import os
# 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"
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
Set your Phoenix endpoint:
import os
os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "http://localhost:6006"
docker run -p 6006:6006 arizephoenix/phoenix:latest
This will expose the Phoenix on localhost:6006
Install packages:
pip install arize-phoenix-otel
Set your Phoenix endpoint:
import os
os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "http://localhost:6006"
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()
By default, notebook instances do not have persistent storage, so your traces will disappear after the notebook is closed. See or use one of the other deployment options to retain traces.
See Google's guide on setting up your environment for the Google Cloud AI Platform. You can also store your Project ID in the CLOUD_ML_PROJECT_ID environment variable.
Use the register function to connect your application to Phoenix:
from phoenix.otel import register
# configure the Phoenix tracer
tracer_provider = register(
project_name="my-llm-app", # Default is 'default'
auto_instrument=True # Auto-instrument your app based on installed OI dependencies
)
Run VertexAI
import vertexai
from vertexai.generative_models import GenerativeModel
vertexai.init(location="us-central1")
model = GenerativeModel("gemini-1.5-flash")
print(model.generate_content("Why is sky blue?").text)
Observe
Now that you have tracing setup, all invocations of Vertex models will be streamed to your running Phoenix for observability and evaluation.
