Python API Overview

The Elsai Guardrails Python API provides a simple and powerful interface for adding guardrails to your LLM applications.

Quick Start

from elsai_guardrails.guardrails import LLMRails, RailsConfig

# Configure
yaml_content = """
llm:
  engine: "openai"
  model: "gpt-4o-mini"
  api_key: "sk-..."

guardrails:
  input_checks: true
  output_checks: true
"""

# Create and use
config = RailsConfig.from_content(yaml_content=yaml_content)
rails = LLMRails(config=config)

# Generate with guardrails
response = rails.generate(
    messages=[{"role": "user", "content": "Hello!"}]
)

Core Classes

LLMRails

High-level class for LLM generation with guardrails.

rails = LLMRails(config=config)
result = rails.generate(messages=messages)

See Basic Usage for details.

GuardrailSystem

Lower-level class for guardrail checks only.

guardrail = GuardrailSystem(config=config)
result = guardrail.check_text("text to check")

RailsConfig

Configuration container.

config = RailsConfig.from_content(yaml_content=yaml_content)

GuardrailConfig

Guardrail-specific configuration.

config = GuardrailConfig(
    check_toxicity=True,
    check_sensitive_data=True
)

Common Patterns

Basic Generation

rails = LLMRails(config=config)
response = rails.generate(messages=[{"role": "user", "content": "Hello"}])

Detailed Results

result = rails.generate(
    messages=[{"role": "user", "content": "test"}],
    return_details=True
)
print(f"Blocked: {result['blocked']}")

Async Generation

result = await rails.generate_async(messages=messages)

Input/Output Validation Only

guardrail = GuardrailSystem(config=config)
input_result = guardrail.check_input("user input")
output_result = guardrail.check_output("llm output")

Next Steps

• Basic Usage - Common use cases
• Advanced Usage - Advanced patterns
• Async Support - Asynchronous operations