Overview

Implements multiple document chunking strategies to optimize retrieval quality in RAG pipelines. It covers semantic, recursive, token-aware, and structure-aware chunking, with fixed-size options and document-type awareness to handle markdown, code, and plain text.

How This Skill Works

Uses LangChain text splitters such as RecursiveCharacterTextSplitter, SemanticChunker, TokenTextSplitter, MarkdownHeaderTextSplitter, and CodeSplitter to produce chunks. Configuration includes chunk size, overlap, separator hierarchy, embedding model for semantic chunking, and document type detection.

When to Use It

• Designing a RAG pipeline for large multi-section documents (PDFs, manuals) where semantic boundaries improve retrieval.
• Handling code blocks or markdown docs with language-aware or header-based chunking.
• Controlling context length by tuning chunk size and overlap to fit embedding model limits.
• Comparing retrieval quality across different chunking strategies during a design or evaluation phase.
• Working with mixed document types and needing document-type detection to apply the right splitter.

Quick Start

1. Step 1: Choose a chunking strategy (RecursiveCharacterTextSplitter, SemanticChunker, etc.) and detect document types.
2. Step 2: Configure chunk size, overlap, separator hierarchy, and embedding model for semantic chunking.
3. Step 3: Wire the chosen splitter into the rag-pipeline-implementation and validate retrieval results.

Best Practices

• Match chunk size to the embedding model's token or character limits.
• Use appropriate overlap to preserve context between adjacent chunks.
• Test retrieval quality across multiple chunking strategies before deployment.
• Leverage document-type detection to select the appropriate splitter for each section.
• Benchmark chunking strategies against real user queries.

Example Use Cases

• Knowledge base search over large manuals using SemanticChunker for accurate results.
• Code search across a repository using CodeSplitter to keep code atoms intact.
• Markdown documentation chunked by headers with MarkdownHeaderTextSplitter for navigable results.
• Technical specs with RecursiveCharacterTextSplitter to preserve boundaries between sections.
• Documents with mixed content using token-aware TokenTextSplitter for efficient retrieval.

Frequently Asked Questions