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Summary

This PR adds comprehensive documentation for building and customizing Presidio Docker images to support additional languages.

Changes

  • Created docs/docker_customization.md with detailed instructions on:
    • Modifying Dockerfiles for language support
    • Configuring YAML files for custom recognizers
    • Building and running custom Docker images
    • Common pitfalls and troubleshooting tips (memory issues, NLP recognizer warnings)
    • Complete examples with docker-compose

Addresses Issue

Closes #1663

This documentation fulfills the request for more elaborate instructions on building custom Docker images for Presidio, specifically covering:

  • Which YAML files to modify for multi-language support
  • Typical pitfalls when adding 10+ languages
  • How to resolve common warnings like "NLP recognizer is not in the list of recognizers"

Testing

Documentation has been reviewed for accuracy and completeness. All code examples follow Presidio's existing patterns.

This document provides a comprehensive guide on how to build and customize Presidio Docker images to support additional languages and configurations, including prerequisites, steps for modification, and troubleshooting tips.
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Thanks! This is a great start! Left some comments for discussion.


Navigate to `presidio-analyzer/Dockerfile` and add your desired spaCy language models.

### Example: Adding Spanish Support
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Presidio supports the installation of spacy, stanza and transformers models using the NLP config, so there is no need to explicitly add those to the Dockerfile. Have you given this a try?

**Problem**: Adding 10+ languages at once can cause the Docker image to run out of memory during build or runtime.

**Solutions**:
- Use smaller spaCy models (e.g., `es_core_news_sm` instead of `es_core_news_lg`)
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Please add a caveat about smaller models likely being less accurate in detecting PII in the text

docker run -d -p 5002:3000 --memory="4g" presidio-analyzer-custom:latest
```
- Build images with only the languages you actually need
- Consider using transformers models which can be more memory-efficient
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Not sure this is true. Do you have a concrete example?

- `md` (medium): ~40MB, balanced
- `lg` (large): ~500MB+, most accurate but resource-intensive

**Recommendation**: Start with `md` models for a good balance.
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Our recommendation is to start with the large models

# Install spaCy language models
RUN python -m spacy download en_core_web_lg
RUN python -m spacy download es_core_news_md
RUN python -m spacy download fr_core_news_md
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If you download models but not configure the NER model configuration, presidio will ignore those models.

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More elaborate description how to build custom Docker images for Presidio

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