intelligent-text-chunking

What problem does it solve?

This skill enables AI systems to split large texts into meaningful chunks while preserving semantic coherence and document structure, improving downstream AI tasks such as training, retrieval-augmented generation (RAG), and memory-constrained processing.

Core Features & Use Cases

• Semantic-aware chunking: split based on topic boundaries and meaning transitions.
• Structure-preserving: maintain document hierarchy and formatting context.
• Language-aware processing: handles accented characters and multilingual content.
• Configurable strategies: supports semantic, structural, fixed-size, and sliding window chunking.
• Context-aware overlap: ensures contextual continuity across chunks.
• Use Case: prepare long manuals or research papers for AI training or retrieval systems, preserving sections and figures.

Quick Start

Load your text and run the chunker with a max_chunk_size of 1024 and an overlap of 0.15 to generate coherent chunks suitable for AI pipelines.