A Python-based comprehensive tagging system for Obsidian vaults that analyzes files, generates appropriate tags based on directory structure and content analysis, and updates frontmatter while preserving existing data.

Features

Directory-based tagging: Automatically generates tags based on vault directory structure
Content analysis: Analyzes file content to extract topics, technologies, and entities
Hierarchical tag structures: Creates organized tag hierarchies using forward slash notation
Language detection: Identifies content language (English, Chinese, mixed, unknown)
Frontmatter management: Updates YAML frontmatter while preserving existing data
Batch processing: Processes entire vaults efficiently with progress tracking
Sensitive content detection: Identifies and appropriately tags sensitive information
Obsidian compatibility: Ensures tags work with Obsidian's features and plugins

Installation

pip install -e .

Development Setup

# Install development dependencies
pip install -e ".[dev,test]"

# Run tests
pytest

# Run tests with coverage
pytest --cov=tagging_system

# Format code
black tagging_system tests

# Lint code
flake8 tagging_system tests

# Type checking
mypy tagging_system

Project Structure

tagging_system/
├── __init__.py
├── core/
│   ├── __init__.py
│   ├── models.py          # Core data models
│   └── interfaces.py      # Abstract interfaces and protocols
├── config/
│   ├── __init__.py
│   └── config.py          # Configuration system
└── implementations/       # Concrete implementations (to be added)

tests/
├── __init__.py
├── conftest.py           # Pytest configuration and fixtures
├── test_models.py        # Tests for core models
├── test_config.py        # Tests for configuration system
└── test_interfaces.py    # Tests for interfaces and protocols

Configuration

The system uses a flexible configuration system that supports both YAML and JSON formats. Configuration includes:

Directory mappings for tag generation
Tag hierarchies and structures
Sensitive content detection patterns
File processing settings
Language detection parameters

Testing

The project uses pytest with hypothesis for property-based testing:

Unit tests: Test specific functionality and edge cases
Property-based tests: Test universal properties across randomized inputs
Integration tests: Test component interactions

Run tests with:

pytest                    # Run all tests
pytest -m unit           # Run only unit tests
pytest -m property       # Run only property-based tests
pytest -v                # Verbose output

Requirements

This implementation addresses the following requirements:

1.1, 1.2, 1.3: Standardized frontmatter structure
Directory-based tag mapping
Content analysis and classification
Tag consistency and validation
Batch processing capabilities

License

MIT License