### Installation

Source: https://microsoft.github.io/presidio/tutorial/00_getting_started

Install Presidio Analyzer and Anonymizer, and download the English language model for spaCy.

```shell
pip install presidio_analyzer
pip install presidio_anonymizer
python -m spacy download en_core_web_lg

```

--------------------------------

### Start a Specific Service with Docker Compose

Source: https://microsoft.github.io/presidio/development

Example of starting only the analyzer service using Docker Compose.

```shell
docker-compose up --build -d presidio-analyzer

```

--------------------------------

### Simple flow

Source: https://microsoft.github.io/presidio/tutorial/00_getting_started

A basic example of using Presidio Analyzer to find PII in a text.

```python
from presidio_analyzer import AnalyzerEngine

text = "His name is Mr. Jones and his phone number is 212-555-5555"

analyzer = AnalyzerEngine()
analyzer_results = analyzer.analyze(text=text, language="en")

print(analyzer_results)

```

--------------------------------

### Install client requirements

Source: https://microsoft.github.io/presidio/samples/deployments/openai-anonymaztion-and-deanonymaztion-best-practices/src/client_app

Install the necessary Python dependencies for the client application.

```bash
cd src/client_app
pip install -r requirements.txt
```

--------------------------------

### Install dependencies

Source: https://microsoft.github.io/presidio/samples/python/example_dicom_image_redactor

Install the required Presidio packages and the spaCy language model.

```bash
!pip install presidio_analyzer presidio_anonymizer presidio_image_redactor
!python -m spacy download en_core_web_lg
```

--------------------------------

### Start Streamlit App

Source: https://microsoft.github.io/presidio/samples/python/streamlit

Launches the Presidio demo website using the Streamlit framework. Ensure all dependencies are installed before running this command.

```bash
streamlit run presidio_streamlit.py
```

--------------------------------

### Install Poetry using Homebrew

Source: https://microsoft.github.io/presidio/development

Instructions for installing Poetry on macOS using Homebrew.

```shell
brew install poetry

```

--------------------------------

### AnalyzerEngine initialization example

Source: https://microsoft.github.io/presidio/api/analyzer_python

Example of how to initialize the AnalyzerEngine.

```python
from presidio_analyzer import AnalyzerEngine

# Set up the engine, loads the NLP module (spaCy model by default)
# and other PII recognizers
analyzer = AnalyzerEngine()


```

--------------------------------

### Pre-commit hook execution example

Source: https://microsoft.github.io/presidio/development

Example output showing ruff and ruff-format hooks running during a git commit.

```text
[INFO] Initializing environment for https://github.com/astral-sh/ruff-pre-commit.
[INFO] Installing environment for https://github.com/astral-sh/ruff-pre-commit.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
ruff.....................................................................Passed
ruff-format..............................................................Failed
- hook id: ruff-format
- files were modified by this hook
  5 files reformatted, 4 files left unchanged


```

--------------------------------

### Install Presidio with GLiNER support

Source: https://microsoft.github.io/presidio/samples/python/gliner

Install the required dependencies using pip.

```bash
pip install 'presidio-analyzer[gliner]'
```

--------------------------------

### Install Poetry using Pip

Source: https://microsoft.github.io/presidio/development

Instructions for installing Poetry, a Python package manager, using Pip.

```shell
pip install poetry

```

--------------------------------

### Install presidio-structured

Source: https://microsoft.github.io/presidio/structured

To install the `presidio-structured` package, run the following command:

```bash
pip install presidio-structured

```

--------------------------------

### Install Presidio Dependencies

Source: https://microsoft.github.io/presidio/samples/python/encrypt_decrypt

Install the necessary Python packages and download the Spacy language model.

```bash
!pip install presidio_analyzer presidio_anonymizer
!python -m spacy download en_core_web_lg
```

--------------------------------

### Install required dependencies

Source: https://microsoft.github.io/presidio/samples/python/example_pdf_annotation

