Instrument and observe your DSPy application via the DSPyInstrumentor
DSPy is a framework for automatically prompting and fine-tuning language models. It provides composable and declarative APIs that allow developers to describe the architecture of their LLM application in the form of a "module" (inspired by PyTorch's nn.Module). It them compiles these modules using "teleprompters" that optimize the module for a particular task. The term "teleprompter" is meant to evoke "prompting at a distance," and could involve selecting few-shot examples, generating prompts, or fine-tuning language models.
Phoenix makes your DSPy applications observable by visualizing the underlying structure of each call to your compiled DSPy module.
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.
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:
from phoenix.otel import registertracer_provider =register( project_name="my-llm-app", # Default is 'default' endpoint="http://localhost:6006",)
Your Phoenix API key can be found on the Keys section of your dashboard.
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.
Install
pipinstallopeninference-instrumentation-dspydspy
Setup
Initialize the DSPyInstrumentor before your application code.
from openinference.instrumentation.dspy import DSPyInstrumentorDSPyInstrumentor().instrument(tracer_provider=tracer_provider)
Run DSPy
Now run invoke your compiled DSPy module. Your traces should appear inside of Phoenix.
classBasicQA(dspy.Signature):"""Answer questions with short factoid answers.""" question = dspy.InputField() answer = dspy.OutputField(desc="often between 1 and 5 words")if__name__=="__main__": turbo = dspy.OpenAI(model="gpt-3.5-turbo") dspy.settings.configure(lm=turbo)withusing_attributes( session_id="my-test-session", user_id="my-test-user", metadata={"test-int": 1,"test-str": "string","test-list": [1, 2, 3],"test-dict": {"key-1": "val-1","key-2": "val-2", }, }, tags=["tag-1", "tag-2"], prompt_template_version="v1.0", prompt_template_variables={"city": "Johannesburg","date": "July 11th", }, ):# Define the predictor. generate_answer = dspy.Predict(BasicQA)# Call the predictor on a particular input. pred =generate_answer( question="What is the capital of the united states?"# noqa: E501 )# noqa: E501print(f"Predicted Answer: {pred.answer}")
Observe
Now that you have tracing setup, all predictions will be streamed to your running Phoenix for observability and evaluation.