docs: sync Core Integrations API reference (presidio) on Docusaurus (#11161)

Co-authored-by: sjrl <10526848+sjrl@users.noreply.github.com>

Author: HaystackBot

docs-website/reference/integrations-api/presidio.md

@@ -0,0 +1,239 @@
+---
+title: "Presidio"
+id: integrations-presidio
+description: "Presidio integration for Haystack"
+slug: "/integrations-presidio"
+---
+
+
+## haystack_integrations.components.preprocessors.presidio.presidio_document_cleaner
+
+### PresidioDocumentCleaner
+
+Anonymizes PII in Haystack Documents using [Microsoft Presidio](https://microsoft.github.io/presidio/).
+
+Accepts a list of Documents, detects personally identifiable information (PII) in their
+text content, and returns new Documents with PII replaced by entity type placeholders
+(e.g. `<PERSON>`, `<EMAIL_ADDRESS>`). Original Documents are not mutated.
+
+Documents without text content are passed through unchanged.
+
+The analyzer and anonymizer engines are loaded on the first call to `run()`,
+or by calling `warm_up()` explicitly beforehand.
+
+### Usage example
+
+```python
+from haystack import Document
+from haystack_integrations.components.preprocessors.presidio import PresidioDocumentCleaner
+
+cleaner = PresidioDocumentCleaner()
+result = cleaner.run(documents=[Document(content="My name is John and my email is john@example.com")])
+print(result["documents"][0].content)
+# My name is <PERSON> and my email is <EMAIL_ADDRESS>
+```
+
+#### __init__
+
+```python
+__init__(
+    *,
+    language: str = "en",
+    entities: list[str] | None = None,
+    score_threshold: float = 0.35
+) -> None
+```
+
+Initializes the PresidioDocumentCleaner.
+
+**Parameters:**
+
+- **language** (<code>str</code>) – Language code for PII detection. Defaults to `"en"`.
+  See [Presidio supported languages](https://microsoft.github.io/presidio/analyzer/languages/).
+- **entities** (<code>list\[str\] | None</code>) – List of PII entity types to detect and anonymize (e.g. `["PERSON", "EMAIL_ADDRESS"]`).
+  If `None`, all supported entity types are used.
+  See [Presidio supported entities](https://microsoft.github.io/presidio/supported_entities/).
+- **score_threshold** (<code>float</code>) – Minimum confidence score (0-1) for a detected entity to be anonymized. Defaults to `0.35`.
+  See [Presidio analyzer documentation](https://microsoft.github.io/presidio/analyzer/).
+
+#### warm_up
+
+```python
+warm_up() -> None
+```
+
+Initializes the Presidio analyzer and anonymizer engines.
+
+This method loads the underlying NLP models. In a Haystack Pipeline,
+this is called automatically before the first run.
+
+#### run
+
+```python
+run(documents: list[Document]) -> dict[str, list[Document]]
+```
+
+Anonymizes PII in the provided Documents.
+
+**Parameters:**
+
+- **documents** (<code>list\[Document\]</code>) – List of Documents whose text content will be anonymized.
+
+**Returns:**
+
+- <code>dict\[str, list\[Document\]\]</code> – A dictionary with key `documents` containing the cleaned Documents.
+
+## haystack_integrations.components.preprocessors.presidio.presidio_entity_extractor
+
+### PresidioEntityExtractor
+
+Detects PII entities in Haystack Documents using Microsoft Presidio Analyzer.
+
+See [Presidio Analyzer](https://microsoft.github.io/presidio/) for details.
+
+Accepts a list of Documents and returns new Documents with detected PII entities stored
+in each Document's metadata under the key `"entities"`. Each entry in the list contains
+the entity type, start/end character offsets, and the confidence score.
+
+Original Documents are not mutated. Documents without text content are passed through unchanged.
+
+The analyzer engine is loaded on the first call to `run()`,
+or by calling `warm_up()` explicitly beforehand.
+
+### Usage example
+
+```python
+from haystack import Document
+from haystack_integrations.components.preprocessors.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}]
+```
+
+#### __init__
+
+```python