Install the necessary libraries for Presidio, PDF processing, and language models.

```bash
!pip install presidio_analyzer
!pip install presidio_anonymizer
!python -m spacy download en_core_web_lg
!pip install pdfminer.six
!pip install pikepdf
```

--------------------------------

### Install Presidio with LangExtract support

Source: https://microsoft.github.io/presidio/samples/python/langextract

Install the necessary dependencies to enable language model-based detection.

```bash
pip install presidio-analyzer[langextract]
```

--------------------------------

### Install Presidio and Dependencies

Source: https://microsoft.github.io/presidio/samples/python/synth_data_with_openai

Installs the necessary Presidio libraries, OpenAI client, pandas, and downloads the English language model for spaCy.

```bash
# download presidio
!pip install presidio_analyzer presidio_anonymizer
!pip install openai pandas
!python -m spacy download en_core_web_lg
```

--------------------------------

### Install Python Dependencies

Source: https://microsoft.github.io/presidio/samples/deployments/openai-anonymaztion-and-deanonymaztion-best-practices/src/api

Navigate to the API source directory and install required Python packages using pip.

```bash
cd src/api
pip install -r requirements.txt

```

--------------------------------

### Install Presidio and spaCy

Source: https://microsoft.github.io/presidio/samples/python/Anonymizing%20known%20values

Installs the necessary Presidio libraries and downloads the English language model for spaCy.

```bash
# download presidio
!pip install presidio_analyzer presidio_anonymizer

!python -m spacy download en_core_web_lg
```

--------------------------------

### Install Presidio and Download spaCy Model

Source: https://microsoft.github.io/presidio/samples/python/getting_entity_values

Installs the necessary Presidio libraries and downloads the English language model for spaCy.

```bash
# download presidio
!pip install presidio_analyzer presidio_anonymizer
!python -m spacy download en_core_web_lg
```

--------------------------------

### Install Dependencies

Source: https://microsoft.github.io/presidio/samples/python/streamlit

Installs the necessary Python packages for the Presidio demo website. It's recommended to do this within a virtual environment.

```bash
pip install -r requirements.txt
```

--------------------------------

### Install Presidio and Dependencies

Source: https://microsoft.github.io/presidio/samples/python/batch_processing

Installs the necessary Presidio libraries and the spaCy language model. Use this to set up your environment before running Presidio.

```python
# download presidio
#!pip install presidio_analyzer presidio_anonymizer
#!python -m spacy download en_core_web_lg
#!pip install pandas

```

--------------------------------

### Installing pre-commit git hooks

Source: https://microsoft.github.io/presidio/development

Command to install pre-commit hooks for automatic code formatting and linting.

```bash
pre-commit install

```

--------------------------------

### Initialize environment configuration

Source: https://microsoft.github.io/presidio/samples/deployments/openai-anonymaztion-and-deanonymaztion-best-practices/src/client_app

Create the .env file from the provided sample template.

```bash
cp .env.sample .env
```

--------------------------------

### Set Up Environment Variables

Source: https://microsoft.github.io/presidio/samples/deployments/openai-anonymaztion-and-deanonymaztion-best-practices/src/api

Copy the sample environment file and edit it to configure your local setup.

```bash
cp .env.sample .env

```

--------------------------------

### Run web interface

Source: https://microsoft.github.io/presidio/samples/deployments/openai-anonymaztion-and-deanonymaztion-best-practices/src/client_app

Start the web server for the chat interface.

```bash
python serve.py
```

--------------------------------

### PhoneRecognizer Usage Example

Source: https://microsoft.github.io/presidio/api/analyzer_python

This snippet shows how to use the PhoneRecognizer to analyze text and get results.

```python
from presidio_analyzer import AnalyzerEngine, RecognizerResult
from presidio_analyzer.recognizer import PhoneRecognizer

# Initialize the analyzer engine
analyzer = AnalyzerEngine()

# Example text containing a phone number
text = "My phone number is 123-456-7890."

# Analyze the text
results = analyzer.analyze(text=text, language='en')

# Print the results
for result in results:
    print(result)
```

