Dor L.

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"] = 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")


# 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.LM(model="gpt-3.5-turbo")
dspy.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 Atlantis?"
    )
    print(f"Question: What is the capital of the Atlantis?")
    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.")

# 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=ARIZE_SPACE_ID,
    api_key=ARIZE_API_KEY,
    project_name=PROJECT_NAME
)

# 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.")