Answer Relevancy

The answer relevancy metric uses LLM-as-a-judge to measure 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

The input and actual_output are required to create an LLMTestCase (and hence required by all metrics) even though they might not be used for metric calculation. Read the How Is It Calculated section below to learn more.

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
)

# To run metric as a standalone
# metric.measure(test_case)
# print(metric.score, metric.reason)

evaluate(test_cases=[test_case], metrics=[metric])

There are SEVEN 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-4o'.
• [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.
• [Optional] verbose_mode: a boolean which when set to True, prints the intermediate steps used to calculate said metric to the console, as outlined in the How Is It Calculated section. Defaulted to False.
• [Optional] evaluation_template: a class of type AnswerRelevancyTemplate, which allows you to override the default prompts used to compute the AnswerRelevancyMetric score. Defaulted to deepeval's AnswerRelevancyTemplate.

As a standalone

You can also run the AnswerRelevancyMetric on a single test case as a standalone, one-off execution.

...

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

caution

This is great for debugging or if you wish to build your own evaluation pipeline, but you will NOT get the benefits (testing reports, Confident AI platform) and all the optimizations (speed, caching, computation) the evaluate() function or deepeval test run offers.

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.

note

You can set the verbose_mode of ANY deepeval metric to True to debug the measure() method:

...

metric = AnswerRelevancyMetric(verbose_mode=True)
metric.measure(test_case)

Customize Your Template

Since deepeval's AnswerRelevancyMetric is evaluated by LLM-as-a-judge, you can likely improve your metric accuracy by overriding deepeval's default prompt templates. This is especially helpful if:

• You're using a custom evaluation LLM, especially for smaller models that have weaker instruction following capabilities.
• You want to customize the examples used in the default AnswerRelevancyTemplate to better align with your expectations.

tip

You can learn what the default AnswerRelevancyTemplate looks like here on GitHub, and should read the How Is It Calculated section above to understand how you can tailor it to your needs.

Here's a quick example of how you can override the statement generation step of the AnswerRelevancyMetric algorithm:

from deepeval.metrics import AnswerRelevancyMetric
from deepeval.metrics.answer_relevancy import AnswerRelevancyTemplate

# Define custom template
class CustomTemplate(AnswerRelevancyTemplate):
    @staticmethod
    def generate_statements(actual_output: str):
        return f"""Given the text, breakdown and generate a list of statements presented.

Example:
Our new laptop model features a high-resolution Retina display for crystal-clear visuals.

{{
    "statements": [
        "The new laptop model has a high-resolution Retina display."
    ]
}}
===== END OF EXAMPLE ======

Text:
{actual_output}

JSON:
"""

# Inject custom template to metric
metric = AnswerRelevancyMetric(evaluation_template=CustomTemplate)
metric.measure(...)