API Reference

Evaluation Dashboards

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Delete Evaluation Dashboard

evaluation_dashboards.archive(strdashboard_id) -> EvaluationDashboard

DELETE/v5/evaluation-dashboards/{dashboard_id}

Soft-delete a dashboard by setting its archived timestamp.

The dashboard row is retained and marked archived rather than physically removed, so it stops appearing in default listings but can still be fetched with include_archived=true; the archived dashboard is returned by this call. Associated widgets and widget results are not deleted or detached. Returns a not-found error if the dashboard does not exist in the caller’s account.

ParametersExpand Collapse

dashboard_id: str

ReturnsExpand Collapse

class EvaluationDashboard: …

id: str

Unique identifier of the dashboard

account_id: str

Account that owns this dashboard

created_at: datetime

When the dashboard was created

formatdate-time

created_by: Identity

The identity that created the entity.

id: str

type: Literal["user", "service_account"]

One of the following:

"user"

"service_account"

object: Optional[Literal["identity"]]

name: str

Dashboard name

tags: Optional[List[str]]

The tags associated with the entity

updated_at: datetime

When the dashboard was last updated

formatdate-time

archived_at: Optional[datetime]

When the dashboard was archived (soft-deleted)

formatdate-time

description: Optional[str]

Dashboard description

error_message: Optional[str]

Error message if computation failed

evaluation_group_id: Optional[str]

Evaluation group ID

evaluation_id: Optional[str]

Evaluation ID

object: Optional[Literal["evaluation_dashboard"]]

widget_order: Optional[List[str]]

Ordered array of widget IDs

widget_results: Optional[List[EvaluationDashboardWidgetResult]]

Widget results for this dashboard. Populated with ‘widget_results’ view.

id: str

Unique identifier of the widget result

account_id: str

Account that owns this widget result

computation_status: Literal["pending", "completed", "failed"]

Status of the computation

One of the following:

"pending"

"completed"

"failed"

created_at: datetime

When the widget result was created

formatdate-time

widget_id: str

Unique identifier of the widget

computation_job_id: Optional[str]

Temporal workflow ID or job ID for async computation tracking

computed_at: Optional[datetime]

Timestamp when computation completed successfully

formatdate-time

computed_result: Optional[Dict[str, object]]

Cached computation results

error_message: Optional[str]

Error message if computation failed

evaluation_group_id: Optional[str]

FK to evaluation_groups. Null if result is for a single evaluation.

evaluation_id: Optional[str]

FK to evaluations. Null if result is for an evaluation group.

object: Optional[Literal["evaluation_dashboard_widget_result"]]

widget: Optional[EvaluationDashboardWidget]

Widget that this result is for

id: str

Unique identifier of the widget

account_id: str

Account that owns this widget

created_at: datetime

When the widget was created

formatdate-time

title: str

Widget title

type: EvaluationWidgetTypeEnum

Widget type

One of the following:

"bar"

"histogram"

"donut"

"scatter"

"metric"

"table"

"markdown"

"heading"

"timeseries"

archived_at: Optional[datetime]

When the widget was archived (soft-deleted)

formatdate-time

config: Optional[Dict[str, object]]

Chart-specific display configuration

object: Optional[Literal["evaluation_dashboard_widget"]]

query: Optional[Query]

Structured query AST for metric computation (SeriesQuery or MetricQuery)

One of the following:

class SeriesQuery: …

Query that returns a series of records (used for table/bar/histogram/donut/scatter widgets).

Used for widget types: table, bar, histogram, donut, scatter. Returns: {“type”: “series”, “data”: […]}

Example SQL equivalent: SELECT category, AVG(score) as avg_score, COUNT(*) as count FROM evaluation_items WHERE score > 0.5 AND category = ‘test’ GROUP BY category ORDER BY avg_score DESC LIMIT 100

select: List[SelectItem]

expression: Expression

Reference to a column from evaluation_items.data

Example: {“type”: “COLUMN”, “column”: “category”}

One of the following:

class ExpressionColumn: …

Reference to a column from evaluation_items.data

Example: {“type”: “COLUMN”, “column”: “category”}

column: str

Column name from evaluation_items.data

source: Optional[str]

