IDP Library

This IDP Library (flows_sdk.implementations) includes the latest performant blocks and functions for building intelligent document processing (IDP).

The IDP Library contains:

• Processing Blocks, which are provided as convenient Python subclasses for plug-and-play use.

• Trigger and Output Blocks, which require use of base classes.

• Helper functions for ease of development.

Note

Most solutions will also require use of base classes, such as Flow or CodeBlock, found in the Source Documentation.

V44

Processing Blocks (v44)

Core Blocks are powerful processing blocks necessary to build intelligent document processing solutions on Hyperscience. They include both manual and machine processing blocks.

Note

Classes in the idp_v44 implementation have parameters with ambiguous typing. For example, a field of type integer could also be represented as a string reference (e.g. that integer gets provided at runtime). The types introduced in the example are therefore incomplete and are likely to be revisioned in a future version.

class flows_sdk.implementations.idp_v44.idp_blocks.IDPCoreBlock(idp_wf_config, reference_name='__idp_core__', title='Start Document Processing Subflow', description='Calls the Document Processing Subflow with the specified settings.', compatibility_spec=CompatibilitySpec(filter_roles=['idp_core'], filter_schema=None, label=None), identifier='IDP_CORE_V44', additional_inputs=None)

Bases: Block

IDP core block includes the steps to process the passed documents into machine-readable output.

Mandatory parameters:

Parameters:

idp_wf_config (IdpWorkflowConfig) – IdpWorkflowConfig object with all flow-level configuration settings for IDP

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique reference name within a flow, a unique one will be generated when not provided

• compatibility_spec (CompatibilitySpec | None) – defines a filter for flows that could be selected in the UI in place of “IDP Core” for the block. Defaults to flows with the role ‘idp_core’. The user will be able to pick any flow that matches the compatibility_spec. This serves to make upgrading to a new compatible version of “IDP Core” (e.g. IDP_CORE_V39) easier in future releases.

• identifier (str) – The identifier of the flow. Defaults to the IDP_CORE_VXX identifier of the out-of-the-box flow that comes with the system.

• additional_inputs (dict[str, Any] | None) – Dictionary containing key-value pairs that will be added to the inputs of the block. If a key is contained in the default inputs, its value will be overwritten by the value provided here. You generally do not need to provide additional_inputs, unless you are creating a custom derivative of the IDP_CODE_Vxx flow and are modifying or extending the list of inputs it accepts.

Example usage:

idp_core = IDPCoreBlock(idp_wf_config=idp_wf_config)

REFERENCE_NAME = '__idp_core__'

class flows_sdk.implementations.idp_v44.idp_blocks.SubmissionBootstrapBlock(reference_name=None, trigger='${workflow.input.$trigger}', layout_release_uuid='${workflow.input.layout_release_uuid}', s3_config='${workflow.input.submission_bootstrap_s3_config}', s3_endpoint_url='${workflow.input.submission_bootstrap_s3_endpoint_url}', ocs_config='${workflow.input.submission_bootstrap_ocs_config}', ocs_host_url='${workflow.input.submission_bootstrap_ocs_host_url}', ocs_username='${workflow.input.submission_bootstrap_ocs_username}', ocs_password='${workflow.input.submission_bootstrap_ocs_password}', ocs_ssl_cert='${workflow.input.submission_bootstrap_ocs_ssl_cert}', http_config='${workflow.input.submission_bootstrap_http_config}', oauth_token_url='${workflow.input.submission_bootstrap_oauth_token_url}', oauth_client_id='${workflow.input.submission_bootstrap_oauth_client_id}', oauth_client_secret='${workflow.input.submission_bootstrap_oauth_client_secret}', oauth_scope='${workflow.input.submission_bootstrap_oauth_scope}', abs_auth_type='${workflow.input.submission_bootstrap_abs_auth_type}', abs_account_url='${workflow.input.submission_bootstrap_abs_account_url}', abs_tenant_id='${workflow.input.submission_bootstrap_abs_tenant_id}', abs_client_id='${workflow.input.submission_bootstrap_abs_client_id}', abs_client_secret='${workflow.input.submission_bootstrap_abs_client_secret}', abs_authority_host='${workflow.input.submission_bootstrap_abs_authority_host}', abs_account_name='${workflow.input.submission_bootstrap_abs_account_name}', abs_account_key='${workflow.input.submission_bootstrap_abs_account_key}', gcs_credentials_json='${workflow.input.submission_bootstrap_gcs_credentials_json}', gcs_workload_identity='${workflow.input.submission_bootstrap_gcs_workload_identity}', max_number_pages_enabled='${workflow.input.submission_bootstrap_max_number_pages_enabled}', max_number_pages='${workflow.input.submission_bootstrap_max_number_pages}', notification_workflow='${workflow.input.notification_workflow_initialization}', workflow_uuid='${workflow.input.workflow_uuid}', workflow_name='${workflow.input.workflow_name}', workflow_version='${workflow.input.workflow_version}', submission='${workflow.input.submission}', oauth_client_auth_method='${workflow.input.submission_bootstrap_oauth_client_auth_method}')

Bases: Block

Submission bootstrap block initializes the submission object and prepares external images or other submission data if needed. This block should be called at the START of every flow that uses blocks included in the library.

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• trigger (str) – the trigger input from a connector - if not set explicitly, it will default to the workflow input trigger (which is where this value is normally found)

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• s3_config (str) – Specify Amazon S3 credentials for submission retrieval store. Expects a json expression: {"aws_access_key_id": "X", "aws_secret_access_key": "Y"}, defaults to workflow_input(Settings.SubmissionBootstrapS3Config)

• s3_endpoint_url (str) – Endpoint URL for S3 submission retrieval store, defaults to workflow_input(Settings.SubmissionBootstrapS3EndpointUrl)

• ocs_config (str) – OCS Configuration for downloading submission data. @deprecated since v43.1; will be removed in v46.0. defaults to workflow_input(Settings.SubmissionBootstrapOCSConfig)

• ocs_host_url (str | None) – Available from V42.3; OCS host URL for submission retrieval store, @deprecated since v43.1; will be removed in v46.0. defaults to workflow_input(Settings.SubmissionBootstrapOCSHostUrl)

• ocs_username (str | None) – Available from V42.3; OCS username for authentication, @deprecated since v43.1; will be removed in v46.0. defaults to workflow_input(Settings.SubmissionBootstrapOCSUsername)

• ocs_password (str | None) – Available from V42.3; OCS password for authentication, @deprecated since v43.1; will be removed in v46.0. defaults to workflow_input(Settings.SubmissionBootstrapOCSPassword)

• ocs_ssl_cert (str | None) – Available from V42.3; CA bundle filename inside $HS_PATH/certs directory or “SKIP” to disable validation, @deprecated since v43.1; will be removed in v46.0. defaults to workflow_input(Settings.SubmissionBootstrapOCSSslCert)

• http_config (str | None) – An optional system secret field expressing the HTTP Configuration for downloading submission data. The content is expected to be a json formatted as: {"username": "X", "password": "Y", "ssl_cert": "CA bundle filename"}, defaults to workflow_input(Settings.SubmissionBootstrapHTTPConfig)

• oauth_token_url (str | None) – OAuth token endpoint for client credentials authentication.

• oauth_client_id (str | None) – Client ID for OAuth client credentials.

• oauth_client_secret (str | None) – Client secret for OAuth client credentials.

• oauth_scope (str | None) – Optional OAuth scope sent with the OAuth client credentials token request.

• abs_auth_type (str | None) – The type of authentication to use for Azure Blob Storage (‘SAS Token Only’ / ‘Service Principal’ / ‘Managed Identity’ / ‘Account Key’), defaults to workflow_input(Settings.SubmissionBootstrapABSAuthType)

• abs_account_url (str | None) – The URL of the Azure Blob Storage account, defaults to workflow_input(Settings.SubmissionBootstrapABSAccountUrl)

• abs_tenant_id (str | None) – The tenant ID of the service principal, defaults to workflow_input(Settings.SubmissionBootstrapABSTenantId)

• abs_client_id (str | None) – The client ID of the service principal or managed identity, defaults to workflow_input(Settings.SubmissionBootstrapABSClientId)

• abs_client_secret (str | None) – The client secret of the service principal, defaults to workflow_input(Settings.SubmissionBootstrapABSClientSecret)

• abs_authority_host (str | None) – The authority host of the service principal, defaults to workflow_input(Settings.SubmissionBootstrapABSAuthorityHost)

• abs_account_name (str | None) – The account name of the Azure Blob Storage account, defaults to workflow_input(Settings.SubmissionBootstrapABSAccountName)

• abs_account_key (str | None) – The account key of the Azure Blob Storage account, defaults to workflow_input(Settings.SubmissionBootstrapABSAccountKey)

• gcs_credentials_json (str) – GCS service account credentials in JSON format, defaults to workflow_input(Settings.SubmissionBootstrapGCSCredentialsJson)

• gcs_workload_identity (str) – Whether to obtain the credentials by using the Workload Identity Federation, applicable for HS installations inside GKE clusters, defaults to workflow_input(Settings.SubmissionBootstrapGCSWorkloadIdentity)

• max_number_pages_enabled (str | None) – An optional parameter (flag) controlling whether the configured max number of pages is taken into account

• max_number_pages (str | None) – An optional parameter specifying the max number of pages for downstream processing. Submissions with pages exceeding this configured value will fail. The default value is set to 10_000 pages.

• notification_workflow (str | None) – Notification flow name to run when the submission is initialized, defaults to workflow_input(Settings.SubmissionBootstrapNotificationWorkflow)

• workflow_uuid (str) – UUID of the triggered workflow version, defaults to workflow_input(Inputs.WorkflowUuid)

• workflow_name (str) – Name of the triggered workflow, defaults to workflow_input(Inputs.WorkflowName)

• workflow_version (str) – Version of the triggered workflow, defaults to workflow_input(Inputs.WorkflowVersion)

• submission (dict[str, Any] | str | None) – Submission object

• oauth_client_auth_method (str | None) – How OAuth client credentials are sent to the token endpoint.

Output: The block returns an object with the following keys:

• submission: Submission object.

• api_params: a dictionary containing the parameters with which the submission was initialized. Extracted from the block’s trigger input.

Example usage:

submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')

IDENTIFIER = 'SUBMISSION_BOOTSTRAP_4'

class flows_sdk.implementations.idp_v44.idp_blocks.MachineCollationBlock(submission, cases, dedupe_files='${workflow.input.machine_collation_dedupe_files}', refresh_retention_period='${workflow.input.machine_collation_refresh_retention_period}', remove_from_cases='${workflow.input.machine_collation_remove_from_cases}', retention_period='${workflow.input.machine_collation_retention_period}', reference_name=None)

Bases: Block

Machine collation block groups files, documents and pages (from the submission) into cases.

Mandatory parameters:

Parameters:

• submission (dict[str, Any] | str) – Submission object

• cases (list[dict[str, Any]] | str) –

  This parameter accepts an array of JSON objects that contain information on how Cases should be created. There are two ways to collate a case: by the filenames specified in the current Submission, or by the ids of the Documents or Pages submitted in previous submissions.

  This parameter follows the given format:

  {
      'cases': [
          'external_case_id': 'HS-1',
          'filename': 'file1.pdf',
          'documents': [42],
          'pages': [43]
      ]
  }
  

Parameters with defaults:

Parameters:

• dedupe_files (bool | str) – Enabling this setting will replace case data from repeated file names within the same case. Cases will retain data from the most recently submitted version of a particular file. Note that this setting does not delete the old data, it just removes it from the case. Keep in mind this option is only relevant when adding to cases. Defaults to workflow_input(Settings.CollationDedupeFiles)

• refresh_retention_period (bool | str) – If True, (re)applies Retention Period parameter to all cases passed into the block. If False, only updates those without a pre-existing deletion date. Defaults to workflow_input(Settings.CollationRefreshRetentionPeriod)

• remove_from_cases (list[dict[str, Any]] | str | None) –

  This parameter accepts an array of JSON objects that contains information on how documents or pages should be removed from a pre-existing case. Currently, the only way to de-collate from a case is by the ids of the Documents or Pages within a given case, defaults to workflow_input(Settings.CollationRemoveFromCases)

  This parameter follows the given format:

  {
      'remove_from_cases': [
          'external_case_id': 'HS-1',
          'documents': [42],
          'pages': [43]
      ]
  }
  

• retention_period (int | str | None) – The number of days to retain cases after they are updated by this block. If passed None, no changes to the deletion date will be made. Defaults to workflow_input(Settings.CollationRetentionPeriod)

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

Output: The block returns an object with the following keys:

• submission: Submission object.

Example usage:

machine_collation = MachineCollationBlock(
    reference_name='machine_collation',
    submission=submission_bootstrap.output('submission'),
    cases=submission_bootstrap.output('api_params.cases'),
    dedupe_files=True,
    remove_from_cases=custom_supervision.output('remove_from_cases')
)

IDENTIFIER = 'MACHINE_COLLATION_2'

class flows_sdk.implementations.idp_v44.idp_blocks.MachineClassificationBlock(submission, api_params, reference_name=None, rotation_correction_enabled='${workflow.input.machine_classification_rotation_correction_enabled}', mobile_processing_enabled='${workflow.input.machine_classification_mobile_processing_enabled}', file_transcription_enabled='${workflow.input.machine_classification_file_transcription_enabled}', layout_release_uuid='${workflow.input.layout_release_uuid}', vpc_registration_threshold='${workflow.input.structured_layout_match_threshold}', nlc_enabled='${workflow.input.semi_structured_classification}', nlc_target_accuracy='${workflow.input.semi_target_accuracy}', nlc_doc_grouping_logic='${workflow.input.semi_doc_grouping_logic}', layout_id_dealbreaker='${workflow.input.layout_id_dealbreaker}', layout_id_dealbreaker_bypass='${workflow.input.layout_id_dealbreaker_bypass}', manual_nlc_enabled='${workflow.input.manual_nlc_enabled}')

Bases: Block

Machine classification block automatically matches documents to structured, semi-structured or additional layouts

Mandatory parameters:

Parameters:

• submission (dict[str, Any] | str) – Submission object

• api_params (dict[str, Any] | str)

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• rotation_correction_enabled (bool | str) – Identifies and corrects the orientation of semi-structured images, defaults to workflow_input(Settings.RotationCorrectionEnabled)

• mobile_processing_enabled (bool | str) – Improves the machine readability of photo captured documents, defaults to workflow_input(Settings.MobileProcessingEnabled)

• file_transcription_enabled (bool | str) – Improves the transcription speed of pdf files. Make sure that the “Image Correction” option is disabled and the pdfs you submit do not contain any rotated images defaults to workflow_input(Settings.FileTranscriptionEnabled)

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• vpc_registration_threshold (str | float) – Structured pages above this threshold will be automatically matched to a layout variation, defaults to workflow_input(Settings.StructuredLayoutMatchThreshold)

• nlc_enabled (str | bool) – Enables workflow to manage a model for automated classification of semi-structured and additional layout variations, defaults to workflow_input(Settings.SemiStructuredClassification)

• nlc_target_accuracy (str | float) – defaults to workflow_input(Settings.SemiTargetAccuracy)

• nlc_doc_grouping_logic (str) – Logic to handle multiple pages matched to the same layout variation a given submission, defaults to workflow_input(Settings.SemiDocGroupingLogic)

• layout_id_dealbreaker (str | bool) – Validates structured document matching by ensuring that the Form ID on the document matches the Layout ID on the corresponding layout, defaults to workflow_input(Settings.LayoutIdDealbreaker)

• layout_id_dealbreaker_bypass (str | bool) – Bypasses Form ID and Layout ID validation if there is no Layout ID present on the Layout Page, defaults to workflow_input(Settings.LayoutIdDealbreakerBypass)

• manual_nlc_enabled (str | bool) – Available from V41.2; enables manual classification when applicable, defaults to workflow_input(Settings.ManualNlcEnabled)

Output: The block returns an object with the following keys:

• submission: Submission with the documents and unassigned_pages fields updated with the classification results.

• needs_manual_classification: A boolean flag indicating whether the submission needs manual classification.

• vpc_metadata: Metadata related to the structured classification process.

• nlc_metadata: Metadata related to the non-structured classification process.

Example usage:

machine_classification = MachineClassificationBlock(
    reference_name='machine_classification',
    submission=case_collation.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    rotation_correction_enabled=idp_wf_config.rotation_correction_enabled,
)

IDENTIFIER = 'MACHINE_CLASSIFICATION_8'

class flows_sdk.implementations.idp_v44.idp_blocks.ManualClassificationBlock(submission, api_params, reference_name=None, layout_release_uuid='${workflow.input.layout_release_uuid}', manual_nlc_enabled='${workflow.input.manual_nlc_enabled}', task_restrictions='${workflow.input.manual_classification_task_restrictions}', notification_workflow='${workflow.input.notification_workflow_classification}', vpc_registration_threshold='${workflow.input.structured_layout_match_threshold}', nlc_metadata=None)

Bases: Block

Manual classification block allows keyers to manually match submissions to their layouts. Keyers may perform manual classification if machine classification cannot automatically match a submission to a layout with high confidence

Mandatory parameters:

Parameters:

• submission (dict[str, Any] | str) – Submission object

• api_params (dict[str, Any] | str)

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• manual_nlc_enabled (str | bool) – Enables manual classification when applicable, defaults to workflow_input(Settings.ManualNlcEnabled)

• task_restrictions (list[Any] | str | None) – Defines what users can access Supervision tasks created by this block, defaults to workflow_input(Settings.ManualClassificationTaskRestrictions)

• notification_workflow (str | None) – Notification flow name to run when the submission enters Manual Classification, defaults to workflow_input(Settings.ManualClassificationNotificationWorkflow)

• vpc_registration_threshold (str | float | None) – Structured pages above this threshold will be automatically matched to a layout variation when performing light vpc registration in the doc-org task, defaults to workflow_input(Settings.StructuredLayoutMatchThreshold)

• nlc_metadata (dict[str, Any] | str | None) – Optional NLC metadata containing predictions per page from machine classification, used to populate best-guess layout suggestions in the manual classification supervision task, defaults to None

Output: The block returns an object with the following keys:

• submission: Submission with the documents and unassigned_pages fields updated with the classification results.

• supervision_metadata: Metadata about the created supervision task.

Example usage:

manual_classification = ManualClassificationBlock(
    reference_name='manual_classification',
    submission=machine_classification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
)

IDENTIFIER = 'MANUAL_CLASSIFICATION_7'

class flows_sdk.implementations.idp_v44.idp_blocks.MachineIdentificationBlock(submission, api_params, layout_release_uuid='${workflow.input.layout_release_uuid}', reference_name=None, manual_field_id_enabled='${workflow.input.manual_field_id_enabled}', force_required_missing_entries_to_sv='${workflow.input.force_required_entries_to_sv}', field_id_target_accuracy='${workflow.input.field_id_target_accuracy}', table_id_target_accuracy='${workflow.input.table_id_target_accuracy}', id_target_accuracies='${workflow.input.identification_target_accuracies}')

Bases: Block

Machine identification automatically identifies fields and tables in the submission

Mandatory parameters:

Parameters:

• submission (dict[str, Any] | str) – Submission object

• api_params (dict[str, Any] | str)

Parameters with defaults:

Parameters:

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• manual_field_id_enabled (bool | str) – Enables manual identification when applicable, defaults to workflow_input(Settings.ManualFieldIdEnabled)

• force_required_missing_entries_to_sv (bool | str) – defaults to workflow_input(Settings.ForceRequiredEntriesToSV)

• field_id_target_accuracy (float | str) – Field ID Target Accuracy, defaults to workflow_input(Settings.FieldIdTargetAccuracy)

• table_id_target_accuracy (float | str) – Table ID Target Accuracy, defaults to workflow_input(Settings.TableIdTargetAccuracy)

• id_target_accuracies (list[dict[str, Any]] | str) – defaults to workflow_input(Settings.IdentificationTargetAccuracies)

Output: The block returns an object with the following keys:

• submission: Submission with its documents updated with the identified fields and tables.

• needs_manual_identification: A boolean flag indicating whether the submission needs Identification Supervision.

• fl_metadata: Metadata related to the Field Identification process.

• tl_metadata: Metadata related to the Table Identification process.

Example usage:

machine_identification = MachineIdentificationBlock(
    reference_name='machine_identification',
    submission=manual_classification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
)

IDENTIFIER = 'MACHINE_IDENTIFICATION_9'

class flows_sdk.implementations.idp_v44.idp_blocks.ManualIdentificationBlock(submission, api_params, reference_name=None, task_restrictions='${workflow.input.manual_identification_task_restrictions}', manual_field_id_enabled='${workflow.input.manual_field_id_enabled}', layout_release_uuid='${workflow.input.layout_release_uuid}', notification_workflow='${workflow.input.notification_workflow_identification}')

Bases: Block

Manual identification allows keyers to complete field identification or table identification tasks, where they draw bounding boxes around the contents of certain fields, table columns or table rows. This identification process ensures that the system will be able to transcribe the correct content in the upcoming transcription process

Mandatory parameters:

Parameters:

• submission (dict[str, Any] | str) – Submission object

• api_params (dict[str, Any] | str)

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• task_restrictions (list[Any] | str | None) – Defines what users can access Supervision tasks created by this block, defaults to workflow_input(Settings.ManualFieldIdTaskRestrictions)

• manual_field_id_enabled (bool | str) – Enables manual identification when applicable, defaults to workflow_input(Settings.ManualFieldIdEnabled)

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• notification_workflow (str) – Notification flow name to run when the submission enters Manual Identification, defaults to workflow_input(Settings.ManualFieldIdNotificationWorkflow)

Output: The block returns an object with the following keys:

• submission: Submission with its documents updated with the identified fields and tables.

Example usage:

manual_identification = ManualIdentificationBlock(
    reference_name='manual_identification',
    submission=machine_identification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    task_restrictions=idp_wf_config.manual_identification_config.task_restrictions,
)

IDENTIFIER = 'MANUAL_IDENTIFICATION_6'

class flows_sdk.implementations.idp_v44.idp_blocks.MachineTranscriptionBlock(submission, api_params, reference_name=None, layout_release_uuid='${workflow.input.layout_release_uuid}', transcription_model='${workflow.input.transcription_model}', finetuning_only_trained_layouts='${workflow.input.finetuning_only_trained_layouts}', force_normalization_errors_to_sv='${workflow.input.force_normalization_errors_to_sv}', force_required_blank_entries_to_sv='${workflow.input.force_required_entries_to_sv}', transcription_overrides='${workflow.input.transcription_overrides}', structured_text_target_accuracy='${workflow.input.structured_text_target_accuracy}', structured_text_confidence_threshold='${workflow.input.structured_text_threshold}', structured_text_acceptable_confidence='${workflow.input.structured_min_leg_threshold}', semi_structured_text_target_accuracy='${workflow.input.semi_structured_text_target_accuracy}', semi_structured_text_confidence_threshold='${workflow.input.semi_structured_text_threshold}', semi_structured_text_acceptable_confidence='${workflow.input.semi_structured_min_leg_threshold}', semi_structured_table_target_accuracy='${workflow.input.semi_structured_table_target_accuracy}', semi_structured_table_confidence_threshold='${workflow.input.semi_structured_table_threshold}', semi_structured_table_acceptable_confidence='${workflow.input.semi_structured_table_min_leg_threshold}', structured_checkbox_target_accuracy='${workflow.input.structured_checkbox_target_accuracy}', structured_checkbox_confidence_threshold='${workflow.input.structured_checkbox_threshold}', structured_checkbox_acceptable_confidence='${workflow.input.checkbox_min_leg_threshold}', structured_signature_target_accuracy='${workflow.input.structured_signature_target_accuracy}', structured_signature_confidence_threshold='${workflow.input.structured_signature_threshold}', structured_signature_acceptable_confidence='${workflow.input.signature_min_leg_threshold}', semi_structured_checkbox_target_accuracy='${workflow.input.semi_structured_checkbox_target_accuracy}', semi_structured_checkbox_confidence_threshold='${workflow.input.semi_structured_checkbox_threshold}', semi_structured_checkbox_acceptable_confidence='${workflow.input.semi_structured_checkbox_min_leg_threshold}', field_target_accuracies='${workflow.input.field_transcription_target_accuracies}', always_supervise_blank_cells='${workflow.input.manual_transcription_always_supervise_blank_cells}', manual_transcription_enabled='${workflow.input.manual_transcription_enabled}', address_parsing_enabled='${workflow.input.address_parsing_enabled}', address_parser_api_provider='${workflow.input.address_parser_api_provider}', address_parser_api_key='${workflow.input.address_parser_api_key}', address_parser_api_options='${workflow.input.address_parser_api_options}')

Bases: Block

Machine transcription automatically transcribes the content of your submission

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• transcription_model (str) – The transcription model that will be used for this flow, defaults to workflow_input(Settings.TranscriptionModel)

• finetuning_only_trained_layouts (str | bool) – defaults to workflow_input(Settings.FinetuningOnlyTrainedLayouts)

• force_normalization_errors_to_sv (str | bool) – defaults to workflow_input(Settings.ForceNormalizationErrorsToSV)

• force_required_blank_entries_to_sv (str | bool) – defaults to workflow_input(Settings.ForceRequiredEntriesToSV)

• transcription_overrides (str) – defaults to workflow_input(Settings.TranscriptionOverrides)

• structured_text_target_accuracy (str | float) – defaults to workflow_input(Settings.StructuredTextTargetAccuracy)

• structured_text_confidence_threshold (str | float) – defaults to workflow_input(Settings.StructuredTextThreshold)

• structured_text_acceptable_confidence (str | float) – defaults to workflow_input(Settings.StructuredTextMinLegThreshold)

• semi_structured_text_target_accuracy (str | float) – defaults to workflow_input(Settings.SemiStructuredTextTargetAccuracy)

• semi_structured_text_confidence_threshold (str | float) – defaults to workflow_input(Settings.SemiStructuredTextThreshold)

• semi_structured_text_acceptable_confidence (str | float) – defaults to workflow_input(Settings.SemiStructuredTextMinLegThreshold)

• semi_structured_table_target_accuracy (str | float) – defaults to workflow_input(Settings.SemiStructuredTableTargetAccuracy)

• semi_structured_table_confidence_threshold (str | float) – defaults to workflow_input(Settings.SemiStructuredTableThreshold)

• semi_structured_table_acceptable_confidence (str | float) – defaults to workflow_input(Settings.SemiStructuredTableMinLegThreshold)

• structured_checkbox_target_accuracy (str | float) – defaults to workflow_input(Settings.StructuredCheckboxTargetAccuracy)

• structured_checkbox_confidence_threshold (str | float) – defaults to workflow_input(Settings.StructuredCheckboxThreshold)

• structured_checkbox_acceptable_confidence (str | float) – defaults to workflow_input(Settings.StructuredCheckboxMinLegThreshold)

• structured_signature_target_accuracy (str | float) – defaults to workflow_input(Settings.StructuredSignatureTargetAccuracy)

• structured_signature_confidence_threshold (str | float) – defaults to workflow_input(Settings.StructuredSignatureThreshold)

• structured_signature_acceptable_confidence (str | float) – defaults to workflow_input(Settings.StructuredSignatureMinLegThreshold)

• semi_structured_checkbox_target_accuracy (str | float) – defaults to workflow_input(Settings.SemiStructuredCheckboxTargetAccuracy)

• semi_structured_checkbox_confidence_threshold (str | float) – defaults to workflow_input(Settings.SemiStructuredCheckboxThreshold)

• semi_structured_checkbox_acceptable_confidence (str | float) – defaults to workflow_input(Settings.SemiStructuredCheckboxMinLegThreshold)

• field_target_accuracies (str) – defaults to workflow_input(Settings.FieldTranscriptionTargetAccuracies)

• always_supervise_blank_cells (str | bool | None) – Available from V41.2; if enabled, always send blank cells in transcribed tables to be supervised manually, regardless of confidence defaults to workflow_input(Settings.ManualTranscriptionAlwaysSuperviseBlankCells)

• manual_transcription_enabled (str | bool | None) – Available from V41.2; enables manual transcription when applicable, defaults to workflow_input(Settings.ManualTranscriptionEnabled)

• address_parsing_enabled (str | bool) – Available from V43.2; toggles AddressParser invocation on machine-transcribed address fields, defaults to workflow_input(AddressParserSettings.AddressParsingEnabled)

• address_parser_api_provider (str) – Available from V43.2; selects the address parsing provider (‘none’ / ‘google’ / ‘smarty’ / ‘geoapify’), defaults to workflow_input(AddressParserSettings.ApiProvider)

• address_parser_api_key (str | None) – Available from V43.2; API key for the configured provider, defaults to workflow_input(AddressParserSettings.ApiKey)

• address_parser_api_options (str | dict | None) – Available from V43.2; provider-specific JSON options, defaults to workflow_input(AddressParserSettings.ApiOptions)

Output: The block returns an object with the following keys:

• submission: Submission with the transcribed field values.

• transcription_metadata: Metadata related to the transcription process.

• needs_manual_transcription: A boolean flag indicating whether the submission needs manual transcription.

• needs_flexible_extraction: A boolean flag indicating whether the submission needs flexible extraction.

Example usage:

machine_transcription = MachineTranscriptionBlock(
    reference_name='machine_transcription',
    submission=manual_identification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
)

IDENTIFIER = 'MACHINE_TRANSCRIPTION_10'

class flows_sdk.implementations.idp_v44.idp_blocks.ManualTranscriptionBlock(submission, api_params, reference_name=None, supervision_transcription_masking='${workflow.input.manual_transcription_supervision_transcription_masking}', table_output_manual_review='${workflow.input.manual_transcription_table_output_manual_review}', always_supervise_blank_cells='${workflow.input.manual_transcription_always_supervise_blank_cells}', task_restrictions='${workflow.input.manual_transcription_task_restrictions}', manual_transcription_enabled='${workflow.input.manual_transcription_enabled}', notification_workflow='${workflow.input.notification_workflow_transcription}', address_parsing_enabled='${workflow.input.address_parsing_enabled}', address_parser_api_provider='${workflow.input.address_parser_api_provider}', address_parser_api_key='${workflow.input.address_parser_api_key}', address_parser_api_options='${workflow.input.address_parser_api_options}', title='Manual Transcription', description='Manually transcribe fields that could not be automatically transcribed')

Bases: Block

Manual transcription lets your keyers manually enter the text found in fields or tables that could not be automatically transcribed

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (bool | str) – Prevents users from inputting invalid characters during Supervision Transcription tasks, defaults to workflow_input(Settings.ManualTranscriptionSupervisionTranscriptionMasking)

• table_output_manual_review (bool | str) – Always generates a table transcription task if the layout contains a table. If disabled, a table transcription task will only be generated if one or more cells have transcribed values below the defined thresholds, defaults to workflow_input(Settings.ManualTranscriptionTableOutputManualReview)

• always_supervise_blank_cells (bool | str) – If enabled, always send blank cells in transcribed tables to be supervised manually, regardless of confidence defaults to workflow_input(Settings.ManualTranscriptionAlwaysSuperviseBlankCells)

• task_restrictions (list[Any] | str | None) – Defines what users can access Supervision tasks for submission from a particular source or submissions matching a specific layout, defaults to workflow_input(Settings.ManualTranscriptionTaskRestrictions)

• manual_transcription_enabled (bool | str) – Enables manual transcription when applicable, defaults to workflow_input(Settings.ManualTranscriptionEnabled)

• notification_workflow (str | None) – Notification flow name to run when the submission enters Manual Transcription, defaults to workflow_input(Settings.ManualTranscriptionNotificationWorkflow)

• address_parsing_enabled (str | bool) – Available from V43.2; toggles AddressParser invocation on manually-transcribed address fields, defaults to workflow_input(AddressParserSettings.AddressParsingEnabled)

• address_parser_api_provider (str) – Available from V43.2; selects the address parsing provider (‘none’ / ‘google’ / ‘smarty’ / ‘geoapify’), defaults to workflow_input(AddressParserSettings.ApiProvider)

• address_parser_api_key (str | None) – Available from V43.2; API key for the configured provider, defaults to workflow_input(AddressParserSettings.ApiKey)

• address_parser_api_options (str | dict | None) – Available from V43.2; provider-specific JSON options, defaults to workflow_input(AddressParserSettings.ApiOptions)

• title (str) – overrides the block’s default title shown in Flow Studio, defaults to ‘Manual Transcription’

• description (str) – overrides the block’s default description shown in Flow Studio, defaults to ‘Manually transcribe fields that could not be automatically transcribed’

Output: The block returns an object with the following keys:

• submission: Submission with the manually transcribed field values.

Example usage:

manual_transcription = ManualTranscriptionBlock(
    reference_name='manual_transcription',
    submission=machine_transcription.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    supervision_transcription_masking=(
        idp_wf_config.manual_transcription_config.supervision_transcription_masking
    ),
    table_output_manual_review=(
        idp_wf_config.manual_transcription_config.table_output_manual_review
    ),
    task_restrictions=idp_wf_config.manual_transcription_config.task_restrictions,
)

IDENTIFIER = 'MANUAL_TRANSCRIPTION_6'

class flows_sdk.implementations.idp_v44.idp_blocks.FlexibleExtractionBlock(submission, api_params, reference_name=None, supervision_transcription_masking='${workflow.input.flexible_extraction_supervision_transcription_masking}', task_restrictions='${workflow.input.flexible_extraction_task_restrictions}', notification_workflow='${workflow.input.notification_workflow_flexible_extraction}', show_machine_predictions='${workflow.input.flexible_extraction_show_machine_predictions}', searchable_pages='${workflow.input.flexible_extraction_searchable_pages}', address_parsing_enabled='${workflow.input.address_parsing_enabled}', address_parser_api_provider='${workflow.input.address_parser_api_provider}', address_parser_api_key='${workflow.input.address_parser_api_key}', address_parser_api_options='${workflow.input.address_parser_api_options}')

Bases: Block

Flexible extraction manually transcribes fields marked for review

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (bool | str | None) – Prevents users from inputting invalid characters during the Flexible Extraction task, defaults to workflow_input(Settings.FlexibleExtractionSupervisionTranscriptionMasking)

• task_restrictions (list[Any] | str | None) – Defines what users can access Supervision tasks created by this block, defaults to workflow_input(Settings.FlexibleExtractionTaskRestrictions)

• notification_workflow (str | None) – Notification flow name to run when the submission enters Flexible Extraction, defaults to workflow_input(Settings.FlexibleExtractionNotificationWorkflow)

• show_machine_predictions (bool | str | None) – Available from V42.3; Shows the machine predictions in the Flexible Extraction Supervision tasks, defaults to workflow_input(Settings.FlexibleExtractionShowMachinePredictions)

• searchable_pages (bool | str | None) – Available from V43.2; When enabled, enables searching by text segments in the Flexible Extraction Supervision tasks, defaults to workflow_input(Settings.FlexibleExtractionSearchablePages)

• address_parsing_enabled (str | bool) – Available from V43.2; toggles AddressParser invocation on flexibly-extracted address fields, defaults to workflow_input(AddressParserSettings.AddressParsingEnabled)

• address_parser_api_provider (str) – Available from V43.2; selects the address parsing provider (‘none’ / ‘google’ / ‘smarty’ / ‘geoapify’), defaults to workflow_input(AddressParserSettings.ApiProvider)

• address_parser_api_key (str | None) – Available from V43.2; API key for the configured provider, defaults to workflow_input(AddressParserSettings.ApiKey)

• address_parser_api_options (str | dict | None) – Available from V43.2; provider-specific JSON options, defaults to workflow_input(AddressParserSettings.ApiOptions)

Output: The block returns an object with the following keys:

• submission: Submission with the fields marked for review updated with the transcribed values.

Example usage:

flexible_extraction = FlexibleExtractionBlock(
    reference_name='flexible_extraction',
    submission=manual_transcription.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    supervision_transcription_masking=(
        idp_wf_config.flexible_extraction_config.supervision_transcription_masking
    ),
    task_restrictions=idp_wf_config.flexible_extraction_config.task_restrictions,
)

IDENTIFIER = 'FLEXIBLE_EXTRACTION_6'

class flows_sdk.implementations.idp_v44.idp_blocks.ReprocessingBlock(reference_name, submission, parent_flow='${workflow.definition.identifier}', idp_wf_config='${workflow.input}', trigger='${workflow.input.$trigger}', reprocessing_enabled='${workflow.input.reprocessing_enabled}')

Bases: Block

Reprocessing block enables a second extraction attempt if documents were initially confidently mis-classified by the machine. When an end-user specifies that a document layout is incorrect during Manual Identification, Manual Transcription, or Flexible Extraction, the flow will reach the Reprocessing block which will mark correctly classified documents as “complete” status. Misclassified documents with “reprocess” status will be marked with status “reprocessing” and processed in a second execution of the flow. This block should be placed before the SubmissionCompleteBlock to enable reprocessing.

How the Reprocessing block works

The Reprocessing block examines whether any documents within the submission have been marked with an incorrect layout, and if so, redirects the submission to the classification step for proper classification. It does this by recursively calling the same flow or subflow with the same inputs and documents, but with the addition of a status key for each document to denote which were marked with an incorrect layout during supervision.

Note

The Reprocessing block works in tandem with the “Mark Incorrect Layout” option presented to users during Manual Identification, Manual Transcription, and Flexible Extraction supervision interfaces introduced in R38. Include the Reprocessing block in all custom flows in order to take advantage of this feature. The feature will remain selectable in supervision interfaces even if the Reprocessing block is not included in the flow.

The initial flow run will pause execution at the Reprocessing block until the recursive flow has completed, at which point the initial flow will resume to finish its execution. If an error occurs during the execution of the recursive flow, that error will be raised to the Reprocessing block of the initial flow.

Note

The recursive flow run is linked with the same correlation_id as the initial flow run and the submission. Users can click the “View related flow runs” link on the left sidebar of the initial flow run to view any recursive flow runs.

As expected, recursively calling the same flow may cause some blocks to run again, resulting in unnecessary work being performed, or work which doesn’t gracefully handle duplications (non-idempotent blocks). To mitigate this, certain Hyperscience-provided blocks - including Submission Initialization, Machine Collation, and Machine Classification - detect when the submission object already contains the output from its block to avoid re-execution. It’s a best practice to build custom blocks such that they are idempotent and reentrant; if that’s not feasible, you can add similar logic to custom blocks which do not depend on the layout for the documents being processed. Common methods for handling this include:

• Iterating through each status key within the submission.document[] collection to see if any documents are set to “reprocess” or “reprocessing”.

• Inspecting if the submission object already contains a key or value explicitly defined by the custom code block.

Recommended Placement for the Reprocessing Block

• The Reprocessing block should not be placed before any supervision blocks which allow users to mark a document with an incorrect layout. These supervision blocks include Manual Identification, Manual Transcription and Flexible Extraction.

• The Reprocessing block should always be placed before the SubmissionComplete Block.

• The Reprocessing block can be used with flows which include multiple branches and conditionals; however, it’s recommended to ensure the Reprocessing block is executed to honor user’s selection of marking layouts incorrectly when supervising documents.

• In general, any custom logic that is to be performed after data has been extracted from documents should be placed after the Reprocessing block. To avoid duplicate executions of this custom logic, it is recommended to separate the document processing blocks - inclusive of the Reprocessing block - from the custom logic. This can be handled in different ways:

  • Split the flow into separate subflows, where the Reprocessing block is in a subflow that does not contain the custom logic.

  • Add a decision to bypass the custom logic if the Reprocessing block was triggered. With this method, the recursive flow run will execute the custom logic, and the initial flow run will bypass the logic.

Mandatory parameters:

Parameters:

submission (Any) – Submission object

Parameters with defaults:

Parameters:

• parent_flow (str) – The identifier of the flow that this block is placed in, used to recursively call the same flow, defaults to ‘${workflow.definition.identifier}’

• idp_wf_config (Any) – The workflow input config to pass through to the recursive flow run, defaults to ‘${workflow.input}’

• trigger (Any) – Value used to trigger and correlate the recursive flow run, defaults to workflow_input(IdpFlowInputs.Trigger)

• reprocessing_enabled (str | bool) – Enables reprocessing of documents marked with an incorrect layout, defaults to workflow_input(Settings.ReprocessingEnabled)

Output: The block returns an object with the following keys:

• submission: Submission updated with the reprocessing results.

• is_reprocessing: A boolean flag indicating whether the submission was sent for reprocessing.

IDENTIFIER = 'REPROCESSING_2'

class flows_sdk.implementations.idp_v44.idp_blocks.SubmissionCompleteBlock(submission, is_reprocessing=False, reference_name=None, nlc_qa_sampling_ratio='${workflow.input.semi_qa_sample_rate}', field_id_qa_enabled='${workflow.input.field_id_qa_enabled}', field_id_qa_sampling_ratio='${workflow.input.field_id_qa_sample_rate}', table_id_qa_enabled='${workflow.input.table_id_qa_enabled}', table_id_qa_sampling_ratio='${workflow.input.table_id_qa_sample_rate}', transcription_qa_enabled='${workflow.input.qa}', transcription_structured_entry_qa_sample_rate='${workflow.input.structured_entry_qa_sample_rate}', transcription_structured_signature_qa_sample_rate='${workflow.input.structured_signature_qa_sample_rate}', transcription_structured_checkbox_qa_sample_rate='${workflow.input.structured_checkbox_qa_sample_rate}', transcription_semi_structured_qa_sample_rate='${workflow.input.semi_structured_qa_sample_rate}', table_cell_transcription_qa_enabled='${workflow.input.table_cell_transcription_qa_enabled}', table_cell_transcription_qa_sample_rate='${workflow.input.table_cell_transcription_qa_sample_rate}', auto_qa_sampling_rate_enabled='${workflow.input.auto_qa_sampling_rate_enabled}', exclude_auto_transcribe_fields_from_qa='${workflow.input.exclude_auto_transcribe_fields_from_qa}', payload=None)

Bases: Block

Submission complete block finalizes submission processing and updates reporting data. This block should be called at the END of every flow that uses blocks included in the flows_sdk.implementations.idp_v34 library. The block requires both the submission object and a payload to be passed in.

Mandatory parameters:

Parameters:

• submission (str) – Submission object

• payload (Any) – Object to pass to downstream systems

Parameters with defaults:

Parameters:

• is_reprocessing (str | bool) – boolean to handle execution when reprocessing, defaults to False

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• nlc_qa_sampling_ratio (str | int | float) – Defines the percentage of pages sampled for Classification QA, defaults to workflow_input(Settings.SemiQaSampleRate)

• field_id_qa_enabled (str | bool) – defaults to workflow_input(Settings.FieldIdQaEnabled)

• field_id_qa_sampling_ratio (str | int | float) – Defines the percentage of fields sampled for Identification QA, defaults to workflow_input(Settings.FieldIdQaSampleRate)

• table_id_qa_enabled (str | bool) – Allows users to verify the location of table cells, defaults to workflow_input(Settings.TableIdQaEnabled)

• table_id_qa_sampling_ratio (str | int | float) – Defines the percentage of tables the system samples for QA, defaults to workflow_input(Settings.TableIdQaSampleRate)

• transcription_qa_enabled (str | bool) – defaults to workflow_input(Settings.TranscriptionQaEnabled)

• transcription_structured_entry_qa_sample_rate (str | float) – Defines the percentage of structured text fields the system samples for Transcription QA, defaults to workflow_input(Settings.TranscriptionStructuredEntryQaSampleRate)

• transcription_structured_signature_qa_sample_rate (str | float) – Defines the percentage of structured signature fields sampled for Transcription QA, defaults to workflow_input(Settings.TranscriptionStructuredSignatureQaSampleRate)

• transcription_structured_checkbox_qa_sample_rate (str | float) – Defines the percentage of structured checkbox fields sampled for Transcription QA, defaults to workflow_input(Settings.TranscriptionStructuredCheckboxQaSampleRate)

• transcription_semi_structured_qa_sample_rate (str | float) – Defines the percentage of semi- structured fields the system samples for Transcription QA, defaults to workflow_input(Settings.TranscriptionSemiStructuredQaSampleRate)

• table_cell_transcription_qa_enabled (str | bool) – defaults to workflow_input(Settings.TableCellTranscriptionQaEnabled)

• table_cell_transcription_qa_sample_rate (str | int | float) – Defines the percentage of cells the system samples for QA. This value is likely to be lower than “Transcription QA Sample Rate” since there are more table cells than fields on any given page, defaults to workflow_input(Settings.TableCellTranscriptionQaSampleRate)

• auto_qa_sampling_rate_enabled (str | bool) – Enables automatic selection of QA sampling rates based on system configuration or heuristics, defaults to workflow_input(Settings.AutoQaSamplingRateEnabled)

• exclude_auto_transcribe_fields_from_qa (str | bool) – Defines whether fields that are marked as auto-transcribe in layout should be excluded from QA or not, defaults to workflow_input(Settings.ExcludeAutoTranscribeFieldsFromQA)

Output: The block returns an object with the following keys:

• submission: Submission with the finalized submission processing and reporting data.

Example usage:

submission_complete = SubmissionCompleteBlock(
    reference_name='complete_submission',
    submission=flexible_extraction.output('submission')
)

IDENTIFIER = 'SUBMISSION_COMPLETE_5'

class flows_sdk.implementations.idp_v44.idp_blocks.SetTransformedOutputBlock(submission, transformed_output, reference_name=None)

Bases: Block

Set Transformed Output block sets the transformed output for the provided submission. This can later be used to send the transformed data downstream using output blocks. For more information see https://docs.hyperscience.ai/#retrieving-transformed-submissions.

Mandatory parameters:

Parameters:

• submission (dict[str, Any] | str) – Submission object

• transformed_output (dict[str, Any] | str) – Object that will be set as the transformed output for this submission

Output: The block returns an object with the following keys:

• submission_id: identifier of the submission for which the transformed output was set

IDENTIFIER = 'SET_TRANSFORMED_OUTPUT'

class flows_sdk.implementations.idp_v44.idp_blocks.IdpCustomSupervisionBlock(submission, task_purpose, supervision_template, data=None, reference_name=None, supervision_transcription_masking=True, task_restrictions=None, page_ids=None, notification_workflow='IDP_SUBMISSION_NOTIFY', chat_flow_identifier=None, title='IDP Custom Supervision', description='IDP Custom Supervision')

Bases: Block

An IDP wrapper for the Custom Supervision block. It has the same functionality, but handles reading/writing data from the IDP database.

Mandatory parameters:

Parameters:

• submission (Any) – Workflow Submission object from upstream IDP Blocks.

• task_purpose (str) – The custom task name that’s given to all supervision tasks that run through a particular instance of a custom supervision block. This can be used to filter tasks in the task queue.

• supervision_template (Any) – JSON used for configuration of the Custom Supervision Task.

Parameters with defaults:

Parameters:

• data (dict[str, Any] | None) – Optional JSON structure that extends the data used to populate the supervision_template. This is intended to append to the data already gathered by the IDP wrapper. If there are any conflicting keys, the keys in this input will take precedent.

• reference_name (str | None) – Unique identifier within a flow. A unique one will be generated when not provided.

• supervision_transcription_masking (str | bool) – Prevents users from inputting invalid characters during the Custom Supervision task. Defaults to True.

• task_restrictions (str | list[Any] | None) – Defines what users can access Supervision tasks created by this block. Defaults to [].

• page_ids (str | list[int] | None) – If left empty, the IDP wrapper will, by default, pull in all pages in the submission. Adding a list of page IDs will override the default list of pages. Defaults to [].

• notification_workflow (str) – Notification flow name to run when the submission enters Custom Supervision. Defaults to IDP_SUBMISSION_NOTIFY_NAME.

• chat_flow_identifier (str | None) – Available from V41.2; optional chat flow identifier to enable Document Chat.

• title (Optional[str]) – UI-visible title of the block. Defaults to ‘IDP Custom Supervision’.

• description (Optional[str]) – Description of the block. Useful for documentation and shown in Flow Studio. Defaults to ‘IDP Custom Supervision’.

Example usage:

idp_custom_supervision = IdpCustomSupervisionBlock(
    reference_name='idp_custom_supervision',
    submission=manual_transcription.output('submission'),
    task_purpose='make_custom_decision',
    supervision_template=[
        {
            'name': 'three_column_template',
            'version': '1.0',
            'thumbnail': {
                'group_by_document': True,
                'group_by_case': True,
            },
            'action': [
                {
                    'name': 'my_tab',
                    'display': 'SSN Info',
                    'input': [
                        {
                            'name': 'ssn_field',
                            'type': 'transcription',
                            'data_identifier': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                            'title': 'SSN',
                        },
                    ],
                    'ui': {
                        'groups': [
                            {
                                'title': 'My Group',
                                'fields': ['ssn_field'],
                            },
                        ],
                    },
                },
            ],
        },
    ],
    data={
        'documents': [
            {
                'id': 2,
                'layout_uuid': 'e31c3cbf-dcea-44fd-a052-902463c6040f',
                'layout_name': 'layout_name',
                'page_count': 2,
                'read_only': False,
            }
        ]
    },
    supervision_transcription_masking=False,
)

IDENTIFIER = 'IDP_CUSTOM_SUPERVISION_4'

class flows_sdk.implementations.idp_v44.idp_blocks.IDPFullPageTranscriptionBlock(submission, reference_name=None, title='Full Page Transcription (Submission)', description='Transcribes the documents included in a submission', app_metadata=None, language_family=None, rerun_segmentation=False)

Bases: Block

IDP Full Page Transcription Block machine-transcribes the pages contained in a submission.

Mandatory parameters:

Parameters:

submission (dict[str, Any] | str) – Submission object that includes the pages that need to be transcribed

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• app_metadata (list[str] | None) – used for capturing information about the flow

• language_family (str | None) – language family to use when instantiating the underlying transcription model. If not provided, the system will default to latin. Language families are single word lowercase strings, e.g., latin, arabic, korean, chinese. You can read more about language families in the “Supported Languages” article in the application documentation: https://help.hyperscience.ai/v44/docs/supported-languages.

• rerun_segmentation (bool) – Available from V42.3; controls whether segmentation should be rerun for already segmented pages.

Output: The block returns an object with the following keys:

• submission: Submission with the combined_segments field populated on its pages with the transcription results.

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')

idp_fpt_block = IDPFullPageTranscriptionBlock(
    reference_name='idp_fpt',
    submission=submission_bootstrap.output('submission'),
)

# reference to the output of IDPFullPageTranscriptionBlock which can be passed as input to
# the next block. For more information on output() and output references, please take a
# look at class Block and its output() method
output_ref = idp_fpt_block.output()

IDENTIFIER = 'FULL_PAGE_TRANSCRIPTION_SUBMISSION_3'

class flows_sdk.implementations.idp_v44.idp_blocks.DocumentRendererBlock(submission, is_reprocessing=False, title='Document Renderer', description='Renders a document into a PDF', reference_name=None, document_rendering_enabled='${workflow.input.document_rendering_enabled}', document_rendering_mime_type='${workflow.input.document_rendering_mime_type}', document_rendering_unit='${workflow.input.document_rendering_unit}', document_rendering_height='${workflow.input.document_rendering_height}', document_rendering_width='${workflow.input.document_rendering_width}', document_rendering_quality='${workflow.input.document_rendering_quality}', document_rendering_mode='${workflow.input.document_rendering_mode}', document_rendering_searchable_pdf_enabled='${workflow.input.document_rendering_searchable_pdf_enabled}')

Bases: Block

Document File Generator Block renders a document into a PDF and stores it in the Blob storage.

This block should be placed ALWAYS after a Reprocessing block (if there is any) so that it is not possible to have more changes in the documents’ organisation.

Mandatory parameters:

Parameters:

submission (dict[str, Any] | str) – Submission object that includes the documents that need to be rendered.

Optional parameters:

Parameters:

• is_reprocessing (str | bool) – boolean to handle execution when reprocessing. Defaults to False.

• title (str) – overrides the block’s default title shown in Flow Studio, defaults to ‘Document Renderer’

• description (str) – overrides the block’s default description shown in Flow Studio, defaults to ‘Renders a document into a PDF’

• document_rendering_enabled (bool | str) – a boolean that enables or disables the document rendering. Default is False.

• document_rendering_mime_type (str) – the mime type of the document to be generated. Default is ‘application/pdf’

• document_rendering_unit (str) – the unit of the document to be generated. Default is ‘in’

• document_rendering_height (str | float) – the height of the document to be generated. Default is 11

• document_rendering_width (str | float) – the width of the document to be generated. Default is 8.5

• document_rendering_quality (str | int) – Quality of pages (images) appended to the resulting document as a percentage (recommended 50%). Higher quality results in a larger file size for the generated PDF.

• document_rendering_mode (str) – Available from V42.0; Mode of the images appended to the resulting document. Supported modes are RGB (default), L (grayscale) and 1 (black-and-white)

• document_rendering_searchable_pdf_enabled (bool | str) – Available from V43.2; add a hidden searchable text layer to rendered PDFs using existing page segment geometry where available. Default is False.

Output: The block returns an object with the following keys:

• submission: Submission with the document_file field populated on its documents with the rendered document file information.

Example usage:

# Use the output of the reprocessing block.
reprocessing = ReprocessingBlock(reference_name='reprocessing')

document_renderer_block = DocumentRendererBlock(
    reference_name='document_renderer',
    submission=reprocessing.output('submission'),
    document_rendering_enabled=True,
    document_rendering_unit='in',
    document_rendering_width=8.5,
    document_rendering_height=11,
    document_rendering_quality=50,
    is_reprocessing=reprocessing.output('is_reprocessing'),
    document_rendering_mode='L'
)

output_ref = document_renderer_block.output()

IDENTIFIER = 'DOCUMENT_RENDERER_2'

class flows_sdk.implementations.idp_v44.idp_blocks.IDPImageCorrectionBlock(submission, reference_name=None, rotation_correction_enabled=True, mobile_processing_enabled=False, language_family=None, title='Image Correction (Submission)', description='Rotate and de-skew documents included in a submission', app_metadata=None)

Bases: Block

IDP Image Correction Block rotates and de-skews tilted/skewed images so that they are more transcription-friendly

Mandatory parameters:

Parameters:

submission (dict[str, Any] | str) – Submission object that includes pages that need to be image-corrected

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• rotation_correction_enabled (bool | str) – Identifies and corrects the orientation of images. Enabled by default.

• mobile_processing_enabled (bool | str) – Improves the machine readability of photo captured documents. Disabled by default.

• language_family (str | None) – language family to use when performing image correction. If not provided, the system will default to latin. Language families are single word lowercase strings, e.g., latin, arabic, korean, chinese. You can read more about language families in the “Supported Languages” article in the application documentation: https://help.hyperscience.ai/v44/docs/supported-languages.

• app_metadata (list[str] | None) – used for capturing information about the flow

Output: The block returns an object with the following keys:

• submission: Submission with the corrected_image_uuid field populated on its pages with the rotated/de-skewed images.

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')

idp_image_correction_block = IDPImageCorrectionBlock(
    reference_name='idp_image_correction',
    submission=submission_bootstrap.output('submission'),
)

# reference to the output of IDPImageCorrectionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look at
# class Block and its output() method
output_ref = idp_image_correction_block.output()

IDENTIFIER = 'IMAGE_CORRECTION_SUBMISSION_2'

class flows_sdk.implementations.idp_v44.idp_blocks.OpticalReasoningCognitionAgentBlock(submission, api_params, model_type='${workflow.input.orca_type}', use_live_model='${workflow.input.orca_use_live_model}', base_model_id='${workflow.input.orca_base_model_uuid}', target_accuracy='${workflow.input.orca_target_accuracy}', sliding_window_size='${workflow.input.orca_sliding_window_size}', max_image_tokens='${workflow.input.orca_max_image_tokens}', max_tokens_per_image='${workflow.input.orca_max_tokens_per_image}', max_new_tokens='${workflow.input.orca_max_new_tokens}', max_num_bboxes='${workflow.input.orca_max_num_bboxes}', thresholds_dict=None, enable_location_hint='${workflow.input.orca_enable_location_hint}', disable_log_redaction=False, reference_name=None)

Bases: Block

Optical Reasoning Cognition Agent block extracts the textual content from your submission. It acts like an intelligent agent that uses visual understanding and reasoning to process submitted documents and pull out the relevant fields. This block requires a GPU to function.

Mandatory parameters:

Parameters:

• submission (dict[str, Any] | str) – Submission object

• api_params (str | dict[str, Any])

Parameters with defaults:

Parameters:

• model_type (str) – The model you would like to install, which will be used to process submissions

• use_live_model (bool | str | None) – Available from V43.2; Whether to use the live ORCA model or a specific version of the model. Default value is set to False.

• base_model_id (str | None) – The UUID of the block asset containing ORCA model weights

• target_accuracy (float | str) – The submission-level machine + human transcription accuracy targeted by the system (fraction between 0.0 and 1.0, e.g. 0.95 for 95 %). Default value is set to 0.95.

• sliding_window_size (int | str) – The number of pages processed by the model at once. Larger values improve throughput but raise GPU memory usage. Default value is set to 10.

• max_image_tokens (int | str | None) – The maximum number of tokens allocated per image by the model. @deprecated since v44.0; will be removed in v47.0. Use max_tokens_per_image instead; this one is applied against the legacy image-token grid rather than the selected model’s own, so existing flows keep the resolution they ran at.

• max_tokens_per_image (int | str | None) – Available from V44.0; The maximum number of tokens allocated per image by the model. This is conceptually similar to setting image resolution. Larger values may increase accuracy on dense / hard to read images but will raise GPU memory usage. Defaults to None, letting the selected base model choose a budget suited to its own vision grid.

• max_new_tokens (int | str) – The maximum number of tokens the model can extract, which should be scaled based on the number and length of field values. Default value is set to 1024.

• max_num_bboxes (int | str) – The maximum number of candidate bounding boxes per field occurrence. Larger values may improve the likelihood of identifying the correct location but may also increase false positives. Default value is set to 3.

• thresholds_dict (dict[str, Any] | None) – Available from V42.2; Field level thresholds passed as a JSON dict. When provided, these will take precedence over any threshold dictionary present in the Binary KV. Default value is set to None.

• enable_location_hint (bool | str | None) – Available from V44.0; When set to True, the model computes approximate locations (bounding boxes) for the extracted fields. Setting it to False lowers GPU memory usage and inference latency, but extracted fields will carry no location information. Default value is set to True.

• disable_log_redaction (bool | str | None) – Available from V44.0; Debug only. When set to True, the VLM blocks skip the redaction of document content (which may contain PII) in their logs. Default value is set to False.

• reference_name (str | None) – unique reference name within a flow, a unique one will be generated when not provided

Output: The block returns an object with the following keys:

• submission: Submission with the fields extracted by the model.

• needs_flexible_extraction: A boolean flag indicating whether the submission needs flexible extraction.

Example usage:

orca_extraction = OpticalReasoningCognitionAgentBlock(
    reference_name='orca_extraction',
    submission=manual_classification.output('submission'),
    api_params=submission_bootstrap.output('api_params'),
)

IDENTIFIER = 'OPTICAL_REASONING_COGNITION_AGENT_GPU'

Additional Blocks (v44)

class flows_sdk.implementations.idp_v44.additional_blocks.JSONOutputsBlock(inputs, blocks=None)

Bases: Outputs

Output block allows users to send JSON data extracted by an IDP flow to other systems for downstream processing Mandatory parameters:

Parameters:

inputs (dict[str, Any]) – Used by the UI to automatically pre-populate the input of newly added blocks. Extended in this class to include 'enabled': True to visualize an enabled/disabled trigger that is controllable via Flow Studio.

Usage when parameters are to be defined in Flow Studio:

outputs = JSONOutputBlock(
    inputs={'result': }
)

class flows_sdk.implementations.idp_v44.additional_blocks.ImageCorrectionBlock(pages, reference_name=None, rotation_correction_enabled=True, mobile_processing_enabled=False, language_family=None, title='Image Correction (Pages)', description='Rotate and de-skew documents', app_metadata=None)

Bases: Block

Image Correction Block rotates and de-skews tilted/skewed images so that they are more transcription-friendly.

Mandatory parameters:

Parameters:

pages (list[dict[str, Any]] | str) – list of Page objects that need to be image-corrected

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• rotation_correction_enabled (bool) – Identifies and corrects the orientation of images. Enabled by default.

• mobile_processing_enabled (bool) – Improves the machine readability of photo captured documents. Disabled by default.

• language_family (str | None) – language family to use when performing image correction. If not provided, the system will default to latin. Language families are single word lowercase strings, e.g., latin, arabic, korean, chinese. You can read more about language families in the “Supported Languages” article in the application documentation: https://help.hyperscience.ai/v44/docs/supported-languages.

• app_metadata (list[str] | None) – used for capturing information about the flow

Output: The block returns an object with the following keys:

• pages: list of Page objects with their corrected_image_uuid field populated with rotated/de-skewed images.

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')

image_correction_block = ImageCorrectionBlock(
    reference_name='image_correction',
    pages=submission_bootstrap.output('submission.unassigned_pages'),
)

# reference to the output of IDPImageCorrectionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look
# at class Block and its output() method
output_ref = image_correction_block.output()

IDENTIFIER = 'IMAGE_CORRECTION_PAGES_2'

class flows_sdk.implementations.idp_v44.additional_blocks.FullPageTranscriptionBlock(pages, reference_name=None, title='Full Page Transcription (Pages)', description='Transcribes documents', language_family=None, app_metadata=None, rerun_segmentation=False)

Bases: Block

Full Page Transcription Block machine-transcribes the provided pages.

Mandatory parameters:

Parameters:

pages (list[dict[str, Any]] | str) – list of Page objects that need to be transcribed

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• language_family (str | None) – language family to use when instantiating the underlying transcription model. If not provided, the system will default to latin. Language families are single word lowercase strings, e.g., latin, arabic, korean, chinese. You can read more about language families in the “Supported Languages” article in the application documentation: https://help.hyperscience.ai/v44/docs/supported-languages.

• app_metadata (list[str] | None) – used for capturing information about the flow

• rerun_segmentation (bool) – Available from V42.3; controls whether segmentation should be rerun for already segmented pages.

Output: The block returns an object with the following keys:

• pages: list of Page objects with their combined_segments field populated with the transcription results.

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')

fpt_block = FullPageTranscriptionBlock(
    reference_name='fpt',
    pages=submission_bootstrap.output('submission.unassigned_pages'),
)

# reference to the output of FullPageTranscriptionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look at
# class Block and its output() method
output_ref = fpt_block.output()

IDENTIFIER = 'FULL_PAGE_TRANSCRIPTION_PAGES_3'

class flows_sdk.implementations.idp_v44.additional_blocks.QASchedulerBlock(flow_identifier, submission, qa_config, is_reprocessing=False, reference_name=None)

Bases: Block

Available from V41.2

Schedules Quality Assurance (QA) tasks for a specific workflow.

This class serves as a specialized block within a flow for scheduling Quality Assurance tasks. It ensures the proper configuration and scheduling processes for a given workflow instance. It leverages provided data and parameters such as identification of the workflow, submission details, QA configuration, and optional reprocessing cases.

Mandatory parameters:

Parameters:

• flow_identifier (str) – A unique string identifying the flow to associate this QA scheduler with.

• submission (dict[str, Any] | str) – A dictionary containing submission details. Will be used as payload to trigger the target flow to be QA’d.

• qa_config (dict[str, Any] | str) – A dictionary containing QA configuration. Will be used as payload to trigger the target flow to be QA’d.

Optional parameters:

Parameters:

• is_reprocessing (bool | str | None) – An optional value indicating whether this block is part of a reprocessing workflow. Defaults to False.

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

Example usage:

qa_scheduler = QASchedulerBlock(
    flow_identifier='my_qa_flow',
    submission={'submission_id': 12345, 'data': 'example_data'},
    qa_config={'sample_rate': 0.1, 'language_family': 'latin'},
    is_reprocessing=False,
)

# reference to the output of QASchedulerBlock which can be passed
 as input to the next block.
# For more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = qa_scheduler.output()

IDENTIFIER = 'QA_SCHEDULER'

class flows_sdk.implementations.idp_v44.additional_blocks.FullPageTranscriptionQABlock(submission, sample_rate, language_family, split_text_segments, min_characters_in_segment=4, task_restrictions=None, reference_name=None)

Bases: Block

Available from V41.2

Full Page Transcription QA Block performs Quality Assurance on the output of Full Page Transcription Block. It samples a percentage of segments for QA and allows users to split segments into smaller tokens for easier QA.

