RAG-Anything: All-in-One Multimodal RAG Framework

🔍 RAG with text, images, tables, and equations simultaneously. That’s RAG-Anything.

RAG-Anything is an open source framework from Hong Kong University for next-generation multimodal RAG. It doesn’t limit itself to text: it processes any type of content in documents.

Capabilities:

• 📄 Text: intelligent chunking and semantic retrieval
• 🖼️ Images: VLM-enhanced queries for visual analysis
• 📊 Tables: extraction and reasoning over structured data
• ➕ Equations: mathematical formula processing

Why does it matter? Traditional RAG systems only work with text. Real documents (scientific PDFs, reports, manuals) mix all these content types. RAG-Anything integrates them in a single query.

Status: 1,000+ ⭐ on GitHub, technical report on arXiv, full multimodal query support.

💡 Explanation in a nutshell

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RAG (Retrieval-Augmented Generation) is a technique for giving an LLM “memory” by connecting it to external documents. The basic version only works with text. RAG-Anything goes further: when you ask about a PDF that has charts and tables, the system understands and responds using all that information, not just the text.

More information at the link 👇

GitHub - HKUDS/RAG-Anything: "RAG-Anything: All-in-One RAG Framework"

"RAG-Anything: All-in-One RAG Framework". Contribute to HKUDS/RAG-Anything development by creating an account on GitHub.

github.com ↗