Column source: ‘data’ or ‘task_result_cache’

type: Optional[Literal["COLUMN"]]

class ExpressionAggregation: …

Aggregation function to apply

Examples: {“type”: “AGGREGATION”, “function”: “AVG”, “column”: “score”} {“type”: “AGGREGATION”, “function”: “COUNT”, “column”: ”*”} {“type”: “AGGREGATION”, “function”: “PERCENTILE”, “column”: “score”, “params”: {“percentile”: 95}}

column: str

Column to aggregate, or '' for COUNT()

function: Literal["COUNT", "SUM", "AVG", 7 more]

Supported aggregation functions

One of the following:

"COUNT"

"SUM"

"AVG"

"MIN"

"MAX"

"STDDEV"

"VARIANCE"

"PERCENTILE"

"COUNT_DISTINCT"

"PERCENTAGE"

evaluation_ids: Optional[List[str]]

Optional subset of evaluation IDs for per-aggregation filtering in evaluation group dashboards.

params: Optional[Dict[str, object]]

Function parameters (e.g., {‘percentile’: 95} for PERCENTILE, {‘percentage_filters’: Filter} for PERCENTAGE)

source: Optional[str]

Column source: ‘data’ or ‘task_result_cache’

type: Optional[Literal["AGGREGATION"]]

alias: Optional[str]

Optional alias for the selected item

evaluation_ids: Optional[List[str]]

Optional subset of evaluation IDs to compute on. Only applicable for evaluation group dashboards. If omitted, computes on all evaluations in the group.

filter: Optional[Filter]

Filter conditions (WHERE clause)

conditions: List[Condition]

column: str

Column name to filter on

operator: Literal["=", "!=", ">", 9 more]

Comparison operator

One of the following:

"="

"!="

">"

"<"

">="

"<="

"IN"

"NOT IN"

"LIKE"

"NOT LIKE"

"IS NULL"

"IS NOT NULL"

source: Optional[str]

Column source: ‘data’ or ‘task_result_cache’

value: Optional[Union[str, float, bool, 2 more]]

Value to compare against. Not required for IS NULL / IS NOT NULL operators.

One of the following:

str

float

bool

List[object]

logical_operators: Optional[List[Literal["AND", "OR"]]]

Logical operators connecting conditions. Length must be len(conditions) - 1

One of the following:

"AND"

"OR"

group_by: Optional[List[str]]

Columns to group by

latest_only: Optional[bool]

When True, the widget computes against rows from only the most recent active evaluation in the group (by EvaluationORM.created_at). Only applicable for evaluation group dashboards. Composes with evaluation_ids (latest within the subset). Cannot be combined with per-aggregation evaluation_ids; the use case enforces these rules.

limit: Optional[int]

Max rows to return

minimum1

order_by: Optional[List[OrderBy]]

Sort order

column: str

Column name to sort by

direction: Optional[Literal["ASC", "DESC"]]

Sort direction

One of the following:

"ASC"

"DESC"

source: Optional[str]

Column source: ‘data’ or ‘task_result_cache’

class MetricQuery: …

Query that returns a single metric value (used for metric widgets).

Used for widget type: metric. Enforces exactly 1 aggregation in select. Returns: {“type”: “metric”, “data”: …}

Example SQL equivalent: SELECT AVG(score) as average_score FROM evaluation_items

select: List[SelectItem]

expression: Expression

Reference to a column from evaluation_items.data

Example: {“type”: “COLUMN”, “column”: “category”}

One of the following:

class ExpressionColumn: …

Reference to a column from evaluation_items.data

Example: {“type”: “COLUMN”, “column”: “category”}

column: str

Column name from evaluation_items.data

source: Optional[str]

Column source: ‘data’ or ‘task_result_cache’

type: Optional[Literal["COLUMN"]]

class ExpressionAggregation: …

Aggregation function to apply

Examples: {“type”: “AGGREGATION”, “function”: “AVG”, “column”: “score”} {“type”: “AGGREGATION”, “function”: “COUNT”, “column”: ”*”} {“type”: “AGGREGATION”, “function”: “PERCENTILE”, “column”: “score”, “params”: {“percentile”: 95}}

column: str

Column to aggregate, or '' for COUNT()

function: Literal["COUNT", "SUM", "AVG", 7 more]

