Multimodal RAG and Parsing: Data Architectures for Semantic Retrieval in Complex Industrial Layouts - Miyaji, Renato; Luz, Arthur; Moulin, Renato; Machado, Leonardo; Corrêa, Pedro L. P. (Anais do Simpósio Brasileiro de Banco de Dados (SBBD), 2026)
Traditional RAG systems struggle with industrial documents by neglecting structural semantics in complex layouts. To mitigate this, we propose a formal Visual Document Understanding ingestion methodology and a Semantic Chunking framework. By orchestrating Vision-Language Models to generate high-fidelity captions for visual elements and grouping them with adjacent text, our pipeline preserves context-rich retrieval units. Experiments on an industrial dataset demonstrate 88.0% accuracy, a 0.91 Hit Rate@5, and 0.89 Context Recall, significantly outperforming OCR baselines (74.0% accuracy) and matching state-of-the-art solutions.