RAGFlow is a deep document understanding RAG engine with 76K+ GitHub stars, focused on extracting maximum knowledge from complex documents. Unlike basic RAG tools that treat documents as plain text, RAGFlow uses layout-aware parsing that understands tables, figures, headers, and document structure.
Supports 20+ document types including PDFs, Word, Excel, PowerPoint, images, and web pages. Template-based chunking strategies allow optimizing extraction for different document types — technical papers, financial reports, legal documents each get specialized parsing.
Multi-recall retrieval combines keyword search (BM25) and semantic vector search for higher-quality results. Retrieved chunks include citation references back to source documents, enabling verifiable AI-generated answers.
The visual interface provides knowledge base management with drag-and-drop upload, chunk preview and editing, conversation testing, and API endpoints for integration. Runs as a Docker stack with its own embedding and reranking models.