Supported aggregation functions

One of the following:

"COUNT"

"SUM"

"AVG"

"MIN"

"MAX"

"STDDEV"

"VARIANCE"

"PERCENTILE"

"COUNT_DISTINCT"

"PERCENTAGE"

evaluation_ids: Optional[List[str]]

Optional subset of evaluation IDs for per-aggregation filtering in evaluation group dashboards.

params: Optional[Dict[str, object]]

Function parameters (e.g., {‘percentile’: 95} for PERCENTILE, {‘percentage_filters’: Filter} for PERCENTAGE)

source: Optional[str]

Column source: ‘data’ or ‘task_result_cache’

type: Optional[Literal["AGGREGATION"]]

alias: Optional[str]

Optional alias for the selected item

evaluation_ids: Optional[List[str]]

Optional subset of evaluation IDs to compute on. Only applicable for evaluation group dashboards. If omitted, computes on all evaluations in the group.

filter: Optional[Filter]

Filter conditions (WHERE clause)

conditions: List[Condition]

column: str

Column name to filter on

operator: Literal["=", "!=", ">", 9 more]

Comparison operator

One of the following:

"="

"!="

">"

"<"

">="

"<="

"IN"

"NOT IN"

"LIKE"

"NOT LIKE"

"IS NULL"

"IS NOT NULL"

source: Optional[str]

Column source: ‘data’ or ‘task_result_cache’

value: Optional[Union[str, float, bool, 2 more]]

Value to compare against. Not required for IS NULL / IS NOT NULL operators.

One of the following:

str

float

bool

List[object]

logical_operators: Optional[List[Literal["AND", "OR"]]]

Logical operators connecting conditions. Length must be len(conditions) - 1

One of the following:

"AND"

"OR"

latest_only: Optional[bool]

When True, the widget computes against rows from only the most recent active evaluation in the group (by EvaluationORM.created_at). Only applicable for evaluation group dashboards. Composes with evaluation_ids (latest within the subset). Cannot be combined with per-aggregation evaluation_ids; the use case enforces these rules.

widgets: Optional[List[EvaluationDashboardWidget]]

Widgets associated with this dashboard. Populated with ‘widgets’ view.

id: str

Unique identifier of the widget

account_id: str

Account that owns this widget

created_at: datetime

When the widget was created

formatdate-time

title: str

Widget title

type: EvaluationWidgetTypeEnum

Widget type

One of the following:

"bar"

"histogram"

"donut"

"scatter"

"metric"

"table"

"markdown"

"heading"

"timeseries"

archived_at: Optional[datetime]

When the widget was archived (soft-deleted)

formatdate-time

config: Optional[Dict[str, object]]

Chart-specific display configuration

object: Optional[Literal["evaluation_dashboard_widget"]]

query: Optional[Query]

Structured query AST for metric computation (SeriesQuery or MetricQuery)

One of the following:

class SeriesQuery: …

Query that returns a series of records (used for table/bar/histogram/donut/scatter widgets).

Used for widget types: table, bar, histogram, donut, scatter. Returns: {“type”: “series”, “data”: […]}

Example SQL equivalent: SELECT category, AVG(score) as avg_score, COUNT(*) as count FROM evaluation_items WHERE score > 0.5 AND category = ‘test’ GROUP BY category ORDER BY avg_score DESC LIMIT 100

select: List[SelectItem]

expression: Expression

Reference to a column from evaluation_items.data

Example: {“type”: “COLUMN”, “column”: “category”}

One of the following:

class ExpressionColumn: …

Reference to a column from evaluation_items.data

Example: {“type”: “COLUMN”, “column”: “category”}

column: str

Column name from evaluation_items.data

source: Optional[str]

Column source: ‘data’ or ‘task_result_cache’

type: Optional[Literal["COLUMN"]]

class ExpressionAggregation: …

Aggregation function to apply

Examples: {“type”: “AGGREGATION”, “function”: “AVG”, “column”: “score”} {“type”: “AGGREGATION”, “function”: “COUNT”, “column”: ”*”} {“type”: “AGGREGATION”, “function”: “PERCENTILE”, “column”: “score”, “params”: {“percentile”: 95}}

column: str

Column to aggregate, or '' for COUNT()

function: Literal["COUNT", "SUM", "AVG", 7 more]

