DocuFiller

Intelligent document filling system with LLM-powered field mapping. Automatically fills DOC/DOCX/PDF templates with data from JSON, supports OCR for scanned documents, and batch processing.

Features

• Multi-format Support: DOC, DOCX, PDF
• Smart Field Detection: 14 pattern types including placeholders, underscores, brackets
• LLM Integration: GigaChat for intelligent field mapping with rule-based fallback
• OCR Support: Process scanned PDFs with Tesseract
• Batch Processing: Fill multiple documents at once
• Database Management: Store organizations, persons, and data templates
• Cross-platform: Windows and Ubuntu support

Quick Start

Installation

pip install -r requirements.txt

Ubuntu Additional Setup

sudo apt install libreoffice tesseract-ocr tesseract-ocr-rus tesseract-ocr-eng

Windows Additional Setup

1. Install Tesseract OCR
2. Add Tesseract to PATH

LLM Setup (Optional)

pip install llm llm-gigachat
llm keys set gigachat YOUR_API_KEY

Usage

Basic Example

from document_filler import DocumentFiller

data = {
    'full_name': 'John Doe',
    'organization': 'Acme Corp',
    'date': '08.11.2025',
    'contract_number': 'CT-2025-001'
}

filler = DocumentFiller()
filler.fill_document('template.docx', data, 'output.docx')

Batch Processing

templates = ['contract.docx', 'invoice.pdf', 'agreement.doc']
filler.fill_multiple(templates, data, output_dir='filled_docs')

Using Database

from database_manager import DatabaseManager

db = DatabaseManager()

org_id = db.add_organization({
    'name': 'Acme Corp',
    'inn': '1234567890',
    'address': '123 Main St'
})

person_id = db.add_person({
    'full_name': 'John Doe',
    'position': 'Director',
    'organization_id': org_id
})

data = db.get_complete_data_for_document(
    organization_id=org_id,
    person_id=person_id
)

filler.fill_document('template.docx', data, 'output.docx')

Supported Field Patterns

• {{field_name}} - Double braces
• {field_name} - Single braces
• [field_name] - Square brackets
• <field_name> - Angle brackets
• _____ - Underscores (3+ chars)
• ... - Dots (3+ chars)
• --- - Dashes (3+ chars)
• Context markers: ФИО, Дата, Подпись, Должность, etc.

Project Structure

docufiller/
├── document_processor.py    # Document format handling
├── field_detector.py         # Field detection and mapping
├── document_filler.py        # Main filling logic
├── database_manager.py       # Database operations
├── requirements.txt          # Dependencies
├── test.py                   # Test suite
└── data/
    └── example_data.json     # Sample data

API Reference

DocumentFiller

fill_document(template_path: str, data: Dict, output_path: str, mapping: Optional[Dict] = None) -> str

Fills a single document with provided data.

fill_multiple(template_paths: List[str], data: Dict, output_dir: str, mapping: Optional[Dict] = None) -> List[str]

Batch processes multiple documents.

FieldDetector

detect_fields_in_docx(doc: Document) -> List[Dict]
detect_fields_in_pdf(pdf_path: str) -> List[Dict]
smart_field_mapping(detected_fields: List[Dict], data: Dict) -> List[Tuple[Dict, Any]]

DatabaseManager

add_organization(org_data: Dict) -> int
add_person(person_data: Dict) -> int
add_data_card(card_name: str, data: Dict, card_type: str = 'general') -> int
get_complete_data_for_document(organization_id: int, person_id: int, data_card_id: int) -> Dict

Testing

python test.py

All tests passed successfully. See TEST_REPORT.txt for details.

Use Cases

• Legal contracts and agreements
• HR documents (employment contracts, NDAs)
• Invoices and financial documents
• Government forms and applications
• Medical records and prescriptions
• Real estate documents

Roadmap

• Web interface
• REST API
• More LLM providers (OpenAI, Claude)
• Excel template support
• Digital signature integration
• Template editor GUI
• Cloud storage integration

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT License - see LICENSE file for details.

Support

For issues and questions, please open an issue on GitHub.

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