shadowai 0.1.4

AI-powered mock data generation library with flexible rule engine

ShadowAI

🚀 An AI-powered intelligent mock data generation library

ShadowAI is a powerful Python library that uses AI technology to generate high-quality simulated data. Through a flexible rule engine, you can easily generate structured JSON data.

🎯 Design Philosophy

ShadowAI provides flexible and easy-to-use API design, supporting various usage scenarios from simple to complex, allowing users to get started quickly while maintaining powerful customization capabilities.

🆚 Comparison with Traditional Mock Libraries

Core Differences

Feature	ShadowAI	Traditional Mock Libraries (like faker.js)
Generation Method	AI-powered intelligent generation	Predefined algorithms
Configuration Complexity	Minimal (description-based)	Medium (requires API combination)
Data Quality	High (semantic understanding)	Medium (template-based)
Business Relevance	Strong (context-aware)	Weak (generic patterns)
Generation Speed	Slow (AI calls)	Very fast (local computation)
Extensibility	High (AI adaptation)	Medium (requires development)

ShadowAI's Unique Advantages

🧠 Intelligent Understanding

# ShadowAI - One line of code, intelligent understanding of business meaning
shadow_ai.generate("company_email")  # Automatically generates company-formatted emails

# Traditional library - Requires manual combination of multiple APIs
faker.internet.email(
    faker.person.firstName(),
    faker.person.lastName(), 
    faker.internet.domainName()
)

🎯 Business Scenario Driven

# ShadowAI - Business rule packages ensure data logical consistency
developer_profile = RulePackage(
    name="senior_developer",
    rules=["name", "email", "programming_language", "years_experience", "github_username"]
)
# Generated data automatically maintains logical relationships: high experience corresponds to advanced programming languages

🔧 Minimal Configuration

# ShadowAI - Descriptive configuration
Rule(
    name="medical_record_id", 
    description="Generate HIPAA-compliant patient ID",
    constraints={"format": "anonymized"}
)

# Traditional library - Requires custom development
def generate_medical_id():
    # Lots of custom logic...

Use Case Selection

✅ Recommended ShadowAI Scenarios

• Complex business testing: Requires logical relationships between data
• Prototype demonstrations: Needs highly realistic sample data
• Industry-specific data: Medical, financial, and other professional domains
• API documentation examples: Automatically generates business-compliant response examples
• Rapid iteration: Frequently adjusting data generation rules

✅ Recommended Traditional Library Scenarios

• High performance requirements: Bulk generation of large amounts of data
• CI/CD pipelines: Automated testing environments
• Simple standard data: Basic names, emails, phone numbers
• Offline environments: No network connection restrictions
• Cost-sensitive: Avoiding AI API call costs

💡 Best Practice Recommendations

Hybrid Usage Strategy - Leverage the advantages of both:

# 1. Use ShadowAI to design data templates
business_template = shadow_ai.generate(complex_business_package)

# 2. Use traditional libraries for bulk data population  
for i in range(1000):
    test_data = apply_template_with_faker(business_template)

Selection Guide:

• 🎯 Pursue data quality and business relevance → Choose ShadowAI
• ⚡ Pursue generation speed and simplicity → Choose Traditional Mock Libraries
• 🔄 Combine both → Get best development experience

✨ Features

• 🤖 AI-driven: Based on Agno framework, supports multiple LLM models
• 📝 Flexible rules: Supports rule records, rule combinations, and rule packages
• 📄 Multi-format support: Supports JSON and YAML format rule definitions
• 🎯 Precise output: Generates structured JSON data
• 📦 Ready to use: Built-in common rule packages
• ⚡ Minimal configuration: Descriptive configuration, quick start

📦 Installation

pip install shadowai

🚀 Quick Start

Basic Usage

from shadow_ai import ShadowAI

# Create ShadowAI instance
shadow_ai = ShadowAI()

# Use string directly
result = shadow_ai.generate("email")
print(result)  # {"email": "[e-mail]"}

# Generate multiple fields
result = shadow_ai.generate(["email", "name", "age"])
print(result)  # {"email": "...", "name": "...", "age": ...}

# Quick method
result = shadow_ai.quick("email", "name", "phone")
print(result)  # {"email": "...", "name": "...", "phone": "..."}

Creating Custom Rules

from shadow_ai import Rule, RuleCombination, RulePackage

# Create single rule
email_rule = Rule(name="email")
company_rule = Rule(name="company_name")

# Generate data
result = shadow_ai.generate(email_rule)
print(result)  # {"email": "[e-mail]"}

# Create rule combination
user_combo = RuleCombination(
    name="user_profile",
    rules=["name", "email", "phone"]
)

# Create rule package
user_package = RulePackage(
    name="user", 
    rules=["username", "email", "age", "location"]
)

result = shadow_ai.generate(user_package)
print(result)  # Complete user information

Using Pre-built Rules

from shadow_ai.rules import email_rule, name_rule
from shadow_ai.rules.packages import person_package

# Use predefined rules
result = shadow_ai.generate(email_rule)
print(result)  # {"email": "[e-mail]"}

# Use predefined packages
result = shadow_ai.generate(person_package)
print(result)
# {
#   "fullname": "John Smith", 
#   "age": 25,
#   "email": "[e-mail]"
# }

Advanced Custom Rules

from shadow_ai import Rule

# Detailed rule configuration
custom_rule = Rule(
    name="company",
    description="Generate a technology company name",
    examples=["TechCorp", "DataFlow", "CloudByte"],
    constraints={"type": "string", "style": "modern"}
)

result = shadow_ai.generate(custom_rule)

📖 Documentation

For detailed documentation, please check the docs/ directory.

🤝 Contributing

Contributions are welcome! Please see CONTRIBUTING.md.

📄 License

MIT License - see LICENSE file.