Paper Title
Enhancing Conversational Artificial Intelligence with Multimodal Retrieval Augmented Generation

Abstract
Recently, retrieval-augmented generation (RAG) based solutions have improved language generation by leveraging an external nonparametric index, showing impressive performance despite constrained model sizes. However, these models are limited to retrieving only textual knowledge. Multimodal RAG (Mu RAG) was introduced to address these limitations of RAG, which leverages image data along with text to utilize the full power of retrieval models. This paper proposes a multimodal retrieval-augmented framework combining Simple RAG and Mu RAG to enhance chatbot experiences by utilizing domain-specific repositories with both text and image data. After explaining Simple RAG and reviewing existing RAG and Mu RAG research, a case study is presented where a company struggles to develop an efficient Multimodal RAG framework for handling image and text data. The proposed comprehensive framework uses the latest libraries and models, uniquely leveraging image data as a knowledge source to improve LLM-generated responses. Recommendations are provided to enhance performance, efficiency, and reliability, ensuring accurate, contextually appropriate, and standards-aligned responses. Keywords - Artificial intelligence (AI), Large Language Model (LLM), Retrieval Augmented Generation (RAG), Multimodal Model, Generative Pre-Trained Transformers (GPT), Lang Chain, Facebook AI Similarity Search (FAISS)