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‎README.md‎

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# Quantum Tutor
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## Overview
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Quantum Tutor is an AI-powered teaching assistant that uses the Socratic method to guide students through learning Data Structures and Algorithms, with a specific focus on sorting algorithms. The system is capable of performing retrieval-augmented generation (RAG) on user-provided documents, making the learning experience tailored to individual needs. This project integrates Next.js, FastAPI, LlamaIndex, Gemini LLM, Groq open-source LLMs, MongoDB, and Docker, leveraging the strengths of these technologies to provide an efficient, scalable, and interactive platform for AI-driven Socratic learning.
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## Tech Stack
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- **Next.js**: For server-side rendering and building the frontend.
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- **FastAPI**: Lightweight Python-based backend for handling API requests.
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- **LlamaIndex**: For indexing and retrieving relevant document content.
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- **LlamaDeploy**: For deploying Llama-based models.
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- **Gemini LLM**: Large language model used for generating human-like responses.
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- **Groq LLM**: Open-source LLM for efficient language model processing.
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- **MongoDB**: A NoSQL database for storing user sessions and application data.
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- **Docker**: Containerization for the FastAPI server and Llama components.
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## Prerequisites
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- Node.js >= 14.x
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- MongoDB >= 4.4
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- Python >= 3.8
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- Git
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- Docker
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## Installation
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1. **Clone the repository**
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```bash
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git clone https://github.com/Quantum-Tutors/quantum-tutor.git
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2. **Navigate to project**
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```bash
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cd quantum-tutor-web
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cd llm-backend
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### LLM SERVER & LLAMA DEPLOY
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3. **Install Docker**
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4. **Docker Compose**
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```bash
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docker compose -f ./docker-compose.yml --project-directory ./ up --build -d --remove-orphans
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5. **Install dependencies For frontend (Next.js):**
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```bash
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cd quantum-tutor-web
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npm install
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6. **Run the application Frontend:**
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```bash
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npm run dev
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# ⚛️ Quantum Tutor: AI Socratic Teaching Assistant
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**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.
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---
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## ✨ Features
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- 🧑‍🏫 **Socratic Learning**: Guides students through DSA concepts using interactive questioning.
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- 🧠 **RAG Personalization**: Tailors content by performing RAG on user-provided documents.
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- 📚 **DSA Focus**: Specialized in Data Structures and Algorithms, particularly sorting algorithms.
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- 🗣️ **Multiple LLMs**: Integrates Gemini LLM and Groq open-source LLMs for diverse AI interactions.
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- 🌐 **Full-Stack Architecture**: Built with Next.js (frontend) and FastAPI (backend).
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---
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## 🧰 Tech Stack
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| Component | Tech |
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|-------------------|-----------------------------------|
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| Frontend | Next.js |
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| Backend | FastAPI, Python |
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| RAG Framework | LlamaIndex |
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| LLMs | Gemini LLM, Groq LLMs |
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| Database | MongoDB |
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| Containerization | Docker |
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| Deployment | LlamaDeploy (for LLM backend) |
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| Language | JavaScript/TypeScript, Python |
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---
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## 🚀 Setup Instructions
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This project consists of two main parts: the `llm-backend` (FastAPI) and `quantum-tutor-web` (Next.js frontend).
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### Prerequisites
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- **Node.js** (>= 14.x)
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- **MongoDB** (>= 4.4)
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- **Python** (>= 3.8)
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- **Git**
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- **Docker**
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### 1. Clone the Repository
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```bash
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git clone https://github.com/Quantum-Tutors/quantum-tutor.git # Replace with actual repo URL if different
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cd quantum-tutor
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```
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### 2. Set up LLM Backend (FastAPI with Docker)
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Navigate to the `llm-backend` directory and use Docker Compose:
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```bash
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cd llm-backend
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docker compose -f ./docker-compose.yml --project-directory ./ up --build -d --remove-orphans
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# This will start your FastAPI server and Llama components in Docker containers.
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cd ..
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```
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### 3. Set up Frontend (Next.js)
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Navigate to the `quantum-tutor-web` directory and install dependencies:
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```bash
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cd quantum-tutor-web
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npm install
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cd ..
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```
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### ⚙️ Configuration
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- Ensure **MongoDB** is running and accessible.
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- 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).
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---
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## 🧑‍💻 Run the Application
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### 1. Start LLM Backend
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The backend should already be running via Docker Compose (from setup step 2).
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### 2. Start Frontend
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Navigate to the `quantum-tutor-web` directory and start the Next.js development server:
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```bash
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cd quantum-tutor-web
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npm run dev
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```
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The frontend application will typically be accessible at `http://localhost:3000`.
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---
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## 📂 Project Structure
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```
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.
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├── llm-backend/ # FastAPI backend for LLM integration and RAG
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│ ├── Dockerfile # Dockerfile for backend
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│ ├── docker-compose.yml # Docker Compose for services (FastAPI, Llama)
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│ ├── main.py # FastAPI application entry point
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│ ├── requirements.txt # Python dependencies
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│ └── ...
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├── quantum-tutor-web/ # Next.js frontend application
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│ ├── public/ # Static assets
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│ ├── src/ # React components, pages, API integrations
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│ ├── package.json # Frontend dependencies and scripts
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│ └── ...
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├── README.md # Project overview and documentation
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├── .gitignore # Git ignore rules
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├── start_llm_server.sh # Convenience script for LLM backend
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└── start_next_server.sh # Convenience script for Next.js frontend
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```
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---
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Built for **AI-powered Education**, **Socratic Learning**, and **Personalized Tutoring**.

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