Examples - Blindfold

Learn how to integrate Blindfold into real-world applications with these practical examples.

AI Chat with OpenAI

Protect user data when building AI chatbots with OpenAI.

Python
JavaScript
Java

import os
from blindfold import Blindfold
from openai import OpenAI

# Initialize clients
blindfold = Blindfold(api_key=os.environ["BLINDFOLD_API_KEY"])
openai_client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

def secure_chat(user_message: str) -> str:
    """Process chat message with privacy protection"""

    # Step 1: Tokenize sensitive data
    protected = blindfold.tokenize(user_message)
    print(f"Protected input: {protected.text}")
    print(f"Detected {protected.entities_count} sensitive entities")

    # Step 2: Send protected text to OpenAI
    response = openai_client.chat.completions.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": protected.text}
        ]
    )

    ai_response = response.choices[0].message.content

    # Step 3: Restore original data in AI response
    final = blindfold.detokenize(ai_response, protected.mapping)

    return final.text

# Usage
user_input = "My name is John Doe, email john@example.com. Help me book a flight."
response = secure_chat(user_input)
print(f"Response: {response}")

import { Blindfold } from '@blindfold/sdk';
import OpenAI from 'openai';

// Initialize clients
const blindfold = new Blindfold({
  apiKey: process.env.BLINDFOLD_API_KEY
});

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY
});

async function secureChat(userMessage) {
  // Step 1: Tokenize sensitive data
  const protected = await blindfold.tokenize(userMessage);
  console.log(`Protected input: ${protected.text}`);
  console.log(`Detected ${protected.entities_count} sensitive entities`);

  // Step 2: Send protected text to OpenAI
  const response = await openai.chat.completions.create({
    model: 'gpt-4',
    messages: [
      { role: 'system', content: 'You are a helpful assistant.' },
      { role: 'user', content: protected.text }
    ]
  });

  const aiResponse = response.choices[0].message.content;

  // Step 3: Restore original data in AI response
  const final = await blindfold.detokenize(aiResponse, protected.mapping);

  return final.text;
}

// Usage
const userInput = "My name is John Doe, email john@example.com. Help me book a flight.";
const response = await secureChat(userInput);
console.log(`Response: ${response}`);

import dev.blindfold.sdk.Blindfold;
import com.openai.OpenAI;

Blindfold blindfold = new Blindfold(System.getenv("BLINDFOLD_API_KEY"));

String secureChat(String userMessage) {
    // Step 1: Tokenize sensitive data
    var protected_ = blindfold.tokenize(userMessage);
    System.out.println("Protected input: " + protected_.getText());
    System.out.println("Detected " + protected_.getEntitiesCount() + " sensitive entities");

    // Step 2: Send protected text to OpenAI
    // (use your preferred OpenAI Java client)
    String aiResponse = callOpenAI(protected_.getText());

    // Step 3: Restore original data in AI response
    var final_ = blindfold.detokenize(aiResponse, protected_.getMapping());

    return final_.getText();
}

// Usage
String userInput = "My name is John Doe, email john@example.com. Help me book a flight.";
String response = secureChat(userInput);
System.out.println("Response: " + response);

What’s Protected:

Personal names (John Doe → <PERSON_1>)
Email addresses (john@example.com → <EMAIL_ADDRESS_1>)
Any other PII in user messages

Benefits:

User data never reaches OpenAI in plain text
Compliant with privacy regulations
Transparent to end users

AI Chat with Anthropic Claude

Protect user data when building AI chatbots with Anthropic Claude.

