University Enrollment and admissions ChatBot

The university admission chatbot is a useful tool designed to provide assistance and information by answering general student queries during university admissions using state-of-the-art language models and vector stores.

Problem Identified:

Handling a high volume of inquiries during university admissions can overwhelm staff and significantly slow down response times. Many students seek general information, which can lead to delays as staff address repetitive queries manually. This inefficiency not only strains resources but also negatively impacts the user experience. To address these challenges, we are developing a university admission chatbot that leverages Generative AI and advanced language models to provide instant, accurate responses, streamline the admissions journey and reduce the workload on university staff.

Project objectives:

• To develop a university chatbot to assist with student queries on admissions processes and enquiries using large language models and vector stores.

• To create a Flask FastAPI-based API with robust endpoints for user authentication, chatbot interaction and admin operations including data embedding and CRUD functions.

• To create a document embedding system allowing admins to mange data files in the vector database ensuring up-to-date and correct data.

Product Impact:

Transformed the university admissions process by efficiently handling high inquiry volumes, streamlining the journey with instant responses, reducing staff workload and enhancing the institution's reputation for technological innovation.

Prerequisites

Before you can start using the ChatBot, make sure you have the following prerequisites installed on your system:

• Python 3.9 or higher but I recommend python 3.12
• Required Python packages (all packages are in requirement.txt)

Required libraries:

-flask -flask_cors -google-generativeai -python-dotenv -langchain -pypdf -chromadb -faiss-cpu -fastapi -pydantic -uvicorn -requests -starlette -mysql-connector-python -fastapi_jwt_auth -passlib

Installation

1. Clone this repository to your local machine.

2. Create a Python virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows, use: venv\Scripts\activate.bat

3. Install the required Python packages:

   pip install -r requirements.txt

4. Create .env file for creating environment variables.

5. Add your Googl API key variable in the file. You can get your API key from here

• For Google API Key: https://makersuite.google.com/app/apikey

  GOOGLE_API_KEY="Insert your google API key here"

Creating and importing database on MySQL

1. Open xamp or MySQL workbench to start the db server. import database located inside backend data folder makesure the database the db server run on port 3306

Run backend API on local server

1. Open cmd in the root folder and write this command to run the live server.

   cd backend
   uvicorn main:app --reload
   the backend API server will be up on port 8000 
   

Run frontend server

1. Open cmd in the root folder and write this command to run the live server.

   cd frontend
   python app.py
   the frontend server will be up on port 5000 

API endpoints

• / -> For accessing homepage
• /login -> For accessing login
• /admin -> For accessing Knowledgebase (its protected endpoint)
• /users -> For accessing registered users (its protected endpoint)

My contact in case

Refere to my profile