Supported aggregation functions

One of the following:

"COUNT"

"SUM"

"AVG"

"MIN"

"MAX"

"STDDEV"

"VARIANCE"

"PERCENTILE"

"COUNT_DISTINCT"

"PERCENTAGE"

evaluation_ids: Optional[List[str]]

Optional subset of evaluation IDs for per-aggregation filtering in evaluation group dashboards.

params: Optional[Dict[str, object]]

Function parameters (e.g., {‘percentile’: 95} for PERCENTILE, {‘percentage_filters’: Filter} for PERCENTAGE)

source: Optional[str]

Column source: ‘data’ or ‘task_result_cache’

type: Optional[Literal["AGGREGATION"]]

alias: Optional[str]

Optional alias for the selected item

evaluation_ids: Optional[List[str]]

Optional subset of evaluation IDs to compute on. Only applicable for evaluation group dashboards. If omitted, computes on all evaluations in the group.

filter: Optional[Filter]

Filter conditions (WHERE clause)

conditions: List[Condition]

column: str

Column name to filter on

operator: Literal["=", "!=", ">", 9 more]

Comparison operator

One of the following:

"="

"!="

">"

"<"

">="

"<="

"IN"

"NOT IN"

"LIKE"

"NOT LIKE"

"IS NULL"

"IS NOT NULL"

source: Optional[str]

Column source: ‘data’ or ‘task_result_cache’

value: Optional[Union[str, float, bool, 2 more]]

Value to compare against. Not required for IS NULL / IS NOT NULL operators.

One of the following:

str

float

bool

List[object]

logical_operators: Optional[List[Literal["AND", "OR"]]]

Logical operators connecting conditions. Length must be len(conditions) - 1

One of the following:

"AND"

"OR"

group_by: Optional[List[str]]

Columns to group by

latest_only: Optional[bool]

When True, the widget computes against rows from only the most recent active evaluation in the group (by EvaluationORM.created_at). Only applicable for evaluation group dashboards. Composes with evaluation_ids (latest within the subset). Cannot be combined with per-aggregation evaluation_ids; the use case enforces these rules.

limit: Optional[int]

Max rows to return

minimum1

order_by: Optional[List[OrderBy]]

Sort order

column: str

Column name to sort by

direction: Optional[Literal["ASC", "DESC"]]

Sort direction

One of the following:

"ASC"

"DESC"

source: Optional[str]

Column source: ‘data’ or ‘task_result_cache’

class MetricQuery: …

Query that returns a single metric value (used for metric widgets).

Used for widget type: metric. Enforces exactly 1 aggregation in select. Returns: {“type”: “metric”, “data”: …}

Example SQL equivalent: SELECT AVG(score) as average_score FROM evaluation_items

select: List[SelectItem]

expression: Expression

Reference to a column from evaluation_items.data

Example: {“type”: “COLUMN”, “column”: “category”}

One of the following:

class ExpressionColumn: …

Reference to a column from evaluation_items.data

Example: {“type”: “COLUMN”, “column”: “category”}

column: str

Column name from evaluation_items.data

source: Optional[str]

Column source: ‘data’ or ‘task_result_cache’

type: Optional[Literal["COLUMN"]]

class ExpressionAggregation: …

Aggregation function to apply

Examples: {“type”: “AGGREGATION”, “function”: “AVG”, “column”: “score”} {“type”: “AGGREGATION”, “function”: “COUNT”, “column”: ”*”} {“type”: “AGGREGATION”, “function”: “PERCENTILE”, “column”: “score”, “params”: {“percentile”: 95}}

column: str

Column to aggregate, or '' for COUNT()

function: Literal["COUNT", "SUM", "AVG", 7 more]

Supported aggregation functions

One of the following:

"COUNT"

"SUM"

"AVG"

"MIN"

"MAX"

"STDDEV"

"VARIANCE"

"PERCENTILE"

"COUNT_DISTINCT"

"PERCENTAGE"

evaluation_ids: Optional[List[str]]

Optional subset of evaluation IDs for per-aggregation filtering in evaluation group dashboards.

params: Optional[Dict[str, object]]

