latest.md

las package

Module contents

class las.Client(credentials: Optional[las.credentials.Credentials] = None)

Bases: object

A low level client to invoke api methods from Lucidtech AI Services.

create_app_client(generate_secret=True, logout_urls=None, callback_urls=None, login_urls=None, default_login_url=None, **optional_args)

Creates an appClient, calls the POST /appClients endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.create_app_client(name='<name>', description='<description>')

• Parameters

  • name (Optional*[str]*) – Name of the appClient

  • description (Optional*[str]*) – Description of the appClient

  • generate_secret (Boolean) – Set to False to ceate a Public app client, default: True

  • logout_urls (List*[str]*) – List of logout urls

  • callback_urls (List*[str]*) – List of callback urls

  • login_urls (List*[str]*) – List of login urls

  • default_login_url (str) – Default login url

• Returns

  AppClient response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

create_asset(content: Union[bytes, bytearray, str, pathlib.Path, io.IOBase], **optional_args)

Creates an asset, calls the POST /assets endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.create_asset(b'<bytes data>')

• Parameters

  • content (Content) – Content to POST

  • name (Optional*[str]*) – Name of the asset

  • description (Optional*[str]*) – Description of the asset

• Returns

  Asset response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

create_batch(**optional_args)

Creates a batch, calls the POST /batches endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.create_batch(description='<description>')

• Parameters

  • name (Optional*[str]*) – Name of the batch

  • description (Optional*[str]*) – Description of the batch

• Returns

  Batch response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

create_data_bundle(model_id, dataset_ids, **optional_args)

Creates a data bundle, calls the POST /models/{modelId}/dataBundles endpoint.

• Parameters

  • model_id (str) – Id of the model

  • dataset_ids (List*[str]*) – Dataset Ids that will be included in the data bundle

  • name (Optional*[str]*) – Name of the data bundle

  • description (Optional*[str]*) – Description of the data bundle

• Returns

  Data Bundle response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

create_dataset(**optional_args)

Creates a dataset, calls the POST /datasets endpoint.

• Parameters

  • name (Optional*[str]*) – Name of the dataset

  • description (Optional*[str]*) – Description of the dataset

• Returns

  Dataset response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

create_document(content: Union[bytes, bytearray, str, pathlib.Path, io.IOBase], content_type: str, *, consent_id: Optional[str] = None, batch_id: Optional[str] = None, dataset_id: Optional[str] = None, ground_truth: Optional[Sequence[Dict[str, str]]] = None)

Creates a document, calls the POST /documents endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.create_document(b'<bytes data>', 'image/jpeg', consent_id='<consent id>')

• Parameters

  • content (Content) – Content to POST

  • content_type (str) – MIME type for the document

  • consent_id (Optional*[str]*) – Id of the consent that marks the owner of the document

  • batch_id (Optional*[str]*) – Id of the associated batch

  • dataset_id (Optional*[str]*) – Id of the associated dataset

  • ground_truth – List of items {‘label’: label, ‘value’: value}

representing the ground truth values for the document :type ground_truth: Optional[Sequence[Dict[str, str]]] :return: Document response from REST API :rtype: dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

create_model(width: int, height: int, field_config: dict, *, preprocess_config: Optional[dict] = None, name: Optional[str] = None, description: Optional[str] = None, **optional_args)

Creates a model, calls the POST /models endpoint.

• Parameters

  • width (int) – The number of pixels to be used for the input image width of your model

  • height (int) – The number of pixels to be used for the input image height of your model

  • field_config (dict) – Specification of the fields that the model is going to predict

  • preprocess_config (dict) – Specification of the processing steps prior to the prediction of an image

  • name (Optional*[str]*) – Name of the model

  • description (Optional*[str]*) – Description of the model

• Returns

  Model response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

create_prediction(document_id: str, model_id: str, *, max_pages: Optional[int] = None, auto_rotate: Optional[bool] = None, image_quality: Optional[str] = None)

Create a prediction on a document using specified model, calls the POST /predictions endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.create_prediction(document_id='<document id>', model_id='<model id>')

• Parameters

  • document_id (str) – Id of the document to run inference and create a prediction on

  • model_id (str) – Id of the model to use for inference

  • max_pages (Optional*[int]*) – Maximum number of pages to run predictions on

  • auto_rotate (Optional*[bool]*) – Whether or not to let the API try different rotations on the document when running predictions

  • image_quality (Optional*[int]*) – image quality for prediction “LOW|HIGH”. high quality could give better result but will also take longer time.

