StudyBuddy AI

Name: Rahul Raj

Institute : Indian Institute of Technology Bhilai

Department : Computer Science and Engineering

An Intelligent Study Companion Powered by Multi-Agent AI

StudyBuddy AI transforms static documents into interactive, adaptive learning experiences using advanced AI technologies. Upload your PDFs and Excel files to get personalized tutoring, automated study plans, AI-powered flashcards, and intelligent document analysis.

✨ Features

🤖 Multi-Agent AI System

• ConductorAgent: Intelligent workflow orchestration and intent analysis
• TutorAgent: Personalized educational responses with source attribution
• PlannerAgent: Structured study plan generation with timelines
• SearchAgent: Web search integration for enhanced learning resources
• FlashcardAgent: Spaced repetition system with adaptive difficulty

📚 Document Intelligence

• Smart Upload: Support for PDF, Excel, and DOCX files
• Semantic Search: Vector-based similarity search across documents
• Context-Aware Chat: Ask questions and get answers with page references
• Automatic Processing: Intelligent chunking and embedding generation

🎯 Learning Tools

• AI Study Plans: Comprehensive, personalized learning paths
• Smart Flashcards: Auto-generated cards with spaced repetition
• Progress Dashboard: Visual analytics and learning statistics
• Interactive Chat: Real-time AI tutoring with workflow visualization

🎨 Modern Interface

• Responsive Design: Works seamlessly on desktop and mobile
• Dark/Light Mode: Adaptive theming for comfortable studying
• Smooth Animations: Framer Motion powered interactions
• Real-time Updates: Live feedback and progress tracking

---

🚀 Quick Start

Prerequisites

• Docker & Docker Compose (recommended)
• Python 3.10+ (for development)
• Node.js 16+ (for frontend development)
• OpenAI API Key (Get one here)

Option 1: Docker Compose (Recommended)

1. Clone the repository

   git clone (https://github.com/Rahul5977/StudyBot)
   cd StudyBuddy

2. Set up environment variables

   cp .env.example .env
   # Edit .env and add your OpenAI API key

3. Start all services

   docker-compose up --build

4. Access the application

   • Frontend: http://localhost:3000
   • Backend API: http://localhost:8000
   • API Documentation: http://localhost:8000/docs
   • Qdrant Vector DB: http://localhost:6333

Option 2: Development Setup

1. Backend Setup

   cd backend
   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   pip install -r requirements.txt
   
   # Start Qdrant with Docker
   docker run -p 6333:6333 qdrant/qdrant:latest
   
   # Start backend server
   uvicorn app.main:app --reload --port 8000

2. Frontend Setup

   cd frontend
   npm install
   npm start

---

🏗️ System Architecture

graph TB
    subgraph "Frontend Layer"
        A[React + Tailwind UI]
        B[Framer Motion Animations]
        C[Real-time State Management]
    end

    subgraph "API Gateway"
        D[FastAPI Backend]
        E[Authentication Middleware]
        F[Rate Limiting & CORS]
    end

    subgraph "AI Engine - LangGraph Multi-Agent System"
        G[ConductorAgent - Orchestrator]
        G --> H[TutorAgent - Educational AI]
        G --> I[PlannerAgent - Study Plans]
        G --> J[SearchAgent - Web Search]
        G --> K[FlashcardAgent - Spaced Repetition]
    end

    subgraph "Data Processing Pipeline"
        L[Document Parser]
        M[Semantic Chunker]
        N[Embedding Generator]
        O[Vector Indexer]
    end

    subgraph "Storage Layer"
        P[Qdrant Vector DB]
        Q[Local File System]
        R[JSON Configuration]
        S[Interaction Logs]
    end

    subgraph "External Services"
        T[OpenAI API]
        U[Tavily Search API]
    end

    A --> D
    D --> G
    G --> T
    J --> U
    H --> P
    I --> P
    L --> M --> N --> O --> P
    D --> Q
    D --> R
    D --> S

