datafog 4.5.0a1

Lightning-fast PII detection and anonymization library with 190x performance advantage

DataFog Python

DataFog is a Python library for detecting and redacting personally identifiable information (PII).

It provides:

• Fast structured PII detection via regex
• Optional NER support via spaCy and GLiNER
• A simple agent-oriented API for LLM applications
• Backward-compatible DataFog and TextService classes

4.5 Focus

DataFog 4.5 is focused on lightweight text PII screening: a small core install, fast regex-based scan/redact helpers, explicit optional extras, and a clearer path toward future middleware use cases. Dedicated Sentry, OpenTelemetry, logging-framework, and cloud DLP adapters are future-facing work and are not part of the 4.5 release.

Installation

# Core install (regex engine)
pip install datafog

# Add spaCy support
pip install datafog[nlp]

# Add GLiNER + spaCy support
pip install datafog[nlp-advanced]

# Add local OCR support
pip install datafog[ocr]

# Add Spark/distributed support
pip install datafog[distributed]

# Everything
pip install datafog[all]

Python 3.13 support is certified for the core SDK, CLI, nlp, nlp-advanced, and ocr install profiles. Donut OCR still requires a model that is available locally before runtime use. distributed and all are not newly certified on Python 3.13 in the 4.5 line.

Quick Start

import datafog

text = "Contact [e-mail] or call [phone]"
clean = datafog.sanitize(text, engine="regex")
print(clean)
# Contact [EMAIL_1] or call [PHONE_1]

For LLM Applications

import datafog

# 1) Scan prompt text before sending to an LLM
prompt = "My SSN is 123-45-6789"
scan_result = datafog.scan_prompt(prompt, engine="regex")
if scan_result.entities:
    print(f"Detected {len(scan_result.entities)} PII entities")

# 2) Redact model output before returning it
output = "Email me at [e-mail]"
safe_result = datafog.filter_output(output, engine="regex")
print(safe_result.redacted_text)
# Email me at [EMAIL_1]

# 3) One-liner redaction
print(datafog.sanitize("Card: 4111-1111-1111-1111", engine="regex"))
# Card: [CREDIT_CARD_1]

German Structured PII

German structured PII is country-specific and opt-in. Use explicit locale selection or entity-type filtering when you want German VAT IDs, German IBANs, tax IDs, postal codes, passports, or residence permits.

import datafog

text = "Steuer-ID 12345678901 liegt vor."

print(datafog.scan(text, engine="regex").entities)
# []

print(datafog.scan(text, engine="regex", locales=["de"]).entities)
# [Entity(type='DE_TAX_ID', text='12345678901', ...)]

Guardrails

import datafog

# Reusable guardrail object
guard = datafog.create_guardrail(engine="regex", on_detect="redact")

@guard
def call_llm() -> str:
    return "Send to [e-mail]"

print(call_llm())
# Send to [EMAIL_1]

Engines

Use the engine that matches your accuracy and dependency constraints:

• regex:
  • Fastest and always available.
  • Best for default structured entities: EMAIL, PHONE, SSN, CREDIT_CARD, IP_ADDRESS, DATE, ZIP_CODE.
  • Use locales=["de"] for German structured IDs such as DE_VAT_ID, DE_IBAN, DE_TAX_ID, DE_POSTAL_CODE, and passport or residence permit numbers.
• spacy:
  • Requires pip install datafog[nlp].
  • Useful for unstructured entities like person and organization names.
• gliner:
  • Requires pip install datafog[nlp-advanced].
  • Stronger NER coverage than regex for unstructured text.
• smart:
  • Cascades regex with optional NER engines.
  • If optional deps are missing, it degrades gracefully and warns.

Optional OCR And Spark Surfaces

DataFog 4.5 keeps the main package story centered on lightweight text PII screening. OCR and Spark remain supported optional surfaces for users who already rely on them, but they are not required for the core import, default scan/redact helpers, or guardrail helpers.

• OCR:
  • Install datafog[ocr] for local image OCR helpers.
  • URL-based image downloading also needs datafog[web,ocr].
  • Tesseract usage requires the system tesseract binary.
  • Python 3.13 is validated for the OCR install profile, Pillow, pytesseract, and system Tesseract smoke checks.
  • Donut OCR requires datafog[nlp-advanced,ocr] and a model already available locally.
• Spark:
  • Install datafog[distributed] for SparkService.
  • Spark PII UDF helpers also require datafog[nlp] and an installed spaCy model.
  • A Java runtime is required by PySpark.

OCR and Spark are not deprecated. Their broader API and packaging overhaul is deferred; the 4.5 goal is to keep them explicit, documented, and isolated from the lightweight core path.

Backward-Compatible APIs

The existing public API remains available.

DataFog class

from datafog import DataFog

result = DataFog().scan_text("Email [e-mail]")
print(result["EMAIL"])

TextService class

from datafog.services import TextService

service = TextService(engine="regex")
result = service.annotate_text_sync("Call [phone]")
print(result["PHONE"])

CLI

# Scan text
datafog scan-text "[e-mail]"

# Redact text
datafog redact-text "[e-mail]"

# Replace text with pseudonyms
datafog replace-text "[e-mail]"

# Hash detected entities
datafog hash-text "[e-mail]"

# Enable German regex identifiers
datafog redact-text "Steuer-ID 12345678901" --locale de

Telemetry

DataFog telemetry is disabled by default.

To opt in:

export DATAFOG_TELEMETRY=1

To force telemetry off:

export DATAFOG_NO_TELEMETRY=1
# or
export DO_NOT_TRACK=1

Telemetry does not include input text or detected PII values.

Development

git clone https://github.com/datafog/datafog-python
cd datafog-python
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate
pip install -e ".[all,dev]"
pip install -r requirements-dev.txt
pytest tests/