• Returns

  Prediction response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

create_secret(data: dict, **optional_args)

Creates an secret, calls the POST /secrets endpoint.

>>> from las.client import Client
>>> client = Client()
>>> data = {'username': '<username>', 'password': '<password>'}
>>> client.create_secret(data, description='<description>')

• Parameters

  • data (str) – Dict containing the data you want to keep secret

  • name (Optional*[str]*) – Name of the secret

  • description (Optional*[str]*) – Description of the secret

• Returns

  Secret response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

create_transition(transition_type: str, *, in_schema: Optional[dict] = None, out_schema: Optional[dict] = None, parameters: Optional[dict] = None, **optional_args)

Creates a transition, calls the POST /transitions endpoint.

>>> import json
>>> from pathlib import Path
>>> from las.client import Client
>>> client = Client()
>>> in_schema = {'$schema': 'https://json-schema.org/draft-04/schema#', 'title': 'in', 'properties': {...} }
>>> out_schema = {'$schema': 'https://json-schema.org/draft-04/schema#', 'title': 'out', 'properties': {...} }
>>> # A typical docker transition
>>> docker_params = {
>>>     'imageUrl': '<image_url>',
>>>     'credentials': {'username': '<username>', 'password': '<password>'}
>>> }
>>> client.create_transition('docker', in_schema=in_schema, out_schema=out_schema, params=docker_params)
>>> # A manual transition with UI
>>> assets = {'jsRemoteComponent': 'las:asset:<hex-uuid>', '<other asset name>': 'las:asset:<hex-uuid>'}
>>> manual_params = {'assets': assets}
>>> client.create_transition('manual', in_schema=in_schema, out_schema=out_schema, params=manual_params)

• Parameters

  • transition_type (str) – Type of transition “docker”|”manual”

  • in_schema (Optional*[dict]*) – Json-schema that defines the input to the transition

  • out_schema (Optional*[dict]*) – Json-schema that defines the output of the transition

  • name (Optional*[str]*) – Name of the transition

  • parameters (Optional*[dict]*) – Parameters to the corresponding transition type

  • description (Optional*[str]*) – Description of the transition

• Returns

  Transition response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

create_user(email: str, *, app_client_id, **optional_args)

Creates a new user, calls the POST /users endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.create_user('<email>', name='John Doe')

• Parameters

  • email (str) – Email to the new user

  • name (Optional*[str]*) – Name of the user

  • avatar (Optional*[str]*) – base64 encoded JPEG avatar of the user

• Returns

  User response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

create_workflow(specification: dict, *, error_config: Optional[dict] = None, completed_config: Optional[dict] = None, **optional_args)

Creates a new workflow, calls the POST /workflows endpoint. Check out Lucidtech’s tutorials for more info on how to create a workflow.

>>> from las.client import Client
>>> from pathlib import Path
>>> client = Client()
>>> specification = {'language': 'ASL', 'version': '1.0.0', 'definition': {...}}
>>> error_config = {'email': '<error-recipient>'}
>>> client.create_workflow(specification, error_config=error_config)

• Parameters

  • specification (dict) – Specification of the workflow, currently supporting ASL: https://states-language.net/spec.html

  • name (Optional*[str]*) – Name of the workflow

  • description (Optional*[str]*) – Description of the workflow

  • error_config (Optional*[dict]*) – Configuration of error handler

  • completed_config (Optional*[dict]*) – Configuration of a job to run whenever a workflow execution ends

• Returns

  Workflow response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

delete_app_client(app_client_id: str)

Delete the appClient with the provided appClientId, calls the DELETE /appClients/{appClientId} endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.delete_app_client('<app_client_id>')

• Parameters

  app_client_id (str) – Id of the appClient

• Returns

  AppClient response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

delete_asset(asset_id: str)

