Generate Goldens 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.

Generate Your Goldens

To get started, simply define a Synthesizer object and pass in your list of existing goldens. Note that you can only generate single-turn goldens from existing single-turn ones, and vice versa.

from deepeval.synthesizer import Synthesizer

synthesizer = Synthesizer()
goldens = 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.

from deepeval.synthesizer import Synthesizer

synthesizer = Synthesizer()
conversational_goldens = synthesizer.generate_conversational_goldens_from_goldens(
  goldens=goldens,
  max_goldens_per_golden=2,
  include_expected_outcome=True,
)

There is ONE mandatory and TWO optional parameter when using the generate_conversational_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_outcome: a boolean which when set to True, will additionally generate an expected_outcome for each synthetic ConversationalGolden. Defaulted to True.

Generate Your Goldens

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