privacy-prompt-library 1.2.0

A privacy-preserving prompt transformation library with smart multiple key combinations that reduce redundancy by 50%+ while maintaining complete accessibility context.

Privacy-Preserving Prompt Library

A comprehensive Python library that transforms user prompts to protect disability privacy while maintaining functional context for AI interactions. Now with smart combination technology that reduces description length by 50%+ while preserving complete accessibility information.

✨ Key Features

🔒 Privacy Protection - Removes medical terms while preserving functional needs
🎯 Smart Combinations - Combines multiple accessibility needs into optimized single descriptions
⚡ 50%+ More Efficient - Dramatically reduces word count without losing information
🧠 AI-Ready - Perfect single-string descriptions for prompt engineering
📚 14 Categories - Comprehensive coverage of disability and accessibility needs

🎯 Purpose

This library helps users interact with AI models without disclosing specific medical conditions by:

• Detecting disability mentions in prompts
• Redacting medical/diagnostic terms
• Adding functional context for better AI responses
• Preserving user privacy and intent
• Smart Combining multiple accessibility needs into efficient single descriptions

🔄 How It Works

Input: "I'm paralyzed and need help finding accessible restaurants"
↓
Output: "I use mobility equipment and need help finding accessible restaurants. I need step-free access to buildings and accessible parking close to entrances."

⚡ Smart Multiple Keys (v1.2.0)

# Instead of 179 words across 3 separate descriptions...
keys = ["visual-impairment", "hearing-impairment", "physical-disability"]
result = transform_multiple_keys(keys)

# Get 1 optimized 76-word description (57% more efficient!)
print(result['output'])
# "I have comprehensive accessibility needs requiring screen reader 
# compatibility with detailed text descriptions... [complete single description]"

🏗️ Architecture

• 14 Disability Categories with comprehensive subgroups
• Pattern Detection Engine for identifying disability mentions
• Redaction Engine for replacing medical terms
• Context Enrichment for adding functional needs
• Privacy Validation to ensure no medical data leaks
• Smart Combination Engine for optimizing multiple accessibility descriptions

📦 Installation

pip install privacy-prompt-library

🚀 Quick Start

Basic Usage - Transform Personal Prompts

from prompt_library import transform_prompt

# Protect your privacy while getting AI help
result = transform_prompt("I'm blind and need coding help")
print(result['output'])
# "I use screen readers and need coding help. Please ensure any visual content 
# includes text descriptions and is compatible with screen readers."

Key-Based Descriptions

from prompt_library import transform_by_key, get_supported_keys

# See what accessibility categories are available
print("Available categories:", get_supported_keys())

# Get a detailed 60-word accessibility description
description = transform_by_key("visual-impairment")  
print(description['output'])
# Returns comprehensive visual accessibility needs without medical terms

# Use in your AI prompts
ai_prompt = f"{description['output']} Help me learn web development."

Smart Multiple Keys (Most Efficient!)

from prompt_library import transform_multiple_keys

# Get 1 optimized description instead of 3 separate ones
keys = ["visual-impairment", "hearing-impairment", "physical-disability"] 
result = transform_multiple_keys(keys)  # Smart combination by default

print(f"Efficiency: {result['validation']['efficiency_gain']} space saved!")
print(f"Single optimized description:\n{result['output']}")

Library Information

from prompt_library import get_library_info
info = get_library_info()
print(f"Version: {info['version']}")
print(f"Features: {', '.join(info['features'])}")

⭐ What's New in v1.2.0: Smart Combination

Transform multiple accessibility needs into one optimized description instead of handling separate strings:

# OLD WAY: Get 3 separate 60-word descriptions (180 words total)
desc1 = transform_by_key("visual-impairment")     # 60 words
desc2 = transform_by_key("hearing-impairment")    # 60 words  
desc3 = transform_by_key("physical-disability")   # 60 words
# Total: 180 words, lots of redundancy

# NEW WAY: Get 1 smart combined description (76 words total)
result = transform_multiple_keys(["visual-impairment", "hearing-impairment", "physical-disability"])
print(f"Efficiency gain: {result['validation']['efficiency_gain']}")  # "57.5%"
print(result['output'])  # One cohesive, optimized description

Benefits:

• 🎯 50-60% more efficient - Dramatically reduced word count
• 🔄 No redundancy - Removes repeated accessibility terminology
• 📝 Better readability - Flows as one natural description
• ⚡ Perfect for AI - Single string ready for prompts

Input: "I'm paralyzed and need help finding accessible restaurants" ↓ Output: "I use mobility equipment and need help finding accessible restaurants. I need step-free access to buildings and accessible parking close to entrances."

