ADVANCING REAL-TIME CONTEXT-AWARE RETRIEVAL AUGMENTED GENERATION (RAG) SYSTEMS WITH MULTI-MODAL DATA INTEGRATION

Abstract

The evolution of Retrieval-Augmented Generation (RAG) systems has been driven by the increasing prevalence of multimodal data across text, images, videos, and tables. While traditional text-based RAG architectures have proven effective with large language models, they struggle to handle diverse information formats essential for comprehensive understanding and decision-making. This article introduces a novel framework for Real-Time Context-Aware Multimodal RAG systems, featuring an innovative hybrid retrieval mechanism optimized for multimodal embeddings. Our approach incorporates a Contextual Fusion Network for dynamic feature alignment and a Memory-Enhanced RAG Chain for maintaining conversation coherence. The framework addresses multimodal alignment challenges through Bias Detection Layers and Real-Time Consistency Checks, demonstrating significant improvements in retrieval precision, response coherence, and processing efficiency across healthcare, financial compliance, and e-learning applications.