PDF Question Answering System Using Retrieval-Augmented Generation (RAG)

This project is a sophisticated question-answering system designed to extract and provide context-aware answers from PDF documents. By integrating advanced Retrieval-Augmented Generation (RAG) techniques and state-of-the-art AI models, the system enables users to interact with their documents in a more efficient and intelligent manner.

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Use Cases

• Academic Research: Quickly extract insights from research papers, reports, or studies.
• Professional Analysis: Navigate lengthy contracts, whitepapers, or manuals with ease.
• Everyday Use: Simplify interactions with dense or complex PDF documents.

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Key Features

• PDF Processing: Upload and process PDF documents for analysis.
• Interactive Q&A: Enter natural-language questions and receive precise answers based on document content.
• Advanced Retrieval: Uses vector-based indexing and similarity scoring for accurate content retrieval.
• User-Friendly Interface: A web application built with Streamlit ensures ease of use and accessibility.

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Technologies Used

Frontend: Streamlit Backend: Python Machine Learning: HuggingFace Transformers for text generation VectorStoreIndex for document indexing Custom retriever and postprocessor for improved accuracy

Installation and Setup

1. Clone the Repository:

   git clone https://github.com/your-repo-name.git
   cd your-repo-name
   

2. Run the Application: Start the Streamlit application:

    streamlit run app.py
   

Upload a PDF and Start Querying

• Upload your desired PDF file through the application interface.
• Enter questions and retrieve contextually accurate responses.

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How It Works

1. PDF Processing:

   • The system reads and processes the uploaded PDF, splitting it into manageable chunks for indexing.

2. Information Retrieval:

   • The indexed content is retrieved using advanced embeddings and similarity scoring.

3. Answer Generation:

   • A pre-trained language model generates context-aware and concise responses based on the retrieved content.

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Technology Stack

• Frontend: Streamlit for an interactive and intuitive user experience.
• Backend:
  • HuggingFace Transformers for natural language understanding and generation.
  • Vector-based retrieval using custom embeddings.
• Programming Language: Python.

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Code Overview

app.py

• A Streamlit application that provides the user interface.
• Handles PDF uploads, question inputs, and displays answers.

rag.py

• Implements the core RAG logic:
  • PDF Processing: Reads and splits the PDF into manageable chunks.
  • Indexing: Creates a vector index for efficient content retrieval.
  • Query Engine: Uses a retriever and postprocessor to answer queries.
  • Response Generation: Generates detailed responses using a transformer model.

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Usage Instructions

1. Upload a PDF file.
2. Wait for the system to process the document.
3. Type your question and click "Get Answer".
4. View the answer generated by the system.

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Future Enhancements

• Multi-Document Support: Enable querying across multiple PDF files.
• Multi-Language Support: Add support for processing documents in multiple languages.
• GPU Support: Implement GPU acceleration for faster processing and response times.
• Additional Formats: Expand support to other document formats such as DOCX and TXT.
• Enhanced UI: Improve the user interface with advanced analytics and visualization features.

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Contributing

We welcome contributions from the community. To contribute:

1. Fork the repository.
2. Create a feature branch.
3. Submit a pull request detailing your contribution.

For any issues or suggestions, please open a discussion or issue on the repository.

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License

This project is licensed under the MIT License. Feel free to use, modify, and distribute it in compliance with the terms of the license.

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Contact

For inquiries or further information, please contact via the repository issue tracker or email (if applicable).