from phoenix.otel import registertracer_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.
Launch your local Phoenix instance:
python3-mphoenix.server.mainserve
For details on customizing a local terminal deployment, see Terminal Setup.
Install packages:
pipinstallarize-phoenix-otel
Connect your application to your instance using:
from phoenix.otel import registertracer_provider =register( project_name="my-llm-app", # Default is 'default' endpoint="http://localhost:6006",)
from phoenix.otel import registertracer_provider =register( project_name="my-llm-app", # Default is 'default' endpoint="http://localhost:6006",)
For more info on using Phoenix with Docker, see Docker
If you don't want to host an instance of Phoenix yourself or use a notebook instance, you can use a persistent instance provided on our site. Sign up for an Arize Phoenix account athttps://app.phoenix.arize.com/login
Install packages:
pipinstallarize-phoenix-otel
Connect your application to your cloud instance:
import osfrom phoenix.otel import register# Add Phoenix API Key for tracingos.environ["PHOENIX_CLIENT_HEADERS"]="api_key=...:..."# configure the Phoenix tracerregister( project_name="my-llm-app", # Default is 'default' endpoint="https://app.phoenix.arize.com/v1/traces",)
Your Phoenix API key can be found on the Keys section of your dashboard.
Initialize the InstructorInstrumentor before your application code.
from openinference.instrumentation.instructor import InstructorInstrumentorInstructorInstrumentor().instrument(tracer_provider=tracer_provider)
Be sure you also instrument the underlying model you're using along with Instructor. For example, if you're using OpenAI calls directly, you would also add:
from openinference.instrumentation.openai import OpenAIInstrumentorOpenAIInstrumentor().instrument(tracer_provider=tracer_provider)
Run Instructor
From here you can use instructor as normal.
import instructorfrom pydantic import BaseModelfrom openai import OpenAI# Define your desired output structureclassUserInfo(BaseModel): name:str age:int# Patch the OpenAI clientclient = instructor.from_openai(OpenAI())# Extract structured data from natural languageuser_info = client.chat.completions.create( model="gpt-3.5-turbo", response_model=UserInfo, messages=[{"role": "user", "content": "John Doe is 30 years old."}],)print(user_info.name)#> John Doeprint(user_info.age)#> 30
Observe
Now that you have tracing setup, all invocations of your underlying model (completions, chat completions, embeddings) and instructor triggers will be streamed to your running Phoenix for observability and evaluation.