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.

tip

Not sure if the ContextualRecallMetric is suitable for your use case? Run the follow command to find out:

deepeval recommend metrics

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

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.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])

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

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)

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 ContextualRecallMetric score is calculated according to the following equation:

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

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.

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.

tip

You can learn what the default ContextualRecallTemplate 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 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(...)