📚 RAG-based PDF Query Application

This is a Streamlit-based application that allows users to upload a PDF document and ask questions about its content. The application uses a Retrieval-Augmented Generation (RAG) approach to retrieve relevant information from the PDF and generate answers using the Groq API.

Features

• PDF Upload: Users can upload a PDF document.
• Text Extraction: The application extracts text from the uploaded PDF.
• Text Chunking: The extracted text is split into smaller chunks for efficient processing.
• FAISS Indexing: The text chunks are embedded using a sentence transformer model and indexed using FAISS for fast similarity search.
• Question Answering: Users can ask questions about the PDF content, and the application retrieves the most relevant chunks and generates an answer using the Groq API.

How It Works

1. PDF Upload: The user uploads a PDF file.
2. Text Extraction: The application extracts text from the PDF.
3. Text Chunking: The text is split into smaller chunks with a specified size and overlap.
4. FAISS Indexing: The text chunks are embedded and indexed using FAISS.
5. Question Answering: The user asks a question, and the application retrieves the most relevant chunks using FAISS.
6. Answer Generation: The retrieved chunks are passed to the Groq API, which generates an answer based on the context.

Requirements

• Python 3.8+
• Streamlit
• PyMuPDF (fitz)
• FAISS
• Sentence Transformers
• Groq API

Installation

1. Clone the repository:

   git clone https://github.com/SUNBALSHEHZADI/rag-pdf-query.git
   cd rag-pdf-query