Contextual Recall

The contextual recall metric uses LLM-as-a-judge to measure 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

Read the How Is It Calculated section below to learn how test case parameters are used for metric calculation.

Usage

The ContextualRecallMetric() can be used for end-to-end evaluation of text-based and multimodal test cases:

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

# 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
)

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

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

from deepeval import evaluate
from deepeval.test_case import LLMTestCase, MLLMImage
from deepeval.metrics import ContextualRecallMetric

# Replace this with the actual retrieved context from your RAG pipeline
retrieval_context = [
    f"The Eiffel Tower {MLLMImage(...)} is a wrought-iron lattice tower built in the late 19th century.",
    f"...",
]

metric = ContextualRecallMetric(
    threshold=0.7,
    model="gpt-4.1",
    include_reason=True
)
test_case = LLMTestCase(
    input=f"Tell me about this landmark in France: {MLLMImage(...)}",
    actual_output=f"This appears to be Eiffel Tower, which is a famous landmark in France"
    expected_output=f"The Eiffel Tower is located in Paris, France. {MLLMImage(...)}",
    retrieval_context=retrieval_context
)

# 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 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-5.4.
• [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 ContextualRecallTemplate, which allows you to override the default prompts used to compute the ContextualRecallMetric score. Defaulted to deepeval's ContextualRecallTemplate.

Within components

You can also run the ContextualRecallMetric within nested components for component-level evaluation.

from deepeval.dataset import Golden
from deepeval.tracing import observe, update_current_span
...

@observe(metrics=[metric])
def inner_component():
    # Set test case at runtime
    test_case = LLMTestCase(input="...", actual_output="...")
    update_current_span(test_case=test_case)
    return

@observe
def llm_app(input: str):
    # Component can be anything from an LLM call, retrieval, agent, tool use, etc.
    inner_component()
    return

evaluate(observed_callback=llm_app, goldens=[Golden(input="Hi!")])

As a standalone

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

...

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

How Is It Calculated?

The ContextualRecallMetric score is calculated according to the following equation:

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.

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.

Customize Your Template

Since deepeval's ContextualRecallMetric 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 ContextualRecallTemplate to better align with your expectations.

Here's a quick example of how you can override the relevancy classification step of the ContextualRecallMetric algorithm:

from deepeval.metrics import ContextualRecallMetric
from deepeval.metrics.contextual_recall import ContextualRecallTemplate

# Define custom template
class CustomTemplate(ContextualRecallTemplate):
    @staticmethod
    def generate_verdicts(expected_output: str, retrieval_context: List[str]):
        return f"""For EACH sentence in the given expected output below, determine whether the sentence can be attributed to the nodes of retrieval contexts.

Example JSON:
{{
    "verdicts": [
        {{
            "verdict": "yes",
            "reason": "..."
        }},
    ]
}}

Expected Output:
{expected_output}

Retrieval Context:
{retrieval_context}

JSON:
"""

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