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

First, set the eval mode:

deepeval set-eval-mode llm         # LLM-as-a-judge (default)
deepeval set-eval-mode hybrid      # LLM extracts, Jev decides
deepeval set-eval-mode system_one  # Jev-as-a-judge, no LLM

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 NINE 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.
• [Optional] system_one_model: the Jev model to use, as a string or a DeepEvalBaseSystemOneModel. Only used under hybrid or system_one eval_mode. Defaulted to jev-latest.
• [Optional] eval_mode: llm, hybrid or system_one, choosing whether an LLM, Jev, or both judge. Defaulted to the configured eval mode (llm unless set).

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?

You can change how the ConversationCompletenessMetric is calculated by setting the eval mode.

LLM-as-a-judge

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".

Hybrid

Under the hybrid eval mode, the LLM still extracts the user intentions, but whether each one was satisfied is answered by Jev, a System One model: one yes/no question per intention, with P(yes) >= 0.5 counted as satisfied. The equation and the LLM-written reason are unchanged. If a Jev call fails, the LLM makes that decision instead.

Jev-as-a-judge

Under the system_one eval mode, Jev judges the whole metric in one request. It is sent the whole conversation and asked three questions:

Each answer becomes a value in $[0, 1]$ and the score is their weighted mean. No LLM is called: the reason lists each answer with its probability, and metric.confidence reports how decisive Jev was.

FAQs