---
title: "PresidioDocumentCleaner"
id: presidiodocumentcleaner
slug: "/presidiodocumentcleaner"
description: "Use `PresidioDocumentCleaner` to replace PII in Document text with entity type placeholders, powered by Microsoft Presidio."
---

# PresidioDocumentCleaner

`PresidioDocumentCleaner` replaces personally identifiable information (PII) in the text content of Documents with entity type placeholders such as `<PERSON>` or `<EMAIL_ADDRESS>`. Original Documents are not mutated. Documents without text content pass through unchanged.

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | In an indexing pipeline, before writing Documents to a Document Store |
| **Mandatory run variables**            | `documents`: A list of Document objects |
| **Output variables**                   | `documents`: A list of Document objects with PII replaced |
| **API reference**                      | [Presidio](/reference/integrations-presidio) |
| **GitHub link**                        | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/presidio |
| **Package name** | `presidio-haystack` |

</div>

## Overview

[Microsoft Presidio](https://microsoft.github.io/presidio/) is an open-source framework for PII detection and anonymization. `PresidioDocumentCleaner` uses Presidio's Analyzer and Anonymizer engines to scan document text and replace detected entities with type placeholders such as `<PERSON>` or `<EMAIL_ADDRESS>`.

This is useful when you want to store sanitized versions of your documents in a Document Store — for example, to prevent sensitive information from being indexed or returned in search results.

If you want to annotate PII without modifying the text, see [`PresidioEntityExtractor`](../extractors/presidioentityextractor.mdx). For sanitizing plain strings such as user queries, see [`PresidioTextCleaner`](./presidiotextcleaner.mdx).

## Configuration

| Parameter | Default | Description |
| --- | --- | --- |
| `language` | `"en"` | ISO 639-1 language code for PII detection. The appropriate spaCy model is selected automatically for [supported languages](#non-english-languages). See [Presidio supported languages](https://microsoft.github.io/presidio/analyzer/languages/). |
| `entities` | `None` | List of PII entity types to detect and anonymize (e.g. `["PERSON", "EMAIL_ADDRESS"]`). If `None`, all supported types are detected. See [supported entities](https://microsoft.github.io/presidio/supported_entities/). |
| `score_threshold` | `0.35` | Minimum confidence score (0–1) for a detected entity to be anonymized. |
| `models` | `None` | Advanced override: explicit list of spaCy model configs, e.g. `[{"lang_code": "fr", "model_name": "fr_core_news_md"}]`. Use this only when you need a specific model variant or a language not in the built-in mapping. If `None`, the model is selected automatically based on `language`. |

## Usage

Install the `presidio-haystack` package to use the `PresidioDocumentCleaner`.

```bash
pip install presidio-haystack
```

### On its own

```python
from haystack import Document
from haystack_integrations.components.preprocessors.presidio import (
    PresidioDocumentCleaner,
)

cleaner = PresidioDocumentCleaner()
result = cleaner.run(
    documents=[
        Document(content="Contact Alice Smith at alice@example.com or 212-555-1234."),
    ],
)
print(result["documents"][0].content)
# Contact <PERSON> at <EMAIL_ADDRESS> or <PHONE_NUMBER>.
```

### In a pipeline

```python
from haystack import Document, Pipeline
from haystack.components.writers import DocumentWriter
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack_integrations.components.preprocessors.presidio import (
    PresidioDocumentCleaner,
)

document_store = InMemoryDocumentStore()

indexing_pipeline = Pipeline()
indexing_pipeline.add_component("cleaner", PresidioDocumentCleaner())
indexing_pipeline.add_component("writer", DocumentWriter(document_store=document_store))
indexing_pipeline.connect("cleaner", "writer")

indexing_pipeline.run(
    {
        "cleaner": {
            "documents": [
                Document(content="Alice Smith's email is alice@example.com"),
                Document(content="Call Bob at 212-555-9876"),
            ],
        },
    },
)
```

### Using Custom Parameters

Use `entities` to limit anonymization to the PII types you actually care about. This reduces false positives and improves performance by skipping recognizers you don't need.

Use `score_threshold` to tune the precision-recall tradeoff. The default `0.35` casts a wide net and may anonymize some false positives. Raise it (e.g. `0.7`) when you need high confidence before replacing text; lower it when missing any PII is the bigger risk.

```python
from haystack_integrations.components.preprocessors.presidio import (
    PresidioDocumentCleaner,
)

cleaner = PresidioDocumentCleaner(
    language="de",
    entities=["PERSON", "EMAIL_ADDRESS"],  # only anonymize names and emails
    score_threshold=0.7,  # higher precision, fewer false positives
)
```

### Non-English languages

For any language in the built-in mapping, just set `language` — the right spaCy model is selected and loaded automatically at warm-up time.

```python
from haystack import Document
from haystack_integrations.components.preprocessors.presidio import (
    PresidioDocumentCleaner,
)

# No `models` parameter needed — de_core_news_lg is selected automatically
cleaner = PresidioDocumentCleaner(language="de")
result = cleaner.run(
    documents=[
        Document(
            content="Mein Name ist Hans Müller und meine E-Mail ist hans@example.com",
        ),
    ],
)
print(result["documents"][0].content)
# Mein Name ist <PERSON> und meine E-Mail ist <EMAIL_ADDRESS>
```

Supported languages and their default models are listed in `PresidioDocumentCleaner.SPACY_DEFAULT_MODELS`. Using a language not in that mapping without providing `models` raises a `ValueError` at warm-up time with a list of the supported language codes.

To use a non-default model variant, or a language outside the built-in mapping, pass `models` explicitly:

```python
cleaner = PresidioDocumentCleaner(
    language="fr",
    models=[{"lang_code": "fr", "model_name": "fr_core_news_md"}],
)
```