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Multimodal Contextual Recall

The multimodal 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 MLLM-Eval, meaning it outputs a reason for its metric score.

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The Multimodal Contextual Recall is the multimodal adaptation of DeepEval's contextual recall metric. It accepts images in addition to text for the input, actual_output, expected_output, and retrieval_context.

Required Arguments​

To use the MultimodalContextualRecallMetric, you'll have to provide the following arguments when creating a MLLMTestCase:

  • input
  • actual_output
  • expected_output
  • retrieval_context

The input and actual_output are required to create an MLLMTestCase (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 MultimodalContextualRecallMetric
from deepeval.test_case import MLLMTestCase, MLLMImage

metric = MultimodalContextualRecallMetric()
test_case = MLLMTestCase(
    input=["Tell me about some landmarks in France"],
    actual_output=[
        "France is home to iconic landmarks like the Eiffel Tower in Paris.",
        MLLMImage(...)
    ],
    expected_output=[
        "The Eiffel Tower is located in Paris, France.",
        MLLMImage(...)
    ],
    retrieval_context=[
        MLLMImage(...),
        "The Eiffel Tower is a wrought-iron lattice tower built in the late 19th century.",
        MLLMImage(...)
    ],
)


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 MultimodalContextualRecallMetric:

  • [Optional] threshold: a float representing the minimum passing threshold, defaulted to 0.5.
  • [Optional] model: a string specifying which of OpenAI's Multimodal GPT models to use, OR any custom MLLM model of type DeepEvalBaseMLLM. 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 MultimodalContextualRecallMetric score is calculated according to the following equation:

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

The MultimodalContextualRecallMetric first uses an MLLM to extract all statements and images in the expected_output, before using the same MLLM to classify whether each statement and image can be attributed to nodes in the retrieval_context.

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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.