# This file was auto-generated by Fern from our API Definition.
import datetime as dt
import typing
from json.decoder import JSONDecodeError
from ...core.api_error import ApiError
from ...core.client_wrapper import AsyncClientWrapper, SyncClientWrapper
from ...core.http_response import AsyncHttpResponse, HttpResponse
from ...core.jsonable_encoder import jsonable_encoder
from ...core.request_options import RequestOptions
from ...core.unchecked_base_model import construct_type
from ...types.cancel_model_run_response import CancelModelRunResponse
from ...types.model_run import ModelRun
from ...types.project_subset_enum import ProjectSubsetEnum
from .types.list_runs_request_project_subset import ListRunsRequestProjectSubset
# this is used as the default value for optional parameters
OMIT = typing.cast(typing.Any, ...)
class RawRunsClient:
def __init__(self, *, client_wrapper: SyncClientWrapper):
self._client_wrapper = client_wrapper
def list(
self,
prompt_id: int,
version_id: int,
*,
ordering: typing.Optional[str] = None,
parent_model: typing.Optional[int] = None,
project: typing.Optional[int] = None,
project_subset: typing.Optional[ListRunsRequestProjectSubset] = None,
request_options: typing.Optional[RequestOptions] = None,
) -> HttpResponse[typing.List[ModelRun]]:
"""
This endpoint is not available in Label Studio Community Edition. [Learn more about Label Studio Enterprise](https://humansignal.com/goenterprise)
Get information (status, metadata, etc) about an existing inference run
Parameters
----------
prompt_id : int
version_id : int
ordering : typing.Optional[str]
Which field to use when ordering the results.
parent_model : typing.Optional[int]
The ID of the parent model for this Inference Run
project : typing.Optional[int]
The ID of the project this Inference Run makes predictions on
project_subset : typing.Optional[ListRunsRequestProjectSubset]
Defines which tasks are operated on (e.g. HasGT will only operate on tasks with a ground truth annotation, but All will operate on all records)
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[typing.List[ModelRun]]
"""
_response = self._client_wrapper.httpx_client.request(
f"api/prompts/{jsonable_encoder(prompt_id)}/versions/{jsonable_encoder(version_id)}/inference-runs",
method="GET",
params={
"ordering": ordering,
"parent_model": parent_model,
"project": project,
"project_subset": project_subset,
},
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
typing.List[ModelRun],
construct_type(
type_=typing.List[ModelRun], # type: ignore
object_=_response.json(),
),
)
return HttpResponse(response=_response, data=_data)
_response_json = _response.json()
except JSONDecodeError:
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json)
def create(
self,
prompt_id: int,
version_id: int,
*,
project: int,
job_id: typing.Optional[str] = OMIT,
only_missing_predictions: typing.Optional[bool] = OMIT,
organization: typing.Optional[int] = OMIT,
predictions_updated_at: typing.Optional[dt.datetime] = OMIT,
project_subset: typing.Optional[ProjectSubsetEnum] = OMIT,
total_correct_predictions: typing.Optional[int] = OMIT,
total_predictions: typing.Optional[int] = OMIT,
total_tasks: typing.Optional[int] = OMIT,
request_options: typing.Optional[RequestOptions] = None,
) -> HttpResponse[ModelRun]:
"""
This endpoint is not available in Label Studio Community Edition. [Learn more about Label Studio Enterprise](https://humansignal.com/goenterprise)
Run a prompt inference.
Parameters
----------
prompt_id : int
version_id : int
project : int
job_id : typing.Optional[str]
Job ID for inference job for a ModelRun e.g. Adala job ID
only_missing_predictions : typing.Optional[bool]
When true, only tasks without successful predictions for this prompt version are submitted for inference.
organization : typing.Optional[int]
predictions_updated_at : typing.Optional[dt.datetime]
project_subset : typing.Optional[ProjectSubsetEnum]
total_correct_predictions : typing.Optional[int]
total_predictions : typing.Optional[int]
total_tasks : typing.Optional[int]
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[ModelRun]
"""
_response = self._client_wrapper.httpx_client.request(
f"api/prompts/{jsonable_encoder(prompt_id)}/versions/{jsonable_encoder(version_id)}/inference-runs",
method="POST",
json={
"job_id": job_id,
"only_missing_predictions": only_missing_predictions,
"organization": organization,
"predictions_updated_at": predictions_updated_at,
"project": project,
"project_subset": project_subset,
"total_correct_predictions": total_correct_predictions,
"total_predictions": total_predictions,
"total_tasks": total_tasks,
},
headers={
"content-type": "application/json",
},
request_options=request_options,
omit=OMIT,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
ModelRun,
construct_type(
type_=ModelRun, # type: ignore
object_=_response.json(),
),
)
return HttpResponse(response=_response, data=_data)
_response_json = _response.json()
except JSONDecodeError:
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json)
def cancel(
self,
prompt_id: int,
version_id: int,
inference_run_id: int,
*,
request_options: typing.Optional[RequestOptions] = None,
) -> HttpResponse[CancelModelRunResponse]:
"""
This endpoint is not available in Label Studio Community Edition. [Learn more about Label Studio Enterprise](https://humansignal.com/goenterprise)
Cancel the inference run for the given api
Parameters
----------
prompt_id : int
version_id : int
inference_run_id : int
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[CancelModelRunResponse]
"""
_response = self._client_wrapper.httpx_client.request(
f"api/prompts/{jsonable_encoder(prompt_id)}/versions/{jsonable_encoder(version_id)}/inference-runs/{jsonable_encoder(inference_run_id)}/cancel",
method="POST",
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
CancelModelRunResponse,
construct_type(
type_=CancelModelRunResponse, # type: ignore
object_=_response.json(),
),
)
return HttpResponse(response=_response, data=_data)
_response_json = _response.json()
except JSONDecodeError:
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json)
class AsyncRawRunsClient:
def __init__(self, *, client_wrapper: AsyncClientWrapper):
self._client_wrapper = client_wrapper
async def list(
self,
prompt_id: int,
version_id: int,
*,
ordering: typing.Optional[str] = None,
parent_model: typing.Optional[int] = None,
project: typing.Optional[int] = None,
project_subset: typing.Optional[ListRunsRequestProjectSubset] = None,
request_options: typing.Optional[RequestOptions] = None,
) -> AsyncHttpResponse[typing.List[ModelRun]]:
"""
This endpoint is not available in Label Studio Community Edition. [Learn more about Label Studio Enterprise](https://humansignal.com/goenterprise)
Get information (status, metadata, etc) about an existing inference run
Parameters
----------
prompt_id : int
version_id : int
ordering : typing.Optional[str]
Which field to use when ordering the results.
