Documentation | Evaluations › retrieve

Evaluations

EvaluationsResource

Methods

client.evaluations.create() ->

Evaluation

post/v5/evaluations

Create Evaluation

client.evaluations.list() -> SyncCursorPage[

Evaluation

]

get/v5/evaluations

List Evaluations

client.evaluations.retrieve(, ) ->

Evaluation

get/v5/evaluations/{evaluation_id}

Get Evaluation

Parameters

evaluation_id: str

include_archived: Optional[

bool

]

views: Optional[List[

EvaluationViews

]]

(default: [])

"tasks"

Returns

Evaluation

id: str

created_at:

datetime

(format: date-time)

created_by:

Identity

The identity that created the entity.

datasets: List[

Dataset

]

name: str

status: Literal["failed", "completed", "running"]

tags: List[str]

The tags associated with the entity

archived_at: Optional[datetime]

(format: date-time)

description: Optional[str]

error_count: Optional[int]

Number of task errors across all items in this evaluation.

metadata: Optional[Dict[str,

object

]]

Metadata key-value pairs for the evaluation

object: Optional[Literal["evaluation"]]

(default: "evaluation")

progress: Optional[EvaluationTasksProgressSchema]

Progress of the evaluation's underlying async job

status_reason: Optional[str]

Reason for evaluation status

tasks: Optional[List[

EvaluationTask

]]

Tasks executed during evaluation. Populated with optional task view.

Request example

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 = client.evaluations.retrieve(
    evaluation_id="evaluation_id",
)
print(evaluation.id)

200Example

{
  "id": "id",
  "created_at": "2019-12-27T18:11:19.117Z",
  "created_by": {
    "id": "id",
    "type": "user",
    "object": "identity"
  },
  "datasets": [
    {
      "id": "id",
      "created_at": "2019-12-27T18:11:19.117Z",
      "created_by": {
        "id": "id",
        "type": "user",
        "object": "identity"
      },
      "current_version_num": 0,
      "name": "name",
      "tags": [
        "string"
      ],
      "archived_at": "2019-12-27T18:11:19.117Z",
      "description": "description",
      "object": "dataset"
    }
  ],
  "name": "name",
  "status": "failed",
  "tags": [
    "string"
  ],
  "archived_at": "2019-12-27T18:11:19.117Z",
  "description": "description",
  "error_count": 0,
  "metadata": {
    "foo": "bar"
  },
  "object": "evaluation",
  "progress": {
    "items": {
      "failed": 0,
      "pending": 0,
      "successful": 0,
      "total": 0,
      "failed_items": [
        {
          "item_id": "item_id",
          "error": "error",
          "error_type": "error_type"
        }
      ]
    },
    "workflows": {
      "completed": 0,
      "failed": 0,
      "pending": 0,
      "total": 0
    }
  },
  "status_reason": "status_reason",
  "tasks": [
    {
      "configuration": {
        "messages": [
          {
            "foo": "bar"
          }
        ],
        "model": "model",
        "audio": {
          "foo": "bar"
        },
        "frequency_penalty": 0,
        "function_call": {
          "foo": "bar"
        },
        "functions": [
          {
            "foo": "bar"
          }
        ],
        "logit_bias": {
          "foo": 0
        },
        "logprobs": true,
        "max_completion_tokens": 0,
        "max_tokens": 0,
        "metadata": {
          "foo": "string"
        },
        "modalities": [
          "string"
        ],
        "n": 0,
        "parallel_tool_calls": true,
        "prediction": {
          "foo": "bar"
        },
        "presence_penalty": 0,
        "reasoning_effort": "reasoning_effort",
        "response_format": {
          "foo": "bar"
        },
        "seed": 0,
        "stop": "stop",
        "store": true,
        "temperature": 0,
        "tool_choice": "tool_choice",
        "tools": [
          {
            "foo": "bar"
          }
        ],
        "top_k": 0,
        "top_logprobs": 0,
        "top_p": 0
      },
      "alias": "alias",
      "task_type": "chat_completion"
    }
  ]
}

client.evaluations.archive() ->

Evaluation

delete/v5/evaluations/{evaluation_id}

Archive Evaluation

client.evaluations.update(, ) ->

Evaluation

patch/v5/evaluations/{evaluation_id}

Update or Restore Evaluation

client.evaluations.retrieve_schema(, ) ->

EvaluationSchemaResponse

get/v5/evaluations/{evaluation_id}/schema

Get schema information for evaluation item data, including field names, types, and occurrence counts.

Parameters

evaluation_id: str

include_archived: Optional[

bool

]

Include archived items in schema analysis

Returns

EvaluationSchemaResponse

evaluation_id: str

The ID of the evaluation

fields: List[

Field

]

List of all discovered fields, ordered alphabetically by field_name

total_items: int

(minimum: 0)

Total number of evaluation items

is_sampled: Optional[bool]

Whether schema was computed from a sample of items (for large evaluations)

object: Optional[Literal["evaluation_schema"]]

(default: "evaluation_schema")

sample_size: Optional[int]

(minimum: 0)

Number of items sampled for schema inference, if applicable

Request example

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_schema_response = client.evaluations.retrieve_schema(
    evaluation_id="evaluation_id",
)
print(evaluation_schema_response.evaluation_id)

200Example

{
  "evaluation_id": "evaluation_id",
  "fields": [
    {
      "data_type": "data_type",
      "field_name": "field_name",
      "item_count": 0,
      "source": "data",
      "object": "field_schema"
    }
  ],
  "total_items": 0,
  "is_sampled": true,
  "object": "evaluation_schema",
  "sample_size": 0
}

client.evaluations.filter() -> SyncCursorPage[

Evaluation

]

post/v5/evaluations/filter

Filter evaluations using metadata and other criteria. Supports up to 10 filters with AND logic.

client.evaluations.retrieve_taxonomy() ->

EvaluationRetrieveTaxonomyResponse

get/v5/evaluations/{evaluation_id}/taxonomy

Get taxonomy JSON for contributor evaluation question tasks.

Domain types

class AutoEvaluationAgentTaskRequestWithItemLocator: ...

class Evaluation: ...

Dict[str,

object

]

class EvaluationSchemaResponse: ...

Schema information for an evaluation's item data structure

EvaluationTask

class EvaluationTasksProgressSchema: ...

Literal["tasks"]

str

class PaginatedListEvaluation: ...

Evaluations

Tasks

EvaluationsResource.TasksResource

Methods

client.evaluations.tasks.add(, ) ->

Evaluation

post/v5/evaluations/{evaluation_id}/tasks

Add a new test criteria (LLM judge, contributor question, etc.) to an existing evaluation. Gated: rejected if any contributor annotation task has been claimed or completed. Kicks off the evaluation workflow so the new task runs against existing items.

client.evaluations.tasks.update(, ) ->

Evaluation

patch/v5/evaluations/{evaluation_id}/tasks/{alias}

Replace a single test criteria's configuration, identified by its alias. Gated: rejected if any contributor annotation task for the evaluation has been claimed or completed.