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Evaluation Dashboards

Patch Evaluation Dashboard

evaluation_dashboards.update(strdashboard_id, EvaluationDashboardUpdateParams**kwargs) -> EvaluationDashboard
PATCH/v5/evaluation-dashboards/{dashboard_id}

Partially update a dashboard’s name, description, tags, or widget_order.

Only these mutable fields can change — the dashboard’s bound evaluation or group is fixed at creation and cannot be reassigned here, and unset fields are left untouched (partial-update semantics). Reorder existing widgets by sending a new widget_order, whose entries are validated to be existing, non-duplicate widget IDs before the update is applied; creating and attaching new widgets is done through the widget sub-endpoints, not by editing widget_order. Archived dashboards cannot be updated and return a not-found error.

ParametersExpand Collapse
dashboard_id: str
description: Optional[str]

Dashboard description

name: Optional[str]

Dashboard name

maxLength256
minLength1
tags: Optional[Sequence[str]]

The tags associated with the entity

widget_order: Optional[Sequence[str]]

Ordered array of widget IDs (for reordering widgets)

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.

Patch Evaluation Dashboard

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.update(
    dashboard_id="dashboard_id",
)
print(evaluation_dashboard.id)
{
  "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
{
  "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"
          }
        ]
      }
    }
  ]
}