Create Question Answering Solutions With Azure AI Language (Part 2)

By Ashwin Venugopal -

 





Create a Knowledge Base

You can write code that defines, trains, and publishes the knowledge base using the REST API or SDK to create a question-answering solution. However, defining and managing a knowledge base is most frequently done through the Language Studio web interface.

To create a knowledge base you:

  • Sign in to Azure portal.

  • Search for Azure AI services using the search field at the top of the portal.

  • Select Create under the Language Service resource.

  • Create a resource in your Azure subscription-
    • Enable the question answering feature.
    • Create or select an Azure AI Search resource to host the knowledge base index.

  • In Create or select an Azure AI Search resource to host the knowledge base index. Language Studio, select your Azure AI Language resource and create a Custom question answering project.

  • Add one or more data sources to populate the knowledge base-
    • URLs for web pages containing FAQs.
    • Files containing structured text from which questions and answers can be derived.
    • Predefined chit-chat datasets that include common conversational questions and responses in a specified style.

  • Edit question and answer pairs in the portal.

Implement Multi-Turn Conversation

Even while you can frequently build an efficient knowledge base with individual question-and-answer pairs, you may occasionally need to ask follow-up questions to get additional information from a user before providing a conclusive response. We call this type of exchange a multi-turn conversation.

You can explicitly define follow-up prompts and responses for already-existing question and answer pairs, or you can enable multi-turn responses when importing questions and answers from an existing web page or document based on its structure.

When you define a follow-up prompt for multi-turn conversation, you can link to an existing answer in the knowledge base or define a new answer specifically for the follow-up. Additionally, you can limit the connected response such that it is only ever seen within the multi-turn dialogue that the initial question started.

Test and Publish a Knowledge Base

After you have defined a knowledge base, you can train its natural language model, and test it before publishing it for use in an application or bot.

Testing a Knowledge Base

With Language Studio, you can test your knowledge base interactively by submitting questions and examining the responses. Along with other possible responses, you can examine the results to see their confidence scores.

Deploying a Knowledge Base

Your knowledge base can be deployed to a REST endpoint where client apps can submit queries and receive responses after you are satisfied with its performance. It is directly deployable from Language Studio.

Conclusion

We have successfully learnt about knowledge base.

 





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