Python
JavaScript
Java

import os
from blindfold import Blindfold
import anthropic

# Initialize clients
blindfold = Blindfold(api_key=os.environ["BLINDFOLD_API_KEY"])
client = anthropic.Anthropic()

def secure_chat(user_message: str) -> str:
    """Process chat message with privacy protection"""

    # Step 1: Tokenize sensitive data
    protected = blindfold.tokenize(user_message)
    print(f"Protected input: {protected.text}")
    print(f"Detected {protected.entities_count} sensitive entities")

    # Step 2: Send protected text to Claude
    response = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=1024,
        messages=[
            {"role": "user", "content": protected.text}
        ]
    )

    ai_response = response.content[0].text

    # Step 3: Restore original data in AI response
    final = blindfold.detokenize(ai_response, protected.mapping)

    return final.text

# Usage
user_input = "My name is John Doe, email john@example.com. Help me book a flight."
response = secure_chat(user_input)
print(f"Response: {response}")

import { Blindfold } from '@blindfold/sdk';
import Anthropic from '@anthropic-ai/sdk';

// Initialize clients
const blindfold = new Blindfold({
  apiKey: process.env.BLINDFOLD_API_KEY
});

const anthropic = new Anthropic();

async function secureChat(userMessage) {
  // Step 1: Tokenize sensitive data
  const protected = await blindfold.tokenize(userMessage);
  console.log(`Protected input: ${protected.text}`);
  console.log(`Detected ${protected.entities_count} sensitive entities`);

  // Step 2: Send protected text to Claude
  const response = await anthropic.messages.create({
    model: 'claude-sonnet-4-20250514',
    max_tokens: 1024,
    messages: [
      { role: 'user', content: protected.text }
    ]
  });

  const aiResponse = response.content[0].text;

  // Step 3: Restore original data in AI response
  const final = await blindfold.detokenize(aiResponse, protected.mapping);

  return final.text;
}

// Usage
const userInput = "My name is John Doe, email john@example.com. Help me book a flight.";
const response = await secureChat(userInput);
console.log(`Response: ${response}`);

import dev.blindfold.sdk.Blindfold;

Blindfold blindfold = new Blindfold(System.getenv("BLINDFOLD_API_KEY"));

String secureChat(String userMessage) {
    // Step 1: Tokenize sensitive data
    var protected_ = blindfold.tokenize(userMessage);
    System.out.println("Protected input: " + protected_.getText());
    System.out.println("Detected " + protected_.getEntitiesCount() + " sensitive entities");

    // Step 2: Send protected text to Claude
    // (use your preferred Anthropic Java client)
    String aiResponse = callClaude(protected_.getText());

    // Step 3: Restore original data in AI response
    var final_ = blindfold.detokenize(aiResponse, protected_.getMapping());

    return final_.getText();
}

// Usage
String userInput = "My name is John Doe, email john@example.com. Help me book a flight.";
String response = secureChat(userInput);
System.out.println("Response: " + response);

Benefits:

User data never reaches Anthropic in plain text
Works with Claude Sonnet, Opus, and Haiku
Same tokenize/detokenize pattern as other providers

Customer Support Ticket Anonymization

Anonymize customer support tickets before storing in databases or sending to third-party analytics.

Python
JavaScript
Java

from blindfold import Blindfold
import os

client = Blindfold(api_key=os.environ["BLINDFOLD_API_KEY"])

def process_support_ticket(ticket_text: str) -> dict:
    """Anonymize and process support ticket"""

    # Redact sensitive data permanently
    result = client.redact(ticket_text)

    # Store anonymized ticket
    ticket_data = {
        "text": result.text,
        "pii_detected": result.entities_count,
        "entity_types": [e.type for e in result.detected_entities]
    }

    return ticket_data

# Example ticket
ticket = """
Customer: Jane Smith
Email: jane.smith@email.com
Phone: +1-555-9876
Issue: Cannot access account after password reset.
SSN for verification: 123-45-6789
"""

processed = process_support_ticket(ticket)
print(f"Anonymized ticket:\n{processed['text']}")
print(f"PII types found: {processed['entity_types']}")

# Output:
# Anonymized ticket:
# Customer: <PERSON>
# Email: <EMAIL_ADDRESS>
# Phone: <PHONE_NUMBER>
# Issue: Cannot access account after password reset.
# SSN for verification: <US_SSN>

import { Blindfold } from '@blindfold/sdk';

const client = new Blindfold({
  apiKey: process.env.BLINDFOLD_API_KEY
});

async function processSupportTicket(ticketText) {
  // Redact sensitive data permanently
  const result = await client.redact(ticketText);

