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Conversation Relevancy

The conversation relevancy metric is a conversational metric that determines whether your LLM chatbot is able to consistently generate relevant responses throughout a conversation.

Required Arguments​

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

  • turns

Additionally, each LLMTestCases in turns requires the following arguments:

  • input
  • actual_output

Example​

Let's take this conversation as an example:

from deepeval.test_case import LLMTestCase, ConversationalTestCase
from deepeval.metrics import ConversationRelevancyMetric

convo_test_case = ConversationalTestCase(
    turns=[LLMTestCase(input="...", actual_output="...")]
)
metric = ConversationRelevancyMetric(threshold=0.5)

metric.measure(convo_test_case)
print(metric.score)
print(metric.reason)

There are seven optional parameters when creating a ConversationRelevancyMetric:

  • [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] window_size: an integer which defines the size of the sliding window of turns used during evaluation. Defaulted to 3.

How Is It Calculated?​

The ConversationRelevancyMetric score is calculated according to the following equation:

Conversation Relevancy=Number of Turns with Relevant Actual OutputsTotal Number of Turns\text{Conversation Relevancy} = \frac{\text{Number of Turns with Relevant Actual Outputs}}{\text{Total Number of Turns}}

The ConversationRelevancyMetric first constructs a sliding windows of turns for each turn, before using an LLM to determine whether the last turn in each sliding window has an actual_output that is relevant to the input based on previous conversational context found in the sliding window.