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High-Level Architecture

graph TB
    subgraph "Frontend Layer"
        A[React + Tailwind UI]
        B[Framer Motion Animations]
        C[Real-time State Management]
    end

    subgraph "API Gateway"
        D[FastAPI Backend]
        E[CORS & Rate Limiting]
    end

    subgraph "AI Engine - Multi-Agent System"
        F[ConductorAgent - Orchestrator]
        F --> G[TutorAgent - Educational AI]
        F --> H[PlannerAgent - Study Plans]
        F --> I[SearchAgent - Web Search]
        F --> J[FlashcardAgent - Spaced Repetition]
    end

    subgraph "Data Processing"
        K[Document Parser]
        L[Semantic Chunker]
        M[Embedding Generator]
    end

    subgraph "Storage"
        N[Qdrant Vector DB]
        O[Local File System]
    end

    subgraph "External APIs"
        P[OpenAI API]
        Q[Tavily Search API]
    end

    A --> D
    D --> F
    F --> P
    I --> Q
    G --> N
    H --> N
    K --> L --> M --> N

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Technology Stack

Component	Technology	Purpose
Frontend	React 18, Tailwind CSS, Framer Motion	Modern, responsive UI with smooth animations
Backend	FastAPI, Python 3.10+	High-performance async API server
AI Framework	LangChain, LangGraph, OpenAI	Multi-agent orchestration and LLM integration
Vector DB	Qdrant	Fast similarity search and document retrieval
Document Processing	PyPDF2, pandas, openpyxl	Extract and process various file formats
Containerization	Docker, Docker Compose	Consistent development and deployment

---

📁 Project Structure

StudyBuddy/
├── 📂 backend/                 # FastAPI backend application
│   ├── 📂 app/
│   │   ├── 📂 agents/         # Multi-agent AI system
│   │   │   ├── conductor.py   # Main orchestrator agent
│   │   │   ├── planner.py     # Study plan generation
│   │   │   └── search_agent.py # Web search integration
│   │   ├── 📂 api/            # API route handlers
│   │   │   ├── routes_chat.py
│   │   │   ├── routes_docs.py
│   │   │   ├── routes_plan.py
│   │   │   └── routes_flashcards.py
│   │   ├── 📂 core/           # Core services and utilities
│   │   │   ├── config.py      # Configuration management
│   │   │   ├── db.py          # Database connections
│   │   │   └── logger.py      # Logging setup
│   │   └── 📂 services/       # Business logic services
│   │       ├── simple_rag.py  # RAG pipeline
│   │       └── embeddings.py  # Vector embeddings
│   ├── requirements.txt
│   └── Dockerfile
├── 📂 frontend/               # React frontend application
│   ├── 📂 src/
│   │   ├── 📂 components/     # React components
│   │   │   ├── Dashboard.jsx  # Analytics dashboard
│   │   │   ├── ChatBox.jsx    # AI chat interface
│   │   │   ├── Flashcards.jsx # Spaced repetition system
│   │   │   └── UploadForm.jsx # File upload handling
│   │   ├── 📂 pages/          # Page components
│   │   │   └── Home.jsx       # Main application layout
│   │   └── 📂 utils/          # Utility functions
│   │       └── api.js         # API communication
│   ├── package.json
│   └── tailwind.config.js
├── 📂 data/                   # Processed documents and metadata
├── 📂 storage/                # Uploaded files storage
├── 📂 logs/                   # Application logs
├── docker-compose.yml         # Multi-service orchestration
├── .env.example              # Environment variables template
└── README.md                 # Project documentation

---

🔧 Configuration

Environment Variables

Create a .env file in the project root:

# OpenAI Configuration
OPENAI_API_KEY=sk-your-openai-api-key-here

# Tavily Search API (optional)
TAVILY_API_KEY=your-tavily-api-key

# Qdrant Configuration
QDRANT_HOST=localhost
QDRANT_PORT=6333
QDRANT_COLLECTION=studybuddy_docs

# Application Settings
DEBUG=true
MAX_FILE_SIZE=52428800  # 50MB
CHUNK_SIZE=1000
CHUNK_OVERLAP=200

# CORS Settings
ALLOWED_ORIGINS=http://localhost:3000,http://localhost:3002

Frontend Configuration

Update frontend/src/utils/api.js if needed:

const API_BASE_URL = process.env.REACT_APP_API_URL || "http://localhost:8000";

---

💻 Usage Guide

1. Upload Documents

1. Navigate to the Upload Documents tab
2. Drag and drop or select PDF/Excel files
3. Wait for processing to complete
4. View processed documents in the file list

2. AI Chat

1. Go to the AI Chat tab
2. Ask questions about your uploaded documents
3. View real-time agent workflow steps
4. Get responses with source page references

Example queries:

• "Explain the main concepts in this document"
• "Create a summary of chapter 3"
• "What are the key formulas mentioned?"

