RAGFlow: Open-Source RAG Engine for Document Understanding

Retrieval-Augmented Generation (RAG) has become the standard architecture for grounding LLM responses in factual data, but most RAG implementations have a fundamental weakness: they treat documents as undifferentiated text, shredding them into arbitrary chunks that lose all structural meaning. RAGFlow takes a fundamentally different approach, combining deep document understanding with LLM-based generation for precise, citation-grounded answers.

RAGFlow is developed by infiniflow and has rapidly gained adoption as a production-grade RAG engine. Its core innovation is the use of layout analysis and vision-language models to understand the actual structure of documents – recognizing headers, paragraphs, tables, charts, figures, and their hierarchical relationships before performing retrieval.

This deep document understanding makes RAGFlow particularly effective for enterprise document scenarios – legal contracts, financial reports, technical manuals, academic papers, and government documents – where the position of information within a structured document is as important as the information itself.

How Does RAGFlow’s Document Processing Pipeline Work?

RAGFlow applies multiple stages of analysis to extract structured understanding from documents.

graph TD
    A[Input Document\nPDF / DOCX / Image] --> B[Layout Analysis\nVisual Structure Detection]
    B --> C[OCR Engine\nText Extraction from Images]
    B --> D[Table Detection\nRow/Column Structure]
    B --> E[Figure Analysis\nChart / Diagram Understanding]
    C --> F[Structure Preservation\nHeaders + Body + Footnotes]
    D --> F
    E --> F
    F --> G[Semantic Chunking\nStructure-Aware Text Splitting]
    G --> H[Vector Embeddings\nDense Retrieval Index]
    G --> I[Keyword Index\nSparse Retrieval]
    H --> J[Hybrid Retrieval\nDense + Sparse Search]
    I --> J
    J --> K[LLM Generation\nAnswer + Citations]

The pipeline preserves document structure through every stage, ensuring that retrieval respects the logical organization of the source material.

What Features Does RAGFlow Offer?

RAGFlow provides a comprehensive set of features spanning document processing, retrieval, and generation.

The combination of deep document parsing with flexible LLM and embedding choices makes RAGFlow adaptable to a wide range of enterprise requirements.

How Does RAGFlow Handle Complex Document Types?

Different document types require fundamentally different parsing strategies, and RAGFlow applies the appropriate approach for each.

Each parsing path is optimized for its source format while producing a consistent structured output for downstream retrieval.

How Does RAGFlow Handle Citation and Attribution?

RAGFlow provides detailed source attribution for every generated answer.

The citation system is designed for enterprise scenarios where answer verification and auditability are critical requirements.

FAQ

What is RAGFlow? RAGFlow is an open-source Retrieval-Augmented Generation (RAG) engine developed by infiniflow that specializes in deep document understanding. Unlike simple RAG systems that chunk documents arbitrarily, RAGFlow uses vision-language models and layout analysis to understand document structure – including tables, charts, figures, and complex layouts – before passing relevant context to an LLM for answer generation.

How does RAGFlow differ from traditional RAG systems? Traditional RAG systems typically split documents into fixed-size text chunks, losing structural information. RAGFlow incorporates deep document parsing using layout analysis and OCR to understand the actual structure of documents – recognizing headers, paragraphs, tables, figures, and their relationships. This preserves the semantic structure of documents and enables more precise retrieval.

What document formats does RAGFlow support? RAGFlow supports a wide range of document formats including PDF, DOCX, Excel, PPTX, TXT, Markdown, images (for OCR), HTML, EPUB, and email files. For each format, it applies the appropriate parsing strategy – layout analysis for PDFs, built-in structure for DOCX, cell-aware parsing for Excel, and OCR for scanned images.

How does RAGFlow handle images and tables in documents? RAGFlow uses vision-language models and layout detection to understand tables, charts, figures, and diagrams within documents. Tables are parsed with cell-level accuracy preserving row-column relationships. Figures are analyzed and described semantically. This enables retrieval and answering based on image and table content, not just text.

Can RAGFlow work with local LLMs? Yes, RAGFlow is designed to work with both cloud API-based LLMs (OpenAI, Claude, Gemini) and local open-source models (Llama, Qwen, DeepSeek, Mistral) through Ollama, vLLM, or llama.cpp. This flexibility allows deployment in air-gapped or privacy-sensitive environments where data cannot be sent to external APIs.

Further Reading

• RAGFlow GitHub Repository – Source code, documentation, and deployment guide
• RAGFlow Official Documentation – User guide and API reference
• RAG Architecture Overview – Introduction to RAG concepts and design
• LayoutLM Paper (ArXiv) – Foundational paper on document layout understanding