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.
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.
DSPy uses LiteLLM under the hood to make some calls. By adding the OpenInference library for LiteLLM, you'll be able to see additional information like token counts on your traces.
Setup
Connect to your Phoenix instance using the register function.
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 DSPy
Now run invoke your compiled DSPy module. Your traces should appear inside of Phoenix.
class BasicQA(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)
with using_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: E501
print(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.
