RAG-Anything
RAG-Anything is an open-source all-in-one multimodal RAG framework that processes and queries documents containing text, images, tables, equations, charts, PDFs, Office files, and other mixed content.
Overview
RAG-Anything helps developers build advanced knowledge retrieval systems by combining document parsing, multimodal content understanding, knowledge graph indexing, vector-graph retrieval, VLM-enhanced querying, and specialized processors for visual, tabular, mathematical, and textual content.
Core Features & Capabilities
Ideal for AI engineers, RAG developers, data engineers, researchers, enterprise knowledge teams, academic teams, document intelligence builders, legal technology teams, financial analysis teams, technical documentation teams, scientific research groups, knowledge management teams, and developers building AI systems over complex multimodal files.
- Process PDFs, Office documents, images, tables, equations, charts, and mixed-format files in one RAG framework
- Build multimodal knowledge graphs that preserve relationships between text, visual elements, structured tables, and formulas
- Use VLM-enhanced querying to combine visual and textual context for richer answers
- Apply vector-graph fusion retrieval and modality-aware ranking for more contextual document search
- Create document intelligence systems for research papers, reports, technical manuals, business files, and enterprise knowledge bases
Trending Use Cases
Why Developers Choose RAG-Anything
Visit the RAG-Anything GitHub repository, install the Python package with pip or clone the project from source, then configure your parser, working directory, LLM provider, embedding function, and vision model function. Start with a single PDF or Office document, enable image, table, and equation processing where needed, and test multimodal queries. For production workflows, review parser dependencies such as MinerU, Docling, PaddleOCR, LibreOffice for Office documents, storage settings, retrieval configuration, and model cost before scaling to larger document collections.
“RAG-Anything helps developers move beyond text-only RAG by turning complex multimodal documents into searchable, queryable, graph-connected knowledge systems.”
Getting Started with RAG-Anything
By combining multimodal document parsing, content decomposition, visual analysis, table interpretation, equation parsing, knowledge graph construction, vector-graph retrieval, VLM-enhanced queries, and open-source Python deployment, RAG-Anything gives AI builders a powerful framework for turning complex mixed-format documents into searchable intelligence.
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RAG-Anything
HKUDS is the creator of RAG-Anything, an open-source all-in-one multimodal RAG framework focused on complex document processing, LightRAG integration, multimodal knowledge graphs, hybrid retrieval, VLM-enhanced queries, and document intelligence workflows.
RAG-Anything is an open-source multimodal retrieval-augmented generation framework designed to help developers build knowledge systems over complex documents that contain more than plain text. Built on LightRAG, it extends traditional RAG pipelines by supporting end-to-end multimodal document processing, document parsing, content decomposition, visual analysis, table interpretation, mathematical equation parsing, multimodal knowledge graph construction, and hybrid retrieval. RAG-Anything can process PDFs, Office documents, images, text files, structured tables, diagrams, formulas, and heterogeneous content, making it useful for academic research, technical documentation, financial reports, enterprise knowledge bases, scientific papers, legal documents, and mixed-format business intelligence workflows.
github.com/hkuds/rag-anything
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