Skip to content

Latest commit

 

History

History
121 lines (86 loc) · 4.08 KB

README.md

File metadata and controls

121 lines (86 loc) · 4.08 KB

⚛️ Quantum Tutor: AI Socratic Teaching Assistant

Quantum Tutor is an AI-powered teaching assistant utilizing the Socratic method to guide students through Data Structures and Algorithms, with a focus on sorting algorithms. It features Retrieval-Augmented Generation (RAG) on user-provided documents for a personalized learning experience.

✨ Features

  • 🧑‍🏫 Socratic Learning: Guides students through DSA concepts using interactive questioning.
  • 🧠 RAG Personalization: Tailors content by performing RAG on user-provided documents.
  • 📚 DSA Focus: Specialized in Data Structures and Algorithms, particularly sorting algorithms.
  • 🗣️ Multiple LLMs: Integrates Gemini LLM and Groq open-source LLMs for diverse AI interactions.
  • 🌐 Full-Stack Architecture: Built with Next.js (frontend) and FastAPI (backend).

🧰 Tech Stack

Component Tech
Frontend Next.js
Backend FastAPI, Python
RAG Framework LlamaIndex
LLMs Gemini LLM, Groq LLMs
Database MongoDB
Containerization Docker
Deployment LlamaDeploy (for LLM backend)
Language JavaScript/TypeScript, Python

🚀 Setup Instructions

This project consists of two main parts: the llm-backend (FastAPI) and quantum-tutor-web (Next.js frontend).

Prerequisites

  • Node.js (>= 14.x)
  • MongoDB (>= 4.4)
  • Python (>= 3.8)
  • Git
  • Docker

1. Clone the Repository

git clone https://github.com/Quantum-Tutors/quantum-tutor.git # Replace with actual repo URL if different
cd quantum-tutor

2. Set up LLM Backend (FastAPI with Docker)

Navigate to the llm-backend directory and use Docker Compose:

cd llm-backend
docker compose -f ./docker-compose.yml --project-directory ./ up --build -d --remove-orphans
# This will start your FastAPI server and Llama components in Docker containers.
cd ..

3. Set up Frontend (Next.js)

Navigate to the quantum-tutor-web directory and install dependencies:

cd quantum-tutor-web
npm install
cd ..

⚙️ Configuration

  • Ensure MongoDB is running and accessible.
  • Set up any necessary environment variables (e.g., API keys for Gemini/Groq, MongoDB connection strings) in both llm-backend and quantum-tutor-web (typically via .env files).

🧑‍💻 Run the Application

1. Start LLM Backend

The backend should already be running via Docker Compose (from setup step 2).

2. Start Frontend

Navigate to the quantum-tutor-web directory and start the Next.js development server:

cd quantum-tutor-web
npm run dev

The frontend application will typically be accessible at http://localhost:3000.

📂 Project Structure

.
├── llm-backend/                # FastAPI backend for LLM integration and RAG
│   ├── Dockerfile              # Dockerfile for backend
│   ├── docker-compose.yml      # Docker Compose for services (FastAPI, Llama)
│   ├── main.py                 # FastAPI application entry point
│   ├── requirements.txt        # Python dependencies
│   └── ...
├── quantum-tutor-web/          # Next.js frontend application
│   ├── public/                 # Static assets
│   ├── src/                    # React components, pages, API integrations
│   ├── package.json            # Frontend dependencies and scripts
│   └── ...
├── README.md                   # Project overview and documentation
├── .gitignore                  # Git ignore rules
├── start_llm_server.sh         # Convenience script for LLM backend
└── start_next_server.sh        # Convenience script for Next.js frontend

Built for AI-powered Education, Socratic Learning, and Personalized Tutoring.