What is Semantic Chunking
Semantic Chunking - Splitting documents into meaningful segments based on content boundaries rather than fixed sizes
Semantic chunking is a document preprocessing technique that splits text into segments aligned with natural content boundaries — paragraphs, topics, or logical sections — rather than using fixed character or token counts. This produces chunks that preserve complete ideas and context, leading to better embedding quality and more relevant search results in RAG and retrieval systems.
How It Works
Semantic chunking analyzes the content structure to identify natural break points. Common approaches include embedding-based splitting (computing embeddings for sentences and splitting where similarity drops), topic boundary detection, and structural parsing (using headings, paragraphs, and formatting cues). Each resulting chunk contains a coherent unit of information.
Technical Details
Embedding-based semantic chunking computes sentence-level embeddings and measures cosine similarity between consecutive sentences. When similarity drops below a threshold, a chunk boundary is inserted. More advanced methods use sliding windows with percentile-based thresholds. For structured documents (HTML, Markdown, PDF), layout analysis identifies headings, sections, and visual boundaries as natural chunk points.
Best Practices
- Target chunk sizes of 200-500 tokens for most retrieval tasks
- Include overlap between chunks to preserve context at boundaries
- Use document structure (headings, sections) as primary split points when available
- Test different chunking strategies on your specific data and measure retrieval quality
- Preserve metadata (source, section title, page number) with each chunk