Pydantic Evals

pip install pydantic-evals "arize[Tracing]" arize-otel openai \
  openinference-instrumentation-openai

export ARIZE_SPACE_ID="<your-space-id>"
export ARIZE_API_KEY="<your-api-key>"
export OPENAI_API_KEY="<your-openai-api-key>"

import os

from openai import OpenAI
from pydantic_evals import Case, Dataset

from arize.otel import register

tracer_provider = register(
    space_id=os.environ["ARIZE_SPACE_ID"],
    api_key=os.environ["ARIZE_API_KEY"],
    project_name="pydantic-evals-tutorial",
)

from openinference.instrumentation.openai import OpenAIInstrumentor
OpenAIInstrumentor().instrument(tracer_provider=tracer_provider)

cases = [
    Case(name="capital of France", inputs="What is the capital of France?", expected_output="Paris"),
    Case(name="author of Romeo and Juliet", inputs="Who wrote Romeo and Juliet?", expected_output="William Shakespeare"),
    Case(name="largest planet", inputs="What is the largest planet in our solar system?", expected_output="Jupiter")
]
dataset = Dataset(cases=cases)

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

def evaluate_case(case):
    response = client.chat.completions.create(
        model="gpt-5.4-mini",
        messages=[{"role": "user", "content": case.inputs}]
    )
    output = response.choices[0].message.content
    print(output)
    is_correct = case.expected_output.lower() in output.strip().lower()
    return is_correct

results = [evaluate_case(case) for case in dataset.cases]

for case, result in zip(dataset.cases, results):
    print(f"Case: {case.name}, Correct: {result}")