Principle:Infiniflow Ragflow Text Chunking
| Knowledge Sources | |
|---|---|
| Domains | RAG, NLP, Document_Processing |
| Last Updated | 2026-02-12 06:00 GMT |
Overview
A text segmentation strategy that divides parsed document content into semantically coherent, retrievable units using format-specific splitting logic.
Description
Text Chunking is the process of dividing parsed document content into chunks suitable for embedding and retrieval. RAGFlow implements 14+ parser-specific chunking strategies via the FACTORY pattern. The naive chunker splits by token count with configurable delimiters. The paper chunker preserves academic structure (abstract, sections, references). The laws chunker splits by legal article numbers. Each chunker produces a list of chunk dictionaries with content and positional metadata.
Usage
Operates automatically within the document parsing pipeline. The chunking strategy is determined by the knowledge base's parser_id configuration.
Theoretical Basis
Chunking quality determines retrieval quality:
- Token-based splitting: Ensures chunks fit within embedding model context windows (typically 512 tokens)
- Semantic boundaries: Prefer splitting at paragraph, section, or sentence boundaries rather than mid-sentence
- Overlap: Some strategies use overlapping windows to preserve context across chunk boundaries