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+---
+layout: integration
+name: Presidio
+description: PII detection and anonymization for Haystack Documents and text strings, powered by Microsoft Presidio.
+authors:
+    - name: deepset
+      socials:
+        github: deepset-ai
+        twitter: deepset_ai
+        linkedin: https://www.linkedin.com/company/deepset-ai/
+    - name: Shahmeer Ali
+      socials:
+        github: SyedShahmeerAli12
+pypi: https://pypi.org/project/presidio-haystack/
+repo: https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/presidio
+type: Custom Component
+report_issue: https://github.com/deepset-ai/haystack-core-integrations/issues
+logo: /logos/microsoft.png
+version: Haystack 2.0
+toc: true
+---
+
+### Table of Contents
+
+- [Overview](#overview)
+- [Installation](#installation)
+- [Usage](#usage)
+  - [Document Cleaning](#document-cleaning)
+  - [Text Cleaning](#text-cleaning)
+  - [Entity Extraction](#entity-extraction)
+- [License](#license)
+
+## Overview
+
+[Microsoft Presidio](https://microsoft.github.io/presidio/) is an open-source library for PII detection and anonymization using NLP-based entity recognition.
+
+`presidio-haystack` provides three Haystack components:
+
+| Component | Input | Purpose |
+|-----------|-------|---------|
+| `PresidioDocumentCleaner` | `list[Document]` | Replace PII in document text with entity type placeholders |
+| `PresidioTextCleaner` | `list[str]` | Replace PII in plain strings — useful for sanitizing user queries |
+| `PresidioEntityExtractor` | `list[Document]` | Detect PII and store entities as structured document metadata |
+
+All components run locally — no external API required. Presidio uses spaCy NLP models under the hood.
+
+## Installation
+
+```bash
+pip install presidio-haystack
+```
+
+`en_core_web_lg` is the recommended English model for best accuracy. For a lighter footprint, `en_core_web_sm` works too — see the [full list of spaCy models](https://spacy.io/models/en) for options.
+
+Each component accepts a `language` parameter (default `"en"`). To use a non-English language, specify the language code, and provide a model mapping, unless you want to use the large one.
+
+
+## Usage
+
+### Document Cleaning
+
+Replace PII in document content before indexing:
+
+```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>.
+```
+
+Original documents are not mutated. Documents with no text content pass through unchanged.
+
+### Text Cleaning
+
+Sanitize user queries before they reach your LLM:
+
+```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>
+```
+
+### Entity Extraction
+
+Detect PII and attach it as structured metadata without modifying the document text:
+
+```python
+from haystack import Document
+from haystack_integrations.components.extractors.presidio import PresidioEntityExtractor
+
+extractor = PresidioEntityExtractor()
+result = extractor.run(documents=[
+    Document(content="Contact Alice at alice@example.com")
+])
+print(result["documents"][0].meta["entities"])
+# [{"entity_type": "PERSON", "start": 8, "end": 13, "score": 0.85},
+#  {"entity_type": "EMAIL_ADDRESS", "start": 17, "end": 34, "score": 1.0}]
+```
+
+All three components accept `language`, `entities`, and `score_threshold` parameters at init time. See [Presidio supported entities](https://microsoft.github.io/presidio/supported_entities/) for the full list of detectable PII types.
+
+## License
+
+`presidio-haystack` is distributed under the terms of the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license.