📚 Research Paper Q&A Assistant

A semantic search and question-answering system for research papers using Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs).

🚀 Overview

This system enables users to:

• Upload research papers (PDF)
• Ask natural language questions
• Receive concise, context-grounded answers with traceable sources

🔧 Built With:

• LangChain
• Hugging Face Transformers
• Pinecone
• Google Gemini 1.5 Pro
• Flask

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🧠 Features

• 📄 PDF parsing with text cleaning
• 🔍 Section segmentation & semantic chunking
• 🧬 Embedding generation with all-MiniLM-L6-v2
• 📦 Vector indexing with Pinecone
• 🤖 Contextual answer generation using Gemini 1.5 Pro
• 🌐 Web UI for upload and Q&A
• ✅ Transparent answers with source document snippets

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Method 1: Using the Setup Script

chmod +x run_app.sh
./run_app.sh

Method 2: Manual Execution

🛠️ Installation & Setup

1. Clone the repository

git clone https://github.com/ajay-del-bot/research_paper_RAG_chain.git
cd research_paper_RAG_chain

2. Create and activate a virtual environment

# For Linux/macOS
python3 -m venv venv
source venv/bin/activate

# For Windows
python -m venv venv
venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

4. Set up environment variables in .env

PINECONE_API_KEY=YOUR_PINNECONE_API_KEY
GOOGLE_API_KEY=YOUR_GOOGLE_API_KEY
INDEX_NAME='test-db'

💻 Running the App

python3 src/server.py

🧪 Future Enhancements

• Support for tables, figures, equations
• Better layout handling for multi-column PDFs
• User authentication & session history
• Integration with multiple LLMs