---
title: "PresidioTextCleaner"
id: presidiotextcleaner
slug: "/presidiotextcleaner"
description: "Use `PresidioTextCleaner` to replace PII in plain strings, powered by Microsoft Presidio."
---

# PresidioTextCleaner

`PresidioTextCleaner` replaces personally identifiable information (PII) in plain strings. It takes a `list[str]` as input and returns a `list[str]`, making it easy to sanitize user queries before they are sent to an LLM.

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

|  |  |
| --- | --- |
| **Most common position in a pipeline** | In a query pipeline, before a Generator or Chat Generator |
| **Mandatory run variables**            | `texts`: A list of strings |
| **Output variables**                   | `texts`: A list of strings with PII replaced |
| **API reference**                      | [Presidio](/reference/integrations-presidio) |
| **GitHub link**                        | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/presidio |

</div>

## Overview

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

This is useful when you want to sanitize user queries before sending them to an LLM, ensuring that no personally identifiable information is passed to the model.

For sanitizing Haystack `Document` objects rather than plain strings, see [`PresidioDocumentCleaner`](./presidiodocumentcleaner.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 `PresidioTextCleaner`.

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

### On its own

```python
from haystack_integrations.components.preprocessors.presidio import PresidioTextCleaner

cleaner = PresidioTextCleaner()
result = cleaner.run(texts=["My name is John Doe, my SSN is 123-45-6789"])
print(result["texts"][0])
# My name is <PERSON>, my SSN is <US_SSN>
```

### In a pipeline

```python
from haystack import Pipeline
from haystack.components.builders import ChatPromptBuilder
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.dataclasses import ChatMessage
from haystack_integrations.components.preprocessors.presidio import PresidioTextCleaner

template = [ChatMessage.from_user("Answer this question: {{query}}")]

query_pipeline = Pipeline()
query_pipeline.add_component("cleaner", PresidioTextCleaner())
query_pipeline.add_component("prompt_builder", ChatPromptBuilder(template=template))
query_pipeline.add_component("llm", OpenAIChatGenerator(model="gpt-4o-mini"))
query_pipeline.connect("cleaner.texts[0]", "prompt_builder.query")
query_pipeline.connect("prompt_builder", "llm")

query_pipeline.run(
    {"cleaner": {"texts": ["My name is John Smith. What is the capital of France?"]}},
)
```

### 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 PresidioTextCleaner

cleaner = PresidioTextCleaner(
    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_integrations.components.preprocessors.presidio import PresidioTextCleaner

# No `models` parameter needed — de_core_news_lg is selected automatically
cleaner = PresidioTextCleaner(language="de")
result = cleaner.run(
    texts=["Hallo, ich bin Thomas Schmidt und meine E-Mail ist thomas@example.com"],
)
print(result["texts"][0])
# Hallo, ich bin <PERSON> und meine E-Mail ist <EMAIL_ADDRESS>
```

Supported languages and their default models are listed in `PresidioTextCleaner.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 = PresidioTextCleaner(
    language="fr",
    models=[{"lang_code": "fr", "model_name": "fr_core_news_md"}],
)
```
