openinference-instrumentation-guardrails 0.1.14

OpenInference Guardrails Instrumentation

OpenInference guardrails Instrumentation

Python auto-instrumentation library for LLM applications implemented with Guardrails

Guards are fully OpenTelemetry-compatible and can be sent to an OpenTelemetry collector for monitoring, such as arize-phoenix.

Installation

pip install openinference-instrumentation-guardrails

Quickstart

This quickstart shows you how to instrument your guardrailed LLM application

Install required packages.

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

Start Phoenix in the background as a collector. By default, it listens on http://localhost:6006. You can visit the app via a browser at the same address. (Phoenix does not send data over the internet. It only operates locally on your machine.)

python -m phoenix.server.main serve

Install the TwoWords validator that's used in the Guard.

guardrails hub install hub://guardrails/two_words

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
from opentelemetry import trace as trace_api
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk import trace as trace_sdk
from opentelemetry.sdk.trace.export import SimpleSpanProcessor
import os

os.environ["OPENAI_API_KEY"] = "YOUR_KEY_HERE"

endpoint = "http://127.0.0.1:6006/v1/traces"
tracer_provider = trace_sdk.TracerProvider()
tracer_provider.add_span_processor(SimpleSpanProcessor(OTLPSpanExporter(endpoint)))
trace_api.set_tracer_provider(tracer_provider)

GuardrailsInstrumentor().instrument()

Set up a simple example of LLM call using a Guard

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)

More Info

• More info on OpenInference and Phoenix
• How to customize spans to track sessions, metadata, etc.
• How to account for private information and span payload customization