  // Store anonymized ticket
  const ticketData = {
    text: result.text,
    pii_detected: result.entities_count,
    entity_types: result.detected_entities.map(e => e.type)
  };

  return ticketData;
}

// Example ticket
const ticket = `
Customer: Jane Smith
Email: jane.smith@email.com
Phone: +1-555-9876
Issue: Cannot access account after password reset.
SSN for verification: 123-45-6789
`;

const processed = await processSupportTicket(ticket);
console.log(`Anonymized ticket:\n${processed.text}`);
console.log(`PII types found: ${processed.entity_types}`);

import dev.blindfold.sdk.Blindfold;

Blindfold client = new Blindfold(System.getenv("BLINDFOLD_API_KEY"));

Map<String, Object> processSupportTicket(String ticketText) {
    // Redact sensitive data permanently
    var result = client.redact(ticketText);

    return Map.of(
        "text", result.getText(),
        "pii_detected", result.getEntitiesCount(),
        "entity_types", result.getDetectedEntities().stream()
            .map(e -> e.getType()).toList()
    );
}

// Example ticket
String ticket = """
    Customer: Jane Smith
    Email: jane.smith@email.com
    Phone: +1-555-9876
    Issue: Cannot access account after password reset.
    SSN for verification: 123-45-6789
    """;

var processed = processSupportTicket(ticket);
System.out.println("Anonymized ticket:\n" + processed.get("text"));
System.out.println("PII types found: " + processed.get("entity_types"));

Use Cases:

Support ticket systems
Customer feedback collection
Quality assurance reviews
Third-party analytics

Displaying Masked Credit Cards

Show partial credit card numbers in user interfaces while protecting full details.

Python
JavaScript
Java

from blindfold import Blindfold

client = Blindfold(api_key="your-api-key")

def display_payment_info(payment_text: str) -> str:
    """Mask payment information for display"""

    result = client.mask(
        text=payment_text,
        masking_char="*",
        chars_to_show=4,
        from_end=True
    )

    return result.text

# Examples
card_info = "Card ending in 4532-7562-9102-3456"
print(display_payment_info(card_info))
# Output: "Card ending in ***************3456"

multiple_cards = """
Primary: 4532-7562-9102-3456
Backup: 5425-2334-3010-9903
"""
print(display_payment_info(multiple_cards))
# Output:
# Primary: ***************3456
# Backup: ***************9903

import { Blindfold } from '@blindfold/sdk';

const client = new Blindfold({ apiKey: 'your-api-key' });

async function displayPaymentInfo(paymentText) {
  const result = await client.mask(
    paymentText,
    {
      masking_char: '*',
      chars_to_show: 4,
      from_end: true
    }
  );

  return result.text;
}

// Examples
const cardInfo = "Card ending in 4532-7562-9102-3456";
console.log(await displayPaymentInfo(cardInfo));
// Output: "Card ending in ***************3456"

const multipleCards = `
Primary: 4532-7562-9102-3456
Backup: 5425-2334-3010-9903
`;
console.log(await displayPaymentInfo(multipleCards));
// Output:
// Primary: ***************3456
// Backup: ***************9903

import dev.blindfold.sdk.Blindfold;

Blindfold client = new Blindfold("your-api-key");

String displayPaymentInfo(String paymentText) {
    var result = client.mask(paymentText, 4, true, "*", null);
    return result.getText();
}

// Examples
String cardInfo = "Card ending in 4532-7562-9102-3456";
System.out.println(displayPaymentInfo(cardInfo));
// Output: "Card ending in ***************3456"

String multipleCards = """
    Primary: 4532-7562-9102-3456
    Backup: 5425-2334-3010-9903
    """;
System.out.println(displayPaymentInfo(multipleCards));
// Output:
// Primary: ***************3456
// Backup: ***************9903

Benefits:

Users can identify their cards
Full numbers stay protected
Compliant with PCI-DSS

Analytics with Hashed Identifiers

Create consistent identifiers for analytics without storing actual PII.

