How does semantic chunking differ from recursive splitting?

Recursive splitting breaks text based on structural characters like paragraphs and sentences, while semantic chunking uses embedding similarity to identify thematic shifts and maintain topical coherence.

Why is chunk overlap important for vector databases?

Overlap prevents context loss at the edges of chunks, ensuring that keywords or concepts split across two segments can still be retrieved effectively during a search.

Can this skill handle source code and technical documentation?

Yes, it includes structure-aware logic specifically for Python code, Markdown, and other structured formats to ensure functions and sections remain intact as semantic units.

What is the best chunk size for general RAG systems?

A common starting point is 512 tokens with a 10-20% overlap. However, this skill helps you adjust sizes based on query type—256 tokens for factoid lookups and 1024 for analytical tasks.

Does this skill include performance evaluation metrics?

Absolutely. It provides a framework to measure retrieval precision, recall, and end-to-end accuracy to help you iterate on your chunking parameters.

RAG Chunking Strategy

Name: RAG Chunking Strategy
Author: pur3v4d3r

bypur3v4d3r

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データサイエンスとML

Implements advanced document chunking strategies to optimize retrieval-augmented generation (RAG) performance and embedding accuracy.

概要

The Chunking Strategy skill provides a comprehensive framework for decomposing large documents into semantically meaningful segments tailored for vector databases and LLM search. It offers five distinct levels of complexity—ranging from basic fixed-size splitting to advanced semantic boundary detection and structure-aware code parsing—ensuring that RAG systems maintain contextual integrity. By balancing retrieval precision with computational efficiency, this skill helps developers minimize data loss and maximize the accuracy of AI-generated responses across diverse document types like Markdown, code, and PDFs.

主な機能

1 GitHub stars
Semantic boundary detection using embedding similarity thresholds
Late chunking and contextual retrieval for long-context models
Structure-aware parsing for code, Markdown, tables, and PDFs
Integrated evaluation framework for measuring retrieval precision and recall
Hierarchical Recursive Character Chunking for structural document integrity

ユースケース

Preparing structured codebases for semantic search and AI-assisted navigation
Optimizing vector search relevance for large enterprise knowledge bases
Tuning RAG pipelines to balance processing latency against retrieval quality

概要

主な機能

1 GitHub stars
Semantic boundary detection using embedding similarity thresholds
Late chunking and contextual retrieval for long-context models
Structure-aware parsing for code, Markdown, tables, and PDFs
Integrated evaluation framework for measuring retrieval precision and recall
Hierarchical Recursive Character Chunking for structural document integrity

ユースケース

Preparing structured codebases for semantic search and AI-assisted navigation
Optimizing vector search relevance for large enterprise knowledge bases
Tuning RAG pipelines to balance processing latency against retrieval quality