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Custom Question Answering

This is the Bot Framework v4 Custom question answering bot sample, which shows how to use advanced features of Cognitive Services question answering, such as Precise answering, support for unstructured sources, Multi-turn conversations, and Active Learning in a bot.

This bot was created using Bot Framework.

Concepts introduced in this sample

Question answering 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:

  • How to use active learning to generate suggestions for your knowledge base.
  • How to use follow-up prompts to create multiple turns of a conversation.
  • How to configure display of precise answers.
  • How to enable/disable querying unstructured sources with the bot.

Prerequisites

Configure knowledge base of the project

  • Follow instructions here to create a Custom question answering project. You will need this project's name to be used as ProjectName in .env file.
  • Visit Language Studio 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.

Connect your bot to the project.

Follow these steps to update .env file.

  • In the Azure Portal, 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 .env file.
  • ProjectName is the name of the project created in Language Studio.

To try this sample

  • Install the Bot Framework Emulator version 4.14.0 or greater from here

  • Clone the repository

    git clone https://github.com/Microsoft/botbuilder-samples.git

  • In a terminal, navigate to samples/javascript_nodejs/48.customQABot-all-features

    cd samples/javascript_nodejs/48.customQABot-all-features

  • Install modules

    npm install

  • Run the sample

    npm start

  • Connect to the bot using Bot Framework Emulator

    1. Launch Bot Framework Emulator
    2. File -> Open Bot
    3. Enter a Bot URL of http://localhost:3978/api/messages

Try Active Learning

  • Try the following utterances:
    1. Surface Book
    2. Power
  • In Language Studio, select inspect to view the scores of the returned answers and compare how close they are.
  • In Bot Framework Emulator, a card is generated with the suggestions.
    • Clicking an option sends a feedback record, which shows as suggestion under Review suggestions in Language Studio.
    • ACTIVE_LEARNING_CARD_TITLE, ACTIVE_LEARNING_CARD_NO_MATCH_TEXT and ACTIVE_LEARNING_CARD_NO_MATCH_RESPONSE in the card can be changed from rootDialog.js.

Try Multi-turn prompt

  • Try the following utterances:
    1. Accessibility
    2. Options
  • You'll notice that multi-turn prompts associated with the question are also returned in the responses.

Try Precise Answering

  • Try the following utterances:
    1. Accessibility
    2. Register
  • You will notice a short answer returned along with a long answer.
  • If testing in Language Studio, you might have to check Include short answer response at the top.
  • You can disable precise answering by setting EnablePreciseAnswer to false in .env file.
  • You can set DisplayPreciseAnswerOnly to true in .env file to display just precise answers in the response.
  • Learn more about precise answering.

Query unstructured content

  • Go to your project in Language Studio. In Manage sources, select + Add source.
  • Select 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/.
  • Select unstructured in the Classify file structure dropdown.
  • Try the following utterances:
    1. Frontline workers
    2. Hybrid work solutions
  • Make sure answers are returned with a high score.
  • To prevent querying unstructured sources, set INCLUDE_UNSTRUCTURED_SOURCES to false in rootDialog.js.

Try Filters

If you want to return answers with only specified metadata, use the following steps:

  • Go to your project in Language Studio. In Edit knowledge bases, under the Metadata column, select + Add
  • Select a QnA to edit and add a key value pair. Add the key value pair Language : Javascript, then select Save changes.
  • Select Test, then Show advanced options, then select the metadata you just added (Language : Javascript). You can also filter answers using a bot by passing it metadata and/or source filters. To do this, edit line 54 in rootDialog.js to something like the code snippet below. For more information, see Query filters. 
    //Add below lines in line no. 54 in rootDialog.js
var filters = {
    metadataFilter: {
        metadata: [
            { key: 'Language', value: 'Javascript' },
        ],
        logicalOperation: 'OR'
    },
    sourceFilter: [],
    logicalOperation: 'AND'
};

qnaMakerDialog.filters = filters;

Microsoft Teams channel group chat fix

To get answers from the service when a bot (named as HelpBot) is added to a Teams channel or Teams group chat, refer to it as @HelpBot How to build a bot?. However, the bot may try to send <at>HelpBot</at> How to build a bot? as a query to the 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 dialogs/rootDialog.js
  • Add the following references: 
    const {
    TurnContext
} = require('botbuilder-core');
  • Modify run function as 
    async run(context, accessor) {
    const dialogSet = new DialogSet(accessor);
    dialogSet.add(this);

    const dialogContext = await dialogSet.createContext(context);

    if (context.activity.channelId === "msteams") {
        context.activity.text = TurnContext.removeRecipientMention(context.request);
    }

    const results = await dialogContext.continueDialog();
    if (results.status === DialogTurnStatus.empty) {
        await dialogContext.beginDialog(this.id);
    }
}

Deploy the bot to Azure

See Deploy your bot to Azure for instructions.

The deployment process assumes you have an account on Microsoft Azure and are able to log into the Microsoft Azure Portal.

If you are new to Microsoft Azure, please refer to Getting started with Azure for guidance on how to get started on Azure.

Further reading