## 🔑 Key-Based Descriptions
Generate detailed 60-word accessibility descriptions from simple category keys:

```python
from prompt_library import transform_by_key, get_supported_keys

# Get all available category keys
supported_keys = get_supported_keys()
print("Available keys:", supported_keys)

# Generate a functional description from a key
description = transform_by_key("visual-impairment")
print(description['output'])
# Output: "I have specific visual accessibility needs requiring comprehensive 
# screen reader compatibility, high contrast display options with customizable 
# color schemes, detailed text descriptions for all visual content including 
# images, alternative format documents in accessible formats, large print 
# materials when needed, audio descriptions for multimedia content, tactile 
# feedback options, keyboard navigation support, and accessible navigation 
# systems that work seamlessly with assistive technology."

# Example usage for different needs:
mobility_desc = transform_by_key("physical-disability")
hearing_desc = transform_by_key("hearing-impairment")
speech_desc = transform_by_key("speech-language-communication-and-swallowing-disability")

print(f"Word count: {len(mobility_desc['output'].split())} words")  # Always 60 words

Available Keys:

• visual-impairment - Visual accessibility needs and assistive technology
• hearing-impairment - Hearing accessibility and communication needs
• physical-disability - Mobility and physical accessibility requirements
• speech-language-communication-and-swallowing-disability - Communication support needs
• speech-intellectual-autism-spectrum-disorders - Cognitive and sensory support
• maxillofacial-disabilities - Facial and oral function considerations
• progressive-chronic-disorders - Adaptive and flexible accommodation needs

🚀 Why Use Smart Combinations?

Problem: Traditional approach requires handling multiple separate descriptions

# Old way: 3 separate descriptions = 180 words + manual combining
visual_desc = transform_by_key("visual-impairment")      # 60 words
hearing_desc = transform_by_key("hearing-impairment")    # 60 words  
mobility_desc = transform_by_key("physical-disability")  # 60 words
# You have to manually combine and remove redundancy

Solution: Smart combination gives you one optimized description

# New way: 1 smart description = 76 words, ready to use
result = transform_multiple_keys(["visual-impairment", "hearing-impairment", "physical-disability"])
ai_prompt = f"{result['output']} Help me plan a conference presentation."
# 57% more efficient, no redundancy, perfect for AI prompts

🔗 Multiple Keys (New in v1.2.0)

Process multiple accessibility categories at once with intelligent combination:

from prompt_library import transform_multiple_keys

# Smart combination (default) - Intelligently merges descriptions, removes redundancy
keys = ["visual-impairment", "hearing-impairment", "physical-disability"]
result = transform_multiple_keys(keys)  # Uses smart_combined by default

print(f"Original total: {sum(result['transformation']['individual_word_counts'])} words")
print(f"Smart combined: {result['transformation']['total_word_count']} words")
print(f"Efficiency gain: {result['validation']['efficiency_gain']}")
print(f"\nCombined description:\n{result['output']}")

# Alternative methods if needed:
separate_result = transform_multiple_keys(keys, combine_method="separate")
simple_combined = transform_multiple_keys(keys, combine_method="simple_combined")
prioritized = transform_multiple_keys(keys, combine_method="prioritized")

Combination Methods:

• "smart_combined" - Default: Intelligently merges while avoiding redundancy (57% more efficient!)
• "separate" - Returns individual descriptions for each key
• "simple_combined" - Basic concatenation of all descriptions
• "prioritized" - Emphasizes first key, adds others as supplementary needs

Smart Combination Benefits:

• 🎯 Efficiency: Reduces word count by ~50-60% while preserving all key information
• 🔄 Deduplication: Removes redundant terms like "accessibility needs" and "screen reader"
• 📝 Coherence: Creates flowing, natural language instead of repetitive chunks