Mandatory parameters:

Parameters:

• submission (dict[str, Any] | str) – Submission object

• sample_rate (int | str) – Defines the percentage of segments the system samples for Quality Assurance

• language_family (str) – Specifies the language family for the class

• split_text_segments (bool | str) – Enabling this option splits segments into smaller, more manageable tokens for easier QA

Parameters with defaults:

Parameters:

• min_characters_in_segment (integer) – Minimum number of characters in a token. Defaults to 4. Minimum value is 1, maximum is 100.

• task_restrictions (Optional[TaskRestrictions]) – Defines what users can access tasks created by this block. Defaults to None, which is treated as an empty list.

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

Example usage:

full_page_transcription_qa = FullPageTranscriptionQABlock(
    reference_name='full_page_transcription_qa',
    submission=manual_transcription.output('submission'),
    sample_rate=10,
    language_family='latin',
    split_text_segments=True,
    min_characters_in_segment=4,
    task_restrictions=[],
)

# reference to the output of FullPageTranscriptionQABlock which can be passed
as input to the
# next block. For more information on output() and output references, please take a look
# at class Block and its output() method
output_ref = full_page_transcription_qa.output()

IDENTIFIER = 'FULL_PAGE_TRANSCRIPTION_QA_2'

class flows_sdk.implementations.idp_v44.additional_blocks.CustomSupervisionBlock(submission, task_purpose, data, supervision_template, reference_name=None, supervision_transcription_masking=True, task_restrictions=None, notification_workflow='IDP_SUBMISSION_NOTIFY', chat_flow_identifier=None, title='Custom Supervision', description='Custom Supervision')

Bases: Block

The CustomSupervisionBlock uses a JSON schema as a form builder to create customer-driven UIs for Supervision tasks.

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• task_purpose (str) – the custom task name that’s given to all supervision tasks that run through a particular instance of a custom supervision block. This can be used to filter tasks in the task queue.

• data (Any) – JSON structure that supplies the data used to populate the supervision_template

• supervision_template (Any) – JSON used for configuration of the Custom Supervision Task.

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (bool) – Prevents users from inputting invalid characters during the Custom Supervision task, defaults to True

• task_restrictions (list[Any] | None) – Defines what users can access Supervision tasks created by this block, defaults to []

• notification_workflow (str) – Notification flow name to run when the submission enters Custom Supervision, defaults to IDP_SUBMISSION_NOTIFY_NAME

• chat_flow_identifier (str | None) – Available from V41.2; optional chat flow identifier to be populated to enable Document Chat in Custom Supervision

• title (Optional[str]) – UI-visible title of the block. Defaults to ‘IDP Custom Supervision’

• description (Optional[str]) – Description of the block. Both useful for documentation purpose and visible to users in the Flow studio. Defaults to ‘IDP Custom Supervision’

Example usage:

custom_supervision = CustomSupervisionBlock(
    reference_name='custom_supervision',
    submission=manual_transcription.output('submission'),
    task_purpose='make_custom_decision',
    supervision_template=[
        {
            'name': 'three_column_template',
            'version': '1.0',
            'thumbnail': {
                'group_by_document': True,
                'group_by_case': True,
            },
            'action': [
                {
                    'name': 'my_tab',
                    'display': 'SSN Info',
                    'input': [
                        {
                            'name': 'ssn_field',
                            'type': 'transcription',
                            'data_identifier': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                            'title': 'SSN',
                        },
                    ],
                    'ui': {
                        'groups': [
                            {
                                'title': 'My Group',
                                'fields': ['ssn_field'],
                            },
                        ],
                    },
                },
            ],
        },
    ],
    data={
        'fields': [
            {
                'id': 967,
                'uuid': 'fe8d4e7a-a82c-448c-85a1-0a3cbbe53b61',
                'value': '111-22-3333',
                'page_id': 15,
                'layout_field_id': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                'validation_overridden': False,
                'bounding_box': [
                    0.09360783305186686,
                    0.25792617589433436,
                    0.6358913805295097,
                    0.28862414740388187,
                ],
                'occurrence_index': 1,
            },
        ],
        'template_fields': [
            {
                'uuid': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                'notes': '',
                'type': 'entry',
                'data_type_uuid': 'b600a4e4-758f-4c4b-a2f4-cbb5dd870a0c',
                'name': 'SSN',
            },
        ],
        'pages': [
            {
                'id': 15,
                'file_page_number': 1,
                'document_page_number': 1,
                'image_url': '/image/2ed3d989-b64a-44bd-a9fb-6e0dcc5ccdb8',
                'form_id': 2,
                'submission_id': 1,
                'external_case_ids': [],
                'read_only': False,
                'filename': '1 page.pdf',
                'form_page_id': 1
            },
        ],
        'documents': [
            {
                'id': 2,
                'layout_uuid': 'e31c3cbf-dcea-44fd-a052-902463c6040f',
                'layout_name': 'layout_name',
                'page_count': 2,
                'read_only': False,
            },
        ],
        'cases': [],
        'selected_choices': [],
    },
    supervision_transcription_masking=False,
)

IDENTIFIER = 'CUSTOM_SUPERVISION_3'

class flows_sdk.implementations.idp_v44.additional_blocks.SoapRequestBlock(method, reference_name=None, endpoint='', body_namespace=None, headers_namespace=None, headers=None, http_headers=None, params=None, use_wsdl=False, wsdl_uri='', wsdl_service_name='', wsdl_port_name='', title='HTTP REST Block', description='HTTP REST block')

Bases: Block

Soap Block allows user to perform SOAP requests

Mandatory parameters:

Parameters:

• method (str) – SOAP method to invoke

• endpoint (str) – absolute url including schema to HTTP endpoint for this request

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• body_namespace (str | None) – SOAP request body namespace url

• headers_namespace (str | None) – SOAP request headers namespace url

• headers (dict[Any, Any] | None) – SOAP headers to use

• http_headers (dict[str, Any] | None) – Available from V42.1; HTTP headers to be sent on all requests

• params (dict[Any, Any] | None) – key-value pair used as query parameters in the request to be inserted as SOAP body

Example usage:

# minimum viable instantiation, the rest of the fields can later be added in the UI
soap_block = SoapRequestBlock(
    reference_name='soap_request',
    title='Soap Request',
    description='Perform soap request',
    method='ExampleMethod',
)
soap_block = SoapRequestBlock(
    reference_name='soap_request',
    title='Soap Request',
    description='Perform soap request',
    method='ExampleMethod',
    endpoint='http://example.org/abc.wso',
    body_namespace='http://www.example.org/abc.countryinfo',
    params={'example': 'stuff'},
    headers={},
    http_headers={}
)
# reference to the output of SoapRequestBlock which can be passed as input to the next block
# for more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = soap_block.output()

IDENTIFIER = 'SOAP_REQ'

class flows_sdk.implementations.idp_v44.additional_blocks.HttpRestBlock(method, endpoint, reference_name=None, headers=None, params=None, payload=None, json=None, timeout=600, authorization_type=None, handled_error_codes=None, auth_params=None, response_encoding=None, title='HTTP REST Block', description='HTTP REST block')

Bases: BaseHttpRestBlock

Http Rest Block allows user to perform HTTP requests

Mandatory parameters:

Parameters:

• method (str) – HTTP method to use

• endpoint (str) – absolute url including schema to HTTP endpoint for this request

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• headers (dict[str, str] | None) – HTTP headers to use

• params (dict[str, Any] | None) – key-value pair used as query parameters in the request

• payload (dict[str, str] | None) – key-value pair to be used as x-www-form-urlencoded data. Mutually exclusive with json

• json (dict[str, Any] | None) – key-value pair to be used as json data. Mutually exclusive with payload

• timeout (int | None) – How long (in seconds) to keep the connection open if no bytes are received from the endpoint. Set to 0 to wait indefinitely (not recommended).

• authorization_type (str | None) – authorization type (none, http_header, oauth_2_client_credentials)

• handled_error_codes (list[int | str] | None) – specify HTTP error codes that will not fail the task, allowing for wildcards with the ‘x’ char. Example: [400, “5xx”]

• auth_params (None | HttpHeaderAuthorizationParams | OAuth2AuthorizationParams) – authorization parameters to be passed when the authorization_type is set to “http_header” or “oauth_2_client_credentials”

• response_encoding (str | None) – Character encoding to use when decoding the response. If not provided, the encoding from the Content-Type header or the requests library’s default detection will be used. Specify a standard encoding name (e.g., ‘utf-8’, ‘ISO-8859-1’, ‘windows-1252’) to override.

For a guide on working with secrets, review the Flows SDK page on “Secure Handling of Secrets” and specifically the section on “Secrets cannot be nested within strings or objects”.

Example usage:

# Minimum viable instantiation
http_rest_block = HttpRestBlock(
    reference_name='http_request',
    title='Http Request',
    description='Perform http request',
    method='GET',
    endpoint='http://www.example.com',
)

# Reference to the output of HttpRestBlock which can be passed as input to the next block
output_ref = http_rest_block.output()

# By default the output is wrapped under "result" (reference it via output('result.X')).
# When the HS_CODE_BLOCK_OUTPUT_WRAPPING_MODE env var selects no_wrapping, it is no longer
# wrapped, so reference it directly via output('X') instead.

# For more information on output() and output references,
# please take a look at class Block and its output() method.

# HTTP header authorization
header_auth_block = HttpRestBlock(
    reference_name='header_auth_request',
    title='Header Auth Request',
    description='Perform request with HTTP header auth',
    method='GET',
    endpoint='https://www.example.com',
    authorization_type='http_header',
    auth_params=HttpHeaderAuthorizationParams(
        authorization_header_name='Authorization',
        authorization_header=workflow_input('header_with_secret'),  # e.g.: Bearer 1234abcd
    ),
)

# OAuth2 Client Credentials authorization
oauth2_block = HttpRestBlock(
    reference_name='oauth2_request',
    title='OAuth2 Request',
    description='Perform request with OAuth2',
    method='GET',
    endpoint='https://api.example.com/v1/resources',
    authorization_type='oauth_2_client_credentials',
    auth_params=OAuth2AuthorizationParams(
        auth_url='https://auth.example.com/oauth2/token',
        client_id=workflow_input('oauth_client_id'),
        client_secret=workflow_input('oauth_client_secret'),
        audience='https://api.example.com',
        scope='read write',
    ),
)

class flows_sdk.implementations.idp_v44.additional_blocks.HyperscienceRestApiBlock(method, app_endpoint, reference_name=None, headers=None, params=None, payload=None, json=None, handled_error_codes=None, timeout=600, response_encoding=None, title='Hyperscience HTTP REST Block', description='Hyperscience HTTP REST block')

Bases: BaseHttpRestBlock

Make Http requests to the Hyperscience platform APIs. This block will automatically take care of prepending the base url and will include the appropriate authentication headers with the request.

Mandatory parameters:

Parameters:

• method (str) – HTTP method to use

• app_endpoint (str) – relative url, beginning with a forward slash, to an HTTP endpoint served by the Hypersciecne platform. For reference on the available endpoints, check out https://docs.hyperscience.ai/

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• headers (dict[str, str] | None) – HTTP headers to use

• params (dict[str, Any] | None) – key-value pair used as query parameters in the request

• payload (dict[str, str] | None) – key-value pair to be used as x-www-form-urlencoded data. Mutually exclusive with json

• json (dict[str, Any] | None) – key-value pair to be used as json data. Mutually exclusive with payload

• handled_error_codes (list[int | str] | None) – specify HTTP error codes that will not fail the task, allowing for wildcards with the ‘x’ char. Example: [400, “5xx”]

• timeout (int | None) – How long (in seconds) to keep the connection open if no bytes are received from the endpoint. Set to 0 to wait indefinitely (not recommended).

• response_encoding (str | None) – Character encoding to use when decoding the response. If not provided, the encoding from the Content-Type header or the requests library’s default detection will be used. Specify a standard encoding name (e.g., ‘utf-8’, ‘ISO-8859-1’, ‘windows-1252’) to override.

Example usage:

hs_rest_block = HyperscienceRestApiBlock(
    reference_name='http_request',
    app_endpoint='/api/healthcheck',
    method='GET',
)

class flows_sdk.implementations.idp_v44.additional_blocks.DatabaseAccessBlock(reference_name=None, db_type='', database='', host='', username='', password='', query='', port=None, options=None, timeout=None, query_params=None, title='DB Block', description='DB access block')

Bases: Block

Database Block allows user to access and perform queries on the specified database

Mandatory parameters:

Parameters:

• db_type (str) – database type (mssql, oracle, postgres)

• database (str) – database name

• host (str) – URL/IP address of the server database is hosted on

• username (str) – database username

• password (str) – database password

• query (str) – parameterized query

Optional parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• port (int | None) – database port number

• options (dict[Any, Any] | None) – dictionary of additional connection string options

• timeout (int | None) – timeout in seconds (mssql and postgres only)

• query_params (dict[Any, Any] | None) – dictionary of values for query placeholders

Example usage:

# minimum viable instantiation, the rest of the fields can later be added in the UI
db_lookup = DatabaseAccessBlock(
    reference_name='db_lookup',
    title='Database Lookup',
    description='Perform database lookup',
)
# note that storing password in plain text form is not recommended
# password can later be entered in the UI or defined as a flow secret.
db_lookup = DatabaseAccessBlock(
    reference_name='db_lookup',
    title='Database Lookup',
    description='Perform database lookup',
    db_type='mssql',
    database='cars',
    host='example.com',
    username='user',
    # we recommend skipping the password field or defining it as a system secret
    # e.g. system_secret('{your_secret_identifier}') (both allow for secret input via. UI)
    password='pw'
    port=1433,
    timeout=200,
    query='SELECT * from CAR',
)
# reference to the output of DBAccessBlock which can be passed as input to the next block
# for more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = db_lookup.output()
# content of output_ref:
{
    "result": [
        {
            "id": 1,
            "brand": "Aston Martin",
            "model": "Vanquish"
        },
        {
            "id": 2,
            "brand": "Jaguar",
            "model": "XE 2018"
        }
    ]
}

Database output for the same query (SELECT * FROM CAR)

IDENTIFIER = 'DB_ACCESS_2'

class flows_sdk.implementations.idp_v44.additional_blocks.EntityRecognitionBlock(pages, regex_map=None, keyword_map=None, ner_enabled=True, ced_enabled=True, signature_enabled=True, page_batch_size=15, page_overlap=1, language_family=None, enable_multiline_detection=True, enable_cross_page_detection=True, deduplicate_entities=False, reference_name=None, title='Entity Recognition', description='Recognizes entities of different types in documents')

Bases: Block

Entity Recognition Block detects entities of different types within the pages.

The block supports three recognition engines that can be enabled independently:

• Named Entity Recognition (NER) — detects Person Names, Organisation/Company Names, and Addresses using a trained ML model.

• Custom Entity Detection (CED) — detects words or word patterns described by regular expressions and keyword configurations. See Regex Map Configuration and Keyword Map Configuration below.

• Signature Detection — detects signatures on pages.

Mandatory parameters:

Parameters:

• pages (list[dict[str, Any]] | str) – list of Page objects

• regex_map (dict[str, str] | str | None) – mapping of entity type names to regex patterns (or 'default'). See Regex Map Configuration below for the full format.

• keyword_map (dict[str, list[str]] | str | None) – mapping of entity type names to keyword search configurations (or ['default']). See Keyword Map Configuration below for the full format.

Optional Parameters:

Parameters:

• ner_enabled (bool | str) – a boolean flag that enables or disables Named Entity Recognition. Default value is True.

• ced_enabled (bool | str) – a boolean flag that enables or disables Custom Entity Detection. Default value is True.

• signature_enabled (bool | str) – a boolean flag that enables or disables Signature Detection. Default value is True.

• page_batch_size (int | None) – Available from V42.3; an integer that specifies the number of pages to be processed in a batch. If set to None, all pages are processed in a single batch. Default value is 15.

• page_overlap (int) – Available from V42.3; an integer that specifies the number of overlapping pages between batches. Default value is 1, which means that the last page of a batch will be repeated as the first page of the next batch. This is to ensure that entities that are located at the border of two batches are not missed.

• language_family (str | None) – Available from V42.3; language family to use when performing segmentation. If not provided, the system will default to latin. Language families are single word lowercase strings, e.g., latin, arabic, korean, chinese. You can read more about language families in the “Supported Languages” article in the application documentation: https://help.hyperscience.ai/v44/docs/supported-languages.

• enable_multiline_detection (bool) – a boolean flag that enables or disables multiline detection in Custom Entity Detection. When enabled, also looks for matches spanning multiple lines on the same page. Default value is True.

• enable_cross_page_detection (bool) – a boolean flag that enables or disables cross-page detection in Custom Entity Detection. When enabled, also looks for matches straddling two consecutive pages. Default value is True.

• deduplicate_entities (bool | str) – a boolean flag that enables or disables deduplication of entities detected by different recognition engines (NER, CED, Signature). When enabled, overlapping entities are resolved using entity-type priority. Default value is False.

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

Output: The block returns an object with the following keys:

• groups: list of EntityGroup objects, containing the detected entities.

Regex Map Configuration

The regex_map parameter is a dictionary that maps entity type names to regular expression patterns. The block searches through the transcribed text and matches patterns corresponding to each regex, regardless of surrounding keywords.

• Use 'default' as the value to use the built-in regex for that entity type.

• Use a custom regex string to define your own pattern.

Example regex_map:

regex_map = {
    # Use built-in regexes for these types
    'ssn': 'default',
    'email': 'default',
    'us phone number': 'default',
    # Custom regex for an account ID like "AB1385 957"
    'account id': '[A-Z]{2}\d{4}\s\d{3}',
}

Keyword Map Configuration

The keyword_map parameter is a dictionary that maps entity type names to a list of keyword search configurations. Unlike regex_map, keyword search looks for entity patterns near specific keywords in the text.

Each value is a list where items can be:

• 'default' — use the built-in keyword configuration for that entity type.

• A dictionary describing a custom keyword-entity pair with the following keys:

  • keywords_regex (required) — defines which keywords to search for:

    • root_regexes (required, list of str) — root keyword patterns, e.g. ['Account', 'Acc'].

    • suffix_sequence (required, list of list of str) — each inner list contains suffix alternatives that combine with roots. For example, [['ID', 'I.D'], ['Number', '#']] generates combinations like Account ID Number, Acc I.D #, etc.

    • is_suffix_optional (optional, bool, default: False) — when True, root words alone also match (without any suffix).

    • custom_targets (optional, list of str) — additional standalone keywords that don’t follow the root+suffix pattern, e.g. ['SSN'].

    • is_case_sensitive (optional, bool, default: False) — whether keyword matching is case-sensitive.

  • entity_regex (required, str) — the regex pattern for the entity value itself.

  • entity_regex_error_tolerance (optional, int, default: 0) — fuzzy matching tolerance. Specifies how many wrong/additional/fewer symbols are allowed in a match.

  • enable_table_search (optional, bool, default: False) — when True, also considers the keyword as a potential column header and searches the column below.

  • enable_closest_word_line_below (optional, bool, default: True) — when True, considers the closest non-overlapping word in the line below the keyword.

  • enable_block_above_search (optional, bool, default: True) — when True, also searches above the keyword.

  • allow_intervening_words (optional, bool, default: True) — when False, rejects matches that have any words between the keyword and the entity.

Example keyword_map:

keyword_map = {
    # Use built-in keyword config for passport numbers
    'passport number': ['default'],
    # Custom keyword config for a patient name entity
    'patient': [
        {
            'keywords_regex': {
                'root_regexes': ['patient', "patient's", 'veteran'],
                'suffix_sequence': [['name']],
                'custom_targets': ['name', 'chart'],
                'is_suffix_optional': True,
            },
            'entity_regex': '.*',
        }
    ],
    # Custom keyword config for an Account ID
    'account id': [
        {
            'keywords_regex': {
                'root_regexes': ['Account', 'Acc'],
                'suffix_sequence': [['ID', 'I.D'], ['Number', 'Num', '#']],
                'is_suffix_optional': True,
            },
            'entity_regex': '[A-Z]{2}\d{4}\s\d{3}',
        }
    ],
}

Built-in Entity Types

The following entity types have built-in ('default') regex and keyword definitions:

ssn, email, employer id, us phone number, date, micr, machine readable line, policy number, loan number, credit card number, customer id, account number, employee id, passport number, uscis number, driver license number, driver license restrictions, i94 number, pin number, ptin number, judgment number, case number, bankruptcy number, application number, file number, routing number, nmls number, appraiser certification number

Use 'default' in regex_map and ['default'] in keyword_map to enable the built-in detection for any of these types.

Example usage:

# This is one way to obtain the pages (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')

entity_recognition_block = EntityRecognitionBlock(
    reference_name='entity_recognition',
    pages=submission_bootstrap.output('submission.unassigned_pages'),
    regex_map=DEFAULT_PII_REGEX_MAP,
    keyword_map=DEFAULT_PII_KEYWORD_MAP,
)

# reference to the output of EntityRecognitionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look at
# class Block and its output() method
output_ref = entity_recognition_block.output()

IDENTIFIER = 'ENTITY_RECOGNITION_2'

class flows_sdk.implementations.idp_v44.additional_blocks.RedactionBlock(redaction_groups, text_content_mode='synthetic_data', sensitive_content_mode='opaque_box', replacements=None, skip_entity_types=None, add_watermark=True, watermark_text=None, reference_name=None, title='Redaction', description='Performs redaction on the provided pages.')

Bases: Block

Redacts and masks the provided entities on the pages based on the corresponding settings.

Mandatory parameters:

Parameters:

redaction_groups (list[dict[str, Any]] | str) – list of EntityGroup objects. Each group contains a list of entities to be redacted / masked together.

Optional Parameters:

Parameters:

• text_content_mode (str) – a discrete value, either “synthetic_data” or “opaque_box”, which defines the masking / redaction method to be performed on text entities. Default value is “synthetic_data”.

• sensitive_content_mode (str) – a discrete value, either “opaque_box”, or “obfuscated_box”. Defines the masking method to be performed on entities with sensitive content. Default value is “opaque_box”.

• replacements (dict[str, dict[str, str]] | None | str) – can be used to define the replacement text for a given entity type and original text. It is a dictionary where keys are entity types and values are dictionaries that map original text to replacement text.

• skip_entity_types (list[str] | None | str) – a list of entity types that should be excluded from redaction / masking.

• add_watermark (bool | str) – a boolean flag that indicates whether to add a watermark to the pages that have redaction / masking applied. Default value is True.

• watermark_text (str | None) – a string that specifies the text to be used in the watermark, if add_watermark is set to True. If not provided, a default watermark text will be used.

Output: The block returns an object with the following keys:

• redacted_groups: list of RedactedGroup objects, containing the redacted images.

Example usage:

redaction = RedactionBlock(
    reference_name='redaction',
    redaction_groups=entity_recognition.output('groups'),
)

IDENTIFIER = 'REDACTION_AND_MASKING'

class flows_sdk.implementations.idp_v44.additional_blocks.FileFilterBlock(trigger='${workflow.input.$trigger}', filter_type='${workflow.input.file_filter_filter_type}', minimum_image_width='${workflow.input.file_filter_minimum_image_width}', minimum_image_height='${workflow.input.file_filter_minimum_image_height}', minimum_file_size_kb='${workflow.input.file_filter_minimum_file_size_kb}', file_extensions_action='${workflow.input.file_filter_file_extensions_action}', file_extensions='${workflow.input.file_filter_file_extensions}', file_extensions_other='${workflow.input.file_filter_file_extensions_other}', reference_name=None, title='File Filter', description='Filters out unwanted files from the input of a flow')

Bases: Block

Available from V40.2

File Filter Block filters out unwanted files from the input of a flow

This block is meant to be placed at the start of the flow/subflow - BEFORE the SubmissionBootstrapBlock, not after it. It requires the output of a connector (e.g. Email Listener) or an API submission as input data, and its output is meant to be consumed by a SubmissionBootstrapBlock.

Mandatory parameters:

This block has no strictly required parameters - all of its filters are optional.

Optional Parameters:

Parameters:

• trigger (str) – the trigger input from a connector - if not set explicitly, it will default to the workflow input trigger (which is where this value is normally found)

• filter_type (str) – a discrete value, either “ALL_FILES” or “IMAGES_ONLY”. Determines what kind of files the filters will be applied to. Default value is “ALL_FILES”. If set to “IMAGES_ONLY”, all filtering options will be applied only to images, other files will pass through.

• minimum_image_width (int | str | None) – image width in pixels - half of the image dimension filter. Any image with less width than configured here will be filtered out. Defaults to None (will do nothing).

• minimum_image_height (int | str | None) – image height in pixels - second half of the image dimension filter. Any image with less height than configured here will be filtered out. Defaults to None (will do nothing).

• minimum_file_size_kb (float | str | None) – represents the file size filter. Any file whose disk size is less than this number (in KB) will be filtered out. May be fractional. Defaults to None (will do nothing).

• file_extensions_action (str) – a discrete value, either “ALLOW”, “DENY” or “NONE”. Represents the function of the file extension filter. “ALLOW” means we only allow the configured file extensions. “DENY” means we only deny the configured file extensions. “NONE” means no file extension filtering takes place. Defaults to “NONE”

• file_extensions (list[str] | str | None) – a list of strings, representing the file extensions used for file extension filtering. Expected if file_extensions_action is not “NONE”. Otherwise, defaults to None (will do nothing).

• file_extensions_other (str | None) – a comma separated list of file extensions used for file extension filtering. Meant for adding extra extensions that are not available in the pre-defined list. Optional, defaults to None (will do nothing). However, it is expected in case the file_extensions parameter contains “other”.

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• title (str) – overrides the block’s default title shown in Flow Studio, defaults to ‘File Filter’

• description (str) – overrides the block’s default description shown in Flow Studio, defaults to ‘Filters out unwanted files from the input of a flow’

Output: The block returns an object with the following keys:

• result.filtered_trigger: The trigger payload with filtered-out files removed, meant to be passed as the trigger input to a SubmissionBootstrapBlock.

Example usage:

# File Filter Block filters out unwanted files from the input of a flow
file_filter_block = FileFilterBlock(
    # trigger: str = workflow_input(Inputs.Trigger),  # usually not necessary to set this
    reference_name='file_filter',
    filter_type='ALL_FILES',
    minimum_image_width=500,
    minimum_image_height=500,
    minimum_file_size_kb=2.5,
    file_extensions_action='ALLOW',
    file_extensions=['pdf', 'png'],
    file_extensions_other='jpg,svg,txt',
)

# reference to the output of FileFilterBlock which can be passed as input to the
# next block (a SubmissionBootstrapBlock)
output_ref = file_filter_block.output('result.filtered_trigger')
submission_bootstrap = SubmissionBootstrapBlock(trigger=output_ref)

When the HS_CODE_BLOCK_OUTPUT_WRAPPING_MODE env var selects no_wrapping, the output is no longer wrapped under result, so reference it directly instead:

output_ref = file_filter_block.output('filtered_trigger')

Example content of output_ref:

{
    "payload": {
        "blob_storage_files": [
            {
                "uuid": "db58606e-32f9-42c9-a0bc-ed9848ff247a",
            }
        ],
        "filtered_files": [
            {
                "uuid": "db58606e-32f9-42c9-a0bc-ed9848ff246e",
                "filtered_by": [
                    "file_size_filter"
                ]
            },
        ]
    }
}

IDENTIFIER = 'FILE_FILTER'

class flows_sdk.implementations.idp_v44.additional_blocks.GeneralPromptingORCABlock(model_type='${workflow.input.orca_type}', base_model_id='${workflow.input.orca_base_model_uuid}', messages=None, target_outputs=None, adapter_id=None, max_new_tokens=None, max_image_tokens=None, max_tokens_per_image=None, enable_location_hint=None, thinking_budget=None, json_schema=None, model_meta_overrides=None, concurrency_config_overrides=None, is_byo_model=False, disable_log_redaction=False, reference_name=None, title='Optical Reasoning & Cognition Agent for General Prompting', description='Optical Reasoning & Cognition Agent for General Prompting')

Bases: Block

General Prompting ORCA Block

This block requires a GPU to function.

Mandatory parameters:

Parameters:

• model_type (str) – Base model to be used for general prompting.

• base_model_id (str) – The UUID of the block asset containing ORCA model weights.

Parameters with defaults:

Parameters:

• messages (list[list[dict[str, Any]]] | str | None) – A list of prompt message threads sent to the model. Each message thread is a list of messages. Each message is a dictionary containing the keys “role” and “content”. - Role is a string, with one of “system”, “user”, “assistant”, depending on what the related content represents. “system” - there is usually a single system prompt in any list of messages, which outlines rules and recommendations for the generative model to follow. “user” - User messages should be messages sent by a person to the model. “assistant” - Assistant messages should be model responses. - Content is a mixed list that can contain text and images. Each content entry is formatted either as {‘type’: ‘image’, ‘image’: image_uuid} or {‘type’: ‘text’, ‘text’: ‘content of the message’}

• target_outputs (list[str] | str | None) – Available from V44.0; A list of completions, one per message thread in messages. When provided, the block scores each completion against its own thread (teacher forcing) instead of generating an answer: the output carries the target text back, along with the per-token confidences derived from the prompt log-probabilities. Because no tokens are generated, this cannot be combined with json_schema, enable_location_hint or thinking_budget. A completion that cannot be scored (e.g. empty, or not aligned with the model’s tokenization) degrades its own item instead of failing the task: that result carries a scoring_error code and no tokens, meaning its confidence is unknown. Defaults to None (the model generates its own answer).

• adapter_id (str | None) – UUID of a previously imported adapter to use. The base model will be used when not provided. Default value is None.

• max_new_tokens (int | str | None) – The maximum number of tokens the model can extract, which should be scaled based on the number and length of field values. Default value is set to 1024.

• max_image_tokens (int | str | None) – The maximum number of tokens allocated per image by the model. @deprecated since v44.0; will be removed in v47.0. Use max_tokens_per_image instead; this one is applied against the legacy image-token grid rather than the selected model’s own, so existing flows keep the resolution they ran at.

• max_tokens_per_image (int | str | None) – Available from V44.0; The maximum number of tokens allocated per image by the model. This is conceptually similar to setting image resolution. Larger values may increase accuracy on dense / hard to read images but will raise GPU memory usage. Defaults to None, letting the selected base model choose a budget suited to its own vision grid.

• enable_location_hint (bool | None) – When set to True, the ORCA general prompting block will generate location hint related outputs. This can be useful for downstream tasks that require bounding boxes within the image. However, enable it will raise GPU memory usage and inference latency.

• thinking_budget (int | str | None) – Available from V43.2; Soft cap on the number of reasoning (“thinking”) tokens the model may generate before it is forced to close its thinking phase and produce the answer. Only applies to thinking-capable models with thinking enabled. Defaults to None (no cap).

• json_schema (dict[str, Any] | None) – Available from V43.2; Optional JSON Schema (a dict) used to constrain the model’s generated output to valid JSON that conforms to the schema. Enables structured JSON output via the SGLang xgrammar grammar backend. When None (the default), decoding is unconstrained. For a reference on how to author JSON Schemas, see https://json-schema.org/understanding-json-schema/reference/type.

• model_meta_overrides (dict[str, Any] | None) – Available from V43.2; A dictionary of arguments used to override the model meta for the vlm_gpu atomic block. Defaults to None (no overrides).

• concurrency_config_overrides (dict[str, Any] | None) – Available from V43.2; A dictionary of arguments used to override the concurrency config (e.g. max_batch_size, num_kv_cache_tokens) computed for the vlm_gpu atomic block. Defaults to None (no overrides).

• is_byo_model (bool) – Available from V43.2; A boolean flag indicating whether the specified base model is a bring-your-own (BYO) model. Defaults to False.

• disable_log_redaction (bool | str | None) – Available from V44.0; Debug only. When set to True, the VLM blocks skip the redaction of document content (which may contain PII) in their logs. Defaults to False.

• reference_name (str | None) – unique reference name within a flow, a unique one will be generated when not provided.

• title (str | None) – UI-visible title of the block. Defaults to ‘Optical Reasoning & Cognition Agent for General Prompting’.

• description (str | None) – Description of the block. Both useful for documentation purpose and visible to users in the Flow studio. Defaults to ‘Optical Reasoning & Cognition Agent for General Prompting’.

Output: The block returns an object with the following keys:

• orca_result: The result generated by the model for the given prompt messages.

• metadata: Metadata related to the general prompting process.

Example usage:

# Here we construct a simple static set of messages. But these can also be dynamic
# and the output of a PythonCode block or other processing blocks.
system_message = {'role': 'system', 'content': [
        {'type': 'text', 'text': 'You are a helpful assistant'}
]}
prompt = {'role': 'user', 'content': [
    {'type': 'image', 'image': workflow_input('some_image_uuid')},
    {'type': 'text', 'text': 'Make a short summary of everything in the image'}
]}
message_thread = [system_message, prompt]

gp_block = GeneralPromptingORCABlock(messages=[message_thread])

IDENTIFIER = 'OPTICAL_REASONING_COGNITION_AGENT_GENERAL_PROMPTING'

class flows_sdk.implementations.idp_v44.additional_blocks.ImagesToPrintableFileBlock(documents, height_in_mm=279.4, width_in_mm=215.9, use_native_dimensions=False, dpi=200, mime_type='application/pdf', quality=50, mode='RGB', reference_name=None)

Bases: Block

This block renders each document’s sequence of images into a separate PDF file and stores each resulting PDF in Blob storage. For every document provided, a new blob is created containing the rendered PDF.

Available from V42.1.

Mandatory parameters:

Parameters:

documents (list[dict[str, Any]]) – List of documents, each containing a document_id and pages. Each page contains image_uuid for image-only output, and may optionally include searchable_words_blob_uuid for generating a searchable PDF text layer.

Optional parameters:

Parameters:

• height_in_mm (float) – the height of the document to be generated in millimeters. Default is 279.4 (US Letter height). Ignored when use_native_dimensions is True.

• width_in_mm (float) – the width of the document to be generated in millimeters. Default is 215.9 (US Letter width). Ignored when use_native_dimensions is True.

• use_native_dimensions (bool) – Available from V42.3; when True, each page uses its source image’s native dimensions instead of being resized to width_in_mm × height_in_mm. This preserves maximum image quality. Default is False. When enabled, the output height_in_mm and width_in_mm represent the bounding box (maximum dimensions across all pages).

• dpi (int) – Available from V42.3; dots per inch to assume for images without embedded DPI metadata. Used to calculate physical dimensions in native dimensions mode. Default is 200 (matching VECTOR_GRAPHICS_DPI used by the pagination pipeline).

• mime_type (str) – the mime type of the document to be generated. Default is ‘application/pdf’

• quality (int) – Quality of pages (images) appended to the resulting document as a percentage (recommended 50%). Higher quality results in a larger file size for the generated PDF.

• mode (str) – Mode of the images appended to the resulting document. Supported modes are RGB (default), L (grayscale) and 1 (black-and-white)

Page-based document shape:

Each page contains the image UUID to render and may optionally include searchable_words_blob_uuid. A page with no searchable words blob renders image-only output; a page with a searchable words blob also emits an invisible searchable text layer for those words.

PDF page order follows the order of entries in pages.

• image_uuid: UUID of the rendered image for that PDF page.

• searchable_words_blob_uuid: Optional UUID of a block-storage JSON blob whose records provide the minimal searchable word data: image_uuid, text, and bbox.

Example usage:

documents = [
    {
        'document_id': '48212a99-c099-42e3-bb45-75664db65341',
        'pages': [
            {'image_uuid': 'f42fc9af-b551-4403-aba6-d0eba2c41973'},
            {'image_uuid': 'f42fc9af-b551-4403-aba6-d0eba2c41974'},
        ],
    },
    {
        'document_id': 'delivery_receipt_xyz',
        'pages': [
            {'image_uuid': 'f42fc9af-b551-4403-aba6-d0eba2c41975'},
        ],
    }
]

# Fixed dimensions mode (default) - all pages resized to specified dimensions
images_renderer_block = ImagesToPrintableFileBlock(
    reference_name='images_renderer',
    height_in_mm=279.4,
    width_in_mm=215.9,
    quality=50,
    mode='L',
    documents=documents
)

# Native dimensions mode - preserves original image quality
images_renderer_native = ImagesToPrintableFileBlock(
    reference_name='images_renderer_native',
    use_native_dimensions=True,
    dpi=200,
    quality=80,
    documents=documents
)

output_ref = images_renderer_block.output()

Searchable words are optional on each page. Store them in Blob storage and
reference the blob from the page::

    documents = [
        {
            'document_id': '48212a99-c099-42e3-bb45-75664db65341',
            'pages': [
                {
                    'image_uuid': 'f42fc9af-b551-4403-aba6-d0eba2c41972',
                },
                {
                    'image_uuid': 'f42fc9af-b551-4403-aba6-d0eba2c41973',
                    'searchable_words_blob_uuid': (
                        'd6f5a4bd-581d-4d3d-9eba-d0382b64fda6'
                    ),
                }
            ],
        }
    ]

The searchable words blob must be UTF-8 JSON with a `words` list::

    {
        "words": [
            {
                "image_uuid": "f42fc9af-b551-4403-aba6-d0eba2c41973",
                "text": "Example",
                "bbox": [0.11, 0.21, 0.19, 0.23]
            },
            {
                "image_uuid": "f42fc9af-b551-4403-aba6-d0eba2c41973",
                "text": "text",
                "bbox": [0.22, 0.21, 0.29, 0.23]
            }
        ]
    }

Only records whose `image_uuid` matches the rendered page image are converted
into searchable words. The blob reader also accepts `segments` as an alias for
the top-level `words` list, `transcription` as an alias for `text`, and
`tight_crop_bounding_box` or `bounding_box` as aliases for `bbox`. Extra fields
are ignored, so a ProcessedSegmentList-shaped blob can be reused directly as long
as those minimal fields are present. Each bbox is normalized to the rendered
image:

* `bbox[0]` / `bbox[1]`: top-left x/y.
* `bbox[2]` / `bbox[3]`: bottom-right x/y.
* `[0.0, 0.0]`: top-left of the rendered image.
* `[1.0, 1.0]`: bottom-right of the rendered image.

Example content of output_ref (fixed dimensions mode):

{
    "rendered_documents": [
        {
            "document_id": "48212a99-c099-42e3-bb45-75664db65341",
            "blob_uuid": "52e81c7f-aef6-4f69-b4a6-91fd770a607d",
            "mime_type": "application/pdf",
            "height_in_mm": 279.4,
            "width_in_mm": 215.9,
            "quality": 50,
            "mode": "L",
            "page_dimensions": [
                {
                    "image_uuid": "f42fc9af-b551-4403-aba6-d0eba2c41973",
                    "width_in_mm": 215.9,
                    "height_in_mm": 279.4,
                },
                {
                    "image_uuid": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
                    "width_in_mm": 215.9,
                    "height_in_mm": 279.4,
                },
            ]
        }
    ]
}

Example content of output_ref (native dimensions mode):

{
    "rendered_documents": [
        {
            "document_id": "48212a99-c099-42e3-bb45-75664db65341",
            "blob_uuid": "52e81c7f-aef6-4f69-b4a6-91fd770a607d",
            "mime_type": "application/pdf",
            "height_in_mm": 297.0,
            "width_in_mm": 215.9,
            "quality": 80,
            "mode": "RGB",
            "page_dimensions": [
                {
                    "image_uuid": "f42fc9af-b551-4403-aba6-d0eba2c41973",
                    "width_in_mm": 210.0,
                    "height_in_mm": 297.0,
                },
                {
                    "image_uuid": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
                    "width_in_mm": 215.9,
                    "height_in_mm": 279.4,
                },
            ]
        }
    ]
}

IDENTIFIER = 'IMAGE_TO_PRINTABLE_FILE'

class flows_sdk.implementations.idp_v44.additional_blocks.VertexAIBlock(model, messages, project, location, service_acc_json=None, workload_identity=False, reference_name=None, generation_config=None, safety_settings=None, extra_body=None, title='GCP VertexAI Generate Content', description='Send prompts (text and files) to a model hosted on GCP Vertex AI')

Bases: Block

Generate content using a GCP Vertex AI model.

Available from V43.1.

Mandatory parameters:

Parameters:

• model (str) – ID of the model to use. For example: gemini-2.5-flash or gemini-2.5-pro

• messages (list[dict[str, Any]]) – A list of messages. Each message has a ‘role’ (e.g. user, model) and ‘content’ (list of text or file contents).

• project (str) – GCP Project

• location (str) – GCP Location. For more information, check out: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/learn/locations

• service_acc_json (str | None) – Full service account auth json. Optional when workload_identity is True. For more information, check out: https://cloud.google.com/iam/docs/service-account-creds

• workload_identity (bool) – When True, authenticate via GCP Workload Identity instead of a service account key. Defaults to False.

Optional parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• generation_config (dict[str, Any] | None) – Generation config. For more information, check out: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/reference/rest/v1/GenerationConfig

• safety_settings (list[dict[str, Any]] | None) – A list of SafetySetting objects passed directly to the Vertex AI API. For more information: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/reference/rest/v1/SafetySetting

• extra_body (dict[str, Any] | None) – Additional fields merged into the API request body.

Output: The block returns an object with the following keys:

• text: The text of the first response candidate, with all of its text parts joined. None when the response has no candidates or the first candidate has no text parts.

• response: The raw generateContent response body returned by the Vertex AI API.

If the request to Vertex AI fails, the block fails.

Example usage:

vertexai = VertexAIBlock(
    reference_name='vertexai',
    model='gemini-2.5-flash',
    messages=[{'role': 'user', 'content': [{'type': 'text', 'text': 'Hello'}]}],
    project='my-gcp-project',
    location='us-central1',
    service_acc_json='${system.secrets.gcp_service_account}',
)

IDENTIFIER = 'VERTEXAI_2'

class flows_sdk.implementations.idp_v44.additional_blocks.AddressParserBlock(transcriptions, address_parsing_enabled='${workflow.input.address_parsing_enabled}', address_parser_api_provider='${workflow.input.address_parser_api_provider}', address_parser_api_key='${workflow.input.address_parser_api_key}', address_parser_api_options='${workflow.input.address_parser_api_options}', reference_name=None, title='Address Parser', description='Parses address-typed transcriptions into structured components')

Bases: Block

Address Parser Block parses address-typed fields into structured components (street, city, zip, etc.), either locally (usaddress for US addresses, deepparse for international addresses) or by routing them through a configured third-party address API.

Mandatory parameters:

Parameters:

transcriptions (list[dict[str, Any]] | str) – list of Transcription objects that need to be parsed

Optional Parameters:

Parameters:

• address_parsing_enabled (bool | str) – flag to enable or disable address parsing, defaults to workflow_input(AddressParserSettings.AddressParsingEnabled)

• address_parser_api_provider (str) – selects the address parsing provider (‘none’ / ‘google’ / ‘smarty’ / ‘geoapify’), defaults to workflow_input(AddressParserSettings.ApiProvider)

• address_parser_api_key (str | None) – API key for the configured provider, defaults to workflow_input(AddressParserSettings.ApiKey)

• address_parser_api_options (str | dict | None) – provider-specific JSON options, defaults to workflow_input(AddressParserSettings.ApiOptions)

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• title (str) – overrides the block’s default title shown in Flow Studio, defaults to ‘Address Parser’

• description (str) – overrides the block’s default description shown in Flow Studio, defaults to ‘Parses address-typed transcriptions into structured components’

Output: The block returns an object with the following keys:

• parsed_addresses: list of AddressData objects

Example usage:

address_parser = AddressParserBlock(
    reference_name='address_parser',
    transcriptions=extract_transcriptions.output('transcriptions'),
    address_parser_api_provider='google',
)

IDENTIFIER = 'ADDRESS_PARSER_2'

Trigger Blocks (v44)

Note

“Triggers” are referred to as “Input Blocks” in our application documentation.

Warning

Flows are built to allow full definition of triggers either in code or manually in the Flow Studio. We recommend defining triggers manually in the Flow Studio. Instructions on how to do so are in our application documentation.

To enable trigger definition via the Flow Studio UI, you need to instantiate the IDPTriggers() convenience class and pass it to the triggers property of the Flow

return Flow(
   ...
   triggers=IDPTriggers()
)

If instead you wish to define specific triggers in Python, use the trigger definitions below.

Folder Listener (v44)

Identifier: FOLDER_TRIGGER

Deprecated in favor of the Universal Folder Listener

Parameters:

Name	Type	Default Value	Title	Description
folder	Path	N/A	Folder to scan for submissions	N/A
file_extensions	string	N/A	File extensions	List of file extensions to monitor for (e.g.: ‘png, jpg, pdf’)
has_meta	boolean	False	Enable metadata	Select this if a metadata file is to be expected along with document files (in XXX_index.txt file)
poll_interval	integer	10	Poll interval	Poll interval in seconds
warmup_interval	integer	15	Warm-up interval	Seconds to wait past document last modified time before processing it
folder_cleanup_delay	integer	86400	Folder cleanup delay	Seconds to wait before cleaning up subfolders
api_params	object	{}	API Parameters	N/A

folder_listener = IOBlock(
    identifier='FOLDER_TRIGGER',
    reference_name='folder_trigger',
    title='Folder listener',
    enabled=True,
    input={
        'folder': '/var/www/forms/forms/folder/',
        'file_extensions': 'png, pdf, jpg',
    },
)

triggers = IDPTriggers(blocks=[folder_listener])

S3 Listener (v44)

Identifier: S3_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
bucket_uri	string	N/A	S3 source URI	S3 URI, where the files will be uploaded (e.g. s3://hs_bucket/prefix1/)
archive_bucket_uri	string	N/A	S3 archive URI	S3 URI to move the processed files to (e.g. s3://hs_bucket/prefix1/)
file_extensions	array	[]	File extensions	Types of file extensions for which to monitor
other_file_extensions	string	N/A	Other file extensions	Comma separated list of file extensions for which to monitor (e.g. ‘png, jpg, pdf’)
has_meta	boolean	false	Include submission level parameters	Select this to ingest JSON files along with document files and submission s3 prefixes. These JSON files can contain information such as metadata, cases and external_id. These JSON file names should match the related files or s3 prefixes (e.g., XXX.jpg.json for XXX.jpg)
aws_access_key_id	string	N/A	AWS ACCESS KEY ID	Access Key used for authentication
aws_secret_access_key	password	N/A	SECRET ACCESS KEY	Secret Key used for authentication
use_ec2_instance_credentials	boolean	true	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used
poll_interval	integer	10	Polling interval (in seconds)	How often the S3 Listener will check the S3 bucket for submissions
warmup_interval	integer	15	Warm-up interval (in seconds)	How long the S3 Listener will wait to process the document after it was first detected
api_params	object	{}	API Parameters	N/A

s3_listener = IOBlock(
    identifier='S3_TRIGGER',
    reference_name='s3_trigger',
    title='S3 Listener',
    enabled=True,
    input={
        'bucket_uri': 's3://hs_bucket/prefix1/',
        'archive_bucket_uri': 's3://hs_bucket/prefix2/',
        'file_extensions': ['png', 'pdf', 'jpg'],
        'aws_access_key_id': 'admin',
        'aws_secret_access_key': system_secret('s3_secret_access_key'),
        'use_ec2_instance_credentials': False,
    },
)
triggers = IDPTriggers(blocks=[s3_listener])

GCS Listener (v44)

Identifier: GCS_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
bucket_uri	string	N/A	GCS Source URI	GCS URI, where the files will be uploaded (e.g. gs://hs_bucket/prefix1/)
archive_bucket_uri	string	N/A	GCS archive URI	GCS URI to move the processed files to (e.g. gs://hs_bucket/prefix2/). Cannot be same as Source URI!
file_extensions	array	N/A	File extensions	Types of file extensions for which to monitor
other_file_extensions	string	N/A	Other file extensions	Comma separated list of file extensions for which to monitor (e.g. ‘png, jpg, pdf’)
has_meta	boolean	false	Include submission level parameters	Select this to ingest JSON files along with document files and submission gcs prefixes. These JSON files can contain information such as metadata, cases and external_id. These JSON file names should match the related files or prefixes (e.g., XXX.jpg.json for XXX.jpg)
credentials_json	json	N/A	GCP Service Account JSON	GCP Service Account JSON used for authentication, must be a valid json.
workload_identity	boolean	false	Use Workload Identity	Credentials are obtained by using the Workload Identity Federation, applicable for HS installations inside GKE clusters.
poll_interval	integer	10	Polling interval (in seconds)	How often the GCS Listener will check the GCS bucket for submissions
warmup_interval	integer	15	Warm-up interval (in seconds)	How long the GCS Listener will wait to process the document after it was first detected
api_params	object	{}	API Parameters	N/A

gcs_listener = IOBlock(
    identifier='GCS_TRIGGER',
    reference_name='gcs_trigger',
    title='GCS Listener',
    enabled=True,
    input={
        'bucket_uri': 'gs://hs_bucket/prefix1/',
        'archive_bucket_uri': 'gs://hs_bucket/prefix2/',
        'file_extensions': ['png', 'pdf', 'jpg'],
        'credentials_json': system_secret('gcs_credentials_json'),
        'workload_identity': False,
    },
)
triggers = IDPTriggers(blocks=[gcs_listener])

Azure Blob Listener (v44)

Identifier: AZURE_BLOB_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
container_uri	string	N/A	Azure source URI	Azure URI, where the files will be uploaded, can contain prefix, can contain trailing slash (e.g.: https://<custom_domain>/<container_name>// https://<account_name>.blob.core.windows.net/<container_name>)
archive_container_uri	string	N/A	Azure archive URI	Azure URI to move the processed files to, can contain prefix, can contain trailing slash. Cannot be same as the source URI.
file_extensions	array	N/A	File extensions	Types of file extensions for which to monitor
other_file_extensions	string	N/A	Other file extensions	Comma separated list of file extensions for which to monitor (e.g. ‘png, jpg, pdf’)
has_meta	boolean	false	Include submission level parameters	Select this to ingest JSON files along with document files and submission Azure container prefixes. These JSON files can contain information such as metadata, cases and external_id. These JSON file names should match the related files or prefixes (e.g., XXX.jpg.json for XXX.jpg)
auth_type [1]	string	service_principal	Authentication type	Authentication type.
client_id	string	N/A	Client ID	Application (client) ID for the corresponding application registered with the Microsoft Entra ID
client_secret	Password	N/A	Client secret	Client secret for the corresponding application registered with the Microsoft Entra ID
tenant_id	string	N/A	Tenant ID	Also called directory ID. Unique identifier of the tenant in which the corresponding application is registered with.
account_name	string	N/A	Storage Account Name	Azure Storage Account name used for authentication
account_key	Password	N/A	Storage Account Key	Azure Storage Account key
authority_host	string	N/A	Authority Host	The authority host URL for the Azure Entra ID. Defaults to https://login.microsoftonline.com
poll_interval	integer	10	Polling interval (in seconds)	How often the Azure Listener will check the container for submissions
warmup_interval	integer	15	Warm-up interval (in seconds)	How long the Azure Listener will wait to process the document after it was first detected
api_params	object	{}	API Parameters	N/A

[1] one of:

service_principal
account_key

azure_blob_listener = IOBlock(
    identifier='AZURE_BLOB_TRIGGER',
    reference_name='azure_blob_trigger',
    title='Azure Blob Listener',
    enabled=True,
    input={
        'container_uri': 'https://hs_container.blob.core.windows.net/prefix1/',
        'archive_container_uri': 'https://hs_container.blob.core.windows.net/prefix2/',
        'file_extensions': ['png', 'pdf', 'jpg'],
        'auth_type': 'service_principal',
        'client_id': '123456',
        'tenant_id': '654321',
        'client_secret': system_secret('azure_blob_client_secret'),
    },
)
triggers = IDPTriggers(blocks=[azure_blob_listener])

Email Listener (v44)

Identifier: IMAP_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
provider [1]	string	imap	Email Provider	N/A
poll_interval	integer	60	Polling interval in seconds	The frequency at which the connection will monitor the email folder for submissions
folder	Path	N/A	Folder to scan for emails	The email folder to retrieve submissions from (e.g., “inbox”)
email_body_treatment	string	“ignore”	Email body treatment	What to do with the body of an email that is ingested. Available options are “process” and “ignore”.
post_process_action	string	“move”	Post process action	What to do with the email after it is processed. Available options are “move” and “delete”.
post_process_move_folder	string	“”	Post process archive folder	Folder to move emails to once they are processed. It will be included only if post_process_action is set to “move”.
api_params	object	{}	API Parameters	N/A
render_headers [2]	array	[]	Headers to include	Headers to render at the top of the email body. Headers will be included only if email_body_treatment is set to “process”.
raw_email_treatment [3]	string	“ignore”	Original email file	If stored, the copy will be found under “raw_email” in the block output. Storing a copy will use additional storage.
attachment_header_treatment [3]	string	“ignore”	Attachment headers	If extracted, the attachment headers will be found under “attachment_metadata” in the block output.

[1] one of:

imap
graph

[2] any of:

To
From
Subject
Date

[3] only available in specific Hyperscience versions:

• version 41: since patch 41.0.24

• version 41.2: since patch 41.2.15

• version 42: since patch 42.0.8

• version 42.1: since patch 42.1.2

• all versions past (and including) 42.2

Additional inputs when "imap" is the selected provider:

Name	Type	Default Value	Title	Description
host	string	N/A	IMAP server address	N/A
port	integer	993	Port Number	N/A
ssl	boolean	True	Use SSL connection	N/A
username	string	N/A	Username	N/A
password	Password	N/A	Password	N/A

Additional inputs when "graph" is the selected provider:

Name	Type	Default Value	Title	Description
client_id	string	N/A	Client id	Application (client) ID for the corresponding application registered with the Microsoft identity platform.
tenant_id	string	N/A	Tenant id	Also called directory ID. Unique identifier of the tenant in which the corresponding application is registered with.
graph_cloud_endpoint [1]	string	Global	Microsoft Graph Cloud Endpoint	Determines base URL used for accessing cloud services through Microsoft Graph.
azure_ad_endpoint [2]	string	AZURE_PUBLIC_CLOUD	Azure AD Endpoint	Determines base URL for the Azure Active Directory (Azure AD) endpoint to acquire token for each national cloud.
client_secret	Password	N/A	Client secret	Client secret for the corresponding application registered with the Microsoft identity platform.

[1] one of:

Global
US_GOV
US_DoD
Germany
China

[2] one of:

AZURE_PUBLIC_CLOUD
AZURE_CHINA
AZURE_GERMANY
AZURE_GOVERNMENT

imap_trigger = IOBlock(
    identifier='IMAP_TRIGGER',
    reference_name='imap_trigger',
    title='IMAP trigger',
    enabled=True,
    input={
        'host': 'example@mail.com',
        'folder': '/var/www/forms/forms/imap/',
        'username': 'admin',
        'password': 'pass',
    }
)

triggers = IDPTriggers(blocks=[imap_trigger])

Box Folder Listener (v44)

Identifier: BOX_FOLDER_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
source_folder_id	string	N/A	Folder to scan for submissions	Use the Box Folder ID found in the URL
target_folder_id	string	N/A	Folder to move completed files	Use the Box Folder ID found in the URL
file_extensions	array	[]	File extensions	Types of file extensions for which to monitor
custom_file_extensions	string	N/A	Other file extension types	Comma separated list of file extensions for which to monitor (e.g. ‘png, jpg, pdf’)
poll_interval	integer	10	Polling interval (in seconds)	How often the connector will check the base folder for submissions
warmup_interval	integer	15	Warm-up interval (in seconds)	How long the connector will wait to process the document after it was last modified
public_key_id	string	N/A	Public-Key ID	ID of the Public-key used for authentication with Box
private_key	Password	N/A	Private-Key	Private key used for authentication with Box
passphrase	Password	N/A	Passphrase	Passphrase used for authentication with Box
client_id	string	N/A	Client ID	Client Id used for authentication with Box
client_secret	Password	N/A	Client Secret	Client Secret used for authentication with Box
enterprise_id	string	N/A	Enterprise ID	Enterprise Id used for authentication with Box
api_params	object	{}	API Parameters	N/A

box_folder_trigger = IOBlock(
    identifier='BOX_FOLDER_TRIGGER',
    reference_name='box_folder_trigger',
    title='Box folder',
    enabled=True,
    input={
        'file_extensions': ['png', 'pdf', 'jpg'],
        'source_folder_id': '24',
        'target_folder_id': '42',
        'public_key_id': 'admin',
        'private_key': 'secret',
        'passphrase': 'password',
        'client_id': 'admin',
        'client_secret': 'secret',
        'enterprise_id': 'admin',
    }
)

triggers = IDPTriggers(blocks=[box_folder_trigger])

Cron Listener (v44)

Identifier: CRON_TRIGGER

Inputs:

Name	Type	Default Value	Title	Description
cron_spec	string	"*/5 * * * *"	Cron specification	N/A
time_zone	string	“US/Eastern”	Time Zone	N/A
api_params	object	{}	API Parameters	N/A

cron_trigger = IOBlock(
    identifier='CRON_TRIGGER',
    reference_name='cron_trigger',
    title='Cron Trigger',
    enabled=True,
    input={
        'cron_spec': '0 10 * * *',
        'time_zone': 'Europe/Sofia',
    }
)

triggers = IDPTriggers(blocks=[cron_trigger])

Salesforce Listener (v44)

Identifier: SALESFORCE_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
channel_name	string	N/A	Channel Name	Configured in the Hyperscience app in Salesforce.
username	string	N/A	Associated Username	Username associated with the Salesforce Connected App.
consumer_key	Password	N/A	Consumer Key	Consumer key used for authentication with Salesforce. Paired with the Salesforce instance from which it was issued.
private_key	Password	N/A	Private Key	Private key used for authentication with Salesforce.
sandbox_environment	boolean	false	Private Key	Enable if the Salesforce environment is a sandbox.
api_params	object	{}	API Parameters	N/A

Message Queue Listener (v44)

Identifier: MQ_LISTENER

Inputs:

Name	Type	Default Value	Title	Description
MQ_TYPE	string	“ACTIVE_MQ”	Message Queue type	Type of the message queue to connect with. Valid values are “ACTIVE_MQ”, “AMAZON_SQS”, “IBM_MQ” and “RABBIT_MQ”
MQ_USERNAME	string	null	Username/Access Key ID	N/A
MQ_PASSWORD	Password	null	Password/Secret Access Key	N/A
MQ_QUEUE_NAME	string	null	Queue Name	Name of the queue (topic) to connect to.
MQ_HOST	string	null	Host Name	N/A
MQ_PORT	integer	5672 when “RABBIT_MQ”, 1414 when “IBM_MQ”, otherwise null	Port Number	N/A
api_params	object	{}	API Parameters	N/A

Additional inputs when "AMAZON_SQS" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_REGION	string	null	AWS Region	N/A
MQ_QUEUE_URL	string	null	Queue URL	N/A
MQ_USE_EC2_INSTANCE_CREDENTIALS	boolean	True	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used.

NOTE: MQ_USERNAME can be used to provide Access Key ID and MQ_PASSWORD to provide Secret Access Key.

Additional inputs when "IBM_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_QUEUE_MANAGER	string	null	Queue Manager (IBM MQ)	N/A
MQ_SSL_CIPHER_SUITE [1]	string	null	SSL cipher suite	SSL cipher suite to use if SSL connection is used.
MQ_CHANNEL	string	null	Channel	N/A
MQ_NO_AUTH_REQUIRED	boolean	false	No auth credentials required	N/A
MQ_MQCSP_AUTHENTICATION_MODE	boolean	false	MQCSP authentication mode	N/A

NOTE: MQ_NO_AUTH_REQUIRED is True, MQ_USERNAME and MQ_PASSWORD are optional.

[1] one of:

"NULL_MD5"
"NULL_SHA"
"RC4_MD5_EXPORT"
"RC4_MD5_US"
"RC4_SHA_US"
"RC2_MD5_EXPORT"
"DES_SHA_EXPORT"
"RC4_56_SHA_EXPORT1024"
"DES_SHA_EXPORT1024"
"TRIPLE_DES_SHA_US"
"TLS_RSA_WITH_NULL_SHA256"
"TLS_RSA_WITH_AES_128_CBC_SHA"
"TLS_RSA_WITH_AES_128_CBC_SHA256"
"TLS_RSA_WITH_AES_256_CBC_SHA"
"TLS_RSA_WITH_AES_256_CBC_SHA256"
"AES_SHA_US"
"TLS_RSA_WITH_DES_CBC_SHA"
"TLS_RSA_WITH_3DES_EDE_CBC_SHA"
"TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256"
"FIPS_WITH_DES_CBC_SHA"
"FIPS_WITH_3DES_EDE_CBC_SHA"

Additional inputs when "RABBIT_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_VIRTUAL_HOST	string	null	Virtual Host (RABBIT MQ)	N/A
MQ_EXCHANGE	string	“”	Exchange	N/A
MQ_ROUTING_KEY	string	“”	Routing key	N/A
MQ_CONNECTION_TYPE	string	“TCP”	Connection Type	“TCP or SSL”

rabbit_mq_listener = IOBlock(
    identifier='MQ_LISTENER',
    reference_name='mq_listener',
    title='RabbitMQ Listener',
    enabled=True,
    input={
        'MQ_TYPE': 'RABBIT_MQ',
        'MQ_QUEUE_NAME': 'some_queue_name',
        'MQ_HOST': 'somehost.com',
        'MQ_USERNAME': 'foo',
        'MQ_PASSWORD': system_secret('rabbit_mq_1_password'),
    }
)

triggers = IDPTriggers(blocks=[rabbit_mq_listener])

Universal Folder Listener (v44)

Identifier: UNIVERSAL_FOLDER_TRIGGER

Inputs:

Name	Type	Default Value	Title	Description
folder	string	N/A	Folder to scan for submissions	Enter the path relative to the base folder. Leave blank to monitor the base folder
file_extensions	array	[]	File extensions	Types of file extensions for which to monitor
other_file_extensions	string	N/A	Other file extensions	Comma separated list of file extensions for which to monitor (e.g. ‘png, jpg, pdf’)
has_meta	boolean	false	Include submission level parameters	Select this to ingest JSON files along with document files and submission folders. These JSON files can contain information such as metadata, cases and external_id. These JSON file names should match the related files or folders (e.g., XXX.jpg.json for XXX.jpg)
poll_interval	integer	10	Polling interval (in seconds)	How often the Folder Listener will check the base folder for submissions
warmup_interval	integer	15	Warm-up interval (in seconds)	How long the Folder Listener will wait to process the document after it was last modified
folder_cleanup_delay	number	24	Empty folder cleanup delay (in hours)	How often the Folder Listener will remove empty folders from the base folder
api_params	object	{}	API Parameters	N/A

universal_folder_listener = IOBlock(
    identifier='UNIVERSAL_FOLDER_TRIGGER',
    reference_name='universal_folder_trigger',
    title='Universal Folder Listener',
    enabled=True,
    input={
        'folder': '/var/www/forms/forms/folder/',
        'file_extensions': 'png, pdf, jpg',
    }
)

triggers = IDPTriggers(blocks=[universal_folder_listener])

Kafka Listener (v44)

Identifier: KAFKA_TRIGGER

Inputs:

Name	Type	Default Value	Title	Description
bootstrap_servers	string	N/A	Bootstrap Servers	A Comma separated list of host:port pairs to use for establishing the initial connection to the cluster
group_id	string	N/A	Group Id	A unique string that identifies the consumer group this consumer belongs to. BLOCK_THREADS_KAFKA_TRIGGER should be used to scale up or down the number of consumers (default: number of available cores)
topics	string	N/A	Topics	Comma separated list of topic names that the block will subscribe to
consumer_config_json	json	N/A	Consumer Config	JSON formatted string containing key-value pairs of Kafka consumer configurations described in the official Kafka documentation
api_params	object	{}	API Parameters	N/A
consume_timeout	integer	-1	Consume Timeout	Maximum time, in seconds, that the consumer will wait for new messages. If messages are received within the specified time, they are processed immediately. However, if no messages are received within that specified time, consumption cycle will be restarted internally to continue fetching messages. This ensures continuous message consumption (default: infinite)
consume_num_messages	integer	1	Consume Num Messages	Sets the maximum number of messages the consumer fetches in a single consumption cycle.If the number of available messages is less than the specified, and timeout is reached, messages will be processed. Consumption cycle will be restarted after the current finishes

kafka_listener = IOBlock(
    identifier='KAFKA_TRIGGER',
    reference_name='kafka_trigger',
    title='Kafka Listener',
    enabled=True,
    input={
        'folder': '/var/www/forms/forms/folder/',
        'file_extensions': 'png, pdf, jpg',
         'bootstrap_servers': 'example.com',
         'group_id': 'test_group',
         'topics': 'test_topic1, test_topic2',
         'consumer_config_json': {},
         'number_of_workers': 1,
         'consume_timeout': 5,
         'consume_num_messages': 10
    }
)

triggers = IDPTriggers(blocks=[kafka_listener])

Output Blocks (v44)

Warning

Flows pass information to downstream systems using the IDPOutputsBlock. This block is specially built to allow full definition either in code or manually in the Flow Studio. We recommend defining outputs manually in the Flow Studio.