Delete the asset with the provided asset_id, calls the DELETE /assets/{assetId} endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.delete_asset('<asset_id>')

• Parameters

  asset_id (str) – Id of the asset

• Returns

  Asset response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

delete_batch(batch_id: str, delete_documents=False)

Delete the batch with the provided batch_id, calls the DELETE /batches/{batchId} endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.delete_batch('<batch_id>')

• Parameters

  • batch_id (str) – Id of the batch

  • delete_documents (bool) – Set to true to delete documents in batch before deleting batch

• Returns

  Batch response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

delete_data_bundle(model_id: str, data_bundle_id: str)

Delete the data bundle with the provided data_bundle_id, calls the DELETE /models/{modelId}/dataBundles/{dataBundleId} endpoint.

• Parameters

  • model_id (str) – Id of the model

  • data_bundle_id (str) – Id of the data bundle

• Returns

  Data Bundle response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

delete_dataset(dataset_id: str, delete_documents: bool = False)

Delete the dataset with the provided dataset_id, calls the DELETE /datasets/{datasetId} endpoint.

• Parameters

  • dataset_id (str) – Id of the dataset

  • delete_documents (bool) – Set to true to delete documents in dataset before deleting dataset

• Returns

  Dataset response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

delete_document(document_id: str)

Delete the document with the provided document_id, calls the DELETE /documents/{documentId} endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.delete_document('<document_id>')

• Parameters

  document_id (str) – Id of the document

• Returns

  Model response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

delete_documents(*, batch_id: Optional[Union[str, List[str]]] = None, consent_id: Optional[Union[str, List[str]]] = None, dataset_id: Optional[Union[str, List[str]]] = None, max_results: Optional[int] = None, next_token: Optional[str] = None, delete_all: Optional[bool] = False)

Delete documents with the provided consent_id, calls the DELETE /documents endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.delete_documents(consent_id='<consent id>')

• Parameters

  • batch_id (Optional*[Queryparam]*) – Ids of the batches to be deleted

  • consent_id (Optional*[Queryparam]*) – Ids of the consents that marks the owner of the document

  • dataset_id (Optional*[Queryparam]*) – Ids of the datasets to be deleted

  • max_results (Optional*[int]*) – Maximum number of documents that will be deleted

  • next_token (Optional*[str]*) – A unique token for each page, use the returned token to retrieve the next page.

  • delete_all – Delete all documents that match the given parameters doing multiple API calls if necessary.

Will throw an error if parameter max_results is also specified. :type delete_all: Optional[bool] :return: Documents response from REST API :rtype: dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

delete_model(model_id: str)

Delete the model with the provided model_id, calls the DELETE /models/{modelId} endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.delete_model('<model_id>')

• Parameters

  model_id (str) – Id of the model

• Returns

  Model response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

delete_secret(secret_id: str)

Delete the secret with the provided secret_id, calls the DELETE /secrets/{secretId} endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.delete_secret('<secret_id>')

• Parameters

  secret_id (str) – Id of the secret

• Returns

  Secret response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

delete_transition(transition_id: str)

Delete the transition with the provided transition_id, calls the DELETE /transitions/{transitionId} endpoint.

Will fail if transition is in use by one or more workflows.

>>> from las.client import Client
>>> client = Client()
>>> client.delete_transition('<transition_id>')

• Parameters

  transition_id (str) – Id of the transition

• Returns

  Transition response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

delete_user(user_id: str)

Delete the user with the provided user_id, calls the DELETE /users/{userId} endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.delete_user('<user_id>')

• Parameters

  user_id (str) – Id of the user

• Returns

  User response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

delete_workflow(workflow_id: str)

Delete the workflow with the provided workflow_id, calls the DELETE /workflows/{workflowId} endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.delete_workflow('<workflow_id>')

• Parameters

  workflow_id (str) – Id of the workflow

• Returns

  Workflow response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

delete_workflow_execution(workflow_id: str, execution_id: str)

Deletes the execution with the provided execution_id from workflow_id, calls the DELETE /workflows/{workflowId}/executions/{executionId} endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.delete_workflow_execution('<workflow_id>', '<execution_id>')

• Parameters

  • workflow_id (str) – Id of the workflow

  • execution_id (str) – Id of the execution

• Returns

  WorkflowExecution response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

execute_transition(transition_id: str)

Start executing a manual transition, calls the POST /transitions/{transitionId}/executions endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.execute_transition('<transition_id>')

• Parameters

  transition_id (str) – Id of the transition

• Returns

  Transition execution response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

execute_workflow(workflow_id: str, content: dict)

Start a workflow execution, calls the POST /workflows/{workflowId}/executions endpoint.

