Question And Answering System Using Rag : Ai

Abstract

This system leverages Retrieval-Augmented Generation (RAG), combining NLP tools like NLTK and GenAI models with a Django-powered web framework. Users can upload and query documents, and the system retrieves and generates contextually relevant answers in real-time. Secure multi-user authentication ensures personalized data access, making it suitable for domains with sensitive or structured data like healthcare, finance, and research.

Objective

To build a scalable, secure, and user-friendly web-based Q&A system that uses RAG to fetch accurate answers from user-provided data, improving decision-making and research efficiency.

Info

• Built using Python and Django for the backend.
• NLTK is used for text preprocessing and tokenization.
• Uses GenAI (via free and open-source models or APIs like Hugging Face Transformers) for answer generation.
• Incorporates RAG architecture: retrieves relevant chunks from uploaded documents, then feeds into generative model.
• Offers multi-user access with authentication and session management.
• Stores documents and user sessions using SQLite (development) or PostgreSQL (production).
• Designed for document-based Q&A: PDFs, Word, or plain text files.
• Emphasis on privacy: users can only access their own data.

Key Features

• 🧠 GenAI with RAG: Combines retrieval and generation for accurate answers.
• 🔐 Authentication System: Django-based multi-user support with secure login/logout.
• 📄 Document Upload & Storage: Users can upload their own content for personalized Q&A.
• 💬 Interactive QA Interface: Ask natural language questions via a clean frontend.
• 🧹 Text Preprocessing: Built-in NLTK pipeline for robust text cleaning and normalization.
• 📊 Usage Tracking & History: Optional dashboard for viewing past queries and document usage.
• 🔍 Search Optimization: Efficient document chunking and indexing for fast retrieval.
• 🧩 Modular Architecture: Easy to integrate other NLP or GenAI tools.

Keywords

RAG, Question Answering, Django, NLTK, GenAI, NLP, Multi-user, Authentication, Open-source, AI Assistant, Document AI, Retrieval-Augmented Generation, Hugging Face, Python, Chatbot, Knowledge Extraction