricing tier for your Language resource. You can use the Free F0 tier to try the service and upgrade later to a paid tier for production.

   

4. Make sure the Responsible AI Notice checkbox is checked.

5. Select Review + Create at the bottom of the page.

6. In the screen that appears, make sure the validation has passed, and that you entered your information correctly. Then select Create.

Next you will need the key and endpoint from the resource to connect your application to the API. You'll paste your key and endpoint into the code later in the quickstart.

1. After the Language resource deploys successfully, click the Go to Resource button under Next Steps.

   

2. On the screen for your resource, select Keys and endpoint on the left pane. You will use one of your keys and your endpoint in the steps below.

   

Your application must be authenticated to send API requests. For production, use a secure way of storing and accessing your credentials. In this example, you will write your credentials to environment variables on the local machine running the application.

To set the environment variable for your Language resource key, open a console window, and follow the instructions for your operating system and development environment.

• To set the LANGUAGE_KEY environment variable, replace your-key with one of the keys for your resource.
• To set the LANGUAGE_ENDPOINT environment variable, replace your-endpoint with the endpoint for your resource.

setx LANGUAGE_KEY your-key

setx LANGUAGE_ENDPOINT your-endpoint

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

In a console window (such as cmd, PowerShell, or Bash), create a new directory for your app, and navigate to it.

mkdir myapp 

cd myapp

Run the npm init command to create a node application with a package.json file.

npm init

Install the npm package:

npm install @azure/ai-language-text

Open the file and copy the below code. Then run the code.

"use strict";

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

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

//Example sentences in different languages to be analyzed
const documents = [
    "This document is written in English.",
    "这是一个用中文写的文件",
];

//Example of how to use the client library to detect language
async function main() {
    console.log("== Language detection sample ==");
  
    const client = new TextAnalysisClient(endpoint, new AzureKeyCredential(key));
  
    const result = await client.analyze("LanguageDetection", documents);
  
    for (const doc of result) {
      if (!doc.error) {
        console.log(
          `ID ${doc.id} - Primary language: ${doc.primaryLanguage.name} (iso6391 name: ${doc.primaryLanguage.iso6391Name})`
        );
      }
    }
}

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

== Language detection sample ==
ID 0 - Primary language: English (iso6391 name: en)
ID 1 - Primary language: Chinese_Simplified (iso6391 name: zh_chs)

Reference documentation | More samples | Package (PyPi) | Library source code

Use this quickstart to create a language detection application with the client library for Python. In the following example, you create a Python application that can identify the language a text sample was written in.

• Azure subscription - Create one for free
• Python 3.8 or later

To use the code sample below, you'll need to deploy an Azure resource. This resource will contain a key and endpoint you'll use to authenticate the API calls you send to the Language service.

1. Use the following link to create a language resource using the Azure portal. You will need to sign in using your Azure subscription.

2. On the Select additional features screen that appears, select Continue to create your resource.

   

3. In the Create language screen, provide the following information:

   Expand table

   Detail	Description
   Subscription	The subscription account that your resource will be associated with. Select your Azure subscription from the drop-down menu.
   Resource group	A resource group is a container that stores the resources you create. Select Create new to create a new resource group.
   Region	The location of your Language resource. Different regions may introduce latency depending on your physical location, but have no impact on the runtime availability of your resource. For this quickstart, either select an available region near you, or choose East US.
   Name	The name for your Language resource. This name will also be used to create an endpoint URL that your applications will use to send API requests.
   Pricing tier	The pricing tier for your Language resource. You can use the Free F0 tier to try the service and upgrade later to a paid tier for production.

   

4. Make sure the Responsible AI Notice checkbox is checked.

5. Select Review + Create at the bottom of the page.

6. In the screen that appears, make sure the validation has passed, and that you entered your information correctly. Then select Create.

Next you will need the key and endpoint from the resource to connect your application to the API. You'll paste your key and endpoint into the code later in the quickstart.

