StudyMate - AI-Powered Learning Assistant

Transform your study materials into an interactive learning experience

Watch Demo in Youtube

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Overview

StudyMate is a full-stack AI learning platform that turns static PDF study materials into an interactive, intelligent tutor. Upload your notes or textbooks, and StudyMate lets you chat with the content, auto-generate quizzes, and track your learning progress — all powered by a RAG pipeline backed by Groq LLM.

Built with FastAPI · LangChain · ChromaDB · Streamlit · PostgreSQL · Docker

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Features

Feature	Description
AI Chat	Ask questions about your uploaded PDFs — context-aware answers via RAG
Quiz Generation	Auto-generate quizzes at multiple difficulty levels with automatic scoring
Learning Analytics	Topic-wise and difficulty-wise performance tracking with personalized recommendations
Authentication	JWT-based user registration, login, and protected API routes
Persistent Storage	PostgreSQL-backed user management and full quiz history

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Architecture

┌─────────────────────────────────────────────────┐
│              Frontend (Streamlit)                │
└────────────────────┬────────────────────────────┘
                     │  HTTP / REST
┌────────────────────▼────────────────────────────┐
│             FastAPI Backend                      │
│   auth.py · analytics.py · models.py            │
└────────────────────┬────────────────────────────┘
                     │
┌────────────────────▼────────────────────────────┐
│               RAG Pipeline                       │
│  PDF → Chunks → Embeddings → ChromaDB           │
└────────┬───────────────────────┬────────────────┘
         │                       │
┌────────▼────────┐   ┌──────────▼──────────────┐
│  ChromaDB       │   │  Groq LLM               │
│  Vector Store   │   │  (Context + Answer)     │
└─────────────────┘   └─────────────────────────┘

Request Flow

User uploads PDF
      │
      ▼
PDF split into chunks
      │
      ▼
Sentence Transformers generate embeddings
      │
      ▼
Embeddings stored in ChromaDB
      │
      ▼
User asks a question
      │
      ▼
Top-k relevant chunks retrieved
      │
      ▼
Context + question sent to Groq LLM
      │
      ▼
AI answer returned to user

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Tech Stack

Backend & API

Component	Technology
API Framework	FastAPI
ORM	SQLAlchemy
Authentication	JWT (PyJWT)
Language	Python 3.10+

AI & RAG

Component	Technology
Orchestration	LangChain
Embeddings	Sentence Transformers (HuggingFace)
Vector Store	ChromaDB
LLM	Groq (LLaMA 3)

Frontend & Database

Component	Technology
UI	Streamlit
Primary Database	PostgreSQL

DevOps

Component	Technology
Containerization	Docker + Docker Compose
CI/CD	GitHub Actions
Testing	Pytest

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Project Structure

StudyMate/
│
├── app/                    # Core application modules
├── db/                     # Database config and migrations
├── rag/                    # RAG pipeline (chunking, embeddings, retrieval)
├── frontend/               # Streamlit UI
│   └── app.py
├── tests/                  # Pytest test suite
│
├── main.py                 # FastAPI entry point
├── auth.py                 # JWT authentication
├── models.py               # SQLAlchemy models
├── schemas.py              # Pydantic schemas
├── analytics.py            # Learning analytics logic
│
├── Dockerfile
├── docker-compose.yml
├── requirements.txt
└── README.md

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Getting Started

Prerequisites

• Python 3.10+
• PostgreSQL
• Docker (optional)
• Groq API key

1. Clone the Repository

git clone https://github.com/sajeerrr/StudyMate.git
cd StudyMate

2. Create and Activate Virtual Environment

python -m venv venv

# Windows
venv\Scripts\activate

# macOS / Linux
source venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

4. Configure Environment Variables

Create a .env file in the root directory:

DATABASE_URL=postgresql://user:password@localhost:5432/studymate
GROQ_API_KEY=your_groq_api_key
SECRET_KEY=your_jwt_secret_key

5. Run the Application

Start FastAPI backend:

uvicorn main:app --reload

Start Streamlit frontend (in a separate terminal):

streamlit run frontend/app.py

Open http://localhost:8501 in your browser.

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Docker Deployment

Build and Run

# Build image
docker build -t studymate .

# Run container
docker run -p 8000:8000 studymate

Using Docker Compose (Recommended)

docker compose up --build

This starts FastAPI, Streamlit, and PostgreSQL together in one command.

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API Reference

After starting the backend, interactive API docs are available at:

Interface	URL
Swagger UI	http://localhost:8000/docs
ReDoc	http://localhost:8000/redoc

Key Endpoints

Method	Endpoint	Description
POST	/auth/register	Register a new user
POST	/auth/login	Login and receive JWT token
POST	/upload	Upload a PDF document
POST	/chat	Ask a question about uploaded material
POST	/quiz/generate	Generate a quiz from study material
GET	/analytics/me	Fetch personal learning analytics

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Testing

pytest

Tests run automatically on every push via GitHub Actions CI.

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Screenshots

AI Chat	Quiz Generation

Dashboard	Quiz History

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