Researchers from Bloomberg and the University of North Carolina at Chapel Hill have unveiled M3DocRAG, a novel multi-modal retrieval-augmented generation (RAG) framework designed to enhance artificial intelligence's ability to process various document contexts. This innovative framework aims to address challenges faced by large language models (LLMs) in understanding complex documents, which often contain a mix of text, tables, images, and intricate flow charts. Current RAG systems struggle with flat data representations and context awareness, leading to inaccuracies in domain-specific queries. M3DocRAG is expected to improve the accuracy of LLMs, achieving up to 97% accuracy on domain questions by integrating structured and unstructured knowledge. The development of this framework reflects ongoing advancements in AI research, particularly in the areas of knowledge retrieval and document processing.