Multimodal Retrieval-Augmented Generation (RAG) builds upon the solid foundation established by standard RAG, which enhances the capabilities of Large Language Models (LLMs) by providing them with relevant text snippets retrieved from a vast corpus. In classic RAG, this retrieval process allows the LLM to access up-to-date and contextually relevant information, resulting in more accurate and informative responses than would be possible if the model relied solely on its internal knowledge base. Multimodal RAG advances this concept by integrating various data modalities beyond just text, such as images, videos, and audio. This approach recognizes that real-world information is often conveyed through multiple channels, and by harnessing these diverse sources, Multimodal RAG enables LLMs to generate richer, more nuanced responses. For instance, an image associated with a text snippet can provide context or additional details that text alone might not convey. Similarly, videos and audio can offer temporal and auditory information, adding layers of meaning that enhance the LLM's ability to understand and respond to user queries comprehensively.