List

List Evaluation Test Cases

agents.evaluation_test_cases.list() -> EvaluationTestCaseListResponse

get/v2/gen-ai/evaluation_test_cases

To list all evaluation test cases, send a GET request to /v2/gen-ai/evaluation_test_cases.

ReturnsExpand Collapse

class EvaluationTestCaseListResponse: …

evaluation_test_cases: Optional[List[APIEvaluationTestCase]]

Alternative way of authentication for internal usage only - should not be exposed to public api

archived_at: Optional[datetime]

created_at: Optional[datetime]

created_by_user_email: Optional[str]

created_by_user_id: Optional[str]

dataset: Optional[Dataset]

created_at: Optional[datetime]

Time created at.

formatdate-time

dataset_name: Optional[str]

Name of the dataset.

dataset_uuid: Optional[str]

UUID of the dataset.

file_size: Optional[str]

The size of the dataset uploaded file in bytes.

formatuint64

has_ground_truth: Optional[bool]

Does the dataset have a ground truth column?

row_count: Optional[int]

Number of rows in the dataset.

formatint64

dataset_name: Optional[str]

dataset_uuid: Optional[str]

description: Optional[str]

latest_version_number_of_runs: Optional[int]

metrics: Optional[List[APIEvaluationMetric]]

description: Optional[str]

inverted: Optional[bool]

If true, the metric is inverted, meaning that a lower value is better.

metric_name: Optional[str]

metric_type: Optional[Literal["METRIC_TYPE_UNSPECIFIED", "METRIC_TYPE_GENERAL_QUALITY", "METRIC_TYPE_RAG_AND_TOOL"]]

Accepts one of the following:

"METRIC_TYPE_UNSPECIFIED"

"METRIC_TYPE_GENERAL_QUALITY"

"METRIC_TYPE_RAG_AND_TOOL"

metric_uuid: Optional[str]

metric_value_type: Optional[Literal["METRIC_VALUE_TYPE_UNSPECIFIED", "METRIC_VALUE_TYPE_NUMBER", "METRIC_VALUE_TYPE_STRING", "METRIC_VALUE_TYPE_PERCENTAGE"]]

Accepts one of the following:

"METRIC_VALUE_TYPE_UNSPECIFIED"

"METRIC_VALUE_TYPE_NUMBER"

"METRIC_VALUE_TYPE_STRING"

"METRIC_VALUE_TYPE_PERCENTAGE"

range_max: Optional[float]

The maximum value for the metric.

formatfloat

range_min: Optional[float]

The minimum value for the metric.

formatfloat

name: Optional[str]

star_metric: Optional[APIStarMetric]

test_case_uuid: Optional[str]

total_runs: Optional[int]

updated_at: Optional[datetime]

updated_by_user_email: Optional[str]

updated_by_user_id: Optional[str]

version: Optional[int]

from gradient import Gradient

client = Gradient()
evaluation_test_cases = client.agents.evaluation_test_cases.list()
print(evaluation_test_cases.evaluation_test_cases)

{
  "evaluation_test_cases": [
    {
      "archived_at": "2023-01-01T00:00:00Z",
      "created_at": "2023-01-01T00:00:00Z",
      "created_by_user_email": "[email protected]",
      "created_by_user_id": "12345",
      "dataset": {
        "created_at": "2023-01-01T00:00:00Z",
        "dataset_name": "example name",
        "dataset_uuid": "123e4567-e89b-12d3-a456-426614174000",
        "file_size": "12345",
        "has_ground_truth": true,
        "row_count": 123
      },
      "dataset_name": "example name",
      "dataset_uuid": "123e4567-e89b-12d3-a456-426614174000",
      "description": "example string",
      "latest_version_number_of_runs": 123,
      "metrics": [
        {
          "description": "example string",
          "inverted": true,
          "metric_name": "example name",
          "metric_type": "METRIC_TYPE_UNSPECIFIED",
          "metric_uuid": "123e4567-e89b-12d3-a456-426614174000",
          "metric_value_type": "METRIC_VALUE_TYPE_UNSPECIFIED",
          "range_max": 123,
          "range_min": 123
        }
      ],
      "name": "example name",
      "star_metric": {
        "metric_uuid": "123e4567-e89b-12d3-a456-426614174000",
        "name": "example name",
        "success_threshold": 123,
        "success_threshold_pct": 123
      },
      "test_case_uuid": "123e4567-e89b-12d3-a456-426614174000",
      "total_runs": 123,
      "updated_at": "2023-01-01T00:00:00Z",
      "updated_by_user_email": "[email protected]",
      "updated_by_user_id": "12345",
      "version": 123
    }
  ]
}

Returns Examples

{
  "evaluation_test_cases": [
    {
      "archived_at": "2023-01-01T00:00:00Z",
      "created_at": "2023-01-01T00:00:00Z",
      "created_by_user_email": "[email protected]",
      "created_by_user_id": "12345",
      "dataset": {
        "created_at": "2023-01-01T00:00:00Z",
        "dataset_name": "example name",
        "dataset_uuid": "123e4567-e89b-12d3-a456-426614174000",
        "file_size": "12345",
        "has_ground_truth": true,
        "row_count": 123
      },
      "dataset_name": "example name",
      "dataset_uuid": "123e4567-e89b-12d3-a456-426614174000",
      "description": "example string",
      "latest_version_number_of_runs": 123,
      "metrics": [
        {
          "description": "example string",
          "inverted": true,
          "metric_name": "example name",
          "metric_type": "METRIC_TYPE_UNSPECIFIED",
          "metric_uuid": "123e4567-e89b-12d3-a456-426614174000",
          "metric_value_type": "METRIC_VALUE_TYPE_UNSPECIFIED",
          "range_max": 123,
          "range_min": 123
        }
      ],
      "name": "example name",
      "star_metric": {
        "metric_uuid": "123e4567-e89b-12d3-a456-426614174000",
        "name": "example name",
        "success_threshold": 123,
        "success_threshold_pct": 123
      },
      "test_case_uuid": "123e4567-e89b-12d3-a456-426614174000",
      "total_runs": 123,
      "updated_at": "2023-01-01T00:00:00Z",
      "updated_by_user_email": "[email protected]",
      "updated_by_user_id": "12345",
      "version": 123
    }
  ]
}