🧠 Project Overview
PDF2QA is an intelligent platform that transforms research documents into structured Question & Answer formats, making it easy for users to extract, organize, and reference information quickly.
The system ensures that users can store their work for future use, while also maintaining data integrity and copyright compliance through instant deletion of copyrighted material.
This combination of functionality and ethical design ensures that research materials remain accurate, compliant, and easily reusable — all within a streamlined interface.
⚙️ Tech Stack
- Language Model: OpenAI GPT-based QA Extraction
- Backend: Python (FastAPI) / Java / Node.js Microservices
- Frontend: Next.js, Tailwind CSS
- Database: PostgreSQL
- Deployment: AWS
- Authentication: AWS Cognito
🔍 Key Features
- Converts research PDFs into structured Q&A datasets
- Allows storage and retrieval of converted materials
- Instant deletion of copyrighted or sensitive documents
- Clean and minimal UI for document uploads and results
- Semantic search for quick information access
- Supports export to JSON or markdown for study and collaboration
📸 Gallery🚀 Challenges & Insights
- Building reliable PDF parsing pipelines for varied document structures
- Ensuring data privacy and copyright compliance
- Balancing response accuracy with processing speed
- Designing a scalable API for document-to-QA transformations
🧩 Impact & Vision
PDF2QA helps researchers, students, and analysts bridge the gap between dense academic writing and accessible knowledge.
It represents a step toward intelligent document understanding, where reading a paper doesn’t mean skimming — it means interacting with it.
🔗 Links
- Live Link: pdf2qa
🏁 Summary
PDF2QA redefines how we interact with research papers — turning static documents into dynamic, queryable knowledge sources.
It’s not just about reading faster — it’s about understanding better, while maintaining ethical use and compliance at every step.