--------------------------------

### Initialize AnalyzerEngine with multiple configuration files

Source: https://microsoft.github.io/presidio/analyzer/analyzer_engine_provider

Split configuration into separate files for the analyzer, NLP engine, and recognizer registry.

```python
from presidio_analyzer import AnalyzerEngine, AnalyzerEngineProvider

analyzer_conf_file = "./analyzer/analyzer-config.yml"
nlp_engine_conf_file = "./analyzer/nlp-config.yml"
recognizer_registry_conf_file = "./analyzer/recognizers-config.yml"

provider = AnalyzerEngineProvider(
    analyzer_engine_conf_file=analyzer_conf_file,
    nlp_engine_conf_file=nlp_engine_conf_file,
    recognizer_registry_conf_file=recognizer_registry_conf_file,
    )
analyzer = provider.create_engine()

results = analyzer.analyze(text="My name is Morris", language="en")
print(results)
```

--------------------------------

### Basic Presidio Analyzer and Anonymizer Configuration

Source: https://microsoft.github.io/presidio/recipes/template

This snippet shows the basic setup for using Presidio's AnalyzerEngine and AnonymizerEngine to detect and anonymize PII in text. Ensure you have the necessary Presidio libraries installed.

```python
from presidio_analyzer import AnalyzerEngine
from presidio_anonymizer import AnonymizerEngine

# Your Presidio configuration
analyzer = AnalyzerEngine()
anonymizer = AnonymizerEngine()

# Analyze and anonymize
results = analyzer.analyze(text=sample_text, language="en")
anonymized = anonymizer.anonymize(text=sample_text, analyzer_results=results)

print(anonymized.text)

```

--------------------------------

### Install spaCy CUDA Dependencies

Source: https://microsoft.github.io/presidio/analyzer/nlp_engines/gpu_usage

Install the specific spaCy package variant that matches your installed CUDA toolkit version.

```bash
pip install "spacy[cuda12x]"  # For CUDA 12.x
# or
pip install "spacy[cuda11x]"  # For CUDA 11.x
```

--------------------------------

### Initialize AnalyzerEngine with a single configuration file

Source: https://microsoft.github.io/presidio/analyzer/analyzer_engine_provider

Use the AnalyzerEngineProvider to load all settings from a single YAML file.

```python
from presidio_analyzer import AnalyzerEngine, AnalyzerEngineProvider

analyzer_conf_file = "./analyzer/analyzer-config-all.yml"

provider = AnalyzerEngineProvider(
    analyzer_engine_conf_file=analyzer_conf_file
    )
analyzer = provider.create_engine()

results = analyzer.analyze(text="My name is Morris", language="en")
print(results)
```

--------------------------------

### Run Arbitrary Scripts with Poetry

Source: https://microsoft.github.io/presidio/development

Examples of running arbitrary scripts within the virtual environment managed by Poetry.

```shell
poetry run ruff check

```

```shell
poetry run pip freeze

```

```shell
poetry run python -m spacy download en_core_web_lg

```

--------------------------------

### Initialize AnalyzerEngine with default configuration

Source: https://microsoft.github.io/presidio/analyzer/analyzer_engine_provider

Create an engine instance without providing specific configuration files to use defaults.

```python
from presidio_analyzer import AnalyzerEngine, AnalyzerEngineProvider

provider = AnalyzerEngineProvider().create_engine()

results = provider.analyze(text="My name is Morris", language="en")
print(results)
```

--------------------------------

### Install AHDS Recognizer

Source: https://microsoft.github.io/presidio/ahds_integration

Install the required package for AHDS entity recognition.

```bash
pip install presidio-analyzer[ahds]
```

