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LLM-Powered Document Knowledge Extractor

🌟 Project Overview

This AI-driven project enables users to:

1. Upload a document (PDF/DOC)
2. Ask AI questions related to the content
3. Generate knowledge graphs to visualize relationships within the document

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🚀 Key Features

• File Upload: Supports PDF and DOC formats for document processing.
• AI-Powered Q&A: Use advanced Natural Language Processing (NLP) to ask questions about the uploaded document.
• Knowledge Graphs: Automatically generate dynamic graphs to represent the document’s key concepts and relationships.

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🛠️ Tech Stack

• Backend: Python (Flask/Django/FastAPI)
• AI: OpenAI/GPT models, Natural Language Processing (NLP)
• Document Parsing:
  • PDFs: PyMuPDF, pdfplumber
  • DOCs: python-docx
• Knowledge Graph Generation: NetworkX, Matplotlib, Graphviz
• Frontend (if applicable): React / Angular / Vue or pure HTML, CSS, JavaScript
• Database: SQLite / PostgreSQL (if needed)

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📋 Installation Guide

1. Clone the repository
   git clone https://github.com/yourusername/yourproject.git

2. Navigate to the project directory
   cd yourproject

3. Install the dependencies
   pip install -r requirements.txt

4. (Optional) Create and activate a virtual environment
   python -m venv venv
source venv/bin/activate (on Windows, use venv\Scripts\activate)

5. Run the backend server
   python app.py

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📊 Usage Steps

1. Run the server and open the web interface or use API endpoints.
2. Upload a document (PDF/DOC).
3. Ask questions about the document.
4. View the generated knowledge graphs that visually represent key insights from the document.

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📡 API Endpoints

1. POST /upload
   Upload a PDF/DOC file for processing.
   Example request:
   curl -X POST -F "file=@yourfile.pdf" http://localhost:5000/upload

2. GET /ask
   Ask questions about the document.
   Example request:
   curl -X GET "http://localhost:5000/ask?question=What is the main topic?"

3. GET /knowledge-graph
   Generate a knowledge graph based on the document’s content.
   Example request:
   curl -X GET http://localhost:5000/knowledge-graph

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🧩 Project Structure

• static/: Frontend assets (if applicable)
• templates/: HTML templates (if applicable)
• models/: AI model scripts and logic
• utils/: Utility functions (PDF parsing, graph generation)
• app.py: Main backend file
• requirements.txt: Project dependencies
• README.md: Project documentation

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🔧 Future Enhancements

1. Expand supported document formats (e.g., DOCX, TXT).
2. Improve AI model accuracy.
3. Enhance graph generation with better layouts and more data visualization techniques.
4. Add real-time collaboration features.

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🤝 Contributing

Contributions are welcome! Follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Commit your changes (git commit -am 'Add new feature').
4. Push to the branch (git push origin feature-branch).
5. Submit a pull request.

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✨ Acknowledgements

• OpenAI GPT models
• PyMuPDF
• NetworkX

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📧 Contact

Feel free to reach out at: aadityashankar21@gmail.com

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