DocQuify is a powerful AI-powered platform that helps users extract relevant information from research papers and technical documentation by asking natural language questions. Upload PDFs and ask context-based queries to get quick, accurate answers — without reading the entire document.
- 📄 PDF Upload: Upload research papers or technical docs for smart processing.
- ❓ Context-Based Question Answering: Ask questions and get answers directly from the uploaded content.
- 🔍 Stay Updated: Understand new tech trends by uploading the latest whitepapers or articles.
- 🔐 Google Login Integration: Secure, seamless sign-in using Clerk’s Google Auth.
- ☁️ AWS S3 Storage: Store and access documents reliably.
- 🧠 AI + Vector Search: Uses OpenAI and Pinecone for semantic search and precise answers.
| Layer | Tech Used |
|---|---|
| Frontend | Next.js, TypeScript |
| Backend | Node.js, Express.js, PostgreSQL |
| Authentication | Clerk (Google Login) |
| Storage | AWS S3 |
| AI & Search | OpenAI API, Pinecone Vector DB |
git clone https://github.com/yourusername/docquify.git
cd docquify
2. Install Dependencies
bash
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npm install
3. Set Environment Variables
Create a .env file in the root directory and add the following:
env
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JWT_SECRET=your_jwt_secret
AWS_S3_BUCKET_NAME=your_bucket_name
AWS_ACCESS_KEY_ID=your_access_key
AWS_SECRET_ACCESS_KEY=your_secret_key
OPENAI_API_KEY=your_openai_key
PINECONE_API_KEY=your_pinecone_key
4. Run the Development Server
bash
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npm run dev
🤝 Contributing
We welcome contributions! To contribute:
Fork the repository
Create a new branch (git checkout -b feature-name)
Make your changes
Submit a pull request
Found a bug or have a suggestion? Feel free to open an issue.
Let me know if:
- You want the markdown as a downloadable `.md` file.
- You have a live deployment link to add.
- You want to include screenshots or a GIF demo.