Key Features
Deep Document Parsing
Vision-based layout analysis that understands:
- Complex tables (merged cells, nested headers)
- Embedded images and charts
- Multi-column layouts
- Headers, footers, page numbers
- Mathematical formulas
Accurate Citations
Every answer includes exact source references — page number, paragraph, and highlighted text.
Agent Workflows
Built-in agent for multi-step reasoning, web search augmentation, and tool use.
Template-Based Chunking
Document-type-aware chunking strategies — different templates for papers, resumes, contracts, manuals.
Multi-Model Support
OpenAI, Anthropic, Google, Ollama, Azure, DeepSeek, Zhipu, and custom endpoints.
Knowledge Base Management
Web UI for uploading, organizing, and managing document collections across teams.
FAQ
Q: What is RAGFlow? A: An open-source RAG engine with deep document understanding. Parses complex PDFs with tables, images, and charts for accurate Q&A with citations. 77K+ GitHub stars.
Q: How is RAGFlow different from basic RAG? A: Basic RAG splits text into chunks by character count. RAGFlow uses vision models to understand document structure — tables remain as tables, images are captioned, and layouts are preserved.