RAGFlow employs sophisticated, semantically-aware chunking strategies that adapt to document structure, avoiding the naive mistakes of simple text splitting. The default approach uses semantic chunking with specially trained models that parse document layouts and understand where natural content boundaries occur—headers, section breaks, page boundaries—to avoid cutting information in the middle of logical units. This is crucial because fixed-size chunking often breaks tables, code blocks, or multi-sentence concepts, degrading retrieval quality. RAGFlow offers multiple chunking modes: sentence-aware chunking splits on sentence boundaries to preserve complete thoughts, document-aware chunking respects document structure like code blocks and tables, and adaptive chunking adjusts chunk size based on content type. The system recognizes that chunk size trades off between semantic precision and keyword frequency preservation—smaller chunks improve semantic relevance matching while larger chunks maintain more keyword context. From v0.17.0 onward, chunking methods are decoupled from OCR/TSR tasks, allowing you to independently select your parsing strategy and chunking approach to optimize for your specific use case. Post-chunking, RAGFlow optionally constructs knowledge graphs from the chunks, adding explicit relationships between concepts. The chunking engine (Infinity v0.6.1 as of v0.24.0) continuously improves to handle increasingly complex document formats.
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.