LiteLLM allows developers to call all LLM APIs using the openAI format. LiteLLM Proxy is a proxy server to call 100+ LLMs in OpenAI format. Both are supported by this auto-instrumentation.
Any calls made to the following functions will be automatically captured by this integration:
completion()
acompletion()
completion_with_retries()
embedding()
aembedding()
image_generation()
aimage_generation()
Launch Phoenix
Install packages:
pipinstallarize-phoenix
Launch Phoenix:
import phoenix as pxpx.launch_app()
Connect your notebook to Phoenix:
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.
You can now use LiteLLM as normal and calls will be traces in Phoenix.
import litellmcompletion_response = litellm.completion(model="gpt-3.5-turbo", messages=[{"content": "What's the capital of China?", "role": "user"}])print(completion_response)