Groq provides low latency and lightning-fast inference for AI models. Arize supports instrumenting Groq API calls, including role types such as system, user, and assistant messages, as well as tool use. You can create a free GroqCloud account and generate a Groq API Key here to get started.
import os
from phoenix.otel import register
# Add Phoenix API Key for tracing
PHOENIX_API_KEY = "ADD YOUR API KEY"
os.environ["PHOENIX_CLIENT_HEADERS"] = f"api_key={PHOENIX_API_KEY}"
os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "https://app.phoenix.arize.com"
# configure the Phoenix tracer
tracer_provider = register(
project_name="my-llm-app", # Default is 'default'
)
Your Phoenix API key can be found on the Keys section of your dashboard.
Launch your local Phoenix instance:
pip install arize-phoenix
phoenix serve
For details on customizing a local terminal deployment, see Terminal Setup.
Install packages:
pip install arize-phoenix-otel
Connect your application to your instance using:
from phoenix.otel import register
tracer_provider = register(
project_name="my-llm-app", # Default is 'default'
endpoint="http://localhost:6006/v1/traces",
)
docker run -p 6006:6006 arizephoenix/phoenix:latest
This will expose the Phoenix on localhost:6006
Install packages:
pip install arize-phoenix-otel
Connect your application to your instance using:
from phoenix.otel import register
tracer_provider = register(
project_name="my-llm-app", # Default is 'default'
endpoint="http://localhost:6006/v1/traces",
)
For more info on using Phoenix with Docker, see Docker
Install packages:
pip install arize-phoenix
Launch Phoenix:
import phoenix as px
px.launch_app()
Connect your notebook to Phoenix:
from phoenix.otel import register
tracer_provider = register(
project_name="my-llm-app", # Default is 'default'
)
By default, notebook instances do not have persistent storage, so your traces will disappear after the notebook is closed. See Persistence or use one of the other deployment options to retain traces.
Initialize the GroqInstrumentor before your application code.
from openinference.instrumentation.groq import GroqInstrumentor
GroqInstrumentor().instrument(tracer_provider=tracer_provider)
Run Groq
A simple Groq application that is now instrumented
import os
from groq import Groq
client = Groq(
# This is the default and can be omitted
api_key=os.environ.get("GROQ_API_KEY"),
)
chat_completion = client.chat.completions.create(
messages=[
{
"role": "user",
"content": "Explain the importance of low latency LLMs",
}
],
model="mixtral-8x7b-32768",
)
print(chat_completion.choices[0].message.content)
Observe
Now that you have tracing setup, all invocations of pipelines will be streamed to your running Phoenix for observability and evaluation.
