Vision-Guided Chunking Is All You Need: Enhancing RAG with Multimodal
  Document Understanding
Authors: name: Vishesh Tripathi, name: Tanmay Odapally, name: Indraneel Das, name: Uday Allu, name: Biddwan Ahmed

Retrieval-Augmented Generation (RAG) systems have revolutionized information
retrieval and question answering, but traditional text-based chunking methods
struggle with complex document structures, multi-page tables, embedded figures,
and contextual dependencies across page boundaries. We present a novel
multimodal document chunking approach that leverages Large Multimodal Models
(LMMs) to process PDF documents in batches while maintaining semantic coherence
and structural integrity. Our method processes documents in configurable page
batches with cross-batch context preservation, enabling accurate handling of
tables spanning multiple pages, embedded visual elements, and procedural
content. We evaluate our approach on a curated dataset of PDF documents with
manually crafted queries, demonstrating improvements in chunk quality and
downstream RAG performance. Our vision-guided approach achieves better accuracy
compared to traditional vanilla RAG systems, with qualitative analysis showing
superior preservation of document structure and semantic coherence.