>>> from las.client import Client
>>> from pathlib import Path
>>> client = Client()
>>> content = {...}
>>> client.execute_workflow('<workflow_id>', content)

• Parameters

  • workflow_id (str) – Id of the workflow

  • content (dict) – Input to the first step of the workflow

• Returns

  Workflow execution response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

get_asset(asset_id: str)

Get asset, calls the GET /assets/{assetId} endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.get_asset(asset_id='<asset id>')

• Parameters

  asset_id (str) – Id of the asset

• Returns

  Asset response from REST API with content

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

get_document(document_id: str)

Get document, calls the GET /documents/{documentId} endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.get_document('<document id>')

• Parameters

  document_id (str) – Id of the document

• Returns

  Document response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

get_log(log_id)

get log, calls the GET /logs/{logId} endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.get_log('<log_id>')

• Parameters

  log_id (str) – Id of the log

• Returns

  Log response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

get_model(model_id: str)

Get a model, calls the GET /models/{modelId} endpoint.

• Parameters

  model_id (str) – The Id of the model

• Returns

  Model response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

get_organization(organization_id: str)

Get an organization, calls the GET /organizations/{organizationId} endpoint.

• Parameters

  organization_id (str) – The Id of the organization

• Returns

  Organization response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

get_transition(transition_id: str)

Get the transition with the provided transition_id, calls the GET /transitions/{transitionId} endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.get_transition('<transition_id>')

• Parameters

  transition_id (str) – Id of the transition

• Returns

  Transition response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

get_transition_execution(transition_id: str, execution_id: str)

Get an execution of a transition, calls the GET /transitions/{transitionId}/executions/{executionId} endpoint

>>> from las.client import Client
>>> client = Client()
>>> client.get_transition_execution('<transition_id>', '<execution_id>')

• Parameters

  • transition_id (str) – Id of the transition

  • execution_id (str) – Id of the executions

• Returns

  Transition execution responses from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

get_user(user_id: str)

Get information about a specific user, calls the GET /users/{user_id} endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.get_user('<user_id>')

• Parameters

  user_id (str) – Id of the user

• Returns

  User response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

get_workflow(workflow_id: str)

Get the workflow with the provided workflow_id, calls the GET /workflows/{workflowId} endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.get_workflow('<workflow_id>')

• Parameters

  workflow_id (str) – Id of the workflow

• Returns

  Workflow response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

get_workflow_execution(workflow_id: str, execution_id: str)

Get a workflow execution, calls the GET /workflows/{workflow_id}/executions/{execution_id} endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.get_workflow_execution('<workflow_id>', '<execution_id>')

• Parameters

  • workflow_id (str) – Id of the workflow that performs the execution

  • execution_id (str) – Id of the execution to get

• Returns

  Workflow execution response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

list_app_clients(*, max_results: Optional[int] = None, next_token: Optional[str] = None)

List appClients available, calls the GET /appClients endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.list_app_clients()

• Parameters

  • max_results (Optional*[int]*) – Maximum number of results to be returned

  • next_token (Optional*[str]*) – A unique token for each page, use the returned token to retrieve the next page.

• Returns

  AppClients response from REST API without the content of each appClient

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

list_assets(*, max_results: Optional[int] = None, next_token: Optional[str] = None)

List assets available, calls the GET /assets endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.list_assets()

• Parameters

  • max_results (Optional*[int]*) – Maximum number of results to be returned

  • next_token (Optional*[str]*) – A unique token for each page, use the returned token to retrieve the next page.

