Sri Sapthagiri College Chatbot: A Comprehensive Guide

Welcome to the official chatbot of Sri Sapthagiri Pre-University College! This guide will walk you through the process of setting up and running this chatbot on your local machine. Whether you're a seasoned developer or just starting, this guide will provide you with all the necessary steps to get the chatbot up and running.

Project Overview

This chatbot is designed to be a virtual assistant for Sri Sapthagiri Pre-University College. It can provide information about the college, including courses, faculty, events, and more. The goal of this project is to provide a user-friendly and interactive way for students, parents, and staff to get the information they need.

Features in Detail

• Course Information: Get details about the science and commerce streams offered at the college, including the different subject combinations available.
• Faculty Information: Learn more about the esteemed faculty members of the college, including their qualifications and departments.
• Event Information: Stay up-to-date with the latest events happening at the college, from the STEM Expo to cultural fests.
• College Information: Get answers to frequently asked questions about the college, such as its location, history, and special achievements.
• Conversational AI: Engage in a natural and interactive conversation with the chatbot.

Getting Started - The Ultimate Guide

This guide will provide you with a step-by-step process to set up the chatbot on your local machine. We'll cover everything from setting up the environment to running the chatbot.

Prerequisites

Before you begin, make sure you have the following software installed on your machine:

• Python (version 3.8 or higher): You can download Python from the official website: https://www.python.org/downloads/
• pip: pip is the package installer for Python. It usually comes with Python, but you can check if it's installed by running pip --version in your terminal.
• Git: Git is a version control system that we'll use to clone the repository. You can download Git from the official website: https://git-scm.com/downloads

Setting up the Environment

To avoid conflicts with other Python projects, it's highly recommended to create a virtual environment for this project.

1. Create a virtual environment:

   python -m venv venv

2. Activate the virtual environment:
   • On Windows:

     .\venv\Scripts\activate

   • On macOS and Linux:

     source venv/bin/activate

Cloning the Repository

Now, let's clone the repository to your local machine.

git clone https://github.com/Mustaqeem-Rafi/Rasa-Chatbot-Project.git
cd Rasa-Chatbot-Project

Installing Dependencies

Next, we'll install all the required dependencies for this project.

pip install -r requirements.txt

Understanding the Project Structure

Here's a brief overview of the key files and directories in the project:

• actions/: This directory contains the custom actions for the chatbot.
• data/: This directory contains the NLU data, stories, and rules for the chatbot.
• models/: This directory will contain the trained models.
• config.yml: This file contains the configuration for the NLU pipeline and policies.
• domain.yml: This file defines the intents, entities, slots, responses, and actions for the chatbot.
• endpoints.yml: This file contains the configuration for the action server and other endpoints.

Training the Model

Now, it's time to train the Rasa model. This will train the NLU model and the Core model.

rasa train

Note on 2025 Rasa Protocols: As of 2025, Rasa may have introduced new training commands or options. Always refer to the official Rasa documentation for the latest information on training models.

Running the Chatbot

To run the chatbot, you'll need to run the Rasa server and the action server in two separate terminals.

Terminal 1: Run the Rasa Server

rasa run

Terminal 2: Run the Action Server

rasa run actions

Once both servers are running, you can interact with the chatbot through the command line in the first terminal.

Troubleshooting Common Issues

• Dependency Issues: If you encounter any issues with the dependencies, make sure you are using the correct version of Python and that you have activated the virtual environment.
• Model Training Issues: If you have issues with training the model, make sure your config.yml and domain.yml files are correctly formatted.
• Action Server Issues: If you have issues with the action server, make sure your endpoints.yml file is correctly configured and that your custom actions in the actions/ directory are free of errors.

Customization and Extension

• Adding New Intents: To add new intents, you'll need to add them to the domain.yml file and provide examples in the data/nlu.yml file.
• Adding New Responses: To add new responses, you'll need to add them to the domain.yml file.
• Adding New Actions: To add new custom actions, you'll need to create a new action class in the actions/actions.py file and add the action to the domain.yml file.

Deployment

This chatbot can be deployed to various platforms, such as a website, Slack, or Facebook Messenger. For detailed instructions on deployment, please refer to the official Rasa documentation.

Contributing

Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

Acknowledgments

• The students who developed this chatbot:
  • Mohammed Tahir
  • Mustaqeem Rafi
  • Hema Sai T
  • Kiran S.K
• The computer science faculty who guided the project:
  • Miss Prabharani S
  • Mr Jeelani