Conversation Completeness

The conversation completeness metric is a conversational metric that determines whether your LLM chatbot is able to complete an end-to-end conversation by satisfying user needs throughout a conversation.

Required Arguments

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

• turns

You must provide the role and content for evaluation to happen. Read the How Is It Calculated section below to learn more.

Usage

The ConversationCompletenessMetric() can be used for end-to-end multi-turn evaluation:

from deepeval.test_case import Turn, ConversationalTestCase
from deepeval.metrics import ConversationCompletenessMetric
from deepeval import evaluate

convo_test_case = ConversationalTestCase(
    turns=[Turn(role="...", content="..."), Turn(role="...", content="...")]
)
metric = ConversationCompletenessMetric(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 SEVEN optional parameters when creating a ConversationCompletenessMetric:

• [Optional] threshold: a number representing the minimum passing threshold. Can also be set to None to run the metric in score-only mode. 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-5.4.
• [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] flaky: a boolean which when set to True, marks the metric as flaky. Defaulted to False.

As a standalone

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

...

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

How Is It Calculated?

The ConversationCompletenessMetric score is calculated according to the following equation:

The ConversationCompletenessMetric assumes that a conversion is only complete if user intentions, such as asking for help to an LLM chatbot, are met by the LLM chatbot.

Hence, the ConversationCompletenessMetric first uses an LLM to extract a list of high level user intentions found in turns (in "user" roles), before using the same LLM to determine whether each intention was met and/or satisfied throughout the conversation by the "assistant".

FAQs