Instructions on how to do so are in our application documentation. Simply include an IDPOutputsBlock in your Python flow definition wherever you wish to send information to downstream systems.

class flows_sdk.implementations.idp_v44.idp_blocks.IDPOutputsBlock(inputs, blocks=None)

Bases: Outputs

Output block allows users to send data extracted by an IDP flow to other systems for downstream processing

Mandatory parameters:

Parameters:

inputs (dict[str, Any]) – Used by the UI to automatically pre-populate the input of newly added blocks. Extended in this class to include 'enabled': True to visualize an enabled/disabled trigger that is controllable via Flow Studio.

Usage when parameters are to be defined in Flow Studio:

outputs = IDPOutputBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')}
)

If instead you wish to define specific outputs in Python, use the outputs definitions below.

HTTP Notifier Output (v44)

The HTTP Notifier (REST) output connection will POST results from the system to a specified HTTP endpoint.

Identifier: COB_HTTP_EXPORT

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
endpoint	string	N/A	Endpoint URL	URL that output notifications will be posted to
endpoint_secrets	string	N/A	Endpoint URL secrets	Endpoint URL Secrets, format: (one per line) secret_key_name=secret_value
timeout	int	600	Timeout (seconds)	How long (in seconds) to keep the connection open if no bytes are received from the endpoint. Set to 0 to wait indefinitely.
authorization_type [2]	string	“none”	Authorization type	Type of authorization
authorization_header_name	string	null	Authorization header name	Authorization header name to be set in the notification request
authorization_header	Password	null	Authorization header value	Authorization header to be set in the notification request
auth_url	string	null	OAuth2 authorization URL	The endpoint for the authorization server
client_id	string	null	Client ID	The client identifier issued to the client during the application registration process
client_secret	Password	null	Client Secret	The client secret issued to the client during the application registration process
scope	string	null	Scope	Scope to request to oauth server
audience	string	null	Audience	Resource service URL where token will be valid
additional_oauth_parameters	json	{}	Additional Oauth Request Parameters	Additional parameters to send when requesting token
ssl_cert_source [3]	string	null	Source	N/A
ssl_cert_path	string	null	Trusted Certificate(s) path	Enter the path relative to the base folder
ssl_cert_value	MultilineText	null	Trusted Certificate(s)	N/A
additional_notification_http_headers	json	null	Additional Notification Request Headers	Key value pairs in json format which will be sent via the request as http headers

[1] one of:

"v5"
"transformed"

[2] one of:

"none"
"http_header"
"oauth_2_client_credentials"

[3] one of:

"env_var"
"ca_bundle_path"
"certificate_value"

cob_http_export = IOBlock(
    identifier='COB_HTTP_EXPORT',
    reference_name='cob_http_export',
    title='HTTP Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'SUBMISSION',
        'endpoint': 'example.com',
        'authorization_type': 'none',
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_http_export],
)

Message Queue Notifier Output (v44)

The Message Queue Notifier Output can configure connections to ActiveMQ, Amazon SQS, IBM MQ, and RabbitMQ message queues.

Identifier: COB_MQ_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
MQ_TYPE	string	“ACTIVE_MQ”	Message Queue type	Type of the message queue to connect with. Valid values are “ACTIVE_MQ”, “AMAZON_SQS”, “IBM_MQ” and “RABBIT_MQ”
MQ_USERNAME	string	null	Username/Access Key ID	N/A
MQ_PASSWORD	Password	null	Password/Secret Access Key	N/A
MQ_QUEUE_NAME	string	null	Queue Name	Name of the queue (topic) to connect to. It applies only to “ACTIVE_MQ”, “IBM_MQ” and “RABBIT_MQ” MQ_TYPEs.
MQ_HOST	string	null	Host Name	N/A
MQ_PORT	integer	5672 when “RABBIT_MQ”, 1414 when “IBM_MQ”, otherwise null	Port Number	N/A

[1] one of:

"v5"
"transformed"

Additional inputs when "AMAZON_SQS" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_REGION	string	null	AWS Region	N/A
MQ_QUEUE_URL	string	null	Queue URL	N/A
MQ_USE_EC2_INSTANCE_CREDENTIALS	boolean	True	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used.
MQ_MESSAGE_GROUP_ID	string	null	Group ID for FIFO queues	N/A
MQ_MESSAGE_METADATA	string	null	Additional SQS Metadata	Input additional metadata key value pairs in the format of { “key”: {“TYPE”: “String”, “VALUE”: “”}, “key2”: {“TYPE”: “Number”, “VALUE”: “12345”}

NOTE: MQ_USERNAME can be used to provide Access Key ID and MQ_PASSWORD to provide Secret Access Key.

Additional inputs when "IBM_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_QUEUE_MANAGER	string	null	Queue Manager (IBM MQ)	N/A
MQ_SSL_CIPHER_SUITE [1]	string	null	SSL cipher suite	SSL cipher suite to use if SSL connection is used.
MQ_CHANNEL	string	null	Channel	N/A
MQ_NO_AUTH_REQUIRED	boolean	false	Channel	N/A
MQ_MQCSP_AUTHENTICATION_MODE	boolean	false	Channel	N/A

NOTE: MQ_NO_AUTH_REQUIRED is True, MQ_USERNAME and MQ_PASSWORD are optional.

[1] one of:

"NULL_MD5"
"NULL_SHA"
"RC4_MD5_EXPORT"
"RC4_MD5_US"
"RC4_SHA_US"
"RC2_MD5_EXPORT"
"DES_SHA_EXPORT"
"RC4_56_SHA_EXPORT1024"
"DES_SHA_EXPORT1024"
"TRIPLE_DES_SHA_US"
"TLS_RSA_WITH_NULL_SHA256"
"TLS_RSA_WITH_AES_128_CBC_SHA"
"TLS_RSA_WITH_AES_128_CBC_SHA256"
"TLS_RSA_WITH_AES_256_CBC_SHA"
"TLS_RSA_WITH_AES_256_CBC_SHA256"
"AES_SHA_US"
"TLS_RSA_WITH_DES_CBC_SHA"
"TLS_RSA_WITH_3DES_EDE_CBC_SHA"
"TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256"
"FIPS_WITH_DES_CBC_SHA"
"FIPS_WITH_3DES_EDE_CBC_SHA"

Additional inputs when "RABBIT_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_VIRTUAL_HOST	string	null	Virtual Host (RABBIT MQ)	N/A
MQ_EXCHANGE	string	“”	Exchange	N/A
MQ_ROUTING_KEY	string	“”	Routing key	N/A
MQ_CONNECTION_TYPE	string	“TCP”	Connection Type	“TCP or SSL”

mq_notifier = IOBlock(
    identifier='COB_MQ_NOTIFIER',
    reference_name='mq_notifier',
    title='MQ Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'SUBMISSION',
        'MQ_USERNAME': 'admin',
        'MQ_PASSWORD': 'pass',
        'MQ_QUEUE_NAME': 'queue',
        'MQ_HOST': 'host',
    },
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[mq_notifier],
)

Box Notifier Output (v44)

Provides an out-of-the-box integration into Box systems.

Identifier: COB_BOX_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
public_key_id	string	N/A	Public-Key ID	ID of the Public-key used for authentication with Box
private_key	Password	N/A	Private-Key	Private key used for authentication with Box
passphrase	Password	N/A	Passphrase	Passphrase used for authentication with Box
client_id	string	N/A	Client ID	Client Id used for authentication with Box
client_secret	Password	N/A	Client Secret	Client Secret used for authentication with Box
enterprise_id	string	N/A	Enterprise ID	Enterprise Id used for authentication with Box
template_key	string	“”	Box Metadata Template Key	Enter the key of the Box Metadata template that you would like to map the Hyperscience metadata to.
static_output_fields_to_map	array	[]	Static Metadata Fields	Specify the Hyperscience fields you want to store in Box metadata.
submission_id	string	“”	Key for Mapping Submission ID	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_id	string	“”	Key for Mapping Document ID	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
submission_state	string	“”	Key for Mapping Submission State	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_state	string	“”	Key for Mapping Document State	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
submission_exceptions	string	“”	Key for Mapping Submission Exceptions	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_exceptions	string	“”	Key for Mapping Document Exceptions	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_layout_uuid	string	“”	Key for Mapping Document Layout Uuid	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_layout_name	string	“”	Key for Mapping Document Layout Name	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
start_time	string	“”	Key for Mapping Start Time	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
complete_time	string	“”	Key for Mapping Complete Time	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_fields_template_mappings	json	{}	JSON for Additional Metadata Fields	Specify additional metadata fields using their key value. Please consult Box Integration setup manual for JSON template and instructions.

[1] one of:

"v5"
"transformed"

box_notifier = IOBlock(
    identifier='COB_BOX_NOTIFIER',
    reference_name='box_notifier',
    title='Box Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'private_key': 'password',
        'passphrase': 'password',
        'public_key_id': 'admin',
        'client_id': 'admin',
        'client_secret': 'secret',
        'enterprise_id': 'admin',
        'template_key': 'key',
    },
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[box_notifier],
)

UiPath Notifier Output (v44)

Provides an out-of-the-box integration into UiPath systems.

Identifier: COB_EXPORT_UIPATH

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
auth_method [2]	string	“basic_auth”	Authentication method	N/A
url	string	N/A	URL	Base URL of the uipath instance
app_id	string	N/A	App ID	App ID provided by UiPath
app_secret	Password	N/A	App Secret	App Secret provided by UiPath
oauth_token_endpoint	string	N/A	OAuth token endpoint	Endpoint to retrieve the token. Using https://cloud.uipath.com/identity_/connect/token by default
organization_unit_id	string	N/A	Organization unit id	Organization unit id
tenant	string	N/A	Tenant	Tenant
username	string	N/A	Username	Username
password	Password	N/A	Password	Password
queue	string	N/A	Queue	Queue

[1] one of:

"v5"
"transformed"

[2] one of:

"basic_auth"
"oauth"

cob_export_uipath = IOBlock(
    identifier='COB_EXPORT_UIPATH',
    reference_name='cob_export_uipath',
    title='CCB Export UiPath',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'FLAT_SUBMISSION',
        'url': 'example.com',
        'tenant': 'tenant',
        'username': 'admin',
        'password': 'pass',
        'queue': 'queue',
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_export_uipath],
)

Salesforce Notifier Output (v44)

v44 ships two Salesforce output blocks. Use the first; the second is kept so existing flows keep working, and is going away.

• Salesforce Notifier Output Block (COB_SALESFORCE_NOTIFIER_2) does not write to Salesforce itself. For every payload of a completed submission it runs an integration flow of your own, passing down the Salesforce credentials, so what gets written, where, and when is ordinary Python in a flow you control. The Salesforce Integration Flow page covers it end to end, with a runnable example to start from.

• Salesforce Notifier (Deprecated) Output Block (COB_SALESFORCE_NOTIFIER) is the block from earlier releases, which updates fields on a Salesforce object from field mappings. Deprecated as of v44.0 and removed in v46.0. It takes the same connection settings, so migrating means moving its field mappings into an integration flow.

Salesforce Notifier Output Block (v44)

Identifier: COB_SALESFORCE_NOTIFIER_2

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. DOCUMENTS runs the integration flow once per extracted document, SUBMISSION once per submission
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
username	string	N/A	Associated Username	Username associated with the Salesforce Connected App
consumer_key	password	N/A	Consumer Key	Consumer key used for authentication with Salesforce. Paired with the Salesforce instance from which it was issued.
private_key	password	N/A	Private Key	Private key used for authentication with Salesforce.
sandbox_environment	boolean	false	Sandbox Environment	Enable if the Salesforce environment is a sandbox.
integration_flow_identifier	FlowIdentifier	N/A	Integration Flow	Identifier of the flow to run for each payload. It receives the credentials above plus submission_id and payload_uuid, and must declare the salesforce_integration role.

[1] one of:

"v5"
"transformed"

cob_salesforce_notifier = IOBlock(
    identifier='COB_SALESFORCE_NOTIFIER_2',
    reference_name='cob_salesforce_notifier',
    title='Salesforce Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        # the IDENTIFIER of your integration flow
        'integration_flow_identifier': 'SALESFORCE_INTEGRATION_FLOW_EXAMPLE',
        'api_version': 'v5',
        'export_type': 'DOCUMENTS',
        # enter username, consumer_key and private_key on the block in Flow Studio rather than here
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_salesforce_notifier],
)

Salesforce Notifier (Deprecated) Output Block (v44)

Deprecated as of v44.0 and removed in v46.0. Use the Salesforce Notifier Output Block above for new flows; see Salesforce Integration Flow.

With the Salesforce Notifier Block, you can configure your flow to send extracted information to Salesforce. The Salesforce Notifier Block can use extracted information to look up, and then update any number of fields on a Salesforce object.

Identifier: COB_SALESFORCE_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
username	string	N/A	Associated Username	Username associated with the Salesforce Connected App
consumer_key	password	N/A	Consumer Key	Consumer key used for authentication with Salesforce. Paired with the Salesforce instance from which it was issued.
private_key	password	N/A	Private Key	Private key used for authentication with Salesforce.
sandbox_environment	boolean	false	Sandbox Environment	Enable if the Salesforce environment is a sandbox.
object_api_name	string	N/A	Object API Name	API name of the object in Salesforce to lookup and update.
new_record_on_lookup_failure	boolean	false	Create new record on lookup failure	When enabled, it will create a new record if the lookup fails to find a record using the lookup parameters. When disabled, lookup will fail if it does not find a record using the lookup parameters.
object_lookup_mapping	json	N/A	Lookup Field Mapping	Specify which Salesforce fields (using their API name) to be queried with which extracted values from the document for the lookup using key-value format. For example: “DOCUMENT_FIELD_NAME”: “OBJECT_FIELD_API_NAME”.
document_fields_mapping	json	N/A	Document Field Mappings	Specify which Salesforce fields (using their API name) you want to update with which extracted data from Hyperscience using key-value format. For example: “DOCUMENT_FIELD_NAME”: “OBJECT_FIELD_API_NAME”.”
overwrite_existing_field_data	boolean	false	Overwrite existing field data	If this is checked and a selected field has data in it, Hyperscience will overwrite that data. If this is unchecked, existing data will not be overwritten and the operation will fail.

[1] one of:

"v5"
"transformed"

cob_salesforce_notifier = IOBlock(
    identifier='COB_SALESFORCE_NOTIFIER',
    reference_name='cob_salesforce_notifier',
    title='Salesforce Notifier (Deprecated) Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'username': 'salesforce_user',
        'consumer_key': 'salesforce_consumer_key',
        'private_key': 'salesforce_private_key',
        'object_api_name': 'salesforce_object_api_name',
        'object_lookup_mapping': {"LAYOUT_VARIATION_XYZ_FIELD_NAME_1": "SF_FIELD_API_NAME_1", "LAYOUT_VARIATION_XYZ_FIELD_NAME_2": "SF_FIELD_API_NAME_2"},
        'document_fields_mapping': {"LAYOUT_VARIATION_XYZ_FIELD_NAME_3": "SF_FIELD_API_NAME_3", "LAYOUT_VARIATION_XYZ_FIELD_NAME_4": "SF_FIELD_API_NAME_4"}
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_salesforce_notifier],
)

S3 Notifier Output (v44)

With the S3 Notifier Block, you can configure your flow to send extracted information to an S3 bucket.

Identifier: COB_S3_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
bucket_uri	string	N/A	S3 URI	S3 URI, where the data should be stored (e.g. s3://hs_bucket/prefix1/prefix2/)
aws_access_key_id	string	N/A	AWS ACCESS KEY ID	Access Key used for authentication
aws_secret_access_key	password	N/A	SECRET ACCESS KEY	Secret Key used for authentication
use_ec2_instance_credentials	boolean	true	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used

[1] one of:

"v5"
"transformed"

cob_s3_notifier = IOBlock(
    identifier='COB_S3_NOTIFIER',
    reference_name='cob_s3_notifier',
    title='S3 Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'FLAT_SUBMISSION',
        'bucket_uri': 's3://hs_bucket/prefix1/prefix2/',
        'aws_access_key_id': 'aws_access',
        'aws_secret_access_key': system_secret('aws_secret_access_key'),
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_s3_notifier],
)

GCS Notifier Output (v44)

With the GCS Notifier Block, you can configure your flow to send extracted information to a GCS bucket.

Identifier: COB_GCS_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
bucket_uri	string	N/A	GCS Source URI	GCS URI, where the files will be uploaded (e.g. gs://hs_bucket/prefix1/)
credentials_json	json	N/A	GCP Service Account JSON	GCP Service Account JSON used for authentication, must be a valid json.
workload_identity	boolean	true	Use Workload Identity	Credentials are obtained by using the Workload Identity Federation, applicable for HS installations inside GKE clusters.

[1] one of:

"v5"
"transformed"

cob_gcs_notifier = IOBlock(
    identifier='COB_GCS_NOTIFIER',
    reference_name='cob_gcs_notifier',
    title='GCS Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'FLAT_SUBMISSION',
        'bucket_uri': 'gs://hs_bucket/prefix1/prefix2/',
        'credentials_json': system_secret('gcs_credentials_json'),
        'workload_identity': False,
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_gcs_notifier],
)

Azure Blob Notifier Output (v44)

With the Azure Blob Notifier Block, you can configure your flow to send extracted information to an Azure Blob container.

Identifier: COB_AZURE_BLOB_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
container_uri	string	N/A	Azure Blob URI	Azure Blob URI, where the data will be stored, can contain prefix, can contain trailing slash (e.g.: https://<custom_domain>/<container_name>/<prefix>/ https://<account_name>.blob.core.windows.net/<container_name>)
auth_type [2]	string	service_principal	Authentication type	Authentication type.
client_id	string	N/A	Client ID	Application (client) ID for the corresponding application registered with the Microsoft Entra ID
client_secret	Password	N/A	Client secret	Client secret for the corresponding application registered with the Microsoft Entra ID
tenant_id	string	N/A	Tenant ID	Also called directory ID. Unique identifier of the tenant in which the corresponding application is registered with.
account_name	string	N/A	Storage Account Name	Azure Storage Account name used for authentication
account_key	Password	N/A	Storage Account Key	Azure Storage Account key
authority_host	string	N/A	Authority Host	The authority host URL for the Azure Entra ID. Defaults to https://login.microsoftonline.com
[1] one of:

"v5"
"transformed"

[2] one of:

service_principal
account_key

cob_azure_blob_notifier = IOBlock(
    identifier='COB_AZURE_BLOB_NOTIFIER',
    reference_name='cob_azure_blob_notifier',
    title='Azure Blob Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'FLAT_SUBMISSION',
        'container_uri': 'https://hs_container.blob.core.windows.net/',
        'auth_type': 'service_principal',
        'client_id': '123456',
        'tenant_id': '654321',
        'client_secret': system_secret('azure_blob_client_secret'),
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_azure_blob_notifier],
)

Kafka Notifier Output (v44)

With the Kafka Notifier Block, you can configure your flow to send extracted information to a kafka topic.

Identifier: COB_KAFKA_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
bootstrap_servers	string	N/A	Bootstrap Servers	A list of Kafka brokers to connect to. For example: “kafka1:9092,kafka2:9092”
topic	string	N/A	Topic	The Kafka topic where your data will be sent
client_id	string	N/A	SECRET ACCESS KEY	An identifier for your application when connecting to Kafka, typically used for tracking and logging purposes
producer_config_json	json	true	Producer Config (JSON)	Configuration settings for the Kafka producer in JSON format. See https://kafka.apache.org/documentation/#producerconfigs for more information
synchronous_publish	boolean	true	Sync Publish	If true, the publish call will block until the message is sent successfully or an error is raised. If false, the publish call will return immediately after enqueueing the message
message_key	string	null	Message Key	The key associated with the message to be sent. This is used to determine which partition the message is sent to. If null, the message will be sent to a random partition

[1] one of:

"v5"
"transformed"

cob_kafka_notifier = IOBlock(
    identifier='COB_KAFKA_NOTIFIER',
    reference_name='cob_kafka_notifier',
    title='Kafka Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'FLAT_SUBMISSION',
        'bootstrap_servers': 'kafka1:9092,kafka2:9092',
        'topic': 'topic',
        'client_id': 'client_id',
        'producer_config_json': {},
        'synchronous_publish': True,
        'message_key': 'key'
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_kafka_notifier],
)

Helper Classes and Functions (v44)

The following classes are used to easily instantiate and manage a flow’s settings.

flows_sdk.implementations.idp_v44.idp_values.get_idp_wf_inputs(idp_wf_config)

A helper function that maps the values provided by an IdpWorkflowConfig to their corresponding IDP keys.

Parameters:

idp_wf_config (IdpWorkflowConfig) – with static values to be filled.

Return type:

dict[str, Any]

Returns:

a dictionary with filled static values, compatible with IDP flows.

Example usage with default values:

flow = Flow(
    input=get_idp_wf_inputs(get_idp_wf_config()),
    ...
)

Example usage:

Example usage when instantiating an IDP flow with custom parameters::

flow = Flow(
    input=get_idp_wf_inputs({an instance of IdpWorkflowConfig}),
    ...
)

flows_sdk.implementations.idp_v44.idp_values.get_idp_wf_config()

A helper function that instantiates an IdpWorkflowConfig pre-filled with default values. IdpWorkflowConfig is the container class for defining flow-level settings.

Return type:

IdpWorkflowConfig

Returns:

a valid, fully constructed IdpWorkflowConfig instance

Example usage when instantiating an IDP flow:

flow = Flow(
    input=get_idp_wf_inputs(get_idp_wf_config()),
    ...
)

class flows_sdk.implementations.idp_v44.idp_values.IdpWorkflowConfig

Convenience dataclass outlining all flow-level config values for IDP.

class flows_sdk.implementations.idp_v44.idp_values.IDPTriggers(blocks=None)

An adapter class making it easier to instantiate flows_sdk.flows.Triggers for IDP flows. In particular, provides defaults for some fields (e.g. title, description, reference_name) and defines api_params_manifest with IDP specific parameters.

Parameters:

blocks (Sequence[IOBlock] | None) – flows_sdk.blocks.IOBlock s to be included as triggers for an IDP Flow. Optional, defaults to an empty list.

Example usage when instantiating an IDP flow:

folder_listener = IOBlock(...)
flow = Flow(
    triggers=IDPTriggers([folder_listener]),
    ...
)

class flows_sdk.implementations.idp_v44.idp_values.IDPWrapperManifest(flow_identifier)

An adapter class making it easier to instantiate flows_sdk.flows.Manifest for IDP wrapper flows. In particular, provides defaults for some fields (e.g. roles) but more importantly, defines all flows_sdk.flows.Parameter that construct the Flow inputs and their UI representation (left-hand side menu in Flow studio).

Parameters:

flow_identifier (str) – A system-wide unique identifier for flows_sdk.flows.Flow

Example usage when instantiating an IDP flow:

flow = Flow(
    manifest=IDPWrapperManifest(flow_identifier='FLOW_IDENTIFIER'),
    ...
)

class flows_sdk.implementations.idp_v44.idp_values.IDPCoreManifest(flow_identifier, roles=None)

An adapter class making it easier to instantiate flows_sdk.flows.Manifest for IDP Core flows. In particular, provides defaults for some fields (e.g. roles, output) but more importantly, defines all flows_sdk.flows.Parameter that construct the Flow inputs and their UI representation (left-hand side menu in Flow studio).

Parameters:

• flow_identifier (str) – A system-wide unique identifier for flows_sdk.flows.Flow

• roles (list[str] | None) – ‘read_only’, ‘supporting’ and ‘idp_core’ are the default roles of every subflow that can replace the default IDP_CORE subflow inside the top-level IDP flows. ‘supporting’ and ‘idp_core’ are mandatory roles that are necessary for all IDP Core flows. ‘supporting’ ensures that the subflow will be auto-deployed together with the top-level IDP flow. ‘idp_core’ ensures that this subflow can be selected in the UI in dropdowns which allow picking an alternative subflow to the default IDP_CORE subflow. If you are implementing a subflow that is not supposed to be a drop-in replacement for IDP_CORE, you will have to subclass or implement your own version of IDPCoreManifest class in order to remove the idp_core role.

Example usage when instantiating an IDP flow:

flow = Flow(
    manifest=IDPCoreManifest(flow_identifier='FLOW_IDENTIFIER'),
    ...
)

Contracts (v44)

class flows_sdk.implementations.idp_v44.contracts.Submission

A Submission object is created when a set of files are submitted into the system together. The system then interprets each file as a one or more Page objects.

id: int

Unique system-generated identifier.

cases: list[Case] | None = None

Array of Case objects that contain Documents and Pages from this Submission.

flow_uuid: UUID

The UUID of the Flow that created this Submission.

flow_name: str

The name of the Flow that created this Submission.

flow_version: int

The version of the Flow that created this Submission.

status: Literal['processing', 'complete', 'reprocessing']

Current status of the Submission.

complete_time: datetime | None = None

Time the entire Submission entered the Complete state, including all of its Supervision tasks.

classification_review_needed: bool | None = None

Indicates whether Classification Supervision is needed for this Submission.

documents: list[Document] | None = None

Array of Document objects identified as being part of this Submission.

unassigned_pages: list[Page]

Array of Page objects included in this Submission that have not been grouped into a Document.

failed_pages: list[FailedPage]

Array of Page objects included in this Submission that failed processing.

class flows_sdk.implementations.idp_v44.contracts.Page

The Page object is the representation of a single image that was submitted into the system.

id: int

Unique system-generated identifier.

submission_page_number: int

Indicates page order for the Submission.

status: Literal['processing', 'failed', 'complete']

Current status of the Page.

page_type: Union[Literal['unassigned_page', 'blank_page', 'other_page', 'additional_form_page', 'document_page', 'unknown_page', 'failed'], str]

The type of the Page.

origin: FilePageOrigin

The origin of the Page, which can be used to determine from which submitted file the Page was extracted.

classification_type: Optional[Literal['manual', 'machine']] = None

The method by which the Page was classified, if applicable. Present when the page_type is not ‘unknown_page’ or ‘unassigned_page’.

machine_layout_page_uuid: UUID | None = None

Unique identifier of the layout page that the machine predicted this Page matches to, if applicable.

manual_layout_page_uuid: UUID | None = None

Unique identifier of the layout page that a keyer assigned this Page to, if applicable.

combined_segments: PageSegments | None = None

The segments for the Page, if applicable.

image_uuid: UUID

Unique identifier of the image for this Page. Refers to the original image as extracted from the source file before any transformations were applied.

image_name: str

The name of the original image file for this Page.

image_width: int

The width of the original image for this Page in pixels.

image_height: int

The height of the original image for this Page in pixels.

image_checksum: str

The checksum of the original image for this Page.

corrected_image_uuid: UUID | None = None

Unique identifier of the corrected image for this Page, if applicable. Refers to the image after transformations such as rotation or perspective correction were applied.

image_correction_type: Optional[Literal['layout_matching', 'image_correction', 'manual']] = None

The type of image correction applied to this Page, if applicable. Available in v43.1 and later.

transformation_matrix: list[list[float]] | None = None

The 3x3 registration homography matrix for the Page, if applicable.

template_image_shapes: list[list[int]] | None = None

Registration shape metadata for the layout image, if applicable. Contains the pre-computed layout shape and the original layout image shape.

submission_image_shape: list[int] | None = None

Registration pre-processing shape of the submission image, if applicable.

correction_model_info: ImageCorrectionModelInfo | None = None

Model version metadata for image correction on this Page, if applicable.

rotation_confidence: float | None = None

The confidence score for the applied rotation correction for the Page, if applicable. Ranges from 0.0 to 1.0, or None if not applicable.

rotation_class: int | None = None

The applied rotation-correction class for the Page, if applicable. Values are 0, 1, 2, or 3, representing the number of 90-degree counterclockwise rotations applied, or None if not applicable.

deskew_confidence: float | None = None

The confidence score for the applied deskew correction for the Page, if applicable. Ranges from 0.0 to 1.0, or None if not applicable.

skew_angle: float | None = None

The applied deskew-correction angle for the Page in degrees, if applicable. This value represents the rotation applied to deskew the image (it may be 0 when no deskew is needed), or None if not applicable.

mobile_correction_result: Optional[Literal['PREDICTED_TRANSFORM', 'THRESHOLDED_AS_SCANNED', 'NO_VANISHING_POINT_CONSENSUS', 'TOO_FEW_DOCUMENT_PIXELS']] = None

Outcome of mobile perspective correction for this Page, if applicable. Indicates whether a perspective transform was applied or why it was not, or None if not applicable.

applied_transform: ImageCorrectionPerspectiveTransform | None = None

Applied mobile perspective transform for this Page, if applicable.

exceptions: list[str]

List of exceptions encountered during processing.

class flows_sdk.implementations.idp_v44.contracts.Document

The Document object is a set of pages that have matched to a pre-defined layout.

id: int

Unique system-generated identifier.

uuid: UUID

Unique system-generated identifier.

status: Literal['processing', 'reprocess', 'reprocessing', 'rejected', 'complete']

Current status of the Document.

complete_time: datetime | None = None

Time the entire Document entered the Complete state, including all of its Supervision tasks.

layout_uuid: UUID

The unique identifier of the layout that this Document was matched to.

layout_name: str

The name of the layout that this Document was matched to.

layout_language: str

The language of the layout that this Document was matched to.

layout_kind: Literal['structured', 'semi_structured', 'additional']

The kind of the layout that this Document was matched to.

layout_tags: list[str]

The list of tags associated with the layout that this Document was matched to.

layout_version_uuid: UUID

The unique identifier of the specific layout version that this Document was matched to.

original_layout_version_uuid: UUID

The unique identifier of the original layout version that this Document was matched to. Used to map partially matched Documents to a specific layout version.

classified_by_user_id: int

The unique identifier of the user who classified this Document. -1 if classified by an automated process.

needs_flexible_extraction_review: bool

Indicates whether the Document requires Flexible Extraction Supervision.

pages: list[DocumentPage]

Array of Page objects included in this Document.

document_fields: list[Field] | None = None

Array of Field objects identified as being part of this Document.

tables: list[Table] | None = None

Array of Table objects identified as being part of this Document.

data_types: list[DataType]

Array of DataType objects identified as being part of this Document.

processed_segments: list[FragmentedSegments] | None = None

Array of FragmentedSegments objects representing the segments that have been extracted for this Document.

document_file: DocumentFile | None = None

The printable file rendered from the Document’s page images, if applicable.

metadata: dict[str, Any]

A dictionary to store any additional metadata related to the Document.

exceptions: list[str]

List of exceptions encountered during processing.

class flows_sdk.implementations.idp_v44.contracts.FailedPage

A FailedPage object represents a Page that failed processing during the execution of a Flow.

id: int

Unique system-generated identifier.

submission_page_number: int

Indicates page order for the Submission.

status: Literal['processing', 'failed', 'complete']

Current status of the Page. For failed pages, this property will be set to failed.

page_type: Literal['unassigned_page', 'blank_page', 'other_page', 'additional_form_page', 'document_page', 'unknown_page', 'failed']

The type of the Page, which can be used to determine how the system attempted to process the Page before it failed.

image_uuid: UUID | None

Unique identifier of the image for this Page, if available. Refers to the original image as extracted from the source file before any transformations were applied.

image_name: str | None

The name of the original image file for this Page, if available.

image_width: int

The width of the original image for this Page in pixels, if available.

image_height: int

The height of the original image for this Page in pixels, if available.

image_checksum: str | None

The checksum of the original image for this Page, if available.

origin: FilePageOrigin | FilenamePageOrigin

The origin of the Page, which can be used to determine from which submitted file the Page was extracted.

exceptions: list[str]

List of exceptions encountered during processing that led to the failure of this Page.

class flows_sdk.implementations.idp_v44.contracts.DocumentPage

Represents a page within a document.

id: int

Unique system-generated identifier of the original Page object.

document_page_id: int

Unique system-generated identifier.

document_page_number: int

Indicates page order for the Document.

classification_type: Literal['manual', 'machine'] = None

The method by which the Page was classified.

layout_page_uuid: UUID | None = None

Unique identifier of the layout page that this Page was matched to, if applicable.

layout_image_uuid: UUID | None = None

Unique identifier of the layout image that this Page was matched to, if applicable.

submission_page_number: int

Indicates page order for the Submission.

status: Literal['processing', 'failed', 'complete']

Current status of the Page.

page_type: Union[Literal['unassigned_page', 'blank_page', 'other_page', 'additional_form_page', 'document_page', 'unknown_page', 'failed'], str]

The type of the Page.

origin: FilePageOrigin

The origin of the Page, which can be used to determine from which submitted file the Page was extracted.

machine_layout_page_uuid: UUID | None = None

Unique identifier of the layout page that the machine predicted this Page matches to, if applicable.

manual_layout_page_uuid: UUID | None = None

Unique identifier of the layout page that a keyer assigned this Page to, if applicable.

combined_segments: PageSegments | None = None

The segments for the Page, if applicable.

image_uuid: UUID

Unique identifier of the image for this Page. Refers to the original image as extracted from the source file before any transformations were applied.

image_name: str

The name of the original image file for this Page.

image_width: int

The width of the original image for this Page in pixels.

image_height: int

The height of the original image for this Page in pixels.

image_checksum: str

The checksum of the original image for this Page.

corrected_image_uuid: UUID | None = None

Unique identifier of the corrected image for this Page, if applicable. Refers to the image after transformations such as rotation or perspective correction were applied.

image_correction_type: Optional[Literal['layout_matching', 'image_correction', 'manual']] = None

The type of image correction applied to this Page, if applicable. Available in v43.1 and later.

transformation_matrix: list[list[float]] | None = None

The 3x3 registration homography matrix for the Page, if applicable.

template_image_shapes: list[list[int]] | None = None

Registration shape metadata for the layout image, if applicable. Contains the pre-computed layout shape and the original layout image shape.

submission_image_shape: list[int] | None = None

Registration pre-processing shape of the submission image, if applicable.

correction_model_info: ImageCorrectionModelInfo | None = None

Model version metadata for image correction on this Page, if applicable.

rotation_confidence: float | None = None

The confidence score for the applied rotation correction for the Page, if applicable. Ranges from 0.0 to 1.0, or None if not applicable.

rotation_class: int | None = None

The applied rotation-correction class for the Page, if applicable. Values are 0, 1, 2, or 3, representing the number of 90-degree counterclockwise rotations applied, or None if not applicable.

deskew_confidence: float | None = None

The confidence score for the applied deskew correction for the Page, if applicable. Ranges from 0.0 to 1.0, or None if not applicable.

skew_angle: float | None = None

The applied deskew-correction angle for the Page in degrees, if applicable. This value represents the rotation applied to deskew the image (it may be 0 when no deskew is needed), or None if not applicable.

mobile_correction_result: Optional[Literal['PREDICTED_TRANSFORM', 'THRESHOLDED_AS_SCANNED', 'NO_VANISHING_POINT_CONSENSUS', 'TOO_FEW_DOCUMENT_PIXELS']] = None

Outcome of mobile perspective correction for this Page, if applicable. Indicates whether a perspective transform was applied or why it was not, or None if not applicable.

applied_transform: ImageCorrectionPerspectiveTransform | None = None

Applied mobile perspective transform for this Page, if applicable.

exceptions: list[str]

List of exceptions encountered during processing.

class flows_sdk.implementations.idp_v44.contracts.Field

Represents a concrete occurrence of a field that has been defined for extraction as part of a layout.

id: int

Unique system-generated identifier.

uuid: UUID

Unique system-generated identifier.

status: Optional[Literal['processing', 'complete']] = None

Current status of the Field.

layout_field_uuid: UUID

The unique identifier of the specific layout field this Field was matched to.

layout_page_uuid: UUID

The unique identifier of the layout page that this Field was matched to.

data_type_uuid: UUID

The unique identifier of the data type of the Field.

source_uuid: UUID | None

Stable layout-level field identifier used to map equivalent Fields across versions of the same layout.

original_source_uuid: UUID | None

The original stable layout-level field identifier. Used to map Fields from partially matched Documents to a specific layout field.

shared_field_uuid: UUID | None

The unique identifier of the shared field, if applicable.

field_number: int

One-based index, defining the number of the Field within the layout.

field_name: str

The name of the layout field that this Field was matched to.

output_name: str

The output name of the layout field that this Field was matched to.

required: bool

Indicates whether this Field was defined as required in the layout.

repeated: bool

For fields in Structured layouts, indicates whether this Field can appear multiple times. Data from repeated fields is extracted only from the Field’s first occurrence in a Document.

dropout: bool

Indicates whether text in the Field’s background is removed from the transcription, as defined in the layout.

multiline: bool

Indicates whether this Field is expected to have multiple lines of text, as defined in the layout.

identification_sv_mode: int

Indicates how Supervision affects the identification of the Field. Possible values are:

• 0 (default) - using the default identification process.

• 2 (auto_identify) - the field will never go to Identification Supervision, regardless of the machine’s confidence.

• 3 (sv_always) - the field will always go to Identification Supervision, regardless of the machine’s confidence.

Ignored when process_manual_identification_type is set.

transcription_sv_mode: int

Indicates how Supervision affects the transcription of the Field. Possible values are:

• 0 (default) - using the default transcription process.

• 1 (consensus) - this field requires multiple sources to agree on the transcription before its processing is complete.

• 2 (auto_transcribe) - the field will never go to Transcription Supervision, regardless of the machine’s confidence.

• 3 (sv_always) - the field will always go to Transcription Supervision, regardless of the machine’s confidence.

language: str | None

The language of the layout field that this Field was matched to.

notes: str | None

Additional notes or comments related to the Field.

transcription_target_accuracy: float | None

The target accuracy for the transcription of this Field, as defined in the layout.

process_manual_identification_type: Optional[Literal['FORCE', 'ALWAYS_FORCE', 'SKIP', 'ALWAYS_SKIP']] = None

Overrides the normal extraction process by forcing or bypassing Identification Supervision for this Field.

When set, this value overrides identification_sv_mode.

process_machine_transcription_type: Optional[Literal['FORCE', 'ALWAYS_FORCE', 'SKIP', 'ALWAYS_SKIP']] = None

Overrides the normal extraction process by forcing or bypassing machine transcription for this Field.

process_manual_transcription_type: Optional[Literal['FORCE', 'ALWAYS_FORCE', 'SKIP', 'ALWAYS_SKIP']] = None

Overrides the normal extraction process by forcing or bypassing Manual Transcription Supervision for this Field.

process_flexible_extraction_type: Optional[Literal['FORCE', 'ALWAYS_FORCE', 'SKIP', 'ALWAYS_SKIP']] = None

Overrides the normal extraction process by forcing or bypassing Flexible Extraction Supervision for this Field.

process_orca_type: Optional[Literal['FORCE', 'ALWAYS_FORCE', 'SKIP', 'ALWAYS_SKIP']] = None

Overrides the normal extraction process by forcing or bypassing ORCA extraction for this Field.

identification_source: Literal['machine', 'human']

The method by which the Field was identified.

identification_confidence: Literal['sure', 'not_sure']

The confidence of the Field’s identification.

occurrence_index: int

Zero-based index of this occurrence among all occurrences for the same layout field and in the same Document.

locations: list[FieldLocation]

Array of locations where the Field appears.

page_number: int

Corresponds to the page number of the first FieldLocation of the Field. If the Field has no locations, will reference the first page of the Document.

bounding_box: list[float] | None

Coordinates of the bounding box of the first FieldLocation of the Field. If the Field has no locations, will be None.

exceptions: list[str]

List of exceptions encountered during processing.

class flows_sdk.implementations.idp_v44.contracts.FieldLocation

Represents the location of a field on a page.

type: Literal['approximate', 'exact']

The type of location.

position: int

One-based index of this location among all locations for the same Field, ordered by reading order. Sort by this property to reconstruct the Field’s content in the sequence it appears in across pages.

page_number: int

Reference to the document_page_number of the Page where the Field is located.

bounding_box: list[float]

The location of the Field on the Page, as an array of four normalized coordinates in the format [start_x, start_y, end_x, end_y], relative to page dimensions.

class flows_sdk.implementations.idp_v44.contracts.Table

Represents a concrete table that has been defined for extraction as part of a layout.

layout_table_uuid: UUID

The unique identifier of the specific layout table this Table was matched to.

layout_table_name: str

The name of the layout table that this Table was matched to.

parent_layout_table_uuid: UUID | None = None

Indicates the layout_table_uuid of the parent Table, if applicable.

process_manual_identification_type: Optional[Literal['FORCE', 'ALWAYS_FORCE', 'SKIP', 'ALWAYS_SKIP']] = None

Overrides the normal extraction process by forcing or bypassing Identification Supervision for this Table.

identification_confidence: Literal['sure', 'not_sure']

The combined Identification confidence for the entire table hierarchy. If any table in the parent/child hierarchy is not confidently predicted, the value will be not_sure even if the individual table prediction is sure.

model_prediction: Literal['sure', 'not_sure']

The Identification confidence for this specific Table only, independent of its parent or child tables.

rows: list[Row]

Array of Row objects identified as being part of this Table.

columns: list[Column]

Array of Column objects identified as being part of this Table.

class flows_sdk.implementations.idp_v44.contracts.Column

Represents a concrete column in a table that has been defined for extraction as part of a layout.

layout_column_uuid: UUID

The unique identifier of the specific layout column.

data_type_uuid: UUID

The unique identifier of the data type of the TableColumn.

source_uuid: UUID | None

Stable layout-level column identifier used to map equivalent Columns across versions of the same layout.

column_number: int

One-based index, defining the number of the Column within the layout.

field_name: str

The name of the Column as defined in the layout.

output_name: str

The output name of the Column as defined in the layout.

required: bool

Indicates whether this Column was defined as required in the layout.

dropout: bool

Indicates whether text in the background of the cells in this Column is removed from the transcription, as defined in the layout.

multiline: bool

Indicates whether this Column is expected to have multiple lines of text, as defined in the layout.

transcription_sv_mode: int

Indicates how Supervision affects the transcription of the cells in this Column. Possible values are:

• 0 (default) - using the default transcription process.

• 1 (consensus) - the cells require multiple sources to agree on the transcription before their processing is complete.

• 2 (auto_transcribe) - the cells will never go to Transcription Supervision, regardless of the machine’s confidence.

• 3 (sv_always) - the cells will always go to Transcription Supervision, regardless of the machine’s confidence.

language: str | None

The language of the Column as defined in the layout.

notes: str | None

Additional notes or comments related to the Column.

transcription_target_accuracy: float | None = None

The target accuracy for the transcription of the cells in this Column, as defined in the layout.

cells: list[Cell]

Array of Cell objects identified as being part of this Column.

class flows_sdk.implementations.idp_v44.contracts.Row

Represents a row of cells in a table that has been defined for extraction as part of a layout.

row_number: int

Indicates the order in which this Row appears in the Table.

parent_row_number: int | None = None

Indicates the row_number of the parent Row, if part of a child Table.

missing_row_iteration: int | None = None

Indicates which iteration of the missing-row-detection algorithm identified this Row.

class flows_sdk.implementations.idp_v44.contracts.Cell

Represents a concrete cell in a table that has been defined for extraction as part of a layout.

id: int

Unique system-generated identifier.

uuid: UUID

Unique system-generated identifier.

status: Optional[Literal['processing', 'complete']] = None

Current status of the Cell.

process_machine_transcription_type: Optional[Literal['FORCE', 'ALWAYS_FORCE', 'SKIP', 'ALWAYS_SKIP']] = None

Overrides the normal extraction process by forcing or bypassing machine transcription for this Cell.

process_manual_transcription_type: Optional[Literal['FORCE', 'ALWAYS_FORCE', 'SKIP', 'ALWAYS_SKIP']] = None

Overrides the normal extraction process by forcing or bypassing Manual Transcription Supervision for this Cell.

process_flexible_extraction_type: Optional[Literal['FORCE', 'ALWAYS_FORCE', 'SKIP', 'ALWAYS_SKIP']] = None

Overrides the normal extraction process by forcing or bypassing Flexible Extraction Supervision for this Cell.

identification_source: Literal['machine', 'human']

The method by which the Cell was identified.

identification_confidence: Literal['sure', 'not_sure']

The confidence of the Cell’s identification.

layout_column_uuid: UUID

The unique identifier of the specific layout column this Cell was matched to.

row_number: int

Reference to the row_number of the Row where the Cell is located.

page_number: int

Reference to the document_page_number of the Page where the Cell is located.

bounding_box: list[float] | None = None

The location of the Cell on the Page, as an array of four normalized coordinates in the format [start_x, start_y, end_x, end_y], relative to page dimensions.

exceptions: list[str]

List of exceptions encountered during processing.

class flows_sdk.implementations.idp_v44.contracts.Case

The Case object represents a set of documents collated for a particular purpose.

id: int

Unique system-generated identifier.

uuid: UUID

Unique system-generated identifier that remains unique when there is a need to migrate case data between systems.

external_case_id: str

User-defined identifier that can be included as part of the Case object during case creation.

notes: str

User-defined string that can be included as part of the Case object during case creation or update.

start_time: datetime

Time the Case was created.

update_time: datetime

Time the Case was last updated.

to_delete_time: datetime | None = None

Time the Case is scheduled to be deleted, if applicable.

submissions: list[int]

Array of ids of Submissions with Pages (or Documents) included in this Case.

submission_files: list[str]

Array of strings representing the names of files comprising the Case.

unassigned_pages: list[int]

Array of ids of Pages that are included in the Case but not yet assigned to a Document.

documents: list[int]

Array of ids of Documents included in the Case.

class flows_sdk.implementations.idp_v44.contracts.FilePageOrigin

Represents the origin of a page when detailed file information is available.

file_uuid: UUID

Unique identifier of the file from which this Page was extracted.

file_name: str | None = None

The name of the file from which this Page was extracted. Available in v43.1 and later.

sub_file_number: int

The number of the sub-file from which this Page was extracted. For example, if a ZIP file contains three PDFs, the Pages extracted from the first PDF would have sub_file_number 1, the second PDF would have sub_file_number 2, and the third PDF would have sub_file_number 3.

file_page_number: int | None

The page number within the file / sub-file. This value is 1-indexed.

file_group: str

The group to which the file belongs.

intermediate_file_uuid: UUID | None = None

UUID of the intermediate PDF blob produced when paginating a non-PDF file type (e.g., DOCX, HTML). None for native PDFs and image-only formats. Available in v43.2 and later.

class flows_sdk.implementations.idp_v44.contracts.FilenamePageOrigin

Represents the origin of a page when the file name is the only available information. This situation can occur when the system fails to extract the referenced file.

file_name: str

The name of the file from which this Page was extracted.

class flows_sdk.implementations.idp_v44.contracts.PageSegments

Represents the segmentation details of a page, including the number of text, checkbox, and signature segments.

uuid: UUID

Unique identifier used to link the Page to its corresponding segments in the file store.

model_version: str

The version of the segmentation model used to extract the segments.

segmentation_date: datetime

The date and time when the segmentation was performed.

n_text_segments: int

The number of text segments identified on the Page.

n_checkbox_segments: int

The number of checkbox segments identified on the Page.

n_signature_segments: int

The number of signature segments identified on the Page.

class flows_sdk.implementations.idp_v44.contracts.DataType

Provides means for configuring the data-extraction and normalization processes.

uuid: UUID

Unique system-generated identifier.

name: str

Human-readable name of the DataType.

entry_type: Literal[1, 2, 3]

Represents the method by which the data is extracted. Possible values are:

• 1 (builtin) - extraction using a built-in model.

• 2 (pattern) - extraction using a regex pattern.

• 3 (list) - extraction by matching against a list of accepted values.

ml_model: Literal['checkbox', 'signature', 'entry', 'barcode']

Top-level category of the DataType.

meta_type: str | None

Sub-category of the DataType. For example, for an entry DataType, the meta_type could be date, address, etc.

class flows_sdk.implementations.idp_v44.contracts.ImageCorrectionModelInfo

Provides information about the models used for image correction.

rotation_model: str

The version of the rotation-detection model used for image correction.

mobile_model: str | None

The version of the mobile-correction model used for image correction, if applicable.

class flows_sdk.implementations.idp_v44.contracts.ImageCorrectionPerspectiveTransform

Provides information about the perspective transform applied during mobile correction.

absolute_matrix: list[list[float]]

The 3x3 perspective (homography) matrix in absolute pixel coordinates.

relative_matrix: list[list[float]]

The 3x3 perspective (homography) matrix in normalized relative coordinates.

relative_corner_points: list[list[float]]

The corner points of the perspective transform, represented as relative coordinates (between 0 and 1) with respect to the original image dimensions.

class flows_sdk.implementations.idp_v44.contracts.DocumentFile

Metadata for a printable file rendered from a document’s page images.

document_uuid: UUID

UUID of the source Document this printable file was rendered for.

uuid: UUID

UUID of the stored rendered file.

checksum: str

Checksum of the stored rendered file.

mime_type: str

MIME type of the rendered file (e.g., application/pdf).

height_in_mm: float

The maximum page height across all rendered pages in millimeters.

width_in_mm: float

The maximum page width across all rendered pages in millimeters.

quality: int

Rendering quality used when generating the printable file.

mode: str

Color mode used during rendering (e.g., RGB, GRAYSCALE, or BW).

download_url: str

API URL for downloading the rendered file.

page_dimensions: list[DocumentFilePage]

Per-page rendered metadata listed in the same order as pages in the output file.

class flows_sdk.implementations.idp_v44.contracts.DocumentFilePage

Metadata for one rendered page in a DocumentFile.

Includes the source page image identifier and the rendered page size in millimeters.

image_uuid: UUID

UUID of the source image used for this rendered page.

width_in_mm: float | None

Rendered page width in millimeters.

height_in_mm: float | None

Rendered page height in millimeters.

class flows_sdk.implementations.idp_v44.contracts.FragmentedSegments

Metadata for a stored artifact that contains fragmented segments from a document.

Each instance represents one derived segment set produced from the document’s original segments using a particular splitting configuration.

uuid: UUID

Unique identifier of the stored fragmented segments.

flex_model_type: str

Specifies the splitting strategy used to derive these fragmented segments from the original segments.

custom_split_char: str | None

Optional custom split-character configuration used when these fragmented segments were derived.

class flows_sdk.implementations.idp_v44.contracts.EntityGroup

The EntityGroup object represents a group of pages and entities extracted from those pages.

identifier: str

Unique system-generated identifier.

pages: list[Page]

Array of Page objects included in this EntityGroup.

entities: list[Entity]

Array of Entity objects included in this EntityGroup.

class flows_sdk.implementations.idp_v44.contracts.Entity

The Entity object presents a single extracted occurrence of a given type.

uuid: UUID

Unique system-generated identifier.

origin: Literal['NER', 'CED', 'Signature']

The origin of the Entity, indicating which method was used for extracting it.

type: str

The type of the Entity (e.g., Person Name, Location, etc.).

text: str

The text extracted from the Entity.

locations: list[EntityLocation]

Array of locations where the Entity appears.

confidence: float

A number between 0 and 1 indicating the confidence the machine has in the accuracy of the Entity’s extraction.

class flows_sdk.implementations.idp_v44.contracts.EntityLocation

Represents the location of an entity on a page.

position: int

One-based index of this location among all locations for the same Entity, ordered by reading order. Sort by this property to reconstruct the Entity’s content in the sequence it appears across pages.

page_number: int

Reference to the submission_page_number of the Page where the Entity is located.

bounding_box: list[float]

The location of the Entity on the Page, as array of four numbers in the format [start_x, start_y, end_x, end_y].

text: str

The text extracted from the Entity location.

raw_text: str

The raw text extracted from the Entity location.

class flows_sdk.implementations.idp_v44.contracts.RedactedGroup

The RedactedGroup object represents a group of redacted pages.

identifier: str

Identifier of the source redaction group.

pages: list[RedactedPage]

Array of RedactedPage objects included in this RedactedGroup.

class flows_sdk.implementations.idp_v44.contracts.RedactedPage

Represents a page with redaction data.

id: int

Unique system-generated identifier.

redaction_data: PageRedactionData

A PageRedactionData object containing the redaction data for the Page.

submission_page_number: int

Indicates page order for the Submission.

status: Literal['processing', 'failed', 'complete']

Current status of the Page.

page_type: Union[Literal['unassigned_page', 'blank_page', 'other_page', 'additional_form_page', 'document_page', 'unknown_page', 'failed'], str]

The type of the Page.

origin: FilePageOrigin

The origin of the Page, which can be used to determine from which submitted file the Page was extracted.

classification_type: Optional[Literal['manual', 'machine']] = None

The method by which the Page was classified, if applicable. Present when the page_type is not ‘unknown_page’ or ‘unassigned_page’.

machine_layout_page_uuid: UUID | None = None

Unique identifier of the layout page that the machine predicted this Page matches to, if applicable.

manual_layout_page_uuid: UUID | None = None

Unique identifier of the layout page that a keyer assigned this Page to, if applicable.

combined_segments: PageSegments | None = None

The segments for the Page, if applicable.

image_uuid: UUID

Unique identifier of the image for this Page. Refers to the original image as extracted from the source file before any transformations were applied.

image_name: str

The name of the original image file for this Page.

image_width: int

The width of the original image for this Page in pixels.

image_height: int

The height of the original image for this Page in pixels.

image_checksum: str

The checksum of the original image for this Page.

corrected_image_uuid: UUID | None = None

Unique identifier of the corrected image for this Page, if applicable. Refers to the image after transformations such as rotation or perspective correction were applied.

image_correction_type: Optional[Literal['layout_matching', 'image_correction', 'manual']] = None

The type of image correction applied to this Page, if applicable. Available in v43.1 and later.

transformation_matrix: list[list[float]] | None = None

The 3x3 registration homography matrix for the Page, if applicable.

template_image_shapes: list[list[int]] | None = None

Registration shape metadata for the layout image, if applicable. Contains the pre-computed layout shape and the original layout image shape.

submission_image_shape: list[int] | None = None

Registration pre-processing shape of the submission image, if applicable.

correction_model_info: ImageCorrectionModelInfo | None = None

Model version metadata for image correction on this Page, if applicable.

rotation_confidence: float | None = None

The confidence score for the applied rotation correction for the Page, if applicable. Ranges from 0.0 to 1.0, or None if not applicable.

rotation_class: int | None = None

The applied rotation-correction class for the Page, if applicable. Values are 0, 1, 2, or 3, representing the number of 90-degree counterclockwise rotations applied, or None if not applicable.

deskew_confidence: float | None = None

The confidence score for the applied deskew correction for the Page, if applicable. Ranges from 0.0 to 1.0, or None if not applicable.

skew_angle: float | None = None

The applied deskew-correction angle for the Page in degrees, if applicable. This value represents the rotation applied to deskew the image (it may be 0 when no deskew is needed), or None if not applicable.

mobile_correction_result: Optional[Literal['PREDICTED_TRANSFORM', 'THRESHOLDED_AS_SCANNED', 'NO_VANISHING_POINT_CONSENSUS', 'TOO_FEW_DOCUMENT_PIXELS']] = None

Outcome of mobile perspective correction for this Page, if applicable. Indicates whether a perspective transform was applied or why it was not, or None if not applicable.

applied_transform: ImageCorrectionPerspectiveTransform | None = None

Applied mobile perspective transform for this Page, if applicable.

exceptions: list[str]

List of exceptions encountered during processing.

class flows_sdk.implementations.idp_v44.contracts.PageRedactionData

Contains the redaction data for a Page.

image_uuid: str

Unique identifier of the primary redacted image for the Page.

debug_image_uuid: str

Unique identifier of a separate debug rendering of the same redacted Page. Contains visual indicators of where redactions were applied.

redactions: list[Redaction]

Array of Redaction objects associated with the Page.

class flows_sdk.implementations.idp_v44.contracts.Redaction

Represents a single redaction applied to a page.

kind: str

The normalized redaction category.

bounding_box: list[float]

The location of the redacted text on the Page, as an array of four numbers in the format [start_x, start_y, end_x, end_y].

original_text: str

The original text before redaction.

redacted_text: str

The replacement text written into the page image. May be empty when text is not replaced (e.g., in opaque/box-based redaction modes).

class flows_sdk.implementations.idp_v44.contracts.Transcription

Represents a transcription of text with a unique identifier.

uuid: UUID

Unique system-generated identifier.

value: str

The transcribed text value.

class flows_sdk.implementations.idp_v44.contracts.AddressData

Represents the address data structure, including parsed, normalized, and verification details.

uuid: UUID

The unique identifier for the address data.

parse_error: str | None = None

The error encountered during the parsing of the address, if any.

normalized_address: str | None = None

The normalized address as a string.

parsed_address_json: ParsedAddress | None = None

The parsed address data, if available.

verification_json: dict[str, Any] | None = None

The verification details of the parsed address, if available.

class flows_sdk.implementations.idp_v44.contracts.ParsedAddress

Represents the parsed address data extracted by the specified parser.

parser: Literal['geoapify', 'google', 'smarty', 'usaddress', 'deepparse']

The parser used to extract the address data.

recipient: str | None = None

The recipient name, if available.

street_number: str | None = None

The street number, if available.

street_name: str | None = None

The street name, if available.

unit: str | None = None

The unit or apartment number, if available.

po_box: str | None = None

The PO box number, if available.

city: str | None = None

The city name, if available.

state: str | None = None

The state or province name, if available.

postal_code: str | None = None

The postal code, if available.

country: str | None = None

The country name, if available.

country_code: str | None = None

The country code, if available.

raw: dict[str, Any] | None = None

The raw address data as a dictionary, if available.

V43

Processing Blocks (v43)

Core Blocks are powerful processing blocks necessary to build intelligent document processing solutions on Hyperscience. They include both manual and machine processing blocks.

Note

Classes in the idp_v43 implementation have parameters with ambiguous typing. For example, a field of type integer could also be represented as a string reference (e.g. that integer gets provided at runtime). The types introduced in the example are therefore incomplete and are likely to be revisioned in a future version.

class flows_sdk.implementations.idp_v43.idp_blocks.IDPCoreBlock(idp_wf_config, reference_name='__idp_core__', title='Start Document Processing Subflow', description='Calls the Document Processing Subflow with the specified settings.', compatibility_spec=CompatibilitySpec(filter_roles=['idp_core'], filter_schema=None, label=None), identifier='IDP_CORE_V43', additional_inputs=None)

Bases: Block

IDP core block includes the steps to process the passed documents into machine-readable output.

Mandatory parameters:

Parameters:

idp_wf_config (IdpWorkflowConfig) – IdpWorkflowConfig object with all flow-level configuration settings for IDP

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique reference name within a flow, a unique one will be generated when not provided

• compatibility_spec (CompatibilitySpec | None) – defines a filter for flows that could be selected in the UI in place of “IDP Core” for the block. Defaults to flows with the role ‘idp_core’. The user will be able to pick any flow that matches the compatibility_spec. This serves to make upgrading to a new compatible version of “IDP Core” (e.g. IDP_CORE_V39) easier in future releases.

• identifier (str) – The identifier of the flow. Defaults to the IDP_CORE_VXX identifier of the out-of-the-box flow that comes with the system.

• additional_inputs (dict[str, Any] | None) – Dictionary containing key-value pairs that will be added to the inputs of the block. If a key is contained in the default inputs, its value will be overwritten by the value provided here. You generally do not need to provide additional_inputs, unless you are creating a custom derivative of the IDP_CODE_Vxx flow and are modifying or extending the list of inputs it accepts.

Example usage:

idp_core = IDPCoreBlock(idp_wf_config=idp_wf_config)

REFERENCE_NAME = '__idp_core__'

class flows_sdk.implementations.idp_v43.idp_blocks.SubmissionBootstrapBlock(reference_name=None, trigger='${workflow.input.$trigger}', layout_release_uuid='${workflow.input.layout_release_uuid}', s3_config='${workflow.input.submission_bootstrap_s3_config}', s3_endpoint_url='${workflow.input.submission_bootstrap_s3_endpoint_url}', ocs_config='${workflow.input.submission_bootstrap_ocs_config}', ocs_host_url='${workflow.input.submission_bootstrap_ocs_host_url}', ocs_username='${workflow.input.submission_bootstrap_ocs_username}', ocs_password='${workflow.input.submission_bootstrap_ocs_password}', ocs_ssl_cert='${workflow.input.submission_bootstrap_ocs_ssl_cert}', http_config='${workflow.input.submission_bootstrap_http_config}', oauth_token_url='${workflow.input.submission_bootstrap_oauth_token_url}', oauth_client_id='${workflow.input.submission_bootstrap_oauth_client_id}', oauth_client_secret='${workflow.input.submission_bootstrap_oauth_client_secret}', oauth_scope='${workflow.input.submission_bootstrap_oauth_scope}', abs_auth_type='${workflow.input.submission_bootstrap_abs_auth_type}', abs_account_url='${workflow.input.submission_bootstrap_abs_account_url}', abs_tenant_id='${workflow.input.submission_bootstrap_abs_tenant_id}', abs_client_id='${workflow.input.submission_bootstrap_abs_client_id}', abs_client_secret='${workflow.input.submission_bootstrap_abs_client_secret}', abs_authority_host='${workflow.input.submission_bootstrap_abs_authority_host}', abs_account_name='${workflow.input.submission_bootstrap_abs_account_name}', abs_account_key='${workflow.input.submission_bootstrap_abs_account_key}', gcs_credentials_json='${workflow.input.submission_bootstrap_gcs_credentials_json}', gcs_workload_identity='${workflow.input.submission_bootstrap_gcs_workload_identity}', max_number_pages_enabled='${workflow.input.submission_bootstrap_max_number_pages_enabled}', max_number_pages='${workflow.input.submission_bootstrap_max_number_pages}', notification_workflow='${workflow.input.notification_workflow_initialization}', workflow_uuid='${workflow.input.workflow_uuid}', workflow_name='${workflow.input.workflow_name}', workflow_version='${workflow.input.workflow_version}', submission='${workflow.input.submission}')

Bases: Block

Submission bootstrap block initializes the submission object and prepares external images or other submission data if needed. This block should be called at the START of every flow that uses blocks included in the library.

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• trigger (str) – the trigger input from a connector - if not set explicitly, it will default to the workflow input trigger (which is where this value is normally found)

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• s3_config (str) – Specify Amazon S3 credentials for submission retrieval store. Expects a json expression: {"aws_access_key_id": "X", "aws_secret_access_key": "Y"}, defaults to workflow_input(Settings.SubmissionBootstrapS3Config)

• s3_endpoint_url (str) – Endpoint URL for S3 submission retrieval store, defaults to workflow_input(Settings.SubmissionBootstrapS3EndpointUrl)

• ocs_config (str) – OCS Configuration for downloading submission data. @deprecated since v43.1; will be removed in v46.0. defaults to workflow_input(Settings.SubmissionBootstrapOCSConfig)

• ocs_host_url (str | None) – Available from V42.3; OCS host URL for submission retrieval store, @deprecated since v43.1; will be removed in v46.0. defaults to workflow_input(Settings.SubmissionBootstrapOCSHostUrl)

• ocs_username (str | None) – Available from V42.3; OCS username for authentication, @deprecated since v43.1; will be removed in v46.0. defaults to workflow_input(Settings.SubmissionBootstrapOCSUsername)

• ocs_password (str | None) – Available from V42.3; OCS password for authentication, @deprecated since v43.1; will be removed in v46.0. defaults to workflow_input(Settings.SubmissionBootstrapOCSPassword)

• ocs_ssl_cert (str | None) – Available from V42.3; CA bundle filename inside $HS_PATH/certs directory or “SKIP” to disable validation, @deprecated since v43.1; will be removed in v46.0. defaults to workflow_input(Settings.SubmissionBootstrapOCSSslCert)

• http_config (str | None) – An optional system secret field expressing the HTTP Configuration for downloading submission data. The content is expected to be a json formatted as: {"username": "X", "password": "Y", "ssl_cert": "CA bundle filename"}, defaults to workflow_input(Settings.SubmissionBootstrapHTTPConfig)

• oauth_token_url (str | None) – OAuth token endpoint for client credentials authentication.

• oauth_client_id (str | None) – Client ID for OAuth client credentials.

