## Complete a batch upload and trigger processing

`datasets.upload.complete_batch(UploadCompleteBatchParams**kwargs)  -> UploadCompleteBatchResponse`

**post** `/api/v1/datasets/upload/complete-batch`

Any TXT files in the batch are converted to csv/jsonl. Batches that still contain documents (pdf/docx/pptx/xlsx/html, or unpacked zip contents) move to awaiting_preprocessing and wait for an explicit launch-preprocessing call. Batches with only tabular formats (csv/json/jsonl/parquet, plus any converted-from-TXT files) move directly to column extraction.

### Parameters

- `dataset_id: str`

  Dataset ID from initiate-batch

- `files: Iterable[File]`

  - `s3_key: str`

    S3 key returned from initiate-batch

  - `file_name: str`

    Original file name

  - `file_format: Literal["csv", "json", "jsonl", 8 more]`

    File format

    - `"csv"`

    - `"json"`

    - `"jsonl"`

    - `"parquet"`

    - `"pdf"`

    - `"docx"`

    - `"pptx"`

    - `"xlsx"`

    - `"html"`

    - `"zip"`

    - `"txt"`

  - `file_size_bytes: float`

    File size in bytes

- `defer_adaption: Optional[bool]`

  When true, the upload is stored but Adaptive Data does not start. The dataset stays in `awaiting_preprocessing` until POST /datasets/:id/start-adaption.

### Returns

- `class UploadCompleteBatchResponse: …`

  - `dataset_id: str`

    Dataset ID

  - `status: str`

    Current dataset status

### Example

```python
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.upload.complete_batch(
    dataset_id="dataset_id",
    files=[{
        "s3_key": "s3_key",
        "file_name": "file_name",
        "file_format": "csv",
        "file_size_bytes": 0,
    }],
)
print(response.dataset_id)
```

#### Response

```json
{
  "dataset_id": "dataset_id",
  "status": "status"
}
```