RAGFlow offers multiple intelligent chunking strategies designed to preserve document meaning and structure, moving beyond naive fixed-size splitting. Default semantic chunking uses specially trained models to identify natural content boundaries—section breaks, paragraph boundaries, table edges, code blocks—avoiding cuts that fragment logical units. This approach recognizes that document structure carries meaning: cutting a sentence in half or separating a header from its paragraph degrades retrieval quality. Sentence-aware chunking splits text on sentence boundaries, ensuring complete thoughts are preserved while keeping chunks smaller. Document-aware chunking respects document structure like headers, code blocks, and tables, treating these as atomic units that shouldn't be split. Adaptive chunking adjusts chunk size based on content type—code blocks remain intact, tables are treated as units, paragraphs are split logically. From v0.17.0 onward, RAGFlow decouples chunking methods from OCR/parser selection, letting you independently choose parsing strategy (DeepDoc, MinerU, Docling) and chunking approach (semantic, sentence-aware, document-aware). This modularity is crucial because the optimal chunking strategy depends on your document type and query patterns. RAGFlow's document engine (Infinity v0.6.1 as of v0.24.0) implements these strategies efficiently, balancing chunk quality against indexing time. You configure chunking through RAGFlow's visual interface per knowledge base, enabling experimentation with different strategies. For tuning, consider: smaller chunks improve semantic precision in similarity search but reduce keyword frequency context; larger chunks preserve keyword context but may include irrelevant material. The semantic chunking approach RAGFlow defaults to generally outperforms both extremes by intelligently adapting size. For specialized use cases (dense tables, code repositories), you can fine-tune chunk size and strategy. RAGFlow's chunking sophistication is a key differentiator from simpler RAG frameworks that use naive fixed-size splitting.
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.