Answer Relevancy

The answer relevancy metric measures the quality of your RAG pipeline's generator by evaluating how relevant the actual_output of your LLM application is compared to the provided input. deepeval's answer relevancy metric is a self-explaining LLM-Eval, meaning it outputs a reason for its metric score.

Required Parameters

To use the AnswerRelevancyMetric, you'll have to provide the following parameters when creating an LLMTestCase:

• input
• actual_output
• retrieval_context

Example

from deepeval import evaluate
from deepeval.metrics import AnswerRelevancyMetric
from deepeval.test_case import LLMTestCase

# Replace this with the actual output from your LLM application
actual_output = "We offer a 30-day full refund at no extra cost."

# Replace this with the actual retrieved context from your RAG pipeline
retrieval_context = ["All customers are eligible for a 30 day full refund at no extra cost."]

metric = AnswerRelevancyMetric(
    threshold=0.7,
    model="gpt-4",
    include_reason=True
)
test_case = LLMTestCase(
    input="What if these shoes don't fit?",
    actual_output=actual_output,
    retrieval_context=retrieval_context
)

metric.measure(test_case)
print(metric.score)
print(metric.reason)

# or evaluate test cases in bulk
evaluate([test_case], [metric])

You can also choose to fallback to Ragas' answer relevancy metric (which has a similar implemention). This however is not capable of generating a reason.

from deepeval.metrics import RAGASAnswerRelevancyMetric