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API Reference
Datasets

Get a dataset by ID

datasets.get(strdataset_id) -> Dataset
GET/api/v1/datasets/{dataset_id}

Get a dataset by ID

ParametersExpand Collapse
dataset_id: str
ReturnsExpand Collapse
class Dataset: …
configured_column_mapping: Optional[ConfiguredColumnMapping]

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

chat: Optional[str]
completion: Optional[str]
context: List[str]
prompt: Optional[str]
created_at: datetime

Timestamp when the dataset was created

formatdate-time
dataset_id: str

Unique dataset identifier

error: Optional[Error]

Error details if the dataset failed. Null otherwise.

message: str

Error message

evaluation_summary: Optional[EvaluationSummary]

Compact evaluation summary. Null if evaluation has not completed.

grade_after: Optional[str]

Letter grade (A-E) after augmentation

grade_before: Optional[str]

Letter grade (A-E) before augmentation

improvement_percent: Optional[float]

Relative improvement percentage

score_after: Optional[float]

Quality score after augmentation

score_before: Optional[float]

Quality score before augmentation

name: Optional[str]

Human-readable name for the dataset

progress: Optional[Progress]

Processing progress. Null when no run is active.

percent: Optional[int]

Progress percentage (0-100)

processed_rows: Optional[int]

Number of rows processed so far

total_rows: Optional[int]

Total rows to process (samples_to_process or row_count)

row_count: Optional[int]

Total number of rows in the dataset

run_id: Optional[str]

ID of the currently active run

status: Literal["pending", "running", "succeeded", "failed"]

Lifecycle status: pending, running, succeeded, or failed

One of the following:
"pending"
"running"
"succeeded"
"failed"
updated_at: datetime

Timestamp of the last update

formatdate-time

Get a dataset by ID

import os
from adaption import Adaption

client = Adaption(
    api_key=os.environ.get("ADAPTION_API_KEY"),  # This is the default and can be omitted
)
dataset = client.datasets.get(
    "dataset_id",
)
print(dataset.dataset_id)
{
  "configured_column_mapping": {
    "chat": "chat",
    "completion": "completion",
    "context": [
      "string"
    ],
    "prompt": "prompt"
  },
  "created_at": "2019-12-27T18:11:19.117Z",
  "dataset_id": "dataset_id",
  "error": {
    "message": "message"
  },
  "evaluation_summary": {
    "grade_after": "grade_after",
    "grade_before": "grade_before",
    "improvement_percent": 0,
    "score_after": 0,
    "score_before": 0
  },
  "name": "name",
  "progress": {
    "percent": 0,
    "processed_rows": 0,
    "total_rows": 0
  },
  "row_count": 0,
  "run_id": "run_id",
  "status": "pending",
  "updated_at": "2019-12-27T18:11:19.117Z"
}
Returns Examples
{
  "configured_column_mapping": {
    "chat": "chat",
    "completion": "completion",
    "context": [
      "string"
    ],
    "prompt": "prompt"
  },
  "created_at": "2019-12-27T18:11:19.117Z",
  "dataset_id": "dataset_id",
  "error": {
    "message": "message"
  },
  "evaluation_summary": {
    "grade_after": "grade_after",
    "grade_before": "grade_before",
    "improvement_percent": 0,
    "score_after": 0,
    "score_before": 0
  },
  "name": "name",
  "progress": {
    "percent": 0,
    "processed_rows": 0,
    "total_rows": 0
  },
  "row_count": 0,
  "run_id": "run_id",
  "status": "pending",
  "updated_at": "2019-12-27T18:11:19.117Z"
}