How do I determine the right chunk size for my RAG app?

Use the built-in benchmarking script (`benchmark-chunking.py`) to compare different sizes against metrics like processing time and context preservation scores.

Which chunking strategy is best for technical documentation?

Recursive chunking is generally best for technical documentation as it respects hierarchical structures like headings and sub-sections, preserving context better than fixed-size methods.

Can this skill handle programming languages?

Yes, it includes specialized recursive splitters for languages like Python, JavaScript, and Go, ensuring functions and classes stay together within chunks.

Does this skill support PDF processing?

Yes, it includes scripts and examples specifically designed to extract text from PDFs while maintaining page boundaries and handling multi-column layouts.

RAG Chunking Strategies

Name: RAG Chunking Strategies
Author: vanman2024

byvanman2024

0•

Data Science & ML

Implements and benchmarks advanced document chunking strategies to optimize retrieval performance and context preservation in RAG pipelines.

The Chunking Strategies skill provides a comprehensive toolkit for developers building Retrieval-Augmented Generation (RAG) systems. It offers production-ready implementations of fixed-size, semantic, recursive, and sentence-based chunking, along with language-aware splitters for source code and Markdown. Beyond simple implementation, it includes a robust benchmarking suite to evaluate chunking performance against processing time, context preservation, and retrieval quality, ensuring that developers can select the optimal configuration for their specific document types and use cases.

Key Features

01Comprehensive strategies including fixed-size, semantic, recursive, and sentence-based chunking

02Production-ready configuration templates and custom splitter interfaces

030 GitHub stars

04Built-in benchmarking tools to measure processing time and context preservation scores

05Support for complex document formats including PDF, Markdown, and technical manuals

06Language-aware splitting for Python, JavaScript, TypeScript, and Java

Use Cases

01Optimizing vector database retrieval by fine-tuning chunk sizes and overlap ratios

02Processing large codebases for AI-assisted development using hierarchical recursive splitting

03Benchmarking different segmentation methods to improve LLM accuracy in specialized domains

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add vanman2024/ai-dev-marketplace chunking-strategies

For use in Claude.ai and ChatGPT

Download Skill