• oauth_client_secret (str | None) – Client secret for OAuth client credentials.

• oauth_scope (str | None) – Optional OAuth scope sent with the OAuth client credentials token request.

• abs_auth_type (str | None) – The type of authentication to use for Azure Blob Storage (‘SAS Token Only’ / ‘Service Principal’ / ‘Managed Identity’ / ‘Account Key’), defaults to workflow_input(Settings.SubmissionBootstrapABSAuthType)

• abs_account_url (str | None) – The URL of the Azure Blob Storage account, defaults to workflow_input(Settings.SubmissionBootstrapABSAccountUrl)

• abs_tenant_id (str | None) – The tenant ID of the service principal, defaults to workflow_input(Settings.SubmissionBootstrapABSTenantId)

• abs_client_id (str | None) – The client ID of the service principal or managed identity, defaults to workflow_input(Settings.SubmissionBootstrapABSClientId)

• abs_client_secret (str | None) – The client secret of the service principal, defaults to workflow_input(Settings.SubmissionBootstrapABSClientSecret)

• abs_authority_host (str | None) – The authority host of the service principal, defaults to workflow_input(Settings.SubmissionBootstrapABSAuthorityHost)

• abs_account_name (str | None) – The account name of the Azure Blob Storage account, defaults to workflow_input(Settings.SubmissionBootstrapABSAccountName)

• abs_account_key (str | None) – The account key of the Azure Blob Storage account, defaults to workflow_input(Settings.SubmissionBootstrapABSAccountKey)

• gcs_credentials_json (str) – GCS service account credentials in JSON format, defaults to workflow_input(Settings.SubmissionBootstrapGCSCredentialsJson)

• gcs_workload_identity (str) – Whether to obtain the credentials by using the Workload Identity Federation, applicable for HS installations inside GKE clusters, defaults to workflow_input(Settings.SubmissionBootstrapGCSWorkloadIdentity)

• max_number_pages_enabled (str | None) – An optional parameter (flag) controlling whether the configured max number of pages is taken into account

• max_number_pages (str | None) – An optional parameter specifying the max number of pages for downstream processing. Submissions with pages exceeding this configured value will fail. The default value is set to 10_000 pages.

• notification_workflow (str | None) – Notification flow name to run when the submission is initialized, defaults to workflow_input(Settings.SubmissionBootstrapNotificationWorkflow)

• workflow_uuid (str) – UUID of the triggered workflow version, defaults to workflow_input(Inputs.WorkflowUuid)

• workflow_name (str) – Name of the triggered workflow, defaults to workflow_input(Inputs.WorkflowName)

• workflow_version (str) – Version of the triggered workflow, defaults to workflow_input(Inputs.WorkflowVersion)

• submission (dict[str, Any] | str | None) – Submission object

Output: The block returns an object with the following keys:

• submission: Submission object.

• api_params: a dictionary containing the parameters with which the submission was initialized. Extracted from the block’s trigger input.

Example usage:

submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')

IDENTIFIER = 'SUBMISSION_BOOTSTRAP_3'

output(key=None)

Submission bootstrap ends with a CodeBlock, which nests the actual outputs under the ‘result’ key. This method overrides the default output in the same manner that flows_sdk.blocks.Routing.CodeBlock does for convenience. When the no_wrapping output mode is selected, the output is emitted unwrapped, so the reference points to the raw output instead.

Parameters:

key (str | None) –

Optionally provide a key to directly get a nested property. When not provided, the entire ‘result’ will be returned. For example, we have:

{
    "result": {
        "a": {
            "b": 42
        }
    }
}

• skipping the key output()` will return ``{"a": {"b": 42}}

• calling with output("a") will result in {"b": 42}

• calling with output("a.b") will return 42

Return type:

str

Returns:

the value under the provided key

class flows_sdk.implementations.idp_v43.idp_blocks.MachineCollationBlock(submission, cases, dedupe_files='${workflow.input.machine_collation_dedupe_files}', refresh_retention_period='${workflow.input.machine_collation_refresh_retention_period}', remove_from_cases='${workflow.input.machine_collation_remove_from_cases}', retention_period='${workflow.input.machine_collation_retention_period}', reference_name=None)

Bases: Block

Machine collation block groups files, documents and pages (from the submission) into cases.

Mandatory parameters:

Parameters:

• submission (dict[str, Any] | str) – Submission object

• cases (list[dict[str, Any]] | str) –

  This parameter accepts an array of JSON objects that contain information on how Cases should be created. There are two ways to collate a case: by the filenames specified in the current Submission, or by the ids of the Documents or Pages submitted in previous submissions.

  This parameter follows the given format:

  {
      'cases': [
          'external_case_id': 'HS-1',
          'filename': 'file1.pdf',
          'documents': [42],
          'pages': [43]
      ]
  }
  

Parameters with defaults:

Parameters:

• dedupe_files (bool | str) – Enabling this setting will replace case data from repeated file names within the same case. Cases will retain data from the most recently submitted version of a particular file. Note that this setting does not delete the old data, it just removes it from the case. Keep in mind this option is only relevant when adding to cases. Defaults to workflow_input(Settings.CollationDedupeFiles)

• refresh_retention_period (bool | str) – If True, (re)applies Retention Period parameter to all cases passed into the block. If False, only updates those without a pre-existing deletion date. Defaults to workflow_input(Settings.CollationRefreshRetentionPeriod)

• remove_from_cases (list[dict[str, Any]] | str | None) –

  This parameter accepts an array of JSON objects that contains information on how documents or pages should be removed from a pre-existing case. Currently, the only way to de-collate from a case is by the ids of the Documents or Pages within a given case, defaults to workflow_input(Settings.CollationRemoveFromCases)

  This parameter follows the given format:

  {
      'remove_from_cases': [
          'external_case_id': 'HS-1',
          'documents': [42],
          'pages': [43]
      ]
  }
  

• retention_period (int | str | None) – The number of days to retain cases after they are updated by this block. If passed None, no changes to the deletion date will be made. Defaults to workflow_input(Settings.CollationRetentionPeriod)

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

Output: The block returns an object with the following keys:

• submission: Submission object.

Example usage:

machine_collation = MachineCollationBlock(
    reference_name='machine_collation',
    submission=submission_bootstrap.output('submission'),
    cases=submission_bootstrap.output('api_params.cases'),
    dedupe_files=True,
    remove_from_cases=custom_supervision.output('remove_from_cases')
)

IDENTIFIER = 'MACHINE_COLLATION_2'

class flows_sdk.implementations.idp_v43.idp_blocks.MachineClassificationBlock(submission, api_params, reference_name=None, rotation_correction_enabled='${workflow.input.machine_classification_rotation_correction_enabled}', mobile_processing_enabled='${workflow.input.machine_classification_mobile_processing_enabled}', file_transcription_enabled='${workflow.input.machine_classification_file_transcription_enabled}', layout_release_uuid='${workflow.input.layout_release_uuid}', vpc_registration_threshold='${workflow.input.structured_layout_match_threshold}', nlc_enabled='${workflow.input.semi_structured_classification}', nlc_target_accuracy='${workflow.input.semi_target_accuracy}', nlc_doc_grouping_logic='${workflow.input.semi_doc_grouping_logic}', layout_id_dealbreaker='${workflow.input.layout_id_dealbreaker}', layout_id_dealbreaker_bypass='${workflow.input.layout_id_dealbreaker_bypass}', manual_nlc_enabled='${workflow.input.manual_nlc_enabled}')

Bases: Block

Machine classification block automatically matches documents to structured, semi-structured or additional layouts

Mandatory parameters:

Parameters:

• submission (dict[str, Any] | str) – Submission object

• api_params (dict[str, Any] | str)

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• rotation_correction_enabled (bool | str) – Identifies and corrects the orientation of semi-structured images, defaults to workflow_input(Settings.RotationCorrectionEnabled)

• mobile_processing_enabled (bool | str) – Improves the machine readability of photo captured documents, defaults to workflow_input(Settings.MobileProcessingEnabled)

• file_transcription_enabled (bool | str) – Improves the transcription speed of pdf files. Make sure that the “Image Correction” option is disabled and the pdfs you submit do not contain any rotated images defaults to workflow_input(Settings.FileTranscriptionEnabled)

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• vpc_registration_threshold (str | float) – Structured pages above this threshold will be automatically matched to a layout variation, defaults to workflow_input(Settings.StructuredLayoutMatchThreshold)

• nlc_enabled (str | bool) – Enables workflow to manage a model for automated classification of semi-structured and additional layout variations, defaults to workflow_input(Settings.SemiStructuredClassification)

• nlc_target_accuracy (str | float) – defaults to workflow_input(Settings.SemiTargetAccuracy)

• nlc_doc_grouping_logic (str) – Logic to handle multiple pages matched to the same layout variation a given submission, defaults to workflow_input(Settings.SemiDocGroupingLogic)

• layout_id_dealbreaker (str | bool) – Validates structured document matching by ensuring that the Form ID on the document matches the Layout ID on the corresponding layout, defaults to workflow_input(Settings.LayoutIdDealbreaker)

• layout_id_dealbreaker_bypass (str | bool) – Bypasses Form ID and Layout ID validation if there is no Layout ID present on the Layout Page, defaults to workflow_input(Settings.LayoutIdDealbreakerBypass)

• manual_nlc_enabled (str | bool) – Available from V41.2; enables manual classification when applicable, defaults to workflow_input(Settings.ManualNlcEnabled)

Output: The block returns an object with the following keys:

• submission: Submission with the documents and unassigned_pages fields updated with the classification results.

• needs_manual_classification: A boolean flag indicating whether the submission needs manual classification.

• vpc_metadata: Metadata related to the structured classification process.

• nlc_metadata: Metadata related to the non-structured classification process.

Example usage:

machine_classification = MachineClassificationBlock(
    reference_name='machine_classification',
    submission=case_collation.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    rotation_correction_enabled=idp_wf_config.rotation_correction_enabled,
)

IDENTIFIER = 'MACHINE_CLASSIFICATION_7'

class flows_sdk.implementations.idp_v43.idp_blocks.ManualClassificationBlock(submission, api_params, reference_name=None, layout_release_uuid='${workflow.input.layout_release_uuid}', manual_nlc_enabled='${workflow.input.manual_nlc_enabled}', task_restrictions='${workflow.input.manual_classification_task_restrictions}', notification_workflow='${workflow.input.notification_workflow_classification}', vpc_registration_threshold='${workflow.input.structured_layout_match_threshold}', nlc_metadata=None)

Bases: Block

Manual classification block allows keyers to manually match submissions to their layouts. Keyers may perform manual classification if machine classification cannot automatically match a submission to a layout with high confidence

Mandatory parameters:

Parameters:

• submission (dict[str, Any] | str) – Submission object

• api_params (dict[str, Any] | str)

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• manual_nlc_enabled (str | bool) – Enables manual classification when applicable, defaults to workflow_input(Settings.ManualNlcEnabled)

• task_restrictions (list[Any] | str | None) – Defines what users can access Supervision tasks created by this block, defaults to workflow_input(Settings.ManualClassificationTaskRestrictions)

• notification_workflow (str | None) – Notification flow name to run when the submission enters Manual Classification, defaults to workflow_input(Settings.ManualClassificationNotificationWorkflow)

• vpc_registration_threshold (str | float | None) – Structured pages above this threshold will be automatically matched to a layout variation when performing light vpc registration in the doc-org task, defaults to workflow_input(Settings.StructuredLayoutMatchThreshold)

• nlc_metadata (dict[str, Any] | str | None) – Optional NLC metadata containing predictions per page from machine classification, used to populate best-guess layout suggestions in the manual classification supervision task, defaults to None

Output: The block returns an object with the following keys:

• submission: Submission with the documents and unassigned_pages fields updated with the classification results.

• supervision_metadata: Metadata about the created supervision task.

Example usage:

manual_classification = ManualClassificationBlock(
    reference_name='manual_classification',
    submission=machine_classification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
)

IDENTIFIER = 'MANUAL_CLASSIFICATION_6'

class flows_sdk.implementations.idp_v43.idp_blocks.MachineIdentificationBlock(submission, api_params, layout_release_uuid='${workflow.input.layout_release_uuid}', reference_name=None, manual_field_id_enabled='${workflow.input.manual_field_id_enabled}', force_required_missing_entries_to_sv='${workflow.input.force_required_entries_to_sv}', field_id_target_accuracy='${workflow.input.field_id_target_accuracy}', table_id_target_accuracy='${workflow.input.table_id_target_accuracy}', id_target_accuracies='${workflow.input.identification_target_accuracies}')

Bases: Block

Machine identification automatically identifies fields and tables in the submission

Mandatory parameters:

Parameters:

• submission (dict[str, Any] | str) – Submission object

• api_params (dict[str, Any] | str)

Parameters with defaults:

Parameters:

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• manual_field_id_enabled (bool | str) – Enables manual identification when applicable, defaults to workflow_input(Settings.ManualFieldIdEnabled)

• force_required_missing_entries_to_sv (bool | str) – defaults to workflow_input(Settings.ForceRequiredEntriesToSV)

• field_id_target_accuracy (float | str) – Field ID Target Accuracy, defaults to workflow_input(Settings.FieldIdTargetAccuracy)

• table_id_target_accuracy (float | str) – Table ID Target Accuracy, defaults to workflow_input(Settings.TableIdTargetAccuracy)

• id_target_accuracies (list[dict[str, Any]] | str) – defaults to workflow_input(Settings.IdentificationTargetAccuracies)

Output: The block returns an object with the following keys:

• submission: Submission with its documents updated with the identified fields and tables.

• needs_manual_identification: A boolean flag indicating whether the submission needs Identification Supervision.

• fl_metadata: Metadata related to the Field Identification process.

• tl_metadata: Metadata related to the Table Identification process.

Example usage:

machine_identification = MachineIdentificationBlock(
    reference_name='machine_identification',
    submission=manual_classification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
)

IDENTIFIER = 'MACHINE_IDENTIFICATION_9'

class flows_sdk.implementations.idp_v43.idp_blocks.ManualIdentificationBlock(submission, api_params, reference_name=None, task_restrictions='${workflow.input.manual_identification_task_restrictions}', manual_field_id_enabled='${workflow.input.manual_field_id_enabled}', layout_release_uuid='${workflow.input.layout_release_uuid}', notification_workflow='${workflow.input.notification_workflow_identification}')

Bases: Block

Manual identification allows keyers to complete field identification or table identification tasks, where they draw bounding boxes around the contents of certain fields, table columns or table rows. This identification process ensures that the system will be able to transcribe the correct content in the upcoming transcription process

Mandatory parameters:

Parameters:

• submission (dict[str, Any] | str) – Submission object

• api_params (dict[str, Any] | str)

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• task_restrictions (list[Any] | str | None) – Defines what users can access Supervision tasks created by this block, defaults to workflow_input(Settings.ManualFieldIdTaskRestrictions)

• manual_field_id_enabled (bool | str) – Enables manual identification when applicable, defaults to workflow_input(Settings.ManualFieldIdEnabled)

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• notification_workflow (str) – Notification flow name to run when the submission enters Manual Identification, defaults to workflow_input(Settings.ManualFieldIdNotificationWorkflow)

Output: The block returns an object with the following keys:

• submission: Submission with its documents updated with the identified fields and tables.

Example usage:

manual_identification = ManualIdentificationBlock(
    reference_name='manual_identification',
    submission=machine_identification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    task_restrictions=idp_wf_config.manual_identification_config.task_restrictions,
)

IDENTIFIER = 'MANUAL_IDENTIFICATION_6'

class flows_sdk.implementations.idp_v43.idp_blocks.MachineTranscriptionBlock(submission, api_params, reference_name=None, layout_release_uuid='${workflow.input.layout_release_uuid}', transcription_model='${workflow.input.transcription_model}', finetuning_only_trained_layouts='${workflow.input.finetuning_only_trained_layouts}', force_normalization_errors_to_sv='${workflow.input.force_normalization_errors_to_sv}', force_required_blank_entries_to_sv='${workflow.input.force_required_entries_to_sv}', transcription_overrides='${workflow.input.transcription_overrides}', structured_text_target_accuracy='${workflow.input.structured_text_target_accuracy}', structured_text_confidence_threshold='${workflow.input.structured_text_threshold}', structured_text_acceptable_confidence='${workflow.input.structured_min_leg_threshold}', semi_structured_text_target_accuracy='${workflow.input.semi_structured_text_target_accuracy}', semi_structured_text_confidence_threshold='${workflow.input.semi_structured_text_threshold}', semi_structured_text_acceptable_confidence='${workflow.input.semi_structured_min_leg_threshold}', semi_structured_table_target_accuracy='${workflow.input.semi_structured_table_target_accuracy}', semi_structured_table_confidence_threshold='${workflow.input.semi_structured_table_threshold}', semi_structured_table_acceptable_confidence='${workflow.input.semi_structured_table_min_leg_threshold}', structured_checkbox_target_accuracy='${workflow.input.structured_checkbox_target_accuracy}', structured_checkbox_confidence_threshold='${workflow.input.structured_checkbox_threshold}', structured_checkbox_acceptable_confidence='${workflow.input.checkbox_min_leg_threshold}', structured_signature_target_accuracy='${workflow.input.structured_signature_target_accuracy}', structured_signature_confidence_threshold='${workflow.input.structured_signature_threshold}', structured_signature_acceptable_confidence='${workflow.input.signature_min_leg_threshold}', semi_structured_checkbox_target_accuracy='${workflow.input.semi_structured_checkbox_target_accuracy}', semi_structured_checkbox_confidence_threshold='${workflow.input.semi_structured_checkbox_threshold}', semi_structured_checkbox_acceptable_confidence='${workflow.input.semi_structured_checkbox_min_leg_threshold}', field_target_accuracies='${workflow.input.field_transcription_target_accuracies}', always_supervise_blank_cells='${workflow.input.manual_transcription_always_supervise_blank_cells}', manual_transcription_enabled='${workflow.input.manual_transcription_enabled}', address_parsing_enabled='${workflow.input.address_parsing_enabled}', address_parser_api_provider='${workflow.input.address_parser_api_provider}', address_parser_api_key='${workflow.input.address_parser_api_key}', address_parser_api_options='${workflow.input.address_parser_api_options}')

Bases: Block

Machine transcription automatically transcribes the content of your submission

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• transcription_model (str) – The transcription model that will be used for this flow, defaults to workflow_input(Settings.TranscriptionModel)

• finetuning_only_trained_layouts (str | bool) – defaults to workflow_input(Settings.FinetuningOnlyTrainedLayouts)

• force_normalization_errors_to_sv (str | bool) – defaults to workflow_input(Settings.ForceNormalizationErrorsToSV)

• force_required_blank_entries_to_sv (str | bool) – defaults to workflow_input(Settings.ForceRequiredEntriesToSV)

• transcription_overrides (str) – defaults to workflow_input(Settings.TranscriptionOverrides)

• structured_text_target_accuracy (str | float) – defaults to workflow_input(Settings.StructuredTextTargetAccuracy)

• structured_text_confidence_threshold (str | float) – defaults to workflow_input(Settings.StructuredTextThreshold)

• structured_text_acceptable_confidence (str | float) – defaults to workflow_input(Settings.StructuredTextMinLegThreshold)

• semi_structured_text_target_accuracy (str | float) – defaults to workflow_input(Settings.SemiStructuredTextTargetAccuracy)

• semi_structured_text_confidence_threshold (str | float) – defaults to workflow_input(Settings.SemiStructuredTextThreshold)

• semi_structured_text_acceptable_confidence (str | float) – defaults to workflow_input(Settings.SemiStructuredTextMinLegThreshold)

• semi_structured_table_target_accuracy (str | float) – defaults to workflow_input(Settings.SemiStructuredTableTargetAccuracy)

• semi_structured_table_confidence_threshold (str | float) – defaults to workflow_input(Settings.SemiStructuredTableThreshold)

• semi_structured_table_acceptable_confidence (str | float) – defaults to workflow_input(Settings.SemiStructuredTableMinLegThreshold)

• structured_checkbox_target_accuracy (str | float) – defaults to workflow_input(Settings.StructuredCheckboxTargetAccuracy)

• structured_checkbox_confidence_threshold (str | float) – defaults to workflow_input(Settings.StructuredCheckboxThreshold)

• structured_checkbox_acceptable_confidence (str | float) – defaults to workflow_input(Settings.StructuredCheckboxMinLegThreshold)

• structured_signature_target_accuracy (str | float) – defaults to workflow_input(Settings.StructuredSignatureTargetAccuracy)

• structured_signature_confidence_threshold (str | float) – defaults to workflow_input(Settings.StructuredSignatureThreshold)

• structured_signature_acceptable_confidence (str | float) – defaults to workflow_input(Settings.StructuredSignatureMinLegThreshold)

• semi_structured_checkbox_target_accuracy (str | float) – defaults to workflow_input(Settings.SemiStructuredCheckboxTargetAccuracy)

• semi_structured_checkbox_confidence_threshold (str | float) – defaults to workflow_input(Settings.SemiStructuredCheckboxThreshold)

• semi_structured_checkbox_acceptable_confidence (str | float) – defaults to workflow_input(Settings.SemiStructuredCheckboxMinLegThreshold)

• field_target_accuracies (str) – defaults to workflow_input(Settings.FieldTranscriptionTargetAccuracies)

• always_supervise_blank_cells (str | bool | None) – Available from V41.2; if enabled, always send blank cells in transcribed tables to be supervised manually, regardless of confidence defaults to workflow_input(Settings.ManualTranscriptionAlwaysSuperviseBlankCells)

• manual_transcription_enabled (str | bool | None) – Available from V41.2; enables manual transcription when applicable, defaults to workflow_input(Settings.ManualTranscriptionEnabled)

• address_parsing_enabled (str | bool) – Available from V43.2; toggles AddressParser invocation on machine-transcribed address fields, defaults to workflow_input(AddressParserSettings.AddressParsingEnabled)

• address_parser_api_provider (str) – Available from V43.2; selects the address parsing provider (‘none’ / ‘google’ / ‘smarty’ / ‘geoapify’), defaults to workflow_input(AddressParserSettings.ApiProvider)

• address_parser_api_key (str | None) – Available from V43.2; API key for the configured provider, defaults to workflow_input(AddressParserSettings.ApiKey)

• address_parser_api_options (str | dict | None) – Available from V43.2; provider-specific JSON options, defaults to workflow_input(AddressParserSettings.ApiOptions)

Output: The block returns an object with the following keys:

• submission: Submission with the transcribed field values.

• transcription_metadata: Metadata related to the transcription process.

• needs_manual_transcription: A boolean flag indicating whether the submission needs manual transcription.

• needs_flexible_extraction: A boolean flag indicating whether the submission needs flexible extraction.

Example usage:

machine_transcription = MachineTranscriptionBlock(
    reference_name='machine_transcription',
    submission=manual_identification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
)

IDENTIFIER = 'MACHINE_TRANSCRIPTION_10'

class flows_sdk.implementations.idp_v43.idp_blocks.ManualTranscriptionBlock(submission, api_params, reference_name=None, supervision_transcription_masking='${workflow.input.manual_transcription_supervision_transcription_masking}', table_output_manual_review='${workflow.input.manual_transcription_table_output_manual_review}', always_supervise_blank_cells='${workflow.input.manual_transcription_always_supervise_blank_cells}', task_restrictions='${workflow.input.manual_transcription_task_restrictions}', manual_transcription_enabled='${workflow.input.manual_transcription_enabled}', notification_workflow='${workflow.input.notification_workflow_transcription}', address_parsing_enabled='${workflow.input.address_parsing_enabled}', address_parser_api_provider='${workflow.input.address_parser_api_provider}', address_parser_api_key='${workflow.input.address_parser_api_key}', address_parser_api_options='${workflow.input.address_parser_api_options}', title='Manual Transcription', description='Manually transcribe fields that could not be automatically transcribed')

Bases: Block

Manual transcription lets your keyers manually enter the text found in fields or tables that could not be automatically transcribed

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (bool | str) – Prevents users from inputting invalid characters during Supervision Transcription tasks, defaults to workflow_input(Settings.ManualTranscriptionSupervisionTranscriptionMasking)

• table_output_manual_review (bool | str) – Always generates a table transcription task if the layout contains a table. If disabled, a table transcription task will only be generated if one or more cells have transcribed values below the defined thresholds, defaults to workflow_input(Settings.ManualTranscriptionTableOutputManualReview)

• always_supervise_blank_cells (bool | str) – If enabled, always send blank cells in transcribed tables to be supervised manually, regardless of confidence defaults to workflow_input(Settings.ManualTranscriptionAlwaysSuperviseBlankCells)

• task_restrictions (list[Any] | str | None) – Defines what users can access Supervision tasks for submission from a particular source or submissions matching a specific layout, defaults to workflow_input(Settings.ManualTranscriptionTaskRestrictions)

• manual_transcription_enabled (bool | str) – Enables manual transcription when applicable, defaults to workflow_input(Settings.ManualTranscriptionEnabled)

• notification_workflow (str | None) – Notification flow name to run when the submission enters Manual Transcription, defaults to workflow_input(Settings.ManualTranscriptionNotificationWorkflow)

• address_parsing_enabled (str | bool) – Available from V43.2; toggles AddressParser invocation on manually-transcribed address fields, defaults to workflow_input(AddressParserSettings.AddressParsingEnabled)

• address_parser_api_provider (str) – Available from V43.2; selects the address parsing provider (‘none’ / ‘google’ / ‘smarty’ / ‘geoapify’), defaults to workflow_input(AddressParserSettings.ApiProvider)

• address_parser_api_key (str | None) – Available from V43.2; API key for the configured provider, defaults to workflow_input(AddressParserSettings.ApiKey)

• address_parser_api_options (str | dict | None) – Available from V43.2; provider-specific JSON options, defaults to workflow_input(AddressParserSettings.ApiOptions)

• title (str) – overrides the block’s default title shown in Flow Studio, defaults to ‘Manual Transcription’

• description (str) – overrides the block’s default description shown in Flow Studio, defaults to ‘Manually transcribe fields that could not be automatically transcribed’

Output: The block returns an object with the following keys:

• submission: Submission with the manually transcribed field values.

Example usage:

manual_transcription = ManualTranscriptionBlock(
    reference_name='manual_transcription',
    submission=machine_transcription.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    supervision_transcription_masking=(
        idp_wf_config.manual_transcription_config.supervision_transcription_masking
    ),
    table_output_manual_review=(
        idp_wf_config.manual_transcription_config.table_output_manual_review
    ),
    task_restrictions=idp_wf_config.manual_transcription_config.task_restrictions,
)

IDENTIFIER = 'MANUAL_TRANSCRIPTION_5'

class flows_sdk.implementations.idp_v43.idp_blocks.FlexibleExtractionBlock(submission, api_params, reference_name=None, supervision_transcription_masking='${workflow.input.flexible_extraction_supervision_transcription_masking}', task_restrictions='${workflow.input.flexible_extraction_task_restrictions}', notification_workflow='${workflow.input.notification_workflow_flexible_extraction}', show_machine_predictions='${workflow.input.flexible_extraction_show_machine_predictions}', searchable_pages='${workflow.input.flexible_extraction_searchable_pages}', address_parsing_enabled='${workflow.input.address_parsing_enabled}', address_parser_api_provider='${workflow.input.address_parser_api_provider}', address_parser_api_key='${workflow.input.address_parser_api_key}', address_parser_api_options='${workflow.input.address_parser_api_options}')

Bases: Block

Flexible extraction manually transcribes fields marked for review

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (bool | str | None) – Prevents users from inputting invalid characters during the Flexible Extraction task, defaults to workflow_input(Settings.FlexibleExtractionSupervisionTranscriptionMasking)

• task_restrictions (list[Any] | str | None) – Defines what users can access Supervision tasks created by this block, defaults to workflow_input(Settings.FlexibleExtractionTaskRestrictions)

• notification_workflow (str | None) – Notification flow name to run when the submission enters Flexible Extraction, defaults to workflow_input(Settings.FlexibleExtractionNotificationWorkflow)

• show_machine_predictions (bool | str | None) – Available from V42.3; Shows the machine predictions in the Flexible Extraction Supervision tasks, defaults to workflow_input(Settings.FlexibleExtractionShowMachinePredictions)

• searchable_pages (bool | str | None) – Available from V43.2; When enabled, enables searching by text segments in the Flexible Extraction Supervision tasks, defaults to workflow_input(Settings.FlexibleExtractionSearchablePages)

• address_parsing_enabled (str | bool) – Available from V43.2; toggles AddressParser invocation on flexibly-extracted address fields, defaults to workflow_input(AddressParserSettings.AddressParsingEnabled)

• address_parser_api_provider (str) – Available from V43.2; selects the address parsing provider (‘none’ / ‘google’ / ‘smarty’ / ‘geoapify’), defaults to workflow_input(AddressParserSettings.ApiProvider)

• address_parser_api_key (str | None) – Available from V43.2; API key for the configured provider, defaults to workflow_input(AddressParserSettings.ApiKey)

• address_parser_api_options (str | dict | None) – Available from V43.2; provider-specific JSON options, defaults to workflow_input(AddressParserSettings.ApiOptions)

Output: The block returns an object with the following keys:

• submission: Submission with the fields marked for review updated with the transcribed values.

Example usage:

flexible_extraction = FlexibleExtractionBlock(
    reference_name='flexible_extraction',
    submission=manual_transcription.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    supervision_transcription_masking=(
        idp_wf_config.flexible_extraction_config.supervision_transcription_masking
    ),
    task_restrictions=idp_wf_config.flexible_extraction_config.task_restrictions,
)

IDENTIFIER = 'FLEXIBLE_EXTRACTION_5'

class flows_sdk.implementations.idp_v43.idp_blocks.ReprocessingBlock(reference_name, submission, parent_flow='${workflow.definition.identifier}', idp_wf_config='${workflow.input}', trigger='${workflow.input.$trigger}', reprocessing_enabled='${workflow.input.reprocessing_enabled}')

Bases: Block

Reprocessing block enables a second extraction attempt if documents were initially confidently mis-classified by the machine. When an end-user specifies that a document layout is incorrect during Manual Identification, Manual Transcription, or Flexible Extraction, the flow will reach the Reprocessing block which will mark correctly classified documents as “complete” status. Misclassified documents with “reprocess” status will be marked with status “reprocessing” and processed in a second execution of the flow. This block should be placed before the SubmissionCompleteBlock to enable reprocessing.

How the Reprocessing block works

The Reprocessing block examines whether any documents within the submission have been marked with an incorrect layout, and if so, redirects the submission to the classification step for proper classification. It does this by recursively calling the same flow or subflow with the same inputs and documents, but with the addition of a status key for each document to denote which were marked with an incorrect layout during supervision.

Note

The Reprocessing block works in tandem with the “Mark Incorrect Layout” option presented to users during Manual Identification, Manual Transcription, and Flexible Extraction supervision interfaces introduced in R38. Include the Reprocessing block in all custom flows in order to take advantage of this feature. The feature will remain selectable in supervision interfaces even if the Reprocessing block is not included in the flow.

The initial flow run will pause execution at the Reprocessing block until the recursive flow has completed, at which point the initial flow will resume to finish its execution. If an error occurs during the execution of the recursive flow, that error will be raised to the Reprocessing block of the initial flow.

Note

The recursive flow run is linked with the same correlation_id as the initial flow run and the submission. Users can click the “View related flow runs” link on the left sidebar of the initial flow run to view any recursive flow runs.

As expected, recursively calling the same flow may cause some blocks to run again, resulting in unnecessary work being performed, or work which doesn’t gracefully handle duplications (non-idempotent blocks). To mitigate this, certain Hyperscience-provided blocks - including Submission Initialization, Machine Collation, and Machine Classification - detect when the submission object already contains the output from its block to avoid re-execution. It’s a best practice to build custom blocks such that they are idempotent and reentrant; if that’s not feasible, you can add similar logic to custom blocks which do not depend on the layout for the documents being processed. Common methods for handling this include:

• Iterating through each status key within the submission.document[] collection to see if any documents are set to “reprocess” or “reprocessing”.

• Inspecting if the submission object already contains a key or value explicitly defined by the custom code block.

Recommended Placement for the Reprocessing Block

• The Reprocessing block should not be placed before any supervision blocks which allow users to mark a document with an incorrect layout. These supervision blocks include Manual Identification, Manual Transcription and Flexible Extraction.

• The Reprocessing block should always be placed before the SubmissionComplete Block.

• The Reprocessing block can be used with flows which include multiple branches and conditionals; however, it’s recommended to ensure the Reprocessing block is executed to honor user’s selection of marking layouts incorrectly when supervising documents.

• In general, any custom logic that is to be performed after data has been extracted from documents should be placed after the Reprocessing block. To avoid duplicate executions of this custom logic, it is recommended to separate the document processing blocks - inclusive of the Reprocessing block - from the custom logic. This can be handled in different ways:

  • Split the flow into separate subflows, where the Reprocessing block is in a subflow that does not contain the custom logic.

  • Add a decision to bypass the custom logic if the Reprocessing block was triggered. With this method, the recursive flow run will execute the custom logic, and the initial flow run will bypass the logic.

Mandatory parameters:

Parameters:

submission (Any) – Submission object

Parameters with defaults:

Parameters:

• parent_flow (str) – The identifier of the flow that this block is placed in, used to recursively call the same flow, defaults to ‘${workflow.definition.identifier}’

• idp_wf_config (Any) – The workflow input config to pass through to the recursive flow run, defaults to ‘${workflow.input}’

• trigger (Any) – Value used to trigger and correlate the recursive flow run, defaults to workflow_input(IdpFlowInputs.Trigger)

• reprocessing_enabled (str | bool) – Enables reprocessing of documents marked with an incorrect layout, defaults to workflow_input(Settings.ReprocessingEnabled)

Output: The block returns an object with the following keys:

• submission: Submission updated with the reprocessing results.

• is_reprocessing: A boolean flag indicating whether the submission was sent for reprocessing.

IDENTIFIER = 'REPROCESSING_2'

class flows_sdk.implementations.idp_v43.idp_blocks.SubmissionCompleteBlock(submission, is_reprocessing=False, reference_name=None, nlc_qa_sampling_ratio='${workflow.input.semi_qa_sample_rate}', field_id_qa_enabled='${workflow.input.field_id_qa_enabled}', field_id_qa_sampling_ratio='${workflow.input.field_id_qa_sample_rate}', table_id_qa_enabled='${workflow.input.table_id_qa_enabled}', table_id_qa_sampling_ratio='${workflow.input.table_id_qa_sample_rate}', transcription_qa_enabled='${workflow.input.qa}', transcription_structured_entry_qa_sample_rate='${workflow.input.structured_entry_qa_sample_rate}', transcription_structured_signature_qa_sample_rate='${workflow.input.structured_signature_qa_sample_rate}', transcription_structured_checkbox_qa_sample_rate='${workflow.input.structured_checkbox_qa_sample_rate}', transcription_semi_structured_qa_sample_rate='${workflow.input.semi_structured_qa_sample_rate}', table_cell_transcription_qa_enabled='${workflow.input.table_cell_transcription_qa_enabled}', table_cell_transcription_qa_sample_rate='${workflow.input.table_cell_transcription_qa_sample_rate}', auto_qa_sampling_rate_enabled='${workflow.input.auto_qa_sampling_rate_enabled}', exclude_auto_transcribe_fields_from_qa='${workflow.input.exclude_auto_transcribe_fields_from_qa}', payload=None)

Bases: Block

Submission complete block finalizes submission processing and updates reporting data. This block should be called at the END of every flow that uses blocks included in the flows_sdk.implementations.idp_v34 library. The block requires both the submission object and a payload to be passed in.

Mandatory parameters:

Parameters:

• submission (str) – Submission object

• payload (Any) – Object to pass to downstream systems

Parameters with defaults:

Parameters:

• is_reprocessing (str | bool) – boolean to handle execution when reprocessing, defaults to False

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• nlc_qa_sampling_ratio (str | int | float) – Defines the percentage of pages sampled for Classification QA, defaults to workflow_input(Settings.SemiQaSampleRate)

• field_id_qa_enabled (str | bool) – defaults to workflow_input(Settings.FieldIdQaEnabled)

• field_id_qa_sampling_ratio (str | int | float) – Defines the percentage of fields sampled for Identification QA, defaults to workflow_input(Settings.FieldIdQaSampleRate)

• table_id_qa_enabled (str | bool) – Allows users to verify the location of table cells, defaults to workflow_input(Settings.TableIdQaEnabled)

• table_id_qa_sampling_ratio (str | int | float) – Defines the percentage of tables the system samples for QA, defaults to workflow_input(Settings.TableIdQaSampleRate)

• transcription_qa_enabled (str | bool) – defaults to workflow_input(Settings.TranscriptionQaEnabled)

• transcription_structured_entry_qa_sample_rate (str | float) – Defines the percentage of structured text fields the system samples for Transcription QA, defaults to workflow_input(Settings.TranscriptionStructuredEntryQaSampleRate)

• transcription_structured_signature_qa_sample_rate (str | float) – Defines the percentage of structured signature fields sampled for Transcription QA, defaults to workflow_input(Settings.TranscriptionStructuredSignatureQaSampleRate)

• transcription_structured_checkbox_qa_sample_rate (str | float) – Defines the percentage of structured checkbox fields sampled for Transcription QA, defaults to workflow_input(Settings.TranscriptionStructuredCheckboxQaSampleRate)

• transcription_semi_structured_qa_sample_rate (str | float) – Defines the percentage of semi- structured fields the system samples for Transcription QA, defaults to workflow_input(Settings.TranscriptionSemiStructuredQaSampleRate)

• table_cell_transcription_qa_enabled (str | bool) – defaults to workflow_input(Settings.TableCellTranscriptionQaEnabled)

• table_cell_transcription_qa_sample_rate (str | int | float) – Defines the percentage of cells the system samples for QA. This value is likely to be lower than “Transcription QA Sample Rate” since there are more table cells than fields on any given page, defaults to workflow_input(Settings.TableCellTranscriptionQaSampleRate)

• auto_qa_sampling_rate_enabled (str | bool) – Enables automatic selection of QA sampling rates based on system configuration or heuristics, defaults to workflow_input(Settings.AutoQaSamplingRateEnabled)

• exclude_auto_transcribe_fields_from_qa (str | bool) – Defines whether fields that are marked as auto-transcribe in layout should be excluded from QA or not, defaults to workflow_input(Settings.ExcludeAutoTranscribeFieldsFromQA)

Output: The block returns an object with the following keys:

• submission: Submission with the finalized submission processing and reporting data.

Example usage:

submission_complete = SubmissionCompleteBlock(
    reference_name='complete_submission',
    submission=flexible_extraction.output('submission')
)

IDENTIFIER = 'SUBMISSION_COMPLETE_5'

class flows_sdk.implementations.idp_v43.idp_blocks.SetTransformedOutputBlock(submission, transformed_output, reference_name=None)

Bases: Block

Set Transformed Output block sets the transformed output for the provided submission. This can later be used to send the transformed data downstream using output blocks. For more information see https://docs.hyperscience.ai/#retrieving-transformed-submissions.

Mandatory parameters:

Parameters:

• submission (dict[str, Any] | str) – Submission object

• transformed_output (dict[str, Any] | str) – Object that will be set as the transformed output for this submission

Output: The block returns an object with the following keys:

• submission_id: identifier of the submission for which the transformed output was set

IDENTIFIER = 'SET_TRANSFORMED_OUTPUT'

class flows_sdk.implementations.idp_v43.idp_blocks.IdpCustomSupervisionBlock(submission, task_purpose, supervision_template, data=None, reference_name=None, supervision_transcription_masking=True, task_restrictions=None, page_ids=None, notification_workflow='IDP_SUBMISSION_NOTIFY', chat_flow_identifier=None, title='IDP Custom Supervision', description='IDP Custom Supervision')

Bases: Block

An IDP wrapper for the Custom Supervision block. It has the same functionality, but handles reading/writing data from the IDP database.

Mandatory parameters:

Parameters:

• submission (Any) – Workflow Submission object from upstream IDP Blocks.

• task_purpose (str) – The custom task name that’s given to all supervision tasks that run through a particular instance of a custom supervision block. This can be used to filter tasks in the task queue.

• supervision_template (Any) – JSON used for configuration of the Custom Supervision Task.

Parameters with defaults:

Parameters:

• data (dict[str, Any] | None) – Optional JSON structure that extends the data used to populate the supervision_template. This is intended to append to the data already gathered by the IDP wrapper. If there are any conflicting keys, the keys in this input will take precedent.

• reference_name (str | None) – Unique identifier within a flow. A unique one will be generated when not provided.

• supervision_transcription_masking (str | bool) – Prevents users from inputting invalid characters during the Custom Supervision task. Defaults to True.

• task_restrictions (str | list[Any] | None) – Defines what users can access Supervision tasks created by this block. Defaults to [].

• page_ids (str | list[int] | None) – If left empty, the IDP wrapper will, by default, pull in all pages in the submission. Adding a list of page IDs will override the default list of pages. Defaults to [].

• notification_workflow (str) – Notification flow name to run when the submission enters Custom Supervision. Defaults to IDP_SUBMISSION_NOTIFY_NAME.

• chat_flow_identifier (str | None) – Available from V41.2; optional chat flow identifier to enable Document Chat.

• title (Optional[str]) – UI-visible title of the block. Defaults to ‘IDP Custom Supervision’.

• description (Optional[str]) – Description of the block. Useful for documentation and shown in Flow Studio. Defaults to ‘IDP Custom Supervision’.

Example usage:

idp_custom_supervision = IdpCustomSupervisionBlock(
    reference_name='idp_custom_supervision',
    submission=manual_transcription.output('submission'),
    task_purpose='make_custom_decision',
    supervision_template=[
        {
            'name': 'three_column_template',
            'version': '1.0',
            'thumbnail': {
                'group_by_document': True,
                'group_by_case': True,
            },
            'action': [
                {
                    'name': 'my_tab',
                    'display': 'SSN Info',
                    'input': [
                        {
                            'name': 'ssn_field',
                            'type': 'transcription',
                            'data_identifier': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                            'title': 'SSN',
                        },
                    ],
                    'ui': {
                        'groups': [
                            {
                                'title': 'My Group',
                                'fields': ['ssn_field'],
                            },
                        ],
                    },
                },
            ],
        },
    ],
    data={
        'documents': [
            {
                'id': 2,
                'layout_uuid': 'e31c3cbf-dcea-44fd-a052-902463c6040f',
                'layout_name': 'layout_name',
                'page_count': 2,
                'read_only': False,
            }
        ]
    },
    supervision_transcription_masking=False,
)

IDENTIFIER = 'IDP_CUSTOM_SUPERVISION_4'

class flows_sdk.implementations.idp_v43.idp_blocks.IDPFullPageTranscriptionBlock(submission, reference_name=None, title='Full Page Transcription (Submission)', description='Transcribes the documents included in a submission', app_metadata=None, language_family=None, rerun_segmentation=False)

Bases: Block

IDP Full Page Transcription Block machine-transcribes the pages contained in a submission.

Mandatory parameters:

Parameters:

submission (dict[str, Any] | str) – Submission object that includes the pages that need to be transcribed

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• app_metadata (list[str] | None) – used for capturing information about the flow

• language_family (str | None) – language family to use when instantiating the underlying transcription model. If not provided, the system will default to latin. Language families are single word lowercase strings, e.g., latin, arabic, korean, chinese. You can read more about language families in the “Supported Languages” article in the application documentation: https://help.hyperscience.ai/v43/docs/supported-languages.

• rerun_segmentation (bool) – Available from V42.3; controls whether segmentation should be rerun for already segmented pages.

Output: The block returns an object with the following keys:

• submission: Submission with the combined_segments field populated on its pages with the transcription results.

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')

idp_fpt_block = IDPFullPageTranscriptionBlock(
    reference_name='idp_fpt',
    submission=submission_bootstrap.output('submission'),
)

# reference to the output of IDPFullPageTranscriptionBlock which can be passed as input to
# the next block. For more information on output() and output references, please take a
# look at class Block and its output() method
output_ref = idp_fpt_block.output()

IDENTIFIER = 'FULL_PAGE_TRANSCRIPTION_SUBMISSION_3'

class flows_sdk.implementations.idp_v43.idp_blocks.DocumentRendererBlock(submission, is_reprocessing=False, title='Document Renderer', description='Renders a document into a PDF', reference_name=None, document_rendering_enabled='${workflow.input.document_rendering_enabled}', document_rendering_mime_type='${workflow.input.document_rendering_mime_type}', document_rendering_unit='${workflow.input.document_rendering_unit}', document_rendering_height='${workflow.input.document_rendering_height}', document_rendering_width='${workflow.input.document_rendering_width}', document_rendering_quality='${workflow.input.document_rendering_quality}', document_rendering_mode='${workflow.input.document_rendering_mode}', document_rendering_searchable_pdf_enabled='${workflow.input.document_rendering_searchable_pdf_enabled}')

Bases: Block

Document File Generator Block renders a document into a PDF and stores it in the Blob storage.

This block should be placed ALWAYS after a Reprocessing block (if there is any) so that it is not possible to have more changes in the documents’ organisation.

Mandatory parameters:

Parameters:

submission (dict[str, Any] | str) – Submission object that includes the documents that need to be rendered.

Optional parameters:

Parameters:

• is_reprocessing (str | bool) – boolean to handle execution when reprocessing. Defaults to False.

• title (str) – overrides the block’s default title shown in Flow Studio, defaults to ‘Document Renderer’

• description (str) – overrides the block’s default description shown in Flow Studio, defaults to ‘Renders a document into a PDF’

• document_rendering_enabled (bool | str) – a boolean that enables or disables the document rendering. Default is False.

• document_rendering_mime_type (str) – the mime type of the document to be generated. Default is ‘application/pdf’

• document_rendering_unit (str) – the unit of the document to be generated. Default is ‘in’

• document_rendering_height (str | float) – the height of the document to be generated. Default is 11

• document_rendering_width (str | float) – the width of the document to be generated. Default is 8.5

• document_rendering_quality (str | int) – Quality of pages (images) appended to the resulting document as a percentage (recommended 50%). Higher quality results in a larger file size for the generated PDF.

• document_rendering_mode (str) – Available from V42.0; Mode of the images appended to the resulting document. Supported modes are RGB (default), L (grayscale) and 1 (black-and-white)

• document_rendering_searchable_pdf_enabled (bool | str) – Available from V43.2; add a hidden searchable text layer to rendered PDFs using existing page segment geometry where available. Default is False.

Output: The block returns an object with the following keys:

• submission: Submission with the document_file field populated on its documents with the rendered document file information.

Example usage:

# Use the output of the reprocessing block.
reprocessing = ReprocessingBlock(reference_name='reprocessing')

document_renderer_block = DocumentRendererBlock(
    reference_name='document_renderer',
    submission=reprocessing.output('submission'),
    document_rendering_enabled=True,
    document_rendering_unit='in',
    document_rendering_width=8.5,
    document_rendering_height=11,
    document_rendering_quality=50,
    is_reprocessing=reprocessing.output('is_reprocessing'),
    document_rendering_mode='L'
)

output_ref = document_renderer_block.output()

IDENTIFIER = 'DOCUMENT_RENDERER_2'

class flows_sdk.implementations.idp_v43.idp_blocks.IDPImageCorrectionBlock(submission, reference_name=None, rotation_correction_enabled=True, mobile_processing_enabled=False, language_family=None, title='Image Correction (Submission)', description='Rotate and de-skew documents included in a submission', app_metadata=None)

Bases: Block

IDP Image Correction Block rotates and de-skews tilted/skewed images so that they are more transcription-friendly

Mandatory parameters:

Parameters:

submission (dict[str, Any] | str) – Submission object that includes pages that need to be image-corrected

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• rotation_correction_enabled (bool | str) – Identifies and corrects the orientation of images. Enabled by default.

• mobile_processing_enabled (bool | str) – Improves the machine readability of photo captured documents. Disabled by default.

• language_family (str | None) – language family to use when performing image correction. If not provided, the system will default to latin. Language families are single word lowercase strings, e.g., latin, arabic, korean, chinese. You can read more about language families in the “Supported Languages” article in the application documentation: https://help.hyperscience.ai/v43/docs/supported-languages.

• app_metadata (list[str] | None) – used for capturing information about the flow

Output: The block returns an object with the following keys:

• submission: Submission with the corrected_image_uuid field populated on its pages with the rotated/de-skewed images.

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')

idp_image_correction_block = IDPImageCorrectionBlock(
    reference_name='idp_image_correction',
    submission=submission_bootstrap.output('submission'),
)

# reference to the output of IDPImageCorrectionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look at
# class Block and its output() method
output_ref = idp_image_correction_block.output()

IDENTIFIER = 'IMAGE_CORRECTION_SUBMISSION_2'

class flows_sdk.implementations.idp_v43.idp_blocks.OpticalReasoningCognitionAgentBlock(submission, api_params, model_type='${workflow.input.orca_type}', use_live_model='${workflow.input.orca_use_live_model}', base_model_id='${workflow.input.orca_base_model_uuid}', target_accuracy='${workflow.input.orca_target_accuracy}', sliding_window_size='${workflow.input.orca_sliding_window_size}', max_image_tokens='${workflow.input.orca_max_image_tokens}', max_new_tokens='${workflow.input.orca_max_new_tokens}', max_num_bboxes='${workflow.input.orca_max_num_bboxes}', thresholds_dict=None, reference_name=None, disable_log_redaction=False)

Bases: Block

Optical Reasoning Cognition Agent block extracts the textual content from your submission. It acts like an intelligent agent that uses visual understanding and reasoning to process submitted documents and pull out the relevant fields. This block requires a GPU to function.

Mandatory parameters:

Parameters:

• submission (dict[str, Any] | str) – Submission object

• api_params (str | dict[str, Any])

• target_accuracy (float | str) – The submission-level machine + human transcription accuracy targeted by the system (fraction between 0.0 and 1.0, e.g. 0.95 for 95 %).

Parameters with defaults:

Parameters:

• model_type (str) – The model you would like to install, which will be used to process submissions

• use_live_model (bool | str | None) – Available from V43.2; Whether to use the live ORCA model or a specific version of the model. Default value is set to False.

• base_model_id (str | None) – The UUID of the block asset containing ORCA model weights

• sliding_window_size (int | str) – The number of pages processed by the model at once. Larger values improve throughput but raise GPU memory usage. Default value is set to 10.

• max_image_tokens (int | str) – The maximum number of tokens allocated per image by the model. This is conceptually similar to setting image resolution. Larger values may increase accuracy on dense / hard to read images but will raise GPU memory usage. Default value is set to 1280.

• max_new_tokens (int | str) – The maximum number of tokens the model can extract, which should be scaled based on the number and length of field values. Default value is set to 1024.

• max_num_bboxes (int | str) – The maximum number of candidate bounding boxes per field occurrence. Larger values may improve the likelihood of identifying the correct location but may also increase false positives. Default value is set to 3.

• thresholds_dict (dict[str, Any] | None) – Available from V42.2; Field level thresholds passed as a JSON dict. When provided, these will take precedence over any threshold dictionary present in the Binary KV. Default value is set to None.

• reference_name (str | None) – unique reference name within a flow, a unique one will be generated when not provided

• disable_log_redaction (bool | str | None) – Available from V43.2; Debug only. When set to True, the VLM blocks skip the redaction of document content (which may contain PII) in their logs. Default value is set to False.

Output: The block returns an object with the following keys:

• submission: Submission with the fields extracted by the model.

• needs_flexible_extraction: A boolean flag indicating whether the submission needs flexible extraction.

IDENTIFIER = 'OPTICAL_REASONING_COGNITION_AGENT_GPU'

Additional Blocks (v43)

class flows_sdk.implementations.idp_v43.additional_blocks.JSONOutputsBlock(inputs, blocks=None)

Bases: Outputs

Output block allows users to send JSON data extracted by an IDP flow to other systems for downstream processing Mandatory parameters:

Parameters:

inputs (dict[str, Any]) – Used by the UI to automatically pre-populate the input of newly added blocks. Extended in this class to include 'enabled': True to visualize an enabled/disabled trigger that is controllable via Flow Studio.

Usage when parameters are to be defined in Flow Studio:

outputs = JSONOutputBlock(
    inputs={'result': }
)

class flows_sdk.implementations.idp_v43.additional_blocks.ImageCorrectionBlock(pages, reference_name=None, rotation_correction_enabled=True, mobile_processing_enabled=False, language_family=None, title='Image Correction (Pages)', description='Rotate and de-skew documents', app_metadata=None)

Bases: Block

Image Correction Block rotates and de-skews tilted/skewed images so that they are more transcription-friendly.

Mandatory parameters:

Parameters:

pages (list[dict[str, Any]] | str) – list of Page objects that need to be image-corrected

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• rotation_correction_enabled (bool) – Identifies and corrects the orientation of images. Enabled by default.

• mobile_processing_enabled (bool) – Improves the machine readability of photo captured documents. Disabled by default.

• language_family (str | None) – language family to use when performing image correction. If not provided, the system will default to latin. Language families are single word lowercase strings, e.g., latin, arabic, korean, chinese. You can read more about language families in the “Supported Languages” article in the application documentation: https://help.hyperscience.ai/v43/docs/supported-languages.

• app_metadata (list[str] | None) – used for capturing information about the flow

Output: The block returns an object with the following keys:

• pages: list of Page objects with their corrected_image_uuid field populated with rotated/de-skewed images.

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')

image_correction_block = ImageCorrectionBlock(
    reference_name='image_correction',
    pages=submission_bootstrap.output('submission.unassigned_pages'),
)

# reference to the output of IDPImageCorrectionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look
# at class Block and its output() method
output_ref = image_correction_block.output()

IDENTIFIER = 'IMAGE_CORRECTION_PAGES_2'

class flows_sdk.implementations.idp_v43.additional_blocks.FullPageTranscriptionBlock(pages, reference_name=None, title='Full Page Transcription (Pages)', description='Transcribes documents', language_family=None, app_metadata=None, rerun_segmentation=False)

Bases: Block

Full Page Transcription Block machine-transcribes the provided pages.

Mandatory parameters:

Parameters:

pages (list[dict[str, Any]] | str) – list of Page objects that need to be transcribed

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• language_family (str | None) – language family to use when instantiating the underlying transcription model. If not provided, the system will default to latin. Language families are single word lowercase strings, e.g., latin, arabic, korean, chinese. You can read more about language families in the “Supported Languages” article in the application documentation: https://help.hyperscience.ai/v43/docs/supported-languages.

• app_metadata (list[str] | None) – used for capturing information about the flow

• rerun_segmentation (bool) – Available from V42.3; controls whether segmentation should be rerun for already segmented pages.

Output: The block returns an object with the following keys:

• pages: list of Page objects with their combined_segments field populated with the transcription results.

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')

fpt_block = FullPageTranscriptionBlock(
    reference_name='fpt',
    pages=submission_bootstrap.output('submission.unassigned_pages'),
)

# reference to the output of FullPageTranscriptionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look at
# class Block and its output() method
output_ref = fpt_block.output()

IDENTIFIER = 'FULL_PAGE_TRANSCRIPTION_PAGES_3'

class flows_sdk.implementations.idp_v43.additional_blocks.QASchedulerBlock(flow_identifier, submission, qa_config, is_reprocessing=False, reference_name=None)

Bases: Block

Available from V41.2

Schedules Quality Assurance (QA) tasks for a specific workflow.

This class serves as a specialized block within a flow for scheduling Quality Assurance tasks. It ensures the proper configuration and scheduling processes for a given workflow instance. It leverages provided data and parameters such as identification of the workflow, submission details, QA configuration, and optional reprocessing cases.

Mandatory parameters:

Parameters:

• flow_identifier (str) – A unique string identifying the flow to associate this QA scheduler with.

• submission (dict[str, Any] | str) – A dictionary containing submission details. Will be used as payload to trigger the target flow to be QA’d.

• qa_config (dict[str, Any] | str) – A dictionary containing QA configuration. Will be used as payload to trigger the target flow to be QA’d.

Optional parameters:

Parameters:

• is_reprocessing (bool | str | None) – An optional value indicating whether this block is part of a reprocessing workflow. Defaults to False.

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

Example usage:

qa_scheduler = QASchedulerBlock(
    flow_identifier='my_qa_flow',
    submission={'submission_id': 12345, 'data': 'example_data'},
    qa_config={'sample_rate': 0.1, 'language_family': 'latin'},
    is_reprocessing=False,
)

# reference to the output of QASchedulerBlock which can be passed
 as input to the next block.
# For more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = qa_scheduler.output()

IDENTIFIER = 'QA_SCHEDULER'

class flows_sdk.implementations.idp_v43.additional_blocks.FullPageTranscriptionQABlock(submission, sample_rate, language_family, split_text_segments, min_characters_in_segment=4, task_restrictions=None, reference_name=None)

Bases: Block

Available from V41.2

Full Page Transcription QA Block performs Quality Assurance on the output of Full Page Transcription Block. It samples a percentage of segments for QA and allows users to split segments into smaller tokens for easier QA.

Mandatory parameters:

Parameters:

• submission (dict[str, Any] | str) – Submission object

• sample_rate (int | str) – Defines the percentage of segments the system samples for Quality Assurance

• language_family (str) – Specifies the language family for the class

• split_text_segments (bool | str) – Enabling this option splits segments into smaller, more manageable tokens for easier QA

Parameters with defaults:

Parameters:

• min_characters_in_segment (integer) – Minimum number of characters in a token. Defaults to 4. Minimum value is 1, maximum is 100.

• task_restrictions (Optional[TaskRestrictions]) – Defines what users can access tasks created by this block. Defaults to None, which is treated as an empty list.

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

Example usage:

full_page_transcription_qa = FullPageTranscriptionQABlock(
    reference_name='full_page_transcription_qa',
    submission=manual_transcription.output('submission'),
    sample_rate=10,
    language_family='latin',
    split_text_segments=True,
    min_characters_in_segment=4,
    task_restrictions=[],
)

# reference to the output of FullPageTranscriptionQABlock which can be passed
as input to the
# next block. For more information on output() and output references, please take a look
# at class Block and its output() method
output_ref = full_page_transcription_qa.output()

IDENTIFIER = 'FULL_PAGE_TRANSCRIPTION_QA_2'

class flows_sdk.implementations.idp_v43.additional_blocks.CustomSupervisionBlock(submission, task_purpose, data, supervision_template, reference_name=None, supervision_transcription_masking=True, task_restrictions=None, notification_workflow='IDP_SUBMISSION_NOTIFY', chat_flow_identifier=None, title='Custom Supervision', description='Custom Supervision')

Bases: Block

The CustomSupervisionBlock uses a JSON schema as a form builder to create customer-driven UIs for Supervision tasks.

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• task_purpose (str) – the custom task name that’s given to all supervision tasks that run through a particular instance of a custom supervision block. This can be used to filter tasks in the task queue.

• data (Any) – JSON structure that supplies the data used to populate the supervision_template

• supervision_template (Any) – JSON used for configuration of the Custom Supervision Task.

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (bool) – Prevents users from inputting invalid characters during the Custom Supervision task, defaults to True

• task_restrictions (list[Any] | None) – Defines what users can access Supervision tasks created by this block, defaults to []

• notification_workflow (str) – Notification flow name to run when the submission enters Custom Supervision, defaults to IDP_SUBMISSION_NOTIFY_NAME

• chat_flow_identifier (str | None) – Available from V41.2; optional chat flow identifier to be populated to enable Document Chat in Custom Supervision

• title (Optional[str]) – UI-visible title of the block. Defaults to ‘IDP Custom Supervision’

• description (Optional[str]) – Description of the block. Both useful for documentation purpose and visible to users in the Flow studio. Defaults to ‘IDP Custom Supervision’

Example usage:

custom_supervision = CustomSupervisionBlock(
    reference_name='custom_supervision',
    submission=manual_transcription.output('submission'),
    task_purpose='make_custom_decision',
    supervision_template=[
        {
            'name': 'three_column_template',
            'version': '1.0',
            'thumbnail': {
                'group_by_document': True,
                'group_by_case': True,
            },
            'action': [
                {
                    'name': 'my_tab',
                    'display': 'SSN Info',
                    'input': [
                        {
                            'name': 'ssn_field',
                            'type': 'transcription',
                            'data_identifier': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                            'title': 'SSN',
                        },
                    ],
                    'ui': {
                        'groups': [
                            {
                                'title': 'My Group',
                                'fields': ['ssn_field'],
                            },
                        ],
                    },
                },
            ],
        },
    ],
    data={
        'fields': [
            {
                'id': 967,
                'uuid': 'fe8d4e7a-a82c-448c-85a1-0a3cbbe53b61',
                'value': '111-22-3333',
                'page_id': 15,
                'layout_field_id': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                'validation_overridden': False,
                'bounding_box': [
                    0.09360783305186686,
                    0.25792617589433436,
                    0.6358913805295097,
                    0.28862414740388187,
                ],
                'occurrence_index': 1,
            },
        ],
        'template_fields': [
            {
                'uuid': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                'notes': '',
                'type': 'entry',
                'data_type_uuid': 'b600a4e4-758f-4c4b-a2f4-cbb5dd870a0c',
                'name': 'SSN',
            },
        ],
        'pages': [
            {
                'id': 15,
                'file_page_number': 1,
                'document_page_number': 1,
                'image_url': '/image/2ed3d989-b64a-44bd-a9fb-6e0dcc5ccdb8',
                'form_id': 2,
                'submission_id': 1,
                'external_case_ids': [],
                'read_only': False,
                'filename': '1 page.pdf',
                'form_page_id': 1
            },
        ],
        'documents': [
            {
                'id': 2,
                'layout_uuid': 'e31c3cbf-dcea-44fd-a052-902463c6040f',
                'layout_name': 'layout_name',
                'page_count': 2,
                'read_only': False,
            },
        ],
        'cases': [],
        'selected_choices': [],
    },
    supervision_transcription_masking=False,
)

IDENTIFIER = 'CUSTOM_SUPERVISION_3'

class flows_sdk.implementations.idp_v43.additional_blocks.SoapRequestBlock(method, reference_name=None, endpoint='', body_namespace=None, headers_namespace=None, headers=None, http_headers=None, params=None, use_wsdl=False, wsdl_uri='', wsdl_service_name='', wsdl_port_name='', title='HTTP REST Block', description='HTTP REST block')

Bases: Block

Soap Block allows user to perform SOAP requests

Mandatory parameters:

Parameters:

• method (str) – SOAP method to invoke

• endpoint (str) – absolute url including schema to HTTP endpoint for this request

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• body_namespace (str | None) – SOAP request body namespace url

• headers_namespace (str | None) – SOAP request headers namespace url

• headers (dict[Any, Any] | None) – SOAP headers to use

• http_headers (dict[str, Any] | None) – Available from V42.1; HTTP headers to be sent on all requests

• params (dict[Any, Any] | None) – key-value pair used as query parameters in the request to be inserted as SOAP body

Example usage:

# minimum viable instantiation, the rest of the fields can later be added in the UI
soap_block = SoapRequestBlock(
    reference_name='soap_request',
    title='Soap Request',
    description='Perform soap request',
    method='ExampleMethod',
)
soap_block = SoapRequestBlock(
    reference_name='soap_request',
    title='Soap Request',
    description='Perform soap request',
    method='ExampleMethod',
    endpoint='http://example.org/abc.wso',
    body_namespace='http://www.example.org/abc.countryinfo',
    params={'example': 'stuff'},
    headers={},
    http_headers={}
)
# reference to the output of SoapRequestBlock which can be passed as input to the next block
# for more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = soap_block.output()

IDENTIFIER = 'SOAP_REQ'

class flows_sdk.implementations.idp_v43.additional_blocks.HttpRestBlock(method, endpoint, reference_name=None, headers=None, params=None, payload=None, json=None, timeout=600, authorization_type=None, handled_error_codes=None, auth_params=None, response_encoding=None, title='HTTP REST Block', description='HTTP REST block')

Bases: BaseHttpRestBlock

Http Rest Block allows user to perform HTTP requests

Mandatory parameters:

Parameters:

• method (str) – HTTP method to use

• endpoint (str) – absolute url including schema to HTTP endpoint for this request

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• headers (dict[str, str] | None) – HTTP headers to use

• params (dict[str, Any] | None) – key-value pair used as query parameters in the request

• payload (dict[str, str] | None) – key-value pair to be used as x-www-form-urlencoded data. Mutually exclusive with json

• json (dict[str, Any] | None) – key-value pair to be used as json data. Mutually exclusive with payload

• timeout (int | None) – How long (in seconds) to keep the connection open if no bytes are received from the endpoint. Set to 0 to wait indefinitely (not recommended).

