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MassimilianoPili
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chunking-strategy

Implement fixed-size, recursive, structure-aware, and semantic chunking for RAG systems.

Updated Mar 2, 2026
Search Tags:#document processing#embeddings#llm#rag#vector search#chunking
One-click install
npx skills add https://github.com/MassimilianoPili/claude-code-config --skill chunking-strategy-massimilianopili
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: chunking-strategy
Source: https://github.com/MassimilianoPili/claude-code-config/tree/main/skills/chunking-strategy
Command: npx skills add https://github.com/MassimilianoPili/claude-code-config --skill chunking-strategy-massimilianopili

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires langchain, sentence-transformers, nltk, spacy, tiktoken, pdfminer.six, python-docx, unstructured, chromadb, pinecone-client, weaviate-client, ragas, memory-profiler, pytest, dask, ray, plotly, streamlit, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of breaking down large documents into optimal segments (chunks) for retrieval-augmented generation (RAG) systems, improving the accuracy and relevance of AI responses.

Core Features & Use Cases

  • Diverse Chunking Strategies: Implements fixed-size, recursive, structure-aware, and semantic chunking methods.
  • Parameter Tuning: Offers guidance on adjusting chunk size, overlap, and strategy selection based on document type and use case.
  • Performance Evaluation: Provides frameworks and metrics for assessing chunking effectiveness.
  • Use Case: When building a RAG system for customer support documentation, use this Skill to ensure that relevant information is retrieved efficiently, leading to faster and more accurate answers for customer queries.

Quick Start

Use the chunking-strategy skill to split the document 'user_guide.pdf' into semantically meaningful chunks.

Frequently Asked Questions about chunking-strategy

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What is the best way to split documents into chunks for RAG systems?▼

Semantic chunking improves RAG retrieval by splitting documents into semantically meaningful segments rather than arbitrary text blocks, ensuring that embeddings capture complete contextual information for more accurate vector search results.

How do I chunk a PDF for vector search and LLM embeddings?▼

You can chunk PDFs for vector search by applying structure-aware chunking strategies that respect document layout, combined with parameter tuning for chunk size and overlap, to generate optimal text segments for embedding models.

What chunking strategy should I use for customer support documentation?▼

For customer support documentation, structure-aware or semantic chunking strategies are recommended. They preserve context within sections, ensuring relevant information is retrieved efficiently for faster, more accurate answers to customer queries.

How do I evaluate chunking effectiveness in a document processing pipeline?▼

You evaluate chunking effectiveness by using provided performance evaluation frameworks and metrics to measure retrieval accuracy and relevance. This assesses how well different chunking strategies perform within your RAG system.

Can I use recursive and fixed-size chunking methods with LangChain?▼

Yes, you can use fixed-size and recursive chunking methods with LangChain. The implementation supports parameter tuning for chunk size and overlap, allowing strategy selection based on document type and specific use case.

When should I avoid fixed-size chunking for LLM document processing?▼

Avoid fixed-size chunking for documents with complex layouts or distinct structural elements. In these cases, structure-aware or semantic chunking methods preserve context better, preventing fragmented information and improving retrieval accuracy.

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