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Contributing to Presidio Recipes Gallery

Thank you for your interest in contributing! Recipes help others learn how to customize Presidio for specific domains.

What Makes a Good Recipe?

A good recipe should:

  1. Focus on a Specific Domain: Target a well-defined scenario (e.g., "Financial chatbot logs", "Clinical notes", "Customer support tickets")

  2. Be Reproducible: Include working code that others can run

  3. Follow the Research Pattern: Build upon the end-to-end approach from presidio-research, showing:

  4. Data Synthesis: How to generate or obtain test data
  5. Configuration: Your Presidio setup with any custom recognizers

  6. Evaluation: Metrics (precision, recall, F₂) showing performance

  7. Keep It Simple: Focus on getting a working example first. Detailed documentation can come later.

Recipe Format

Follow Presidio Research Examples

Your recipe should follow the end-to-end evaluation approach from presidio-research.

Key reference notebooks: - Evaluate Presidio Analyzer - Complete evaluation workflow example - Generate Synthetic Data - Using Presidio Evaluator data generator

For straightforward examples, use one notebook:

your-recipe-name/
├── recipe.ipynb          # Main notebook with data synthesis → evaluation
└── README.md             # Brief overview (optional)

For complex scenarios, break into separate files:

your-recipe-name/
├── 1_generate_data.ipynb # Data synthesis (use Presidio Evaluator or custom)
├── 2_configure.ipynb     # Presidio setup with custom recognizers
├── 3_evaluate.ipynb      # Run evaluation and analysis
└── README.md             # Overview and instructions

Or as Python scripts:

your-recipe-name/
├── generate_data.py      # Generate test data
├── configure.py          # Presidio setup
├── evaluate.py           # Run evaluation
└── README.md             # Overview

Required Components

Your recipe should include: - Data Synthesis: Generate synthetic data using Presidio Evaluator or your own method - Presidio Configuration: Show your setup (default, custom recognizers, or custom models) - Evaluation: Measure and report precision, recall, F₂ score, latency - Key Findings: Brief summary of results and when to use this approach

Quick Contribution Steps

  1. Fork the repo and create a new branch

  2. Create your recipe folder under docs/recipes/your-recipe-name/

  3. Use the template: Copy template.md or start with a notebook

  4. Focus on working code first: Don't worry about making it perfect

  5. Submit a PR: We'll help refine it during review

Evaluation Metrics

Include at minimum: - Precision: Percentage of detected entities that were correct - Recall: Percentage of actual PII that was detected
- F₂ Score: Recall-weighted F-score (emphasizes catching all PII) - Latency: Average processing time per sample

Examples to Learn From

Presidio Research Notebooks (recommended starting point): - Evaluate Presidio Analyzer - Complete end-to-end evaluation example - Generate Synthetic Data - Presidio Evaluator data generator - Other presidio-research notebooks - Additional examples and tools

Additional Resources: - Presidio Samples: Integration patterns and usage examples

Questions?

License

By contributing, you agree to license your work under the MIT License.