Multimodal Retrieval-Augmented Generation in Knowledge Systems: A Framework for Enhanced Semantic Search and Response Accuracy

Maintaining structured and up-to-date documentation remains a critical challenge in knowledge management, especially as organizational data becomes increasingly unstructured and multimodal.This paper presents a framework for a Multimodal Retrieval-Augmented Generation (RAG) Knowledge Database Assistant, designed to enhance semantic search and improve the accuracy of generated responses.By combining retrieval-augmented generation techniques with support for diverse data modalities (e.g., text, images, and structured metadata), the proposed system mitigates hallucination risks and increases the reliability of information access.The framework enables precise, contextaware question answering, even when underlying knowledge repositories are incomplete or inconsistently maintained.Our approach demonstrates how multimodal integration and RAG pipelines can form a robust foundation for next-generation knowledge systems.