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Generate From Goldens

DeepEval enables you to generate synthetic Goldens from an existing set of Goldens, without requiring any documents or context. This is ideal for quickly expanding or adding more complexity to your evaluation dataset.

LangChain
tip

By default, generate_goldens_from_goldens extracts StylingConfig from your existing Golden, but it is recommended to provide a StylingConfig explicitly for better accuracy and consistency.

Generate Your Goldens​

To get started, simply define a Synthesizer object and pass in your list of existing Goldens to the generate_goldens_from_goldens method.

from deepeval.synthesizer import Synthesizer

synthesizer = Synthesizer()
synthesizer.generate_goldens_from_goldens(
  goldens=goldens,
  max_goldens_per_golden=2,
  include_expected_output=True,
)

There is ONE mandatory and TWO optional parameter when using the generate_goldens_from_goldens method:

  • goldens: a list of existing Goldens from which the new Goldens will be generated.
  • [Optional] max_goldens_per_golden: the maximum number of goldens to be generated per golden. Defaulted to 2.
  • [Optional] include_expected_output: a boolean which when set to True, will additionally generate an expected_output for each synthetic Golden. Defaulted to True.
info

If your existing Goldens include context, the synthesizer will utilize these contexts to generate synthetic Goldens, ensuring they are grounded in truth. If no context is present, the synthesizer will employ the generate_from_scratch method to create additional inputs based on provided inputs.