Add guide for customizing Presidio Docker images

[SAIRAMSSSS]

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.

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[omri374]

Thanks! This is a great start! Left some comments for discussion.

[omri374]

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?

[omri374]

Please add a caveat about smaller models likely being less accurate in detecting PII in the text

[omri374]

Not sure this is true. Do you have a concrete example?

[omri374]

Our recommendation is to start with the large models

[omri374]

If you download models but not configure the NER model configuration, presidio will ignore those models.

[Copilot]

Pull request overview

This PR adds a new documentation file providing guidance on building and customizing Presidio Docker images for multi-language support, addressing issue #1663.

Changes:

• New comprehensive documentation file docs/docker_customization.md with instructions on Dockerfile modifications, YAML configurations, common pitfalls, and docker-compose examples

[Copilot]

The Docker run command uses port 5002 for the external mapping but the Dockerfile default PORT environment variable is 3000 (as seen in the actual Dockerfile line 13). This creates confusion about which internal port the service is actually running on.

The documentation should be consistent with the actual Presidio Dockerfile which uses PORT=3000 by default. The command should either be:

• docker run -d -p 5002:3000 presidio-analyzer-custom:latest (using default PORT=3000)
• Or document that users can override the PORT environment variable if needed

[Copilot]

The link to "Analyzer Engine Provider" documentation appears to be inconsistent with the actual file name. The link uses /analyzer/analyzer_engine_provider/ (suggesting a directory), but the actual file in the repository is analyzer/analyzer_engine_provider.md (a single markdown file).

The correct link format should be:
[Analyzer Engine Provider](https://microsoft.github.io/presidio/analyzer/analyzer_engine_provider/)

This likely works in practice due to how MkDocs handles URLs, but it's better to be consistent with the actual file structure for clarity.

Suggested change

	- [Analyzer Engine Provider](https://microsoft.github.io/presidio/analyzer/analyzer_engine_provider/)
	- [Analyzer Engine Provider](https://microsoft.github.io/presidio/analyzer/analyzer_engine_provider.md)

[Copilot]

The docker-compose.yml example uses version '3.8', but the actual Presidio docker-compose.yml files in the repository don't specify a version (which is the recommended practice for modern Docker Compose). The version field is deprecated in the latest Docker Compose specification.

Consider either:

1. Removing the version: '3.8' line to follow current best practices
2. Adding a note that the version field is optional in modern Docker Compose

This is a minor point but helps keep the documentation aligned with current Docker Compose conventions.

Suggested change

version: '3.8'

[Copilot]

The recommendation to "Consider using transformers models which can be more memory-efficient" may be misleading. Transformers models are typically more memory-intensive than smaller spaCy models, not less. The advantage of transformers is usually better accuracy for certain tasks, not memory efficiency.

If the intent is to suggest using a single multilingual transformer model instead of multiple language-specific spaCy models, this should be clarified. Otherwise, this recommendation could confuse users about the memory characteristics of transformers vs spaCy models.

Consider revising to: "Consider using a single multilingual transformers model instead of multiple language-specific spaCy models, which can reduce the total model size" or removing this point if it's not accurate for the intended use case.

Suggested change

	- Consider using transformers models which can be more memory-efficient
	- Consider using a single multilingual transformers model instead of multiple language-specific spaCy models, which can reduce the total model size

[Copilot]

The Dockerfile example provided is incomplete and doesn't match the actual Presidio architecture. The real Presidio Dockerfile:

1. Uses Python 3.12-slim (not 3.11-slim) as of the current version
2. Uses Poetry for dependency management, not direct pip install
3. Installs models via a configuration-driven approach using install_nlp_models.py, not direct spacy download commands
4. Has a more complex structure with environment variables, non-root user setup, and health checks

This simplified example could mislead users into thinking they should manually add RUN python -m spacy download commands, when they should actually be modifying the YAML configuration files. Either remove this example or replace it with accurate guidance on modifying the NLP configuration file (e.g., conf/default.yaml or using a custom configuration file via build args).

