rag-chunking-strategy

name	rag-chunking-strategy
description	Document chunking with multiple strategies including semantic, recursive, and fixed-size chunking
allowed-tools	["Read","Write","Edit","Bash","Glob","Grep"]
graph	{"domains":["domain:software-engineering"],"specializations":["specialization:ai-agents-conversational"],"skillAreas":["skill-area:retrieval-augmented-generation","skill-area:natural-language-processing"],"roles":["role:ml-engineer","role:backend-engineer"],"workflows":["workflow:ml-model-lifecycle","workflow:feature-development"]}

RAG Chunking Strategy Skill

Capabilities

• Implement multiple document chunking strategies
• Configure semantic chunking based on content boundaries
• Set up recursive character text splitting
• Design fixed-size chunking with overlap
• Implement document-aware chunking (markdown, code, etc.)
• Optimize chunk sizes for retrieval quality

Target Processes

• rag-pipeline-implementation
• chunking-strategy-design

Implementation Details

Chunking Strategies

1. RecursiveCharacterTextSplitter: Hierarchical splitting with separators
2. SemanticChunker: Embedding-based semantic boundaries
3. TokenTextSplitter: Token-aware splitting
4. MarkdownHeaderTextSplitter: Structure-aware markdown splitting
5. CodeSplitter: Language-aware code chunking

Configuration Options

• Chunk size (characters or tokens)
• Chunk overlap percentage
• Separator hierarchy
• Embedding model for semantic chunking
• Document type detection

Best Practices

• Match chunk size to embedding model limits
• Use appropriate overlap for context preservation
• Test retrieval quality with different strategies
• Consider document structure in strategy selection

Dependencies

• langchain-text-splitters
• sentence-transformers (for semantic chunking)