• Returns

  Assets response from REST API without the content of each asset

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

list_batches(*, max_results: Optional[int] = None, next_token: Optional[str] = None)

List batches available, calls the GET /batches endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.list_batches()

• Parameters

  • max_results (Optional*[int]*) – Maximum number of results to be returned

  • next_token (Optional*[str]*) – A unique token for each page, use the returned token to retrieve the next page.

• Returns

  Batches response from REST API without the content of each batch

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

list_data_bundles(model_id, *, max_results: Optional[int] = None, next_token: Optional[str] = None)

List data bundles available, calls the GET /models/{modelId}/dataBundles endpoint.

• Parameters

  • model_id (str) – Id of the model

  • max_results (Optional*[int]*) – Maximum number of results to be returned

  • next_token (Optional*[str]*) – A unique token for each page, use the returned token to retrieve the next page.

• Returns

  Data Bundles response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

list_datasets(*, max_results: Optional[int] = None, next_token: Optional[str] = None)

List datasets available, calls the GET /datasets endpoint.

• Parameters

  • max_results (Optional*[int]*) – Maximum number of results to be returned

  • next_token (Optional*[str]*) – A unique token for each page, use the returned token to retrieve the next page.

• Returns

  Datasets response from REST API without the content of each dataset

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

list_documents(*, batch_id: Optional[Union[str, List[str]]] = None, consent_id: Optional[Union[str, List[str]]] = None, dataset_id: Optional[Union[str, List[str]]] = None, max_results: Optional[int] = None, next_token: Optional[str] = None)

List documents available for inference, calls the GET /documents endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.list_documents(batch_id='<batch_id>', consent_id='<consent_id>')

• Parameters

  • batch_id (Optional*[Queryparam]*) – Ids of batches that contains the documents of interest

  • consent_id (Optional*[Queryparam]*) – Ids of the consents that marks the owner of the document

  • dataset_id (Optional*[Queryparam]*) – Ids of datasets that contains the documents of interest

  • max_results (Optional*[int]*) – Maximum number of results to be returned

  • next_token (Optional*[str]*) – A unique token for each page, use the returned token to retrieve the next page.

• Returns

  Documents response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

list_logs(*, workflow_id: Optional[str] = None, workflow_execution_id: Optional[str] = None, transition_id: Optional[str] = None, transition_execution_id: Optional[str] = None, max_results: Optional[int] = None, next_token: Optional[str] = None)

List logs, calls the GET /logs endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.list_logs()

• Parameters

  • workflow_id (Optional*[str]*) – Only show logs from this workflow

  • workflow_execution_id (Optional*[str]*) – Only show logs from this workflow execution

  • transition_id (Optional*[str]*) – Only show logs from this transition

  • transition_execution_id (Optional*[str]*) – Only show logs from this transition execution

  • max_results (Optional*[int]*) – Maximum number of results to be returned

  • next_token (Optional*[str]*) – A unique token for each page, use the returned token to retrieve the next page.

• Returns

  Logs response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

list_models(*, max_results: Optional[int] = None, next_token: Optional[str] = None)

List models available, calls the GET /models endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.list_models()

• Parameters

  • max_results (Optional*[int]*) – Maximum number of results to be returned

  • next_token (Optional*[str]*) – A unique token for each page, use the returned token to retrieve the next page.

• Returns

  Models response from REST API without the content of each model

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

list_predictions(*, max_results: Optional[int] = None, next_token: Optional[str] = None)

List predictions available, calls the GET /predictions endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.list_predictions()

• Parameters

  • max_results (Optional*[int]*) – Maximum number of results to be returned

  • next_token (Optional*[str]*) – A unique token for each page, use the returned token to retrieve the next page.

• Returns

  Predictions response from REST API without the content of each prediction

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

list_secrets(*, max_results: Optional[int] = None, next_token: Optional[str] = None)

List secrets available, calls the GET /secrets endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.list_secrets()

• Parameters

  • max_results (Optional*[int]*) – Maximum number of results to be returned

  • next_token (Optional*[str]*) – A unique token for each page, use the returned token to retrieve the next page.