1. After the Language resource deploys successfully, click the Go to Resource button under Next Steps.

   

2. On the screen for your resource, select Keys and endpoint on the left pane. You will use one of your keys and your endpoint in the steps below.

   

Your application must be authenticated to send API requests. For production, use a secure way of storing and accessing your credentials. In this example, you will write your credentials to environment variables on the local machine running the application.

To set the environment variable for your Language resource key, open a console window, and follow the instructions for your operating system and development environment.

• To set the LANGUAGE_KEY environment variable, replace your-key with one of the keys for your resource.
• To set the LANGUAGE_ENDPOINT environment variable, replace your-endpoint with the endpoint for your resource.

setx LANGUAGE_KEY your-key

setx LANGUAGE_ENDPOINT your-endpoint

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

After installing Python, you can install the client library with:

pip install azure-ai-textanalytics==5.2.0

Create a new Python file and copy the below code. Then run the code.

# This example requires environment variables named "LANGUAGE_KEY" and "LANGUAGE_ENDPOINT"
language_key = os.environ.get('LANGUAGE_KEY')
language_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(language_key)
    text_analytics_client = TextAnalyticsClient(
            endpoint=language_endpoint, 
            credential=ta_credential)
    return text_analytics_client

client = authenticate_client()

# Example method for detecting the language of text
def language_detection_example(client):
    try:
        documents = ["Ce document est rédigé en Français."]
        response = client.detect_language(documents = documents, country_hint = 'us')[0]
        print("Language: ", response.primary_language.name)

    except Exception as err:
        print("Encountered exception. {}".format(err))
language_detection_example(client)

Language:  French

Reference documentation

Use this quickstart to send language detection requests using the REST API. In the following example, you'll use cURL to identify the language that a text sample was written in.

• Azure subscription - Create one for free

To use the code sample below, you'll need to deploy an Azure resource. This resource will contain a key and endpoint you'll use to authenticate the API calls you send to the Language service.

1. Use the following link to create a language resource using the Azure portal. You will need to sign in using your Azure subscription.

2. On the Select additional features screen that appears, select Continue to create your resource.

   

3. In the Create language screen, provide the following information:

   Expand table

   Detail	Description
   Subscription	The subscription account that your resource will be associated with. Select your Azure subscription from the drop-down menu.
   Resource group	A resource group is a container that stores the resources you create. Select Create new to create a new resource group.
   Region	The location of your Language resource. Different regions may introduce latency depending on your physical location, but have no impact on the runtime availability of your resource. For this quickstart, either select an available region near you, or choose East US.
   Name	The name for your Language resource. This name will also be used to create an endpoint URL that your applications will use to send API requests.
   Pricing tier	The pricing tier for your Language resource. You can use the Free F0 tier to try the service and upgrade later to a paid tier for production.

   

4. Make sure the Responsible AI Notice checkbox is checked.

5. Select Review + Create at the bottom of the page.

6. In the screen that appears, make sure the validation has passed, and that you entered your information correctly. Then select Create.

Next you will need the key and endpoint from the resource to connect your application to the API. You'll paste your key and endpoint into the code later in the quickstart.

1. After the Language resource deploys successfully, click the Go to Resource button under Next Steps.

   

2. On the screen for your resource, select Keys and endpoint on the left pane. You will use one of your keys and your endpoint in the steps below.

   

Your application must be authenticated to send API requests. For production, use a secure way of storing and accessing your credentials. In this example, you will write your credentials to environment variables on the local machine running the application.

To set the environment variable for your Language resource key, open a console window, and follow the instructions for your operating system and development environment.

• To set the LANGUAGE_KEY environment variable, replace your-key with one of the keys for your resource.
• To set the LANGUAGE_ENDPOINT environment variable, replace your-endpoint with the endpoint for your resource.

setx LANGUAGE_KEY your-key

setx LANGUAGE_ENDPOINT your-endpoint

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

In a code editor, create a new file named test_languagedetection_payload.json and copy the following JSON example. This example request will be sent to the API in the next step.

