Paper Bold AI

Paper Bold AI is an AI-powered web application designed for academic paper analysis. Built with a Retrieval Augmented Generation (RAG) architecture, it helps researchers and students efficiently extract key insights from complex academic papers in PDF format.

The application addresses the time-consuming challenge of understanding lengthy research papers by providing instant summaries, identifying technical approaches, and enabling conversational Q&A about the paper's content.

Paper Bold AI implements a complete RAG pipeline:

1. Content Extraction: Converts PDF documents to text using PyPDF2
2. Intelligent Chunking: Dynamically segments text based on document size and structure
3. Vector Embedding: Transforms text chunks into semantic vectors using Google's embedding model
4. Vector Storage: Organizes vectors in a Chroma database for quick retrieval
5. Context Matching: ConversationalRetrievalChain query processing
6. Response Generation: Gemini-powered answer synthesis

Deploy Link

• https://paper-bold.onrender.com/

Dataset

This application processes PDF files uploaded by users. No pre-prepared dataset is used. Each PDF is processed after upload and converted into a vector database.

Features and Use Cases

• Bilingual Interface: Full support for both Turkish and English throughout the application
• Adaptive Document Processing: Automatically adjusts chunking parameters based on PDF size for optimal performance
• AI-Powered Summarization: Generates concise, focused summaries highlighting key findings and methodologies
• Technical Element Detection: Automatically identifies and extracts models, algorithms, and technical approaches mentioned in papers
• Contextual Question Answering: Allows natural language queries about any aspect of the paper with accurate, citation-based responses
• Responsive Design: Fully functional across desktop and mobile devices

Ideal For:

• Academic researchers conducting literature reviews
• Students navigating complex research papers
• Research teams sharing and discussing technical publications
• Anyone needing to quickly extract specific information from academic papers

Technologies Used

• Backend: Python, Flask
• Frontend: HTML, CSS, JavaScript
• GenAI: Gemini 1.5 Flash, Google Embedding, LangChain
• Vector Database: Chroma
• PDF Processing: PyPDF2

Local Installation Steps

1. Clone the repository:

   git clone https://github.com/enesmanan/paper-bold.git
   cd paper-bold

2. Create and activate a virtual environment:

    python -m venv venv
    source venv/bin/activate  # Linux/Mac
    venv\Scripts\activate     # Windows

3. Install required packages:

   pip install -r requirements.txt

4. Create a .env file and add your Google API key:

   GOOGLE_API_KEY=your_api_key
   

5. Run the application:

   python app.py

6. Go to http://localhost:5000 in your browser

Contact

Please get in touch if you have any questions about the project.

• E-mail: enesmanan768@gmail.com
• GitHub: github.com/enesmanan
• LinkedIn: linkedin.com/in/enesfehmimanan

Project Structure

paper-bold/
├── app.py                  
├── static/                 
│   ├── style.css           
│   └── images/             
├── templates/              
│   ├── index.html          
│   └── viewer.html         
├── uploads/                
├── chroma_db/              
├── requirements.txt       
└── README.md