PaperWise 🤖📚

AI-Powered Research Paper Analysis for PhD Students and Researchers

PaperWise is an intelligent AI agent system that provides deep, critical analysis of research papers, going beyond simple summarization to offer the kind of insights that PhD students and Principal Investigators need for their research.

✨ Features

• 🔍 Deep Analysis: Critical evaluation of methodology, results, and implications
• 🤖 Multi-Agent System: Specialized AI agents working collaboratively
• 📊 Research-Focused: Designed specifically for PhD students and PIs
• 💬 Interactive Queries: Ask specific questions about any aspect of the paper
• 📋 Structured Output: Clear, organized analysis with actionable insights
• 📄 Multi-format Support: Advanced PDF parsing with table and figure extraction

🎯 What PaperWise Analyzes

PaperWise helps researchers understand:

• The "What" and "Why": Problem statements, hypotheses, and motivations
• The "How": Methodology, experimental design, and novelty
• The "Results and Impact": Key findings, interpretations, and limitations
• The "So What": Contribution to the field and relevance to your work

🎥 Demo

🚀 Watch PaperWise in action - From upload to comprehensive analysis in seconds

🏗️ Architecture

Frontend

• React 18 with TypeScript for type safety
• Tailwind CSS for modern, responsive design
• Drag-and-drop file upload interface
• Real-time analysis progress tracking
• Markdown rendering for rich analysis display

Backend

• FastAPI for high-performance REST API
• Multi-agent system using LangChain
• Meta Llama models for intelligent analysis
• Advanced PDF processing with PyMuPDF

AI Agents

• 📄 PDF Parser Agent: Extracts text, tables, and figures
• 🔬 Methodology Agent: Analyzes experimental design and methods
• 📊 Results Agent: Interprets findings and statistical significance
• 🌐 Contextualization Agent: Compares with existing literature
• 🎯 Orchestrator Agent: Coordinates and synthesizes all analyses

🚀 Quick Start

Prerequisites

• Python 3.8+
• Node.js 16+
• Llama API key

1. Clone and Setup

git clone <repository-url>
cd PaperWise

2. Set Environment Variable

export LLAMA_API_KEY='your-llama-api-key-here'

3. Run the Application

# Start both backend and frontend
./start-paperwise.sh

# Or start individually:
# Backend
cd backend
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt
python -m uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

# Frontend (in new terminal)
cd frontend
npm install
npm start

4. Open Your Browser

Navigate to http://localhost:3000

🛠️ Tech Stack

Component	Technology	Version
Frontend	React, TypeScript, Tailwind CSS	18.2.0
Backend	FastAPI, Python	0.104+
AI/ML	Meta Llama, LangChain	Latest
Document Processing	PyMuPDF, LangChain	Latest
Package Management	uv (Python), npm	Latest
Deployment	Docker	Ready

📖 Usage

1. Upload a PDF: Drag and drop or click to upload a research paper
2. Optional Query: Add specific questions about the paper
3. Analyze: Click "Analyze Paper" to start the AI analysis
4. Review Results: View comprehensive analysis including:
   • Executive summary
   • Key insights
   • Detailed analysis
   • Recommendations for different stakeholders

🔧 Configuration

Environment Variables

Variable	Description	Default
LLAMA_API_KEY	Your Llama API key	Required
LLAMA_BASE_URL	Llama API base URL	https://api.llama.com/compat/v1/
LLAMA_MODEL	Llama model to use	Llama-4-Maverick-17B-128E-Instruct-FP8
LLAMA_TEMPERATURE	Model temperature	0.1
UPLOAD_DIR	File upload directory	uploads
MAX_FILE_SIZE	Maximum file size	50MB
CHUNK_SIZE	Text chunk size	1000
CHUNK_OVERLAP	Chunk overlap size	200

📚 Documentation

For detailed setup instructions, API documentation, and development guides, see the Documentation.

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

🐛 Troubleshooting

Common Issues

• PDF Parsing Errors: Ensure PDF is not password-protected or corrupted
• API Key Issues: Verify Llama API key is valid and has sufficient credits
• Memory Issues: Reduce chunk size for large documents
• Timeout Errors: Increase timeout settings for complex analyses

Getting Help

• Check the Documentation
• Review Troubleshooting Guide
• Open an Issue for bugs or feature requests

📄 License

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

🙏 Acknowledgments

• Built with Meta Llama models
• Powered by LangChain for AI orchestration
• UI components from Lucide React

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Made with ❤️ for the research community