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: Anais do XLI Simpósio Brasileiro de Bancos de Dados, 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.