Instrument and observe your DSPy application with OpenInference, DSPyInstrumentor, and view traces in Arize.
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 then compiles these modules using "teleprompters" that optimize the module for a particular task.
OpenInference and Arize make your DSPy applications observable by visualizing the underlying structure of each call to your compiled DSPy module. This often involves instrumenting DSPy itself and LiteLLM, which DSPy frequently uses for model interactions.
Follow the tutorial notebook for an in-depth example. Note: you may need to adapt parts of the notebook's original OTel setup to the Arize OTel setup described below.
Launch Arize
To get started, sign up for a free Arize account and get your Space ID and API Key.
Install
Install dspy-ai, the OpenInference instrumentors for DSPy and LiteLLM (if used by DSPy for model calls), and Arize OTel packages.
# Install DSPy framework
pip install dspy-ai
# Install OpenInference instrumentor for DSPy
pip install openinference-instrumentation-dspy
# Install LiteLLM and its OpenInference instrumentor (DSPy often uses LiteLLM)
pip install litellm openinference-instrumentation-litellm
# Install Arize OTel
pip install arize-otel
DSPy frequently uses LiteLLM under the hood to make LLM calls. By adding the OpenInference instrumentor for LiteLLM, you'll get more detailed traces, including token counts.
API Key Setup
Configure the API key for the underlying LLM that DSPy (via LiteLLM or directly) will use. For example, if using OpenAI models:
export OPENAI_API_KEY='your_openai_api_key'
# Other keys like ANTHROPIC_API_KEY, COHERE_API_KEY, etc., if using those models via LiteLLM.
Setup Tracing
Connect to Arize using arize.otel.register and apply the instrumentors.
# Import Arize OTel registration
from arize.otel import register
# Import OpenInference instrumentors
from openinference.instrumentation.dspy import DSPyInstrumentor
from openinference.instrumentation.litellm import LiteLLMInstrumentor # For underlying LLM calls via LiteLLM
# Setup OTel and Arize exporter
tracer_provider = register(
space_id="YOUR_SPACE_ID",
api_key="YOUR_API_KEY",
project_name="my-dspy-app"
)
# Instrument DSPy
DSPyInstrumentor().instrument(tracer_provider=tracer_provider)
# Instrument LiteLLM (recommended for full visibility into DSPy's LLM calls)
LiteLLMInstrumentor().instrument(tracer_provider=tracer_provider)
print("DSPy and LiteLLM instrumented for Arize.")
Run DSPy Example
Now run your compiled DSPy module. The example below uses dspy.OpenAI which might make direct calls or go via a configured LiteLLM route depending on DSPy version and setup. The LiteLLMInstrumentor helps capture these calls.
import dspy
import os
from openinference.semconv.trace import SpanAttributes # For using_attributes
from openinference.instrumentation import using_attributes # For custom attributes
# Ensure OPENAI_API_KEY is set in your environment
# os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"
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__":
# Configure the LLM. DSPy can use various LLMs, often via LiteLLM.
# For OpenAI models, ensure OPENAI_API_KEY is set.
# The dspy.OpenAI client might internally use LiteLLM or a direct OpenAI client.
turbo = dspy.OpenAI(model="gpt-3.5-turbo")
dspy.settings.configure(lm=turbo)
# Example of adding custom attributes to traces using OpenInference context manager
with using_attributes(
session_id="my-dspy-session-001",
user_id="user-dspy-example",
metadata={
"environment": "testing",
"dspy_module": "BasicQA",
},
tags=["dspy", "qa"],
prompt_template_version="1.0",
prompt_template_variables={
"signature_desc": BasicQA.__doc__.strip()
}
):
# 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?"
)
print(f"Question: What is the capital of the United States?")
print(f"Predicted Answer: {pred.answer}")
pred_europe = generate_answer(
question="What is the capital of France?"
)
print(f"Question: What is the capital of France?")
print(f"Predicted Answer: {pred_europe.answer}")
print("DSPy example run complete. Check Arize for traces.")
Observe
Now that you have tracing setup, all instrumented calls within your DSPy application, including underlying LLM interactions captured by the LiteLLM (or other) instrumentor, will be streamed to your Arize account.
