intelligent-text-chunking

Documents

Split large texts into meaningful, AI-optimized chunks while preserving semantic coherence and document structure. Use when processing large documents for AI training, RAG systems, or when you need to break down content while maintaining context and relationships.

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Intelligent Text Chunking

Overview

A sophisticated text segmentation skill that splits large texts into meaningful, AI-optimized chunks while preserving semantic coherence, document structure, and contextual relationships. Essential for processing large documents for AI training, RAG systems, and memory-constrained applications.

Capabilities

Semantic Awareness: Respects topic boundaries and meaning transitions
Structure Preservation: Maintains document hierarchy and formatting context
Multi-language Support: Handles accented characters and diverse writing systems
Configurable Strategies: Multiple chunking approaches for different use cases
Context Preservation: Intelligent overlap management for context continuity
Quality Optimization: Balances chunk size with content coherence

Chunking Strategies

1. Semantic Chunking

Split text based on meaning and topic boundaries rather than arbitrary size limits.

2. Structural Chunking

Follow document organization (headings, sections, lists) for natural divisions.

3. Fixed-Size Chunking

Create consistent-sized chunks with intelligent boundary selection.

4. Sliding Window Chunking

Create overlapping chunks for enhanced context preservation.

Implementation Examples

Language-Aware Processing

# Multi-language sentence detection
sentence_patterns = {
    'english': ['.', '!', '?', ';'],
    'chuukese': ['.', '!', '?'],
    'general': ['.', '!', '?', ';', '。', '！', '？']
}

def detect_language_patterns(text):
    has_accents = bool(re.search(r'[áéíóúàèìòùāēīōūâêîôû]', text))
    return 'chuukese' if has_accents else 'english'

Basic Usage

from .intelligent_chunker import IntelligentTextChunker, ChunkType

chunker = IntelligentTextChunker(
    max_chunk_size=1024,
    overlap_ratio=0.15,
    preserve_sentences=True
)

chunks = chunker.chunk_document(text, ChunkType.SEMANTIC)

Best Practices

Size Balancing: Balance chunk size with content coherence
Context Preservation: Use appropriate overlap for your use case
Language Awareness: Configure for specific languages when known
Quality Validation: Check chunk quality with sample reviews
Use Case Optimization: Choose strategy based on downstream use

Dependencies

re: Regular expression pattern matching
spacy: Advanced sentence segmentation
nltk: Natural language processing utilities
langdetect: Language identification support