How to Create a QnA Bot in Azure
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This Azure tutorial, we will discuss how to create a QnA bot in azure. You can follow the step by step tutorial to create a QnA bot in Microsoft azure.
Microsoft Bot Framework is an amazing framework that people are using it more and more to build Bots these days because it is so simple to put together a Bot very quick.
When talking about the bot framework, and chatbots in general, people usually assume that these are all using some clever logic and Natural Language Processing (NLP) to deliver a chunk of business logic via a natural language interface.
With the bot framework this is most likely implemented by wiring up the Language Understanding Intelligent Service (LUIS): originally a stand-alone, (optionally) self-training, natural language understanding service, but now part of Microsoft Research’s Cognitive Services – previously “Project Oxford” – a collection of extremely powerful machine learning APIs for processing images, video, text, speech, to extract meaning.
As a simple bot application let’s create a conversational bot. So this article will be based on creating a bot application that supports QnA or else known as FAQ bot in Azure.
Create a QnA Bot in Azure
As the first step, you must log in to your azure portal and need to create a new service => Search for “QnA Maker” and then create the service with selecting the resource group and pricing tires.
The following image contains the creating azure QnA service.
As per the following image, we should provide a name for the service and select the subscription with the location.
So the deployment is now ready for the usage. The following image contains the success message of the completeness of the deployment.
Then you can navigate to https://www.qnamaker.ai/Create to create the QnA Bot. There you need to login from the account you have created your service. Now you will see what are the available Azure directories and according to the directory, your subscription and your available bot services you created.
In the qnamaker, there are 5 steps to create the QnA Maker bot app.
Step 1: Create QnAMaker app in azure. (this is what we did previously)
Step 2: Connecting the app created in azure.
Step 3: Naming your knowledge base (QnA Maker knowledge base (KB) is own content, such as FAQs or product manuals)
Step 4: Populate the Knowledgebase using FAQ page, pdf, .doc, .docx, .xlsx, containing questions and answers in sequence.
Step 5: Create your Knowledge Base.
Let’s create this.
The following image contains creating the knowledge base for the QnA Maker.
In step 2, we can select the QnA Service which we created in Azure and select the language you need to chat.
In the step 3, you can set the name. Then in the step 4, you need to add a URL or File to extract the question and answers. Here for this I have mentioned the Xamarin FAQ page in docs.microsoft.com.
The next option is Chit-Chat
This is a new option in qnamaker. Adding chit-chat to your bot makes it more conversational and engaging. The chit-chat feature in QnA maker allows you to easily add a pre-populated set of the top chit-chat, into your knowledge base (KB). This can be a starting point for your bot’s personality, and it will save you the time and cost of writing them from scratch.
Then the last step: creating the knowledge base.
Finally, now you can see your knowledge base with the extracted question and answers.
And also from here, you can just add a new question and answer pair. Like the below image, you can just add new questions.
Likewise, you can add any questions and answers. At the bottom of the page, you can see the imported Chit-chat pre-trained question and answers.
Finally now we can save and train the knowledge base.
Now we came to the finalizing points of our QnA Bot.
Now we will test the knowledge base.
Now you can publish your Bot Service.
So, now you can see the request URI of your deployed bot application. You can just request this URI and get back the response as the answer.
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Likewise, you can create this as a sample bot application. In addition, you can use Luis service to understand the user’s input and match words and then it will return an answer with much confidence.
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