Guardrails Library Overview

The Elsai Guardrails library provides core classes and functions for implementing guardrails in your LLM applications.

Core Classes

GuardrailSystem

The main class for performing guardrail checks on text content.

Key Features:

• Toxicity detection
• Sensitive data detection
• Content classification
• Input and output validation

See GuardrailSystem for details.

LLMRails

High-level class that combines LLM generation with guardrail checks.

Key Features:

• Integrated LLM and guardrails
• Automatic input/output validation
• Detailed result reporting
• Async support

See LLMRails for details.

GuardrailResult

Result object returned from guardrail checks.

Contains:

• passed: Whether checks passed
• toxicity: Toxicity detection results
• sensitive_data: Sensitive data detection results
• semantic_class: Content classification result
• message: Human-readable message

See GuardrailResult for details.

Configuration Classes

RailsConfig

Configuration container for the entire rails system.

GuardrailConfig

Configuration for guardrail behavior and thresholds.

Quick Example

from elsai_guardrails.guardrails import GuardrailSystem, GuardrailConfig

# Create guardrail system
config = GuardrailConfig(
    check_toxicity=True,
    check_sensitive_data=True,
    check_semantic=True
)
guardrail = GuardrailSystem(config=config)

# Check text
result = guardrail.check_text("Hello, this is a test")
print(f"Passed: {result.passed}")

Next Steps

• GuardrailSystem - Detailed API reference
• LLMRails - High-level usage
• GuardrailResult - Result object details