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Multimodal Contextual Relevancy

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

info

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

Required Arguments​

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

  • input
  • actual_output
  • retrieval_context
note

Similar to MultimodalContextualPrecisionMetric, the MultimodalContextualRelevancyMetric uses retrieval_context from your multimodal RAG pipeline for evaluation.

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 MultimodalContextualRelevancyMetric
from deepeval.test_case import MLLMTestCase, MLLMImage

metric = MultimodalContextualRelevancyMetric()
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(...)
    ],
    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 MultimodalContextualRelevancyMetric:

  • [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 MultimodalContextualRelevancyMetric score is calculated according to the following equation:

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

Although similar to how the MultimodalAnswerRelevancyMetric is calculated, the MultimodalContextualRelevancyMetric first uses an MLLM to extract all statements and images in the retrieval_context instead, before using the same MLLM to classify whether each statement and image is relevant to the input.