3. Study Plans

1. Switch to the Study Plans tab
2. Enter a topic (e.g., "Machine Learning Fundamentals")
3. Click "Generate Plan" to create a structured learning path
4. Edit and customize the generated plan

4. Flashcards

1. Visit the Flashcards tab
2. Generate flashcards from your documents
3. Review cards with spaced repetition algorithm
4. Rate difficulty to improve future scheduling

5. Dashboard

Monitor your learning progress:

• Study plan completion rates
• Flashcard review statistics
• Recent activity timeline
• Document processing status

---

🔌 API Reference

Core Endpoints

Document Management

POST   /api/documents/upload     # Upload and process documents
GET    /api/documents            # List all documents
GET    /api/documents/{id}       # Get document details
DELETE /api/documents/{id}       # Remove document

AI Chat

POST   /api/chat                 # Send message to AI
GET    /api/chat/history         # Get conversation history
GET    /api/chat/logs            # Get interaction logs

Study Plans

POST   /api/plan/create          # Generate study plan
GET    /api/plans                # List user plans
PUT    /api/plans/{id}           # Update existing plan

Flashcards

GET    /api/flashcards           # Get due flashcards
POST   /api/flashcards/generate  # Generate new flashcards
POST   /api/flashcards/review    # Submit review result
GET    /api/flashcards/stats     # Get learning statistics

Example API Usage

// Upload a document
const formData = new FormData();
formData.append("file", selectedFile);

const response = await fetch("http://localhost:8000/api/documents/upload", {
  method: "POST",
  body: formData,
});

// Send chat message
const chatResponse = await fetch("http://localhost:8000/api/chat", {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({
    message: "Explain machine learning concepts",
    session_id: "unique-session-id",
  }),
});

---

🧪 Testing

Backend Tests

cd backend
python -m pytest tests/ -v

# Run specific test categories
python -m pytest tests/test_agents.py -v        # Agent tests
python -m pytest tests/test_api.py -v           # API tests
python -m pytest tests/test_integration.py -v   # Integration tests

Frontend Tests

cd frontend
npm test

# Run tests with coverage
npm test -- --coverage

# Run tests in watch mode
npm test -- --watch

End-to-End Testing

# Start all services first
docker-compose up -d

# Run E2E tests
npm run test:e2e

---

🚀 Deployment

Production Deployment with Docker

1. Build production images

   docker-compose -f docker-compose.prod.yml build

2. Deploy with environment variables

   docker-compose -f docker-compose.prod.yml up -d

Kubernetes Deployment

# k8s/deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: studybuddy-backend
spec:
  replicas: 3
  selector:
    matchLabels:
      app: studybuddy-backend
  template:
    metadata:
      labels:
        app: studybuddy-backend
    spec:
      containers:
        - name: backend
          image: studybuddy/backend:latest
          ports:
            - containerPort: 8000
          env:
            - name: OPENAI_API_KEY
              valueFrom:
                secretKeyRef:
                  name: studybuddy-secrets
                  key: openai-api-key

Cloud Deployment Options

• AWS: ECS/EKS with RDS and S3
• Google Cloud: GKE with Cloud SQL and Cloud Storage
• Azure: AKS with Azure Database and Blob Storage
• Railway/Render: Simple deployment for smaller scale

---

🔍 Monitoring & Debugging

Health Checks

# Backend health
curl http://localhost:8000/ping

# Qdrant health
curl http://localhost:6333/

# Check document processing status
curl http://localhost:8000/api/documents

Logs

# View backend logs
docker-compose logs backend

# View all logs
docker-compose logs -f

# Check specific service logs
tail -f logs/app.log

Performance Monitoring

• Backend metrics: Available at /metrics endpoint
• Frontend performance: React DevTools
• Database queries: Qdrant dashboard at :6333

