Upload File

In this example we will upload a CSV file to the stored on the Kelvin Platform and receive the confirmation.

There are also options to add custom metadata to help classify and filter file listings.

Kelvin APIKelvin API Client (Python)

curl -X "POST" \
"https://<url.kelvin.ai>/api/v4/filestorage/upload" \
-H "Authorization: Bearer <Your Current Token>" \
-H "Accept: application/json" \
-H "Content-Type: multipart/form-data" \
-F 'file=@/home/ubuntu/test_data.csv;type=text/plain" \
-F 'metadata={"customkey": "Custom metadata information"}'

The response will look like this;

{
"file_id": "66fce65f-d43f-4424-89b8-e1dd01f9193b",
"file_name": "test_data.csv",
"file_size": 6,
"checksum": "66a045b452102c59d840ec097d59d9467e13a3f34f6494e539ffd32c1bb35f18",
"created": "2024-03-19T10:29:55.673471Z",
"source": "krn:user:demo@kelvin.ai",
"metadata": {
    "customkey": "Custom metadata information"
}
}

from kelvin.api.client import Client

# Login
client = Client(config={"url": "https://<url.kelvin.ai>", "username": "<your_username>"})
client.login(password="<your_password>")

# Upload file to filestore
response = client.filestorage.upload_file(
    file='/home/ubuntu/model_weights.h5', 
    metadata={"customkey": "Custom metadata information"}
)

print(response)

You will see the output like this;

file_id='d9a5a89b-7a72-4ff3-a7b6-eaefec4875ad' file_name='model_weights.h5' file_size=3066 checksum='1b342f992ac36da9018e84ba81623f3a862d351497bae2a129fe70df02e085ad' source='krn:user:demo@kelvin.ai' created=datetime.datetime(2024, 3, 24, 1, 51, 54, 699950, tzinfo=datetime.timezone.utc) metadata={"customkey": "Custom metadata information"}