💡 The paper introduces RAG-Anything, a unified framework that enhances multimodal knowledge retrieval by integrating cross-modal relationships and semantic matching. The problem addressed is that current Retrieval-Augmented Generation frameworks are limited to textual content, creating gaps when processing multimodal documents that contain a combination of text, images, tables, and mathematical expressions.

The proposed method, RAG-Anything, reconceptualizes multimodal content as interconnected knowledge entities, introducing dual-graph construction to capture both cross-modal relationships and textual semantics within a unified representation. The framework develops cross-modal hybrid retrieval that combines structural knowledge navigation with semantic matching, enabling effective reasoning over heterogeneous content where relevant evidence spans multiple modalities.

The results show that RAG-Anything demonstrates superior performance on challenging multimodal benchmarks, achieving significant improvements over state-of-the-art methods. The performance gains are particularly pronounced on long documents where traditional approaches fail. The framework establishes a new paradigm for multimodal knowledge access, eliminating the architectural fragmentation that constrains current systems. The RAG-Anything framework is open-sourced, making it available for further development and application. Overall, the paper contributes to the development of a more comprehensive and effective knowledge retrieval system that can handle multimodal content.