Python
JavaScript
Java

from blindfold import Blindfold
import json

client = Blindfold(api_key="your-api-key")

def track_user_event(user_email: str, event: str) -> dict:
    """Track user events with hashed identifiers"""

    # Hash email for consistent user ID
    hashed = client.hash(
        text=f"User: {user_email}",
        hash_type="sha256",
        hash_prefix="user_",
        hash_length=16
    )

    # Extract hashed ID
    user_id = hashed.text.replace("User: ", "")

    # Create analytics event
    analytics_event = {
        "user_id": user_id,
        "event": event,
        "timestamp": "2024-01-20T10:00:00Z"
    }

    return analytics_event

# Track events for same user
events = [
    track_user_event("john@example.com", "page_view"),
    track_user_event("john@example.com", "button_click"),
    track_user_event("john@example.com", "purchase")
]

# All events have same hashed user_id (deterministic)
for event in events:
    print(json.dumps(event, indent=2))

# Output (same user_id for all):
# {
#   "user_id": "user_a3f8b9c2d4e5f6g7",
#   "event": "page_view",
#   ...
# }

import { Blindfold } from '@blindfold/sdk';

const client = new Blindfold({ apiKey: 'your-api-key' });

async function trackUserEvent(userEmail, event) {
  // Hash email for consistent user ID
  const hashed = await client.hash(
    `User: ${userEmail}`,
    {
      hash_type: 'sha256',
      hash_prefix: 'user_',
      hash_length: 16
    }
  );

  // Extract hashed ID
  const userId = hashed.text.replace('User: ', '');

  // Create analytics event
  return {
    user_id: userId,
    event: event,
    timestamp: new Date().toISOString()
  };
}

// Track events for same user
const events = await Promise.all([
  trackUserEvent('john@example.com', 'page_view'),
  trackUserEvent('john@example.com', 'button_click'),
  trackUserEvent('john@example.com', 'purchase')
]);

// All events have same hashed user_id (deterministic)
events.forEach(event => {
  console.log(JSON.stringify(event, null, 2));
});

import dev.blindfold.sdk.Blindfold;

Blindfold client = new Blindfold("your-api-key");

Map<String, Object> trackUserEvent(String userEmail, String event) {
    // Hash email for consistent user ID
    var hashed = client.hash(
        "User: " + userEmail,
        "sha256", "user_", 16, null
    );

    String userId = hashed.getText().replace("User: ", "");

    return Map.of(
        "user_id", userId,
        "event", event,
        "timestamp", Instant.now().toString()
    );
}

// Track events for same user
var event1 = trackUserEvent("john@example.com", "page_view");
var event2 = trackUserEvent("john@example.com", "button_click");
var event3 = trackUserEvent("john@example.com", "purchase");
// All events have same hashed user_id (deterministic)

Benefits:

Consistent user tracking
No PII in analytics database
GDPR-friendly approach

Generating Test Data

Create realistic test data with synthetic PII for development and testing.

Python
JavaScript

from blindfold import Blindfold

client = Blindfold(api_key="your-api-key")

def generate_test_data(template: str, language: str = "en") -> list[str]:
    """Generate multiple test data samples"""

    samples = []
    for _ in range(5):
        result = client.synthesize(
            text=template,
            language=language
        )
        samples.append(result.text)

    return samples

# Generate test user profiles
template = """
Name: John Doe
Email: john.doe@company.com
Phone: +1-555-1234
Location: New York
Company: TechCorp
"""

test_profiles = generate_test_data(template)

print("Generated Test Profiles:")
for i, profile in enumerate(test_profiles, 1):
    print(f"\n=== Profile {i} ===")
    print(profile)

# Output (synthetic data):
# === Profile 1 ===
# Name: Michael Smith
# Email: michael.smith@company.com
# Phone: +1-555-9876
# Location: Boston
# Company: DataSystems
#
# === Profile 2 ===
# Name: Sarah Johnson
# Email: sarah.johnson@company.com
# ...

