Get a dataset by ID

GET/api/v1/datasets/{dataset_id}

Get a dataset by ID

Path ParametersExpand Collapse

dataset_id: string

ReturnsExpand Collapse

Dataset object { dataset_id, kind, source_dataset_id, 12 more }

dataset_id: string

Unique dataset identifier

kind: "uploaded" or "combined" or "invented" or 3 more

How this dataset came about. uploaded was supplied by you, combined merges several datasets, invented was generated from a prompt, augmented adds rows retrieved from the curated pool to another dataset, and translated / localized add translated copies of another dataset’s rows.

One of the following:

"uploaded"

"combined"

"invented"

"augmented"

"translated"

"localized"

source_dataset_id: string

The dataset this one was derived from. Set when kind is augmented, translated or localized. Null for uploaded and invented, and for combined, which has several sources rather than one.

name: string

Human-readable name for the dataset

status: "pending" or "running" or "succeeded" or "failed"

Lifecycle status: pending, running, succeeded, or failed

One of the following:

"pending"

"running"

"succeeded"

"failed"

created_at: string

Timestamp when the dataset was created

formatdate-time

created_by_user_id: string

User who created the dataset

formatuuid

updated_at: string

Timestamp of the last update

formatdate-time

row_count: number

Total number of rows in the dataset

configured_column_mapping: object { prompt, completion, chat, 2 more }

User-configured column mapping. Null if not yet configured.

prompt: string

completion: string

chat: string

context: array of string

image: string

evaluation_summary: object { grade_before, grade_after, score_before, 2 more }

Compact evaluation summary. Null if evaluation has not completed.

grade_before: string

Letter grade (A-E) before adaptation

grade_after: string

Letter grade (A-E) after adaptation

score_before: number

Quality score before adaptation

score_after: number

Quality score after adaptation

improvement_percent: number

Relative improvement percentage

run_id: string

ID of the currently active run

progress: object { percent, processed_rows, total_rows }

Processing progress. Null when no run is active.

percent: number

Progress percentage (0-100)

processed_rows: number

Number of rows processed so far

total_rows: number

Total rows to process (samples_to_process or row_count)

error_data: object { message, code, level }

Error details if the dataset failed. Null otherwise.

message: string

Error message

code: string

Stable error code when the failure was structured (e.g. E0100)

level: "error" or "warning"

Severity when known

One of the following:

"error"

"warning"

image_column_formats: map["embedded_bytes" or "url" or "file_reference"]

Per-column export encoding for detected image columns (column name → format). Use with GET /datasets/{dataset_id}/download: look up the active image column (mapped image column that is also in configured_column_mapping.context) to determine how each row’s original_image is encoded. Null or empty when no image columns were detected.

One of the following:

"embedded_bytes"

"url"

"file_reference"

Get a dataset by ID

curl https://api.prod.adaptionlabs.ai/api/v1/datasets/$DATASET_ID \
    -H "Authorization: Bearer $ADAPTION_API_KEY"

{
  "dataset_id": "dataset_id",
  "kind": "augmented",
  "source_dataset_id": "550e8400-e29b-41d4-a716-446655440000",
  "name": "name",
  "status": "pending",
  "created_at": "2019-12-27T18:11:19.117Z",
  "created_by_user_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "updated_at": "2019-12-27T18:11:19.117Z",
  "row_count": 0,
  "configured_column_mapping": {
    "prompt": "prompt",
    "completion": "completion",
    "chat": "chat",
    "context": [
      "string"
    ],
    "image": "image"
  },
  "evaluation_summary": {
    "grade_before": "grade_before",
    "grade_after": "grade_after",
    "score_before": 0,
    "score_after": 0,
    "improvement_percent": 0
  },
  "run_id": "run_id",
  "progress": {
    "percent": 0,
    "processed_rows": 0,
    "total_rows": 0
  },
  "error_data": {
    "message": "message",
    "code": "code",
    "level": "error"
  },
  "image_column_formats": {
    "foo": "embedded_bytes"
  }
}

Returns Examples

{
  "dataset_id": "dataset_id",
  "kind": "augmented",
  "source_dataset_id": "550e8400-e29b-41d4-a716-446655440000",
  "name": "name",
  "status": "pending",
  "created_at": "2019-12-27T18:11:19.117Z",
  "created_by_user_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "updated_at": "2019-12-27T18:11:19.117Z",
  "row_count": 0,
  "configured_column_mapping": {
    "prompt": "prompt",
    "completion": "completion",
    "chat": "chat",
    "context": [
      "string"
    ],
    "image": "image"
  },
  "evaluation_summary": {
    "grade_before": "grade_before",
    "grade_after": "grade_after",
    "score_before": 0,
    "score_after": 0,
    "improvement_percent": 0
  },
  "run_id": "run_id",
  "progress": {
    "percent": 0,
    "processed_rows": 0,
    "total_rows": 0
  },
  "error_data": {
    "message": "message",
    "code": "code",
    "level": "error"
  },
  "image_column_formats": {
    "foo": "embedded_bytes"
  }
}