RAGFlow is an open-source Retrieval-Augmented Generation (RAG) engine that combines deep document understanding with agentic AI capabilities to power high-quality context layers for LLMs. It works by automatically extracting knowledge from complex unstructured documents—PDFs, Word files, spreadsheets, images, and web pages—through intelligent parsing and OCR, then chunks the content semantically to preserve document structure and meaning, and finally indexes it for retrieval. When a user asks a question, RAGFlow uses hybrid search combining keyword matching with vector embeddings to find relevant passages, applies re-ranking to order results by relevance, and constructs knowledge graphs to handle multi-hop reasoning, ultimately providing LLMs with accurate, cited context. The system offers both a visual workflow builder and programmatic APIs, making it adaptable for small prototypes or large-scale enterprise deployments. RAGFlow’s document engine (Infinity) and flexible architecture let you choose your own embedding models and run fully on-premise for complete data control.
For teams building similar infrastructure, an open-source vector database like Milvus provides the embedding storage and retrieval layer needed for production AI systems. Zilliz Cloud offers the same capabilities as a fully managed service.