import { Blindfold } from '@blindfold/sdk';

const client = new Blindfold({ apiKey: 'your-api-key' });

async function generateTestData(template, language = 'en', count = 5) {
  const samples = [];

  for (let i = 0; i < count; i++) {
    const result = await client.synthesize(template, { language });
    samples.push(result.text);
  }

  return samples;
}

// Generate test user profiles
const template = `
Name: John Doe
Email: john.doe@company.com
Phone: +1-555-1234
Location: New York
Company: TechCorp
`;

const testProfiles = await generateTestData(template);

console.log('Generated Test Profiles:');
testProfiles.forEach((profile, i) => {
  console.log(`\n=== Profile ${i + 1} ===`);
  console.log(profile);
});

Use Cases:

Development environments
Automated testing
Demo environments
Training datasets

Encrypting Sensitive Configuration

Encrypt sensitive configuration values before storing in databases.

Python
JavaScript

from blindfold import Blindfold
import os

client = Blindfold(api_key=os.environ["BLINDFOLD_API_KEY"])

# Encryption key (store securely!)
ENCRYPTION_KEY = os.environ["ENCRYPTION_KEY"]

def encrypt_config(config_text: str) -> str:
    """Encrypt sensitive configuration"""

    result = client.encrypt(
        text=config_text,
        encryption_key=ENCRYPTION_KEY
    )

    return result.text

def decrypt_config(encrypted_text: str) -> str:
    """Decrypt configuration (use decrypt endpoint)"""
    # Note: Implement decrypt endpoint call
    pass

# Example: Encrypt API keys before storing
config = """
database_url: postgresql://user:pass@host/db
api_key: sk-1234567890abcdef
secret_token: abc123xyz789
"""

encrypted = encrypt_config(config)
print("Encrypted configuration:")
print(encrypted)

# Store encrypted version in database
# Later, decrypt when needed

import { Blindfold } from '@blindfold/sdk';

const client = new Blindfold({
  apiKey: process.env.BLINDFOLD_API_KEY
});

const ENCRYPTION_KEY = process.env.ENCRYPTION_KEY;

async function encryptConfig(configText) {
  const result = await client.encrypt(
    configText,
    { encryption_key: ENCRYPTION_KEY }
  );

  return result.text;
}

// Example: Encrypt API keys before storing
const config = `
database_url: postgresql://user:pass@host/db
api_key: sk-1234567890abcdef
secret_token: abc123xyz789
`;

const encrypted = await encryptConfig(config);
console.log('Encrypted configuration:');
console.log(encrypted);

// Store encrypted version in database

Benefits:

Protect secrets at rest
Reversible encryption
Centralized key management

Multi-Language Support & Custom Entities

Blindfold automatically detects PII across 6+ languages and supports custom entity types for industry-specific data.

Automatic Multilingual Detection

Python
JavaScript

from blindfold import Blindfold

client = Blindfold(api_key="your-api-key")

# Automatic language detection - no configuration needed
# Spanish example
spanish_text = "Mi nombre es María García, email: maria@ejemplo.es, teléfono: +34 912 345 678"
result_es = client.tokenize(spanish_text)
print(f"Spanish: {result_es.text}")
# Output: "Mi nombre es <PERSON_1>, email: <EMAIL_ADDRESS_1>, teléfono: <PHONE_NUMBER_1>"

# German example
german_text = "Ich heiße Hans Müller, E-Mail: hans@beispiel.de, wohne in Berlin"
result_de = client.tokenize(german_text)
print(f"German: {result_de.text}")
# Output: "Ich heiße <PERSON_1>, E-Mail: <EMAIL_ADDRESS_1>, wohne in <LOCATION_1>"

# French example
french_text = "Je m'appelle Marie Dupont, email: marie@exemple.fr, tél: +33 1 42 86 82 00"
result_fr = client.tokenize(french_text)
print(f"French: {result_fr.text}")
# Output: "Je m'appelle <PERSON_1>, email: <EMAIL_ADDRESS_1>, tél: <PHONE_NUMBER_1>"

