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Add rows to a dataset from the curated pool
API Reference
Datasets

Add rows to a dataset from the curated pool

datasets.augment(strdataset_id, DatasetAugmentParams**kwargs) -> DatasetAugmentResponse
POST/api/v1/datasets/{dataset_id}/augment

Retrieves additional rows matching this dataset and returns them as a new dataset, leaving this one unchanged. The new dataset carries this dataset’s rows alongside the retrieved ones, so it can be trained on or downloaded directly. Omit both row counts to receive the recommended fill and its cost as a quote; a run needs explicit row counts, or fill_to_recommended=true to fill any count you leave out with its recommended value. Poll GET /datasets/{dataset_id}/status on the returned id until it reaches succeeded. Set estimate=true to price the request without starting a run.

ParametersExpand Collapse
dataset_id: str
domain_rows: Optional[float]

How many additional rows to retrieve from topics matching this dataset. Omit domain_rows and general_rows to receive the recommended fill and its cost as a quote; a run needs a count, or fill_to_recommended=true.

minimum0
maximum100000
general_rows: Optional[float]

How many additional rows to retrieve from other topics, to broaden the dataset. Omit domain_rows and general_rows to receive the recommended fill and its cost as a quote; a run needs a count, or fill_to_recommended=true.

minimum0
maximum100000
similar_rows: Optional[float]

How many new rows to generate in the style of this dataset. A sample of its rows is analysed for topic, language and prompt/completion conventions, and new rows are generated to match. Combines with domain_rows and general_rows in one run, and is never filled from the recommendation. Naming it makes the request explicit rather than a quote. Defaults to 0.

minimum0
maximum100000
training_type: Optional[Literal["instruction_dataset", "preference_pairs"]]

Shape of the rows to retrieve. instruction_dataset (default) retrieves prompt/completion pairs for SFT; preference_pairs retrieves chosen/rejected pairs for DPO.

One of the following:
"instruction_dataset"
"preference_pairs"
estimate: Optional[bool]

When true, validates the request and returns the estimated credit cost without starting a run or creating a dataset.

idempotency_key: Optional[str]

Opaque key that makes a retried request safe. A second request carrying the same key returns the dataset the first one created instead of starting and charging for a second run.

maxLength255
ReturnsExpand Collapse
class DatasetAugmentResponse: …
dataset_id: Optional[str]

The augmented dataset. It carries this dataset’s rows plus the retrieved ones, and is ready to download once its status reaches succeeded. Null for an estimate, which creates nothing.

status: Optional[Literal["pending", "running", "awaiting_input", 2 more]]

Status of the augmented dataset at the moment this response was sent, in the same vocabulary GET /datasets/{dataset_id}/status reports. Poll that endpoint until it reaches succeeded or failed. An idempotent replay of a finished run returns its terminal status here.

One of the following:
"pending"
"running"
"awaiting_input"
"succeeded"
"failed"
domain_rows: float

Rows this run retrieves from topics matching the dataset, or the recommended fill an estimate priced.

general_rows: float

Rows this run retrieves from other topics, to broaden the dataset, or the recommended fill an estimate priced.

estimated_credits_consumed: float

Credits this run consumes, charged against the requested rows.

estimate: bool

Whether this was an estimate-only request (no run started).

Add rows to a dataset from the curated pool

import os
from adaption import Adaption

client = Adaption(
    api_key=os.environ.get("ADAPTION_API_KEY"),  # This is the default and can be omitted
)
response = client.datasets.augment(
    dataset_id="dataset_id",
)
print(response.dataset_id)
{
  "dataset_id": "550e8400-e29b-41d4-a716-446655440000",
  "status": "running",
  "domain_rows": 17000,
  "general_rows": 0,
  "estimated_credits_consumed": 40,
  "estimate": true
}
Returns Examples
{
  "dataset_id": "550e8400-e29b-41d4-a716-446655440000",
  "status": "running",
  "domain_rows": 17000,
  "general_rows": 0,
  "estimated_credits_consumed": 40,
  "estimate": true
}