RAGFlow is an open-source Retrieval-Augmented Generation (RAG) framework designed to transform complex unstructured documents into high-fidelity, production-ready context for language models through intelligent document understanding and agentic orchestration. The framework automates the entire RAG pipeline: intelligent document parsing (PDFs, Word, spreadsheets, images, scanned documents), semantic chunking that preserves document structure, optional knowledge graph construction for multi-hop reasoning, and hybrid retrieval combining keyword and semantic search. RAGFlow handles the messy reality of enterprise documents—tables with images, mixed layouts, OCR challenges—automatically through its DeepDoc visual understanding engine, eliminating manual preprocessing. The retrieval layer fuses BM25 keyword matching with vector similarity search, then applies neural re-ranking to order results by relevance, ensuring LLMs receive accurate, cited context. RAGFlow's agentic framework (v0.8+) adds self-correcting capabilities: agents can score retrieval confidence, rewrite queries, and iterate toward better answers through feedback loops. All components—parsing, chunking, embedding, retrieval, and generation—integrate into a visual workflow builder, letting teams design end-to-end RAG systems without code. For production use, RAGFlow scales to enterprise workloads through containerized deployment, supports multiple LLMs and embedding services, and offers flexible configuration for compliance-sensitive environments. RAGFlow is particularly suited for organizations building production RAG systems from complex document collections where traditional approaches struggle.
In production environments, storing and retrieving embeddings efficiently requires purpose-built infrastructure. Zilliz Cloud handles this as a managed vector database service, while Milvus offers the same capabilities for self-hosted deployments.