StudyBuddy AI

StudyBuddy AI is a Next.js application that helps students study more efficiently by processing documents (PDFs, DOCX, TXT) and creating flashcards, quizzes, and concept summaries using RAG (Retrieval Augmented Generation).

Features

• 📄 Upload and process academic documents
• 🔍 Extract key concepts and information
• 🧠 Generate flashcards for effective studying
• 📝 Create quizzes to test understanding
• 📊 Track progress and identify areas for improvement

MongoDB Migration

This project now uses MongoDB (instead of PostgreSQL) for data storage, providing:

• 🚀 Simpler setup and maintenance
• 🔄 Flexible schema for evolving data models
• 🔍 Built-in vector search capabilities (via MongoDB Atlas)
• 🔗 Native integration with Node.js applications

Setup & Configuration

Prerequisites

• Node.js (v16+)
• MongoDB Atlas account (free tier is sufficient)
• npm or yarn package manager

Initial Setup

1. Clone the repository

   git clone https://github.com/yourusername/StudyBuddy-ai.git
   cd StudyBuddy-ai

2. Install dependencies

   npm install --legacy-peer-deps

3. Configure MongoDB

   Follow the instructions in MONGODB_SETUP.md to set up your MongoDB Atlas database with vector search capabilities.

4. Environment Variables

   Create a .env.local file in the project root with:

   DATABASE_URL="mongodb+srv://<username>:<password>@<cluster>.mongodb.net/<dbname>?retryWrites=true&w=majority"
   

   Replace the placeholders with your actual MongoDB connection details.

5. Run the application

   npm run dev

   Access the application at http://localhost:3000

Document Processing Flow

1. User uploads a document through the UI
2. Backend processes the document:
   • Saves file temporarily
   • Loads and parses content
   • Splits content into manageable chunks
   • Generates embeddings for each chunk
   • Stores document metadata and chunks in MongoDB
3. The stored documents are available for search and used to generate flashcards, quizzes, and concept summaries

Testing the API Endpoint

To test the document processing API:

1. Use Postman or a similar tool to send a POST request to /api/process-document
2. Set the request body to form-data format
3. Add a key file with a file as the value (PDF, DOCX, or TXT)
4. Optionally add a userId key if you have authentication set up
5. Send the request and check the response

Example response:

{
  "success": true,
  "message": "Document processed and ingested successfully.",
  "documentId": "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx",
  "fileName": "your-document.pdf",
  "flashcards": [...],
  "quizzes": [...]
}

License

MIT License

Acknowledgements

• Next.js
• MongoDB Atlas
• LangChain
• HuggingFace Transformers