redact-proxy 0.1.1

The ngrok for PHI. HIPAA-compliant wrappers for LLM APIs.

Redact Proxy

The ngrok for PHI.

Drop-in replacements for OpenAI, Anthropic, and Gemini SDKs that automatically redact PHI before sending to the API.

Installation

# Basic (OpenAI + fast detection)
pip install redact-proxy

# With additional providers
pip install redact-proxy[anthropic]
pip install redact-proxy[gemini]

# With enhanced detection
pip install redact-proxy[balanced]   # Adds Presidio NER
pip install redact-proxy[accurate]   # Adds transformer model

# Everything
pip install redact-proxy[all]

Quick Start

OpenAI

# Before (not HIPAA-safe)
from openai import OpenAI

# After (HIPAA-safe) - just change the import!
from redact_proxy import OpenAI

client = OpenAI(phi_detection="fast")

# Same API, PHI automatically redacted
response = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {"role": "user", "content": "Patient John Smith, DOB 01/15/1980, has diabetes"}
    ]
)
# OpenAI receives: "Patient [NAME], DOB [DATE], has diabetes"

Anthropic

from redact_proxy import Anthropic

client = Anthropic(phi_detection="fast")

response = client.messages.create(
    model="claude-3-opus-20240229",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Patient John Smith has diabetes"}
    ]
)

Gemini

from redact_proxy import Gemini

client = Gemini(phi_detection="fast")

response = client.generate_content(
    "Patient John Smith has diabetes"
)

# Or use chat
chat = client.start_chat()
response = chat.send_message("Patient John Smith has diabetes")

Detection Modes

Mode	Speed	Method	Use Case
fast	~1-5ms	Regex patterns	Real-time chat, most users
balanced	~20-50ms	Patterns + Presidio NER	Better name detection
accurate	~100-500ms	Patterns + Presidio + Transformer	Batch processing, high-risk

# Choose your mode
client = OpenAI(phi_detection="fast")      # Default - fastest
client = OpenAI(phi_detection="balanced")  # Better accuracy
client = OpenAI(phi_detection="accurate")  # Best accuracy

PHI Types Detected

• Names: Patient, provider, family member names
• Dates: DOB, visit dates, all date formats
• Ages: All age formats (65 y/o, 65-year-old, etc.)
• SSN: Social Security Numbers
• MRN: Medical Record Numbers
• Medicare/Medicaid IDs
• Phone/Fax numbers
• Email addresses
• Addresses: Street, city, state, ZIP
• URLs and IP addresses

Advanced Usage

Custom Placeholder

client = OpenAI(
    phi_detection="fast",
    redact_placeholder="<REDACTED:{phi_type}>"
)
# Output: "Patient <REDACTED:NAME> has diabetes"

Direct Detection

from redact_proxy import PHIDetector

detector = PHIDetector(mode="fast")

# Just detect
findings = detector.detect("Patient John Smith, DOB 01/15/1980")
for f in findings:
    print(f"{f.phi_type}: {f.text} (confidence: {f.confidence})")

# Detect and redact
redacted_text, findings = detector.redact("Patient John Smith, DOB 01/15/1980")
print(redacted_text)  # "Patient [NAME], DOB [DATE]"

Why Redact Proxy?

1. One-line migration: Just change your import
2. Zero infrastructure: Works entirely locally
3. Fast: Pattern-based detection in milliseconds
4. Configurable: Choose speed vs accuracy tradeoff
5. Comprehensive: Covers all 18 HIPAA Safe Harbor identifiers

License

MIT

Links

• Website: redact.health
• Documentation: docs.redact.health/proxy
• Issues: GitHub Issues