How to build and train a LUIS (Language Understanding Intelligent Service) model programmatically

 To build and train a LUIS (Language Understanding Intelligent Service) model programmatically, especially for the scenario you provided in the image, follow these detailed steps:

Prerequisites

1. Azure Subscription: Ensure you have an active Azure subscription.
2. LUIS Resource: Create a Language Understanding (LUIS) resource in Azure.
3. Development Environment: Set up your development environment (e.g., Visual Studio, VS Code) and ensure you have installed the necessary SDKs.

Step 1: Create a LUIS Resource in Azure

1. Log in to the Azure Portal:
   • Go to the Azure Portal.
2. Create a LUIS Resource:
   • Click on Create a resource.
   • Search for "Language Understanding" and select Language Understanding (LUIS).
   • Provide the necessary details such as name, subscription, and resource group.
   • Click Review + Create and then Create.
3. Retrieve API Keys:
   • After creating the resource, go to your LUIS resource in the portal.
   • Navigate to Keys and Endpoint to get your authoring key and endpoint.

Step 2: Set Up Development Environment

1. Install SDKs:

   • For C#, use NuGet to install the Microsoft.Azure.CognitiveServices.Language.LUIS.Authoring package.
   • For Node.js, use npm to install @azure/cognitiveservices-luis-authoring.
   • For Python, use pip to install azure-cognitiveservices-language-luis.

2. Create a New Project:

   • Open your IDE (e.g., Visual Studio for C#).
   • Create a new Console Application project.

Step 3: Build and Train a LUIS Model Programmatically

Here’s how you would complete the code provided in the image using C#:

1. Initialize the LUIS Authoring Client:

csharp code

using Microsoft.Azure.CognitiveServices.Language.LUIS.Authoring;
using Microsoft.Azure.CognitiveServices.Language.LUIS.Authoring.Models;
using System;
using System.Threading.Tasks;

namespace LUISApp
{
    class Program
    {
        static async Task Main(string[] args)
        {
            string authoringKey = "YOUR_AUTHORING_KEY";
            string authoringEndpoint = "YOUR_AUTHORING_ENDPOINT";
            string appId = "YOUR_LUIS_APP_ID";
            string versionId = "0.1";

            var client = new LUISAuthoringClient(new ApiKeyServiceClientCredentials(authoringKey))
            {
                Endpoint = authoringEndpoint
            };

            // Adding a new phrase list
            var phraselistId = await client.Features.AddPhraseListAsync(
                appId, 
                versionId, 
                new PhraselistCreateObject
                {
                    EnabledForAllModels = false,
                    IsExchangeable = true,
                    Name = "PL1",
                    Phrases = "item1,item2,item3,item4,item5"
                });

            Console.WriteLine($"Phrase list created with ID: {phraselistId}");
        }
    }
}

• Replace "YOUR_AUTHORING_KEY", "YOUR_AUTHORING_ENDPOINT", and "YOUR_LUIS_APP_ID" with your actual values.

2. Train the Model:

csharp

// Train the LUIS model
await client.Train.TrainAsync(appId, versionId);

// Check training status
var status = await client.Train.GetStatusAsync(appId, versionId);
while (status.Any(s => s.Details.Status == "InProgress"))
{
    Console.WriteLine("Waiting for training to complete...");
    await Task.Delay(1000);
    status = await client.Train.GetStatusAsync(appId, versionId);
}
Console.WriteLine("Training completed.");

3. Publish the Model:

// Publish the LUIS model
await client.Apps.PublishAsync(appId, new ApplicationPublishObject
{
    VersionId = versionId,
    IsStaging = false,
    Region = "westus"
});
Console.WriteLine("App published.");

Step 4: Test the Model

1. Test in LUIS Portal:

   • Go to LUIS.ai and log in with your Azure credentials.
   • You should see the phrase list you added and can test utterances against it.

2. Integrate with Applications:

   • Use the published model's endpoint to integrate it with your applications (e.g., chatbots, custom applications).

Step 5: Automate and Scale

1. Automate:

   • Incorporate this process into a CI/CD pipeline using Azure DevOps or GitHub Actions to automatically update and deploy LUIS models.

2. Scale:

   • If managing multiple LUIS apps (e.g., for different chatbots), you can loop through app IDs and automate updates across all apps.

By following these steps, you can successfully build, train, and deploy LUIS models programmatically. This allows for scalable and automated updates to your Language Understanding services in Azure.

use case :- 

You have 100 chatbots that each has its own Language Understanding model.
Frequently, you must add the same phrases to each model.
You need to programmatically update the Language Understanding models to include the new phrases.
How should you complete the code?