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Conversational G-Eval

LLM-as-a-judge
Chatbot metric
Custom metric

The conversational G-Eval is an adopted version of deepeval's popular GEval metric but for evaluating entire conversations instead.

It is currently the best way to define custom criteria to evaluate multi-turn conversations in deepeval. By defining a custom ConversationalGEval, you can easily determine whether your LLM chatbot is able to consistently generate responses that are up to standard with your custom criteria throughout a conversation.

Required Arguments​

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

  • turns

You'll also want to supply any additional arguments such as retrieval_context and tools_called in turns if your evaluation criteria depends on these parameters.

Usage​

To create a custom metric that evaluates entire LLM conversations, simply instantiate a ConversationalGEval class and define an evaluation criteria in everyday language:

from deepeval import evaluate
from deepeval.test_case import Turn, TurnParams, ConversationalTestCase
from deepeval.metrics import ConversationalGEval

convo_test_case = ConversationalTestCase(
    turns=[Turn(role="...", content="..."), Turn(role="...", content="...")]
)
professionalism_metric = ConversationalGEval(
    name="Professionalism",
    criteria="Determine whether the assistant has acted professionally based on the content."
)

# 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 THREE mandatory and SIX optional parameters required when instantiating an ConversationalGEval class:

  • name: name of metric. This will not affect the evaluation.
  • criteria: a description outlining the specific evaluation aspects for each test case.
  • [Optional] evaluation_params: a list of type TurnParams, include only the parameters that are relevant for evaluation. Defaulted to [TurnParams.CONTENT].
  • [Optional] evaluation_steps: a list of strings outlining the exact steps the LLM should take for evaluation. If evaluation_steps is not provided, ConversationalGEval will generate a series of evaluation_steps on your behalf based on the provided criteria. You can only provide either evaluation_steps OR criteria, and not both.
  • [Optional] threshold: the 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] 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.
danger

For accurate and valid results, only turn parameters that are mentioned in criteria/evaluation_steps should be included as a member of evaluation_params.

As a standalone​

You can also run the ConversationalGEval 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 ConversationalGEval is an adapted version of GEval, so alike GEval, the ConversationalGEval metric is a two-step algorithm that first generates a series of evaluation_steps using chain of thoughts (CoTs) based on the given criteria, before using the generated evaluation_steps to determine the final score using the evaluation_params presented in each turn.

Unlike regular GEval though, the ConversationalGEval takes the entire conversation history into account during evaluation.

tip

Similar to the original G-Eval paper, the ConversationalGEval metric uses the probabilities of the LLM output tokens to normalize the score by calculating a weighted summation. This step was introduced in the paper to minimize bias in LLM scoring, and is automatically handled by deepeval (unless you're using a custom LLM).