Class Evals (1.121.0) 

Evals(api_client_: google.genai._api_client.BaseApiClient)

API documentation for Evals class.

Methods

batch_evaluate

batch_evaluate(
    *,
    dataset: typing.Union[
        vertexai._genai.types.EvaluationDataset,
        vertexai._genai.types.EvaluationDatasetDict,
    ],
    metrics: list[
        typing.Union[vertexai._genai.types.Metric, vertexai._genai.types.MetricDict]
    ],
    dest: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.EvaluateDatasetConfig,
            vertexai._genai.types.EvaluateDatasetConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.EvaluateDatasetOperation

Evaluates a dataset based on a set of given metrics.

create_evaluation_run

create_evaluation_run(
    *,
    name: str,
    display_name: typing.Optional[str] = None,
    data_source: vertexai._genai.types.EvaluationRunDataSource,
    dest: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.CreateEvaluationRunConfig,
            vertexai._genai.types.CreateEvaluationRunConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.EvaluationRun

Creates an EvaluationRun.

evaluate

evaluate(
    *,
    dataset: typing.Union[
        vertexai._genai.types.EvaluationDataset,
        vertexai._genai.types.EvaluationDatasetDict,
        list[
            typing.Union[
                vertexai._genai.types.EvaluationDataset,
                vertexai._genai.types.EvaluationDatasetDict,
            ]
        ],
    ],
    metrics: typing.Optional[
        list[
            typing.Union[vertexai._genai.types.Metric, vertexai._genai.types.MetricDict]
        ]
    ] = None,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.EvaluateMethodConfig,
            vertexai._genai.types.EvaluateMethodConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.EvaluationResult

Evaluates candidate responses in the provided dataset(s) using the specified metrics.

evaluate_instances

evaluate_instances(
    *, metric_config: vertexai._genai.types._EvaluateInstancesRequestParameters
) -> vertexai._genai.types.EvaluateInstancesResponse

Evaluates an instance of a model.

generate_rubrics

generate_rubrics(
    *,
    src: typing.Union[str, pd.DataFrame, vertexai._genai.types.EvaluationDataset],
    rubric_group_name: str,
    prompt_template: typing.Optional[str] = None,
    generator_model_config: typing.Optional[genai_types.AutoraterConfigOrDict] = None,
    rubric_content_type: typing.Optional[types.RubricContentType] = None,
    rubric_type_ontology: typing.Optional[list[str]] = None,
    predefined_spec_name: typing.Optional[
        typing.Union[str, types.PrebuiltMetric]
    ] = None,
    metric_spec_parameters: typing.Optional[dict[str, typing.Any]] = None,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.RubricGenerationConfig,
            vertexai._genai.types.RubricGenerationConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.EvaluationDataset

Generates rubrics for each prompt in the source and adds them as a new column structured as a dictionary.

You can generate rubrics by providing either:

  1. A predefined_spec_name to use a Vertex AI backend recipe.
  2. A prompt_template along with other configuration parameters (generator_model_config, rubric_content_type, rubric_type_ontology) for custom rubric generation.

These two modes are mutually exclusive.

get_evaluation_item

get_evaluation_item(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.GetEvaluationItemConfig,
            vertexai._genai.types.GetEvaluationItemConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.EvaluationItem

Retrieves an EvaluationItem from the resource name.

get_evaluation_run

get_evaluation_run(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.GetEvaluationRunConfig,
            vertexai._genai.types.GetEvaluationRunConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.EvaluationRun

Retrieves an EvaluationRun from the resource name.

get_evaluation_set

get_evaluation_set(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.GetEvaluationSetConfig,
            vertexai._genai.types.GetEvaluationSetConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.EvaluationSet

Retrieves an EvaluationSet from the resource name.

run

run() -> vertexai._genai.types.EvaluateInstancesResponse

Evaluates an instance of a model.

This should eventually call _evaluate_instances()

run_inference

run_inference(
    *,
    model: typing.Union[str, typing.Callable[[typing.Any], typing.Any]],
    src: typing.Union[
        str, pandas.core.frame.DataFrame, vertexai._genai.types.EvaluationDataset
    ],
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.EvalRunInferenceConfig,
            vertexai._genai.types.EvalRunInferenceConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.EvaluationDataset

Runs inference on a dataset for evaluation.