def create(
        self,
        *,
        data_source_config: eval_create_params.DataSourceConfig,
        testing_criteria: Iterable[eval_create_params.TestingCriterion],
        metadata: Optional[Metadata] | NotGiven = NOT_GIVEN,
        name: str | NotGiven = NOT_GIVEN,
        # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
        # The extra values given here take precedence over values defined on the client or passed to this method.
        extra_headers: Headers | None = None,
        extra_query: Query | None = None,
        extra_body: Body | None = None,
        timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
    ) -> EvalCreateResponse:
        """
        Create the structure of an evaluation that can be used to test a model's
        performance. An evaluation is a set of testing criteria and the config for a
        data source, which dictates the schema of the data used in the evaluation. After
        creating an evaluation, you can run it on different models and model parameters.
        We support several types of graders and datasources. For more information, see
        the [Evals guide](https://platform.openai.com/docs/guides/evals).

        Args:
          data_source_config: The configuration for the data source used for the evaluation runs. Dictates the
              schema of the data used in the evaluation.

          testing_criteria: A list of graders for all eval runs in this group. Graders can reference
              variables in the data source using double curly braces notation, like
              `{{item.variable_name}}`. To reference the model's output, use the `sample`
              namespace (ie, `{{sample.output_text}}`).

          metadata: Set of 16 key-value pairs that can be attached to an object. This can be useful
              for storing additional information about the object in a structured format, and
              querying for objects via API or the dashboard.

              Keys are strings with a maximum length of 64 characters. Values are strings with
              a maximum length of 512 characters.

          name: The name of the evaluation.

          extra_headers: Send extra headers

          extra_query: Add additional query parameters to the request

          extra_body: Add additional JSON properties to the request

          timeout: Override the client-level default timeout for this request, in seconds
        """
        return self._post(
            "/evals",
            body=maybe_transform(
                {
                    "data_source_config": data_source_config,
                    "testing_criteria": testing_criteria,
                    "metadata": metadata,
                    "name": name,
                },
                eval_create_params.EvalCreateParams,
            ),
            options=make_request_options(
                extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
            ),
            cast_to=EvalCreateResponse,
        )

        Create the structure of an evaluation that can be used to test a model's
        performance. An evaluation is a set of testing criteria and the config for a
        data source, which dictates the schema of the data used in the evaluation. After
        creating an evaluation, you can run it on different models and model parameters.
        We support several types of graders and datasources. For more information, see
        the [Evals guide](https://platform.openai.com/docs/guides/evals).

        Args:
          data_source_config: The configuration for the data source used for the evaluation runs. Dictates the
              schema of the data used in the evaluation.

          testing_criteria: A list of graders for all eval runs in this group. Graders can reference
              variables in the data source using double curly braces notation, like
              `{{item.variable_name}}`. To reference the model's output, use the `sample`
              namespace (ie, `{{sample.output_text}}`).

          metadata: Set of 16 key-value pairs that can be attached to an object. This can be useful
              for storing additional information about the object in a structured format, and
              querying for objects via API or the dashboard.

              Keys are strings with a maximum length of 64 characters. Values are strings with
              a maximum length of 512 characters.

          name: The name of the evaluation.

          extra_headers: Send extra headers

          extra_query: Add additional query parameters to the request

          extra_body: Add additional JSON properties to the request

          timeout: Override the client-level default timeout for this request, in seconds
        
src/openai/resources/evals/evals.py