Guardrails AI

Instrument LLM applications using the Guardrails AI framework

Google Colab

Setup Guardrails AI with a DatasetEmbeddingsGuard

This guide helps you setup instrumentation for your guardrail using Openinference. For more information on Guardrails, click here.

Python

In this example we will instrument a small program that uses the Guardrails AI framework to protect their LLM calls.

pip install openinference-instrumentation-guardrails openinference-instrumentation-guardrails guardrails-ai arize-otel opentelemetry-sdk opentelemetry-exporter-otlp

Set up GuardrailsInstrumentor to trace your guardrails application and sends the traces to Phoenix at the endpoint defined below.

from openinference.instrumentation.guardrails import GuardrailsInstrumentor
# Import open-telemetry dependencies
from arize_otel import register_otel, Endpoints

# Setup OTEL via our convenience function
register_otel(
    endpoints = Endpoints.ARIZE,
    space_id = "your-space-id", # in app space settings page
    api_key = "your-api-key", # in app space settings page
    model_id = "your-model-id", # name this to whatever you would like
)
GuardrailsInstrumentor().instrument()

To test, run the following code and observe your traces in Arize.

from guardrails import Guard
from guardrails.hub import TwoWords
import openai

guard = Guard().use(
    TwoWords(),
)

response = guard(
    llm_api=openai.chat.completions.create,
    prompt="What is another name for America?",
    model="gpt-3.5-turbo",
    max_tokens=1024,
)

print(response)