+__init__(
+    *,
+    language: str = "en",
+    entities: list[str] | None = None,
+    score_threshold: float = 0.35
+) -> None
+```
+
+Initializes the PresidioEntityExtractor.
+
+**Parameters:**
+
+- **language** (<code>str</code>) – Language code for PII detection. Defaults to `"en"`.
+  See [Presidio supported languages](https://microsoft.github.io/presidio/analyzer/languages/).
+- **entities** (<code>list\[str\] | None</code>) – List of PII entity types to detect (e.g. `["PERSON", "EMAIL_ADDRESS"]`).
+  If `None`, all supported entity types are detected.
+  See [Presidio supported entities](https://microsoft.github.io/presidio/supported_entities/).
+- **score_threshold** (<code>float</code>) – Minimum confidence score (0-1) for a detected entity to be included. Defaults to `0.35`.
+  See [Presidio analyzer documentation](https://microsoft.github.io/presidio/analyzer/).
+
+#### warm_up
+
+```python
+warm_up() -> None
+```
+
+Initializes the Presidio analyzer engine.
+
+This method loads the underlying NLP models. In a Haystack Pipeline,
+this is called automatically before the first run.
+
+#### run
+
+```python
+run(documents: list[Document]) -> dict[str, list[Document]]
+```
+
+Detects PII entities in the provided Documents.
+
+**Parameters:**
+
+- **documents** (<code>list\[Document\]</code>) – List of Documents to analyze for PII entities.
+
+**Returns:**
+
+- <code>dict\[str, list\[Document\]\]</code> – A dictionary with key `documents` containing Documents with detected entities
+  stored in metadata under the key `"entities"`.
+
+## haystack_integrations.components.preprocessors.presidio.presidio_text_cleaner
+
+### PresidioTextCleaner
+
+Anonymizes PII in plain strings using [Microsoft Presidio](https://microsoft.github.io/presidio/).
+
+Accepts a list of strings, detects personally identifiable information (PII), and returns
+a new list of strings with PII replaced by entity type placeholders (e.g. `<PERSON>`).
+Useful for sanitizing user queries before they are sent to an LLM.
+
+The analyzer and anonymizer engines are loaded on the first call to `run()`,
+or by calling `warm_up()` explicitly beforehand.
+
+### Usage example
+
+```python
+from haystack_integrations.components.preprocessors.presidio import PresidioTextCleaner
+
+cleaner = PresidioTextCleaner()
+result = cleaner.run(texts=["Hi, I am John Smith, call me at 212-555-1234"])
+print(result["texts"][0])
+# Hi, I am <PERSON>, call me at <PHONE_NUMBER>
+```
+
+#### __init__
+
+```python
+__init__(
+    *,
+    language: str = "en",
+    entities: list[str] | None = None,
+    score_threshold: float = 0.35
+) -> None
+```
+
+Initializes the PresidioTextCleaner.
+
+**Parameters:**
+
+- **language** (<code>str</code>) – Language code for PII detection. Defaults to `"en"`.
+  See [Presidio supported languages](https://microsoft.github.io/presidio/analyzer/languages/).
+- **entities** (<code>list\[str\] | None</code>) – List of PII entity types to detect and anonymize (e.g. `["PERSON", "PHONE_NUMBER"]`).
+  If `None`, all supported entity types are used.
+  See [Presidio supported entities](https://microsoft.github.io/presidio/supported_entities/).
+- **score_threshold** (<code>float</code>) – Minimum confidence score (0-1) for a detected entity to be anonymized. Defaults to `0.35`.
+  See [Presidio analyzer documentation](https://microsoft.github.io/presidio/analyzer/).
+
+#### warm_up
+
+```python
+warm_up() -> None
+```
+
+Initializes the Presidio analyzer and anonymizer engines.
+
+This method loads the underlying NLP models. In a Haystack Pipeline,
+this is called automatically before the first run.
+
+#### run
+
+```python
+run(texts: list[str]) -> dict[str, list[str]]
+```
+
+Anonymizes PII in the provided strings.
+
+**Parameters:**
+
+- **texts** (<code>list\[str\]</code>) – List of strings to anonymize.
+
+**Returns:**
+
+- <code>dict\[str, list\[str\]\]</code> – A dictionary with key `texts` containing the cleaned strings.