RAGFlow is an innovative open-source Retrieval-Augmented Generation (RAG) engine that revolutionizes how businesses handle document-based knowledge extraction. Built on deep document understanding capabilities, RAGFlow provides:
- Multi-format support: Processes Word documents, slides, Excel files, text, images, scanned copies, structured data, and web pages
- Advanced document analysis: Features upgraded Document Layout Analysis and knowledge graph extraction
- Multi-modal capabilities: Can interpret images within PDF or DOCX files
- Internet integration: Supports web search (Tavily) for deep research capabilities
- Customizable architecture: Configurable LLMs and embedding models with multiple recall and fused re-ranking
- Transparent results: Provides traceable citations and visualization of text chunking for human verification
The system offers a streamlined RAG orchestration suitable for both personal use and large-scale enterprise applications. Its intuitive APIs enable seamless business integration while maintaining explainability and truthfulness in responses. RAGFlow stands out by handling literally unlimited tokens and finding "needles in data haystacks" across complex document formats.
Technical requirements include:
- CPU ≥ 4 cores
- RAM ≥ 16GB
- Disk ≥ 50GB
- Docker ≥ 24.0.0
The project welcomes community contributions and maintains an active roadmap for continuous improvement of its document understanding and generation capabilities.