The Language service includes a custom question answering feature that enables you to create a knowledge base of question and answer pairs that can be queried using natural language input

This service provides a framework for developing, publishing, and managing bots on Azure.

The [Custom question answering feature in Language Service][LS] enables you to build, train and publish a simple question and answer bot based on FAQ URLs, structured and unstructured documents or editorial content in minutes. In this sample, we demonstrate:

• This project requires a Language resource with Custom question answering enabled and Microsoft Azure Account

To create a knowledge base, you must first provision a Language service resource in your Azure subscription.

Click Review and Create and then click Create. Wait for the deployment of the Language service that will support your custom question answering knowledge base.

login into the language studio .If it's your first time logging in, you'll see a window appear that lets you choose a language resource.

Select Create a new language resource. Then enter information for your new resource, such as a name, location and resource group.

The first challenge in creating a user support bot is to use the Language service to create a knowledge base. You can use the Language Studio's custom question answering feature to create, train, publish, and manage knowledge bases.

Bot Framework v4 Custom question answering bot sample. This sample demonstrates usage of advanced features of Custom question answering like [Precise answering][PA], support for unstructured sources along with [Multi-turn conversations][MT] and [Active Learning][AL] in a bot.

This bot has been created using [Bot Framework][BF], it shows how to create a bot that uses the [Custom question answering feature in Language Service][LS].

After creating a set of question-and-answer pairs, you must save it. This process analyzes your literal questions and answers and applies a built-in natural language processing model to match appropriate answers to questions, even when they are not phrased exactly as specified in your question definitions. Then you can use the built-in test interface in the Language Studio to test your knowledge base by submitting questions and reviewing the answers that are returned.

When you're satisfied with your knowledge base, deploy it. Then you can use it over its REST interface. To access the knowledge base, client applications require:

The knowledge base ID The knowledge base endpoint The knowledge base authorization key

After you've created and deployed a knowledge base, you can deliver it to users through a bot.

• Follow instructions [here][Quickstart] to create a Custom question answering project. You will need this project's name to be used as ProjectName in appsettings.json.
• Visit [Language Studio][LS] and open created project.
• Go to Edit knowledge base -> Click on ... -> Click on Import questions and answers -> Click on Import as TSV.
• Import SampleForCQA.tsv file.
• You can test your knowledge base by clicking on Test option.
• Go to Deploy knowledge base and click on Deploy.

Follow these steps to update appsettings.json.

• In the [Azure Portal][Azure], go to your resource.
• Go to Keys and Endpoint under Resource Management.
• Copy one of the keys as value of LanguageEndpointKey and Endpoint as value of LanguageEndpointHostName in appsettings.json.
• ProjectName is the name of the project created in [Language Studio][LS].

Create a bot for the knowledge base The knowledge base provides a back-end service that client applications can use to answer questions through some sort of user interface. Commonly, these client applications are bots. To make the knowledge base available to a bot, you must publish it as a service that can be accessed over HTTP. You can then use the Azure Bot Service to create and host a bot that uses the knowledge base to answer user questions.

At the left of the Language Studio page, click Deploy knowledge base.

At the top of the page, click Deploy, and the click Deploy again.

After the service has been deployed, click Create a bot. This opens the Azure portal in a new browser tab so you can create a Web App Bot in your Azure subscription.

In the Azure portal, create a Web App Bot with the following settings

• Go to your project in [Language Studio][LS] -> In Manage sources click on + Add source
• Click on URLs and add https://www.microsoft.com/en-us/microsoft-365/blog/2022/01/27/from-empowering-frontline-workers-to-accessibility-improvements-heres-whats-new-in-microsoft-365/ and select unstructured in the Classify file structure dropdown.
• Try the following utterances:
  1. Frontline workers
  2. Hybrid work solutions
• You can observe that, answers are returned with high score.
• You can set _includeUnstructuredSources to false in QnAMakerBaseDialog.cs to prevent querying unstructured sources.

• Go to your project in [Language Studio][LS] -> In Edit knowledge bases -> Under Metadata column click on + Add
• Select a QnA to edit and add a key value pair, say Language : CSharp, and click on Save changes.
• Click on Test and select metadata that you just added(Language : CSharp) by clicking on Show advanced options.
• This will return answers with specified metadata only.
• You can filter answers using bot as well by passing metadata and/or source filters. Edit line no. 81 in QnAMakerBaseDialog.cs to something like below. Learn more.

  var filters = new Filters
  {
      MetadataFilter = new MetadataFilter()
      {
          LogicalOperation = Bot.Builder.AI.QnA.JoinOperator.AND.ToString()
      },
      LogicalOperation = Bot.Builder.AI.QnA.JoinOperator.AND.ToString()
  };
  filters.MetadataFilter.Metadata.Add(new KeyValuePair<string, string>("Language", "CSharp"));
  filters.SourceFilter.Add("SampleForCQA.tsv");
  filters.SourceFilter.Add("SampleActiveLearningImport.tsv");
  
  // Initialize Filters with filters in line No. 81

When your bot is ready to be delivered to users, you can connect it to multiple channels; making it possible for users to interact with it through web chat, email, Microsoft Teams, and other common communication media.

When a bot (named as HelpBot) is added to a Teams channel or Teams group chat, you will have to refer it as @HelpBot How to build a bot? to get answers from the service. However, bot tries to send <at>HelpBot</at> How to build a bot? as query to Custom question answering service which may not give expected results for question to bot. The following code removes <at>HelpBot</at> mentions of the bot from the message and sends the remaining text as query to the service.

• Goto Bots/QnABot.cs
• Add References

  using Microsoft.Bot.Connector;
  using System.Text.RegularExpressions;

• Modify OnTurnAsync function as:

  public override async Task OnTurnAsync(ITurnContext turnContext, CancellationToken cancellationToken = default)
      {
          // Teams group chat
          if (turnContext.Activity.ChannelId.Equals(Channels.Msteams))
          {
              turnContext.Activity.Text = turnContext.Activity.RemoveRecipientMention();
          }
          
          await base.OnTurnAsync(turnContext, cancellationToken);
  
          // Save any state changes that might have occurred during the turn.
          await ConversationState.SaveChangesAsync(turnContext, false, cancellationToken);
          await UserState.SaveChangesAsync(turnContext, false, cancellationToken);
      }

• [How bots work][90]
• Question Answering Documentation
• Channels and Bot Connector Service
• [Active learning Documentation][AL]
• [Multi-turn Conversations][MT]
• [Precise Answering][PA]