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Role Adherence

The role adherence metric is a conversational metric that determines whether your LLM chatbot is able to adhere to its given role throughout a conversation.

tip

The RoleAdherenceMetric is particular useful for a role-playing usecase.

Required Arguments​

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

  • turns
  • chatbot_role

Additionally, each LLMTestCases in turns requires the following arguments:

  • input
  • actual_output

Example​

Let's take this conversation as an example:

from deepeval import evaluate
from deepeval.test_case import LLMTestCase, ConversationalTestCase
from deepeval.metrics import RoleAdherenceMetric

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

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

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

There are SIX optional parameters when creating a RoleAdherenceMetric:

  • [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.

As a standalone​

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

...

metric.measure(convo_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 RoleAdherenceMetric score is calculated according to the following equation:

Role Adherence=Number of Turns that Adhered to Chatbot Role in ConversationTotal Number of Turns in Conversation\text{Role Adherence} = \frac{\text{Number of Turns that Adhered to Chatbot Role in Conversation}}{\text{Total Number of Turns in Conversation}}

The RoleAdherenceMetric first loops through each turn individually before using an LLM to determine which one of them does not adhere to the specified chatbot_role using previous turns as context.