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.

tip

Here is a detailed guide on RAG evaluation, which we highly recommend as it explains everything about deepeval's RAG metrics.

Required Arguments

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

• input
• actual_output

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."

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
)

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

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

There are five optional parameters when creating an AnswerRelevancyMetric:

• [Optional] threshold: a float representing the minimum passing threshold, defaulted to 0.5.
• [Optional] model: a string specifying which of OpenAI's GPT models to use, OR any custom LLM model of type DeepEvalBaseLLM. Defaulted to 'gpt-4-0125-preview'.
• [Optional] include_reason: a boolean which when set to True, will include a reason for its evaluation score. Defaulted to True.
• [Optional] strict_mode: a boolean which when set to True, enforces a binary metric score: 1 for perfection, 0 otherwise. It also overrides the current threshold and sets it to 1. Defaulted to False.
• [Optional] async_mode: a boolean which when set to True, enables concurrent execution within the measure() method. Defaulted to True.

How Is It Calculated?

The AnswerRelevancyMetric score is calculated according to the following equation:

$$\text{Answer Relevancy} = \frac{\text{Number of Relevant Statements}}{\text{Total Number of Statements}}$$

The AnswerRelevancyMetric first uses an LLM to extract all statements made in the actual_output, before using the same LLM to classify whether each statement is relevant to the input.