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📚 AI Research Paper Assistant - RAG-based Q&A System

🚀 Project Overview

The AI Research Paper Assistant is a Retrieval-Augmented Generation (RAG) application that enables users to interact with AI research papers. Users can upload a research paper, ask domain-specific questions, and receive context-aware answers generated using LLMs.

🔍 Key Features:

  • Upload research papers in PDF format.
  • Semantic search for retrieving relevant sections.
  • Summarization of key paper sections.
  • Interactive Q&A based on user queries.
  • Citation assistance for proper referencing.

🎯 Problem Statement

Understanding complex AI research papers can be challenging. This project aims to simplify knowledge extraction by enabling users to ask questions and receive relevant, summarized insights.

🏗️ Tech Stack

  • Text Extraction: PyPDF2, PDFMiner, Tesseract (OCR)
  • Vectorization: Sentence Transformers (all-MiniLM-L6-v2)
  • Vector Database: Pinecone, FAISS, or Weaviate
  • LLM Integration: OpenAI GPT-4, Flan-T5
  • Frontend: Streamlit, Flask, or FastAPI
  • Deployment: Streamlit Cloud, Hugging Face Spaces, AWS

🔄 Workflow

  1. Upload & Process Paper 📄

    • Extract text while preserving section hierarchy.
    • Handle multi-column layouts and citations.

  2. Preprocessing & Embedding Creation 🔢

    • Chunk text (200-500 words per segment).
    • Convert text into embeddings using Sentence Transformers.
    • Store embeddings in a vector database.

  3. Query Processing & Response Generation 🎯

    • User submits a query.
    • Perform semantic search to fetch relevant chunks.
    • Use an LLM (GPT-4 or Flan-T5) to generate answers based on retrieved text.

  4. Frontend Interaction 🌐

    • Users upload papers, enter queries, and view AI-generated responses.

🔬 Example Use Case

Query: "What is the main contribution of the paper?"
Processing: Retrieve key sections (Abstract, Conclusion) & generate response.
Output: "The main contribution of this paper is the introduction of a novel transformer-based architecture that improves efficiency by 25% while maintaining state-of-the-art performance."

🛠️ How to Run Locally

# Clone the repository
git clone https://github.com/your-repo/ai-research-assistant.git
cd ai-research-assistant

# Install dependencies
pip install -r requirements.txt

# Run the application
streamlit run app.py

🚀 Future Enhancements

  • Support for multi-paper retrieval 📑
  • Integration with Google Scholar API for external references 🌍
  • More fine-tuned LLM models for improved accuracy 🤖

📜 License

This project is licensed under the MIT License.

🤝 Contributing

Contributions are welcome! Feel free to open issues or submit pull requests.

🌟 Star this repo if you find it useful!