--------------------------------

### Initialize and Run FlairRecognizer

Source: https://microsoft.github.io/presidio/samples/python/flair_recognizer

Demonstrates how to instantiate the FlairRecognizer, add it to the registry, and analyze text. Note that the model download is large (over 2GB) on the first execution.

```python
if __name__ == "__main__":

    from presidio_analyzer import AnalyzerEngine, RecognizerRegistry

    flair_recognizer = (
        FlairRecognizer()
    )  # This would download a very large (+2GB) model on the first run

    registry = RecognizerRegistry()
    registry.add_recognizer(flair_recognizer)

    analyzer = AnalyzerEngine(registry=registry)

    results = analyzer.analyze(
        "My name is Christopher and I live in Irbid.",
        language="en",
        return_decision_process=True,
    )
    for result in results:
        print(result)
        print(result.analysis_explanation)
```

```python
if __name__ == "__main__": from presidio_analyzer import AnalyzerEngine, RecognizerRegistry flair_recognizer = ( FlairRecognizer() ) # This would download a very large (+2GB) model on the first run registry = RecognizerRegistry() registry.add_recognizer(flair_recognizer) analyzer = AnalyzerEngine(registry=registry) results = analyzer.analyze( "My name is Christopher and I live in Irbid.", language="en", return_decision_process=True, ) for result in results: print(result) print(result.analysis_explanation)
```

--------------------------------

### Example configuration file structure

Source: https://microsoft.github.io/presidio/analyzer/analyzer_engine_provider

A sample YAML configuration file defining supported languages, NLP settings, and recognizer registry.

```yaml
supported_languages: 
- en
default_score_threshold: 0

nlp_configuration:
  nlp_engine_name: spacy
  models:
  -
    lang_code: en
    model_name: en_core_web_lg
  -
    lang_code: es
    model_name: es_core_news_md
  ner_model_configuration:
    model_to_presidio_entity_mapping:
      PER: PERSON
      PERSON: PERSON
      LOC: LOCATION
      LOCATION: LOCATION
      GPE: LOCATION
      ORG: ORGANIZATION
      DATE: DATE_TIME
      TIME: DATE_TIME
      NORP: NRP

    low_confidence_score_multiplier: 0.4
    low_score_entity_names:
    - ORGANIZATION
    - ORG
    default_score: 0.85

recognizer_registry:
  global_regex_flags: 26
  recognizers: 
  - name: CreditCardRecognizer
    supported_languages: 
      - en
    supported_entity: IT_FISCAL_CODE
    type: predefined

  - name: ItFiscalCodeRecognizer
    type: predefined
```

--------------------------------

### Deanonymize Payload Example

Source: https://microsoft.github.io/presidio/anonymizer

Example JSON payload for the deanonymize HTTP endpoint.

```json
{
"text": "My name is S184CMt9Drj7QaKQ21JTrpYzghnboTF9pn/neN8JME0=",
"deanonymizers": {
    "PERSON": {
        "type": "decrypt",
        "key": "WmZq4t7w!z%C&F)J"
    }
},
"anonymizer_results": [
    {
        "start": 11,
        "end": 55,
        "entity_type": "PERSON"
    }
]}
```

--------------------------------

### Running e2e tests on Mac/Linux/WSL

Source: https://microsoft.github.io/presidio/development

Steps to set up and run end-to-end tests using pytest on Unix-like systems.

```bash
# Create a virtualenv named presidio-e2e (needs to be done only on the first run)
python -m venv presidio-e2e
# Activate the virtualenv
source presidio-e2e/bin/activate
# Install e2e-tests requirements using pip
pip install -r requirements.txt
# Run pytest
pytest
# Deactivate the virtualenv
deactivate