parent_model : typing.Optional[int]
The ID of the parent model for this Inference Run
project : typing.Optional[int]
The ID of the project this Inference Run makes predictions on
project_subset : typing.Optional[ListRunsRequestProjectSubset]
Defines which tasks are operated on (e.g. HasGT will only operate on tasks with a ground truth annotation, but All will operate on all records)
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[typing.List[ModelRun]]
"""
_response = await self._client_wrapper.httpx_client.request(
f"api/prompts/{jsonable_encoder(prompt_id)}/versions/{jsonable_encoder(version_id)}/inference-runs",
method="GET",
params={
"ordering": ordering,
"parent_model": parent_model,
"project": project,
"project_subset": project_subset,
},
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
typing.List[ModelRun],
construct_type(
type_=typing.List[ModelRun], # type: ignore
object_=_response.json(),
),
)
return AsyncHttpResponse(response=_response, data=_data)
_response_json = _response.json()
except JSONDecodeError:
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json)
async def create(
self,
prompt_id: int,
version_id: int,
*,
project: int,
job_id: typing.Optional[str] = OMIT,
only_missing_predictions: typing.Optional[bool] = OMIT,
organization: typing.Optional[int] = OMIT,
predictions_updated_at: typing.Optional[dt.datetime] = OMIT,
project_subset: typing.Optional[ProjectSubsetEnum] = OMIT,
total_correct_predictions: typing.Optional[int] = OMIT,
total_predictions: typing.Optional[int] = OMIT,
total_tasks: typing.Optional[int] = OMIT,
request_options: typing.Optional[RequestOptions] = None,
) -> AsyncHttpResponse[ModelRun]:
"""
This endpoint is not available in Label Studio Community Edition. [Learn more about Label Studio Enterprise](https://humansignal.com/goenterprise)
Run a prompt inference.
Parameters
----------
prompt_id : int
version_id : int
project : int
job_id : typing.Optional[str]
Job ID for inference job for a ModelRun e.g. Adala job ID
only_missing_predictions : typing.Optional[bool]
When true, only tasks without successful predictions for this prompt version are submitted for inference.
organization : typing.Optional[int]
predictions_updated_at : typing.Optional[dt.datetime]
project_subset : typing.Optional[ProjectSubsetEnum]
total_correct_predictions : typing.Optional[int]
total_predictions : typing.Optional[int]
total_tasks : typing.Optional[int]
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[ModelRun]
"""
_response = await self._client_wrapper.httpx_client.request(
f"api/prompts/{jsonable_encoder(prompt_id)}/versions/{jsonable_encoder(version_id)}/inference-runs",
method="POST",
json={
"job_id": job_id,
"only_missing_predictions": only_missing_predictions,
"organization": organization,
"predictions_updated_at": predictions_updated_at,
"project": project,
"project_subset": project_subset,
"total_correct_predictions": total_correct_predictions,
"total_predictions": total_predictions,
"total_tasks": total_tasks,
},
headers={
"content-type": "application/json",
},
request_options=request_options,
omit=OMIT,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
ModelRun,
construct_type(
type_=ModelRun, # type: ignore
object_=_response.json(),
),
)
return AsyncHttpResponse(response=_response, data=_data)
_response_json = _response.json()
except JSONDecodeError:
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json)
async def cancel(
self,
prompt_id: int,
version_id: int,
inference_run_id: int,
*,
request_options: typing.Optional[RequestOptions] = None,
) -> AsyncHttpResponse[CancelModelRunResponse]:
"""
This endpoint is not available in Label Studio Community Edition. [Learn more about Label Studio Enterprise](https://humansignal.com/goenterprise)
Cancel the inference run for the given api
Parameters
----------
prompt_id : int
version_id : int
inference_run_id : int
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[CancelModelRunResponse]
"""
_response = await self._client_wrapper.httpx_client.request(
f"api/prompts/{jsonable_encoder(prompt_id)}/versions/{jsonable_encoder(version_id)}/inference-runs/{jsonable_encoder(inference_run_id)}/cancel",
method="POST",
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
CancelModelRunResponse,
construct_type(
type_=CancelModelRunResponse, # type: ignore
object_=_response.json(),
),
)
return AsyncHttpResponse(response=_response, data=_data)
_response_json = _response.json()
except JSONDecodeError:
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json)