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GET
/
v1
/
predictions
!pip install vlmrun

from vlmrun.client import VLMRun
from vlmrun.client.types import PredictionResponse

client = VLMRun(api_key="<VLMRUN_API_KEY>")
response: list[PredictionResponse] = client.predictions.list()

[
  {
    "usage": {
      "elements_processed": 123,
      "credits_used": 123,
      "steps": 123,
      "message": "<string>",
      "duration_seconds": 0
    },
    "id": "<string>",
    "created_at": "2023-11-07T05:31:56Z",
    "completed_at": "2023-11-07T05:31:56Z",
    "response": "<unknown>",
    "status": "pending",
    "domain": "<string>"
  }
]
Get the list of predictions for the current user. 
!pip install vlmrun

from vlmrun.client import VLMRun
from vlmrun.client.types import PredictionResponse

client = VLMRun(api_key="<VLMRUN_API_KEY>")
response: list[PredictionResponse] = client.predictions.list()

Authorizations

​
Authorization
string
header
required

Bearer authentication header of the form Bearer <token>, where <token> is your auth token.

Query Parameters

​
skip
integer
default:0

Number of items to skip

Required range: x >= 0
​
limit
integer | null
default:10

Maximum number of items to return

Required range: 1 <= x <= 1000

Response

Successful Response

​
usage
CreditUsageResponse · object

The usage metrics for the request.

​
id
string

Unique identifier of the response.

​
created_at
string<date-time>

Date and time when the request was created (in UTC timezone)

​
completed_at
string<date-time> | null

Date and time when the response was completed (in UTC timezone)

​
response
any | null

The response from the model. May be an empty dict/list when the model found no extractable content (valid for status=completed).

​
status
enum<string>
default:pending

The status of the job.

Available options:
pending,
enqueued,
running,
completed,
failed,
paused
​
domain
string | null

The domain of the prediction (e.g. document.invoice, image.caption).