```

--------------------------------

### Anonymize Payload Example

Source: https://microsoft.github.io/presidio/anonymizer

Example JSON payload for the anonymize HTTP endpoint.

```json
{
"text": "hello world, my name is Jane Doe. My number is: 034453334",
"anonymizers": {
    "PHONE_NUMBER": {
        "type": "mask",
        "masking_char": "*",
        "chars_to_mask": 4,
        "from_end": true
    }
},
"analyzer_results": [
    {
        "start": 24,
        "end": 32,
        "score": 0.8,
        "entity_type": "NAME"
    },
    {
        "start": 24,
        "end": 28,
        "score": 0.8,
        "entity_type": "FIRST_NAME"
    },
    {
        "start": 29,
        "end": 32,
        "score": 0.6,
        "entity_type": "LAST_NAME"
    },
    {
        "start": 48,
        "end": 57,
        "score": 0.95,
        "entity_type": "PHONE_NUMBER"
    }
]}
```

--------------------------------

### Install Presidio Packages

Source: https://microsoft.github.io/presidio/samples/python/ner_model_configuration

Use pip to install the necessary analyzer and anonymizer packages.

```bash
# download presidio
!pip install presidio_analyzer presidio_anonymizer
```

--------------------------------

### Install Presidio AHDS Package

Source: https://microsoft.github.io/presidio/samples/python/ahds

Install the required Presidio package with AHDS support.

```bash
pip install "presidio-analyzer[ahds]"
```

--------------------------------

### Install Presidio Image Redactor

Source: https://microsoft.github.io/presidio/image-redactor

Install the package and the required spaCy language model.

```bash
pip install presidio-image-redactor
python -m spacy download en_core_web_lg
```

--------------------------------

### Checking for linting issues locally

Source: https://microsoft.github.io/presidio/development

Command to check for PEP8 compliance and style guide issues using ruff.

```bash
poetry run ruff check

```

--------------------------------

### Set up Ollama via Docker

Source: https://microsoft.github.io/presidio/samples/python/langextract

Commands to start the Ollama service and pull a model using Docker Compose.

```bash
docker compose up -d ollama
docker exec presidio-ollama-1 ollama pull qwen2.5:1.5b
docker exec presidio-ollama-1 ollama list
```

--------------------------------

### Install Presidio Image Redactor

Source: https://microsoft.github.io/presidio/getting_started/getting_started_images

Install the required Python package for image redaction.

```bash
pip install presidio-image-redactor
```

--------------------------------

### Verify CUDA Installation

Source: https://microsoft.github.io/presidio/analyzer/nlp_engines/gpu_usage

Check if the NVIDIA driver and compiler are correctly installed on the system.

```bash
nvidia-smi
nvcc --version
```

--------------------------------

### Install AHDS Surrogate Operator

Source: https://microsoft.github.io/presidio/ahds_integration

Install the required package for AHDS surrogate generation.

```bash
pip install presidio-anonymizer[ahds]
```

--------------------------------

### Set up Ollama natively

Source: https://microsoft.github.io/presidio/samples/python/langextract

Commands to pull and run a model directly on the host system.

```bash
ollama pull qwen2.5:1.5b
ollama run qwen2.5:1.5b
```

--------------------------------

### RecognizerRegistryProvider Example

Source: https://microsoft.github.io/presidio/api/analyzer_python

Example of a recognizer registry configuration.

```json
{
  "supported_languages": ["de", "es"],
  "recognizers": [
    {
      "name": "Zip code Recognizer",
      "supported_language": "en",
      "patterns": [
        {
          "name": "zip code (weak)",
          "regex": "(\b\d{5}(?:\\-\d{4})?\b)",
          "score": 0.01
        }
      ],
      "context": ["zip", "code"],
      "supported_entity": "ZIP"
    }
  ]
}
```

--------------------------------

### Load and Display Standard Example Image

Source: https://microsoft.github.io/presidio/samples/python/image_redaction_allow_list_approach

Loads a standard image file and displays it.

```python
image = Image.open("../../image-redactor/ocr_text.png")
display(image)
```