• Returns

  Secrets response from REST API without the username of each secret

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

list_transition_executions(transition_id: str, *, status: Optional[Union[str, List[str]]] = None, execution_id: Optional[Union[str, List[str]]] = None, max_results: Optional[int] = None, next_token: Optional[str] = None, sort_by: Optional[str] = None, order: Optional[str] = None)

List executions in a transition, calls the GET /transitions/{transitionId}/executions endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.list_transition_executions('<transition_id>', '<status>')

• Parameters

  • transition_id (str) – Id of the transition

  • status (Optional*[Queryparam]*) – Statuses of the executions

  • order (Optional*[str]*) – Order of the executions, either ‘ascending’ or ‘descending’

  • sort_by (Optional*[str]*) – the sorting variable of the executions, either ‘endTime’, or ‘startTime’

  • execution_id (Optional*[Queryparam]*) – Ids of the executions

  • max_results (Optional*[int]*) – Maximum number of results to be returned

  • next_token (Optional*[str]*) – A unique token for each page, use the returned token to retrieve the next page.

• Returns

  Transition executions responses from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

list_transitions(*, transition_type: Optional[Union[str, List[str]]] = None, max_results: Optional[int] = None, next_token: Optional[str] = None)

List transitions, calls the GET /transitions endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.list_transitions('<transition_type>')

• Parameters

  • transition_type (Optional*[Queryparam]*) – Types of transitions

  • max_results (Optional*[int]*) – Maximum number of results to be returned

  • next_token (Optional*[str]*) – A unique token for each page, use the returned token to retrieve the next page.

• Returns

  Transitions response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

list_users(*, max_results: Optional[int] = None, next_token: Optional[str] = None)

List users, calls the GET /users endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.list_users()

• Parameters

  • max_results (Optional*[int]*) – Maximum number of results to be returned

  • next_token (Optional*[str]*) – A unique token for each page, use the returned token to retrieve the next page.

• Returns

  Users response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

list_workflow_executions(workflow_id: str, *, status: Optional[Union[str, List[str]]] = None, sort_by: Optional[str] = None, order: Optional[str] = None, max_results: Optional[int] = None, next_token: Optional[str] = None)

List executions in a workflow, calls the GET /workflows/{workflowId}/executions endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.list_workflow_executions('<workflow_id>', '<status>')

• Parameters

  • workflow_id (str) – Id of the workflow

  • order (Optional*[str]*) – Order of the executions, either ‘ascending’ or ‘descending’

  • sort_by (Optional*[str]*) – the sorting variable of the executions, either ‘endTime’, or ‘startTime’

  • status (Optional*[Queryparam]*) – Statuses of the executions

  • max_results (Optional*[int]*) – Maximum number of results to be returned

  • next_token (Optional*[str]*) – A unique token for each page, use the returned token to retrieve the next page.

• Returns

  Workflow executions responses from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

list_workflows(*, max_results: Optional[int] = None, next_token: Optional[str] = None)

List workflows, calls the GET /workflows endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.list_workflows()

• Parameters

  • max_results (Optional*[int]*) – Maximum number of results to be returned

  • next_token (Optional*[str]*) – A unique token for each page, use the returned token to retrieve the next page.

• Returns

  Workflows response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

send_heartbeat(transition_id: str, execution_id: str)

Send heartbeat for a manual execution to signal that we are still working on it. Must be done at minimum once every 60 seconds or the transition execution will time out, calls the POST /transitions/{transitionId}/executions/{executionId}/heartbeats endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.send_heartbeat('<transition_id>', '<execution_id>')

• Parameters

  • transition_id (str) – Id of the transition

  • execution_id (str) – Id of the transition execution

• Returns

  Empty response

• Return type

  None

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

update_app_client(app_client_id, **optional_args)

Updates an appClient, calls the PATCH /appClients/{appClientId} endpoint.

• Parameters

  • app_client_id (str) – Id of the appClient

  • name (Optional*[str]*) – Name of the appClient

  • description (Optional*[str]*) – Description of the appClient

• Returns

  AppClient response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

update_asset(asset_id: str, **optional_args)

Updates an asset, calls the PATCH /assets/{assetId} endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.update_asset('<asset id>', content=b'<bytes data>')

• Parameters

  • asset_id (str) – Id of the asset

  • content (Optional*[Content]*) – Content to PATCH

  • name (Optional*[str]*) – Name of the asset

  • description (Optional*[str]*) – Description of the asset

• Returns

  Asset response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

update_batch(batch_id, **optional_args)

Updates a batch, calls the PATCH /batches/{batchId} endpoint.

