doc-parse-convert 0.6.0

Utilities for document content extraction and conversion

Guthman's Document Parsing Utilities

A collection of utilities for document content extraction and conversion, including:

• PDF document processing and content extraction
• EPUB to HTML/TXT/PDF conversion
• Support for AI-assisted document content extraction
• Hierarchical document structure extraction with page ranges

🔒 Security

This repository uses Gitleaks to prevent accidentally committing secrets. See SECURITY.md for our security policy and GITLEAKS_SETUP.md for setup instructions.

Before contributing: Install Gitleaks to scan for secrets automatically. See INSTALLATION_INSTRUCTIONS.md for details.

Installation

# Install directly from the repository
pip install git+https://github.com/Guthman/doc-parse-convert.git

# For development installation (from local clone)
pip install -e .

System Dependencies

In addition to Python dependencies, this library requires the following external tools for certain functionality:

Required for EPUB/PDF/HTML Conversion

• Pandoc: Used for EPUB to PDF conversion

  • Windows: Install from pandoc.org/installing.html or using choco install pandoc
  • macOS: Install using Homebrew: brew install pandoc
  • Linux: Install using package manager: apt-get install pandoc or yum install pandoc

• wkhtmltopdf: Used for HTML to PDF conversion

  • Windows: Install from wkhtmltopdf.org/downloads.html or using choco install wkhtmltopdf
  • macOS: Install using Homebrew: brew install wkhtmltopdf
  • Linux: Install using package manager: apt-get install wkhtmltopdf or yum install wkhtmltopdf

Feature Dependency Matrix

Feature	Required System Dependencies
PDF content extraction	None
EPUB to HTML conversion	None
EPUB to TXT conversion	None
EPUB to PDF conversion	Pandoc
HTML to PDF conversion	wkhtmltopdf
HTML to Markdown conversion	None (but requires GCS bucket and Jina API credentials)

Configuration

The utilities require various configuration values and credentials. These can be provided in several ways:

1. Environment Variables: Create a .env file in your project root with the following variables:

   JINA_API_KEY=your_jina_api_key
   GCP_SERVICE_ACCOUNT=your_service_account_json
   AI_DEBUG_DIR=path/to/debug/directory  # For saving debug information when AI extraction fails
   

2. Processing Configuration: When using the document processors, provide a ProcessingConfig object with your settings:

   from doc_parse_convert import ProcessingConfig, ExtractionStrategy
   
   config = ProcessingConfig(
       project_id="your-project-id",
       vertex_ai_location="your-location",
       gemini_model_name="gemini-2.5-flash",
       use_application_default_credentials=True,
       toc_extraction_strategy=ExtractionStrategy.NATIVE,
       content_extraction_strategy=ExtractionStrategy.AI
   )
   

3. Required Tools:

   • Pandoc: For EPUB to PDF conversion
   • wkhtmltopdf: For HTML to PDF conversion Make sure these tools are installed and available in your system PATH.

Debugging AI Extraction

If you encounter issues with AI extraction, you can enable debugging by setting the AI_DEBUG_DIR environment variable:

# On Windows
$env:AI_DEBUG_DIR = "C:\path\to\debug\directory"

# On Linux/Mac
export AI_DEBUG_DIR=/path/to/debug/directory

When set, the library will save:

• Timestamped debug directories for each error
• Problematic images that caused API errors
• Error details and request information
• Complete API error diagnostics

This helps troubleshoot issues with the Vertex AI API, particularly "InvalidArgument" errors related to image sizes or content.

Usage

PDF Content Extraction

from doc_parse_convert import ProcessingConfig, ExtractionStrategy, PDFProcessor

# Configure the processor
config = ProcessingConfig(
    toc_extraction_strategy=ExtractionStrategy.NATIVE,
    content_extraction_strategy=ExtractionStrategy.NATIVE
)

# Process a PDF file
processor = PDFProcessor(config)
processor.load("document.pdf")

# Extract table of contents
chapters = processor.get_table_of_contents()
for chapter in chapters:
    print(f"{chapter.title} (pages {chapter.start_page+1}-{chapter.end_page+1})")

# Extract content from a specific chapter
chapter_content = processor.extract_chapter_text(chapters[0])

# Don't forget to close the processor when finished
processor.close()

EPUB Conversion

from doc_parse_convert import convert_epub_to_html, convert_epub_to_txt, convert_epub_to_pdf

# Convert EPUB to HTML
html_content = convert_epub_to_html("book.epub")

# Convert EPUB to TXT
text_content = convert_epub_to_txt("book.epub")

# Convert EPUB to PDF
pdf_path = convert_epub_to_pdf("book.epub", output_folder="output_folder")

Converting PDF Pages to Images

from doc_parse_convert.utils.image import ImageConverter

# Using context manager (recommended)
with ImageConverter('document.pdf', format='png') as converter:
    for page_number, page_data in converter:
        with open(f'document_page_{page_number+1}.png', 'wb') as f:
            f.write(page_data)

# Alternative approach
converter = ImageConverter('document.pdf', format='jpg')
try:
    for page_number, page_data in converter:
        with open(f'document_page_{page_number+1}.jpg', 'wb') as f:
            f.write(page_data)
finally:
    converter.close()

Document Structure Extraction

from doc_parse_convert import ProcessingConfig, PDFProcessor, DocumentStructureExtractor

# Configure the processor
config = ProcessingConfig(
    toc_extraction_strategy=ExtractionStrategy.NATIVE
)

# Process a PDF file
processor = PDFProcessor(config)
processor.load("document.pdf")

# Extract hierarchical document structure with page ranges
structure_extractor = DocumentStructureExtractor(processor)
document_structure = structure_extractor.extract_structure()

# Export structure in different formats
json_structure = structure_extractor.export_structure("json")
xml_structure = structure_extractor.export_structure("xml")

# Extract text by sections
section_texts = structure_extractor.extract_text_by_section("output_folder")

Using the Processor Factory

from doc_parse_convert import ProcessingConfig, ProcessorFactory

# Configure processing options
config = ProcessingConfig()

# Automatically create the appropriate processor based on file type
processor = ProcessorFactory.create_processor("document.pdf", config)

# Use the processor
chapters = processor.get_table_of_contents()

# Always close the processor when done
processor.close()

Examples

See the examples/ directory for detailed usage examples:

• usage_example.ipynb: Jupyter notebook with example code and configuration
• image_converter_example.py: Example of converting PDF pages to PNG and JPG images