🧠 Multimodal RAG System

A Retrieval-Augmented Generation (RAG) system that enables semantic search and Q&A over PDF documents using both text and images. Powered by CLIP embeddings, FAISS, and Groq.

🌟 Key Features

• Multimodal Search: Query documents using text or upload images to find similar visual content.
• Unified Embeddings: Uses CLIP to project text and images into a shared 512-dim vector space.
• Smart Parsing: Extracts text, tables, and images from PDFs using OCR (Tesseract).
• High Performance: FAISS for fast vector retrieval and Groq for instant LLM responses.
• Interactive UI: ChatGPT-style interface built with Streamlit.

🛠️ Tech Stack

• Embeddings: CLIP (OpenAI)
• Vector Store: FAISS
• LLM: Groq (Llama-3/Mixtral)
• Frontend: Streamlit
• OCR: Tesseract

🚀 Quick Start

1. Setup

# Clone & Install
git clone [https://github.com/yourusername/multimodal-rag-system.git](https://github.com/yourusername/multimodal-rag-system.git)
cd multimodal-rag-system
pip install -r requirements.txt

# Install Tesseract (Required for OCR)
sudo apt-get install tesseract-ocr  # Ubuntu
brew install tesseract              # macOS