Function parameters (e.g., {‘percentile’: 95} for PERCENTILE, {‘percentage_filters’: Filter} for PERCENTAGE)

source: Optional[str]

Column source: ‘data’ or ‘task_result_cache’

type: Optional[Literal["AGGREGATION"]]

alias: Optional[str]

Optional alias for the selected item

evaluation_ids: Optional[List[str]]

Optional subset of evaluation IDs to compute on. Only applicable for evaluation group dashboards. If omitted, computes on all evaluations in the group.

filter: Optional[Filter]

Filter conditions (WHERE clause)

conditions: List[Condition]

column: str

Column name to filter on

operator: Literal["=", "!=", ">", 9 more]

Comparison operator

One of the following:

"="

"!="

">"

"<"

">="

"<="

"IN"

"NOT IN"

"LIKE"

"NOT LIKE"

"IS NULL"

"IS NOT NULL"

source: Optional[str]

Column source: ‘data’ or ‘task_result_cache’

value: Optional[Union[str, float, bool, 2 more]]

Value to compare against. Not required for IS NULL / IS NOT NULL operators.

One of the following:

str

float

bool

List[object]

logical_operators: Optional[List[Literal["AND", "OR"]]]

Logical operators connecting conditions. Length must be len(conditions) - 1

One of the following:

"AND"

"OR"

latest_only: Optional[bool]

When True, the widget computes against rows from only the most recent active evaluation in the group (by EvaluationORM.created_at). Only applicable for evaluation group dashboards. Composes with evaluation_ids (latest within the subset). Cannot be combined with per-aggregation evaluation_ids; the use case enforces these rules.

Delete Evaluation Dashboard

Python

HTTP

TypeScript

Python

Go

import os
from scale_gp_beta import SGPClient

client = SGPClient(
    api_key=os.environ.get("SGP_API_KEY"),  # This is the default and can be omitted
)
evaluation_dashboard = client.evaluation_dashboards.archive(
    "dashboard_id",
)
print(evaluation_dashboard.id)

200 example

{
  "id": "id",
  "account_id": "account_id",
  "created_at": "2019-12-27T18:11:19.117Z",
  "created_by": {
    "id": "id",
    "type": "user",
    "object": "identity"
  },
  "name": "name",
  "tags": [
    "string"
  ],
  "updated_at": "2019-12-27T18:11:19.117Z",
  "archived_at": "2019-12-27T18:11:19.117Z",
  "description": "description",
  "error_message": "error_message",
  "evaluation_group_id": "evaluation_group_id",
  "evaluation_id": "evaluation_id",
  "object": "evaluation_dashboard",
  "widget_order": [
    "string"
  ],
  "widget_results": [
    {
      "id": "id",
      "account_id": "account_id",
      "computation_status": "pending",
      "created_at": "2019-12-27T18:11:19.117Z",
      "widget_id": "widget_id",
      "computation_job_id": "computation_job_id",
      "computed_at": "2019-12-27T18:11:19.117Z",
      "computed_result": {
        "foo": "bar"
      },
      "error_message": "error_message",
      "evaluation_group_id": "evaluation_group_id",
      "evaluation_id": "evaluation_id",
      "object": "evaluation_dashboard_widget_result",
      "widget": {
        "id": "id",
        "account_id": "account_id",
        "created_at": "2019-12-27T18:11:19.117Z",
        "title": "title",
        "type": "bar",
        "archived_at": "2019-12-27T18:11:19.117Z",
        "config": {
          "foo": "bar"
        },
        "object": "evaluation_dashboard_widget",
        "query": {
          "select": [
            {
              "expression": {
                "column": "column",
                "source": "source",
                "type": "COLUMN"
              },
              "alias": "alias"
            }
          ],
          "evaluation_ids": [
            "string"
          ],
          "filter": {
            "conditions": [
              {
                "column": "column",
                "operator": "=",
                "source": "source",
                "value": "string"
              }
            ],
            "logicalOperators": [
              "AND"
            ]
          },
          "groupBy": [
            "string"
          ],
          "latest_only": true,
          "limit": 1,
          "orderBy": [
            {
              "column": "column",
              "direction": "ASC",
              "source": "source"
            }
          ]
        }
      }
    }
  ],
  "widgets": [
    {
      "id": "id",
      "account_id": "account_id",
      "created_at": "2019-12-27T18:11:19.117Z",
      "title": "title",
      "type": "bar",
      "archived_at": "2019-12-27T18:11:19.117Z",
      "config": {
        "foo": "bar"
      },
      "object": "evaluation_dashboard_widget",
      "query": {
        "select": [
          {
            "expression": {
              "column": "column",
              "source": "source",
              "type": "COLUMN"
            },
            "alias": "alias"
          }
        ],
        "evaluation_ids": [
          "string"
        ],
        "filter": {
          "conditions": [
            {
              "column": "column",
              "operator": "=",
              "source": "source",
              "value": "string"
            }
          ],
          "logicalOperators": [
            "AND"
          ]
        },
        "groupBy": [
          "string"
        ],
        "latest_only": true,
        "limit": 1,
        "orderBy": [
          {
            "column": "column",
            "direction": "ASC",
            "source": "source"
          }
        ]
      }
    }
  ]
}