# Mixed languages in one request
mixed_text = "Contact: John Doe (john@example.com), Client: María García (maria@ejemplo.es)"
result_mixed = client.tokenize(mixed_text)
print(f"Mixed: {result_mixed.text}")
# Automatically detects entities in both English and Spanish

import { Blindfold } from '@blindfold/sdk';

const client = new Blindfold({ apiKey: 'your-api-key' });

// Automatic language detection - no configuration needed
// Spanish example
const spanishText = "Mi nombre es María García, email: maria@ejemplo.es, teléfono: +34 912 345 678";
const resultEs = await client.tokenize(spanishText);
console.log(`Spanish: ${resultEs.text}`);

// German example
const germanText = "Ich heiße Hans Müller, E-Mail: hans@beispiel.de, wohne in Berlin";
const resultDe = await client.tokenize(germanText);
console.log(`German: ${resultDe.text}`);

// French example
const frenchText = "Je m'appelle Marie Dupont, email: marie@exemple.fr, tél: +33 1 42 86 82 00";
const resultFr = await client.tokenize(frenchText);
console.log(`French: ${resultFr.text}`);

// Mixed languages in one request
const mixedText = "Contact: John Doe (john@example.com), Client: María García (maria@ejemplo.es)";
const resultMixed = await client.tokenize(mixedText);
console.log(`Mixed: ${resultMixed.text}`);

Native Language Support (Tier 1 - Highest Accuracy):

🇺🇸 English, 🇩🇪 German, 🇫🇷 French, 🇪🇸 Spanish, 🇮🇹 Italian, 🇵🇹 Portuguese, 🇳🇱 Dutch, 🇵🇱 Polish, 🇷🇺 Russian

Zero-Shot Support (Tier 2 - High Accuracy):

🇨🇿 Czech, 🇸🇰 Slovak, 🇩🇰 Danish, 🇸🇪 Swedish, 🇳🇴 Norwegian, 🇷🇴 Romanian

Key Features:

Automatic detection - no language parameter needed
Mix multiple languages in the same request
Works on 15+ languages without configuration

Custom Entity Detection

Define custom entities for industry-specific identifiers using zero-shot learning.

Python
JavaScript

from blindfold import Blindfold

client = Blindfold(api_key="your-api-key")

# E-commerce example with custom entities
order_text = "Order #ORD-2024-XYZ for customer CUST-456, SKU: PROD-789-BLU"
result = client.tokenize(
    order_text,
    entities=["order number", "customer id", "product sku"]
)
print(result.text)
# Output: "Order #<CUSTOM_1> for customer <CUSTOM_2>, SKU: <CUSTOM_3>"

# Healthcare example with custom entities
medical_text = "Patient admitted to Ward 5B, Doctor ID: DOC-123, Procedure Code: PROC-456"
result = client.tokenize(
    medical_text,
    entities=["ward number", "doctor identifier", "procedure code"]
)
print(result.text)

# Mix standard and custom entities
mixed_text = "Customer John Doe (ID: CUST-789) ordered item SKU-456 on 2024-01-15"
result = client.tokenize(
    mixed_text,
    entities=["PERSON", "EMAIL_ADDRESS", "customer id", "product sku", "DATE_TIME"]
)
print(result.text)
# Detects both standard PII (PERSON, DATE_TIME) and custom entities (customer id, product sku)

import { Blindfold } from '@blindfold/sdk';

const client = new Blindfold({ apiKey: 'your-api-key' });

// E-commerce example with custom entities
const orderText = "Order #ORD-2024-XYZ for customer CUST-456, SKU: PROD-789-BLU";
const result = await client.tokenize(
  orderText,
  { entities: ["order number", "customer id", "product sku"] }
);
console.log(result.text);

// Healthcare example with custom entities
const medicalText = "Patient admitted to Ward 5B, Doctor ID: DOC-123, Procedure Code: PROC-456";
const result2 = await client.tokenize(
  medicalText,
  { entities: ["ward number", "doctor identifier", "procedure code"] }
);
console.log(result2.text);

// Mix standard and custom entities
const mixedText = "Customer John Doe (ID: CUST-789) ordered item SKU-456 on 2024-01-15";
const result3 = await client.tokenize(
  mixedText,
  { entities: ["PERSON", "EMAIL_ADDRESS", "customer id", "product sku", "DATE_TIME"] }
);
console.log(result3.text);

Custom Entity Benefits:

No training required - describe entities in natural language
Works across any industry or domain
Combine with 40+ pre-trained entity types
Zero-shot detection adapts to your use case

Batch Processing

Process multiple texts efficiently in parallel.