• authorization_type (str | None) – authorization type (none, http_header, oauth_2_client_credentials)

• handled_error_codes (list[int | str] | None) – specify HTTP error codes that will not fail the task, allowing for wildcards with the ‘x’ char. Example: [400, “5xx”]

• auth_params (None | HttpHeaderAuthorizationParams | OAuth2AuthorizationParams) – authorization parameters to be passed when the authorization_type is set to “http_header” or “oauth_2_client_credentials”

• response_encoding (str | None) – Character encoding to use when decoding the response. If not provided, the encoding from the Content-Type header or the requests library’s default detection will be used. Specify a standard encoding name (e.g., ‘utf-8’, ‘ISO-8859-1’, ‘windows-1252’) to override.

For a guide on working with secrets, review the Flows SDK page on “Secure Handling of Secrets” and specifically the section on “Secrets cannot be nested within strings or objects”.

Example usage:

# Minimum viable instantiation
http_rest_block = HttpRestBlock(
    reference_name='http_request',
    title='Http Request',
    description='Perform http request',
    method='GET',
    endpoint='http://www.example.com',
)

# Reference to the output of HttpRestBlock which can be passed as input to the next block
output_ref = http_rest_block.output()

# By default the output is wrapped under "result" (reference it via output('result.X')).
# When the HS_CODE_BLOCK_OUTPUT_WRAPPING_MODE env var selects no_wrapping, it is no longer
# wrapped, so reference it directly via output('X') instead.

# For more information on output() and output references,
# please take a look at class Block and its output() method.

# HTTP header authorization
header_auth_block = HttpRestBlock(
    reference_name='header_auth_request',
    title='Header Auth Request',
    description='Perform request with HTTP header auth',
    method='GET',
    endpoint='https://www.example.com',
    authorization_type='http_header',
    auth_params=HttpHeaderAuthorizationParams(
        authorization_header_name='Authorization',
        authorization_header=workflow_input('header_with_secret'),  # e.g.: Bearer 1234abcd
    ),
)

# OAuth2 Client Credentials authorization
oauth2_block = HttpRestBlock(
    reference_name='oauth2_request',
    title='OAuth2 Request',
    description='Perform request with OAuth2',
    method='GET',
    endpoint='https://api.example.com/v1/resources',
    authorization_type='oauth_2_client_credentials',
    auth_params=OAuth2AuthorizationParams(
        auth_url='https://auth.example.com/oauth2/token',
        client_id=workflow_input('oauth_client_id'),
        client_secret=workflow_input('oauth_client_secret'),
        audience='https://api.example.com',
        scope='read write',
    ),
)

class flows_sdk.implementations.idp_v43.additional_blocks.HyperscienceRestApiBlock(method, app_endpoint, reference_name=None, headers=None, params=None, payload=None, json=None, handled_error_codes=None, timeout=600, response_encoding=None, title='Hyperscience HTTP REST Block', description='Hyperscience HTTP REST block')

Bases: BaseHttpRestBlock

Make Http requests to the Hyperscience platform APIs. This block will automatically take care of prepending the base url and will include the appropriate authentication headers with the request.

Mandatory parameters:

Parameters:

• method (str) – HTTP method to use

• app_endpoint (str) – relative url, beginning with a forward slash, to an HTTP endpoint served by the Hypersciecne platform. For reference on the available endpoints, check out https://docs.hyperscience.ai/

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• headers (dict[str, str] | None) – HTTP headers to use

• params (dict[str, Any] | None) – key-value pair used as query parameters in the request

• payload (dict[str, str] | None) – key-value pair to be used as x-www-form-urlencoded data. Mutually exclusive with json

• json (dict[str, Any] | None) – key-value pair to be used as json data. Mutually exclusive with payload

• handled_error_codes (list[int | str] | None) – specify HTTP error codes that will not fail the task, allowing for wildcards with the ‘x’ char. Example: [400, “5xx”]

• timeout (int | None) – How long (in seconds) to keep the connection open if no bytes are received from the endpoint. Set to 0 to wait indefinitely (not recommended).

• response_encoding (str | None) – Character encoding to use when decoding the response. If not provided, the encoding from the Content-Type header or the requests library’s default detection will be used. Specify a standard encoding name (e.g., ‘utf-8’, ‘ISO-8859-1’, ‘windows-1252’) to override.

Example usage:

hs_rest_block = HyperscienceRestApiBlock(
    reference_name='http_request',
    app_endpoint='/api/healthcheck',
    method='GET',
)

class flows_sdk.implementations.idp_v43.additional_blocks.DatabaseAccessBlock(reference_name=None, db_type='', database='', host='', username='', password='', query='', port=None, options=None, timeout=None, query_params=None, title='DB Block', description='DB access block')

Bases: Block

Database Block allows user to access and perform queries on the specified database

Mandatory parameters:

Parameters:

• db_type (str) – database type (mssql, oracle, postgres)

• database (str) – database name

• host (str) – URL/IP address of the server database is hosted on

• username (str) – database username

• password (str) – database password

• query (str) – parameterized query

Optional parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• port (int | None) – database port number

• options (dict[Any, Any] | None) – dictionary of additional connection string options

• timeout (int | None) – timeout in seconds (mssql and postgres only)

• query_params (dict[Any, Any] | None) – dictionary of values for query placeholders

Example usage:

# minimum viable instantiation, the rest of the fields can later be added in the UI
db_lookup = DatabaseAccessBlock(
    reference_name='db_lookup',
    title='Database Lookup',
    description='Perform database lookup',
)
# note that storing password in plain text form is not recommended
# password can later be entered in the UI or defined as a flow secret.
db_lookup = DatabaseAccessBlock(
    reference_name='db_lookup',
    title='Database Lookup',
    description='Perform database lookup',
    db_type='mssql',
    database='cars',
    host='example.com',
    username='user',
    # we recommend skipping the password field or defining it as a system secret
    # e.g. system_secret('{your_secret_identifier}') (both allow for secret input via. UI)
    password='pw'
    port=1433,
    timeout=200,
    query='SELECT * from CAR',
)
# reference to the output of DBAccessBlock which can be passed as input to the next block
# for more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = db_lookup.output()
# content of output_ref:
{
    "result": [
        {
            "id": 1,
            "brand": "Aston Martin",
            "model": "Vanquish"
        },
        {
            "id": 2,
            "brand": "Jaguar",
            "model": "XE 2018"
        }
    ]
}

Database output for the same query (SELECT * FROM CAR)

IDENTIFIER = 'DB_ACCESS_2'

class flows_sdk.implementations.idp_v43.additional_blocks.LlamaBlock(model_id, prompt, reference_name=None, consent=False, tokenize_only=False, predict_extra_kwargs=None, title='Llama Text Generation', description='Text generation using the Llama 2 model')

Bases: Block

Text generation using the Llama 2 model by Meta.

Deprecated from version 41.1

This block utilizes Llama 2 by Meta. Llama 2 is licensed under the LLAMA 2 Community License (https://ai.meta.com/llama/license/). By using the Llama Block, you acknowledge that you have received a copy of the LLAMA 2 Community License.

Note: This block is still experimental and not ready for production use.

Mandatory parameters:

Parameters:

• model_id (str) – UUID, identifier of the asset with the ML model. Model assets are not part of the Hyperscience bundle and must first be downloaded and imported into the system before they can be used. Please contact your Hyperscience representative to discuss the usecase and the appropriate model and to get instructions on downloading the models. Once downloaded, here are the available model asset UUIDs: b3da05c7-061c-4a58-af6d-4c905d2e618f - Llama 2, 13B, 8-bit quantized

• prompt (str) – The prompt to generate text from. Use a CodeBlock ahead of the LlamaBlock in order to build the prompt. The context window of the model is limited to 4096 tokens. If the prompt has more tokens, the block would return an error. The total input and output length also cannot exceed 4096 tokens, which means that if the input is too long, the output may be truncated. Also see tokenize_only for a more detailed explanation of how to handle longer texts.

Optional parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• consent (bool | None) – Has the business user read and acknowledged the Llama 2 legal notice. Unless the flow would be used in CI automation, this should be set to False so that a business user can read and acknowledge the notice in the Hyperscience UI. Setting this to True in the Python definition of the flow is also treated as acknowledgement of the notice on behalf of the entity that will be using the flow.

• tokenize_only (bool | None) – In the context of LLMs, tokens correspond roughly to words, partial words and punctuation. A rough way to compute the number of tokens in a text is number_of_tokens = 1.25 * number_of_words. The Llama block works with a context window of 4096 tokens. If a longer prompt is passed in, it would return an error. Also, if the prompt is shorter, but still close to 4096, the output may be truncated. Since the length of the prompt in tokens cannot be known ahead of time, sometimes the flow may need to make dynamic decisions whether to pass the text to the Llama block, or to split it into multiple chunks, or to use some alternative method for processing it (e.g. a custom supervision manual task). The tokenize_only option enables flows to take such dynamic decisions based on the length of the prompt in tokens. The default is false - it instructs the block to run the ML model inference over the prompt, i.e. to generate a text response. When true is passed, the model will simply tokenize the prompt without running inference and return the list of tokens - this will work regardless of the length of the prompt. A subsequent Decision task can then route longer prompts to an alternative path in the flow, while routing shorter prompts to the LlamaBlock for actual text generation.

• predict_extra_kwargs (dict[Any, Any] | None) – Extra keyword arguments used for model inference. (Used to fine-tune the model prediction. Not recommended to change this unless you know what you are doing)

Example usage:

llama = LlamaBlock(
    reference_name='llama',
    model_id='b3da05c7-061c-4a58-af6d-4c905d2e618f',
    prompt='${previous_block.output.llama_prompt}',
)

IDENTIFIER = 'LLAMA'

class flows_sdk.implementations.idp_v43.additional_blocks.EntityRecognitionBlock(pages, regex_map=None, keyword_map=None, ner_enabled=True, ced_enabled=True, signature_enabled=True, page_batch_size=15, page_overlap=1, language_family=None, enable_multiline_detection=True, enable_cross_page_detection=True, deduplicate_entities=False, reference_name=None, title='Entity Recognition', description='Recognizes entities of different types in documents')

Bases: Block

Entity Recognition Block detects entities of different types within the pages.

The block supports three recognition engines that can be enabled independently:

• Named Entity Recognition (NER) — detects Person Names, Organisation/Company Names, and Addresses using a trained ML model.

• Custom Entity Detection (CED) — detects words or word patterns described by regular expressions and keyword configurations. See Regex Map Configuration and Keyword Map Configuration below.

• Signature Detection — detects signatures on pages.

Mandatory parameters:

Parameters:

• pages (list[dict[str, Any]] | str) – list of Page objects

• regex_map (dict[str, str] | str | None) – mapping of entity type names to regex patterns (or 'default'). See Regex Map Configuration below for the full format.

• keyword_map (dict[str, list[str]] | str | None) – mapping of entity type names to keyword search configurations (or ['default']). See Keyword Map Configuration below for the full format.

Optional Parameters:

Parameters:

• ner_enabled (bool | str) – a boolean flag that enables or disables Named Entity Recognition. Default value is True.

• ced_enabled (bool | str) – a boolean flag that enables or disables Custom Entity Detection. Default value is True.

• signature_enabled (bool | str) – a boolean flag that enables or disables Signature Detection. Default value is True.

• page_batch_size (int | None) – Available from V42.3; an integer that specifies the number of pages to be processed in a batch. If set to None, all pages are processed in a single batch. Default value is 15.

• page_overlap (int) – Available from V42.3; an integer that specifies the number of overlapping pages between batches. Default value is 1, which means that the last page of a batch will be repeated as the first page of the next batch. This is to ensure that entities that are located at the border of two batches are not missed.

• language_family (str | None) – Available from V42.3; language family to use when performing segmentation. If not provided, the system will default to latin. Language families are single word lowercase strings, e.g., latin, arabic, korean, chinese. You can read more about language families in the “Supported Languages” article in the application documentation: https://help.hyperscience.ai/v43/docs/supported-languages.

• enable_multiline_detection (bool) – a boolean flag that enables or disables multiline detection in Custom Entity Detection. When enabled, also looks for matches spanning multiple lines on the same page. Default value is True.

• enable_cross_page_detection (bool) – a boolean flag that enables or disables cross-page detection in Custom Entity Detection. When enabled, also looks for matches straddling two consecutive pages. Default value is True.

• deduplicate_entities (bool | str) – a boolean flag that enables or disables deduplication of entities detected by different recognition engines (NER, CED, Signature). When enabled, overlapping entities are resolved using entity-type priority. Default value is False.

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

Output: The block returns an object with the following keys:

• groups: list of EntityGroup objects, containing the detected entities.

Regex Map Configuration

The regex_map parameter is a dictionary that maps entity type names to regular expression patterns. The block searches through the transcribed text and matches patterns corresponding to each regex, regardless of surrounding keywords.

• Use 'default' as the value to use the built-in regex for that entity type.

• Use a custom regex string to define your own pattern.

Example regex_map:

regex_map = {
    # Use built-in regexes for these types
    'ssn': 'default',
    'email': 'default',
    'us phone number': 'default',
    # Custom regex for an account ID like "AB1385 957"
    'account id': '[A-Z]{2}\d{4}\s\d{3}',
}

Keyword Map Configuration

The keyword_map parameter is a dictionary that maps entity type names to a list of keyword search configurations. Unlike regex_map, keyword search looks for entity patterns near specific keywords in the text.

Each value is a list where items can be:

• 'default' — use the built-in keyword configuration for that entity type.

• A dictionary describing a custom keyword-entity pair with the following keys:

  • keywords_regex (required) — defines which keywords to search for:

    • root_regexes (required, list of str) — root keyword patterns, e.g. ['Account', 'Acc'].

    • suffix_sequence (required, list of list of str) — each inner list contains suffix alternatives that combine with roots. For example, [['ID', 'I.D'], ['Number', '#']] generates combinations like Account ID Number, Acc I.D #, etc.

    • is_suffix_optional (optional, bool, default: False) — when True, root words alone also match (without any suffix).

    • custom_targets (optional, list of str) — additional standalone keywords that don’t follow the root+suffix pattern, e.g. ['SSN'].

    • is_case_sensitive (optional, bool, default: False) — whether keyword matching is case-sensitive.

  • entity_regex (required, str) — the regex pattern for the entity value itself.

  • entity_regex_error_tolerance (optional, int, default: 0) — fuzzy matching tolerance. Specifies how many wrong/additional/fewer symbols are allowed in a match.

  • enable_table_search (optional, bool, default: False) — when True, also considers the keyword as a potential column header and searches the column below.

  • enable_closest_word_line_below (optional, bool, default: True) — when True, considers the closest non-overlapping word in the line below the keyword.

  • enable_block_above_search (optional, bool, default: True) — when True, also searches above the keyword.

  • allow_intervening_words (optional, bool, default: True) — when False, rejects matches that have any words between the keyword and the entity.

Example keyword_map:

keyword_map = {
    # Use built-in keyword config for passport numbers
    'passport number': ['default'],
    # Custom keyword config for a patient name entity
    'patient': [
        {
            'keywords_regex': {
                'root_regexes': ['patient', "patient's", 'veteran'],
                'suffix_sequence': [['name']],
                'custom_targets': ['name', 'chart'],
                'is_suffix_optional': True,
            },
            'entity_regex': '.*',
        }
    ],
    # Custom keyword config for an Account ID
    'account id': [
        {
            'keywords_regex': {
                'root_regexes': ['Account', 'Acc'],
                'suffix_sequence': [['ID', 'I.D'], ['Number', 'Num', '#']],
                'is_suffix_optional': True,
            },
            'entity_regex': '[A-Z]{2}\d{4}\s\d{3}',
        }
    ],
}

Built-in Entity Types

The following entity types have built-in ('default') regex and keyword definitions:

ssn, email, employer id, us phone number, date, micr, machine readable line, policy number, loan number, credit card number, customer id, account number, employee id, passport number, uscis number, driver license number, driver license restrictions, i94 number, pin number, ptin number, judgment number, case number, bankruptcy number, application number, file number, routing number, nmls number, appraiser certification number

Use 'default' in regex_map and ['default'] in keyword_map to enable the built-in detection for any of these types.

Example usage:

# This is one way to obtain the pages (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')

entity_recognition_block = EntityRecognitionBlock(
    reference_name='entity_recognition',
    pages=submission_bootstrap.output('submission.unassigned_pages'),
    regex_map=DEFAULT_PII_REGEX_MAP,
    keyword_map=DEFAULT_PII_KEYWORD_MAP,
)

# reference to the output of EntityRecognitionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look at
# class Block and its output() method
output_ref = entity_recognition_block.output()

IDENTIFIER = 'ENTITY_RECOGNITION_2'

class flows_sdk.implementations.idp_v43.additional_blocks.RedactionBlock(redaction_groups, text_content_mode='synthetic_data', sensitive_content_mode='opaque_box', replacements=None, skip_entity_types=None, add_watermark=True, watermark_text=None, reference_name=None, title='Redaction', description='Performs redaction on the provided pages.')

Bases: Block

Redacts and masks the provided entities on the pages based on the corresponding settings.

Mandatory parameters:

Parameters:

redaction_groups (list[dict[str, Any]] | str) – list of EntityGroup objects. Each group contains a list of entities to be redacted / masked together.

Optional Parameters:

Parameters:

• text_content_mode (str) – a discrete value, either “synthetic_data” or “opaque_box”, which defines the masking / redaction method to be performed on text entities. Default value is “synthetic_data”.

• sensitive_content_mode (str) – a discrete value, either “opaque_box”, or “obfuscated_box”. Defines the masking method to be performed on entities with sensitive content. Default value is “opaque_box”.

• replacements (dict[str, dict[str, str]] | None | str) – can be used to define the replacement text for a given entity type and original text. It is a dictionary where keys are entity types and values are dictionaries that map original text to replacement text.

• skip_entity_types (list[str] | None | str) – a list of entity types that should be excluded from redaction / masking.

• add_watermark (bool | str) – a boolean flag that indicates whether to add a watermark to the pages that have redaction / masking applied. Default value is True.

• watermark_text (str | None) – a string that specifies the text to be used in the watermark, if add_watermark is set to True. If not provided, a default watermark text will be used.

Output: The block returns an object with the following keys:

• redacted_groups: list of RedactedGroup objects, containing the redacted images.

Example usage:

redaction = RedactionBlock(
    reference_name='redaction',
    redaction_groups=entity_recognition.output('groups'),
)

IDENTIFIER = 'REDACTION_AND_MASKING'

class flows_sdk.implementations.idp_v43.additional_blocks.FileFilterBlock(trigger='${workflow.input.$trigger}', filter_type='${workflow.input.file_filter_filter_type}', minimum_image_width='${workflow.input.file_filter_minimum_image_width}', minimum_image_height='${workflow.input.file_filter_minimum_image_height}', minimum_file_size_kb='${workflow.input.file_filter_minimum_file_size_kb}', file_extensions_action='${workflow.input.file_filter_file_extensions_action}', file_extensions='${workflow.input.file_filter_file_extensions}', file_extensions_other='${workflow.input.file_filter_file_extensions_other}', reference_name=None, title='File Filter', description='Filters out unwanted files from the input of a flow')

Bases: Block

Available from V40.2

File Filter Block filters out unwanted files from the input of a flow

This block is meant to be placed at the start of the flow/subflow - BEFORE the SubmissionBootstrapBlock, not after it. It requires the output of a connector (e.g. Email Listener) or an API submission as input data, and its output is meant to be consumed by a SubmissionBootstrapBlock.

Mandatory parameters:

This block has no strictly required parameters - all of its filters are optional.

Optional Parameters:

Parameters:

• trigger (str) – the trigger input from a connector - if not set explicitly, it will default to the workflow input trigger (which is where this value is normally found)

• filter_type (str) – a discrete value, either “ALL_FILES” or “IMAGES_ONLY”. Determines what kind of files the filters will be applied to. Default value is “ALL_FILES”. If set to “IMAGES_ONLY”, all filtering options will be applied only to images, other files will pass through.

• minimum_image_width (int | str | None) – image width in pixels - half of the image dimension filter. Any image with less width than configured here will be filtered out. Defaults to None (will do nothing).

• minimum_image_height (int | str | None) – image height in pixels - second half of the image dimension filter. Any image with less height than configured here will be filtered out. Defaults to None (will do nothing).

• minimum_file_size_kb (float | str | None) – represents the file size filter. Any file whose disk size is less than this number (in KB) will be filtered out. May be fractional. Defaults to None (will do nothing).

• file_extensions_action (str) – a discrete value, either “ALLOW”, “DENY” or “NONE”. Represents the function of the file extension filter. “ALLOW” means we only allow the configured file extensions. “DENY” means we only deny the configured file extensions. “NONE” means no file extension filtering takes place. Defaults to “NONE”

• file_extensions (list[str] | str | None) – a list of strings, representing the file extensions used for file extension filtering. Expected if file_extensions_action is not “NONE”. Otherwise, defaults to None (will do nothing).

• file_extensions_other (str | None) – a comma separated list of file extensions used for file extension filtering. Meant for adding extra extensions that are not available in the pre-defined list. Optional, defaults to None (will do nothing). However, it is expected in case the file_extensions parameter contains “other”.

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• title (str) – overrides the block’s default title shown in Flow Studio, defaults to ‘File Filter’

• description (str) – overrides the block’s default description shown in Flow Studio, defaults to ‘Filters out unwanted files from the input of a flow’

Output: The block returns an object with the following keys:

• result.filtered_trigger: The trigger payload with filtered-out files removed, meant to be passed as the trigger input to a SubmissionBootstrapBlock.

Example usage:

# File Filter Block filters out unwanted files from the input of a flow
file_filter_block = FileFilterBlock(
    # trigger: str = workflow_input(Inputs.Trigger),  # usually not necessary to set this
    reference_name='file_filter',
    filter_type='ALL_FILES',
    minimum_image_width=500,
    minimum_image_height=500,
    minimum_file_size_kb=2.5,
    file_extensions_action='ALLOW',
    file_extensions=['pdf', 'png'],
    file_extensions_other='jpg,svg,txt',
)

# reference to the output of FileFilterBlock which can be passed as input to the
# next block (a SubmissionBootstrapBlock)
output_ref = file_filter_block.output('result.filtered_trigger')
submission_bootstrap = SubmissionBootstrapBlock(trigger=output_ref)

When the HS_CODE_BLOCK_OUTPUT_WRAPPING_MODE env var selects no_wrapping, the output is no longer wrapped under result, so reference it directly instead:

output_ref = file_filter_block.output('filtered_trigger')

Example content of output_ref:

{
    "payload": {
        "blob_storage_files": [
            {
                "uuid": "db58606e-32f9-42c9-a0bc-ed9848ff247a",
            }
        ],
        "filtered_files": [
            {
                "uuid": "db58606e-32f9-42c9-a0bc-ed9848ff246e",
                "filtered_by": [
                    "file_size_filter"
                ]
            },
        ]
    }
}

IDENTIFIER = 'FILE_FILTER'

class flows_sdk.implementations.idp_v43.additional_blocks.GeneralPromptingORCABlock(model_type='${workflow.input.orca_type}', base_model_id='${workflow.input.orca_base_model_uuid}', messages=None, adapter_id=None, max_new_tokens=None, max_image_tokens=None, enable_location_hint=None, thinking_budget=None, json_schema=None, model_meta_overrides=None, concurrency_config_overrides=None, is_byo_model=False, reference_name=None, target_outputs=None, disable_log_redaction=False, title='Optical Reasoning & Cognition Agent for General Prompting', description='Optical Reasoning & Cognition Agent for General Prompting')

Bases: Block

General Prompting ORCA Block

This block requires a GPU to function.

Mandatory parameters:

Parameters:

• model_type (str) – Base model to be used for general prompting.

• base_model_id (str) – The UUID of the block asset containing ORCA model weights.

Parameters with defaults:

Parameters:

• messages (list[list[dict[str, Any]]] | str | None) – A list of prompt message threads sent to the model. Each message thread is a list of messages. Each message is a dictionary containing the keys “role” and “content”. - Role is a string, with one of “system”, “user”, “assistant”, depending on what the related content represents. “system” - there is usually a single system prompt in any list of messages, which outlines rules and recommendations for the generative model to follow. “user” - User messages should be messages sent by a person to the model. “assistant” - Assistant messages should be model responses. - Content is a mixed list that can contain text and images. Each content entry is formatted either as {‘type’: ‘image’, ‘image’: image_uuid} or {‘type’: ‘text’, ‘text’: ‘content of the message’}

• adapter_id (str | None) – UUID of a previously imported adapter to use. The base model will be used when not provided. Default value is None.

• max_new_tokens (int | str | None) – The maximum number of tokens the model can extract, which should be scaled based on the number and length of field values. Default value is set to 1024.

• max_image_tokens (int | str | None) – The maximum number of tokens allocated per image by the model. This is conceptually similar to setting image resolution. Larger values may increase accuracy on dense / hard to read images but will raise GPU memory usage. Default value is set to 1280.

• enable_location_hint (bool | None) – When set to True, the ORCA general prompting block will generate location hint related outputs. This can be useful for downstream tasks that require bounding boxes within the image. However, enable it will raise GPU memory usage and inference latency.

• thinking_budget (int | str | None) – Available from V43.2; Soft cap on the number of reasoning (“thinking”) tokens the model may generate before it is forced to close its thinking phase and produce the answer. Only applies to thinking-capable models with thinking enabled. Defaults to None (no cap).

• json_schema (dict[str, Any] | None) – Available from V43.2; Optional JSON Schema (a dict) used to constrain the model’s generated output to valid JSON that conforms to the schema. Enables structured JSON output via the SGLang xgrammar grammar backend. When None (the default), decoding is unconstrained. For a reference on how to author JSON Schemas, see https://json-schema.org/understanding-json-schema/reference/type.

• model_meta_overrides (dict[str, Any] | None) – Available from V43.2; A dictionary of arguments used to override the model meta for the vlm_gpu atomic block. Defaults to None (no overrides).

• concurrency_config_overrides (dict[str, Any] | None) – Available from V43.2; A dictionary of arguments used to override the concurrency config (e.g. max_batch_size, num_kv_cache_tokens) computed for the vlm_gpu atomic block. Defaults to None (no overrides).

• is_byo_model (bool) – Available from V43.2; A boolean flag indicating whether the specified base model is a bring-your-own (BYO) model. Defaults to False.

• reference_name (str | None) – unique reference name within a flow, a unique one will be generated when not provided.

• target_outputs (list[str] | str | None) – Available from V43.2; A list of completions, one per message thread in messages. When provided, the block scores each completion against its own thread (teacher forcing) instead of generating an answer: the output carries the target text back, along with the per-token confidences derived from the prompt log-probabilities. Because no tokens are generated, this cannot be combined with json_schema, enable_location_hint or thinking_budget. Defaults to None (the model generates its own answer).

• disable_log_redaction (bool | str | None) – Available from V43.2; Debug only. When set to True, the VLM blocks skip the redaction of document content (which may contain PII) in their logs. Defaults to False.

• title (str | None) – UI-visible title of the block. Defaults to ‘Optical Reasoning & Cognition Agent for General Prompting’.

• description (str | None) – Description of the block. Both useful for documentation purpose and visible to users in the Flow studio. Defaults to ‘Optical Reasoning & Cognition Agent for General Prompting’.

Output: The block returns an object with the following keys:

• orca_result: The result generated by the model for the given prompt messages.

• metadata: Metadata related to the general prompting process.

Example usage:

# Here we construct a simple static set of messages. But these can also be dynamic
# and the output of a PythonCode block or other processing blocks.
system_message = {'role': 'system', 'content': [
        {'type': 'text', 'text': 'You are a helpful assistant'}
]}
prompt = {'role': 'user', 'content': [
    {'type': 'image', 'image': workflow_input('some_image_uuid')},
    {'type': 'text', 'text': 'Make a short summary of everything in the image'}
]}
message_thread = [system_message, prompt]

gp_block = GeneralPromptingORCABlock(messages=[message_thread])

IDENTIFIER = 'OPTICAL_REASONING_COGNITION_AGENT_GENERAL_PROMPTING'

class flows_sdk.implementations.idp_v43.additional_blocks.ImagesToPrintableFileBlock(documents, height_in_mm=279.4, width_in_mm=215.9, use_native_dimensions=False, dpi=200, mime_type='application/pdf', quality=50, mode='RGB', reference_name=None)

Bases: Block

This block renders each document’s sequence of images into a separate PDF file and stores each resulting PDF in Blob storage. For every document provided, a new blob is created containing the rendered PDF.

Available from V42.1.

Mandatory parameters:

Parameters:

documents (list[dict[str, Any]]) – List of documents, each containing a document_id and pages. Each page contains image_uuid for image-only output, and may optionally include searchable_words_blob_uuid for generating a searchable PDF text layer.

Optional parameters:

Parameters:

• height_in_mm (float) – the height of the document to be generated in millimeters. Default is 279.4 (US Letter height). Ignored when use_native_dimensions is True.

• width_in_mm (float) – the width of the document to be generated in millimeters. Default is 215.9 (US Letter width). Ignored when use_native_dimensions is True.

• use_native_dimensions (bool) – Available from V42.3; when True, each page uses its source image’s native dimensions instead of being resized to width_in_mm × height_in_mm. This preserves maximum image quality. Default is False. When enabled, the output height_in_mm and width_in_mm represent the bounding box (maximum dimensions across all pages).

• dpi (int) – Available from V42.3; dots per inch to assume for images without embedded DPI metadata. Used to calculate physical dimensions in native dimensions mode. Default is 200 (matching VECTOR_GRAPHICS_DPI used by the pagination pipeline).

• mime_type (str) – the mime type of the document to be generated. Default is ‘application/pdf’

• quality (int) – Quality of pages (images) appended to the resulting document as a percentage (recommended 50%). Higher quality results in a larger file size for the generated PDF.

• mode (str) – Mode of the images appended to the resulting document. Supported modes are RGB (default), L (grayscale) and 1 (black-and-white)

Page-based document shape:

Each page contains the image UUID to render and may optionally include searchable_words_blob_uuid. A page with no searchable words blob renders image-only output; a page with a searchable words blob also emits an invisible searchable text layer for those words.

PDF page order follows the order of entries in pages.

• image_uuid: UUID of the rendered image for that PDF page.

• searchable_words_blob_uuid: Optional UUID of a block-storage JSON blob whose records provide the minimal searchable word data: image_uuid, text, and bbox.

Example usage:

documents = [
    {
        'document_id': '48212a99-c099-42e3-bb45-75664db65341',
        'pages': [
            {'image_uuid': 'f42fc9af-b551-4403-aba6-d0eba2c41973'},
            {'image_uuid': 'f42fc9af-b551-4403-aba6-d0eba2c41974'},
        ],
    },
    {
        'document_id': 'delivery_receipt_xyz',
        'pages': [
            {'image_uuid': 'f42fc9af-b551-4403-aba6-d0eba2c41975'},
        ],
    }
]

# Fixed dimensions mode (default) - all pages resized to specified dimensions
images_renderer_block = ImagesToPrintableFileBlock(
    reference_name='images_renderer',
    height_in_mm=279.4,
    width_in_mm=215.9,
    quality=50,
    mode='L',
    documents=documents
)

# Native dimensions mode - preserves original image quality
images_renderer_native = ImagesToPrintableFileBlock(
    reference_name='images_renderer_native',
    use_native_dimensions=True,
    dpi=200,
    quality=80,
    documents=documents
)

output_ref = images_renderer_block.output()

Searchable words are optional on each page. Store them in Blob storage and
reference the blob from the page::

    documents = [
        {
            'document_id': '48212a99-c099-42e3-bb45-75664db65341',
            'pages': [
                {
                    'image_uuid': 'f42fc9af-b551-4403-aba6-d0eba2c41972',
                },
                {
                    'image_uuid': 'f42fc9af-b551-4403-aba6-d0eba2c41973',
                    'searchable_words_blob_uuid': (
                        'd6f5a4bd-581d-4d3d-9eba-d0382b64fda6'
                    ),
                }
            ],
        }
    ]

The searchable words blob must be UTF-8 JSON with a `words` list::

    {
        "words": [
            {
                "image_uuid": "f42fc9af-b551-4403-aba6-d0eba2c41973",
                "text": "Example",
                "bbox": [0.11, 0.21, 0.19, 0.23]
            },
            {
                "image_uuid": "f42fc9af-b551-4403-aba6-d0eba2c41973",
                "text": "text",
                "bbox": [0.22, 0.21, 0.29, 0.23]
            }
        ]
    }

Only records whose `image_uuid` matches the rendered page image are converted
into searchable words. The blob reader also accepts `segments` as an alias for
the top-level `words` list, `transcription` as an alias for `text`, and
`tight_crop_bounding_box` or `bounding_box` as aliases for `bbox`. Extra fields
are ignored, so a ProcessedSegmentList-shaped blob can be reused directly as long
as those minimal fields are present. Each bbox is normalized to the rendered
image:

* `bbox[0]` / `bbox[1]`: top-left x/y.
* `bbox[2]` / `bbox[3]`: bottom-right x/y.
* `[0.0, 0.0]`: top-left of the rendered image.
* `[1.0, 1.0]`: bottom-right of the rendered image.

Example content of output_ref (fixed dimensions mode):

{
    "rendered_documents": [
        {
            "document_id": "48212a99-c099-42e3-bb45-75664db65341",
            "blob_uuid": "52e81c7f-aef6-4f69-b4a6-91fd770a607d",
            "mime_type": "application/pdf",
            "height_in_mm": 279.4,
            "width_in_mm": 215.9,
            "quality": 50,
            "mode": "L",
            "page_dimensions": [
                {
                    "image_uuid": "f42fc9af-b551-4403-aba6-d0eba2c41973",
                    "width_in_mm": 215.9,
                    "height_in_mm": 279.4,
                },
                {
                    "image_uuid": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
                    "width_in_mm": 215.9,
                    "height_in_mm": 279.4,
                },
            ]
        }
    ]
}

Example content of output_ref (native dimensions mode):

{
    "rendered_documents": [
        {
            "document_id": "48212a99-c099-42e3-bb45-75664db65341",
            "blob_uuid": "52e81c7f-aef6-4f69-b4a6-91fd770a607d",
            "mime_type": "application/pdf",
            "height_in_mm": 297.0,
            "width_in_mm": 215.9,
            "quality": 80,
            "mode": "RGB",
            "page_dimensions": [
                {
                    "image_uuid": "f42fc9af-b551-4403-aba6-d0eba2c41973",
                    "width_in_mm": 210.0,
                    "height_in_mm": 297.0,
                },
                {
                    "image_uuid": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
                    "width_in_mm": 215.9,
                    "height_in_mm": 279.4,
                },
            ]
        }
    ]
}

IDENTIFIER = 'IMAGE_TO_PRINTABLE_FILE'

class flows_sdk.implementations.idp_v43.additional_blocks.VertexAIBlock(model, messages, project, location, service_acc_json=None, workload_identity=False, reference_name=None, generation_config=None, safety_settings=None, extra_body=None, title='GCP VertexAI Generate Content', description='Send prompts (text and files) to a model hosted on GCP Vertex AI')

Bases: Block

Generate content using a GCP Vertex AI model.

Available from V43.1.

Mandatory parameters:

Parameters:

• model (str) – ID of the model to use. For example: gemini-2.5-flash or gemini-2.5-pro

• messages (list[dict[str, Any]]) – A list of messages. Each message has a ‘role’ (e.g. user, model) and ‘content’ (list of text or file contents).

• project (str) – GCP Project

• location (str) – GCP Location. For more information, check out: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/learn/locations

• service_acc_json (str | None) – Full service account auth json. Optional when workload_identity is True. For more information, check out: https://cloud.google.com/iam/docs/service-account-creds

• workload_identity (bool) – When True, authenticate via GCP Workload Identity instead of a service account key. Defaults to False.

Optional parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• generation_config (dict[str, Any] | None) – Generation config. For more information, check out: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/reference/rest/v1/GenerationConfig

• safety_settings (list[dict[str, Any]] | None) – A list of SafetySetting objects passed directly to the Vertex AI API. For more information: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/reference/rest/v1/SafetySetting

• extra_body (dict[str, Any] | None) – Additional fields merged into the API request body.

Example usage:

vertexai = VertexAIBlock(
    reference_name='vertexai',
    model='gemini-2.5-flash',
    messages=[{'role': 'user', 'content': [{'type': 'text', 'text': 'Hello'}]}],
    project='my-gcp-project',
    location='us-central1',
    service_acc_json='${system.secrets.gcp_service_account}',
)

IDENTIFIER = 'VERTEXAI_2'

class flows_sdk.implementations.idp_v43.additional_blocks.AddressParserBlock(fields, address_parsing_enabled='${workflow.input.address_parsing_enabled}', address_parser_api_provider='${workflow.input.address_parser_api_provider}', address_parser_api_key='${workflow.input.address_parser_api_key}', address_parser_api_options='${workflow.input.address_parser_api_options}', reference_name=None, title='Address Parser', description='Parses address-typed fields into structured components')

Bases: Block

Address Parser Block parses address-typed fields into structured components (street, city, zip, etc.), either locally (usaddress for US addresses, deepparse for international addresses) or by routing them through a configured third-party address API.

Mandatory parameters:

Parameters:

fields (list[dict[str, Any]] | str) – list of address fields to parse, each a dict with field_uuid, data_type_kind set to ‘address’, address_type, and transcription

Optional Parameters:

Parameters:

• address_parsing_enabled (bool | str) – flag to enable or disable address parsing, defaults to workflow_input(AddressParserSettings.AddressParsingEnabled)

• address_parser_api_provider (str) – selects the address parsing provider (‘none’ / ‘google’ / ‘smarty’ / ‘geoapify’), defaults to workflow_input(AddressParserSettings.ApiProvider)

• address_parser_api_key (str | None) – API key for the configured provider, defaults to workflow_input(AddressParserSettings.ApiKey)

• address_parser_api_options (str | dict | None) – provider-specific JSON options, defaults to workflow_input(AddressParserSettings.ApiOptions)

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• title (str) – overrides the block’s default title shown in Flow Studio, defaults to ‘Address Parser’

• description (str) – overrides the block’s default description shown in Flow Studio, defaults to ‘Parses address-typed fields into structured components’

Output: The block returns an object with the following keys:

• results: list of parsed address results, one per input field.

Example usage:

address_parser = AddressParserBlock(
    reference_name='address_parser',
    fields=flexible_extraction.output('submission.documents[0].fields'),
    address_parser_api_provider='google',
)

IDENTIFIER = 'ADDRESS_PARSER'

Trigger Blocks (v43)

Note

“Triggers” are referred to as “Input Blocks” in our application documentation.

Warning

Flows are built to allow full definition of triggers either in code or manually in the Flow Studio. We recommend defining triggers manually in the Flow Studio. Instructions on how to do so are in our application documentation.

To enable trigger definition via the Flow Studio UI, you need to instantiate the IDPTriggers() convenience class and pass it to the triggers property of the Flow

return Flow(
   ...
   triggers=IDPTriggers()
)

If instead you wish to define specific triggers in Python, use the trigger definitions below.

Folder Listener (v43)

Identifier: FOLDER_TRIGGER

Deprecated in favor of the Universal Folder Listener

Parameters:

Name	Type	Default Value	Title	Description
folder	Path	N/A	Folder to scan for submissions	N/A
file_extensions	string	N/A	File extensions	List of file extensions to monitor for (e.g.: ‘png, jpg, pdf’)
has_meta	boolean	False	Enable metadata	Select this if a metadata file is to be expected along with document files (in XXX_index.txt file)
poll_interval	integer	10	Poll interval	Poll interval in seconds
warmup_interval	integer	15	Warm-up interval	Seconds to wait past document last modified time before processing it
folder_cleanup_delay	integer	86400	Folder cleanup delay	Seconds to wait before cleaning up subfolders
api_params	object	{}	API Parameters	N/A

folder_listener = IOBlock(
    identifier='FOLDER_TRIGGER',
    reference_name='folder_trigger',
    title='Folder listener',
    enabled=True,
    input={
        'folder': '/var/www/forms/forms/folder/',
        'file_extensions': 'png, pdf, jpg',
    },
)

triggers = IDPTriggers(blocks=[folder_listener])

S3 Listener (v43)

Identifier: S3_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
bucket_uri	string	N/A	S3 source URI	S3 URI, where the files will be uploaded (e.g. s3://hs_bucket/prefix1/)
archive_bucket_uri	string	N/A	S3 archive URI	S3 URI to move the processed files to (e.g. s3://hs_bucket/prefix1/)
file_extensions	array	[]	File extensions	Types of file extensions for which to monitor
other_file_extensions	string	N/A	Other file extensions	Comma separated list of file extensions for which to monitor (e.g. ‘png, jpg, pdf’)
has_meta	boolean	false	Include submission level parameters	Select this to ingest JSON files along with document files and submission s3 prefixes. These JSON files can contain information such as metadata, cases and external_id. These JSON file names should match the related files or s3 prefixes (e.g., XXX.jpg.json for XXX.jpg)
aws_access_key_id	string	N/A	AWS ACCESS KEY ID	Access Key used for authentication
aws_secret_access_key	password	N/A	SECRET ACCESS KEY	Secret Key used for authentication
use_ec2_instance_credentials	boolean	true	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used
poll_interval	integer	10	Polling interval (in seconds)	How often the S3 Listener will check the S3 bucket for submissions
warmup_interval	integer	15	Warm-up interval (in seconds)	How long the S3 Listener will wait to process the document after it was first detected
api_params	object	{}	API Parameters	N/A

s3_listener = IOBlock(
    identifier='S3_TRIGGER',
    reference_name='s3_trigger',
    title='S3 Listener',
    enabled=True,
    input={
        'bucket_uri': 's3://hs_bucket/prefix1/',
        'archive_bucket_uri': 's3://hs_bucket/prefix2/',
        'file_extensions': ['png', 'pdf', 'jpg'],
        'aws_access_key_id': 'admin',
        'aws_secret_access_key': system_secret('s3_secret_access_key'),
        'use_ec2_instance_credentials': False,
    },
)
triggers = IDPTriggers(blocks=[s3_listener])

GCS Listener (v43)

Identifier: GCS_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
bucket_uri	string	N/A	GCS Source URI	GCS URI, where the files will be uploaded (e.g. gs://hs_bucket/prefix1/)
archive_bucket_uri	string	N/A	GCS archive URI	GCS URI to move the processed files to (e.g. gs://hs_bucket/prefix2/). Cannot be same as Source URI!
file_extensions	array	N/A	File extensions	Types of file extensions for which to monitor
other_file_extensions	string	N/A	Other file extensions	Comma separated list of file extensions for which to monitor (e.g. ‘png, jpg, pdf’)
has_meta	boolean	false	Include submission level parameters	Select this to ingest JSON files along with document files and submission gcs prefixes. These JSON files can contain information such as metadata, cases and external_id. These JSON file names should match the related files or prefixes (e.g., XXX.jpg.json for XXX.jpg)
credentials_json	json	N/A	GCP Service Account JSON	GCP Service Account JSON used for authentication, must be a valid json.
workload_identity	boolean	false	Use Workload Identity	Credentials are obtained by using the Workload Identity Federation, applicable for HS installations inside GKE clusters.
poll_interval	integer	10	Polling interval (in seconds)	How often the GCS Listener will check the GCS bucket for submissions
warmup_interval	integer	15	Warm-up interval (in seconds)	How long the GCS Listener will wait to process the document after it was first detected
api_params	object	{}	API Parameters	N/A

gcs_listener = IOBlock(
    identifier='GCS_TRIGGER',
    reference_name='gcs_trigger',
    title='GCS Listener',
    enabled=True,
    input={
        'bucket_uri': 'gs://hs_bucket/prefix1/',
        'archive_bucket_uri': 'gs://hs_bucket/prefix2/',
        'file_extensions': ['png', 'pdf', 'jpg'],
        'credentials_json': system_secret('gcs_credentials_json'),
        'workload_identity': False,
    },
)
triggers = IDPTriggers(blocks=[gcs_listener])

Azure Blob Listener (v43)

Identifier: AZURE_BLOB_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
container_uri	string	N/A	Azure source URI	Azure URI, where the files will be uploaded, can contain prefix, can contain trailing slash (e.g.: https://<custom_domain>/<container_name>// https://<account_name>.blob.core.windows.net/<container_name>)
archive_container_uri	string	N/A	Azure archive URI	Azure URI to move the processed files to, can contain prefix, can contain trailing slash. Cannot be same as the source URI.
file_extensions	array	N/A	File extensions	Types of file extensions for which to monitor
other_file_extensions	string	N/A	Other file extensions	Comma separated list of file extensions for which to monitor (e.g. ‘png, jpg, pdf’)
has_meta	boolean	false	Include submission level parameters	Select this to ingest JSON files along with document files and submission Azure container prefixes. These JSON files can contain information such as metadata, cases and external_id. These JSON file names should match the related files or prefixes (e.g., XXX.jpg.json for XXX.jpg)
auth_type [1]	string	service_principal	Authentication type	Authentication type.
client_id	string	N/A	Client ID	Application (client) ID for the corresponding application registered with the Microsoft Entra ID
client_secret	Password	N/A	Client secret	Client secret for the corresponding application registered with the Microsoft Entra ID
tenant_id	string	N/A	Tenant ID	Also called directory ID. Unique identifier of the tenant in which the corresponding application is registered with.
account_name	string	N/A	Storage Account Name	Azure Storage Account name used for authentication
account_key	Password	N/A	Storage Account Key	Azure Storage Account key
authority_host	string	N/A	Authority Host	The authority host URL for the Azure Entra ID. Defaults to https://login.microsoftonline.com
poll_interval	integer	10	Polling interval (in seconds)	How often the Azure Listener will check the container for submissions
warmup_interval	integer	15	Warm-up interval (in seconds)	How long the Azure Listener will wait to process the document after it was first detected
api_params	object	{}	API Parameters	N/A

[1] one of:

service_principal
account_key

azure_blob_listener = IOBlock(
    identifier='AZURE_BLOB_TRIGGER',
    reference_name='azure_blob_trigger',
    title='Azure Blob Listener',
    enabled=True,
    input={
        'container_uri': 'https://hs_container.blob.core.windows.net/prefix1/',
        'archive_container_uri': 'https://hs_container.blob.core.windows.net/prefix2/',
        'file_extensions': ['png', 'pdf', 'jpg'],
        'auth_type': 'service_principal',
        'client_id': '123456',
        'tenant_id': '654321',
        'client_secret': system_secret('azure_blob_client_secret'),
    },
)
triggers = IDPTriggers(blocks=[azure_blob_listener])

Email Listener (v43)

Identifier: IMAP_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
provider [1]	string	imap	Email Provider	N/A
poll_interval	integer	60	Polling interval in seconds	The frequency at which the connection will monitor the email folder for submissions
folder	Path	N/A	Folder to scan for emails	The email folder to retrieve submissions from (e.g., “inbox”)
email_body_treatment	string	“ignore”	Email body treatment	What to do with the body of an email that is ingested. Available options are “process” and “ignore”.
post_process_action	string	“move”	Post process action	What to do with the email after it is processed. Available options are “move” and “delete”.
post_process_move_folder	string	“”	Post process archive folder	Folder to move emails to once they are processed. It will be included only if post_process_action is set to “move”.
api_params	object	{}	API Parameters	N/A
render_headers [2]	array	[]	Headers to include	Headers to render at the top of the email body. Headers will be included only if email_body_treatment is set to “process”.
raw_email_treatment [3]	string	“ignore”	Original email file	If stored, the copy will be found under “raw_email” in the block output. Storing a copy will use additional storage.
attachment_header_treatment [3]	string	“ignore”	Attachment headers	If extracted, the attachment headers will be found under “attachment_metadata” in the block output.

[1] one of:

imap
graph

[2] any of:

To
From
Subject
Date

[3] only available in specific Hyperscience versions:

• version 41: since patch 41.0.24

• version 41.2: since patch 41.2.15

• version 42: since patch 42.0.8

• version 42.1: since patch 42.1.2

• all versions past (and including) 42.2

Additional inputs when "imap" is the selected provider:

Name	Type	Default Value	Title	Description
host	string	N/A	IMAP server address	N/A
port	integer	993	Port Number	N/A
ssl	boolean	True	Use SSL connection	N/A
username	string	N/A	Username	N/A
password	Password	N/A	Password	N/A

Additional inputs when "graph" is the selected provider:

Name	Type	Default Value	Title	Description
client_id	string	N/A	Client id	Application (client) ID for the corresponding application registered with the Microsoft identity platform.
tenant_id	string	N/A	Tenant id	Also called directory ID. Unique identifier of the tenant in which the corresponding application is registered with.
graph_cloud_endpoint [1]	string	Global	Microsoft Graph Cloud Endpoint	Determines base URL used for accessing cloud services through Microsoft Graph.
azure_ad_endpoint [2]	string	AZURE_PUBLIC_CLOUD	Azure AD Endpoint	Determines base URL for the Azure Active Directory (Azure AD) endpoint to acquire token for each national cloud.
client_secret	Password	N/A	Client secret	Client secret for the corresponding application registered with the Microsoft identity platform.

[1] one of:

Global
US_GOV
US_DoD
Germany
China

[2] one of:

AZURE_PUBLIC_CLOUD
AZURE_CHINA
AZURE_GERMANY
AZURE_GOVERNMENT

imap_trigger = IOBlock(
    identifier='IMAP_TRIGGER',
    reference_name='imap_trigger',
    title='IMAP trigger',
    enabled=True,
    input={
        'host': 'example@mail.com',
        'folder': '/var/www/forms/forms/imap/',
        'username': 'admin',
        'password': 'pass',
    }
)

triggers = IDPTriggers(blocks=[imap_trigger])

Box Folder Listener (v43)

Identifier: BOX_FOLDER_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
source_folder_id	string	N/A	Folder to scan for submissions	Use the Box Folder ID found in the URL
target_folder_id	string	N/A	Folder to move completed files	Use the Box Folder ID found in the URL
file_extensions	array	[]	File extensions	Types of file extensions for which to monitor
custom_file_extensions	string	N/A	Other file extension types	Comma separated list of file extensions for which to monitor (e.g. ‘png, jpg, pdf’)
poll_interval	integer	10	Polling interval (in seconds)	How often the connector will check the base folder for submissions
warmup_interval	integer	15	Warm-up interval (in seconds)	How long the connector will wait to process the document after it was last modified
public_key_id	string	N/A	Public-Key ID	ID of the Public-key used for authentication with Box
private_key	Password	N/A	Private-Key	Private key used for authentication with Box
passphrase	Password	N/A	Passphrase	Passphrase used for authentication with Box
client_id	string	N/A	Client ID	Client Id used for authentication with Box
client_secret	Password	N/A	Client Secret	Client Secret used for authentication with Box
enterprise_id	string	N/A	Enterprise ID	Enterprise Id used for authentication with Box
api_params	object	{}	API Parameters	N/A

box_folder_trigger = IOBlock(
    identifier='BOX_FOLDER_TRIGGER',
    reference_name='box_folder_trigger',
    title='Box folder',
    enabled=True,
    input={
        'file_extensions': ['png', 'pdf', 'jpg'],
        'source_folder_id': '24',
        'target_folder_id': '42',
        'public_key_id': 'admin',
        'private_key': 'secret',
        'passphrase': 'password',
        'client_id': 'admin',
        'client_secret': 'secret',
        'enterprise_id': 'admin',
    }
)

triggers = IDPTriggers(blocks=[box_folder_trigger])

Cron Listener (v43)

Identifier: CRON_TRIGGER

Inputs:

Name	Type	Default Value	Title	Description
cron_spec	string	"*/5 * * * *"	Cron specification	N/A
time_zone	string	“US/Eastern”	Time Zone	N/A
api_params	object	{}	API Parameters	N/A

cron_trigger = IOBlock(
    identifier='CRON_TRIGGER',
    reference_name='cron_trigger',
    title='Cron Trigger',
    enabled=True,
    input={
        'cron_spec': '0 10 * * *',
        'time_zone': 'Europe/Sofia',
    }
)

triggers = IDPTriggers(blocks=[cron_trigger])

Salesforce Listener (v43)

Identifier: SALESFORCE_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
channel_name	string	N/A	Channel Name	Configured in the Hyperscience app in Salesforce.
username	string	N/A	Associated Username	Username associated with the Salesforce Connected App.
consumer_key	Password	N/A	Consumer Key	Consumer key used for authentication with Salesforce. Paired with the Salesforce instance from which it was issued.
private_key	Password	N/A	Private Key	Private key used for authentication with Salesforce.
sandbox_environment	boolean	false	Private Key	Enable if the Salesforce environment is a sandbox.
api_params	object	{}	API Parameters	N/A

Message Queue Listener (v43)

Identifier: MQ_LISTENER

Inputs:

Name	Type	Default Value	Title	Description
MQ_TYPE	string	“ACTIVE_MQ”	Message Queue type	Type of the message queue to connect with. Valid values are “ACTIVE_MQ”, “AMAZON_SQS”, “IBM_MQ” and “RABBIT_MQ”
MQ_USERNAME	string	null	Username/Access Key ID	N/A
MQ_PASSWORD	Password	null	Password/Secret Access Key	N/A
MQ_QUEUE_NAME	string	null	Queue Name	Name of the queue (topic) to connect to.
MQ_HOST	string	null	Host Name	N/A
MQ_PORT	integer	5672 when “RABBIT_MQ”, 1414 when “IBM_MQ”, otherwise null	Port Number	N/A
api_params	object	{}	API Parameters	N/A

Additional inputs when "AMAZON_SQS" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_REGION	string	null	AWS Region	N/A
MQ_QUEUE_URL	string	null	Queue URL	N/A
MQ_USE_EC2_INSTANCE_CREDENTIALS	boolean	True	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used.

NOTE: MQ_USERNAME can be used to provide Access Key ID and MQ_PASSWORD to provide Secret Access Key.

Additional inputs when "IBM_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_QUEUE_MANAGER	string	null	Queue Manager (IBM MQ)	N/A
MQ_SSL_CIPHER_SUITE [1]	string	null	SSL cipher suite	SSL cipher suite to use if SSL connection is used.
MQ_CHANNEL	string	null	Channel	N/A
MQ_NO_AUTH_REQUIRED	boolean	false	No auth credentials required	N/A
MQ_MQCSP_AUTHENTICATION_MODE	boolean	false	MQCSP authentication mode	N/A

NOTE: MQ_NO_AUTH_REQUIRED is True, MQ_USERNAME and MQ_PASSWORD are optional.

[1] one of:

"NULL_MD5"
"NULL_SHA"
"RC4_MD5_EXPORT"
"RC4_MD5_US"
"RC4_SHA_US"
"RC2_MD5_EXPORT"
"DES_SHA_EXPORT"
"RC4_56_SHA_EXPORT1024"
"DES_SHA_EXPORT1024"
"TRIPLE_DES_SHA_US"
"TLS_RSA_WITH_NULL_SHA256"
"TLS_RSA_WITH_AES_128_CBC_SHA"
"TLS_RSA_WITH_AES_128_CBC_SHA256"
"TLS_RSA_WITH_AES_256_CBC_SHA"
"TLS_RSA_WITH_AES_256_CBC_SHA256"
"AES_SHA_US"
"TLS_RSA_WITH_DES_CBC_SHA"
"TLS_RSA_WITH_3DES_EDE_CBC_SHA"
"TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256"
"FIPS_WITH_DES_CBC_SHA"
"FIPS_WITH_3DES_EDE_CBC_SHA"

Additional inputs when "RABBIT_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_VIRTUAL_HOST	string	null	Virtual Host (RABBIT MQ)	N/A
MQ_EXCHANGE	string	“”	Exchange	N/A
MQ_ROUTING_KEY	string	“”	Routing key	N/A
MQ_CONNECTION_TYPE	string	“TCP”	Connection Type	“TCP or SSL”

rabbit_mq_listener = IOBlock(
    identifier='MQ_LISTENER',
    reference_name='mq_listener',
    title='RabbitMQ Listener',
    enabled=True,
    input={
        'MQ_TYPE': 'RABBIT_MQ',
        'MQ_QUEUE_NAME': 'some_queue_name',
        'MQ_HOST': 'somehost.com',
        'MQ_USERNAME': 'foo',
        'MQ_PASSWORD': system_secret('rabbit_mq_1_password'),
    }
)

triggers = IDPTriggers(blocks=[rabbit_mq_listener])

Universal Folder Listener (v43)

Identifier: UNIVERSAL_FOLDER_TRIGGER

Inputs:

Name	Type	Default Value	Title	Description
folder	string	N/A	Folder to scan for submissions	Enter the path relative to the base folder. Leave blank to monitor the base folder
file_extensions	array	[]	File extensions	Types of file extensions for which to monitor
other_file_extensions	string	N/A	Other file extensions	Comma separated list of file extensions for which to monitor (e.g. ‘png, jpg, pdf’)
has_meta	boolean	false	Include submission level parameters	Select this to ingest JSON files along with document files and submission folders. These JSON files can contain information such as metadata, cases and external_id. These JSON file names should match the related files or folders (e.g., XXX.jpg.json for XXX.jpg)
poll_interval	integer	10	Polling interval (in seconds)	How often the Folder Listener will check the base folder for submissions
warmup_interval	integer	15	Warm-up interval (in seconds)	How long the Folder Listener will wait to process the document after it was last modified
folder_cleanup_delay	number	24	Empty folder cleanup delay (in hours)	How often the Folder Listener will remove empty folders from the base folder
api_params	object	{}	API Parameters	N/A

universal_folder_listener = IOBlock(
    identifier='UNIVERSAL_FOLDER_TRIGGER',
    reference_name='universal_folder_trigger',
    title='Universal Folder Listener',
    enabled=True,
    input={
        'folder': '/var/www/forms/forms/folder/',
        'file_extensions': 'png, pdf, jpg',
    }
)

triggers = IDPTriggers(blocks=[universal_folder_listener])

Kafka Listener (v43)

Identifier: KAFKA_TRIGGER

Inputs:

Name	Type	Default Value	Title	Description
bootstrap_servers	string	N/A	Bootstrap Servers	A Comma separated list of host:port pairs to use for establishing the initial connection to the cluster
group_id	string	N/A	Group Id	A unique string that identifies the consumer group this consumer belongs to. BLOCK_THREADS_KAFKA_TRIGGER should be used to scale up or down the number of consumers (default: number of available cores)
topics	string	N/A	Topics	Comma separated list of topic names that the block will subscribe to
consumer_config_json	json	N/A	Consumer Config	JSON formatted string containing key-value pairs of Kafka consumer configurations described in the official Kafka documentation
api_params	object	{}	API Parameters	N/A
consume_timeout	integer	-1	Consume Timeout	Maximum time, in seconds, that the consumer will wait for new messages. If messages are received within the specified time, they are processed immediately. However, if no messages are received within that specified time, consumption cycle will be restarted internally to continue fetching messages. This ensures continuous message consumption (default: infinite)
consume_num_messages	integer	1	Consume Num Messages	Sets the maximum number of messages the consumer fetches in a single consumption cycle.If the number of available messages is less than the specified, and timeout is reached, messages will be processed. Consumption cycle will be restarted after the current finishes

kafka_listener = IOBlock(
    identifier='KAFKA_TRIGGER',
    reference_name='kafka_trigger',
    title='Kafka Listener',
    enabled=True,
    input={
        'folder': '/var/www/forms/forms/folder/',
        'file_extensions': 'png, pdf, jpg',
         'bootstrap_servers': 'example.com',
         'group_id': 'test_group',
         'topics': 'test_topic1, test_topic2',
         'consumer_config_json': {},
         'number_of_workers': 1,
         'consume_timeout': 5,
         'consume_num_messages': 10
    }
)

triggers = IDPTriggers(blocks=[kafka_listener])

Output Blocks (v43)

Warning

Flows pass information to downstream systems using the IDPOutputsBlock. This block is specially built to allow full definition either in code or manually in the Flow Studio. We recommend defining outputs manually in the Flow Studio.

Instructions on how to do so are in our application documentation. Simply include an IDPOutputsBlock in your Python flow definition wherever you wish to send information to downstream systems.

class flows_sdk.implementations.idp_v43.idp_blocks.IDPOutputsBlock(inputs, blocks=None)

Bases: Outputs

Output block allows users to send data extracted by an IDP flow to other systems for downstream processing

Mandatory parameters:

Parameters:

inputs (dict[str, Any]) – Used by the UI to automatically pre-populate the input of newly added blocks. Extended in this class to include 'enabled': True to visualize an enabled/disabled trigger that is controllable via Flow Studio.

Usage when parameters are to be defined in Flow Studio:

outputs = IDPOutputBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')}
)

If instead you wish to define specific outputs in Python, use the outputs definitions below.

HTTP Notifier Output (v43)

The HTTP Notifier (REST) output connection will POST results from the system to a specified HTTP endpoint.

Identifier: COB_HTTP_EXPORT

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
endpoint	string	N/A	Endpoint URL	URL that output notifications will be posted to
endpoint_secrets	string	N/A	Endpoint URL secrets	Endpoint URL Secrets, format: (one per line) secret_key_name=secret_value
timeout	int	600	Timeout (seconds)	How long (in seconds) to keep the connection open if no bytes are received from the endpoint. Set to 0 to wait indefinitely.
authorization_type [2]	string	“none”	Authorization type	Type of authorization
authorization_header_name	string	null	Authorization header name	Authorization header name to be set in the notification request
authorization_header	Password	null	Authorization header value	Authorization header to be set in the notification request
auth_url	string	null	OAuth2 authorization URL	The endpoint for the authorization server
client_id	string	null	Client ID	The client identifier issued to the client during the application registration process
client_secret	Password	null	Client Secret	The client secret issued to the client during the application registration process
scope	string	null	Scope	Scope to request to oauth server
audience	string	null	Audience	Resource service URL where token will be valid
additional_oauth_parameters	json	{}	Additional Oauth Request Parameters	Additional parameters to send when requesting token
ssl_cert_source [3]	string	null	Source	N/A
ssl_cert_path	string	null	Trusted Certificate(s) path	Enter the path relative to the base folder
ssl_cert_value	MultilineText	null	Trusted Certificate(s)	N/A
additional_notification_http_headers	json	null	Additional Notification Request Headers	Key value pairs in json format which will be sent via the request as http headers

[1] one of:

"v5"
"transformed"

[2] one of:

"none"
"http_header"
"oauth_2_client_credentials"

[3] one of:

"env_var"
"ca_bundle_path"
"certificate_value"

cob_http_export = IOBlock(
    identifier='COB_HTTP_EXPORT',
    reference_name='cob_http_export',
    title='HTTP Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'SUBMISSION',
        'endpoint': 'example.com',
        'authorization_type': 'none',
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_http_export],
)

Message Queue Notifier Output (v43)

The Message Queue Notifier Output can configure connections to ActiveMQ, Amazon SQS, IBM MQ, and RabbitMQ message queues.

Identifier: COB_MQ_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
MQ_TYPE	string	“ACTIVE_MQ”	Message Queue type	Type of the message queue to connect with. Valid values are “ACTIVE_MQ”, “AMAZON_SQS”, “IBM_MQ” and “RABBIT_MQ”
MQ_USERNAME	string	null	Username/Access Key ID	N/A
MQ_PASSWORD	Password	null	Password/Secret Access Key	N/A
MQ_QUEUE_NAME	string	null	Queue Name	Name of the queue (topic) to connect to. It applies only to “ACTIVE_MQ”, “IBM_MQ” and “RABBIT_MQ” MQ_TYPEs.
MQ_HOST	string	null	Host Name	N/A
MQ_PORT	integer	5672 when “RABBIT_MQ”, 1414 when “IBM_MQ”, otherwise null	Port Number	N/A

[1] one of:

"v5"
"transformed"

Additional inputs when "AMAZON_SQS" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_REGION	string	null	AWS Region	N/A
MQ_QUEUE_URL	string	null	Queue URL	N/A
MQ_USE_EC2_INSTANCE_CREDENTIALS	boolean	True	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used.
MQ_MESSAGE_GROUP_ID	string	null	Group ID for FIFO queues	N/A
MQ_MESSAGE_METADATA	string	null	Additional SQS Metadata	Input additional metadata key value pairs in the format of { “key”: {“TYPE”: “String”, “VALUE”: “”}, “key2”: {“TYPE”: “Number”, “VALUE”: “12345”}

NOTE: MQ_USERNAME can be used to provide Access Key ID and MQ_PASSWORD to provide Secret Access Key.

