HKUDS/RAG-Anything

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

What it solves

Traditional RAG systems are primarily text-focused and struggle to process documents containing mixed content like images, tables, and mathematical equations. RAG-Anything provides an all-in-one framework that eliminates the need for multiple specialized tools by enabling seamless processing and querying across all these modalities within a single system.

How it works

It implements a multi-stage multimodal pipeline built on LightRAG:

  1. Document Parsing: Uses MinerU for high-fidelity extraction of text, visual elements, and tables from PDFs, Office documents, and images.
  2. Content Analysis: Automatically categorizes content and routes it through specialized analyzers (e.g., a Visual Content Analyzer for images, a Structured Data Interpreter for tables, and a Mathematical Expression Parser for LaTeX formulas).
  3. Knowledge Graph Indexing: Transforms multimodal elements into structured entities and maps cross-modal relationships, preserving the original document hierarchy.
  4. Modality-Aware Retrieval: Combines vector similarity search with graph traversal to retrieve content, using adaptive ranking to weight results based on the query's modality preferences.

Who it’s for

This tool is designed for users dealing with rich, mixed-content documents, specifically those in academic research, technical documentation, financial reporting, and enterprise knowledge management.

Highlights

  • End-to-End Pipeline: Handles everything from ingestion and parsing to multimodal query answering.
  • Universal Format Support: Processes PDFs, Office files (DOCX, PPTX, XLSX), and images.
  • Multimodal Knowledge Graph: Automatically extracts entities and discovers relationships across different content types.
  • VLM-Enhanced Query: Integrates Vision Language Models to analyze images and textual context together for deeper insights.
  • Hybrid Retrieval: Fuses vector search and graph traversal for more comprehensive information recovery.

Related

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    HKUDS/LightRAG

    A lightweight, graph-based RAG framework that combines knowledge graphs and vector embeddings to provide deep contextual understanding and efficient incremental updates.

  • Project
    Alibaba-NLP/VRAG

    A multi-turn multimodal RAG framework that uses agentic reinforcement learning to enable VLMs to reason and retrieve information across text, images, and videos.

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    ggozad/haiku.rag

    An agentic, local-first RAG system that provides cited answers from documents using hybrid search and multimodal capabilities.

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    yixuantt/MultiHop-RAG

    A QA dataset and benchmarking tool designed to evaluate the ability of RAG pipelines to retrieve and reason across multiple documents and metadata.