Documentation Index
Fetch the complete documentation index at: https://www.adaline.ai/docs/llms.txt
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DatasetsClient
adaline.datasets manages datasets as a whole — create a dataset, look up metadata and columns, rename, or delete. Row-level and column-level operations live on the nested .rows and .columns sub-clients. Every method is async.
Access
from adaline.main import Adaline
adaline = Adaline()
datasets = adaline.datasets # DatasetsClient
from adaline.clients import DatasetsClient
Sub-clients
DatasetsClient exposes two nested namespaces:
| Attribute | Client | Covers |
|---|
adaline.datasets.rows | DatasetRowsClient | List / create / update / delete dataset rows |
adaline.datasets.columns | DatasetColumnsClient | Create / update / delete columns + fetch_dynamic to resolve dynamic columns |
adaline_api:
from adaline_api.models.dataset import Dataset
from adaline_api.models.dataset_summary import DatasetSummary
from adaline_api.models.create_dataset_request import CreateDatasetRequest
from adaline_api.models.update_dataset_request import UpdateDatasetRequest
from adaline_api.models.list_datasets_response import ListDatasetsResponse
list()
List datasets in a project (paginated).
async def list(
*,
project_id: str,
sort: Optional[SortOrderInput] = None,
created_after: Optional[int] = None,
created_before: Optional[int] = None,
limit: Optional[int] = None,
cursor: Optional[str] = None,
) -> ListDatasetsResponse
Parameters
| Name | Type | Required | Description |
|---|
project_id | str | Yes | Project whose datasets should be returned. |
sort | Optional[SortOrderInput] | No | "createdAt:asc" or "createdAt:desc". |
created_after | Optional[int] | No | Unix milliseconds. |
created_before | Optional[int] | No | Unix milliseconds. |
limit | Optional[int] | No | Page size (default 50, max 200). |
cursor | Optional[str] | No | Opaque cursor from a previous response’s pagination.next_cursor. |
Returns
ListDatasetsResponse with { data: list[DatasetSummary]; pagination: Pagination }.
Example
response = await adaline.datasets.list(
project_id="project_abc123",
sort="createdAt:desc",
limit=50,
)
for dataset in response.data:
print(dataset.id, dataset.title)
create()
Create a new dataset. You can seed initial columns in the same call.
async def create(*, dataset: CreateDatasetRequest) -> Dataset
Parameters
| Name | Type | Required | Description |
|---|
dataset | CreateDatasetRequest | Yes | Dataset definition — project_id, title, optional icon, optional initial columns. |
Returns
Dataset — the created dataset with every column assigned a server-generated id.
Example
from adaline_api.models.create_dataset_request import CreateDatasetRequest
dataset = await adaline.datasets.create(
dataset=CreateDatasetRequest(
project_id="project_abc123",
title="Support triage eval set",
icon={"type": "emoji", "value": "📚"},
columns=[
{"name": "question", "type": "input"},
{"name": "expected", "type": "input"},
],
)
)
print(f"Created {dataset.id} with {len(dataset.columns)} columns")
get()
Fetch a single dataset (metadata + full column list, not rows).
async def get(*, dataset_id: str) -> Dataset
Parameters
| Name | Type | Required | Description |
|---|
dataset_id | str | Yes | Dataset identifier. |
Example
dataset = await adaline.datasets.get(dataset_id="dataset_abc123")
for column in dataset.columns:
print(column.name, column.type)
update()
Update dataset-level metadata (title, icon).
async def update(
*,
dataset_id: str,
dataset: UpdateDatasetRequest,
) -> DatasetSummary
Returns
DatasetSummary — the updated dataset metadata (returns the summary shape, not the full Dataset with columns).
Example
from adaline_api.models.update_dataset_request import UpdateDatasetRequest
await adaline.datasets.update(
dataset_id="dataset_abc123",
dataset=UpdateDatasetRequest(title="Renamed dataset"),
)
delete()
Permanently delete a dataset and all of its columns and rows. Irreversible.
async def delete(*, dataset_id: str) -> None
See Also