Additional inputs when "IBM_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_QUEUE_MANAGER	string	null	Queue Manager (IBM MQ)	N/A
MQ_SSL_CIPHER_SUITE [1]	string	null	SSL cipher suite	SSL cipher suite to use if SSL connection is used.
MQ_CHANNEL	string	null	Channel	N/A
MQ_NO_AUTH_REQUIRED	boolean	false	Channel	N/A
MQ_MQCSP_AUTHENTICATION_MODE	boolean	false	Channel	N/A

NOTE: MQ_NO_AUTH_REQUIRED is True, MQ_USERNAME and MQ_PASSWORD are optional.

[1] one of:

"NULL_MD5"
"NULL_SHA"
"RC4_MD5_EXPORT"
"RC4_MD5_US"
"RC4_SHA_US"
"RC2_MD5_EXPORT"
"DES_SHA_EXPORT"
"RC4_56_SHA_EXPORT1024"
"DES_SHA_EXPORT1024"
"TRIPLE_DES_SHA_US"
"TLS_RSA_WITH_NULL_SHA256"
"TLS_RSA_WITH_AES_128_CBC_SHA"
"TLS_RSA_WITH_AES_128_CBC_SHA256"
"TLS_RSA_WITH_AES_256_CBC_SHA"
"TLS_RSA_WITH_AES_256_CBC_SHA256"
"AES_SHA_US"
"TLS_RSA_WITH_DES_CBC_SHA"
"TLS_RSA_WITH_3DES_EDE_CBC_SHA"
"TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256"
"FIPS_WITH_DES_CBC_SHA"
"FIPS_WITH_3DES_EDE_CBC_SHA"

Additional inputs when "RABBIT_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_VIRTUAL_HOST	string	null	Virtual Host (RABBIT MQ)	N/A
MQ_EXCHANGE	string	“”	Exchange	N/A
MQ_ROUTING_KEY	string	“”	Routing key	N/A
MQ_CONNECTION_TYPE	string	“TCP”	Connection Type	“TCP or SSL”

mq_notifier = IOBlock(
    identifier='COB_MQ_NOTIFIER',
    reference_name='mq_notifier',
    title='MQ Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'SUBMISSION',
        'MQ_USERNAME': 'admin',
        'MQ_PASSWORD': 'pass',
        'MQ_QUEUE_NAME': 'queue',
        'MQ_HOST': 'host',
    },
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[mq_notifier],
)

Box Notifier Output (v43)

Provides an out-of-the-box integration into Box systems.

Identifier: COB_BOX_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
public_key_id	string	N/A	Public-Key ID	ID of the Public-key used for authentication with Box
private_key	Password	N/A	Private-Key	Private key used for authentication with Box
passphrase	Password	N/A	Passphrase	Passphrase used for authentication with Box
client_id	string	N/A	Client ID	Client Id used for authentication with Box
client_secret	Password	N/A	Client Secret	Client Secret used for authentication with Box
enterprise_id	string	N/A	Enterprise ID	Enterprise Id used for authentication with Box
template_key	string	“”	Box Metadata Template Key	Enter the key of the Box Metadata template that you would like to map the Hyperscience metadata to.
static_output_fields_to_map	array	[]	Static Metadata Fields	Specify the Hyperscience fields you want to store in Box metadata.
submission_id	string	“”	Key for Mapping Submission ID	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_id	string	“”	Key for Mapping Document ID	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
submission_state	string	“”	Key for Mapping Submission State	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_state	string	“”	Key for Mapping Document State	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
submission_exceptions	string	“”	Key for Mapping Submission Exceptions	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_exceptions	string	“”	Key for Mapping Document Exceptions	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_layout_uuid	string	“”	Key for Mapping Document Layout Uuid	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_layout_name	string	“”	Key for Mapping Document Layout Name	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
start_time	string	“”	Key for Mapping Start Time	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
complete_time	string	“”	Key for Mapping Complete Time	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_fields_template_mappings	json	{}	JSON for Additional Metadata Fields	Specify additional metadata fields using their key value. Please consult Box Integration setup manual for JSON template and instructions.

[1] one of:

"v5"
"transformed"

box_notifier = IOBlock(
    identifier='COB_BOX_NOTIFIER',
    reference_name='box_notifier',
    title='Box Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'private_key': 'password',
        'passphrase': 'password',
        'public_key_id': 'admin',
        'client_id': 'admin',
        'client_secret': 'secret',
        'enterprise_id': 'admin',
        'template_key': 'key',
    },
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[box_notifier],
)

UiPath Notifier Output (v43)

Provides an out-of-the-box integration into UiPath systems.

Identifier: COB_EXPORT_UIPATH

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
auth_method [2]	string	“basic_auth”	Authentication method	N/A
url	string	N/A	URL	Base URL of the uipath instance
app_id	string	N/A	App ID	App ID provided by UiPath
app_secret	Password	N/A	App Secret	App Secret provided by UiPath
oauth_token_endpoint	string	N/A	OAuth token endpoint	Endpoint to retrieve the token. Using https://cloud.uipath.com/identity_/connect/token by default
organization_unit_id	string	N/A	Organization unit id	Organization unit id
tenant	string	N/A	Tenant	Tenant
username	string	N/A	Username	Username
password	Password	N/A	Password	Password
queue	string	N/A	Queue	Queue

[1] one of:

"v5"
"transformed"

[2] one of:

"basic_auth"
"oauth"

cob_export_uipath = IOBlock(
    identifier='COB_EXPORT_UIPATH',
    reference_name='cob_export_uipath',
    title='CCB Export UiPath',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'FLAT_SUBMISSION',
        'url': 'example.com',
        'tenant': 'tenant',
        'username': 'admin',
        'password': 'pass',
        'queue': 'queue',
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_export_uipath],
)

Salesforce Notifier Output (v43)

With the Salesforce Notifier Block, you can configure your flow to send extracted information to Salesforce. The Salesforce Notifier Block can use extracted information to look up, and then update any number of fields on a Salesforce object. The Salesforce Notifier Block is available in both SaaS and on-premise versions of the Hyperscience application.

Identifier: COB_SALESFORCE_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
username	string	N/A	Associated Username	Username associated with the Salesforce Connected App
consumer_key	password	N/A	Consumer Key	Consumer key used for authentication with Salesforce. Paired with the Salesforce instance from which it was issued.
private_key	password	N/A	Private Key	Private key used for authentication with Salesforce.
sandbox_environment	boolean	false	Private Key	Enable if the Salesforce environment is a sandbox.
object_api_name	string	N/A	Object API Name	API name of the object in Salesforce to lookup and update.
new_record_on_lookup_failure	boolean	false	Create new record on lookup failure	When enabled, it will create a new record if the lookup fails to find a record using the lookup parameters. When disabled, lookup will fail if it does not find a record using the lookup parameters.
object_lookup_mapping	json	N/A	Lookup Field Mapping	Specify which Salesforce fields (using their API name) to be queried with which extracted values from the document for the lookup using key-value format. For example: “DOCUMENT_FIELD_NAME”: “OBJECT_FIELD_API_NAME”.
document_fields_mapping	json	N/A	Document Field Mappings	Specify which Salesforce fields (using their API name) you want to update with which extracted data from Hyperscience using key-value format. For example: “DOCUMENT_FIELD_NAME”: “OBJECT_FIELD_API_NAME”.”
overwrite_existing_field_data	boolean	false	Overwrite existing field data	If this is checked and a selected field has data in it, Hyperscience will overwrite that data. If this is unchecked, existing data will not be overwritten and the operation will fail.

[1] one of:

"v5"
"transformed"

cob_salesforce_notifier = IOBlock(
    identifier='COB_SALESFORCE_NOTIFIER',
    reference_name='cob_salesforce_notifier',
    title='Salesforce Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'username': 'salesforce_user',
        'consumer_key': 'salesforce_consumer_key',
        'private_key': 'salesforce_private_key',
        'object_api_name': 'salesforce_object_api_name',
        'object_lookup_mapping': {"LAYOUT_VARIATION_XYZ_FIELD_NAME_1": "SF_FIELD_API_NAME_1", "LAYOUT_VARIATION_XYZ_FIELD_NAME_2": "SF_FIELD_API_NAME_2"},
        'document_fields_mapping': {"LAYOUT_VARIATION_XYZ_FIELD_NAME_3": "SF_FIELD_API_NAME_3", "LAYOUT_VARIATION_XYZ_FIELD_NAME_4": "SF_FIELD_API_NAME_4"}
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_salesforce_notifier],
)

S3 Notifier Output (v43)

With the S3 Notifier Block, you can configure your flow to send extracted information to an S3 bucket.

Identifier: COB_S3_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
bucket_uri	string	N/A	S3 URI	S3 URI, where the data should be stored (e.g. s3://hs_bucket/prefix1/prefix2/)
aws_access_key_id	string	N/A	AWS ACCESS KEY ID	Access Key used for authentication
aws_secret_access_key	password	N/A	SECRET ACCESS KEY	Secret Key used for authentication
use_ec2_instance_credentials	boolean	true	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used

[1] one of:

"v5"
"transformed"

cob_s3_notifier = IOBlock(
    identifier='COB_S3_NOTIFIER',
    reference_name='cob_s3_notifier',
    title='S3 Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'FLAT_SUBMISSION',
        'bucket_uri': 's3://hs_bucket/prefix1/prefix2/',
        'aws_access_key_id': 'aws_access',
        'aws_secret_access_key': system_secret('aws_secret_access_key'),
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_s3_notifier],
)

GCS Notifier Output (v43)

With the GCS Notifier Block, you can configure your flow to send extracted information to a GCS bucket.

Identifier: COB_GCS_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
bucket_uri	string	N/A	GCS Source URI	GCS URI, where the files will be uploaded (e.g. gs://hs_bucket/prefix1/)
credentials_json	json	N/A	GCP Service Account JSON	GCP Service Account JSON used for authentication, must be a valid json.
workload_identity	boolean	true	Use Workload Identity	Credentials are obtained by using the Workload Identity Federation, applicable for HS installations inside GKE clusters.

[1] one of:

"v5"
"transformed"

cob_gcs_notifier = IOBlock(
    identifier='COB_GCS_NOTIFIER',
    reference_name='cob_gcs_notifier',
    title='GCS Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'FLAT_SUBMISSION',
        'bucket_uri': 'gs://hs_bucket/prefix1/prefix2/',
        'credentials_json': system_secret('gcs_credentials_json'),
        'workload_identity': False,
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_gcs_notifier],
)

Azure Blob Notifier Output (v43)

With the Azure Blob Notifier Block, you can configure your flow to send extracted information to an Azure Blob container.

Identifier: COB_AZURE_BLOB_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
container_uri	string	N/A	Azure Blob URI	Azure Blob URI, where the data will be stored, can contain prefix, can contain trailing slash (e.g.: https://<custom_domain>/<container_name>/<prefix>/ https://<account_name>.blob.core.windows.net/<container_name>)
auth_type [2]	string	service_principal	Authentication type	Authentication type.
client_id	string	N/A	Client ID	Application (client) ID for the corresponding application registered with the Microsoft Entra ID
client_secret	Password	N/A	Client secret	Client secret for the corresponding application registered with the Microsoft Entra ID
tenant_id	string	N/A	Tenant ID	Also called directory ID. Unique identifier of the tenant in which the corresponding application is registered with.
account_name	string	N/A	Storage Account Name	Azure Storage Account name used for authentication
account_key	Password	N/A	Storage Account Key	Azure Storage Account key
authority_host	string	N/A	Authority Host	The authority host URL for the Azure Entra ID. Defaults to https://login.microsoftonline.com
[1] one of:

"v5"
"transformed"

[2] one of:

service_principal
account_key

cob_azure_blob_notifier = IOBlock(
    identifier='COB_AZURE_BLOB_NOTIFIER',
    reference_name='cob_azure_blob_notifier',
    title='Azure Blob Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'FLAT_SUBMISSION',
        'container_uri': 'https://hs_container.blob.core.windows.net/',
        'auth_type': 'service_principal',
        'client_id': '123456',
        'tenant_id': '654321',
        'client_secret': system_secret('azure_blob_client_secret'),
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_azure_blob_notifier],
)

Kafka Notifier Output (v43)

With the Kafka Notifier Block, you can configure your flow to send extracted information to a kafka topic.

Identifier: COB_KAFKA_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
bootstrap_servers	string	N/A	Bootstrap Servers	A list of Kafka brokers to connect to. For example: “kafka1:9092,kafka2:9092”
topic	string	N/A	Topic	The Kafka topic where your data will be sent
client_id	string	N/A	SECRET ACCESS KEY	An identifier for your application when connecting to Kafka, typically used for tracking and logging purposes
producer_config_json	json	true	Producer Config (JSON)	Configuration settings for the Kafka producer in JSON format. See https://kafka.apache.org/documentation/#producerconfigs for more information
synchronous_publish	boolean	true	Sync Publish	If true, the publish call will block until the message is sent successfully or an error is raised. If false, the publish call will return immediately after enqueueing the message
message_key	string	null	Message Key	The key associated with the message to be sent. This is used to determine which partition the message is sent to. If null, the message will be sent to a random partition

[1] one of:

"v5"
"transformed"

cob_kafka_notifier = IOBlock(
    identifier='COB_KAFKA_NOTIFIER',
    reference_name='cob_kafka_notifier',
    title='Kafka Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'FLAT_SUBMISSION',
        'bootstrap_servers': 'kafka1:9092,kafka2:9092',
        'topic': 'topic',
        'client_id': 'client_id',
        'producer_config_json': {},
        'synchronous_publish': True,
        'message_key': 'key'
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_kafka_notifier],
)

Helper Classes and Functions (v43)

The following classes are used to easily instantiate and manage a flow’s settings.

flows_sdk.implementations.idp_v43.idp_values.get_idp_wf_inputs(idp_wf_config)

A helper function that maps the values provided by an IdpWorkflowConfig to their corresponding IDP keys.

Parameters:

idp_wf_config (IdpWorkflowConfig) – with static values to be filled.

Return type:

dict[str, Any]

Returns:

a dictionary with filled static values, compatible with IDP flows.

Example usage with default values:

flow = Flow(
    input=get_idp_wf_inputs(get_idp_wf_config()),
    ...
)

Example usage:

Example usage when instantiating an IDP flow with custom parameters::

flow = Flow(
    input=get_idp_wf_inputs({an instance of IdpWorkflowConfig}),
    ...
)

flows_sdk.implementations.idp_v43.idp_values.get_idp_wf_config()

A helper function that instantiates an IdpWorkflowConfig pre-filled with default values. IdpWorkflowConfig is the container class for defining flow-level settings.

Return type:

IdpWorkflowConfig

Returns:

a valid, fully constructed IdpWorkflowConfig instance

Example usage when instantiating an IDP flow:

flow = Flow(
    input=get_idp_wf_inputs(get_idp_wf_config()),
    ...
)

class flows_sdk.implementations.idp_v43.idp_values.IdpWorkflowConfig

Convenience dataclass outlining all flow-level config values for IDP.

class flows_sdk.implementations.idp_v43.idp_values.IDPTriggers(blocks=None)

An adapter class making it easier to instantiate flows_sdk.flows.Triggers for IDP flows. In particular, provides defaults for some fields (e.g. title, description, reference_name) and defines api_params_manifest with IDP specific parameters.

Parameters:

blocks (Sequence[IOBlock] | None) – flows_sdk.blocks.IOBlock s to be included as triggers for an IDP Flow. Optional, defaults to an empty list.

Example usage when instantiating an IDP flow:

folder_listener = IOBlock(...)
flow = Flow(
    triggers=IDPTriggers([folder_listener]),
    ...
)

class flows_sdk.implementations.idp_v43.idp_values.IDPWrapperManifest(flow_identifier)

An adapter class making it easier to instantiate flows_sdk.flows.Manifest for IDP wrapper flows. In particular, provides defaults for some fields (e.g. roles) but more importantly, defines all flows_sdk.flows.Parameter that construct the Flow inputs and their UI representation (left-hand side menu in Flow studio).

Parameters:

flow_identifier (str) – A system-wide unique identifier for flows_sdk.flows.Flow

Example usage when instantiating an IDP flow:

flow = Flow(
    manifest=IDPWrapperManifest(flow_identifier='FLOW_IDENTIFIER'),
    ...
)

class flows_sdk.implementations.idp_v43.idp_values.IDPCoreManifest(flow_identifier, roles=None)

An adapter class making it easier to instantiate flows_sdk.flows.Manifest for IDP Core flows. In particular, provides defaults for some fields (e.g. roles, output) but more importantly, defines all flows_sdk.flows.Parameter that construct the Flow inputs and their UI representation (left-hand side menu in Flow studio).

Parameters:

• flow_identifier (str) – A system-wide unique identifier for flows_sdk.flows.Flow

• roles (list[str] | None) – ‘read_only’, ‘supporting’ and ‘idp_core’ are the default roles of every subflow that can replace the default IDP_CORE subflow inside the top-level IDP flows. ‘supporting’ and ‘idp_core’ are mandatory roles that are necessary for all IDP Core flows. ‘supporting’ ensures that the subflow will be auto-deployed together with the top-level IDP flow. ‘idp_core’ ensures that this subflow can be selected in the UI in dropdowns which allow picking an alternative subflow to the default IDP_CORE subflow. If you are implementing a subflow that is not supposed to be a drop-in replacement for IDP_CORE, you will have to subclass or implement your own version of IDPCoreManifest class in order to remove the idp_core role.

Example usage when instantiating an IDP flow:

flow = Flow(
    manifest=IDPCoreManifest(flow_identifier='FLOW_IDENTIFIER'),
    ...
)

Contracts (v43)

class flows_sdk.implementations.idp_v43.contracts.Submission

A Submission object is created when a set of files are submitted into the system together. The system then interprets each file as a one or more Page objects.

id: int

Unique system-generated identifier.

cases: list[Case] | None = None

Array of Case objects that contain Documents and Pages from this Submission.

flow_uuid: UUID

The UUID of the Flow that created this Submission.

flow_name: str

The name of the Flow that created this Submission.

flow_version: int

The version of the Flow that created this Submission.

status: Literal['processing', 'complete', 'reprocessing']

Current status of the Submission.

complete_time: datetime | None = None

Time the entire Submission entered the Complete state, including all of its Supervision tasks.

classification_review_needed: bool | None = None

Indicates whether Classification Supervision is needed for this Submission.

documents: list[Document] | None = None

Array of Document objects identified as being part of this Submission.

unassigned_pages: list[Page]

Array of Page objects included in this Submission that have not been grouped into a Document.

failed_pages: list[FailedPage]

Array of Page objects included in this Submission that failed processing.

class flows_sdk.implementations.idp_v43.contracts.Page

The Page object is the representation of a single image that was submitted into the system.

id: int

Unique system-generated identifier.

submission_page_number: int

Indicates page order for the Submission.

status: Literal['processing', 'failed', 'complete']

Current status of the Page.

page_type: Union[Literal['unassigned_page', 'blank_page', 'other_page', 'additional_form_page', 'document_page', 'unknown_page', 'failed'], str]

The type of the Page.

origin: FilePageOrigin

The origin of the Page, which can be used to determine from which submitted file the Page was extracted.

classification_type: Optional[Literal['manual', 'machine']] = None

The method by which the Page was classified, if applicable. Present when the page_type is not ‘unknown_page’ or ‘unassigned_page’.

machine_layout_page_uuid: UUID | None = None

Unique identifier of the layout page that the machine predicted this Page matches to, if applicable.

manual_layout_page_uuid: UUID | None = None

Unique identifier of the layout page that a keyer assigned this Page to, if applicable.

combined_segments: PageSegments | None = None

The segments for the Page, if applicable.

image_uuid: UUID

Unique identifier of the image for this Page. Refers to the original image as extracted from the source file before any transformations were applied.

image_name: str

The name of the original image file for this Page.

image_width: int

The width of the original image for this Page in pixels.

image_height: int

The height of the original image for this Page in pixels.

image_checksum: str

The checksum of the original image for this Page.

corrected_image_uuid: UUID | None = None

Unique identifier of the corrected image for this Page, if applicable. Refers to the image after transformations such as rotation or perspective correction were applied.

image_correction_type: Optional[Literal['layout_matching', 'image_correction', 'manual']] = None

The type of image correction applied to this Page, if applicable. Available in v43.1 and later.

transformation_matrix: list[list[float]] | None = None

The 3x3 registration homography matrix for the Page, if applicable.

template_image_shapes: list[list[int]] | None = None

Registration shape metadata for the layout image, if applicable. Contains the pre-computed layout shape and the original layout image shape.

submission_image_shape: list[int] | None = None

Registration pre-processing shape of the submission image, if applicable.

correction_model_info: ImageCorrectionModelInfo | None = None

Model version metadata for image correction on this Page, if applicable.

rotation_confidence: float | None = None

The confidence score for the applied rotation correction for the Page, if applicable. Ranges from 0.0 to 1.0, or None if not applicable.

rotation_class: int | None = None

The applied rotation-correction class for the Page, if applicable. Values are 0, 1, 2, or 3, representing the number of 90-degree counterclockwise rotations applied, or None if not applicable.

deskew_confidence: float | None = None

The confidence score for the applied deskew correction for the Page, if applicable. Ranges from 0.0 to 1.0, or None if not applicable.

skew_angle: float | None = None

The applied deskew-correction angle for the Page in degrees, if applicable. This value represents the rotation applied to deskew the image (it may be 0 when no deskew is needed), or None if not applicable.

mobile_correction_result: Optional[Literal['PREDICTED_TRANSFORM', 'THRESHOLDED_AS_SCANNED', 'NO_VANISHING_POINT_CONSENSUS', 'TOO_FEW_DOCUMENT_PIXELS']] = None

Outcome of mobile perspective correction for this Page, if applicable. Indicates whether a perspective transform was applied or why it was not, or None if not applicable.

applied_transform: ImageCorrectionPerspectiveTransform | None = None

Applied mobile perspective transform for this Page, if applicable.

exceptions: list[str]

List of exceptions encountered during processing.

class flows_sdk.implementations.idp_v43.contracts.Document

The Document object is a set of pages that have matched to a pre-defined layout.

id: int

Unique system-generated identifier.

uuid: UUID

Unique system-generated identifier.

status: Literal['processing', 'reprocess', 'reprocessing', 'rejected', 'complete']

Current status of the Document.

complete_time: datetime | None = None

Time the entire Document entered the Complete state, including all of its Supervision tasks.

layout_uuid: UUID

The unique identifier of the layout that this Document was matched to.

layout_name: str

The name of the layout that this Document was matched to.

layout_language: str

The language of the layout that this Document was matched to.

layout_kind: Literal['structured', 'semi_structured', 'additional']

The kind of the layout that this Document was matched to.

layout_tags: list[str]

The list of tags associated with the layout that this Document was matched to.

layout_version_uuid: UUID

The unique identifier of the specific layout version that this Document was matched to.

original_layout_version_uuid: UUID

The unique identifier of the original layout version that this Document was matched to. Used to map partially matched Documents to a specific layout version.

classified_by_user_id: int

The unique identifier of the user who classified this Document. -1 if classified by an automated process.

needs_flexible_extraction_review: bool

Indicates whether the Document requires Flexible Extraction Supervision.

pages: list[DocumentPage]

Array of Page objects included in this Document.

document_fields: list[Field] | None = None

Array of Field objects identified as being part of this Document.

tables: list[Table] | None = None

Array of Table objects identified as being part of this Document.

data_types: list[DataType]

Array of DataType objects identified as being part of this Document.

processed_segments: list[FragmentedSegments] | None = None

Array of FragmentedSegments objects representing the segments that have been extracted for this Document.

document_file: DocumentFile | None = None

The printable file rendered from the Document’s page images, if applicable.

metadata: dict[str, Any]

A dictionary to store any additional metadata related to the Document.

exceptions: list[str]

List of exceptions encountered during processing.

class flows_sdk.implementations.idp_v43.contracts.FailedPage

A FailedPage object represents a Page that failed processing during the execution of a Flow.

id: int

Unique system-generated identifier.

submission_page_number: int

Indicates page order for the Submission.

status: Literal['processing', 'failed', 'complete']

Current status of the Page. For failed pages, this property will be set to failed.

page_type: Literal['unassigned_page', 'blank_page', 'other_page', 'additional_form_page', 'document_page', 'unknown_page', 'failed']

The type of the Page, which can be used to determine how the system attempted to process the Page before it failed.

image_uuid: UUID | None

Unique identifier of the image for this Page, if available. Refers to the original image as extracted from the source file before any transformations were applied.

image_name: str | None

The name of the original image file for this Page, if available.

image_width: int

The width of the original image for this Page in pixels, if available.

image_height: int

The height of the original image for this Page in pixels, if available.

image_checksum: str | None

The checksum of the original image for this Page, if available.

origin: FilePageOrigin | FilenamePageOrigin

The origin of the Page, which can be used to determine from which submitted file the Page was extracted.

exceptions: list[str]

List of exceptions encountered during processing that led to the failure of this Page.

class flows_sdk.implementations.idp_v43.contracts.DocumentPage

Represents a page within a document.

id: int

Unique system-generated identifier of the original Page object.

document_page_id: int

Unique system-generated identifier.

document_page_number: int

Indicates page order for the Document.

classification_type: Literal['manual', 'machine'] = None

The method by which the Page was classified.

layout_page_uuid: UUID | None = None

Unique identifier of the layout page that this Page was matched to, if applicable.

layout_image_uuid: UUID | None = None

Unique identifier of the layout image that this Page was matched to, if applicable.

submission_page_number: int

Indicates page order for the Submission.

status: Literal['processing', 'failed', 'complete']

Current status of the Page.

page_type: Union[Literal['unassigned_page', 'blank_page', 'other_page', 'additional_form_page', 'document_page', 'unknown_page', 'failed'], str]

The type of the Page.

origin: FilePageOrigin

The origin of the Page, which can be used to determine from which submitted file the Page was extracted.

machine_layout_page_uuid: UUID | None = None

Unique identifier of the layout page that the machine predicted this Page matches to, if applicable.

manual_layout_page_uuid: UUID | None = None

Unique identifier of the layout page that a keyer assigned this Page to, if applicable.

combined_segments: PageSegments | None = None

The segments for the Page, if applicable.

image_uuid: UUID

Unique identifier of the image for this Page. Refers to the original image as extracted from the source file before any transformations were applied.

image_name: str

The name of the original image file for this Page.

image_width: int

The width of the original image for this Page in pixels.

image_height: int

The height of the original image for this Page in pixels.

image_checksum: str

The checksum of the original image for this Page.

corrected_image_uuid: UUID | None = None

Unique identifier of the corrected image for this Page, if applicable. Refers to the image after transformations such as rotation or perspective correction were applied.

image_correction_type: Optional[Literal['layout_matching', 'image_correction', 'manual']] = None

The type of image correction applied to this Page, if applicable. Available in v43.1 and later.

transformation_matrix: list[list[float]] | None = None

The 3x3 registration homography matrix for the Page, if applicable.

template_image_shapes: list[list[int]] | None = None

Registration shape metadata for the layout image, if applicable. Contains the pre-computed layout shape and the original layout image shape.

submission_image_shape: list[int] | None = None

Registration pre-processing shape of the submission image, if applicable.

correction_model_info: ImageCorrectionModelInfo | None = None

Model version metadata for image correction on this Page, if applicable.

rotation_confidence: float | None = None

The confidence score for the applied rotation correction for the Page, if applicable. Ranges from 0.0 to 1.0, or None if not applicable.

rotation_class: int | None = None

The applied rotation-correction class for the Page, if applicable. Values are 0, 1, 2, or 3, representing the number of 90-degree counterclockwise rotations applied, or None if not applicable.

deskew_confidence: float | None = None

The confidence score for the applied deskew correction for the Page, if applicable. Ranges from 0.0 to 1.0, or None if not applicable.

skew_angle: float | None = None

The applied deskew-correction angle for the Page in degrees, if applicable. This value represents the rotation applied to deskew the image (it may be 0 when no deskew is needed), or None if not applicable.

mobile_correction_result: Optional[Literal['PREDICTED_TRANSFORM', 'THRESHOLDED_AS_SCANNED', 'NO_VANISHING_POINT_CONSENSUS', 'TOO_FEW_DOCUMENT_PIXELS']] = None

Outcome of mobile perspective correction for this Page, if applicable. Indicates whether a perspective transform was applied or why it was not, or None if not applicable.

applied_transform: ImageCorrectionPerspectiveTransform | None = None

Applied mobile perspective transform for this Page, if applicable.

exceptions: list[str]

List of exceptions encountered during processing.

class flows_sdk.implementations.idp_v43.contracts.Field

Represents a concrete occurrence of a field that has been defined for extraction as part of a layout.

id: int

Unique system-generated identifier.

uuid: UUID

Unique system-generated identifier.

status: Optional[Literal['processing', 'complete']] = None

Current status of the Field.

layout_field_uuid: UUID

The unique identifier of the specific layout field this Field was matched to.

layout_page_uuid: UUID

The unique identifier of the layout page that this Field was matched to.

data_type_uuid: UUID

The unique identifier of the data type of the Field.

source_uuid: UUID | None

Stable layout-level field identifier used to map equivalent Fields across versions of the same layout.

original_source_uuid: UUID | None

The original stable layout-level field identifier. Used to map Fields from partially matched Documents to a specific layout field.

shared_field_uuid: UUID | None

The unique identifier of the shared field, if applicable.

field_number: int

One-based index, defining the number of the Field within the layout.

field_name: str

The name of the layout field that this Field was matched to.

output_name: str

The output name of the layout field that this Field was matched to.

required: bool

Indicates whether this Field was defined as required in the layout.

repeated: bool

For fields in Structured layouts, indicates whether this Field can appear multiple times. Data from repeated fields is extracted only from the Field’s first occurrence in a Document.

dropout: bool

Indicates whether text in the Field’s background is removed from the transcription, as defined in the layout.

multiline: bool

Indicates whether this Field is expected to have multiple lines of text, as defined in the layout.

identification_sv_mode: int

Indicates how Supervision affects the identification of the Field. Possible values are:

• 0 (default) - using the default identification process.

• 2 (auto_identify) - the field will never go to Identification Supervision, regardless of the machine’s confidence.

• 3 (sv_always) - the field will always go to Identification Supervision, regardless of the machine’s confidence.

Ignored when process_manual_identification_type is set.

transcription_sv_mode: int

Indicates how Supervision affects the transcription of the Field. Possible values are:

• 0 (default) - using the default transcription process.

• 1 (consensus) - this field requires multiple sources to agree on the transcription before its processing is complete.

• 2 (auto_transcribe) - the field will never go to Transcription Supervision, regardless of the machine’s confidence.

• 3 (sv_always) - the field will always go to Transcription Supervision, regardless of the machine’s confidence.

language: str | None

The language of the layout field that this Field was matched to.

notes: str | None

Additional notes or comments related to the Field.

transcription_target_accuracy: float | None

The target accuracy for the transcription of this Field, as defined in the layout.

process_manual_identification_type: Optional[Literal['FORCE', 'ALWAYS_FORCE', 'SKIP', 'ALWAYS_SKIP']] = None

Overrides the normal extraction process by forcing or bypassing Identification Supervision for this Field.

When set, this value overrides identification_sv_mode.

process_machine_transcription_type: Optional[Literal['FORCE', 'ALWAYS_FORCE', 'SKIP', 'ALWAYS_SKIP']] = None

Overrides the normal extraction process by forcing or bypassing machine transcription for this Field.

process_flexible_extraction_type: Optional[Literal['FORCE', 'ALWAYS_FORCE', 'SKIP', 'ALWAYS_SKIP']] = None

Overrides the normal extraction process by forcing or bypassing Flexible Extraction Supervision for this Field.

process_orca_type: Optional[Literal['FORCE', 'ALWAYS_FORCE', 'SKIP', 'ALWAYS_SKIP']] = None

Overrides the normal extraction process by forcing or bypassing ORCA extraction for this Field.

identification_source: Literal['machine', 'human']

The method by which the Field was identified.

identification_confidence: Literal['sure', 'not_sure']

The confidence of the Field’s identification.

occurrence_index: int

Zero-based index of this occurrence among all occurrences for the same layout field and in the same Document.

locations: list[FieldLocation]

Array of locations where the Field appears.

page_number: int

Corresponds to the page number of the first FieldLocation of the Field. If the Field has no locations, will reference the first page of the Document.

bounding_box: list[float] | None

Coordinates of the bounding box of the first FieldLocation of the Field. If the Field has no locations, will be None.

exceptions: list[str]

List of exceptions encountered during processing.

class flows_sdk.implementations.idp_v43.contracts.FieldLocation

Represents the location of a field on a page.

type: Literal['approximate', 'exact']

The type of location.

position: int

One-based index of this location among all locations for the same Field, ordered by reading order. Sort by this property to reconstruct the Field’s content in the sequence it appears in across pages.

page_number: int

Reference to the document_page_number of the Page where the Field is located.

bounding_box: list[float]

The location of the Field on the Page, as an array of four normalized coordinates in the format [start_x, start_y, end_x, end_y], relative to page dimensions.

class flows_sdk.implementations.idp_v43.contracts.Table

Represents a concrete table that has been defined for extraction as part of a layout.

layout_table_uuid: UUID

The unique identifier of the specific layout table this Table was matched to.

layout_table_name: str

The name of the layout table that this Table was matched to.

parent_layout_table_uuid: UUID | None = None

Indicates the layout_table_uuid of the parent Table, if applicable.

process_manual_identification_type: Optional[Literal['FORCE', 'ALWAYS_FORCE', 'SKIP', 'ALWAYS_SKIP']] = None

Overrides the normal extraction process by forcing or bypassing Identification Supervision for this Table.

identification_confidence: Literal['sure', 'not_sure']

The combined Identification confidence for the entire table hierarchy. If any table in the parent/child hierarchy is not confidently predicted, the value will be not_sure even if the individual table prediction is sure.

model_prediction: Literal['sure', 'not_sure']

The Identification confidence for this specific Table only, independent of its parent or child tables.

rows: list[Row]

Array of Row objects identified as being part of this Table.

columns: list[Column]

Array of Column objects identified as being part of this Table.

class flows_sdk.implementations.idp_v43.contracts.Column

Represents a concrete column in a table that has been defined for extraction as part of a layout.

layout_column_uuid: UUID

The unique identifier of the specific layout column.

data_type_uuid: UUID

The unique identifier of the data type of the TableColumn.

source_uuid: UUID | None

Stable layout-level column identifier used to map equivalent Columns across versions of the same layout.

column_number: int

One-based index, defining the number of the Column within the layout.

field_name: str

The name of the Column as defined in the layout.

output_name: str

The output name of the Column as defined in the layout.

required: bool

Indicates whether this Column was defined as required in the layout.

dropout: bool

Indicates whether text in the background of the cells in this Column is removed from the transcription, as defined in the layout.

multiline: bool

Indicates whether this Column is expected to have multiple lines of text, as defined in the layout.

transcription_sv_mode: int

Indicates how Supervision affects the transcription of the cells in this Column. Possible values are:

• 0 (default) - using the default transcription process.

• 1 (consensus) - the cells require multiple sources to agree on the transcription before their processing is complete.

• 2 (auto_transcribe) - the cells will never go to Transcription Supervision, regardless of the machine’s confidence.

• 3 (sv_always) - the cells will always go to Transcription Supervision, regardless of the machine’s confidence.

language: str | None

The language of the Column as defined in the layout.

notes: str | None

Additional notes or comments related to the Column.

transcription_target_accuracy: float | None = None

The target accuracy for the transcription of the cells in this Column, as defined in the layout.

cells: list[Cell]

Array of Cell objects identified as being part of this Column.

class flows_sdk.implementations.idp_v43.contracts.Row

Represents a row of cells in a table that has been defined for extraction as part of a layout.

row_number: int

Indicates the order in which this Row appears in the Table.

parent_row_number: int | None = None

Indicates the row_number of the parent Row, if part of a child Table.

missing_row_iteration: int | None = None

Indicates which iteration of the missing-row-detection algorithm identified this Row.

class flows_sdk.implementations.idp_v43.contracts.Cell

Represents a concrete cell in a table that has been defined for extraction as part of a layout.

id: int

Unique system-generated identifier.

uuid: UUID

Unique system-generated identifier.

status: Optional[Literal['processing', 'complete']] = None

Current status of the Cell.

process_machine_transcription_type: Optional[Literal['FORCE', 'ALWAYS_FORCE', 'SKIP', 'ALWAYS_SKIP']] = None

Overrides the normal extraction process by forcing or bypassing machine transcription for this Cell.

process_flexible_extraction_type: Optional[Literal['FORCE', 'ALWAYS_FORCE', 'SKIP', 'ALWAYS_SKIP']] = None

Overrides the normal extraction process by forcing or bypassing Flexible Extraction Supervision for this Cell.

identification_source: Literal['machine', 'human']

The method by which the Cell was identified.

identification_confidence: Literal['sure', 'not_sure']

The confidence of the Cell’s identification.

layout_column_uuid: UUID

The unique identifier of the specific layout column this Cell was matched to.

row_number: int

Reference to the row_number of the Row where the Cell is located.

page_number: int

Reference to the document_page_number of the Page where the Cell is located.

bounding_box: list[float] | None = None

The location of the Cell on the Page, as an array of four normalized coordinates in the format [start_x, start_y, end_x, end_y], relative to page dimensions.

exceptions: list[str]

List of exceptions encountered during processing.

class flows_sdk.implementations.idp_v43.contracts.Case

The Case object represents a set of documents collated for a particular purpose.

id: int

Unique system-generated identifier.

uuid: UUID

Unique system-generated identifier that remains unique when there is a need to migrate case data between systems.

external_case_id: str

User-defined identifier that can be included as part of the Case object during case creation.

notes: str

User-defined string that can be included as part of the Case object during case creation or update.

start_time: datetime

Time the Case was created.

update_time: datetime

Time the Case was last updated.

to_delete_time: datetime | None = None

Time the Case is scheduled to be deleted, if applicable.

submissions: list[int]

Array of ids of Submissions with Pages (or Documents) included in this Case.

submission_files: list[str]

Array of strings representing the names of files comprising the Case.

unassigned_pages: list[int]

Array of ids of Pages that are included in the Case but not yet assigned to a Document.

documents: list[int]

Array of ids of Documents included in the Case.

class flows_sdk.implementations.idp_v43.contracts.FilePageOrigin

Represents the origin of a page when detailed file information is available.

file_uuid: UUID

Unique identifier of the file from which this Page was extracted.

file_name: str | None = None

The name of the file from which this Page was extracted. Available in v43.1 and later.

sub_file_number: int

The number of the sub-file from which this Page was extracted. For example, if a ZIP file contains three PDFs, the Pages extracted from the first PDF would have sub_file_number 1, the second PDF would have sub_file_number 2, and the third PDF would have sub_file_number 3.

file_page_number: int | None

The page number within the file / sub-file. This value is 1-indexed.

file_group: str

The group to which the file belongs.

intermediate_file_uuid: UUID | None = None

UUID of the intermediate PDF blob produced when paginating a non-PDF file type (e.g., DOCX, HTML). None for native PDFs and image-only formats. Available in v43.2 and later.

class flows_sdk.implementations.idp_v43.contracts.FilenamePageOrigin

Represents the origin of a page when the file name is the only available information. This situation can occur when the system fails to extract the referenced file.

file_name: str

The name of the file from which this Page was extracted.

class flows_sdk.implementations.idp_v43.contracts.PageSegments

Represents the segmentation details of a page, including the number of text, checkbox, and signature segments.

uuid: UUID

Unique identifier used to link the Page to its corresponding segments in the file store.

model_version: str

The version of the segmentation model used to extract the segments.

segmentation_date: datetime

The date and time when the segmentation was performed.

n_text_segments: int

The number of text segments identified on the Page.

n_checkbox_segments: int

The number of checkbox segments identified on the Page.

n_signature_segments: int

The number of signature segments identified on the Page.

class flows_sdk.implementations.idp_v43.contracts.DataType

Provides means for configuring the data-extraction and normalization processes.

uuid: UUID

Unique system-generated identifier.

entry_type: Literal[1, 2, 3]

Represents the method by which the data is extracted. Possible values are:

• 1 (builtin) - extraction using a built-in model.

• 2 (pattern) - extraction using a regex pattern.

• 3 (list) - extraction by matching against a list of accepted values.

ml_model: Literal['checkbox', 'signature', 'entry', 'barcode']

Top-level category of the DataType.

meta_type: str | None

Sub-category of the DataType. For example, for an entry DataType, the meta_type could be date, address, etc.

pattern: str | None

A regex pattern used for extracting data when entry_type is pattern.

normalization_strip_charset: str | None

A string of characters to be removed from the extracted value during normalization.

normalization_map: dict[str, str] | None

A mapping of values to their normalized forms. Used during normalization to convert extracted values to a standard format.

accepted_values: list[str] | None

A list of values that are considered valid for this DataType. Used when entry_type is list.

raw_results: bool | None

Indicates whether the raw extracted results should be returned without normalization.

class flows_sdk.implementations.idp_v43.contracts.ImageCorrectionModelInfo

Provides information about the models used for image correction.

rotation_model: str

The version of the rotation-detection model used for image correction.

mobile_model: str | None

The version of the mobile-correction model used for image correction, if applicable.

class flows_sdk.implementations.idp_v43.contracts.ImageCorrectionPerspectiveTransform

Provides information about the perspective transform applied during mobile correction.

absolute_matrix: list[list[float]]

The 3x3 perspective (homography) matrix in absolute pixel coordinates.

relative_matrix: list[list[float]]

The 3x3 perspective (homography) matrix in normalized relative coordinates.

relative_corner_points: list[list[float]]

The corner points of the perspective transform, represented as relative coordinates (between 0 and 1) with respect to the original image dimensions.

class flows_sdk.implementations.idp_v43.contracts.DocumentFile

Metadata for a printable file rendered from a document’s page images.

document_uuid: UUID

UUID of the source Document this printable file was rendered for.

uuid: UUID

UUID of the stored rendered file.

checksum: str

Checksum of the stored rendered file.

mime_type: str

MIME type of the rendered file (e.g., application/pdf).

height_in_mm: float

The maximum page height across all rendered pages in millimeters.

width_in_mm: float

The maximum page width across all rendered pages in millimeters.

quality: int

Rendering quality used when generating the printable file.

mode: str

Color mode used during rendering (e.g., RGB, GRAYSCALE, or BW).

download_url: str

API URL for downloading the rendered file.

page_dimensions: list[DocumentFilePage]

Per-page rendered metadata listed in the same order as pages in the output file.

class flows_sdk.implementations.idp_v43.contracts.DocumentFilePage

Metadata for one rendered page in a DocumentFile.

Includes the source page image identifier and the rendered page size in millimeters.

image_uuid: UUID

UUID of the source image used for this rendered page.

width_in_mm: float | None

Rendered page width in millimeters.

height_in_mm: float | None

Rendered page height in millimeters.

class flows_sdk.implementations.idp_v43.contracts.FragmentedSegments

Metadata for a stored artifact that contains fragmented segments from a document.

Each instance represents one derived segment set produced from the document’s original segments using a particular splitting configuration.

uuid: UUID

Unique identifier of the stored fragmented segments.

flex_model_type: str

Specifies the splitting strategy used to derive these fragmented segments from the original segments.

custom_split_char: str | None

Optional custom split-character configuration used when these fragmented segments were derived.

class flows_sdk.implementations.idp_v43.contracts.EntityGroup

The EntityGroup object represents a group of pages and entities extracted from those pages.

identifier: str

Unique system-generated identifier.

pages: list[Page]

Array of Page objects included in this EntityGroup.

entities: list[Entity]

Array of Entity objects included in this EntityGroup.

class flows_sdk.implementations.idp_v43.contracts.Entity

The Entity object presents a single extracted occurrence of a given type.

uuid: UUID

Unique system-generated identifier.

origin: Literal['NER', 'CED', 'Signature']

The origin of the Entity, indicating which method was used for extracting it.

type: str

The type of the Entity (e.g., Person Name, Location, etc.).

text: str

The text extracted from the Entity.

locations: list[EntityLocation]

Array of locations where the Entity appears.

confidence: float

A number between 0 and 1 indicating the confidence the machine has in the accuracy of the Entity’s extraction.

class flows_sdk.implementations.idp_v43.contracts.EntityLocation

Represents the location of an entity on a page.

position: int

One-based index of this location among all locations for the same Entity, ordered by reading order. Sort by this property to reconstruct the Entity’s content in the sequence it appears across pages.

page_number: int

Reference to the submission_page_number of the Page where the Entity is located.

bounding_box: list[float]

The location of the Entity on the Page, as array of four numbers in the format [start_x, start_y, end_x, end_y].

text: str

The text extracted from the Entity location.

raw_text: str

The raw text extracted from the Entity location.

class flows_sdk.implementations.idp_v43.contracts.RedactedGroup

The RedactedGroup object represents a group of redacted pages.

identifier: str

Identifier of the source redaction group.

pages: list[RedactedPage]

Array of RedactedPage objects included in this RedactedGroup.

class flows_sdk.implementations.idp_v43.contracts.RedactedPage

Represents a page with redaction data.

id: int

Unique system-generated identifier.

redaction_data: PageRedactionData

A PageRedactionData object containing the redaction data for the Page.

submission_page_number: int

Indicates page order for the Submission.

status: Literal['processing', 'failed', 'complete']

Current status of the Page.

page_type: Union[Literal['unassigned_page', 'blank_page', 'other_page', 'additional_form_page', 'document_page', 'unknown_page', 'failed'], str]

The type of the Page.

origin: FilePageOrigin

The origin of the Page, which can be used to determine from which submitted file the Page was extracted.

classification_type: Optional[Literal['manual', 'machine']] = None

The method by which the Page was classified, if applicable. Present when the page_type is not ‘unknown_page’ or ‘unassigned_page’.

machine_layout_page_uuid: UUID | None = None

Unique identifier of the layout page that the machine predicted this Page matches to, if applicable.

manual_layout_page_uuid: UUID | None = None

Unique identifier of the layout page that a keyer assigned this Page to, if applicable.

combined_segments: PageSegments | None = None

The segments for the Page, if applicable.

image_uuid: UUID

Unique identifier of the image for this Page. Refers to the original image as extracted from the source file before any transformations were applied.

image_name: str

The name of the original image file for this Page.

image_width: int

The width of the original image for this Page in pixels.

image_height: int

The height of the original image for this Page in pixels.

image_checksum: str

The checksum of the original image for this Page.

corrected_image_uuid: UUID | None = None

Unique identifier of the corrected image for this Page, if applicable. Refers to the image after transformations such as rotation or perspective correction were applied.

image_correction_type: Optional[Literal['layout_matching', 'image_correction', 'manual']] = None

The type of image correction applied to this Page, if applicable. Available in v43.1 and later.

transformation_matrix: list[list[float]] | None = None

The 3x3 registration homography matrix for the Page, if applicable.

template_image_shapes: list[list[int]] | None = None

Registration shape metadata for the layout image, if applicable. Contains the pre-computed layout shape and the original layout image shape.

submission_image_shape: list[int] | None = None

Registration pre-processing shape of the submission image, if applicable.

correction_model_info: ImageCorrectionModelInfo | None = None

Model version metadata for image correction on this Page, if applicable.

rotation_confidence: float | None = None

The confidence score for the applied rotation correction for the Page, if applicable. Ranges from 0.0 to 1.0, or None if not applicable.

rotation_class: int | None = None

The applied rotation-correction class for the Page, if applicable. Values are 0, 1, 2, or 3, representing the number of 90-degree counterclockwise rotations applied, or None if not applicable.

deskew_confidence: float | None = None

The confidence score for the applied deskew correction for the Page, if applicable. Ranges from 0.0 to 1.0, or None if not applicable.

skew_angle: float | None = None

The applied deskew-correction angle for the Page in degrees, if applicable. This value represents the rotation applied to deskew the image (it may be 0 when no deskew is needed), or None if not applicable.

mobile_correction_result: Optional[Literal['PREDICTED_TRANSFORM', 'THRESHOLDED_AS_SCANNED', 'NO_VANISHING_POINT_CONSENSUS', 'TOO_FEW_DOCUMENT_PIXELS']] = None

Outcome of mobile perspective correction for this Page, if applicable. Indicates whether a perspective transform was applied or why it was not, or None if not applicable.

applied_transform: ImageCorrectionPerspectiveTransform | None = None

Applied mobile perspective transform for this Page, if applicable.

exceptions: list[str]

List of exceptions encountered during processing.

class flows_sdk.implementations.idp_v43.contracts.PageRedactionData

Contains the redaction data for a Page.

image_uuid: str

Unique identifier of the primary redacted image for the Page.

debug_image_uuid: str

Unique identifier of a separate debug rendering of the same redacted Page. Contains visual indicators of where redactions were applied.

redactions: list[Redaction]

Array of Redaction objects associated with the Page.

class flows_sdk.implementations.idp_v43.contracts.Redaction

Represents a single redaction applied to a page.

kind: str

The normalized redaction category.

bounding_box: list[float]

The location of the redacted text on the Page, as an array of four numbers in the format [start_x, start_y, end_x, end_y].

original_text: str

The original text before redaction.

redacted_text: str

The replacement text written into the page image. May be empty when text is not replaced (e.g., in opaque/box-based redaction modes).

V42

Processing Blocks (v42)

Core Blocks are powerful processing blocks necessary to build intelligent document processing solutions on Hyperscience. They include both manual and machine processing blocks.

Note

Classes in the idp_v42 implementation have parameters with ambiguous typing. For example, a field of type integer could also be represented as a string reference (e.g. that integer gets provided at runtime). The types introduced in the example are therefore incomplete and are likely to be revisioned in a future version.

class flows_sdk.implementations.idp_v42.idp_blocks.IDPCoreBlock(idp_wf_config, reference_name='__idp_core__', title='Start Document Processing Subflow', description='Calls the Document Processing Subflow with the specified settings.', compatibility_spec=CompatibilitySpec(filter_roles=['idp_core'], filter_schema=None, label=None), identifier='IDP_CORE_V42', additional_inputs=None)

Bases: Block

IDP core block includes the steps to process the passed documents into machine-readable output.

Mandatory parameters:

Parameters:

idp_wf_config (IdpWorkflowConfig) – IdpWorkflowConfig object with all flow-level configuration settings for IDP

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique reference name within a flow, a unique one will be generated when not provided

• compatibility_spec (CompatibilitySpec | None) – defines a filter for flows that could be selected in the UI in place of “IDP Core” for the block. Defaults to flows with the role ‘idp_core’. The user will be able to pick any flow that matches the compatibility_spec. This serves to make upgrading to a new compatible version of “IDP Core” (e.g. IDP_CORE_V39) easier in future releases.

• identifier (str) – The identifier of the flow. Defaults to the IDP_CORE_VXX identifier of the out-of-the-box flow that comes with the system.

• additional_inputs (dict[str, Any] | None) – Dictionary containing key-value pairs that will be added to the inputs of the block. If a key is contained in the default inputs, its value will be overwritten by the value provided here. You generally do not need to provide additional_inputs, unless you are creating a custom derivative of the IDP_CODE_Vxx flow and are modifying or extending the list of inputs it accepts.

Example usage:

idp_core = IDPCoreBlock(idp_wf_config=idp_wf_config)

REFERENCE_NAME = '__idp_core__'

class flows_sdk.implementations.idp_v42.idp_blocks.SubmissionBootstrapBlock(reference_name=None, trigger='${workflow.input.$trigger}', layout_release_uuid='${workflow.input.layout_release_uuid}', s3_config='${workflow.input.submission_bootstrap_s3_config}', s3_endpoint_url='${workflow.input.submission_bootstrap_s3_endpoint_url}', ocs_config='${workflow.input.submission_bootstrap_ocs_config}', ocs_host_url='${workflow.input.submission_bootstrap_ocs_host_url}', ocs_username='${workflow.input.submission_bootstrap_ocs_username}', ocs_password='${workflow.input.submission_bootstrap_ocs_password}', ocs_ssl_cert='${workflow.input.submission_bootstrap_ocs_ssl_cert}', http_config='${workflow.input.submission_bootstrap_http_config}', abs_auth_type='${workflow.input.submission_bootstrap_abs_auth_type}', abs_account_url='${workflow.input.submission_bootstrap_abs_account_url}', abs_tenant_id='${workflow.input.submission_bootstrap_abs_tenant_id}', abs_client_id='${workflow.input.submission_bootstrap_abs_client_id}', abs_client_secret='${workflow.input.submission_bootstrap_abs_client_secret}', abs_authority_host='${workflow.input.submission_bootstrap_abs_authority_host}', abs_account_name='${workflow.input.submission_bootstrap_abs_account_name}', abs_account_key='${workflow.input.submission_bootstrap_abs_account_key}', gcs_credentials_json='${workflow.input.submission_bootstrap_gcs_credentials_json}', gcs_workload_identity='${workflow.input.submission_bootstrap_gcs_workload_identity}', max_number_pages_enabled='${workflow.input.submission_bootstrap_max_number_pages_enabled}', max_number_pages='${workflow.input.submission_bootstrap_max_number_pages}', notification_workflow='${workflow.input.notification_workflow_initialization}', workflow_uuid='${workflow.input.workflow_uuid}', workflow_name='${workflow.input.workflow_name}', workflow_version='${workflow.input.workflow_version}', submission='${workflow.input.submission}')

Bases: Block

Submission bootstrap block initializes the submission object and prepares external images or other submission data if needed. This block should be called at the START of every flow that uses blocks included in the library.

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• trigger (str) – the trigger input from a connector - if not set explicitly, it will default to the workflow input trigger (which is where this value is normally found)

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• s3_config (str) – Specify Amazon S3 credentials for submission retrieval store. Expects a json expression: {"aws_access_key_id": "X", "aws_secret_access_key": "Y"}, defaults to workflow_input(Settings.SubmissionBootstrapS3Config)

• s3_endpoint_url (str) – Endpoint URL for S3 submission retrieval store, defaults to workflow_input(Settings.SubmissionBootstrapS3EndpointUrl)

• ocs_config (str) – OCS Configuration for downloading submission data, defaults to workflow_input(Settings.SubmissionBootstrapOCSConfig)

• ocs_host_url (str | None) – Available from V42.3; OCS host URL for submission retrieval store, defaults to workflow_input(Settings.SubmissionBootstrapOCSHostUrl)

• ocs_username (str | None) – Available from V42.3; OCS username for authentication, defaults to workflow_input(Settings.SubmissionBootstrapOCSUsername)

• ocs_password (str | None) – Available from V42.3; OCS password for authentication, defaults to workflow_input(Settings.SubmissionBootstrapOCSPassword)

• ocs_ssl_cert (str | None) – Available from V42.3; CA bundle filename inside $HS_PATH/certs directory or “SKIP” to disable validation, defaults to workflow_input(Settings.SubmissionBootstrapOCSSslCert)

• http_config (str | None) – An optional system secret field expressing the HTTP Configuration for downloading submission data. The content is expected to be a json formatted as: {"username": "X", "password": "Y", "ssl_cert": "CA bundle filename"}, defaults to workflow_input(Settings.SubmissionBootstrapHTTPConfig)

• gcs_credentials_json (str) – GCS service account credentials in JSON format, defaults to workflow_input(Settings.SubmissionBootstrapGCSCredentialsJson)

• gcs_workload_identity (str) – Whether to obtain the credentials by using the Workload Identity Federation, applicable for HS installations inside GKE clusters, defaults to workflow_input(Settings.SubmissionBootstrapGCSWorkloadIdentity)

• max_number_pages_enabled (str | None) – An optional parameter (flag) controlling whether the configured max number of pages is taken into account

• max_number_pages (str | None) – An optional parameter specifying the max number of pages for downstream processing. Submissions with pages exceeding this configured value will fail. The default value is set to 10_000 pages.

• notification_workflow (str | None) – Notification flow name to run when the submission is initialized, defaults to workflow_input(Settings.SubmissionBootstrapNotificationWorkflow)

• workflow_uuid (str) – UUID of the triggered workflow version, defaults to workflow_input(Inputs.WorkflowUuid)

• workflow_name (str) – Name of the triggered workflow, defaults to workflow_input(Inputs.WorkflowName)

• workflow_version (str) – Version of the triggered workflow, defaults to workflow_input(Inputs.WorkflowVersion)

• submission (Any) – Submission object

Example usage:

submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')

IDENTIFIER = 'SUBMISSION_BOOTSTRAP_2'

output(key=None)

Submission bootstrap ends with a CodeBlock, which nests the actual outputs under the ‘result’ key. This method overrides the default output in the same manner that flows_sdk.blocks.Routing.CodeBlock does for convenience.

Parameters:

key (str | None) –

Optionally provide a key to directly get a nested property. When not provided, the entire ‘result’ will be returned. For example, we have:

{
    "result": {
        "a": {
            "b": 42
        }
    }
}

• skipping the key output()` will return ``{"a": {"b": 42}}

• calling with output("a") will result in {"b": 42}

• calling with output("a.b") will return 42

Return type:

str

Returns:

the value under the provided key

class flows_sdk.implementations.idp_v42.idp_blocks.MachineCollationBlock(submission, cases, dedupe_files='${workflow.input.machine_collation_dedupe_files}', refresh_retention_period='${workflow.input.machine_collation_refresh_retention_period}', remove_from_cases='${workflow.input.machine_collation_remove_from_cases}', retention_period='${workflow.input.machine_collation_retention_period}', reference_name=None)

Bases: Block

Machine collation block groups files, documents and pages (from the submission) into cases.

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• cases (Any) –

  This parameter accepts an array of JSON objects that contain information on how Cases should be created. There are two ways to collate a case: by the filenames specified in the current Submission, or by the ids of the Documents or Pages submitted in previous submissions.