{
    "kind": "LanguageDetection",
    "parameters": {
        "modelVersion": "latest"
    },
    "analysisInput":{
        "documents":[
            {
                "id":"1",
                "text": "This is a document written in English."
            }
        ]
    }
}

Save test_detection_payload.json somewhere on your computer. For example, your desktop.

Use the following commands to send the API request using the program you're using. Copy the command into your terminal, and run it.

Save test_languagedetection_payload.json somewhere on your computer. For example, your desktop.

Use the following commands to send the API request using the program you're using. Copy the command into your terminal, and run it.

curl -X POST "%LANGUAGE_ENDPOINT%/language/:analyze-text?api-version=2023-11-15-preview" ^
-H "Content-Type: application/json" ^
-H "Ocp-Apim-Subscription-Key: %LANGUAGE_KEY%" ^
-d "@C:\Users\<myaccount>\Desktop\test_languagedetection_payload.json"

curl.exe -X POST $env:LANGUAGE_ENDPOINT/language/:analyze-text?api-version=2023-11-15-preview `
-H "Content-Type: application/json" `
-H "Ocp-Apim-Subscription-Key: $env:LANGUAGE_KEY" `
-d "@C:\Users\<myaccount>\Desktop\test_languagedetection_payload.json"

curl -X POST $LANGUAGE_ENDPOINT/language/:analyze-text?api-version=2023-11-15-preview \
-H "Content-Type: application/json" \
-H "Ocp-Apim-Subscription-Key: $LANGUAGE_KEY" \
-d "@/home/mydir/test_languagedetection_payload.json"

curl -X POST $LANGUAGE_ENDPOINT/language/:analyze-text?api-version=2023-11-15-preview \
-H "Content-Type: application/json" \
-H "Ocp-Apim-Subscription-Key: $LANGUAGE_KEY" \
-d "@/home/mydir/test_languagedetection_payload.json"

Use the following commands to send the API request using the program you're using. Replace /home/mydir/test_detection_payload.json with the location of the example JSON request file you created in the previous step.

curl -X POST $LANGUAGE_ENDPOINT/language/:analyze-text?api-version=2023-11-15-preview \
-H "Content-Type: application/json" \
-H "Ocp-Apim-Subscription-Key: $LANGUAGE_KEY" \
-d "@/home/mydir/test_detection_payload.json"

{
    "kind": "LanguageDetectionResults",
    "results": {
        "documents": [
            {
                "id": "1",
                "detectedLanguage": {
                    "name": "English",
                    "iso6391Name": "en",
                    "confidenceScore": 1.0,
                    "script": "Latin",
                    "scriptCode": "Latn"
                },
                "warnings": []
            }
        ],
        "errors": [],
        "modelVersion": "2023-12-01"
    }
}

Use the following commands to delete the environment variables you created for this quickstart.

reg delete "HKCU\Environment" /v LANGUAGE_KEY /f

reg delete "HKCU\Environment" /v LANGUAGE_ENDPOINT /f

unset LANGUAGE_KEY

unset LANGUAGE_ENDPOINT

unset LANGUAGE_KEY

unset LANGUAGE_ENDPOINT

• Create a Project in Foundry in the Azure AI Foundry Portal

Using the left side pane, select Playgrounds. Then select the Try the Language Playground button.

The Language Playground consists of four sections:

• Top banner: You can select any of the currently available Language services here.
• Right pane: This pane is where you can find the Configuration options for the service, such as the API and model version, along with features specific to the service.
• Center pane: This pane is where you enter your text for processing. After the operation is run, some results are shown here.
• Right pane: This pane is where Details of the run operation are shown.

Here you can select the Language Detection capability by choosing the top banner tile, Detect language.

Detect language is designed to identify the language typed in text.

In Configuration there are the following options:

After your operation is completed, the Details section contains the following fields for the most detected language and script: