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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.

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Install

pip install openinference-instrumentation-dspy openinference-instrumentation-litellm dspy
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.

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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
)

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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}")

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Observe

Now that you have tracing setup, all predictions will be streamed to your running Phoenix for observability and evaluation.
Traces and spans from an instrumented DSPy custom module.
Traces and spans from an instrumented DSPy custom module.

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