Semantic chunking

Skill Goodnight77/rag-skills/skills/chunking/semantic-chunking

Use semantic boundaries and embedding similarity to chunk text for higher-relevance retrieval.From its SKILL.md

Install

npx -y skills add Goodnight77/rag-skills --skill semantic-chunking

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SKILL.md

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Overview

Semantic chunking divides documents into segments based on natural language boundaries and semantic meaning rather than fixed character counts. This approach preserves context integrity and improves retrieval relevance by keeping related ideas together.

Problem Statement

Fixed-size chunking (e.g., 1000 tokens) often cuts through mid-sentence, mid-paragraph, or mid-concept, resulting in:

Fragments that lose semantic meaning
Context bleeding across unrelated chunks
Poor retrieval relevance for semantic search
Difficulty for LLMs to understand isolated fragments

Key Concepts

Semantic Boundaries: Natural break points in text (paragraphs, sections, sentences)
Context Preservation: Keeping related ideas and arguments together
Sentence Boundary Detection: Using NLP tools to identify proper sentence ends
Paragraph Cohesion: Identifying when a new thought or argument begins
Overlapping Windows: Small overlaps to preserve context between adjacent chunks

Implementation Guide

Step 1: Detect Sentence Boundaries

Use sentence boundary detection rather than simple period splitting. This handles abbreviations, decimal numbers, and other edge cases.

Why: Simple period/regex splitting fails on "Dr. Smith", "3.14", "U.S.A.", etc.

Step 2: Group Sentences into Semantic Units

Group sentences into chunks based on semantic cohesion rather than fixed counts.

Why: Similarity-based chunking adapts to document density - dense technical sections get smaller chunks, narrative sections get larger ones.

Step 3: Apply Structure-Based Chunking

Leverage document structure (headings, markdown, HTML tags) to identify natural sections.

Why: Document structure often encodes semantic boundaries (chapters, sections, subsections).

Step 4: Implement Adaptive Sizing

Adjust chunk sizes based on content characteristics (code blocks, lists, tables).

Why: Different content types need different chunking strategies for optimal retrieval.

For practical implementations, compare Sentence-BERT, LlamaIndex Semantic Chunker, LangChain RecursiveCharacterTextSplitter, and spaCy sentence segmentation.

When to Use This Skill

Documents with clear semantic structure (articles, reports, documentation)
When retrieval quality is more important than fixed-size guarantees
For narrative or explanatory content where context matters
When using semantic search (vector embeddings) for retrieval
For Q&A systems that need coherent context for answers

When NOT to Use This Skill

Code repositories where line-level precision matters
Log files or structured data with fixed formats
When you need exact token count guarantees (e.g., for API limits)
Streaming data where chunk boundaries can't be deferred
When using exact keyword matching (BM25) where chunk size matters less

Related Skills

Hierarchical Chunking - For nested document structures
Sliding Window Chunking - For overlapping context preservation
Hybrid Search BM25 Dense - Combining search methods

Metrics & Success Criteria

Retrieval Relevance: Retrieved chunks should contain complete, self-contained information
Context Preservation: Related concepts remain in same chunk
Coverage: No significant information lost in chunk boundaries
Token Efficiency: Chunks balanced between too small (lossy) and too large (noisy)
Semantic Coherence: Chunks represent coherent thoughts or sections