• Parameters

  • batch_id (str) – Id of the batch

  • name (Optional*[str]*) – Name of the batch

  • description (Optional*[str]*) – Description of the batch

• Returns

  Batch response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

update_data_bundle(model_id: str, data_bundle_id: str, **optional_args)

Updates a data bundle, calls the PATCH /models/{modelId}/dataBundles/{dataBundleId} endpoint.

• Parameters

  • model_id (str) – Id of the model

  • data_bundle_id (str) – Id of the data bundle

  • name (Optional*[str]*) – Name of the data bundle

  • description (Optional*[str]*) – Description of the data bundle

• Returns

  Data Bundle response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

update_dataset(dataset_id, **optional_args)

Updates a dataset, calls the PATCH /datasets/{datasetId} endpoint.

• Parameters

  • dataset_id (str) – Id of the dataset

  • name (Optional*[str]*) – Name of the dataset

  • description (Optional*[str]*) – Description of the dataset

• Returns

  Dataset response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

update_document(document_id: str, ground_truth: Optional[Sequence[Dict[str, Union[str, None, bool]]]] = None, *, dataset_id: Optional[str] = None)

Update ground truth for a document, calls the PATCH /documents/{documentId} endpoint. Updating ground truth means adding the ground truth data for the particular document. This enables the API to learn from past mistakes.

• Parameters

  • document_id (str) – Id of the document

  • dataset_id (Optional*[str]*) – Id of the dataset you want to associate your document with

  • ground_truth (Optional*[Sequence[Dict[str, Union[Optional[str], bool]]]]*) – List of items {label: value} representing the ground truth values for the document

• Returns

  Document response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

update_model(model_id: str, *, width: Optional[int] = None, height: Optional[int] = None, field_config: Optional[dict] = None, preprocess_config: Optional[dict] = None, status: Optional[str] = None, **optional_args)

Updates a model, calls the PATCH /models/{modelId} endpoint.

• Parameters

  • model_id (Optional*[str]*) – The Id of the model

  • width (Optional*[int]*) – The number of pixels to be used for the input image width of your model

  • height (Optional*[int]*) – The number of pixels to be used for the input image height of your model

  • field_config (dict) – Specification of the fields that the model is going to predict

  • preprocess_config (dict) – Specification of the processing steps prior to the prediction of an image

  • status (Optional*[str]*) – New status for the model

  • name (Optional*[str]*) – Name of the model

  • description (Optional*[str]*) – Description of the model

• Returns

  Model response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

update_organization(organization_id: str, **optional_args)

Updates an organization, calls the PATCH /organizations/{organizationId} endpoint.

• Parameters

  • organization_id (Optional*[str]*) – The Id of the organization

  • name (Optional*[str]*) – Name of the organization

  • description (Optional*[str]*) – Description of the organization

• Returns

  Organization response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

update_secret(secret_id: str, *, data: Optional[dict] = None, **optional_args)

Updates an secret, calls the PATCH /secrets/secretId endpoint.

>>> from las.client import Client
>>> client = Client()
>>> data = {'username': '<username>', 'password': '<password>'}
>>> client.update_secret('<secret id>', data, description='<description>')

• Parameters

  • secret_id (str) – Id of the secret

  • data (Optional*[dict]*) – Dict containing the data you want to keep secret

  • name (Optional*[str]*) – Name of the secret

  • description (Optional*[str]*) – Description of the secret

• Returns

  Secret response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

update_transition(transition_id: str, *, in_schema: Optional[dict] = None, out_schema: Optional[dict] = None, assets: Optional[dict] = None, environment: Optional[dict] = None, environment_secrets: Optional[list] = None, **optional_args)

Updates a transition, calls the PATCH /transitions/{transitionId} endpoint.

