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Contextual Recall

The contextual recall metric measures the quality of your RAG pipeline's retriever by evaluating the extent of which the retrieval_context aligns with the expected_output. deepeval's contextual recall metric is a self-explaining LLM-Eval, meaning it outputs a reason for its metric score.

Required Arguments​

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

  • input
  • actual_output
  • expected_output
  • retrieval_context

Example​

from deepeval import evaluate
from deepeval.metrics import ContextualRecallMetric
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 expected output from your RAG generator
expected_output = "You are eligible for 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 = ContextualRecallMetric(
    threshold=0.7,
    model="gpt-4",
    include_reason=True
)
test_case = LLMTestCase(
    input="What if these shoes don't fit?",
    actual_output=actual_output,
    expected_output=expected_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 six optional parameters when creating a ContextualRecallMetric:

  • [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.

How Is It Calculated?​

The ContextualRecallMetric score is calculated according to the following equation:

Contextual Recall=Number of Attributable StatementsTotal Number of Statements\text{Contextual Recall} = \frac{\text{Number of Attributable Statements}}{\text{Total Number of Statements}}

The ContextualRecallMetric first uses an LLM to extract all statements made in the expected_output, before using the same LLM to classify whether each statement can be attributed to nodes in the retrieval_context.

info

We use the expected_output instead of the actual_output because we're measuring the quality of the RAG retriever for a given ideal output.

A higher contextual recall score represents a greater ability of the retrieval system to capture all relevant information from the total available relevant set within your knowledge base.