large-document-processing

Documents

Process large documents (200+ pages) with structure preservation, intelligent parsing, and memory-efficient handling. Use when working with complex formatted documents, multi-level hierarchies, or when you need to extract structured data from large files like PDFs, DOCX, or text files.

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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

Memory Management: Use chunked processing for documents >100MB
Parallel Processing: Leverage multiple workers for batch operations
Structure Validation: Verify hierarchy detection accuracy
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