Integration: Presidio
PII detection and anonymization for Haystack Documents and text strings, powered by Microsoft Presidio.
Table of Contents
Overview
Microsoft 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
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 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:
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:
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:
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 for the full list of detectable PII types.
License
presidio-haystack is distributed under the terms of the
Apache-2.0 license.