According to @godofprompt, the release of RAG-Anything marks a breakthrough in AI retrieval by integrating multimodal knowledge, enabling AI systems to process not just text but also charts, tables, diagrams, and mathematical expressions as interconnected knowledge entities (source: @godofprompt on Twitter, Oct 26, 2025). Traditional RAG (Retrieval-Augmented Generation) pipelines only process text, missing up to 60% of valuable information typically found in non-textual formats within research papers, financial reports, and medical studies. RAG-Anything introduces a dual-graph construction to map and retrieve relationships across content types, allowing AI models to provide richer, more contextually complete answers. This unified approach offers significant business opportunities in sectors like healthcare, finance, and technical research, where decision-making relies on multiple data modalities. By outperforming existing systems on benchmarks—especially for long-context, multimodal documents—RAG-Anything sets a new standard for enterprise AI knowledge retrieval and opens pathways for advanced document understanding solutions.