RagFlow

RAGFlow is an open-source retrieval-augmented generation (RAG) engine that fuses cutting-edge RAG technology with agent capabilities to build a high-quality context layer for large language models. Designed for enterprise-grade applications, it aims to solve the hallucination problem of traditional RAG systems by providing traceable, citation-backed answers.

Key Features

• Deep document understanding: Uses OCR and document structure recognition models to intelligently parse unstructured data in complex formats such as PDF, Word, Excel, and images
• Hybrid search and reranking: Combines vector search, the BM25 algorithm, and custom scoring mechanisms, with configurable embedding and reranking models
• Visual chunking: Provides a visual view of the text chunking process, supporting manual intervention and adjustment
• Citation tracing: Grounds generated answers in traceable citations, improving transparency and trustworthiness
• Agent orchestration platform: A unified visual workflow platform integrating RAG, tool calls, and the MCP protocol

Use Cases In enterprise knowledge management, teams can use RAGFlow to build intelligent customer service systems that quickly retrieve accurate information from massive document collections and give well-sourced answers. In financial analysis, analysts can extract key data from research reports, financial statements, and other complex documents for intelligent Q&A. In compliance-heavy industries such as law and healthcare, RAGFlow's citation tracing ensures every answer has a clear source behind it.

Unique Advantages RAGFlow's core strength is its "Deep Document Understanding" technology: compared with other RAG frameworks, it parses the structure and semantics of complex-format documents more precisely. The project uses the Apache 2.0 open-source license, has an active community (26,000+ GitHub stars), and provides a complete UI plus one-click Docker deployment, sharply lowering the barrier to deploying enterprise-grade RAG systems.