LLM-as-a-judge evaluator 정의하는 방법

from langsmith import evaluate, traceable, wrappers, Client
from openai import OpenAI
# Assumes you've installed pydantic
from pydantic import BaseModel

# Optionally wrap the OpenAI client to trace all model calls.
oai_client = wrappers.wrap_openai(OpenAI())

def valid_reasoning(inputs: dict, outputs: dict) -> bool:
    """Use an LLM to judge if the reasoning and the answer are consistent."""
    instructions = """
Given the following question, answer, and reasoning, determine if the reasoning
for the answer is logically valid and consistent with the question and the answer."""

    class Response(BaseModel):
        reasoning_is_valid: bool

    msg = f"Question: {inputs['question']}\nAnswer: {outputs['answer']}\nReasoning: {outputs['reasoning']}"
    response = oai_client.beta.chat.completions.parse(
        model="gpt-4o",
        messages=[{"role": "system", "content": instructions,}, {"role": "user", "content": msg}],
        response_format=Response
    )
    return response.choices[0].message.parsed.reasoning_is_valid

# Optionally add the 'traceable' decorator to trace the inputs/outputs of this function.
@traceable
def dummy_app(inputs: dict) -> dict:
    return {"answer": "hmm i'm not sure", "reasoning": "i didn't understand the question"}

ls_client = Client()
dataset = ls_client.create_dataset("big questions")
examples = [
    {"inputs": {"question": "how will the universe end"}},
    {"inputs": {"question": "are we alone"}},
]
ls_client.create_examples(dataset_id=dataset.id, examples=examples)

results = evaluate(
    dummy_app,
    data=dataset,
    evaluators=[valid_reasoning]
)