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Contextual Relevancy

The contextual relevancy metric measures the quality of your RAG pipeline's retriever by evaluating the overall relevance of the information presented in your retrieval_context for a given input. deepeval's contextual relevancy metric is a self-explaining LLM-Eval, meaning it outputs a reason for its metric score.

Required Arguments​

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

  • input
  • actual_output
  • retrieval_context
note

Similar to ContextualPrecisionMetric, the ContextualRelevancyMetric uses retrieval_context from your RAG pipeline for evaluation.

Example​

from deepeval import evaluate
from deepeval.metrics import ContextualRelevancyMetric
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 = ContextualRelevancyMetric(
    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])

There are five optional parameters when creating a ContextualRelevancyMetricMetric:

  • [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-turbo'.
  • [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 ContextualRelevancyMetric score is calculated according to the following equation:

Contextual Relevancy=Number of Relevant StatementsTotal Number of Statements\text{Contextual Relevancy} = \frac{\text{Number of Relevant Statements}}{\text{Total Number of Statements}}

Although similar to how the AnswerRelevancyMetric is calculated, the ContextualRelevancyMetric first uses an LLM to extract all statements made in the retrieval_context instead, before using the same LLM to classify whether each statement is relevant to the input.