---
title: Combine datasets | Adaption
description: Validate and merge multiple Adaptive Data datasets into one new dataset with the Python SDK.
---

Combine datasets when related training examples are spread across multiple Adaptive Data results. Combining creates a new dataset and leaves every source dataset unchanged.

Each source must belong to your organization and have completed an Adaptive Data run. The examples assume you have [configured the Python SDK](/introduction/getting-started/index.md).

## Validate the source datasets

Validate the datasets before combining them. Validation checks compatibility without writing data:

```
from adaption import Adaption


client = Adaption()


dataset_ids = [
    "dataset_abc123",
    "dataset_def456",
]


validation = client.datasets.combine.validate(dataset_ids=dataset_ids)


for issue in validation.issues:
    print(issue.level, issue.code, issue.message)


if not validation.compatible:
    raise ValueError("The datasets cannot be combined")


print(validation.training_type, validation.total_estimated_rows)
```

Each validation issue has a `level`, stable `code`, and user-facing `message`:

- **Errors** block the combine. For example, a source might not be ready, might belong to another organization, or might mix text and multimodal data.
- **Warnings** describe changes the combine operation can make automatically. It can cast mismatched column types to a common type or resolve mixed training types to an instruction dataset.

See the [`combine.validate` reference](/api/python/resources/datasets/subresources/combine/methods/validate/index.md) for all validation fields and issue codes.

## Combine the datasets

Pass the same source IDs to `combine.create` and give the output dataset a name:

```
from uuid import uuid4


idempotency_key = str(uuid4())


combined = client.datasets.combine.create(
    dataset_ids=dataset_ids,
    name="Combined support data",
    idempotency_key=idempotency_key,
)


print(combined.dataset_id)
```

The request runs synchronously and returns the new `dataset_id` after the merge finishes. There is no public combine-job polling flow.

Keep the same `idempotency_key` if you retry the request. A retry with that key returns the dataset created by the first request instead of starting another merge. The key is optional, but supplying one is useful if a long-running request loses its connection.

Datasets with the same training type preserve that type. Combining instruction and preference datasets produces an instruction dataset. Other warning-level reconciliation is reported by validation before you start the merge.

See the [`combine.create` reference](/api/python/resources/datasets/subresources/combine/methods/create/index.md) for current request requirements and response fields.