--------------------------------

### Configure NLP engine via file

Source: https://microsoft.github.io/presidio/analyzer/customizing_nlp_models

Pass the path of a configuration file to the NlpEngineProvider to initialize the engine.

```python
from presidio_analyzer import AnalyzerEngine, RecognizerRegistry
from presidio_analyzer.nlp_engine import NlpEngineProvider

LANGUAGES_CONFIG_FILE = "./docs/analyzer/languages-config.yml"

# Create NLP engine based on configuration file
provider = NlpEngineProvider(conf_file=LANGUAGES_CONFIG_FILE)
nlp_engine_with_spanish = provider.create_engine()

# Pass created NLP engine and supported_languages to the AnalyzerEngine
analyzer = AnalyzerEngine(
    nlp_engine=nlp_engine_with_spanish, 
    supported_languages=["en", "es"]
)

# Analyze in different languages
results_spanish = analyzer.analyze(text="Mi nombre es David", language="es")
print(results_spanish)

results_english = analyzer.analyze(text="My name is David", language="en")
print(results_english)
```

--------------------------------

### Install Project Dependencies with Poetry

Source: https://microsoft.github.io/presidio/development

Command to create a virtual environment for a Presidio service and install all requirements, including development dependencies, using Poetry.

```shell
poetry install --all-extras

```

--------------------------------

### Display template samples

Source: https://microsoft.github.io/presidio/samples/python/synth_data_with_openai

Prints the first five templates from the loaded list.

```python
print("Example templates:")
templates[:5]
```

--------------------------------

### Reinstall CuPy for CUDA

Source: https://microsoft.github.io/presidio/analyzer/nlp_engines/gpu_usage

Update the CuPy installation to match the specific CUDA version installed on the host.

```bash
pip uninstall cupy cupy-cuda11x cupy-cuda12x
pip install cupy-cuda12x  # Match your CUDA version
```

--------------------------------

### Initialize and use FlairRecognizer

Source: https://microsoft.github.io/presidio/samples/python/flair_recognizer

Example showing how to register the FlairRecognizer with the AnalyzerEngine and perform text analysis.

```python
from presidio_analyzer import AnalyzerEngine, RecognizerRegistry
flair_recognizer = FlairRecognizer()
registry = RecognizerRegistry()
registry.add_recognizer(flair_recognizer)
analyzer = AnalyzerEngine(registry=registry)
results = analyzer.analyze(
 "My name is Christopher and I live in Irbid.",
 language="en",
 return_decision_process=True,
)
for result in results:
 print(result)
 print(result.analysis_explanation)
```

--------------------------------

### Configure Demo Parameters

Source: https://microsoft.github.io/presidio/samples/fabric/artifacts/presidio_and_spark

Define configuration variables for data scaling, file paths, and Delta table output settings.

```python
num_duplicates = 5000 # for the scale part
csv_path = "Files/presidio/fabric_sample_data.csv"
is_write_to_delta = True
table_namne = "presidio_demo_table"
partitions_number = 100
```

--------------------------------

### SpacyRecognizer Example

Source: https://microsoft.github.io/presidio/api/analyzer_python

Example of how SpacyRecognizer processes entities and builds results.

```python
logger.debug(
                    f"Skipping entity {ner_entity.label_} "
                    f"as it is not in the supported entities list"
                )
                continue

            textual_explanation = self.DEFAULT_EXPLANATION.format(ner_entity.label_)
            explanation = self.build_explanation(ner_score, textual_explanation)
            spacy_result = RecognizerResult(
                entity_type=ner_entity.label_,
                start=ner_entity.start_char,
                end=ner_entity.end_char,
                score=ner_score,
                analysis_explanation=explanation,
                recognition_metadata={
                    RecognizerResult.RECOGNIZER_NAME_KEY: self.name,
                    RecognizerResult.RECOGNIZER_IDENTIFIER_KEY: self.id,
                },
            )
            results.append(spacy_result)

        return results
```