Suggested change

	WORKDIR /usr/bin/presidio-analyzer
	# Copy requirements
	COPY requirements.txt .
	RUN pip install -r requirements.txt
	# 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
	# Copy application code
	COPY . .
	EXPOSE 3000
	CMD ["python", "app.py"]
	FROM python:3.12-slim
	# Install system dependencies and Poetry
	RUN apt-get update && \
	apt-get install -y --no-install-recommends curl build-essential && \
	pip install --no-cache-dir poetry && \
	rm -rf /var/lib/apt/lists/*
	WORKDIR /usr/bin/presidio-analyzer
	# Copy project metadata and install Python dependencies with Poetry
	COPY pyproject.toml poetry.lock ./
	RUN poetry install --no-dev --no-interaction --no-ansi
	# Copy NLP configuration which controls which spaCy models are installed
	# Customize this YAML file (or provide your own via build args) instead of
	# adding `python -m spacy download ...` commands in the Dockerfile.
	COPY conf ./conf
	# Install NLP models based on the configuration file
	RUN poetry run python -m presidio_analyzer.install_nlp_models --conf-file conf/default.yaml
	# Copy application code
	COPY . .
	# Create and switch to a non-root user
	RUN useradd -m presidio && chown -R presidio /usr/bin/presidio-analyzer
	USER presidio
	EXPOSE 3000
	# Simple health check endpoint is expected at /health
	HEALTHCHECK --interval=30s --timeout=5s --retries=3 \
	CMD curl -fsS http://localhost:3000/health || exit 1
	# Start the analyzer service (command may vary by version; see official Dockerfile)
	CMD ["poetry", "run", "gunicorn", "-b", "0.0.0.0:3000", "presidio_analyzer.entrypoints.app:app"]

[Copilot]

The Dockerfile example specifies Python 3.11-slim, but the actual Presidio Dockerfile uses Python 3.12-slim (as seen in line 1 of the actual Dockerfile). While Python 3.11 is supported by Presidio according to the installation documentation, the example should match the current official Dockerfile to avoid confusion.

Consider updating to Python 3.12-slim to match the current official implementation, or add a note that users can use any supported Python version (3.10-3.13 according to installation.md).

Suggested change

	FROM python:3.11-slim
	FROM python:3.12-slim

[Copilot]

The documentation incorrectly describes how to add language models to the Dockerfile. The actual Presidio Dockerfile uses a configuration-based approach where models are installed automatically via the install_nlp_models.py script that reads from configuration YAML files (like default.yaml), not by directly adding RUN python -m spacy download commands in the Dockerfile.

The correct approach is to modify the NLP configuration file (e.g., presidio_analyzer/conf/default.yaml) to specify which models should be installed. The Dockerfile already contains the logic to read this configuration and install the models automatically during the build process at line 36: RUN poetry run python install_nlp_models.py --conf_file ${NLP_CONF_FILE}

This section should be rewritten to reflect the actual architecture and direct users to modify the YAML configuration files instead.

[Copilot]

This YAML configuration section is incorrect. The default_recognizers.yaml file already contains a supported_languages list at the top level (line 1-2 of the actual file), and it uses the format:

supported_languages: 
  - en

The documentation should clarify that users need to:

1. Modify the NLP configuration file (e.g., presidio_analyzer/conf/default.yaml) to add language models
2. Update the top-level supported_languages list in default_recognizers.yaml to include new language codes
3. Optionally add or update individual recognizers with language-specific context words

The current documentation incorrectly suggests adding a supported_languages block under the recognizers configuration without explaining the proper structure.

[Copilot]

The warning about "NLP recognizer is not in the list of recognizers" is misleading. This warning typically occurs when the NLP engine configuration (spacy models) doesn't match the recognizer registry configuration, not just when language configuration doesn't match installed models.

The solution provided is incomplete. Based on the actual Presidio architecture:

1. The default_recognizers.yaml file controls which recognizers are loaded and which languages they support
2. The NLP configuration file (e.g., default.yaml) controls which spaCy models are installed
3. These two must be aligned: if you add Spanish support, you need BOTH the Spanish spaCy model in the NLP config AND Spanish language support declared in the recognizer registry

The documentation should clarify that this warning appears when recognizers are configured for a language but no NLP model is configured for that language in the NLP configuration file.

[Copilot]

The documentation doesn't mention that users need to update the NLP configuration file (default.yaml) to specify which language models to install. Based on the actual Presidio Dockerfile (line 36), models are installed by running install_nlp_models.py --conf_file ${NLP_CONF_FILE}, which reads from the configuration file.

For multi-language support, users should either:

1. Modify presidio_analyzer/conf/default.yaml to add additional models, OR
2. Create a custom NLP configuration file (e.g., spacy_multilingual.yaml which already exists in the repo) and pass it as a build arg

The current documentation focuses on modifying the Dockerfile directly, which is not the recommended approach according to the actual Presidio architecture.