Large Document Processing

Overview

A comprehensive skill for processing large documents (200+ pages) with structure preservation, intelligent parsing, and memory-efficient handling. Designed for documents with complex formatting, hierarchical structures, and multi-level indentation.

Capabilities

• Multi-format Support: DOCX, PDF, and text files
• Structure Preservation: Maintains document hierarchy, indentation, and formatting
• Memory Efficiency: Chunked processing to handle very large documents
• Intelligent Parsing: Recognizes headings, lists, dictionary entries, and semantic boundaries
• Progress Tracking: Real-time processing status and error recovery
• Metadata Extraction: Comprehensive document analysis and statistics

Core Components

1. Advanced Document Parser

Parse complex document structures while preserving formatting and hierarchy.

Key Features:

• Hierarchical structure detection (levels 1-10)
• Formatting preservation (bold, italic, fonts, sizes)
• Page-by-page processing for memory efficiency
• Intelligent content classification
• Multi-language support with accent character handling

2. Implementation Pattern

from .large_document_processor import LargeDocumentProcessor, ProcessingConfig

# Configure processing
config = ProcessingConfig(
    chunk_size_pages=50,
    parallel_workers=4,
    preserve_formatting=True
)

# Initialize processor
processor = LargeDocumentProcessor(config)

# Process document
results = processor.process_large_document(
    input_file="large_document.docx",
    output_dir="output/processed"
)

3. Intelligent Text Chunking

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)

Output Formats

• Structured JSON: Complete document hierarchy and metadata
• Plain text: Clean extracted text with optional formatting markers
• Chunked data: AI-ready text segments with overlap and metadata
• Statistics report: Processing metrics and quality analysis

Best Practices

1. Memory Management: Use chunked processing for documents >100MB
2. Parallel Processing: Leverage multiple workers for batch operations
3. Structure Validation: Verify hierarchy detection accuracy
4. Progress Tracking: Provide user feedback for long-running operations

Dependencies

• python-docx: DOCX file processing
• PyMuPDF: Advanced PDF processing
• Pillow: Image processing for embedded content
• pathlib: Cross-platform path handling