Response Relevance

from uptrain import EvalLLM, Evals

OPENAI_API_KEY = "sk-********************"  # Insert your OpenAI key here

data = [{
    "question": "What are the benefits of regular exercise?",
    "response": "Regular exercise has well-documented health benefits. On a related note, the importance of routine health check-ups in preventive healthcare cannot be emphasized enough. It allows for early detection of potential health issues and facilitates proactive management for individuals."
}]

eval_llm = EvalLLM(openai_api_key=OPENAI_API_KEY)

res = eval_llm.evaluate(
    data = data,
    checks = [Evals.RESPONSE_RELEVANCE]
)

[
   {
      "score_response_relevance": 0.0,
      "explanation_response_relevance": "The LLM response contains a lot of additional irrelevant information because it goes off on a tangent about routine health check-ups and preventive healthcare, which is not directly related to the benefits of regular exercise. This additional information distracts from the main topic and does not directly address the user query.\"\n\n \"The LLM response doesn't answer the user query at all because it completely ignores the specific benefits of regular exercise. Instead, it focuses on the importance of routine health check-ups in preventive healthcare. The user asked about the benefits of regular exercise, and the response fails to address this aspect, leading to potential dissatisfaction."
   }
]