Creating a bot with Azure Bot Service

Posted on by Panther

Introduction

Azure Bot Service is a cloud-based service for building, deploying, and managing conversational bots. With Azure Bot Service, developers can easily create intelligent bots that can communicate with users through a variety of channels, including Skype, Slack, Facebook Messenger, and more.

In this tutorial, we will walk through the process of creating a bot using Azure Bot Service, starting with setting up the bot framework and building the bot’s functionality.

Prerequisites

To follow along with this tutorial, you will need the following:

  • An Azure subscription
  • Basic knowledge of programming concepts (C#)
  • Basic knowledge of Microsoft Bot Framework

Setting up the Bot Framework

Before you can create a bot using Azure Bot Service, you will first need to set up the Bot Framework. The Bot Framework is a set of tools and templates provided by Microsoft for building and deploying bots.

1. Create a new bot project

To get started, open Visual Studio and create a new bot project. In the “New Project” dialog, select “Bot Application” under the “Visual C#” node.

2. Test the Bot Framework

Once you have created a new bot project, you will need to test the Bot Framework to ensure that it is set up correctly. To do this, navigate to the root directory of your bot project and open the “Program.cs” file.

Add the following code to the “Main” method:

var builder = new BotBuilder();
builder.UseBotFramework();
var bot = new Bot(builder);

bot.OnMessage(async (context, next) =>
{
    await context.Reply("Hello World!");
});

bot.Run();

This code will create a simple bot that responds to all messages with “Hello World!”.

Run your bot project and verify that it is working correctly by opening a web browser and navigating to “http://localhost:3979/api/messages”. You should see a message from your bot saying “Hello World!”.

Creating the Bot in Azure Bot Service

Now that you have set up the Bot Framework, you can use Azure Bot Service to create and deploy your bot.

1. Create a new bot registration

To create a new bot in Azure Bot Service, navigate to the Azure Portal and select “Create a resource” > “AI + Machine Learning” > “Web App Bot”.

Fill out the necessary information in the “Bot Service” form, including the bot name, subscription, resource group, pricing tier, and bot template.

After you have filled out the form, select “Create” to create the new bot registration.

2. Configure the bot

Once you have created the new bot registration, you will need to configure its settings.

To do this, navigate to the “Bot Management” tab in the Azure Portal and select the bot that you just created. From there, you can configure the bot’s application settings, channels, and authentication settings.

3. Deploy the bot

After you have configured the bot’s settings, you can deploy it to Azure Bot Service.

To deploy the bot, navigate to the “Deployments” tab in the Azure Portal and select “Configure Deployment” > “GitHub” (or your preferred version control system).

Follow the prompts to connect your bot to your version control repository and specify the branch to deploy from.

Once you have configured the deployment, select “Deploy” to deploy your bot to Azure Bot Service.

Building the Bot’s Functionality

Now that you have created and deployed the bot to Azure Bot Service, you can begin building its functionality.

1. Add a dialog

A dialog is the basic unit of conversation in a bot. Dialogs are responsible for receiving messages from users and generating responses.

To add a new dialog to your bot project, create a new C# class and inherit from the “IDialog” interface.

[Serializable]
public class SampleDialog : IDialog<object>
{
    public async Task StartAsync(IDialogContext context)
    {
        context.Wait(MessageReceivedAsync);
    }

    public async Task MessageReceivedAsync(IDialogContext context, IAwaitable<IMessageActivity> argument)
    {
        var message = await argument;

        // Respond to the user's message here
    }
}

In the “StartAsync” method, the dialog is initialized and the “MessageReceivedAsync” method is called to handle incoming messages from the user.

2. Add a prompt

A prompt is a message that is sent to the user to elicit a response. Prompts can be used to gather information from the user or confirm an action.

To add a prompt to your dialog, use the “PromptDialog” class provided by the Bot Framework.

PromptDialog.Text(context, ResumeAfterPrompt, "Please enter your name.");

In this example, a text prompt is sent to the user asking for their name. Once the user responds, the “ResumeAfterPrompt” method is called to process the user’s response.

3. Add LuisRecognizer

LuisRecognizer is a cognitive service provided by Microsoft that uses natural language processing to understand user input. It can be used to interpret user input and perform actions based on the user’s intent.

To add LuisRecognizer to your bot, you will need to create a new LuisModel instance and pass it to the “LuisDialog” class.

[LuisModel("YourModelId", "YourSubscriptionId", "YourEndpointKey")]
public class LuisDialog : LuisDialog<object>
{
    [LuisIntent("YourIntentName")]
    public async Task YourIntent(IDialogContext context, LuisResult result)
    {
        // Perform action based on user's intent
    }
}

In this example, a new “LuisDialog” class is created that has an intent handler for a specific intent (“YourIntentName”).

4. Add QnAMakerDialog

QnAMakerDialog is a cognitive service that uses machine learning to provide instant answers to user questions. It can be used to provide automated support for common customer inquiries.

To add QnAMakerDialog to your bot, you will need to create a new QnAMakerService instance and pass it to the “QnAMakerDialog” class.

public class QnADialog : QnAMakerDialog
{
    public QnADialog() : base(new QnAMakerService(
         new QnAMakerAttribute("YourSubscriptionId", "YourKnowledgeBaseId", "YourEndpointKey"))))
    {
    }
}

In this example, a new “QnADialog” class is created that uses a QnAMakerService instance to provide responses to user questions.

Deploying Changes to Azure Bot Service

Once you have added the desired functionality to your bot project, you can deploy your changes to Azure Bot Service.

To deploy your changes, commit your changes to your version control repository and wait for the build and deployment process to complete.

Once your changes have been deployed, your bot will be updated with the new functionality, ready to communicate with users through a variety of channels.

Conclusion

Azure Bot Service provides an easy-to-use platform for building, deploying, and managing conversational bots. By following the steps outlined in this tutorial, you can create an intelligent bot that can interact with users through a variety of channels.

With the ability to add dialogs, prompts, LuisRecognizer, and QnAMakerDialog, you can build a bot that can understand and respond to user input, providing valuable insights and assistance to users in real-time.

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