  This parameter follows the given format:

  {
      'cases': [
          'external_case_id': 'HS-1',
          'filename': 'file1.pdf',
          'documents': [42],
          'pages': [43]
      ]
  }
  

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• dedupe_files (bool | str) – Enabling this setting will replace case data from repeated file names within the same case. Cases will retain data from the most recently submitted version of a particular file. Note that this setting does not delete the old data, it just removes it from the case. Keep in mind this option is only relevant when adding to cases. Defaults to workflow_input(Settings.CollationDedupeFiles)

• remove_from_cases (Any | None) –

  This parameter accepts an array of JSON objects that contains information on how documents or pages should be removed from a pre-existing case. Currently, the only way to de-collate from a case is by the ids of the Documents or Pages within a given case, defaults to workflow_input(Settings.CollationRemoveFromCases)

  This parameter follows the given format:

  {
      'remove_from_cases': [
          'external_case_id': 'HS-1',
          'documents': [42],
          'pages': [43]
      ]
  }
  

• retention_period (int | str | None) – The number of days to retain cases after they are updated by this block. If passed None, no changes to the deletion date will be made. Defaults to workflow_input(Settings.CollationRetentionPeriod)

• refresh_retention_period (bool | str) – If True, (re)applies Retention Period parameter to all cases passed into the block. If False, only updates those without a pre-existing deletion date. Defaults to workflow_input(Settings.CollationRefreshRetentionPeriod)

Example usage:

machine_collation = MachineCollationBlock(
    reference_name='machine_collation',
    submission=submission_bootstrap.output('result.submission'),
    cases=submission_bootstrap.output('result.api_params.cases'),
    dedupe_files=True,
    remove_from_cases=custom_supervision.output('remove_from_cases')
)

IDENTIFIER = 'MACHINE_COLLATION_2'

class flows_sdk.implementations.idp_v42.idp_blocks.MachineClassificationBlock(submission, api_params, reference_name=None, rotation_correction_enabled='${workflow.input.machine_classification_rotation_correction_enabled}', mobile_processing_enabled='${workflow.input.machine_classification_mobile_processing_enabled}', file_transcription_enabled='${workflow.input.machine_classification_file_transcription_enabled}', layout_release_uuid='${workflow.input.layout_release_uuid}', vpc_registration_threshold='${workflow.input.structured_layout_match_threshold}', nlc_enabled='${workflow.input.semi_structured_classification}', nlc_target_accuracy='${workflow.input.semi_target_accuracy}', nlc_doc_grouping_logic='${workflow.input.semi_doc_grouping_logic}', layout_id_dealbreaker='${workflow.input.layout_id_dealbreaker}', layout_id_dealbreaker_bypass='${workflow.input.layout_id_dealbreaker_bypass}', manual_nlc_enabled='${workflow.input.manual_nlc_enabled}')

Bases: Block

Machine classification block automatically matches documents to structured, semi-structured or additional layouts

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• rotation_correction_enabled (bool | str) – Identifies and corrects the orientation of semi-structured images, defaults to workflow_input(Settings.RotationCorrectionEnabled)

• mobile_processing_enabled (bool | str) – Improves the machine readability of photo captured documents, defaults to workflow_input(Settings.MobileProcessingEnabled)

• file_transcription_enabled (bool | str) – Improves the transcription speed of pdf files. Make sure that the “Image Correction” option is disabled and the pdfs you submit do not contain any rotated images defaults to workflow_input(Settings.FileTranscriptionEnabled)

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• vpc_registration_threshold (str | float) – Structured pages above this threshold will be automatically matched to a layout variation, defaults to workflow_input(Settings.StructuredLayoutMatchThreshold)

• manual_nlc_enabled (str) – Available from V41.2; enables manual classification when applicable, defaults to workflow_input(Settings.ManualNlcEnabled)

• nlc_enabled (str | bool) – Enables workflow to manage a model for automated classification of semi-structured and additional layout variations, defaults to workflow_input(Settings.SemiStructuredClassification)

• nlc_target_accuracy (str | float) – defaults to workflow_input(Settings.SemiTargetAccuracy)

• nlc_doc_grouping_logic (str) – Logic to handle multiple pages matched to the same layout variationin a given submission, defaults to workflow_input(Settings.SemiDocGroupingLogic)

Example usage:

machine_classification = MachineClassificationBlock(
    reference_name='machine_classification',
    submission=case_collation.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    rotation_correction_enabled=idp_wf_config.rotation_correction_enabled,
)

IDENTIFIER = 'MACHINE_CLASSIFICATION_7'

class flows_sdk.implementations.idp_v42.idp_blocks.ManualClassificationBlock(submission, api_params, reference_name=None, layout_release_uuid='${workflow.input.layout_release_uuid}', manual_nlc_enabled='${workflow.input.manual_nlc_enabled}', task_restrictions='${workflow.input.manual_classification_task_restrictions}', notification_workflow='${workflow.input.notification_workflow_classification}', vpc_registration_threshold='${workflow.input.structured_layout_match_threshold}')

Bases: Block

Manual classification block allows keyers to manually match submissions to their layouts. Keyers may perform manual classification if machine classification cannot automatically match a submission to a layout with high confidence

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• manual_nlc_enabled (str | bool) – Enables manual classification when applicable, defaults to workflow_input(Settings.ManualNlcEnabled)

• task_restrictions (list[Any] | str | None) – Defines what users can access Supervision tasks created by this block, defaults to workflow_input(Settings.ManualClassificationTaskRestrictions)

• notification_workflow (str) – Notification flow name to run when the submission enters Manual Classification, defaults to workflow_input(Settings.ManualClassificationNotificationWorkflow)

• nlc_enabled – Enables workflow to manage a model for automated classification of semi-structured and additional layout variations, defaults to workflow_input(Settings.SemiStructuredClassification)

• nlc_target_accuracy – Classification Target Accuracy, defaults to workflow_input(Settings.SemiTargetAccuracy)

• mobile_processing_enabled – Improves the machine readability of photo captured document, defaults to workflow_input(Settings.MobileProcessingEnabled)

• vpc_registration_threshold (str | None) – Structured pages above this threshold will be automatically matched to a layout variation when performing light vpc registration in the doc-org task, defaults to workflow_input(Settings.StructuredLayoutMatchThreshold)

Example usage:

manual_classification = ManualClassificationBlock(
    reference_name='manual_classification',
    submission=machine_classification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
)

IDENTIFIER = 'MANUAL_CLASSIFICATION_6'

class flows_sdk.implementations.idp_v42.idp_blocks.MachineIdentificationBlock(submission, api_params, layout_release_uuid='${workflow.input.layout_release_uuid}', reference_name=None, manual_field_id_enabled='${workflow.input.manual_field_id_enabled}', force_required_missing_entries_to_sv='${workflow.input.force_required_entries_to_sv}', field_id_target_accuracy='${workflow.input.field_id_target_accuracy}', table_id_target_accuracy='${workflow.input.table_id_target_accuracy}', id_target_accuracies='${workflow.input.identification_target_accuracies}')

Bases: Block

Machine identification automatically identify fields and tables in the submission

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• layout_release_uuid (Any) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• manual_field_id_enabled (str) – Enables manual identification when applicable, defaults to workflow_input(Settings.ManualFieldIdEnabled)

• force_required_missing_entries_to_sv (str) – defaults to workflow_input( Settings.ForceRequiredEntriesToSV )

• field_id_target_accuracy (str) – Field ID Target Accuracy, defaults to workflow_input(Settings.FieldIdTargetAccuracy)

• table_id_target_accuracy (str) – Table ID Target Accuracy, defaults to workflow_input(Settings.TableIdTargetAccuracy)

• id_target_accuracies (str) – defaults to workflow_input( Settings.IdentificationTargetAccuracies )

Example usage:

machine_identification = MachineIdentificationBlock(
    reference_name='machine_identification',
    submission=manual_classification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
)

IDENTIFIER = 'MACHINE_IDENTIFICATION_8'

class flows_sdk.implementations.idp_v42.idp_blocks.ManualIdentificationBlock(submission, api_params, reference_name=None, task_restrictions='${workflow.input.manual_identification_task_restrictions}', manual_field_id_enabled='${workflow.input.manual_field_id_enabled}', layout_release_uuid='${workflow.input.layout_release_uuid}', notification_workflow='${workflow.input.notification_workflow_identification}')

Bases: Block

Manual identification allows keyers to complete field identification or table identification tasks, where they draw bounding boxes around the contents of certain fields, table columns or table rows. This identification process ensures that the system will be able to transcribe the correct content in the upcoming transcription process

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• task_restrictions (list[Any] | str | None) – Defines what users can access Supervision tasks created by this block, defaults to workflow_input(Settings.ManualFieldIdTaskRestrictions)

• manual_field_id_enabled (str) – Enables manual identification when applicable, defaults to workflow_input(Settings.ManualFieldIdEnabled)

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• notification_workflow (str) – Notification flow name to run when the submission enters Manual Identification, defaults to workflow_inputSettings.ManualFieldIdNotificationWorkflow

Example usage:

manual_identification = ManualIdentificationBlock(
    reference_name='manual_identification',
    submission=machine_identification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    task_restrictions=idp_wf_config.manual_identification_config.task_restrictions,
)

IDENTIFIER = 'MANUAL_IDENTIFICATION_5'

class flows_sdk.implementations.idp_v42.idp_blocks.MachineTranscriptionBlock(submission, api_params, reference_name=None, layout_release_uuid='${workflow.input.layout_release_uuid}', transcription_model='${workflow.input.transcription_model}', finetuning_only_trained_layouts='${workflow.input.finetuning_only_trained_layouts}', force_normalization_errors_to_sv='${workflow.input.force_normalization_errors_to_sv}', force_required_blank_entries_to_sv='${workflow.input.force_required_entries_to_sv}', transcription_overrides='${workflow.input.transcription_overrides}', structured_text_target_accuracy='${workflow.input.structured_text_target_accuracy}', structured_text_confidence_threshold='${workflow.input.structured_text_threshold}', structured_text_acceptable_confidence='${workflow.input.structured_min_leg_threshold}', semi_structured_text_target_accuracy='${workflow.input.semi_structured_text_target_accuracy}', semi_structured_text_confidence_threshold='${workflow.input.semi_structured_text_threshold}', semi_structured_text_acceptable_confidence='${workflow.input.semi_structured_min_leg_threshold}', semi_structured_table_target_accuracy='${workflow.input.semi_structured_table_target_accuracy}', semi_structured_table_confidence_threshold='${workflow.input.semi_structured_table_threshold}', semi_structured_table_acceptable_confidence='${workflow.input.semi_structured_table_min_leg_threshold}', structured_checkbox_target_accuracy='${workflow.input.structured_checkbox_target_accuracy}', structured_checkbox_confidence_threshold='${workflow.input.structured_checkbox_threshold}', structured_checkbox_acceptable_confidence='${workflow.input.checkbox_min_leg_threshold}', structured_signature_target_accuracy='${workflow.input.structured_signature_target_accuracy}', structured_signature_confidence_threshold='${workflow.input.structured_signature_threshold}', structured_signature_acceptable_confidence='${workflow.input.signature_min_leg_threshold}', semi_structured_checkbox_target_accuracy='${workflow.input.semi_structured_checkbox_target_accuracy}', semi_structured_checkbox_confidence_threshold='${workflow.input.semi_structured_checkbox_threshold}', semi_structured_checkbox_acceptable_confidence='${workflow.input.semi_structured_checkbox_min_leg_threshold}', field_target_accuracies='${workflow.input.field_transcription_target_accuracies}', column_target_accuracies='${workflow.input.column_transcription_target_accuracies}', always_supervise_blank_cells='${workflow.input.manual_transcription_always_supervise_blank_cells}', manual_transcription_enabled='${workflow.input.manual_transcription_enabled}')

Bases: Block

Machine transcription automatically transcribes the content of your submission

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• transcription_model (str) – The transcription model that will be used for this flow, defaults to workflow_input(Settings.TranscriptionModel)

• finetuning_only_trained_layouts (str) – defaults to workflow_input( Settings.FinetuningOnlyTrainedLayouts )

• force_normalization_errors_to_sv (str) – defaults to workflow_input( Settings.ForceNormalizationErrorsToSV )

• force_required_blank_entries_to_sv (str) – defaults to workflow_input( Settings.ForceRequiredEntriesToSV )

• transcription_overrides (str) – defaults to workflow_input( Settings.TranscriptionOverrides )

• structured_text_target_accuracy (str) – defaults to workflow_input( Settings.StructuredTextTargetAccuracy )

• structured_text_confidence_threshold (str | float) – defaults to workflow_input( Settings.StructuredTextThreshold )

• structured_text_acceptable_confidence (str | float) – defaults to workflow_input( Settings.StructuredTextMinLegThreshold )

• semi_structured_text_target_accuracy (str | float) – defaults to workflow_input( Settings.SemiStructuredTextTargetAccuracy )

• semi_structured_text_confidence_threshold (str) – defaults to workflow_input( Settings.SemiStructuredTextThreshold )

• semi_structured_text_acceptable_confidence (str) – defaults to workflow_input( Settings.SemiStructuredTextMinLegThreshold )

• structured_checkbox_target_accuracy (str) – defaults to workflow_input( Settings.StructuredCheckboxTargetAccuracy )

• structured_checkbox_confidence_threshold (str) – defaults to workflow_input( Settings.StructuredCheckboxThreshold )

• structured_checkbox_acceptable_confidence (str) – defaults to workflow_input( Settings.StructuredCheckboxMinLegThreshold )

• structured_signature_target_accuracy (str) – defaults to workflow_input( Settings.StructuredSignatureTargetAccuracy )

• structured_signature_confidence_threshold (str) – defaults to workflow_input( Settings.StructuredSignatureThreshold )

• structured_signature_acceptable_confidence (str) – defaults to workflow_input( Settings.StructuredSignatureMinLegThreshold )

• semi_structured_checkbox_target_accuracy (str) – defaults to workflow_input( Settings.SemiStructuredCheckboxTargetAccuracy )

• semi_structured_checkbox_confidence_threshold (str) – defaults to workflow_input( Settings.SemiStructuredCheckboxThreshold )

• semi_structured_checkbox_acceptable_confidence (str) – defaults to workflow_input( Settings.SemiStructuredCheckboxMinLegThreshold )

• field_target_accuracies (str) – defaults to workflow_input( Settings.FieldTargetAccuracies )

• column_target_accuracies (str) – Deprecated since R40.1 and will be removed in R43 defaults to workflow_input( Settings.ColumnTargetAccuracies )

• always_supervise_blank_cells (str | bool | None) – Available from V41.2; if enabled, always send blank cells in transcribed tables to be supervised manually, regardless of confidence defaults to workflow_input(Settings.ManualTranscriptionAlwaysSuperviseBlankCells)

• manual_transcription_enabled (str | None) – Available from V41.2; enables manual transcription when applicable, defaults to workflow_input(Settings.ManualTranscriptionEnabled)

Example usage:

machine_transcription = MachineTranscriptionBlock(
    reference_name='machine_transcription',
    submission=manual_identification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
)

IDENTIFIER = 'MACHINE_TRANSCRIPTION_9'

class flows_sdk.implementations.idp_v42.idp_blocks.ManualTranscriptionBlock(submission, api_params, reference_name=None, supervision_transcription_masking='${workflow.input.manual_transcription_supervision_transcription_masking}', table_output_manual_review='${workflow.input.manual_transcription_table_output_manual_review}', always_supervise_blank_cells='${workflow.input.manual_transcription_always_supervise_blank_cells}', task_restrictions='${workflow.input.manual_transcription_task_restrictions}', manual_transcription_enabled='${workflow.input.manual_transcription_enabled}', notification_workflow='${workflow.input.notification_workflow_transcription}', title='Manual Transcription', description='Manually transcribe fields that could not be automatically transcribed')

Bases: Block

Manual transcription lets your keyers manually enter the text found in fields or tables that could not be automatically transcribed

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (bool | str) – Prevents users from inputting invalid characters during Supervision Transcription tasks, defaults to workflow_input(Settings.ManualTranscriptionSupervisionTranscriptionMasking)

• table_output_manual_review (bool | str) – Always generates a table transcription task if the layout contains a table. If disabled, a table transcription task will only be generated if one or more cells have transcribed values below the defined thresholds, defaults to workflow_input(Settings.ManualTranscriptionTableOutputManualReview)

• always_supervise_blank_cells (bool | str) – If enabled, always send blank cells in transcribed tables to be supervised manually, regardless of confidence defaults to workflow_input(Settings.ManualTranscriptionAlwaysSuperviseBlankCells)

• task_restrictions (list[Any] | str | None) – Defines what users can access Supervision tasks for submission from a particular source or submissions matching a specific layout, defaults to workflow_input(Settings.ManualTranscriptionTaskRestrictions)

• manual_transcription_enabled (str) – Enables manual transcription when applicable, defaults to workflow_input(Settings.ManualTranscriptionEnabled)

• notification_workflow (str) – Notification flow name to run when the submission enters Manual Transcription, defaults to workflow_input(Settings.ManualTranscriptionNotificationWorkflow)

Example usage:

manual_transcription = ManualTranscriptionBlock(
    reference_name='manual_transcription',
    submission=machine_transcription.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    supervision_transcription_masking=(
        idp_wf_config.manual_transcription_config.supervision_transcription_masking
    ),
    table_output_manual_review=(
        idp_wf_config.manual_transcription_config.table_output_manual_review
    ),
    task_restrictions=idp_wf_config.manual_transcription_config.task_restrictions,
)

IDENTIFIER = 'MANUAL_TRANSCRIPTION_5'

class flows_sdk.implementations.idp_v42.idp_blocks.FlexibleExtractionBlock(submission, api_params, reference_name=None, supervision_transcription_masking='${workflow.input.flexible_extraction_supervision_transcription_masking}', task_restrictions='${workflow.input.flexible_extraction_task_restrictions}', notification_workflow='${workflow.input.notification_workflow_flexible_extraction}', show_machine_predictions='${workflow.input.flexible_extraction_show_machine_predictions}')

Bases: Block

Flexible extraction manually transcribes fields marked for review

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (bool | str | None) – Prevents users from inputting invalid characters during the Flexible Extraction task, defaults to workflow_input(Settings.FlexibleExtractionSupervisionTranscriptionMasking)

• task_restrictions (list[Any] | str | None) – Defines what users can access Supervision tasks created by this block, defaults workflow_input(Settings.FlexibleExtractionTaskRestrictions)

• notification_workflow (str) – Notification flow name to run when the submission enters Flexible Extraction, defaults to workflow_input(Settings.FlexibleExtractionNotificationWorkflow)

• show_machine_predictions (bool | str | None) – Available from V42.3; Shows the machine predictions in the Flexible Extraction Supervision tasks, defaults to workflow_input(Settings.FlexibleExtractionShowMachinePredictions)

Example usage:

flexible_extraction = FlexibleExtractionBlock(
    reference_name='flexible_extraction',
    submission=manual_transcription.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    supervision_transcription_masking=(
        idp_wf_config.flexible_extraction_config.supervision_transcription_masking
    ),
    task_restrictions=idp_wf_config.flexible_extraction_config.task_restrictions,
)

IDENTIFIER = 'FLEXIBLE_EXTRACTION_5'

class flows_sdk.implementations.idp_v42.idp_blocks.ReprocessingBlock(reference_name, submission, parent_flow='${workflow.definition.identifier}', idp_wf_config='${workflow.input}', trigger='${workflow.input.$trigger}', reprocessing_enabled='${workflow.input.reprocessing_enabled}')

Bases: Block

Reprocessing block enables a second extraction attempt if documents were initially confidently mis-classified by the machine. When an end-user specifies that a document layout is incorrect during Manual Identification, Manual Transcription, or Flexible Extraction, the flow will reach the Reprocessing block which will mark correctly classified documents as “complete” status. Misclassified documents with “reprocess” status will be marked with status “reprocessing” and processed in a second execution of the flow. This block should be placed before the SubmissionCompleteBlock to enable reprocessing.

How the Reprocessing block works

The Reprocessing block examines whether any documents within the submission have been marked with an incorrect layout, and if so, redirects the submission to the classification step for proper classification. It does this by recursively calling the same flow or subflow with the same inputs and documents, but with the addition of a status key for each document to denote which were marked with an incorrect layout during supervision.

Note

The Reprocessing block works in tandem with the “Mark Incorrect Layout” option presented to users during Manual Identification, Manual Transcription, and Flexible Extraction supervision interfaces introduced in R38. Include the Reprocessing block in all custom flows in order to take advantage of this feature. The feature will remain selectable in supervision interfaces even if the Reprocessing block is not included in the flow.

The initial flow run will pause execution at the Reprocessing block until the recursive flow has completed, at which point the initial flow will resume to finish its execution. If an error occurs during the execution of the recursive flow, that error will be raised to the Reprocessing block of the initial flow.

Note

The recursive flow run is linked with the same correlation_id as the initial flow run and the submission. Users can click the “View related flow runs” link on the left sidebar of the initial flow run to view any recursive flow runs.

As expected, recursively calling the same flow may cause some blocks to run again, resulting in unnecessary work being performed, or work which doesn’t gracefully handle duplications (non-idempotent blocks). To mitigate this, certain Hyperscience-provided blocks - including Submission Initialization, Machine Collation, and Machine Classification - detect when the submission object already contains the output from its block to avoid re-execution. It’s a best practice to build custom blocks such that they are idempotent and reentrant; if that’s not feasible, you can add similar logic to custom blocks which do not depend on the layout for the documents being processed. Common methods for handling this include:

• Iterating through each status key within the submission.document[] collection to see if any documents are set to “reprocess” or “reprocessing”.

• Inspecting if the submission object already contains a key or value explicitly defined by the custom code block.

Recommended Placement for the Reprocessing Block

• The Reprocessing block should not be placed before any supervision blocks which allow users to mark a document with an incorrect layout. These supervision blocks include Manual Identification, Manual Transcription and Flexible Extraction.

• The Reprocessing block should always be placed before the SubmissionCompete Block.

• The Reprocessing block can be used with flows which include multiple branches and conditionals; however, it’s recommended to ensure the Reprocessing block is executed to honor user’s selection of marking layouts incorrectly when supervising documents.

• In general, any custom logic that is to be performed after data has been extracted from documents should be placed after the Reprocessing block. To avoid duplicate executions of this custom logic, it is recommended to separate the document processing blocks - inclusive of the Reprocessing block - from the custom logic. This can be handled in different ways:

  • Split the flow into separate subflows, where the Reprocessing block is in a subflow that does not contain the custom logic.

  • Add a decision to bypass the custom logic if the Reprocessing block was triggered. With this method, the recursive flow run will execute the custom logic, and the initial flow run will bypass the logic.

Mandatory parameters:

Parameters:

submission (Any) – Submission object

IDENTIFIER = 'REPROCESSING_2'

class flows_sdk.implementations.idp_v42.idp_blocks.SubmissionCompleteBlock(submission, is_reprocessing=False, reference_name=None, nlc_qa_sampling_ratio='${workflow.input.semi_qa_sample_rate}', field_id_qa_enabled='${workflow.input.field_id_qa_enabled}', field_id_qa_sampling_ratio='${workflow.input.field_id_qa_sample_rate}', table_id_qa_enabled='${workflow.input.table_id_qa_enabled}', table_id_qa_sampling_ratio='${workflow.input.table_id_qa_sample_rate}', transcription_qa_enabled='${workflow.input.qa}', transcription_structured_entry_qa_sample_rate='${workflow.input.structured_entry_qa_sample_rate}', transcription_structured_signature_qa_sample_rate='${workflow.input.structured_signature_qa_sample_rate}', transcription_structured_checkbox_qa_sample_rate='${workflow.input.structured_checkbox_qa_sample_rate}', transcription_semi_structured_qa_sample_rate='${workflow.input.semi_structured_qa_sample_rate}', table_cell_transcription_qa_enabled='${workflow.input.table_cell_transcription_qa_enabled}', table_cell_transcription_qa_sample_rate='${workflow.input.table_cell_transcription_qa_sample_rate}', auto_qa_sampling_rate_enabled='${workflow.input.auto_qa_sampling_rate_enabled}', exclude_auto_transcribe_fields_from_qa='${workflow.input.exclude_auto_transcribe_fields_from_qa}', payload=None)

Bases: Block

Submission complete block finalizes submission processing and updates reporting data. This block should be called at the END of every flow that uses blocks included in the flows_sdk.implementations.idp_v34 library. The block requires both the submission object and a payload to be passed in.

Mandatory parameters:

Parameters:

• submission (str) – Submission object

• payload (Any) – Object to pass to downstream systems

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• nlc_qa_sampling_ratio (str | int | float) – Defines the percentage of documents sampled for Classification QA, defaults to workflow_input(Settings.SemiQaSampleRate)

• field_id_qa_enabled (str | bool) – defaults to workflow_input(Settings.FieldIdQaEnabled)

• field_id_qa_sampling_ratio (str | int | float) – Defines the percentage of documents sampled for Identification QA, defaults to workflow_input(Settings.FieldIdQaSampleRate)

• table_id_qa_enabled (str | bool) – Allows users to verify the location of table cells, defaults to workflow_input(Settings.TableIdQaEnabled)

• table_id_qa_sampling_ratio (str | int | float) – Defines the percentage of tables the system samples for QA, defaults to workflow_input(Settings.TableIdQaSampleRate)

• transcription_qa_enabled (str | bool) – defaults to workflow_input(Settings.TranscriptionQaEnabled)

• transcription_qa_sampling_ratio – Defines the percentage of fields sampled for Transcription QA, defaults to workflow_input(Settings.TranscriptionQaSampleRate)

• table_cell_transcription_qa_enabled (str | bool) – defaults to workflow_input(Settings.TableCellTranscriptionQaEnabled)

• table_cell_transcription_qa_sample_rate (str | int | float) – Defines the percentage of cells the system samples for QA. This value is likely to be lower than “Transcription QA Sample Rate” since there are more table cells than fields on any given page, defaults to workflow_input(Settings.TableCellTranscriptionQaSampleRate)

• exclude_auto_transcribe_fields_from_qa (str | bool) – Defines fields that are marked as auto-transcribe in layout should be excluded from QA or not. defaults to workflow_input(Settings.ExcludeAutoTranscribeFieldsFromQA)

• is_reprocessing (str | bool) – boolean to handle execution when reprocessing defaults to False

Example usage:

submission_complete = SubmissionCompleteBlock(
    reference_name='complete_submission',
    submission=flexible_extraction.output('submission')
)

IDENTIFIER = 'SUBMISSION_COMPLETE_4'

class flows_sdk.implementations.idp_v42.idp_blocks.SetTransformedOutputBlock(submission, transformed_output, reference_name=None)

Bases: Block

Set Transformed Output block sets the transformed output for the provided submission. This can later be used to send the transformed data downstream using output blocks. For more information see https://docs.hyperscience.ai/#retrieving-transformed-submissions.

Mandatory parameters:

Parameters:

• submission (str) – Submission object

• transformed_output (str) – Object that will be set as the transformed output for this submission

IDENTIFIER = 'SET_TRANSFORMED_OUTPUT'

class flows_sdk.implementations.idp_v42.idp_blocks.IdpCustomSupervisionBlock(submission, task_purpose, supervision_template, data=None, reference_name=None, supervision_transcription_masking=True, task_restrictions=None, page_ids=None, notification_workflow='IDP_SUBMISSION_NOTIFY', chat_flow_identifier=None, title='IDP Custom Supervision', description='IDP Custom Supervision')

Bases: Block

An IDP wrapper for the Custom Supervision block. It has the same functionality, but handles reading/writing data from the IDP database.

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• task_purpose (str) – The custom task name that’s given to all supervision tasks that run through a particular instance of a custom supervision block. This can be used to filter tasks in the task queue.

• supervision_template (Any) – JSON used for configuration of the Custom Supervision Task.

Parameters with defaults:

Parameters:

• data (dict[str, Any] | None) – Optional JSON structure that extends the data used to populate the supervision_template. This is intended to append to the data already gathered by the IDP wrapper. If there are any conflicting keys, the keys in this input will take precedent.

• reference_name (str | None) – Unique identifier within a flow. A unique one will be generated when not provided.

• supervision_transcription_masking (str | bool) – Prevents users from inputting invalid characters during the Custom Supervision task. Defaults to True.

• task_restrictions (str | list[Any] | None) – Defines what users can access Supervision tasks created by this block. Defaults to [].

• page_ids (str | list[int] | None) – If left empty, the IDP wrapper will, by default, pull in all pages in the submission. Adding a list of page IDs will override the default list of pages. Defaults to [].

• notification_workflow (str) – Notification flow name to run when the submission enters Custom Supervision. Defaults to IDP_SUBMISSION_NOTIFY_NAME.

• chat_flow_identifier (str | None) – Available from V41.2; optional chat flow identifier to enable Document Chat.

• title (Optional[str]) – UI-visible title of the block. Defaults to ‘IDP Custom Supervision’.

• description (Optional[str]) – Description of the block. Useful for documentation and shown in Flow Studio. Defaults to ‘IDP Custom Supervision’.

Example usage:

idp_custom_supervision = IdpCustomSupervisionBlock(
    reference_name='idp_custom_supervision',
    submission=manual_transcription.output('submission'),
    task_purpose='make_custom_decision',
    supervision_template=[
        {
            'name': 'three_column_template',
            'version': '1.0',
            'thumbnail': {
                'group_by_document': True,
                'group_by_case': True,
            },
            'action': [
                {
                    'name': 'my_tab',
                    'display': 'SSN Info',
                    'input': [
                        {
                            'name': 'ssn_field',
                            'type': 'transcription',
                            'data_identifier': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                            'title': 'SSN',
                        },
                    ],
                    'ui': {
                        'groups': [
                            {
                                'title': 'My Group',
                                'fields': ['ssn_field'],
                            },
                        ],
                    },
                },
            ],
        },
    ],
    data={
        'documents': [
            {
                'id': 2,
                'layout_uuid': 'e31c3cbf-dcea-44fd-a052-902463c6040f',
                'layout_name': 'layout_name',
                'page_count': 2,
                'read_only': False,
            }
        ]
    },
    supervision_transcription_masking=False,
)

IDENTIFIER = 'IDP_CUSTOM_SUPERVISION_3'

class flows_sdk.implementations.idp_v42.idp_blocks.IDPFullPageTranscriptionBlock(submission, reference_name=None, title='Full Page Transcription (Submission)', description='Transcribes the documents included in a submission', app_metadata=None, language_family=None, include_raw_segments=True, rerun_segmentation=True)

Bases: Block

IDP Full Page Transcription Block machine-transcribes the documents contained in a submission

Mandatory parameters:

Parameters:

submission (str) – submission object that includes the raw documents that need to be transcribed

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• app_metadata (list[str] | None) – used for capturing information about the flow

• language_family (str | None) – language family to use when instantiating the underlying transcription model. If not provided, the system will default to latin. Language families are single word lowercase strings, e.g., latin, arabic, korean, chinese. You can read more about language families in the “Supported Languages” article in the application documentation: https://help.hyperscience.ai/v42/docs/supported-languages.

• include_raw_segments (bool) – Available from V42.2; controls whether the extracted segments should be included directly in the output.

• rerun_segmentation (bool) – Available from V42.3; controls whether segmentation should be rerun for already segmented pages.

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# IDPFullPageTranscriptionBlock transcribes the documents contained in the submission object
idp_fpt_block = IDPFullPageTranscriptionBlock(
    reference_name='idp_fpt',
    submission=submission_bootstrap.output(),
)
# reference to the output of IDPFullPageTranscriptionBlock which can be passed as input to
# the next block. For more information on output() and output references, please take a
# look at class Block and its output() method
output_ref = idp_fpt_block.output()

IDENTIFIER = 'FULL_PAGE_TRANSCRIPTION_SUBMISSION_2'

class flows_sdk.implementations.idp_v42.idp_blocks.DocumentRendererBlock(submission, is_reprocessing=False, title='Document Renderer', description='Renders a document into a PDF', reference_name=None, document_rendering_enabled='${workflow.input.document_rendering_enabled}', document_rendering_mime_type='${workflow.input.document_rendering_mime_type}', document_rendering_unit='${workflow.input.document_rendering_unit}', document_rendering_height='${workflow.input.document_rendering_height}', document_rendering_width='${workflow.input.document_rendering_width}', document_rendering_quality='${workflow.input.document_rendering_quality}', document_rendering_mode='${workflow.input.document_rendering_mode}')

Bases: Block

Document File Generator Block renders a document into a PDF and stores it in the Blob storage.

This block should be placed ALWAYS after a Reprocessing block (if there is any) so that it is not possible to have more changes in the documents’ organisation.

Mandatory parameters:

Parameters:

submission (str) – submission object that includes the raw documents that need to be rendered.

Optional parameters:

Parameters:

• document_rendering_enabled (bool | str) – a boolean that enables or disables the document rendering. Default is False.

• document_rendering_mime_type (str) – the mime type of the document to be generated. Default is ‘application/pdf’

• document_rendering_unit (str) – the unit of the document to be generated. Default is ‘inches’

• document_rendering_height (str | float) – the height of the document to be generated. Default is 11

• document_rendering_width (str | float) – the width of the document to be generated. Default is 8.5

• document_rendering_quality (str | int) – Quality of pages (images) appended to the resulting document as a percentage (recommended 50%). Higher quality results in a larger file size for the generated PDF.

• is_reprocessing (str | bool) – boolean to handle execution when reprocessing. Defaults to False.

• document_rendering_mode (str) – Available from V42.0; Mode of the images appended to the resulting document. Supported modes are RGB (default), L (grayscale) and 1 (black-and-white)

Example usage:

# Use the output of the reprocessing block.
reprocessing = ReprocessingBlock(reference_name='reprocessing')

document_renderer_block = DocumentRendererBlock(
    reference_name='document_renderer',
    submission=reprocessing.output(submission),
    document_rendering_enabled=True,
    document_rendering_unit='in',
    document_rendering_width=8.5,
    document_rendering_height=11,
    document_rendering_quality=50,
    is_reprocessing=reprocessing.output('is_reprocessing')
    document_rendering_mode='L'
)

output_ref = document_renderer_block.output()

Example content of output_ref:

{
    'document_files': {
        [
            {
                'document_id': "1",
                'blob_uuid': 'f42fc9af-b551-4403-aba6-d0eba2c41973',
                'mime_type': 'application/pdf',
                'height_in_mm': 279.4,
                'width_in_mm': 215.9,
                'quality': 50,
                'mode': 'L'
            }
        ]
    },
    'submission': {
        ...
    }
}

IDENTIFIER = 'DOCUMENT_RENDERER'

class flows_sdk.implementations.idp_v42.idp_blocks.IDPImageCorrectionBlock(submission, reference_name=None, rotation_correction_enabled=True, mobile_processing_enabled=False, language_family=None, title='Image Correction (Submission)', description='Rotate and de-skew documents included in a submission', app_metadata=None)

Bases: Block

IDP Image Correction Block rotates and de-skews tilted/skewed images so that they are more transcription-friendly

Mandatory parameters:

Parameters:

submission (str) – submission object that includes the raw documents that need to be image-corrected

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• rotation_correction_enabled (bool) – Identifies and corrects the orientation of images. Enabled by default.

• mobile_processing_enabled (bool) – Improves the machine readability of photo captured documents. Disabled by default.

• language_family (str | None) – language family to use when performing image correction. If not provided, the system will default to latin. Language families are single word lowercase strings, e.g., latin, arabic, korean, chinese. You can read more about language families in the “Supported Languages” article in the application documentation: https://help.hyperscience.ai/v42/docs/supported-languages.

• app_metadata (list[str] | None) – used for capturing information about the flow

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# IDPImageCorrectionBlock takes in the submission object and outputs in the same format with
# its images
idp_image_correct_block = IDPImageCorrectionBlock(
    reference_name='idp_image_correct',
    submission=submission_bootstrap.output(),
)
# reference to the output of IDPImageCorrectionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look at
# class Block and its output() method
output_ref = idp_image_correct_block.output()

IDENTIFIER = 'IMAGE_CORRECTION_SUBMISSION'

class flows_sdk.implementations.idp_v42.idp_blocks.OpticalReasoningCognitionAgentBlock(submission, api_params, model_type='${workflow.input.orca_type}', base_model_id='${workflow.input.orca_base_model_uuid}', target_accuracy='${workflow.input.orca_target_accuracy}', sliding_window_size='${workflow.input.orca_sliding_window_size}', max_image_tokens='${workflow.input.orca_max_image_tokens}', max_new_tokens='${workflow.input.orca_max_new_tokens}', max_num_bboxes='${workflow.input.orca_max_num_bboxes}', thresholds_dict=None, reference_name=None)

Bases: Block

Optical Reasoning Cognition Agent block extracts the textual content from your submission. It acts like an intelligent agent that uses visual understanding and reasoning to process submitted documents and pull out the relevant fields. This block requires a GPU to function.

Mandatory parameters:

Parameters:

• submission (dict[str, Any] | str) – Submission object

• api_params (str | dict[str, Any])

• target_accuracy (float | str) – The submission-level machine + human transcription accuracy targeted by the system (fraction between 0.0 and 1.0, e.g. 0.95 for 95 %).

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique reference name within a flow, a unique one will be generated when not provided

• model_type (str) – The model you would like to install, which will be used to process submissions

• base_model_id (str) – The UUID of the block asset containing ORCA model weights

• sliding_window_size (int | str) – The number of pages processed by the model at once. Larger values improve throughput but raise GPU memory usage. Default value is set to 8.

• max_new_tokens (int | str) – The maximum number of tokens the model can extract, which should be scaled based on the number and length of field values. Default value is set to 1024.

• max_image_tokens (int | str) – The maximum number of tokens allocated per image by the model. This is conceptually similar to setting image resolution. Larger values may increase accuracy on dense / hard to read images but will raise GPU memory usage. Default value is set to 1280.

• max_num_bboxes (int | str) – The maximum number of candidate bounding boxes per field occurrence. Larger values may improve the likelihood of identifying the correct location but may also increase false positives.’ Default value is set to 3.

• thresholds_dict (dict[str, Any] | str | None) – Available from V42.2; Field level thresholds passed as a JSON dict. When provided, these will take precedence over any threshold dictionary present in the Binary KV. Default value is set to None.

IDENTIFIER = 'OPTICAL_REASONING_COGNITION_AGENT_GPU'

Additional Blocks (v42)

class flows_sdk.implementations.idp_v42.additional_blocks.JSONOutputsBlock(inputs, blocks=None)

Bases: Outputs

Output block allows users to send JSON data extracted by an IDP flow to other systems for downstream processing Mandatory parameters:

Parameters:

inputs (dict[str, Any]) – Used by the UI to automatically pre-populate the input of newly added blocks. Extended in this class to include 'enabled': True to visualize an enabled/disabled trigger that is controllable via Flow Studio.

Usage when parameters are to be defined in Flow Studio:

outputs = JSONOutputBlock(
    inputs={'result': }
)

class flows_sdk.implementations.idp_v42.additional_blocks.ImageCorrectionBlock(pages, reference_name=None, rotation_correction_enabled=True, mobile_processing_enabled=False, language_family=None, title='Image Correction (Pages)', description='Rotate and de-skew documents', app_metadata=None)

Bases: Block

Image Correction Block rotates and de-skews tilted/skewed images so that they are more transcription-friendly Mandatory parameters:

Parameters:

pages (str) – list of page object that includes the raw documents that need to be image-corrected

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• rotation_correction_enabled (bool) – Identifies and corrects the orientation of images. Enabled by default.

• mobile_processing_enabled (bool) – Improves the machine readability of photo captured documents. Disabled by default.

• language_family (str | None) – language family to use when performing image correction. If not provided, the system will default to latin. Language families are single word lowercase strings, e.g., latin, arabic, korean, chinese. You can read more about language families in the “Supported Languages” article in the application documentation: https://help.hyperscience.ai/v42/docs/supported-languages.

• app_metadata (list[str] | None) – used for capturing information about the flow

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# ImageCorrectionBlock takes in the submission object and outputs in the same format with
# its images
image_correct_block = ImageCorrectionBlock(
    reference_name='image_correct',
    pages=submission_bootstrap.output(submission.unassigned_pages),
)
# reference to the output of IDPImageCorrectionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look
# at class Block and its output() method
output_ref = image_correct_block.output()

IDENTIFIER = 'IMAGE_CORRECTION_PAGES'

class flows_sdk.implementations.idp_v42.additional_blocks.FullPageTranscriptionBlock(pages, reference_name=None, title='Full Page Transcription (Pages)', description='Transcribes documents', language_family=None, app_metadata=None, include_raw_segments=True, rerun_segmentation=True)

Bases: Block

Full Page Transcription Block machine-transcribes the documents contained in a submission

Mandatory parameters:

Parameters:

pages (str) – list of pages that includes the raw documents that need to be transcribed

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• language_family (str | None) – language family to use when instantiating the underlying transcription model. If not provided, the system will default to latin. Language families are single word lowercase strings, e.g., latin, arabic, korean, chinese. You can read more about language families in the “Supported Languages” article in the application documentation: https://help.hyperscience.ai/v42/docs/supported-languages.

• include_raw_segments (bool) – Available from V42.2; controls whether the extracted segments should be included directly in the output.

• rerun_segmentation (bool) – Available from V42.3; controls whether segmentation should be rerun for already segmented pages.

• app_metadata (list[str] | None) – used for capturing information about the flow

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# FullPageTranscriptionBlock transcribes the documents contained in the submission object
fpt_block = FullPageTranscriptionBlock(
    reference_name='fpt',
    pages=submission_bootstrap.output(submission.unassigned_pages),
)
# reference to the output of FullPageTranscriptionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look at
# class Block and its output() method
output_ref = fpt_block.output()

IDENTIFIER = 'FULL_PAGE_TRANSCRIPTION_PAGES_2'

class flows_sdk.implementations.idp_v42.additional_blocks.QASchedulerBlock(flow_identifier, submission, qa_config, is_reprocessing=False, reference_name=None)

Bases: Block

Available from V41.2

Schedules Quality Assurance (QA) tasks for a specific workflow.

This class serves as a specialized block within a flow for scheduling Quality Assurance tasks. It ensures the proper configuration and scheduling processes for a given workflow instance. It leverages provided data and parameters such as identification of the workflow, submission details, QA configuration, and optional reprocessing cases.

Mandatory parameters:

Parameters:

• flow_identifier (str) – A unique string identifying the flow to associate this QA scheduler with.

• submission (dict[str, Any] | str) – A dictionary containing submission details. Will be used as payload to trigger the target flow to be QA’d.

• qa_config (dict[str, Any] | str) – A dictionary containing QA configuration. Will be used as payload to trigger the target flow to be QA’d.

Optional parameters:

Parameters:

• is_reprocessing (bool | str | None) – An optional value indicating whether this block is part of a reprocessing workflow. Defaults to False.

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

Example usage:

qa_scheduler = QASchedulerBlock(
    flow_identifier='my_qa_flow',
    submission={'submission_id': 12345, 'data': 'example_data'},
    qa_config={'sample_rate': 0.1, 'language_family': 'latin'},
    is_reprocessing=False,
)

# reference to the output of QASchedulerBlock which can be passed
 as input to the next block.
# For more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = qa_scheduler.output()

IDENTIFIER = 'QA_SCHEDULER'

class flows_sdk.implementations.idp_v42.additional_blocks.FullPageTranscriptionQABlock(submission, sample_rate, language_family, split_text_segments, min_characters_in_segment=4, task_restrictions=None, reference_name=None)

Bases: Block

Available from V41.2

Full Page Transcription QA Block performs Quality Assurance on the output of Full Page Transcription Block. It samples a percentage of segments for QA and allows users to split segments into smaller tokens for easier QA.

Mandatory parameters:

Parameters:

• submission (dict[str, Any] | str) – Submission object

• sample_rate (int | str) – Defines the percentage of segments the system samples for Quality Assurance

• language_family (str) – Specifies the language family for the class

• split_text_segments (bool | str) – Enabling this option splits segments into smaller, more manageable tokens for easier QA

Parameters with defaults:

Parameters:

• min_characters_in_segment (integer) – Minimum number of characters in a token. Defaults to 4. Minimum value is 1, maximum is 100.

• task_restrictions (Optional[TaskRestrictions]) – Defines what users can access tasks created by this block. Defaults to None, which is treated as an empty list.

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

Example usage:

full_page_transcription_qa = FullPageTranscriptionQABlock(
    reference_name='full_page_transcription_qa',
    submission=manual_transcription.output('submission'),
    sample_rate=10,
    language_family='latin',
    split_text_segments=True,
    min_characters_in_segment=4,
    task_restrictions=[],
)

# reference to the output of FullPageTranscriptionQABlock which can be passed
as input to the
# next block. For more information on output() and output references, please take a look
# at class Block and its output() method
output_ref = full_page_transcription_qa.output()

IDENTIFIER = 'FULL_PAGE_TRANSCRIPTION_QA'

class flows_sdk.implementations.idp_v42.additional_blocks.CustomSupervisionBlock(submission, task_purpose, data, supervision_template, reference_name=None, supervision_transcription_masking=True, task_restrictions=None, notification_workflow='IDP_SUBMISSION_NOTIFY', chat_flow_identifier=None, title='Custom Supervision', description='Custom Supervision')

Bases: Block

The CustomSupervisionBlock uses a JSON schema as a form builder to create customer-driven UIs for Supervision tasks.

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• task_purpose (str) – the custom task name that’s given to all supervision tasks that run through a particular instance of a custom supervision block. This can be used to filter tasks in the task queue.

• data (Any) – JSON structure that supplies the data used to populate the supervision_template

• supervision_template (Any) – JSON used for configuration of the Custom Supervision Task.

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (bool) – Prevents users from inputting invalid characters during the Custom Supervision task, defaults to True

• task_restrictions (list[Any] | None) – Defines what users can access Supervision tasks created by this block, defaults to []

• notification_workflow (str) – Notification flow name to run when the submission enters Custom Supervision, defaults to IDP_SUBMISSION_NOTIFY_NAME

• chat_flow_identifier (str | None) – Available from V41.2; optional chat flow identifier to be populated to enable Document Chat in Custom Supervision

• title (Optional[str]) – UI-visible title of the block. Defaults to ‘IDP Custom Supervision’

• description (Optional[str]) – Description of the block. Both useful for documentation purpose and visible to users in the Flow studio. Defaults to ‘IDP Custom Supervision’

Example usage:

custom_supervision = CustomSupervisionBlock(
    reference_name='custom_supervision',
    submission=manual_transcription.output('submission'),
    task_purpose='make_custom_decision',
    supervision_template=[
        {
            'name': 'three_column_template',
            'version': '1.0',
            'thumbnail': {
                'group_by_document': True,
                'group_by_case': True,
            },
            'action': [
                {
                    'name': 'my_tab',
                    'display': 'SSN Info',
                    'input': [
                        {
                            'name': 'ssn_field',
                            'type': 'transcription',
                            'data_identifier': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                            'title': 'SSN',
                        },
                    ],
                    'ui': {
                        'groups': [
                            {
                                'title': 'My Group',
                                'fields': ['ssn_field'],
                            },
                        ],
                    },
                },
            ],
        },
    ],
    data={
        'fields': [
            {
                'id': 967,
                'uuid': 'fe8d4e7a-a82c-448c-85a1-0a3cbbe53b61',
                'value': '111-22-3333',
                'page_id': 15,
                'layout_field_id': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                'validation_overridden': False,
                'bounding_box': [
                    0.09360783305186686,
                    0.25792617589433436,
                    0.6358913805295097,
                    0.28862414740388187,
                ],
                'occurrence_index': 1,
            },
        ],
        'template_fields': [
            {
                'uuid': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                'notes': '',
                'type': 'entry',
                'data_type_uuid': 'b600a4e4-758f-4c4b-a2f4-cbb5dd870a0c',
                'name': 'SSN',
            },
        ],
        'pages': [
            {
                'id': 15,
                'file_page_number': 1,
                'document_page_number': 1,
                'image_url': '/image/2ed3d989-b64a-44bd-a9fb-6e0dcc5ccdb8',
                'form_id': 2,
                'submission_id': 1,
                'external_case_ids': [],
                'read_only': False,
                'filename': '1 page.pdf',
                'form_page_id': 1
            },
        ],
        'documents': [
            {
                'id': 2,
                'layout_uuid': 'e31c3cbf-dcea-44fd-a052-902463c6040f',
                'layout_name': 'layout_name',
                'page_count': 2,
                'read_only': False,
            },
        ],
        'cases': [],
        'selected_choices': [],
    },
    supervision_transcription_masking=False,
)

IDENTIFIER = 'CUSTOM_SUPERVISION_3'

class flows_sdk.implementations.idp_v42.additional_blocks.SoapRequestBlock(method, reference_name=None, endpoint='', body_namespace=None, headers_namespace=None, headers=None, http_headers=None, params=None, use_wsdl=False, wsdl_uri='', wsdl_service_name='', wsdl_port_name='', title='HTTP REST Block', description='HTTP REST block')

Bases: Block

Soap Block allows user to perform SOAP requests Mandatory parameters:

Parameters:

• method (str) – SOAP method to invoke

• endpoint (str) – absolute url including schema to HTTP endpoint for this request

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• body_namespace (str | None) – SOAP request body namespace url

• headers_namespace (str | None) – SOAP request headers namespace url

• headers (dict[Any, Any] | None) – SOAP headers to use

• http_headers (dict[str, Any] | None) – Available from V42.1; HTTP headers to be sent on all requests

• params (dict[Any, Any] | None) – key-value pair used as query parameters in the request to be inserted as SOAP body

Example usage:

# minimum viable instantiation, the rest of the fields can later be added in the UI
soap_block = SoapRequestBlock(
    reference_name='soap_request',
    title='Soap Request',
    description='Perform soap request',
    method='ExampleMethod',
)
soap_block = SoapRequestBlock(
    reference_name='soap_request',
    title='Soap Request',
    description='Perform soap request',
    method='ExampleMethod',
    endpoint='http://example.org/abc.wso',
    body_namespace='http://www.example.org/abc.countryinfo',
    params={'example': 'stuff'},
    headers={},
    http_headers={}
)
# reference to the output of SoapRequestBlock which can be passed as input to the next block
# for more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = soap_block.output()

IDENTIFIER = 'SOAP_REQ'

class flows_sdk.implementations.idp_v42.additional_blocks.HttpRestBlock(method, endpoint, reference_name=None, headers=None, params=None, payload=None, json=None, timeout=600, authorization_type=None, handled_error_codes=None, auth_params=None, response_encoding=None, title='HTTP REST Block', description='HTTP REST block')

Bases: BaseHttpRestBlock

Http Rest Block allows user to perform HTTP requests Mandatory parameters:

Parameters:

• method (str) – HTTP method to use

• endpoint (str) – absolute url including schema to HTTP endpoint for this request

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• headers (dict[str, str] | None) – HTTP headers to use

• params (dict[str, Any] | None) – key-value pair used as query parameters in the request

• payload (dict[str, str] | None) – key-value pair to be used as x-www-form-urlencoded data. Mutually exclusive with json

• json (dict[str, Any] | None) – key-value pair to be used as json data. Mutually exclusive with payload

• timeout (int | None) – How long (in seconds) to keep the connection open if no bytes are received from the endpoint. Set to 0 to wait indefinitely (not recommended).

• authorization_type (str | None) – authorization type (none, http_header, oauth_2_client_credentials)

• handled_error_codes (list[int | str] | None) – specify HTTP error codes that will not fail the task, allowing for wildcards with the ‘x’ char. Example: [400, “5xx”]

• auth_params (None | HttpHeaderAuthorizationParams | OAuth2AuthorizationParams) – authorization parameters to be passed when the authorization_type is set to “http_header” or “oauth_2_client_credentials”

• response_encoding (str | None) – Available from V42.3; Character encoding to use when decoding the response. If not provided, the encoding from the Content-Type header or the requests library’s default detection will be used. Specify a standard encoding name (e.g., ‘utf-8’, ‘ISO-8859-1’, ‘windows-1252’) to override.

Example usage:

# minimum viable instantiation, the rest of the fields can later be added in the UI
http_rest_block = HttpRestBlock(
    reference_name='http_request',
    title='Http Request',
    description='Perform http request',
    method='GET',
)
http_rest_block = HttpRestBlock(
    reference_name='http_request',
    title='Http Request',
    description='Perform http request',
    method='GET',
    endpoint='https://sheets.googleapis.com/v4/spreadsheets/example',
)
# reference to the output of HttpRestBlock which can be passed as input to the next block
# for more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = http_rest_block.output()

class flows_sdk.implementations.idp_v42.additional_blocks.HyperscienceRestApiBlock(method, app_endpoint, reference_name=None, headers=None, params=None, payload=None, json=None, handled_error_codes=None, timeout=600, response_encoding=None, title='Hyperscience HTTP REST Block', description='Hyperscience HTTP REST block')

Bases: BaseHttpRestBlock

Make Http requests to the Hyperscience platform APIs. This block will automatically take care of prepending the base url and will include the appropriate authentication headers with the request. Mandatory parameters:

Parameters:

• method (str) – HTTP method to use

• app_endpoint (str) – relative url, beginning with a forward slash, to an HTTP endpoint served by the Hypersciecne platform. For reference on the available endpoints, check out https://docs.hyperscience.ai/

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• headers (dict[str, str] | None) – HTTP headers to use

• params (dict[str, Any] | None) – key-value pair used as query parameters in the request

• payload (dict[str, str] | None) – key-value pair to be used as x-www-form-urlencoded data. Mutually exclusive with json

• json (dict[str, Any] | None) – key-value pair to be used as json data. Mutually exclusive with payload

• handled_error_codes (list[int | str] | None) – specify HTTP error codes that will not fail the task, allowing for wildcards with the ‘x’ char. Example: [400, “5xx”]

• timeout (int | None) – How long (in seconds) to keep the connection open if no bytes are received from the endpoint. Set to 0 to wait indefinitely (not recommended).

• response_encoding (str | None) – Available from V42.3; Character encoding to use when decoding the response. If not provided, the encoding from the Content-Type header or the requests library’s default detection will be used. Specify a standard encoding name (e.g., ‘utf-8’, ‘ISO-8859-1’, ‘windows-1252’) to override.

Example usage:

hs_rest_block = HyperscienceRestApiBlock(
    reference_name='http_request',
    app_endpoint='/api/healthcheck',
    method='GET',
)

class flows_sdk.implementations.idp_v42.additional_blocks.DatabaseAccessBlock(reference_name=None, db_type='', database='', host='', username='', password='', query='', port=None, options=None, timeout=None, query_params=None, title='DB Block', description='DB access block')

Bases: Block

Database Block allows user to access and perform queries on the specified database Mandatory parameters:

Parameters:

• db_type (str) – database type (mssql, oracle, postgres)

• host (str) – URL/IP address of the server database is hosted on

• database (str) – database name

• username (str) – database username

• password (str) – database password

• query (str) – parameterized query

Optional parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• port (int | None) – database port number

• options (dict[Any, Any] | None) – dictionary of additional connection string options

• query_params (dict[Any, Any] | None) – dictionary of values for query placeholders

• timeout (int | None) – timeout in seconds (mssql and postgres only)

Example usage:

# minimum viable instantiation, the rest of the fields can later be added in the UI
db_lookup = DatabaseAccessBlock(
    reference_name='db_lookup',
    title='Database Lookup',
    description='Perform database lookup',
)
# note that storing password in plain text form is not recommended
# password can later be entered in the UI or defined as a flow secret.
db_lookup = DatabaseAccessBlock(
    reference_name='db_lookup',
    title='Database Lookup',
    description='Perform database lookup',
    db_type='mssql',
    database='cars',
    host='example.com',
    username='user',
    # we recommend skipping the password field or defining it as a system secret
    # e.g. system_secret('{your_secret_identifier}') (both allow for secret input via. UI)
    password='pw'
    port=1433,
    timeout=200,
    query='SELECT * from CAR',
)
# reference to the output of DBAccessBlock which can be passed as input to the next block
# for more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = db_lookup.output()
# content of output_ref:
{
    "result": [
        {
            "id": 1,
            "brand": "Aston Martin",
            "model": "Vanquish"
        },
        {
            "id": 2,
            "brand": "Jaguar",
            "model": "XE 2018"
        }
    ]
}

Database output for the same query (SELECT * FROM CAR)

IDENTIFIER = 'DB_ACCESS_2'

class flows_sdk.implementations.idp_v42.additional_blocks.LLMInstallBlock(model_name='ORCA', cloudsmith_key='', user_consent=False, reference_name=None, title='Install LLM/VLM', description='Sets up the necessary LLM or VLM in the HS instance.', from_cloudsmith=False)

Bases: Block

The LLM Install Block sets up the necessary LLM or VLM in the HS instance. It begins by verifying whether the model is already present and, if not, retrieves it from S3 for SaaS instances or Cloudsmith for on-premises instances.

Mandatory parameters:

Parameters:

• model_name (str) – Name of the LLM model to install (Choose from the available models)

• from_cloudsmith (bool | str) – Available from V42.2; Download from Cloudsmith flag

• cloudsmith_key (str) – Cloudsmith API key (Only when running on on-premise instance)

• user_consent (bool | str) – User consent (The block will fail if set to False)

The block outputs the detailed model name and model UUID of the chosen LLM.

Example usage:

llm_install = LLMInstallBlock(
    model_name='ORCA',
    from_cloudsmith=True,
    cloudsmith_key='cloudsmith_key',
    user_consent=True
)

Example output:

{
    "model": "orca",
    "model_uuid": "14554188-cf8e-4f10-9057-d1df2f710072"
}

IDENTIFIER = 'LLM_INSTALL'

class flows_sdk.implementations.idp_v42.additional_blocks.LlamaBlock(model_id, prompt, reference_name=None, consent=False, tokenize_only=False, predict_extra_kwargs=None, title='Llama Text Generation', description='Text generation using the Llama 2 model')

Bases: Block

Text generation using the Llama 2 model by Meta.

Deprecated from version 41.1

This block utilizes Llama 2 by Meta. Llama 2 is licensed under the LLAMA 2 Community License (https://ai.meta.com/llama/license/). By using the Llama Block, you acknowledge that you have received a copy of the LLAMA 2 Community License.

Note: This block is still experimental and not ready for production use.

Mandatory parameters:

Parameters:

• model_id (str) – UUID, identifier of the asset with the ML model. Model assets are not part of the Hyperscience bundle and must first be downloaded and imported into the system before they can be used. Please contact your Hyperscience representative to discuss the usecase and the appropriate model and to get instructions on downloading the models. Once downloaded, here are the available model asset UUIDs: b3da05c7-061c-4a58-af6d-4c905d2e618f - Llama 2, 13B, 8-bit quantized

• prompt (str) – The prompt to generate text from. Use a CodeBlock ahead of the LlamaBlock in order to build the prompt. The context window of the model is limited to 4096 tokens. If the prompt has more tokens, the block would return an error. The total input and output length also cannot exceed 4096 tokens, which means that if the input is too long, the output may be truncated. Also see tokenize_only for a more detailed explanation of how to handle longer texts.

Optional parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• consent (bool | None) – Has the business user read and acknowledged the Llama 2 legal notice. Unless the flow would be used in CI automation, this should be set to False so that a business user can read and acknowledge the notice in the Hyperscience UI. Setting this to True in the Python definition of the flow is also treated as acknowledgement of the notice on behalf of the entity that will be using the flow.

• tokenize_only (bool | None) – In the context of LLMs, tokens correspond roughly to words, partial words and punctuation. A rough way to compute the number of tokens in a text is number_of_tokens = 1.25 * number_of_words. The Llama block works with a context window of 4096 tokens. If a longer prompt is passed in, it would return an error. Also, if the prompt is shorter, but still close to 4096, the output may be truncated. Since the length of the prompt in tokens cannot be known ahead of time, sometimes the flow may need to make dynamic decisions whether to pass the text to the Llama block, or to split it into multiple chunks, or to use some alternative method for processing it (e.g. a custom supervision manual task). The tokenize_only option enables flows to take such dynamic decisions based on the length of the prompt in tokens. The default is false - it instructs the block to run the ML model inference over the prompt, i.e. to generate a text response. When true is passed, the model will simply tokenize the prompt without running inference and return the list of tokens - this will work regardless of the length of the prompt. A subsequent Decision task can then route longer prompts to an alternative path in the flow, while routing shorter prompts to the LlamaBlock for actual text generation.

• predict_extra_kwargs (dict[Any, Any] | None) – Extra keyword arguments used for model inference. (Used to fine-tune the model prediction. Not recommended to change this unless you know what you are doing)

Example usage:

llama = LlamaBlock(
    reference_name='llama',
    model_id='b3da05c7-061c-4a58-af6d-4c905d2e618f',
    prompt='${previous_block.output.llama_prompt}',
)

IDENTIFIER = 'LLAMA'

class flows_sdk.implementations.idp_v42.additional_blocks.EntityRecognitionBlock(pages, regex_map=None, keyword_map=None, enhanced_output=False, ner_enabled=True, ced_enabled=True, signature_enabled=True, page_batch_size=None, page_overlap=1, language_family=None, reference_name=None, title='Entity Recognition', description='Recognizes entities of different types in documents')

Bases: Block

Entity Recognition Block detects entities of different types within the documents contained in a submission

This block requires the output of the Full Page Transcription Block as input data.

Mandatory parameters:

Parameters:

• pages (str) – list of pages that includes the raw documents with the transcribed information from the FPT block

• regex_map (dict[str, str] | None) – a map that specifies different regex patterns that the block should look for, a default one is present

• keyword_map (dict[str, list[str]] | None) – a map that specifies different keyword patterns that the block should look for, a default one is present

Optional Parameters:

Parameters:

• enhanced_output (bool) – a boolean flag that enables toggling between simple and enhanced output formats. Default value is False.

• ner_enabled (bool) – a boolean flag that enables or disables Named Entity Recognition. Default value is True.

• ced_enabled (bool) – a boolean flag that enables or disables Custom Entity Detection. Default value is True.

• signature_enabled (bool) – a boolean flag that enables or disables Signature Detection. Default value is True.

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• page_batch_size (int | None) – Available from V42.3; an integer that specifies the number of pages to be processed in a batch. If not provided, the block will process all pages in a single batch.

• page_overlap (int) – Available from V42.3; an integer that specifies the number of overlapping pages between batches. Default value is 1, which means that the last page of a batch will be repeated as the first page of the next batch. This is to ensure that entities that are located at the border of two batches are not missed.

• language_family (str | None) – Available from V42.3; language family to use when performing segmentation. If not provided, the system will default to latin. Language families are single word lowercase strings, e.g., latin, arabic, korean, chinese. You can read more about language families in the “Supported Languages” article in the application documentation: https://help.hyperscience.ai/v42/docs/supported-languages.

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')

# EntityRecognitionBlock detects entities within the documents contained in the submission
entity_recognition_block = EntityRecognitionBlock(
    reference_name='entity_recognition',
    pages=submission_bootstrap.output(submission.unassigned_pages),
    regex_map=DEFAULT_PII_REGEX_MAP,
    keyword_map=DEFAULT_PII_KEYWORD_MAP,
    enhanced_output=False,
)

# reference to the output of EntityRecognitionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look at
# class Block and its output() method
output_ref = entity_recognition_block.output()

Example content of output_ref:

{
    "documents": [
        {
            "file_uuid": "da2c6117-43c4-4b30-84c4-cdc59cc3fa4f",
            "entities": [
                {
                    "text": "John Smith",
                    "type": "Person Name",
                    "positions": [
                        [0.5, 0.1, 0.55, 0.13],
                        [0.6, 0.2, 0.65, 0.23],
                        [0.7, 0.3, 0.75, 0.33],
                    ],
                    "page_ids": [0, 0, 1],
                    "origin": "NER",
                },
                {
                    "text": "<SIGNED>",
                    "type": "signature",
                    "positions": [[0.701171875, 0.732421875, 0.92578125, 0.8681640625]],
                    "page_ids": [0],
                    "origin": "Signature",
                },
            ]
        },
        {
            "file_uuid": "4aa38f84-e5af-4cdc-ba5c-edd60f0a6b89",
            "entities": [
                ...
            ]
        }
    ],
    "redaction_annotations": {
        "ner": [ ... ],
        "ced": [ ... ],
    }
}

IDENTIFIER = 'ENTITY_RECOGNITION'

class flows_sdk.implementations.idp_v42.additional_blocks.FileFilterBlock(trigger='${workflow.input.$trigger}', filter_type='${workflow.input.file_filter_filter_type}', minimum_image_width='${workflow.input.file_filter_minimum_image_width}', minimum_image_height='${workflow.input.file_filter_minimum_image_height}', minimum_file_size_kb='${workflow.input.file_filter_minimum_file_size_kb}', file_extensions_action='${workflow.input.file_filter_file_extensions_action}', file_extensions='${workflow.input.file_filter_file_extensions}', file_extensions_other='${workflow.input.file_filter_file_extensions_other}', reference_name=None, title='File Filter', description='Filters out unwanted files from the input of a flow')

Bases: Block

Available from V40.2

File Filter Block filters out unwanted files from the input of a flow

This block is meant to be placed at the start of the flow/subflow - BEFORE the SubmissionBootstrapBlock, not after it. It requires the output of a connector (e.g. Email Listener) or an API submission as input data, and its output is meant to be consumed by a SubmissionBootstrapBlock.

Mandatory parameters:

This block has no strictly required parameters - all of its filters are optional.

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• trigger (str) – the trigger input from a connector - if not set explicitly, it will default to the workflow input trigger (which is where this value is normally found)

• filter_type (str) – a discrete value, either “ALL_FILES” or “IMAGES_ONLY”. Determines what kind of files the filters will be applied to. Default value is “ALL_FILES”. If set to “IMAGES_ONLY”, all filtering options will be applied only to images, other files will pass through.

• minimum_image_width (str | None) – image width in pixels - half of the image dimension filter. Any image with less width than configured here will be filtered out. Defaults to None (will do nothing).

• minimum_image_height (str | None) – image height in pixels - second half of the image dimension filter. Any image with less height than configured here will be filtered out. Defaults to None (will do nothing).

• minimum_file_size_kb (str | None) – represents the file size filter. Any file whose disk size is less than this number (in KB) will be filtered out. May be fractional. Defaults to None (will do nothing).

• file_extensions_action (str) – a discrete value, either “ALLOW”, “DENY” or “NONE”. Represents the function of the file extension filter. “ALLOW” means we only allow the configured file extensions. “DENY” means we only deny the configured file extensions. “NONE” means no file extension filtering takes place. Defaults to “NONE”

• file_extensions (str | None) – a list of strings, representing the file extensions used for file extension filtering. Expected if file_extensions_action is not “NONE”. Otherwise, defaults to None (will do nothing).

• file_extensions_other (str | None) – a comma separated list of file extensions used for file extension filtering. Meant for adding extra extensions that are not available in the pre-defined list. Optional, defaults to None (will do nothing). However, it is expected in case the file_extensions parameter contains “other”.

Example usage:

# File Filter Block filters out unwanted files from the input of a flow
file_filter_block = FileFilterBlock(
    # trigger: str = workflow_input(Inputs.Trigger),  # usually not necessary to set this
    reference_name='file_filter',
    filter_type='ALL_FILES',
    minimum_image_width=500,
    minimum_image_height=500,
    minimum_file_size_kb=2.5,
    file_extensions_action='ALLOW',
    file_extensions=['pdf', 'png'],
    file_extensions_other='jpg,svg,txt',
)

# reference to the output of FileFilterBlock which can be passed as input to the
# next block (a SubmissionBootstrapBlock)
output_ref = file_filter_block.output('result.filtered_trigger')
submission_bootstrap = SubmissionBootstrapBlock(trigger=output_ref)

Example content of output_ref:

{
    "payload": {
        "blob_storage_files": [
            {
                "uuid": "db58606e-32f9-42c9-a0bc-ed9848ff247a",
            }
        ],
        "filtered_files": [
            {
                "uuid": "db58606e-32f9-42c9-a0bc-ed9848ff246e",
                "filtered_by": [
                    "file_size_filter"
                ]
            },
        ]
    }
}

IDENTIFIER = 'FILE_FILTER'

class flows_sdk.implementations.idp_v42.additional_blocks.GeneralPromptingORCABlock(model_type, base_model_id, messages=None, adapter_id=None, max_new_tokens=None, max_image_tokens=None, enable_location_hint=None, reference_name=None)

Bases: Block

General Prompting ORCA Block

This block requires a GPU to function.

Mandatory parameters:

Parameters:

• model_type (str) – Base model to be used for general prompting. This is usually the output of the LLMInstallBlock.

• base_model_id (str) – The UUID of the block asset containing ORCA model weights. This is usually the output of the LLMInstallBlock.

Parameters with defaults:

Parameters:

• messages (list[list[dict[str, Any]]] | str | None) – A list of prompt message threads sent to the model. Each message thread is a list of messages. Each message is a dictionary containing the keys “role” and “content”. - Role is a string, with one of “system”, “user”, “assistant”, depending on what the related content represents. “system” - there is usually a single system prompt in any list of messages, which outlines rules and recommendations for the generative model to follow. “user” - User messages should be messages sent by a person to the model. “assistant” - Assistant messages should be model responses. - Content is a mixed list that can contain text and images. Each content entry is formatted either as {‘type’: ‘image’, ‘image’: image_uuid} or {‘type’: ‘text’, ‘text’: ‘content of the message’}

• adapter_id (str | None) – UUID of a previously imported adapter to use. The base model will be used when not provided. Default value is None.

• max_new_tokens (int | str | None) – The maximum number of tokens the model can extract, which should be scaled based on the number and length of field values. Default value is set to 1024.

• max_image_tokens (int | str | None) – The maximum number of tokens allocated per image by the model. This is conceptually similar to setting image resolution. Larger values may increase accuracy on dense / hard to read images but will raise GPU memory usage. Default value is set to 1280.

• enable_location_hint (bool | None) – Available from V42.3; When set to True, the ORCA general prompting block will generate location hint related outputs. This can be useful for downstream tasks that require bounding boxes within the image. However, enable it will raise GPU memory usage and inference latency.

• reference_name (str | None) – unique reference name within a flow, a unique one will be generated when not provided.

Example usage:

# This is one way to obtain the model_type and base_model_id
llm_install_block = LLMInstallBlock(user_consent=True)

# Here we construct a simple static set of messages. But these can also be dynamic
# and the output of a PythonCode block or other processing blocks.
system_message = {'role': 'system', 'content': [
        {'type': 'text', 'text': 'You are a helpful assistant'}
]}
prompt = {'role': 'user', 'content': [
    {'type': 'image', 'image': workflow_input('some_image_uuid')},
    {'type': 'text', 'text': 'Make a short summary of everything in the image'}
]}
message_thread = [system_message, prompt]

gp_block = GeneralPromptingORCABlock(
    model_type=llm_install.output('model'),
    base_model_id=llm_install.output('model_uuid'),
    messages=[message_thread]
)

IDENTIFIER = 'OPTICAL_REASONING_COGNITION_AGENT_GENERAL_PROMPTING'

class flows_sdk.implementations.idp_v42.additional_blocks.ImagesToPrintableFileBlock(documents, height_in_mm=279.4, width_in_mm=215.9, use_native_dimensions=False, dpi=200, mime_type='application/pdf', quality=50, mode='RGB', reference_name=None)

Bases: Block

This block renders each document’s sequence of images into a separate PDF file and stores each resulting PDF in Blob storage. For every document provided, a new blob is created containing the rendered PDF.

