PagePal AI - AI-Powered Study Assistant

A full-stack RAG (Retrieval-Augmented Generation) application that allows students to upload PDFs of course materials (lecture slides, syllabi, textbooks, assignments) and ask questions about them using AI.

🚀 Features

• PDF Upload & Processing: Upload multiple PDF files for analysis
• AI-Powered Q&A: Ask questions about your course materials
• Conversational Interface: Chat-like experience with your documents
• Real-time Processing: Instant document processing and querying
• Beautiful UI: Modern interface built with Next.js and Shadcn/ui
• File Management: Upload, delete, and manage your course materials

🏗️ Architecture

• Frontend: Next.js 15 with TypeScript, Tailwind CSS, Shadcn/ui
• Backend: Python FastAPI service
• AI/ML: Google Gemini 2.5 Flash LLM, RAG
• Vector Database: Pinecone for document embeddings
• Document Processing: LangChain for text extraction and chunking

📋 Prerequisites

• Node.js 18+
• Python 3.8+
• Pinecone account (free tier available)
• Google AI Studio account (free tier available)

🗄️ Pinecone Setup

1. Create Pinecone Account

1. Go to Pinecone Console
2. Sign up for a free account
3. Get your API key and environment from the console

2. Create Your Index

1. In Pinecone Console, click "Create Index"
2. Index Name: Choose a unique name (e.g., "pagepal-docs")
3. Dimensions: 768 (for the embedding model we use)
4. Metric: Cosine
5. Cloud: Choose your preferred region
6. Click "Create Index"

��️ Installation

1. Clone the Repository

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

2. Frontend Setup

cd frontend
npm install

3. Backend Setup

cd backend/python-service
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

4. Environment Configuration

Backend (.env):

cd backend/python-service
cp .env.example .env
# Edit .env with your API keys

Frontend (.env.local):

cd frontend
cp .env.example .env.local
# Edit .env.local if needed

🔑 Environment Variables

Backend (.env)

• PINECONE_API_KEY: Your Pinecone API key
• PINECONE_INDEX_NAME: Your Pinecone index name (e.g., "pagepal-docs")
• GEMINI_API_KEY: Your Google AI Studio API key

Frontend (.env.local)

• NEXT_PUBLIC_API_URL: Backend API URL (default: http://localhost:8000)

🚀 Running the Application

1. Start Backend Service

cd backend/python-service
source venv/bin/activate  # On Windows: venv\Scripts\activate
python rag_service.py

Backend will run on: http://localhost:8000

2. Start Frontend

cd frontend
npm run dev

Frontend will run on: http://localhost:3000

3. Use the Application

1. Open http://localhost:3000
2. Upload your PDF files (lecture slides, syllabi, etc.)
3. Wait for processing to complete
4. Start asking questions about your materials!

�� API Endpoints

• POST /process-pdf: Upload and process PDF files
• POST /query: Ask questions about uploaded documents
• DELETE /delete-file/{file_name}: Delete file chunks from database
• GET /health: Health check endpoint

📝 License

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

�� Acknowledgments

• Built with Next.js
• UI components from Shadcn/ui
• AI powered by Google Gemini
• Vector database by Pinecone