MLSA-SRM's Chatbot Service

This is a service built for answering Frequently Answered Questions for a closed domain using Azure's Cognitive Search (read more about it here) to find answers in generally formatted documents (e.g. Product Manuals or Club Manifests). A default instance is currently deployed at our website. This service can be instantiated and customised as per need.

Built With

Software	Version
Adobe XD	24.0.22
Zeplin	4.0.2
Python 3	3.7.1
QnAMaker	June 2020

Deployed using Microsoft Azure's App Service

Pre-requisites

Software	Tested With
Python 3	3.7.1
QnAMaker	June 2020

Getting Started

• Clone the repository.

git clone https://github.com/MLSA-SRM/bot-gateway-rest-api

• Now install all required libraries through requirements.txt

pip install requirements.txt

• The project contains a tested live feedback service, with an option to have the feedback sent to an E-mail, or uploaded to a SQL Database (defaulted to E-mail). Code marked as Potential Feedback Service can be uncommented to test the SQL-based Feedback
• Now create a file with the name .env. Add all API Keys (mentioned in following example) inside the .env file as text. Click here to know more about hidden API Keys as Environment Variables.

SQL_USER=examplekeyvalue123
SQL_PWD=examplepassword#123
SQL_HOST=examplehost123
SQL_DB=exampledbconn#123
MAIL_USER_ID=exampleemailid
MAIL_USER_PWD=exampleemailpwd

• You can find all required keys in the settings.py, as imported environment variables.
• Lastly, update the URLs fetching the QnAMaker's API at Line: 256 in bot.js (you can find it here) as per the API credentials provided by your hosted QnAMaker Service.
• Now run the Flask app app.py

python app.py

• In your browser open http://localhost:5000 (or :{port-number} as specified by the Flask's development server)

More Info

For more info, or having a chatbot of your own - contact us: Microsoft Learn Student Ambassadors SRM