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G-Eval

G-Eval is a custom, LLM evaluated metric. This means its score is calculated using an LLM. G-Eval is the most verstile type of metric deepeval has to offer, and is capable of evaluating almost any use cases.

Required Arguments

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

  • input
  • actual_output

You'll also need to supply any additional arguments such as expected_output and context if your evaluation criteria depends on these parameters.

Example

To create a custom metric that uses LLMs for evaluation, simply instantiate an GEval class and define an evaluation criteria in everyday language:

from deepeval.metrics import GEval
from deepeval.test_case import LLMTestCaseParams

coherence_metric = GEval(
name="Coherence",
criteria="Coherence - determine if the actual output is coherent with the input.",
# NOTE: you can only provide either criteria or evaluation_steps, and not both
evaluation_steps=["Check whether the sentences in 'actual output' aligns with that in 'input'"],
evaluation_params=[LLMTestCaseParams.INPUT, LLMTestCaseParams.ACTUAL_OUTPUT],
)

There are three mandatory and two optional parameters required when instantiating an GEval class:

  • name: name of metric
  • criteria: a description outlining the specific evaluation aspects for each test case.
  • evaluation_params: a list of type LLMTestCaseParams. Include only the parameters that are relevant for evaluation.
  • [Optional] evaluation_steps: a list of strings outlining the exact steps the LLM should take for evaluation. 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-4-1106-preview'.
danger

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

As mentioned in the metrics introduction section, all of deepeval's metrics return a score ranging from 0 - 1, and a metric is only successful if the evaluation score is equal to or greater than threshold, and GEval is no exception. You can access the score and reason for each individual GEval metric:

from deepeval.test_case import LLMTestCase
...

test_case = LLMTestCase(
input="The sun is shining bright today",
actual_output="The weather's getting really hot."
)

coherence_metric.measure(test_case)
print(coherence_metric.score)
print(coherence_metric.reason)
note

Remember, you can configure deepeval to use Azure OpenAI for all LLM-based metrics.