Python
JavaScript

import asyncio
from blindfold import AsyncBlindfold

async def process_batch(texts: list[str]) -> list:
    """Process multiple texts concurrently"""

    async with AsyncBlindfold(api_key="your-api-key") as client:
        # Process all texts in parallel
        tasks = [client.tokenize(text) for text in texts]
        results = await asyncio.gather(*tasks)

        return results

# Example: Process 100 customer messages
messages = [
    f"Customer {i}: email{i}@example.com"
    for i in range(100)
]

results = asyncio.run(process_batch(messages))
print(f"Processed {len(results)} messages")
print(f"Average entities per message: {sum(r.entities_count for r in results) / len(results):.2f}")

import { Blindfold } from '@blindfold/sdk';

const client = new Blindfold({ apiKey: 'your-api-key' });

async function processBatch(texts) {
  // Process all texts in parallel
  const results = await Promise.all(
    texts.map(text => client.tokenize(text))
  );

  return results;
}

// Example: Process 100 customer messages
const messages = Array.from(
  { length: 100 },
  (_, i) => `Customer ${i}: email${i}@example.com`
);

const results = await processBatch(messages);
console.log(`Processed ${results.length} messages`);

const avgEntities = results.reduce((sum, r) => sum + r.entities_count, 0) / results.length;
console.log(`Average entities per message: ${avgEntities.toFixed(2)}`);

Performance Tips:

Use async/parallel processing for batches
Implement rate limiting
Use connection pooling
Handle errors gracefully

Express.js Middleware

Create middleware to automatically protect routes.

import express from 'express';
import { Blindfold } from '@blindfold/sdk';

const app = express();
const client = new Blindfold({ apiKey: process.env.BLINDFOLD_API_KEY });

// Middleware to protect request bodies
const protectPII = async (req, res, next) => {
  try {
    if (req.body && req.body.text) {
      const protected = await client.tokenize(req.body.text);

      // Store mapping in session for later detokenization
      req.session.tokenMapping = protected.mapping;

      // Replace text with protected version
      req.body.text = protected.text;
      req.body.pii_detected = protected.entities_count;
    }

    next();
  } catch (error) {
    res.status(500).json({ error: 'Failed to protect data' });
  }
};

// Apply middleware to specific routes
app.post('/api/chat', express.json(), protectPII, async (req, res) => {
  // req.body.text is now protected
  // Process with AI...
  res.json({ message: 'Processed securely' });
});

app.listen(3000);

Best Practices Summary

1. Always Use Environment Variables

export BLINDFOLD_API_KEY="your-key-here"
export ENCRYPTION_KEY="your-encryption-key"

2. Handle Errors Gracefully

try:
    result = client.tokenize(text)
except AuthenticationError:
    # Handle auth error
    pass
except NetworkError:
    # Retry with exponential backoff
    pass

3. Store Mappings Securely

Use encrypted session storage
Implement TTL for mappings
Clear mappings after use

4. Use Appropriate Methods

Tokenize: When you need to restore data later
Mask: For UI display
Redact: For permanent removal
Hash: For analytics identifiers
Synthesize: For test data

5. Monitor Usage

Track API usage in dashboard
Set up rate limit alerts
Monitor error rates

Cookbook

Complete, runnable examples you can clone and use as a starting point. Each example is a self-contained project with setup instructions.

