HKUDS/RAG-Anything

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

What it solves

Traditional RAG (Retrieval-Augmented Generation) systems are often limited to plain text, struggling to interpret complex documents that contain images, tables, mathematical equations, and charts. RAG-Anything provides a unified framework to process, index, and query these diverse, multimodal document types without needing multiple specialized tools.

How it works

It uses a multi-stage pipeline built on the LightRAG framework. First, it parses documents (PDFs, Office files, etc.) using tools like MinerU to decompose them into structured elements. It then uses specialized analyzers to interpret visual content, tables, and equations. These elements are stored in a Multimodal Knowledge Graph that captures both semantic content and hierarchical relationships. Finally, it uses a hybrid retrieval system that combines vector similarity search with graph traversal to provide context-aware answers to user queries.

Who it’s for

This framework is designed for researchers, developers, and enterprises working with complex technical documentation, financial reports, or academic papers where information is spread across text, visual diagrams, and structured data.

Highlights

  • End-to-End Multimodal Pipeline: Handles everything from document ingestion and parsing to intelligent query answering.
  • Universal Format Support: Processes PDFs, Office documents (DOCX, PPTX, XLSX), images, and more.
  • Specialized Analyzers: Dedicated processing for images, tables, and LaTeX mathematical expressions.
  • Multimodal Knowledge Graph: Automatically extracts entities and cross-modal relationships to improve retrieval accuracy.
  • Hybrid Retrieval: Combines vector search and graph-based traversal for better contextual understanding.

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