• ⚡ Performance: Single cohesive description instead of multiple separate strings

💡 Usage Examples

Scenario 1: Getting a Functional Description for AI Prompts

from prompt_library import transform_by_key

# Instead of saying "I'm blind", use a functional description
visual_needs = transform_by_key("visual-impairment")
prompt = f"{visual_needs['output']} Can you help me learn Python programming?"

# This gives the AI detailed context about your accessibility needs
# without revealing medical information

Scenario 2: Multiple Accessibility Needs

from prompt_library import transform_multiple_keys

# Smart combination automatically optimizes for efficiency
multiple_needs = transform_multiple_keys(
    ["visual-impairment", "physical-disability"]
)
# Result is a single, optimized 58-word description instead of 120 words

ai_prompt = f"{multiple_needs['output']} Help me set up a home office workspace."
print(f"Efficiency: {multiple_needs['validation']['efficiency_gain']} space saved!")

Scenario 3: Building Accessibility Profiles

# Create comprehensive accessibility descriptions
keys = ["visual-impairment", "hearing-impairment", "physical-disability"]
accessibility_profile = []

for key in keys:
    description = transform_by_key(key)
    accessibility_profile.append(description['output'])

combined_profile = " ".join(accessibility_profile)
print(f"Complete accessibility profile: {combined_profile}")

Scenario 3: Privacy-Safe Prompt Enhancement

# Transform personal prompts to be privacy-safe
personal_prompt = "I have multiple sclerosis and need help with work accommodations"
safe_prompt = transform_prompt(personal_prompt)

print("Original:", personal_prompt)
print("Safe version:", safe_prompt['output'])
# Result protects medical info while preserving functional needs

Command Line Interface

# Transform a prompt via CLI
privacy-prompt "I have ADHD and need focus strategies"

# Get library information
privacy-prompt --info

# Custom privacy level
privacy-prompt --privacy-level medium "Your prompt here"

Async Support

from prompt_library import PromptLibrary

library = PromptLibrary()
await library.initialize()
result = await library.transform_prompt("I'm autistic and need help with social situations")
print(result['output'])

📋 Categories Supported

1. Physical Disabilities
2. Visual Impairments
3. Hearing Impairments
4. Speech & Language
5. Intellectual Disabilities
6. Learning Disabilities
7. Autism Spectrum
8. Developmental Disabilities
9. Mental Health
10. Emotional & Behavioral
11. Invisible Disabilities
12. Multiple Disabilities
13. Neurological
14. Genetic & Rare Disorders

🔒 Privacy Guarantee

• ✅ No medical terms in output
• ✅ No diagnostic language
• ✅ Functional descriptions only
• ✅ Complete user anonymity

🐍 Python Package Features

This repository now includes a fully-featured Python package with:

• Modern Packaging: Uses pyproject.toml and is available on PyPI
• Async Support: Full async/await compatibility
• CLI Tool: Command-line interface for easy integration
• Type Hints: Complete typing for better development experience
• Testing: Comprehensive test suite with pytest

Python Installation & Usage

# Install from PyPI
pip install privacy-prompt-library

# Basic usage
python -c "from prompt_library import transform_prompt; print(transform_prompt('I have autism and need help'))"

# CLI usage
privacy-prompt --info
privacy-prompt "I'm deaf and need communication help"

Development Setup

# Clone repository
git clone https://github.com/git-markkuria/kanuni-layer-sdk.git
cd kanuni-layer-sdk

# Install in development mode
pip install -e .

# Run tests
pytest tests/

📁 Repository Structure

├── prompt_library/          # Python package
│   ├── core/               # Core processing engines
│   ├── engines/            # Context and redaction engines
│   ├── data/               # JSON data files
│   └── cli.py              # Command-line interface
├── src/                    # JavaScript/Node.js version
├── tests/                  # Python tests
├── pyproject.toml          # Python packaging config
└── package.json            # Node.js config

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.