---

🤝 Contributing

Development Setup

1. Fork and clone the repository
2. Create a feature branch

   git checkout -b feature/amazing-feature

3. Set up development environment

   docker-compose up -d qdrant  # Start only Qdrant
   # Then run backend and frontend separately

4. Make your changes and test
5. Submit a pull request

Code Style

• Backend: Black formatter, flake8 linter
• Frontend: Prettier formatter, ESLint
• Commits: Conventional commit messages

# Format code
cd backend && black . && flake8
cd frontend && npm run format && npm run lint

Adding New Features

1. New AI Agents: Extend the agents/ directory
2. API Endpoints: Add routes in api/ directory
3. Frontend Components: Follow the existing component structure
4. Tests: Always include tests for new functionality

---

🐛 Troubleshooting

Common Issues

Qdrant Connection Errors

# Ensure Qdrant is running
docker ps | grep qdrant

# Restart Qdrant
docker-compose restart qdrant

OpenAI API Errors

• Check your API key in .env
• Verify API quota and usage limits
• Ensure the API key has necessary permissions

File Upload Issues

• Check file size limits (default 50MB)
• Verify supported file formats (PDF, Excel, DOCX)
• Ensure sufficient disk space in storage/ directory

Frontend Build Errors

# Clear cache and reinstall
cd frontend
rm -rf node_modules package-lock.json
npm install

Debug Mode

Enable debug logging:

DEBUG=true
LOG_LEVEL=DEBUG

Getting Help

• 📧 Email: your-email@example.com
• 💬 Discord: Join our community
• 🐛 Issues: GitHub Issues
• 📖 Documentation: Full docs

---

📊 Performance & Scalability

Current Capabilities

• Concurrent Users: 50+ simultaneous users
• Document Size: Up to 50MB per file
• Response Time: <3 seconds for most queries
• Vector Search: <100ms for similarity queries

Scaling Recommendations

• Horizontal Scaling: Add more backend replicas
• Database Scaling: Use Qdrant Cloud for production
• Caching: Implement Redis for response caching
• Load Balancing: Use Nginx or cloud load balancers

---

🛡️ Security & Privacy

Security Features

• Input Validation: All inputs sanitized and validated
• Rate Limiting: API endpoints protected against abuse
• CORS: Configured for specific allowed origins
• File Validation: Strict file type and size checking

Privacy Considerations

• Data Retention: Documents stored locally by default
• API Keys: Never logged or exposed in responses
• User Data: No personal information stored without consent
• Encryption: All API communication over HTTPS in production

GDPR Compliance

• Data Portability: Export user data on request
• Right to Deletion: Complete data removal capability
• Consent Management: Opt-in for data processing
• Audit Logging: Track all data access and modifications

---

📈 Roadmap

Version 2.0 (Q4 2025)

• Multi-user support with authentication
• Real-time collaboration on study plans
• Mobile app (React Native)
• Advanced analytics dashboard
• Integration with popular LMS platforms

Version 2.1 (Q1 2026)

• Voice interaction with speech-to-text
• Automated quiz generation
• Progress sharing and social features
• Multi-language support
• Offline mode capabilities

Long-term Vision

• Custom AI model fine-tuning
• AR/VR learning experiences
• Marketplace for study materials
• Corporate learning platform
• AI tutoring certification

---

📄 License

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

MIT License

Copyright (c) 2024 StudyBuddy AI Team

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

---

🙏 Acknowledgments

Special thanks to the amazing open-source community and the following projects that made StudyBuddy AI possible:

• OpenAI - For providing powerful language models
• Qdrant - For the excellent vector database
• LangChain - For the comprehensive LLM framework
• FastAPI - For the high-performance web framework
• React - For the amazing frontend framework
• Tailwind CSS - For the utility-first CSS framework

---

⭐ Star History

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Made with ❤️ by the StudyBuddy AI Team

🌟 Star on GitHub • 🐛 Report Bug • 💡 Request Feature