openinference
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)