Returns Examples

200 example

{
  "id": "id",
  "account_id": "account_id",
  "created_at": "2019-12-27T18:11:19.117Z",
  "created_by": {
    "id": "id",
    "type": "user",
    "object": "identity"
  },
  "name": "name",
  "tags": [
    "string"
  ],
  "updated_at": "2019-12-27T18:11:19.117Z",
  "archived_at": "2019-12-27T18:11:19.117Z",
  "description": "description",
  "error_message": "error_message",
  "evaluation_group_id": "evaluation_group_id",
  "evaluation_id": "evaluation_id",
  "object": "evaluation_dashboard",
  "widget_order": [
    "string"
  ],
  "widget_results": [
    {
      "id": "id",
      "account_id": "account_id",
      "computation_status": "pending",
      "created_at": "2019-12-27T18:11:19.117Z",
      "widget_id": "widget_id",
      "computation_job_id": "computation_job_id",
      "computed_at": "2019-12-27T18:11:19.117Z",
      "computed_result": {
        "foo": "bar"
      },
      "error_message": "error_message",
      "evaluation_group_id": "evaluation_group_id",
      "evaluation_id": "evaluation_id",
      "object": "evaluation_dashboard_widget_result",
      "widget": {
        "id": "id",
        "account_id": "account_id",
        "created_at": "2019-12-27T18:11:19.117Z",
        "title": "title",
        "type": "bar",
        "archived_at": "2019-12-27T18:11:19.117Z",
        "config": {
          "foo": "bar"
        },
        "object": "evaluation_dashboard_widget",
        "query": {
          "select": [
            {
              "expression": {
                "column": "column",
                "source": "source",
                "type": "COLUMN"
              },
              "alias": "alias"
            }
          ],
          "evaluation_ids": [
            "string"
          ],
          "filter": {
            "conditions": [
              {
                "column": "column",
                "operator": "=",
                "source": "source",
                "value": "string"
              }
            ],
            "logicalOperators": [
              "AND"
            ]
          },
          "groupBy": [
            "string"
          ],
          "latest_only": true,
          "limit": 1,
          "orderBy": [
            {
              "column": "column",
              "direction": "ASC",
              "source": "source"
            }
          ]
        }
      }
    }
  ],
  "widgets": [
    {
      "id": "id",
      "account_id": "account_id",
      "created_at": "2019-12-27T18:11:19.117Z",
      "title": "title",
      "type": "bar",
      "archived_at": "2019-12-27T18:11:19.117Z",
      "config": {
        "foo": "bar"
      },
      "object": "evaluation_dashboard_widget",
      "query": {
        "select": [
          {
            "expression": {
              "column": "column",
              "source": "source",
              "type": "COLUMN"
            },
            "alias": "alias"
          }
        ],
        "evaluation_ids": [
          "string"
        ],
        "filter": {
          "conditions": [
            {
              "column": "column",
              "operator": "=",
              "source": "source",
              "value": "string"
            }
          ],
          "logicalOperators": [
            "AND"
          ]
        },
        "groupBy": [
          "string"
        ],
        "latest_only": true,
        "limit": 1,
        "orderBy": [
          {
            "column": "column",
            "direction": "ASC",
            "source": "source"
          }
        ]
      }
    }
  ]
}