>>> import json
>>> from pathlib import Path
>>> from las.client import Client
>>> client = Client()
>>> client.update_transition('<transition-id>', name='<name>', description='<description>')

• Parameters

  • transition_id (str) – Id of the transition

  • name (Optional*[str]*) – Name of the transition

  • description (Optional*[str]*) – Description of the transition

  • in_schema (Optional*[dict]*) – Json-schema that defines the input to the transition

  • out_schema (Optional*[dict]*) – Json-schema that defines the output of the transition

  • assets (Optional*[dict]*) – A dictionary where the values are assetIds that can be used in a manual transition

  • environment (Optional*[dict]*) – Environment variables to use for a docker transition

  • environment_secrets –

A list of secretIds that contains environment variables to use for a docker transition :type environment_secrets: Optional[list] :return: Transition response from REST API :rtype: dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

update_transition_execution(transition_id: str, execution_id: str, status: str, *, output: Optional[dict] = None, error: Optional[dict] = None, start_time: Optional[Union[str, datetime.datetime]] = None)

Ends the processing of the transition execution, calls the PATCH /transitions/{transition_id}/executions/{execution_id} endpoint.

>>> from las.client import Client
>>> client = Client()
>>> output = {...}
>>> client.update_transition_execution('<transition_id>', '<execution_id>', 'succeeded', output)
>>> error = {"message": 'The execution could not be processed due to ...'}
>>> client.update_transition_execution('<transition_id>', '<execution_id>', 'failed', error)

• Parameters

  • transition_id (str) – Id of the transition that performs the execution

  • execution_id (str) – Id of the execution to update

  • status (str) – Status of the execution ‘succeeded|failed’

  • output (Optional*[dict]*) – Output from the execution, required when status is ‘succeded’

  • error (Optional*[dict]*) – Error from the execution, required when status is ‘failed’, needs to contain ‘message’

  • start_time (Optional*[str]*) – start time that will replace the original start time of the execution

• Returns

  Transition execution response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

update_user(user_id: str, **optional_args)

Updates a user, calls the PATCH /users/{userId} endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.update_user('<user id>', name='John Doe')

• Parameters

  • user_id (str) – Id of the user

  • name (Optional*[str]*) – Name of the user

  • avatar (Optional*[str]*) – base64 encoded JPEG avatar of the user

• Returns

  User response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

update_workflow(workflow_id: str, *, error_config: Optional[dict] = None, completed_config: Optional[dict] = None, **optional_args)

Updates a workflow, calls the PATCH /workflows/{workflowId} endpoint.

>>> import json
>>> from pathlib import Path
>>> from las.client import Client
>>> client = Client()
>>> client.update_workflow('<workflow-id>', name='<name>', description='<description>')

• Parameters

  • workflow_id – Id of the workflow

  • name (Optional*[str]*) – Name of the workflow

  • description (Optional*[str]*) – Description of the workflow

  • error_config (Optional*[dict]*) – Configuration of error handler

  • completed_config (Optional*[dict]*) – Configuration of a job to run whenever a workflow execution ends

• Returns

  Workflow response from REST API

• Return type

  dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

update_workflow_execution(workflow_id: str, execution_id: str, next_transition_id: str)

Retry or end the processing of a workflow execution, calls the PATCH /workflows/{workflow_id}/executions/{execution_id} endpoint.

>>> from las.client import Client
>>> client = Client()
>>> client.update_workflow_execution('<workflow_id>', '<execution_id>', '<next_transition_id>')

• Parameters

  • workflow_id (str) – Id of the workflow that performs the execution

  • execution_id (str) – Id of the execution to update

  • next_transition_id – the next transition to transition into, to end the workflow-execution,

use: las:transition:commons-failed :type next_transition_id: str :return: Workflow execution response from REST API :rtype: dict

• Raises

  InvalidCredentialsException, TooManyRequestsException, LimitExceededException, requests.exception.RequestException

class las.Credentials(client_id: str, client_secret: str, api_key: str, auth_endpoint: str, api_endpoint: str)

Bases: object

Used to fetch and store credentials and to generate/cache an access token.

• Parameters

  • client_id – The client id

  • client_secret – The client secret

  • api_key – The api key

  • auth_endpoint – The auth endpoint

  • api_endpoint – The api endpoint

property access_token(: str)