OpenAI + Python

Tokenize user messages before GPT, detokenize responses

OpenAI + Node.js

TypeScript/Node.js OpenAI integration

LangChain (Python)

PII-safe chains with RunnableLambda

LangChain (Node.js)

PII-safe LangChain.js chains with RunnableLambda

GDPR + OpenAI (Python)

EU region, gdpr_eu policy, batch processing

GDPR + OpenAI (Node.js)

EU region, gdpr_eu policy — TypeScript

HIPAA Chatbot (Python)

Multi-turn chat with hipaa_us policy, PHI redaction

HIPAA Chatbot (Node.js)

Multi-turn healthcare chatbot — TypeScript

E2B Data Analyst (Python)

AI writes analysis code from tokenized data, E2B runs it on real data

E2B Data Analyst (Node.js)

AI data analyst with E2B sandbox — TypeScript

FastAPI Middleware

Auto-tokenize request bodies in FastAPI

Express Middleware

Auto-tokenize request bodies in Express.js

RAG + OpenAI (Python)

PII-safe RAG with ChromaDB — redact at ingestion, tokenize at query

RAG + OpenAI (Node.js)

TypeScript RAG pipeline with ChromaDB and PII protection

RAG + LangChain (Python)

BlindfoldPIITransformer + blindfold_protect() with FAISS

RAG + LangChain (Node.js)

LangChain.js RAG with inline PII protection

RAG + LlamaIndex (Python)

Custom BlindfoldNodePostprocessor for LlamaIndex

RAG + LlamaIndex (Node.js)

LlamaIndex.TS RAG with PII protection

RAG Customer Support (Python)

GDPR multi-turn EU support chatbot with gdpr_eu policy

RAG Customer Support (Node.js)

TypeScript GDPR multi-turn EU support chatbot

Need More Examples?

Can’t find what you’re looking for? Check out:

Python SDK

Detailed Python SDK documentation

JavaScript SDK

Complete JavaScript SDK guide

Java SDK

Sync and async Java client

REST API

HTTP API reference

Contact Support

Email us for custom integration help

Getting Started

AI Integrations

Compliance

Essentials

Privacy Methods

SDKs & Tools

Guides

​AI Chat with OpenAI

​AI Chat with Anthropic Claude

​Customer Support Ticket Anonymization

​Displaying Masked Credit Cards

​Analytics with Hashed Identifiers

​Generating Test Data

​Encrypting Sensitive Configuration

​Multi-Language Support & Custom Entities

​Automatic Multilingual Detection

​Custom Entity Detection

​Batch Processing

​Express.js Middleware

​Best Practices Summary

​1. Always Use Environment Variables

​2. Handle Errors Gracefully

​3. Store Mappings Securely

​4. Use Appropriate Methods

​5. Monitor Usage

​Cookbook

OpenAI + Python

OpenAI + Node.js

LangChain (Python)

LangChain (Node.js)

GDPR + OpenAI (Python)

GDPR + OpenAI (Node.js)

HIPAA Chatbot (Python)

HIPAA Chatbot (Node.js)

E2B Data Analyst (Python)

E2B Data Analyst (Node.js)

FastAPI Middleware

Express Middleware

RAG + OpenAI (Python)

RAG + OpenAI (Node.js)

RAG + LangChain (Python)

RAG + LangChain (Node.js)

RAG + LlamaIndex (Python)

RAG + LlamaIndex (Node.js)

RAG Customer Support (Python)

RAG Customer Support (Node.js)

​Need More Examples?

Python SDK

JavaScript SDK

Java SDK

REST API

Contact Support

AI Chat with OpenAI

AI Chat with Anthropic Claude

Customer Support Ticket Anonymization

Displaying Masked Credit Cards

Analytics with Hashed Identifiers

Generating Test Data

Encrypting Sensitive Configuration

Multi-Language Support & Custom Entities

Automatic Multilingual Detection

Custom Entity Detection

Batch Processing

Express.js Middleware

Best Practices Summary

1. Always Use Environment Variables

2. Handle Errors Gracefully

3. Store Mappings Securely

4. Use Appropriate Methods

5. Monitor Usage

Cookbook

Need More Examples?