Available from V42.1.

Mandatory parameters:

Parameters:

documents (list[dict[int | str, list[UUID]]]) – List of documents, each containing a document_id and list of image UUIDs

Optional parameters:

Parameters:

• height_in_mm (float) – the height of the document to be generated in millimeters. Default is 279.4 (US Letter height). Ignored when use_native_dimensions is True.

• width_in_mm (float) – the width of the document to be generated in millimeters. Default is 215.9 (US Letter width). Ignored when use_native_dimensions is True.

• use_native_dimensions (bool) – Available from V42.3; when True, each page uses its source image’s native dimensions instead of being resized to width_in_mm × height_in_mm. This preserves maximum image quality. Default is False. When enabled, the output height_in_mm and width_in_mm represent the bounding box (maximum dimensions across all pages).

• dpi (int) – Available from V42.3; dots per inch to assume for images without embedded DPI metadata. Used to calculate physical dimensions in native dimensions mode. Default is 200 (matching VECTOR_GRAPHICS_DPI used by the pagination pipeline).

• mime_type (str) – the mime type of the document to be generated. Default is ‘application/pdf’

• quality (int) – Quality of pages (images) appended to the resulting document as a percentage (recommended 50%). Higher quality results in a larger file size for the generated PDF.

• mode (str) – Mode of the images appended to the resulting document. Supported modes are RGB (default), L (grayscale) and 1 (black-and-white)

Example usage:

documents = [
    {
        'document_id': '48212a99-c099-42e3-bb45-75664db65341',
        'images': ['f42fc9af-b551-4403-aba6-d0eba2c41973', '...'],
    },
    {
        'document_id': 'delivery_receipt_xyz',
        'images': ['f42fc9af-b551-4403-aba6-d0eba2c41974', '...'],
    }
]

# Fixed dimensions mode (default) - all pages resized to specified dimensions
images_renderer_block = ImagesToPrintableFileBlock(
    reference_name='images_renderer',
    height_in_mm=279.4,
    width_in_mm=215.9,
    quality=50,
    mode='L',
    documents=documents
)

# Native dimensions mode - preserves original image quality
images_renderer_native = ImagesToPrintableFileBlock(
    reference_name='images_renderer_native',
    use_native_dimensions=True,
    dpi=200,
    quality=80,
    documents=documents
)

output_ref = images_renderer_block.output()

Example content of output_ref (fixed dimensions mode):

{
    "rendered_documents": [
        {
            "document_id": "48212a99-c099-42e3-bb45-75664db65341",
            "blob_uuid": "52e81c7f-aef6-4f69-b4a6-91fd770a607d",
            "mime_type": "application/pdf",
            "height_in_mm": 279.4,
            "width_in_mm": 215.9,
            "quality": 50,
            "mode": "L",
            "page_dimensions": [
                {
                    "image_uuid": "f42fc9af-b551-4403-aba6-d0eba2c41973",
                    "width_in_mm": 215.9,
                    "height_in_mm": 279.4,
                },
                {
                    "image_uuid": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
                    "width_in_mm": 215.9,
                    "height_in_mm": 279.4,
                },
            ]
        }
    ]
}

Example content of output_ref (native dimensions mode):

{
    "rendered_documents": [
        {
            "document_id": "48212a99-c099-42e3-bb45-75664db65341",
            "blob_uuid": "52e81c7f-aef6-4f69-b4a6-91fd770a607d",
            "mime_type": "application/pdf",
            "height_in_mm": 297.0,
            "width_in_mm": 215.9,
            "quality": 80,
            "mode": "RGB",
            "page_dimensions": [
                {
                    "image_uuid": "f42fc9af-b551-4403-aba6-d0eba2c41973",
                    "width_in_mm": 210.0,
                    "height_in_mm": 297.0,
                },
                {
                    "image_uuid": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
                    "width_in_mm": 215.9,
                    "height_in_mm": 279.4,
                },
            ]
        }
    ]
}

IDENTIFIER = 'IMAGE_TO_PRINTABLE_FILE'

Trigger Blocks (v42)

Note

“Triggers” are referred to as “Input Blocks” in our application documentation.

Warning

Flows are built to allow full definition of triggers either in code or manually in the Flow Studio. We recommend defining triggers manually in the Flow Studio. Instructions on how to do so are in our application documentation.

To enable trigger definition via the Flow Studio UI, you need to instantiate the IDPTriggers() convenience class and pass it to the triggers property of the Flow

return Flow(
   ...
   triggers=IDPTriggers()
)

If instead you wish to define specific triggers in Python, use the trigger definitions below.

Folder Listener (v42)

Identifier: FOLDER_TRIGGER

Deprecated in favor of the Universal Folder Listener

Parameters:

Name	Type	Default Value	Title	Description
folder	Path	N/A	Folder to scan for submissions	N/A
file_extensions	string	N/A	File extensions	List of file extensions to monitor for (e.g.: ‘png, jpg, pdf’)
has_meta	boolean	False	Enable metadata	Select this if a metadata file is to be expected along with document files (in XXX_index.txt file)
poll_interval	integer	10	Poll interval	Poll interval in seconds
warmup_interval	integer	15	Warm-up interval	Seconds to wait past document last modified time before processing it
folder_cleanup_delay	integer	86400	Folder cleanup delay	Seconds to wait before cleaning up subfolders
api_params	object	{}	API Parameters	N/A

folder_listener = IOBlock(
    identifier='FOLDER_TRIGGER',
    reference_name='folder_trigger',
    title='Folder listener',
    enabled=True,
    input={
        'folder': '/var/www/forms/forms/folder/',
        'file_extensions': 'png, pdf, jpg',
    },
)

triggers = IDPTriggers(blocks=[folder_listener])

S3 Listener (v42)

Identifier: S3_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
bucket_uri	string	N/A	S3 source URI	S3 URI, where the files will be uploaded (e.g. s3://hs_bucket/prefix1/)
archive_bucket_uri	string	N/A	S3 archive URI	S3 URI to move the processed files to (e.g. s3://hs_bucket/prefix1/)
file_extensions	array	[]	File extensions	Types of file extensions for which to monitor
other_file_extensions	string	N/A	Other file extensions	Comma separated list of file extensions for which to monitor (e.g. ‘png, jpg, pdf’)
has_meta	boolean	false	Include submission level parameters	Select this to ingest JSON files along with document files and submission s3 prefixes. These JSON files can contain information such as metadata, cases and external_id. These JSON file names should match the related files or s3 prefixes (e.g., XXX.jpg.json for XXX.jpg)
aws_access_key_id	string	N/A	AWS ACCESS KEY ID	Access Key used for authentication
aws_secret_access_key	password	N/A	SECRET ACCESS KEY	Secret Key used for authentication
use_ec2_instance_credentials	boolean	true	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used
poll_interval	integer	10	Polling interval (in seconds)	How often the S3 Listener will check the S3 bucket for submissions
warmup_interval	integer	15	Warm-up interval (in seconds)	How long the S3 Listener will wait to process the document after it was first detected
api_params	object	{}	API Parameters	N/A

s3_listener = IOBlock(
    identifier='S3_TRIGGER',
    reference_name='s3_trigger',
    title='S3 Listener',
    enabled=True,
    input={
        'bucket_uri': 's3://hs_bucket/prefix1/',
        'archive_bucket_uri': 's3://hs_bucket/prefix2/',
        'file_extensions': ['png', 'pdf', 'jpg'],
        'aws_access_key_id': 'admin',
        'aws_secret_access_key': system_secret('s3_secret_access_key'),
        'use_ec2_instance_credentials': False,
    },
)
triggers = IDPTriggers(blocks=[s3_listener])

GCS Listener (v42)

Identifier: GCS_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
bucket_uri	string	N/A	GCS Source URI	GCS URI, where the files will be uploaded (e.g. gs://hs_bucket/prefix1/)
archive_bucket_uri	string	N/A	GCS archive URI	GCS URI to move the processed files to (e.g. gs://hs_bucket/prefix2/). Cannot be same as Source URI!
file_extensions	array	N/A	File extensions	Types of file extensions for which to monitor
other_file_extensions	string	N/A	Other file extensions	Comma separated list of file extensions for which to monitor (e.g. ‘png, jpg, pdf’)
has_meta	boolean	false	Include submission level parameters	Select this to ingest JSON files along with document files and submission gcs prefixes. These JSON files can contain information such as metadata, cases and external_id. These JSON file names should match the related files or prefixes (e.g., XXX.jpg.json for XXX.jpg)
credentials_json	json	N/A	GCP Service Account JSON	GCP Service Account JSON used for authentication, must be a valid json.
workload_identity	boolean	false	Use Workload Identity	Credentials are obtained by using the Workload Identity Federation, applicable for HS installations inside GKE clusters.
poll_interval	integer	10	Polling interval (in seconds)	How often the GCS Listener will check the GCS bucket for submissions
warmup_interval	integer	15	Warm-up interval (in seconds)	How long the GCS Listener will wait to process the document after it was first detected
api_params	object	{}	API Parameters	N/A

gcs_listener = IOBlock(
    identifier='GCS_TRIGGER',
    reference_name='gcs_trigger',
    title='GCS Listener',
    enabled=True,
    input={
        'bucket_uri': 'gs://hs_bucket/prefix1/',
        'archive_bucket_uri': 'gs://hs_bucket/prefix2/',
        'file_extensions': ['png', 'pdf', 'jpg'],
        'credentials_json': system_secret('gcs_credentials_json'),
        'workload_identity': False,
    },
)
triggers = IDPTriggers(blocks=[gcs_listener])

Azure Blob Listener (v42)

Identifier: AZURE_BLOB_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
container_uri	string	N/A	Azure source URI	Azure URI, where the files will be uploaded, can contain prefix, can contain trailing slash (e.g.: https://<custom_domain>/<container_name>// https://<account_name>.blob.core.windows.net/<container_name>)
archive_container_uri	string	N/A	Azure archive URI	Azure URI to move the processed files to, can contain prefix, can contain trailing slash. Cannot be same as the source URI.
file_extensions	array	N/A	File extensions	Types of file extensions for which to monitor
other_file_extensions	string	N/A	Other file extensions	Comma separated list of file extensions for which to monitor (e.g. ‘png, jpg, pdf’)
has_meta	boolean	false	Include submission level parameters	Select this to ingest JSON files along with document files and submission Azure container prefixes. These JSON files can contain information such as metadata, cases and external_id. These JSON file names should match the related files or prefixes (e.g., XXX.jpg.json for XXX.jpg)
auth_type [1]	string	service_principal	Authentication type	Authentication type.
client_id	string	N/A	Client ID	Application (client) ID for the corresponding application registered with the Microsoft Entra ID
client_secret	Password	N/A	Client secret	Client secret for the corresponding application registered with the Microsoft Entra ID
tenant_id	string	N/A	Tenant ID	Also called directory ID. Unique identifier of the tenant in which the corresponding application is registered with.
account_name	string	N/A	Storage Account Name	Azure Storage Account name used for authentication
account_key	Password	N/A	Storage Account Key	Azure Storage Account key
authority_host	string	N/A	Authority Host	The authority host URL for the Azure Entra ID. Defaults to https://login.microsoftonline.com
poll_interval	integer	10	Polling interval (in seconds)	How often the Azure Listener will check the container for submissions
warmup_interval	integer	15	Warm-up interval (in seconds)	How long the Azure Listener will wait to process the document after it was first detected
api_params	object	{}	API Parameters	N/A

[1] one of:

service_principal
account_key

azure_blob_listener = IOBlock(
    identifier='AZURE_BLOB_TRIGGER',
    reference_name='azure_blob_trigger',
    title='Azure Blob Listener',
    enabled=True,
    input={
        'container_uri': 'https://hs_container.blob.core.windows.net/prefix1/',
        'archive_container_uri': 'https://hs_container.blob.core.windows.net/prefix2/',
        'file_extensions': ['png', 'pdf', 'jpg'],
        'auth_type': 'service_principal',
        'client_id': '123456',
        'tenant_id': '654321',
        'client_secret': system_secret('azure_blob_client_secret'),
    },
)
triggers = IDPTriggers(blocks=[azure_blob_listener])

Email Listener (v42)

Identifier: IMAP_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
provider [1]	string	imap	Email Provider	N/A
poll_interval	integer	60	Polling interval in seconds	The frequency at which the connection will monitor the email folder for submissions
folder	Path	N/A	Folder to scan for emails	The email folder to retrieve submissions from (e.g., “inbox”)
email_body_treatment	string	“ignore”	Email body treatment	What to do with the body of an email that is ingested. Available options are “process” and “ignore”.
post_process_action	string	“move”	Post process action	What to do with the email after it is processed. Available options are “move” and “delete”.
post_process_move_folder	string	“”	Post process archive folder	Folder to move emails to once they are processed. It will be included only if post_process_action is set to “move”.
api_params	object	{}	API Parameters	N/A
render_headers [2]	array	[]	Headers to include	Headers to render at the top of the email body. Headers will be included only if email_body_treatment is set to “process”.
raw_email_treatment [3]	string	“ignore”	Original email file	If stored, the copy will be found under “raw_email” in the block output. Storing a copy will use additional storage.
attachment_header_treatment [3]	string	“ignore”	Attachment headers	If extracted, the attachment headers will be found under “attachment_metadata” in the block output.

[1] one of:

imap
graph

[2] any of:

To
From
Subject
Date

[3] only available in specific Hyperscience versions:

• version 41: since patch 41.0.24

• version 41.2: since patch 41.2.15

• version 42: since patch 42.0.8

• version 42.1: since patch 42.1.2

• all versions past (and including) 42.2

Additional inputs when "imap" is the selected provider:

Name	Type	Default Value	Title	Description
host	string	N/A	IMAP server address	N/A
port	integer	993	Port Number	N/A
ssl	boolean	True	Use SSL connection	N/A
username	string	N/A	Username	N/A
password	Password	N/A	Password	N/A

Additional inputs when "graph" is the selected provider:

Name	Type	Default Value	Title	Description
client_id	string	N/A	Client id	Application (client) ID for the corresponding application registered with the Microsoft identity platform.
tenant_id	string	N/A	Tenant id	Also called directory ID. Unique identifier of the tenant in which the corresponding application is registered with.
graph_cloud_endpoint [1]	string	Global	Microsoft Graph Cloud Endpoint	Determines base URL used for accessing cloud services through Microsoft Graph.
azure_ad_endpoint [2]	string	AZURE_PUBLIC_CLOUD	Azure AD Endpoint	Determines base URL for the Azure Active Directory (Azure AD) endpoint to acquire token for each national cloud.
client_secret	Password	N/A	Client secret	Client secret for the corresponding application registered with the Microsoft identity platform.

[1] one of:

Global
US_GOV
US_DoD
Germany
China

[2] one of:

AZURE_PUBLIC_CLOUD
AZURE_CHINA
AZURE_GERMANY
AZURE_GOVERNMENT

imap_trigger = IOBlock(
    identifier='IMAP_TRIGGER',
    reference_name='imap_trigger',
    title='IMAP trigger',
    enabled=True,
    input={
        'host': 'example@mail.com',
        'folder': '/var/www/forms/forms/imap/',
        'username': 'admin',
        'password': 'pass',
    }
)

triggers = IDPTriggers(blocks=[imap_trigger])

Box Folder Listener (v42)

Identifier: BOX_FOLDER_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
source_folder_id	string	N/A	Folder to scan for submissions	Use the Box Folder ID found in the URL
target_folder_id	string	N/A	Folder to move completed files	Use the Box Folder ID found in the URL
file_extensions	array	[]	File extensions	Types of file extensions for which to monitor
custom_file_extensions	string	N/A	Other file extension types	Comma separated list of file extensions for which to monitor (e.g. ‘png, jpg, pdf’)
poll_interval	integer	10	Polling interval (in seconds)	How often the connector will check the base folder for submissions
warmup_interval	integer	15	Warm-up interval (in seconds)	How long the connector will wait to process the document after it was last modified
public_key_id	string	N/A	Public-Key ID	ID of the Public-key used for authentication with Box
private_key	Password	N/A	Private-Key	Private key used for authentication with Box
passphrase	Password	N/A	Passphrase	Passphrase used for authentication with Box
client_id	string	N/A	Client ID	Client Id used for authentication with Box
client_secret	Password	N/A	Client Secret	Client Secret used for authentication with Box
enterprise_id	string	N/A	Enterprise ID	Enterprise Id used for authentication with Box
api_params	object	{}	API Parameters	N/A

box_folder_trigger = IOBlock(
    identifier='BOX_FOLDER_TRIGGER',
    reference_name='box_folder_trigger',
    title='Box folder',
    enabled=True,
    input={
        'file_extensions': ['png', 'pdf', 'jpg'],
        'source_folder_id': '24',
        'target_folder_id': '42',
        'public_key_id': 'admin',
        'private_key': 'secret',
        'passphrase': 'password',
        'client_id': 'admin',
        'client_secret': 'secret',
        'enterprise_id': 'admin',
    }
)

triggers = IDPTriggers(blocks=[box_folder_trigger])

Cron Listener (v42)

Identifier: CRON_TRIGGER

Inputs:

Name	Type	Default Value	Title	Description
cron_spec	string	"*/5 * * * *"	Cron specification	N/A
time_zone	string	“US/Eastern”	Time Zone	N/A
api_params	object	{}	API Parameters	N/A

cron_trigger = IOBlock(
    identifier='CRON_TRIGGER',
    reference_name='cron_trigger',
    title='Cron Trigger',
    enabled=True,
    input={
        'cron_spec': '0 10 * * *',
        'time_zone': 'Europe/Sofia',
    }
)

triggers = IDPTriggers(blocks=[cron_trigger])

Salesforce Listener (v42)

Identifier: SALESFORCE_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
channel_name	string	N/A	Channel Name	Configured in the Hyperscience app in Salesforce.
username	string	N/A	Associated Username	Username associated with the Salesforce Connected App.
consumer_key	Password	N/A	Consumer Key	Consumer key used for authentication with Salesforce. Paired with the Salesforce instance from which it was issued.
private_key	Password	N/A	Private Key	Private key used for authentication with Salesforce.
sandbox_environment	boolean	false	Private Key	Enable if the Salesforce environment is a sandbox.
api_params	object	{}	API Parameters	N/A

Message Queue Listener (v42)

Identifier: MQ_LISTENER

Inputs:

Name	Type	Default Value	Title	Description
MQ_TYPE	string	“ACTIVE_MQ”	Message Queue type	Type of the message queue to connect with. Valid values are “ACTIVE_MQ”, “AMAZON_SQS”, “IBM_MQ” and “RABBIT_MQ”
MQ_USERNAME	string	null	Username/Access Key ID	N/A
MQ_PASSWORD	Password	null	Password/Secret Access Key	N/A
MQ_QUEUE_NAME	string	null	Queue Name	Name of the queue (topic) to connect to.
MQ_HOST	string	null	Host Name	N/A
MQ_PORT	integer	5672 when “RABBIT_MQ”, 1414 when “IBM_MQ”, otherwise null	Port Number	N/A
api_params	object	{}	API Parameters	N/A

Additional inputs when "AMAZON_SQS" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_REGION	string	null	AWS Region	N/A
MQ_QUEUE_URL	string	null	Queue URL	N/A
MQ_USE_EC2_INSTANCE_CREDENTIALS	boolean	True	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used.

NOTE: MQ_USERNAME can be used to provide Access Key ID and MQ_PASSWORD to provide Secret Access Key.

Additional inputs when "IBM_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_QUEUE_MANAGER	string	null	Queue Manager (IBM MQ)	N/A
MQ_SSL_CIPHER_SUITE [1]	string	null	SSL cipher suite	SSL cipher suite to use if SSL connection is used.
MQ_CHANNEL	string	null	Channel	N/A
MQ_NO_AUTH_REQUIRED	boolean	false	No auth credentials required	N/A
MQ_MQCSP_AUTHENTICATION_MODE	boolean	false	MQCSP authentication mode	N/A

NOTE: MQ_NO_AUTH_REQUIRED is True, MQ_USERNAME and MQ_PASSWORD are optional.

[1] one of:

"NULL_MD5"
"NULL_SHA"
"RC4_MD5_EXPORT"
"RC4_MD5_US"
"RC4_SHA_US"
"RC2_MD5_EXPORT"
"DES_SHA_EXPORT"
"RC4_56_SHA_EXPORT1024"
"DES_SHA_EXPORT1024"
"TRIPLE_DES_SHA_US"
"TLS_RSA_WITH_NULL_SHA256"
"TLS_RSA_WITH_AES_128_CBC_SHA"
"TLS_RSA_WITH_AES_128_CBC_SHA256"
"TLS_RSA_WITH_AES_256_CBC_SHA"
"TLS_RSA_WITH_AES_256_CBC_SHA256"
"AES_SHA_US"
"TLS_RSA_WITH_DES_CBC_SHA"
"TLS_RSA_WITH_3DES_EDE_CBC_SHA"
"TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256"
"FIPS_WITH_DES_CBC_SHA"
"FIPS_WITH_3DES_EDE_CBC_SHA"

Additional inputs when "RABBIT_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_VIRTUAL_HOST	string	null	Virtual Host (RABBIT MQ)	N/A
MQ_EXCHANGE	string	“”	Exchange	N/A
MQ_ROUTING_KEY	string	“”	Routing key	N/A
MQ_CONNECTION_TYPE	string	“TCP”	Connection Type	“TCP or SSL”

rabbit_mq_listener = IOBlock(
    identifier='MQ_LISTENER',
    reference_name='mq_listener',
    title='RabbitMQ Listener',
    enabled=True,
    input={
        'MQ_TYPE': 'RABBIT_MQ',
        'MQ_QUEUE_NAME': 'some_queue_name',
        'MQ_HOST': 'somehost.com',
        'MQ_USERNAME': 'foo',
        'MQ_PASSWORD': system_secret('rabbit_mq_1_password'),
    }
)

triggers = IDPTriggers(blocks=[rabbit_mq_listener])

Universal Folder Listener (v42)

Identifier: UNIVERSAL_FOLDER_TRIGGER

Inputs:

Name	Type	Default Value	Title	Description
folder	string	N/A	Folder to scan for submissions	Enter the path relative to the base folder. Leave blank to monitor the base folder
file_extensions	array	[]	File extensions	Types of file extensions for which to monitor
other_file_extensions	string	N/A	Other file extensions	Comma separated list of file extensions for which to monitor (e.g. ‘png, jpg, pdf’)
has_meta	boolean	false	Include submission level parameters	Select this to ingest JSON files along with document files and submission folders. These JSON files can contain information such as metadata, cases and external_id. These JSON file names should match the related files or folders (e.g., XXX.jpg.json for XXX.jpg)
poll_interval	integer	10	Polling interval (in seconds)	How often the Folder Listener will check the base folder for submissions
warmup_interval	integer	15	Warm-up interval (in seconds)	How long the Folder Listener will wait to process the document after it was last modified
folder_cleanup_delay	number	24	Empty folder cleanup delay (in hours)	How often the Folder Listener will remove empty folders from the base folder
api_params	object	{}	API Parameters	N/A

universal_folder_listener = IOBlock(
    identifier='UNIVERSAL_FOLDER_TRIGGER',
    reference_name='universal_folder_trigger',
    title='Universal Folder Listener',
    enabled=True,
    input={
        'folder': '/var/www/forms/forms/folder/',
        'file_extensions': 'png, pdf, jpg',
    }
)

triggers = IDPTriggers(blocks=[universal_folder_listener])

Kafka Listener (v42)

Identifier: KAFKA_TRIGGER

Inputs:

Name	Type	Default Value	Title	Description
bootstrap_servers	string	N/A	Bootstrap Servers	A Comma separated list of host:port pairs to use for establishing the initial connection to the cluster
group_id	string	N/A	Group Id	A unique string that identifies the consumer group this consumer belongs to. BLOCK_THREADS_KAFKA_TRIGGER should be used to scale up or down the number of consumers (default: number of available cores)
topics	string	N/A	Topics	Comma separated list of topic names that the block will subscribe to
consumer_config_json	json	N/A	Consumer Config	JSON formatted string containing key-value pairs of Kafka consumer configurations described in the official Kafka documentation
api_params	object	{}	API Parameters	N/A
consume_timeout	integer	-1	Consume Timeout	Maximum time, in seconds, that the consumer will wait for new messages. If messages are received within the specified time, they are processed immediately. However, if no messages are received within that specified time, consumption cycle will be restarted internally to continue fetching messages. This ensures continuous message consumption (default: infinite)
consume_num_messages	integer	1	Consume Num Messages	Sets the maximum number of messages the consumer fetches in a single consumption cycle.If the number of available messages is less than the specified, and timeout is reached, messages will be processed. Consumption cycle will be restarted after the current finishes

kafka_listener = IOBlock(
    identifier='KAFKA_TRIGGER',
    reference_name='kafka_trigger',
    title='Kafka Listener',
    enabled=True,
    input={
        'folder': '/var/www/forms/forms/folder/',
        'file_extensions': 'png, pdf, jpg',
         'bootstrap_servers': 'example.com',
         'group_id': 'test_group',
         'topics': 'test_topic1, test_topic2',
         'consumer_config_json': {},
         'number_of_workers': 1,
         'consume_timeout': 5,
         'consume_num_messages': 10
    }
)

triggers = IDPTriggers(blocks=[kafka_listener])

Output Blocks (v42)

Warning

Flows pass information to downstream systems using the IDPOutputsBlock. This block is specially built to allow full definition either in code or manually in the Flow Studio. We recommend defining outputs manually in the Flow Studio.

Instructions on how to do so are in our application documentation. Simply include an IDPOutputsBlock in your Python flow definition wherever you wish to send information to downstream systems.

class flows_sdk.implementations.idp_v42.idp_blocks.IDPOutputsBlock(inputs, blocks=None)

Bases: Outputs

Output block allows users to send data extracted by an IDP flow to other systems for downstream processing

Mandatory parameters:

Parameters:

inputs (dict[str, Any]) – Used by the UI to automatically pre-populate the input of newly added blocks. Extended in this class to include 'enabled': True to visualize an enabled/disabled trigger that is controllable via Flow Studio.

Usage when parameters are to be defined in Flow Studio:

outputs = IDPOutputBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')}
)

If instead you wish to define specific outputs in Python, use the outputs definitions below.

HTTP Notifier Output (v42)

The HTTP Notifier (REST) output connection will POST results from the system to a specified HTTP endpoint.

Identifier: COB_HTTP_EXPORT

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
endpoint	string	N/A	Endpoint URL	URL that output notifications will be posted to
endpoint_secrets	string	N/A	Endpoint URL secrets	Endpoint URL Secrets, format: (one per line) secret_key_name=secret_value
timeout	int	600	Timeout (seconds)	How long (in seconds) to keep the connection open if no bytes are received from the endpoint. Set to 0 to wait indefinitely.
authorization_type [2]	string	“none”	Authorization type	Type of authorization
authorization_header_name	string	null	Authorization header name	Authorization header name to be set in the notification request
authorization_header	Password	null	Authorization header value	Authorization header to be set in the notification request
auth_url	string	null	OAuth2 authorization URL	The endpoint for the authorization server
client_id	string	null	Client ID	The client identifier issued to the client during the application registration process
client_secret	Password	null	Client Secret	The client secret issued to the client during the application registration process
scope	string	null	Scope	Scope to request to oauth server
audience	string	null	Audience	Resource service URL where token will be valid
additional_oauth_parameters	json	{}	Additional Oauth Request Parameters	Additional parameters to send when requesting token
ssl_cert_source [3]	string	null	Source	N/A
ssl_cert_path	string	null	Trusted Certificate(s) path	Enter the path relative to the base folder
ssl_cert_value	MultilineText	null	Trusted Certificate(s)	N/A
additional_notification_http_headers	json	null	Additional Notification Request Headers	Key value pairs in json format which will be sent via the request as http headers

[1] one of:

"v5"
"transformed"

[2] one of:

"none"
"http_header"
"oauth_2_client_credentials"

[3] one of:

"env_var"
"ca_bundle_path"
"certificate_value"

cob_http_export = IOBlock(
    identifier='COB_HTTP_EXPORT',
    reference_name='cob_http_export',
    title='HTTP Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'SUBMISSION',
        'endpoint': 'example.com',
        'authorization_type': 'none',
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_http_export],
)

Message Queue Notifier Output (v42)

The Message Queue Notifier Output can configure connections to ActiveMQ, Amazon SQS, IBM MQ, and RabbitMQ message queues.

Identifier: COB_MQ_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
MQ_TYPE	string	“ACTIVE_MQ”	Message Queue type	Type of the message queue to connect with. Valid values are “ACTIVE_MQ”, “AMAZON_SQS”, “IBM_MQ” and “RABBIT_MQ”
MQ_USERNAME	string	null	Username/Access Key ID	N/A
MQ_PASSWORD	Password	null	Password/Secret Access Key	N/A
MQ_QUEUE_NAME	string	null	Queue Name	Name of the queue (topic) to connect to. It applies only to “ACTIVE_MQ”, “IBM_MQ” and “RABBIT_MQ” MQ_TYPEs.
MQ_HOST	string	null	Host Name	N/A
MQ_PORT	integer	5672 when “RABBIT_MQ”, 1414 when “IBM_MQ”, otherwise null	Port Number	N/A

[1] one of:

"v5"
"transformed"

Additional inputs when "AMAZON_SQS" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_REGION	string	null	AWS Region	N/A
MQ_QUEUE_URL	string	null	Queue URL	N/A
MQ_USE_EC2_INSTANCE_CREDENTIALS	boolean	True	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used.
MQ_MESSAGE_GROUP_ID	string	null	Group ID for FIFO queues	N/A
MQ_MESSAGE_METADATA	string	null	Additional SQS Metadata	Input additional metadata key value pairs in the format of { “key”: {“TYPE”: “String”, “VALUE”: “”}, “key2”: {“TYPE”: “Number”, “VALUE”: “12345”}

NOTE: MQ_USERNAME can be used to provide Access Key ID and MQ_PASSWORD to provide Secret Access Key.

Additional inputs when "IBM_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_QUEUE_MANAGER	string	null	Queue Manager (IBM MQ)	N/A
MQ_SSL_CIPHER_SUITE [1]	string	null	SSL cipher suite	SSL cipher suite to use if SSL connection is used.
MQ_CHANNEL	string	null	Channel	N/A
MQ_NO_AUTH_REQUIRED	boolean	false	Channel	N/A
MQ_MQCSP_AUTHENTICATION_MODE	boolean	false	Channel	N/A

NOTE: MQ_NO_AUTH_REQUIRED is True, MQ_USERNAME and MQ_PASSWORD are optional.

[1] one of:

"NULL_MD5"
"NULL_SHA"
"RC4_MD5_EXPORT"
"RC4_MD5_US"
"RC4_SHA_US"
"RC2_MD5_EXPORT"
"DES_SHA_EXPORT"
"RC4_56_SHA_EXPORT1024"
"DES_SHA_EXPORT1024"
"TRIPLE_DES_SHA_US"
"TLS_RSA_WITH_NULL_SHA256"
"TLS_RSA_WITH_AES_128_CBC_SHA"
"TLS_RSA_WITH_AES_128_CBC_SHA256"
"TLS_RSA_WITH_AES_256_CBC_SHA"
"TLS_RSA_WITH_AES_256_CBC_SHA256"
"AES_SHA_US"
"TLS_RSA_WITH_DES_CBC_SHA"
"TLS_RSA_WITH_3DES_EDE_CBC_SHA"
"TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256"
"FIPS_WITH_DES_CBC_SHA"
"FIPS_WITH_3DES_EDE_CBC_SHA"

Additional inputs when "RABBIT_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_VIRTUAL_HOST	string	null	Virtual Host (RABBIT MQ)	N/A
MQ_EXCHANGE	string	“”	Exchange	N/A
MQ_ROUTING_KEY	string	“”	Routing key	N/A
MQ_CONNECTION_TYPE	string	“TCP”	Connection Type	“TCP or SSL”

mq_notifier = IOBlock(
    identifier='COB_MQ_NOTIFIER',
    reference_name='mq_notifier',
    title='MQ Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'SUBMISSION',
        'MQ_USERNAME': 'admin',
        'MQ_PASSWORD': 'pass',
        'MQ_QUEUE_NAME': 'queue',
        'MQ_HOST': 'host',
    },
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[mq_notifier],
)

Box Notifier Output (v42)

Provides an out-of-the-box integration into Box systems.

Identifier: COB_BOX_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
public_key_id	string	N/A	Public-Key ID	ID of the Public-key used for authentication with Box
private_key	Password	N/A	Private-Key	Private key used for authentication with Box
passphrase	Password	N/A	Passphrase	Passphrase used for authentication with Box
client_id	string	N/A	Client ID	Client Id used for authentication with Box
client_secret	Password	N/A	Client Secret	Client Secret used for authentication with Box
enterprise_id	string	N/A	Enterprise ID	Enterprise Id used for authentication with Box
template_key	string	“”	Box Metadata Template Key	Enter the key of the Box Metadata template that you would like to map the Hyperscience metadata to.
static_output_fields_to_map	array	[]	Static Metadata Fields	Specify the Hyperscience fields you want to store in Box metadata.
submission_id	string	“”	Key for Mapping Submission ID	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_id	string	“”	Key for Mapping Document ID	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
submission_state	string	“”	Key for Mapping Submission State	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_state	string	“”	Key for Mapping Document State	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
submission_exceptions	string	“”	Key for Mapping Submission Exceptions	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_exceptions	string	“”	Key for Mapping Document Exceptions	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_layout_uuid	string	“”	Key for Mapping Document Layout Uuid	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_layout_name	string	“”	Key for Mapping Document Layout Name	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
start_time	string	“”	Key for Mapping Start Time	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
complete_time	string	“”	Key for Mapping Complete Time	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_fields_template_mappings	json	{}	JSON for Additional Metadata Fields	Specify additional metadata fields using their key value. Please consult Box Integration setup manual for JSON template and instructions.

[1] one of:

"v5"
"transformed"

box_notifier = IOBlock(
    identifier='COB_BOX_NOTIFIER',
    reference_name='box_notifier',
    title='Box Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'private_key': 'password',
        'passphrase': 'password',
        'public_key_id': 'admin',
        'client_id': 'admin',
        'client_secret': 'secret',
        'enterprise_id': 'admin',
        'template_key': 'key',
    },
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[box_notifier],
)

UiPath Notifier Output (v42)

Provides an out-of-the-box integration into UiPath systems.

Identifier: COB_EXPORT_UIPATH

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
auth_method [2]	string	“basic_auth”	Authentication method	N/A
url	string	N/A	URL	Base URL of the uipath instance
app_id	string	N/A	App ID	App ID provided by UiPath
app_secret	Password	N/A	App Secret	App Secret provided by UiPath
oauth_token_endpoint	string	N/A	OAuth token endpoint	Endpoint to retrieve the token. Using https://cloud.uipath.com/identity_/connect/token by default
organization_unit_id	string	N/A	Organization unit id	Organization unit id
tenant	string	N/A	Tenant	Tenant
username	string	N/A	Username	Username
password	Password	N/A	Password	Password
queue	string	N/A	Queue	Queue

[1] one of:

"v5"
"transformed"

[2] one of:

"basic_auth"
"oauth"

cob_export_uipath = IOBlock(
    identifier='COB_EXPORT_UIPATH',
    reference_name='cob_export_uipath',
    title='CCB Export UiPath',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'FLAT_SUBMISSION',
        'url': 'example.com',
        'tenant': 'tenant',
        'username': 'admin',
        'password': 'pass',
        'queue': 'queue',
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_export_uipath],
)

Salesforce Notifier Output (v42)

With the Salesforce Notifier Block, you can configure your flow to send extracted information to Salesforce. The Salesforce Notifier Block can use extracted information to look up, and then update any number of fields on a Salesforce object. The Salesforce Notifier Block is available in both SaaS and on-premise versions of the Hyperscience application.

Identifier: COB_SALESFORCE_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
username	string	N/A	Associated Username	Username associated with the Salesforce Connected App
consumer_key	password	N/A	Consumer Key	Consumer key used for authentication with Salesforce. Paired with the Salesforce instance from which it was issued.
private_key	password	N/A	Private Key	Private key used for authentication with Salesforce.
sandbox_environment	boolean	false	Private Key	Enable if the Salesforce environment is a sandbox.
object_api_name	string	N/A	Object API Name	API name of the object in Salesforce to lookup and update.
new_record_on_lookup_failure	boolean	false	Create new record on lookup failure	When enabled, it will create a new record if the lookup fails to find a record using the lookup parameters. When disabled, lookup will fail if it does not find a record using the lookup parameters.
object_lookup_mapping	json	N/A	Lookup Field Mapping	Specify which Salesforce fields (using their API name) to be queried with which extracted values from the document for the lookup using key-value format. For example: “DOCUMENT_FIELD_NAME”: “OBJECT_FIELD_API_NAME”.
document_fields_mapping	json	N/A	Document Field Mappings	Specify which Salesforce fields (using their API name) you want to update with which extracted data from Hyperscience using key-value format. For example: “DOCUMENT_FIELD_NAME”: “OBJECT_FIELD_API_NAME”.”
overwrite_existing_field_data	boolean	false	Overwrite existing field data	If this is checked and a selected field has data in it, Hyperscience will overwrite that data. If this is unchecked, existing data will not be overwritten and the operation will fail.

[1] one of:

"v5"
"transformed"

cob_salesforce_notifier = IOBlock(
    identifier='COB_SALESFORCE_NOTIFIER',
    reference_name='cob_salesforce_notifier',
    title='Salesforce Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'username': 'salesforce_user',
        'consumer_key': 'salesforce_consumer_key',
        'private_key': 'salesforce_private_key',
        'object_api_name': 'salesforce_object_api_name',
        'object_lookup_mapping': {"LAYOUT_VARIATION_XYZ_FIELD_NAME_1": "SF_FIELD_API_NAME_1", "LAYOUT_VARIATION_XYZ_FIELD_NAME_2": "SF_FIELD_API_NAME_2"},
        'document_fields_mapping': {"LAYOUT_VARIATION_XYZ_FIELD_NAME_3": "SF_FIELD_API_NAME_3", "LAYOUT_VARIATION_XYZ_FIELD_NAME_4": "SF_FIELD_API_NAME_4"}
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_salesforce_notifier],
)

S3 Notifier Output (v42)

With the S3 Notifier Block, you can configure your flow to send extracted information to an S3 bucket.

Identifier: COB_S3_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
bucket_uri	string	N/A	S3 URI	S3 URI, where the data should be stored (e.g. s3://hs_bucket/prefix1/prefix2/)
aws_access_key_id	string	N/A	AWS ACCESS KEY ID	Access Key used for authentication
aws_secret_access_key	password	N/A	SECRET ACCESS KEY	Secret Key used for authentication
use_ec2_instance_credentials	boolean	true	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used

[1] one of:

"v5"
"transformed"

cob_s3_notifier = IOBlock(
    identifier='COB_S3_NOTIFIER',
    reference_name='cob_s3_notifier',
    title='S3 Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'FLAT_SUBMISSION',
        'bucket_uri': 's3://hs_bucket/prefix1/prefix2/',
        'aws_access_key_id': 'aws_access',
        'aws_secret_access_key': system_secret('aws_secret_access_key'),
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_s3_notifier],
)

GCS Notifier Output (v42)

With the GCS Notifier Block, you can configure your flow to send extracted information to a GCS bucket.

Identifier: COB_GCS_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
bucket_uri	string	N/A	GCS Source URI	GCS URI, where the files will be uploaded (e.g. gs://hs_bucket/prefix1/)
credentials_json	json	N/A	GCP Service Account JSON	GCP Service Account JSON used for authentication, must be a valid json.
workload_identity	boolean	true	Use Workload Identity	Credentials are obtained by using the Workload Identity Federation, applicable for HS installations inside GKE clusters.

[1] one of:

"v5"
"transformed"

cob_gcs_notifier = IOBlock(
    identifier='COB_GCS_NOTIFIER',
    reference_name='cob_gcs_notifier',
    title='GCS Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'FLAT_SUBMISSION',
        'bucket_uri': 'gs://hs_bucket/prefix1/prefix2/',
        'credentials_json': system_secret('gcs_credentials_json'),
        'workload_identity': False,
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_gcs_notifier],
)

Azure Blob Notifier Output (v42)

With the Azure Blob Notifier Block, you can configure your flow to send extracted information to an Azure Blob container.

Identifier: COB_AZURE_BLOB_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
container_uri	string	N/A	Azure Blob URI	Azure Blob URI, where the data will be stored, can contain prefix, can contain trailing slash (e.g.: https://<custom_domain>/<container_name>/<prefix>/ https://<account_name>.blob.core.windows.net/<container_name>)
auth_type [2]	string	service_principal	Authentication type	Authentication type.
client_id	string	N/A	Client ID	Application (client) ID for the corresponding application registered with the Microsoft Entra ID
client_secret	Password	N/A	Client secret	Client secret for the corresponding application registered with the Microsoft Entra ID
tenant_id	string	N/A	Tenant ID	Also called directory ID. Unique identifier of the tenant in which the corresponding application is registered with.
account_name	string	N/A	Storage Account Name	Azure Storage Account name used for authentication
account_key	Password	N/A	Storage Account Key	Azure Storage Account key
authority_host	string	N/A	Authority Host	The authority host URL for the Azure Entra ID. Defaults to https://login.microsoftonline.com
[1] one of:

"v5"
"transformed"

[2] one of:

service_principal
account_key

cob_azure_blob_notifier = IOBlock(
    identifier='COB_AZURE_BLOB_NOTIFIER',
    reference_name='cob_azure_blob_notifier',
    title='Azure Blob Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'FLAT_SUBMISSION',
        'container_uri': 'https://hs_container.blob.core.windows.net/',
        'auth_type': 'service_principal',
        'client_id': '123456',
        'tenant_id': '654321',
        'client_secret': system_secret('azure_blob_client_secret'),
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_azure_blob_notifier],
)

Kafka Notifier Output (v42)

With the Kafka Notifier Block, you can configure your flow to send extracted information to a kafka topic.

Identifier: COB_KAFKA_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
bootstrap_servers	string	N/A	Bootstrap Servers	A list of Kafka brokers to connect to. For example: “kafka1:9092,kafka2:9092”
topic	string	N/A	Topic	The Kafka topic where your data will be sent
client_id	string	N/A	SECRET ACCESS KEY	An identifier for your application when connecting to Kafka, typically used for tracking and logging purposes
producer_config_json	json	true	Producer Config (JSON)	Configuration settings for the Kafka producer in JSON format. See https://kafka.apache.org/documentation/#producerconfigs for more information
synchronous_publish	boolean	true	Sync Publish	If true, the publish call will block until the message is sent successfully or an error is raised. If false, the publish call will return immediately after enqueueing the message
message_key	string	null	Message Key	The key associated with the message to be sent. This is used to determine which partition the message is sent to. If null, the message will be sent to a random partition

[1] one of:

"v5"
"transformed"

cob_kafka_notifier = IOBlock(
    identifier='COB_KAFKA_NOTIFIER',
    reference_name='cob_kafka_notifier',
    title='Kafka Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'FLAT_SUBMISSION',
        'bootstrap_servers': 'kafka1:9092,kafka2:9092',
        'topic': 'topic',
        'client_id': 'client_id',
        'producer_config_json': {},
        'synchronous_publish': True,
        'message_key': 'key'
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_kafka_notifier],
)

Helper Classes and Functions (v42)

The following classes are used to easily instantiate and manage a flow’s settings.

flows_sdk.implementations.idp_v42.idp_values.get_idp_wf_inputs(idp_wf_config)

A helper function that maps the values provided by an IdpWorkflowConfig to their corresponding IDP keys.

Parameters:

idp_wf_config (IdpWorkflowConfig) – with static values to be filled.

Return type:

dict[str, Any]

Returns:

a dictionary with filled static values, compatible with IDP flows.

Example usage with default values:

flow = Flow(
    input=get_idp_wf_inputs(get_idp_wf_config()),
    ...
)

Example usage:

Example usage when instantiating an IDP flow with custom parameters::

flow = Flow(
    input=get_idp_wf_inputs({an instance of IdpWorkflowConfig}),
    ...
)

flows_sdk.implementations.idp_v42.idp_values.get_idp_wf_config()

A helper function that instantiates an IdpWorkflowConfig pre-filled with default values. IdpWorkflowConfig is the container class for defining flow-level settings.

Return type:

IdpWorkflowConfig

Returns:

a valid, fully constructed IdpWorkflowConfig instance

Example usage when instantiating an IDP flow:

flow = Flow(
    input=get_idp_wf_inputs(get_idp_wf_config()),
    ...
)

class flows_sdk.implementations.idp_v42.idp_values.IdpWorkflowConfig

Convenience dataclass outlining all flow-level config values for IDP.

class flows_sdk.implementations.idp_v42.idp_values.IDPTriggers(blocks=None)

An adapter class making it easier to instantiate flows_sdk.flows.Triggers for IDP flows. In particular, provides defaults for some fields (e.g. title, description, reference_name) and defines api_params_manifest with IDP specific parameters.

Parameters:

blocks (Sequence[IOBlock] | None) – flows_sdk.blocks.IOBlock s to be included as triggers for an IDP Flow. Optional, defaults to an empty list.

Example usage when instantiating an IDP flow:

folder_listener = IOBlock(...)
flow = Flow(
    triggers=IDPTriggers([folder_listener]),
    ...
)

class flows_sdk.implementations.idp_v42.idp_values.IDPWrapperManifest(flow_identifier)

An adapter class making it easier to instantiate flows_sdk.flows.Manifest for IDP wrapper flows. In particular, provides defaults for some fields (e.g. roles) but more importantly, defines all flows_sdk.flows.Parameter that construct the Flow inputs and their UI representation (left-hand side menu in Flow studio).

Parameters:

flow_identifier (str) – A system-wide unique identifier for flows_sdk.flows.Flow

Example usage when instantiating an IDP flow:

flow = Flow(
    manifest=IDPWrapperManifest(flow_identifier='FLOW_IDENTIFIER'),
    ...
)

class flows_sdk.implementations.idp_v42.idp_values.IDPCoreManifest(flow_identifier, roles=None)

An adapter class making it easier to instantiate flows_sdk.flows.Manifest for IDP Core flows. In particular, provides defaults for some fields (e.g. roles, output) but more importantly, defines all flows_sdk.flows.Parameter that construct the Flow inputs and their UI representation (left-hand side menu in Flow studio).

Parameters:

• flow_identifier (str) – A system-wide unique identifier for flows_sdk.flows.Flow

• roles (list[str] | None) – ‘read_only’, ‘supporting’ and ‘idp_core’ are the default roles of every subflow that can replace the default IDP_CORE subflow inside the top-level IDP flows. ‘supporting’ and ‘idp_core’ are mandatory roles that are necessary for all IDP Core flows. ‘supporting’ ensures that the subflow will be auto-deployed together with the top-level IDP flow. ‘idp_core’ ensures that this subflow can be selected in the UI in dropdowns which allow picking an alternative subflow to the default IDP_CORE subflow. If you are implementing a subflow that is not supposed to be a drop-in replacement for IDP_CORE, you will have to subclass or implement your own version of IDPCoreManifest class in order to remove the idp_core role.

Example usage when instantiating an IDP flow:

flow = Flow(
    manifest=IDPCoreManifest(flow_identifier='FLOW_IDENTIFIER'),
    ...
)

V41

Processing Blocks (v41)

Core Blocks are powerful processing blocks necessary to build intelligent document processing solutions on Hyperscience. They include both manual and machine processing blocks.

Note

Classes in the idp_v41 implementation have parameters with ambiguous typing. For example, a field of type integer could also be represented as a string reference (e.g. that integer gets provided at runtime). The types introduced in the example are therefore incomplete and are likely to be revisioned in a future version.

class flows_sdk.implementations.idp_v41.idp_blocks.IDPCoreBlock(idp_wf_config, reference_name='__idp_core__', title='Start Document Processing Subflow', description='Calls the Document Processing Subflow with the specified settings.', compatibility_spec=CompatibilitySpec(filter_roles=['idp_core'], filter_schema=None, label=None), identifier='IDP_CORE_V41', additional_inputs=None)

Bases: Block

IDP core block includes the steps to process the passed documents into machine-readable output.

Mandatory parameters:

Parameters:

idp_wf_config (IdpWorkflowConfig) – IdpWorkflowConfig object with all flow-level configuration settings for IDP

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique reference name within a flow, a unique one will be generated when not provided

• compatibility_spec (CompatibilitySpec | None) – defines a filter for flows that could be selected in the UI in place of “IDP Core” for the block. Defaults to flows with the role ‘idp_core’. The user will be able to pick any flow that matches the compatibility_spec. This serves to make upgrading to a new compatible version of “IDP Core” (e.g. IDP_CORE_V39) easier in future releases.

• identifier (str) – The identifier of the flow. Defaults to the IDP_CORE_VXX identifier of the out-of-the-box flow that comes with the system.

• additional_inputs (dict[str, Any] | None) – Dictionary containing key-value pairs that will be added to the inputs of the block. If a key is contained in the default inputs, its value will be overwritten by the value provided here. You generally do not need to provide additional_inputs, unless you are creating a custom derivative of the IDP_CODE_Vxx flow and are modifying or extending the list of inputs it accepts.

Example usage:

idp_core = IDPCoreBlock(idp_wf_config=idp_wf_config)

REFERENCE_NAME = '__idp_core__'

class flows_sdk.implementations.idp_v41.idp_blocks.SubmissionBootstrapBlock(reference_name=None, trigger='${workflow.input.$trigger}', layout_release_uuid='${workflow.input.layout_release_uuid}', s3_config='${workflow.input.submission_bootstrap_s3_config}', s3_endpoint_url='${workflow.input.submission_bootstrap_s3_endpoint_url}', ocs_config='${workflow.input.submission_bootstrap_ocs_config}', http_config='${workflow.input.submission_bootstrap_http_config}', abs_auth_type='${workflow.input.submission_bootstrap_abs_auth_type}', abs_account_url='${workflow.input.submission_bootstrap_abs_account_url}', abs_tenant_id='${workflow.input.submission_bootstrap_abs_tenant_id}', abs_client_id='${workflow.input.submission_bootstrap_abs_client_id}', abs_client_secret='${workflow.input.submission_bootstrap_abs_client_secret}', abs_authority_host='${workflow.input.submission_bootstrap_abs_authority_host}', abs_account_name='${workflow.input.submission_bootstrap_abs_account_name}', abs_account_key='${workflow.input.submission_bootstrap_abs_account_key}', notification_workflow='${workflow.input.notification_workflow_initialization}', workflow_uuid='${workflow.input.workflow_uuid}', workflow_name='${workflow.input.workflow_name}', workflow_version='${workflow.input.workflow_version}', submission='${workflow.input.submission}')

Bases: Block

Submission bootstrap block initializes the submission object and prepares external images or other submission data if needed. This block should be called at the START of every flow that uses blocks included in the library.

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• trigger (str) – the trigger input from a connector - if not set explicitly, it will default to the workflow input trigger (which is where this value is normally found)

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• s3_config (str) – Specify Amazon S3 credentials for submission retrieval store. Expects a json expression: {"aws_access_key_id": "X", "aws_secret_access_key": "Y"}, defaults to workflow_input(Settings.SubmissionBootstrapS3Config)

• s3_endpoint_url (str) – Endpoint URL for S3 submission retrieval store, defaults to workflow_input(Settings.SubmissionBootstrapS3EndpointUrl)

• ocs_config (str) – OCS Configuration for downloading submission data, defaults to workflow_input(Settings.SubmissionBootstrapOCSConfig)

• http_config (str | None) – An optional system secret field expressing the HTTP Configuration for downloading submission data. The content is expected to be a json formatted as: {"username": "X", "password": "Y", "ssl_cert": "CA bundle filename"}, defaults to workflow_input(Settings.SubmissionBootstrapHTTPConfig)

• notification_workflow (str | None) – Notification flow name to run when the submission is initialized, defaults to workflow_input(Settings.SubmissionBootstrapNotificationWorkflow)

• workflow_uuid (str) – UUID of the triggered workflow version, defaults to workflow_input(Inputs.WorkflowUuid)

• workflow_name (str) – Name of the triggered workflow, defaults to workflow_input(Inputs.WorkflowName)

• workflow_version (str) – Version of the triggered workflow, defaults to workflow_input(Inputs.WorkflowVersion)

• submission (Any) – Submission object

Example usage:

submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')

IDENTIFIER = 'SUBMISSION_BOOTSTRAP_2'

output(key=None)

Submission bootstrap ends with a CodeBlock, which nests the actual outputs under the ‘result’ key. This method overrides the default output in the same manner that flows_sdk.blocks.Routing.CodeBlock does for convenience.

Parameters:

key (str | None) –

Optionally provide a key to directly get a nested property. When not provided, the entire ‘result’ will be returned. For example, we have:

{
    "result": {
        "a": {
            "b": 42
        }
    }
}

• skipping the key output()` will return ``{"a": {"b": 42}}

• calling with output("a") will result in {"b": 42}

• calling with output("a.b") will return 42

Return type:

str

Returns:

the value under the provided key

class flows_sdk.implementations.idp_v41.idp_blocks.MachineCollationBlock(submission, cases, dedupe_files='${workflow.input.machine_collation_dedupe_files}', refresh_retention_period='${workflow.input.machine_collation_refresh_retention_period}', remove_from_cases='${workflow.input.machine_collation_remove_from_cases}', retention_period='${workflow.input.machine_collation_retention_period}', reference_name=None)

Bases: Block

Machine collation block groups files, documents and pages (from the submission) into cases.

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• cases (Any) –

  This parameter accepts an array of JSON objects that contain information on how Cases should be created. There are two ways to collate a case: by the filenames specified in the current Submission, or by the ids of the Documents or Pages submitted in previous submissions.

  This parameter follows the given format:

  {
      'cases': [
          'external_case_id': 'HS-1',
          'filename': 'file1.pdf',
          'documents': [42],
          'pages': [43]
      ]
  }
  

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• dedupe_files (bool | str) – Enabling this setting will replace case data from repeated file names within the same case. Cases will retain data from the most recently submitted version of a particular file. Note that this setting does not delete the old data, it just removes it from the case. Keep in mind this option is only relevant when adding to cases. Defaults to workflow_input(Settings.CollationDedupeFiles)

• remove_from_cases (Any | None) –

  This parameter accepts an array of JSON objects that contains information on how documents or pages should be removed from a pre-existing case. Currently, the only way to de-collate from a case is by the ids of the Documents or Pages within a given case, defaults to workflow_input(Settings.CollationRemoveFromCases)

  This parameter follows the given format:

  {
      'remove_from_cases': [
          'external_case_id': 'HS-1',
          'documents': [42],
          'pages': [43]
      ]
  }
  

• retention_period (int | str | None) – The number of days to retain cases after they are updated by this block. If passed None, no changes to the deletion date will be made. Defaults to workflow_input(Settings.CollationRetentionPeriod)

• refresh_retention_period (bool | str) – If True, (re)applies Retention Period parameter to all cases passed into the block. If False, only updates those without a pre-existing deletion date. Defaults to workflow_input(Settings.CollationRefreshRetentionPeriod)

Example usage:

machine_collation = MachineCollationBlock(
    reference_name='machine_collation',
    submission=submission_bootstrap.output('result.submission'),
    cases=submission_bootstrap.output('result.api_params.cases'),
    dedupe_files=True,
    remove_from_cases=custom_supervision.output('remove_from_cases')
)

IDENTIFIER = 'MACHINE_COLLATION_2'

class flows_sdk.implementations.idp_v41.idp_blocks.MachineClassificationBlock(submission, api_params, reference_name=None, rotation_correction_enabled='${workflow.input.machine_classification_rotation_correction_enabled}', mobile_processing_enabled='${workflow.input.machine_classification_mobile_processing_enabled}', file_transcription_enabled='${workflow.input.machine_classification_file_transcription_enabled}', layout_release_uuid='${workflow.input.layout_release_uuid}', vpc_registration_threshold='${workflow.input.structured_layout_match_threshold}', nlc_enabled='${workflow.input.semi_structured_classification}', nlc_target_accuracy='${workflow.input.semi_target_accuracy}', nlc_doc_grouping_logic='${workflow.input.semi_doc_grouping_logic}', layout_id_dealbreaker='${workflow.input.layout_id_dealbreaker}', layout_id_dealbreaker_bypass='${workflow.input.layout_id_dealbreaker_bypass}', manual_nlc_enabled='${workflow.input.manual_nlc_enabled}')

Bases: Block

Machine classification block automatically matches documents to structured, semi-structured or additional layouts

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• rotation_correction_enabled (bool | str) – Identifies and corrects the orientation of semi-structured images, defaults to workflow_input(Settings.RotationCorrectionEnabled)

• mobile_processing_enabled (bool | str) – Improves the machine readability of photo captured documents, defaults to workflow_input(Settings.MobileProcessingEnabled)

• file_transcription_enabled (bool | str) – Improves the transcription speed of pdf files. Make sure that the “Image Correction” option is disabled and the pdfs you submit do not contain any rotated images defaults to workflow_input(Settings.FileTranscriptionEnabled)

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• vpc_registration_threshold (str) – Structured pages above this threshold will be automatically matched to a layout variation, defaults to workflow_input(Settings.StructuredLayoutMatchThreshold)

• manual_nlc_enabled (str) – Available from V41.2; enables manual classification when applicable, defaults to workflow_input(Settings.ManualNlcEnabled)

• nlc_enabled (str) – Enables workflow to manage a model for automated classification of semi-structured and additional layout variations, defaults to workflow_input(Settings.SemiStructuredClassification)

• nlc_target_accuracy (str) – defaults to workflow_input(Settings.SemiTargetAccuracy)

• nlc_doc_grouping_logic (str) – Logic to handle multiple pages matched to the same layout variationin a given submission, defaults to workflow_input(Settings.SemiDocGroupingLogic)

Example usage:

machine_classification = MachineClassificationBlock(
    reference_name='machine_classification',
    submission=case_collation.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    rotation_correction_enabled=idp_wf_config.rotation_correction_enabled,
)

IDENTIFIER = 'MACHINE_CLASSIFICATION_7'

class flows_sdk.implementations.idp_v41.idp_blocks.ManualClassificationBlock(submission, api_params, reference_name=None, layout_release_uuid='${workflow.input.layout_release_uuid}', manual_nlc_enabled='${workflow.input.manual_nlc_enabled}', task_restrictions='${workflow.input.manual_classification_task_restrictions}', notification_workflow='${workflow.input.notification_workflow_classification}', vpc_registration_threshold='${workflow.input.structured_layout_match_threshold}')

Bases: Block

Manual classification block allows keyers to manually match submissions to their layouts. Keyers may perform manual classification if machine classification cannot automatically match a submission to a layout with high confidence

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• manual_nlc_enabled (str) – Enables manual classification when applicable, defaults to workflow_input(Settings.ManualNlcEnabled)

• task_restrictions (list[Any] | str | None) – Defines what users can access Supervision tasks created by this block, defaults to workflow_input(Settings.ManualClassificationTaskRestrictions)

• notification_workflow (str) – Notification flow name to run when the submission enters Manual Classification, defaults to workflow_input(Settings.ManualClassificationNotificationWorkflow)

• nlc_enabled – Enables workflow to manage a model for automated classification of semi-structured and additional layout variations, defaults to workflow_input(Settings.SemiStructuredClassification)

• nlc_target_accuracy – Classification Target Accuracy, defaults to workflow_input(Settings.SemiTargetAccuracy)

• mobile_processing_enabled – Improves the machine readability of photo captured document, defaults to workflow_input(Settings.MobileProcessingEnabled)

• vpc_registration_threshold (str | None) – Structured pages above this threshold will be automatically matched to a layout variation when performing light vpc registration in the doc-org task, defaults to workflow_input(Settings.StructuredLayoutMatchThreshold)

Example usage:

manual_classification = ManualClassificationBlock(
    reference_name='manual_classification',
    submission=machine_classification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
)

IDENTIFIER = 'MANUAL_CLASSIFICATION_5'

class flows_sdk.implementations.idp_v41.idp_blocks.MachineIdentificationBlock(submission, api_params, layout_release_uuid='${workflow.input.layout_release_uuid}', reference_name=None, manual_field_id_enabled='${workflow.input.manual_field_id_enabled}', force_required_missing_entries_to_sv='${workflow.input.force_required_entries_to_sv}', field_id_target_accuracy='${workflow.input.field_id_target_accuracy}', table_id_target_accuracy='${workflow.input.table_id_target_accuracy}', id_target_accuracies='${workflow.input.identification_target_accuracies}')

Bases: Block

Machine identification automatically identify fields and tables in the submission

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• layout_release_uuid (Any) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• manual_field_id_enabled (str) – Enables manual identification when applicable, defaults to workflow_input(Settings.ManualFieldIdEnabled)

• force_required_missing_entries_to_sv (str) – defaults to workflow_input( Settings.ForceRequiredEntriesToSV )

• field_id_target_accuracy (str) – Field ID Target Accuracy, defaults to workflow_input(Settings.FieldIdTargetAccuracy)

• table_id_target_accuracy (str) – Table ID Target Accuracy, defaults to workflow_input(Settings.TableIdTargetAccuracy)

• id_target_accuracies (str) – defaults to workflow_input( Settings.IdentificationTargetAccuracies )

Example usage:

machine_identification = MachineIdentificationBlock(
    reference_name='machine_identification',
    submission=manual_classification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
)

IDENTIFIER = 'MACHINE_IDENTIFICATION_8'

class flows_sdk.implementations.idp_v41.idp_blocks.ManualIdentificationBlock(submission, api_params, reference_name=None, task_restrictions='${workflow.input.manual_identification_task_restrictions}', manual_field_id_enabled='${workflow.input.manual_field_id_enabled}', layout_release_uuid='${workflow.input.layout_release_uuid}', notification_workflow='${workflow.input.notification_workflow_identification}')

Bases: Block

Manual identification allows keyers to complete field identification or table identification tasks, where they draw bounding boxes around the contents of certain fields, table columns or table rows. This identification process ensures that the system will be able to transcribe the correct content in the upcoming transcription process

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• task_restrictions (list[Any] | str | None) – Defines what users can access Supervision tasks created by this block, defaults to workflow_input(Settings.ManualFieldIdTaskRestrictions)

• manual_field_id_enabled (str) – Enables manual identification when applicable, defaults to workflow_input(Settings.ManualFieldIdEnabled)

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• notification_workflow (str) – Notification flow name to run when the submission enters Manual Identification, defaults to workflow_inputSettings.ManualFieldIdNotificationWorkflow

Example usage:

manual_identification = ManualIdentificationBlock(
    reference_name='manual_identification',
    submission=machine_identification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    task_restrictions=idp_wf_config.manual_identification_config.task_restrictions,
)

IDENTIFIER = 'MANUAL_IDENTIFICATION_5'

class flows_sdk.implementations.idp_v41.idp_blocks.MachineTranscriptionBlock(submission, api_params, reference_name=None, layout_release_uuid='${workflow.input.layout_release_uuid}', transcription_model='${workflow.input.transcription_model}', finetuning_only_trained_layouts='${workflow.input.finetuning_only_trained_layouts}', force_normalization_errors_to_sv='${workflow.input.force_normalization_errors_to_sv}', force_required_blank_entries_to_sv='${workflow.input.force_required_entries_to_sv}', transcription_overrides='${workflow.input.transcription_overrides}', structured_text_target_accuracy='${workflow.input.structured_text_target_accuracy}', structured_text_confidence_threshold='${workflow.input.structured_text_threshold}', structured_text_acceptable_confidence='${workflow.input.structured_min_leg_threshold}', semi_structured_text_target_accuracy='${workflow.input.semi_structured_text_target_accuracy}', semi_structured_text_confidence_threshold='${workflow.input.semi_structured_text_threshold}', semi_structured_text_acceptable_confidence='${workflow.input.semi_structured_min_leg_threshold}', semi_structured_table_target_accuracy='${workflow.input.semi_structured_table_target_accuracy}', semi_structured_table_confidence_threshold='${workflow.input.semi_structured_table_threshold}', semi_structured_table_acceptable_confidence='${workflow.input.semi_structured_table_min_leg_threshold}', structured_checkbox_target_accuracy='${workflow.input.structured_checkbox_target_accuracy}', structured_checkbox_confidence_threshold='${workflow.input.structured_checkbox_threshold}', structured_checkbox_acceptable_confidence='${workflow.input.checkbox_min_leg_threshold}', structured_signature_target_accuracy='${workflow.input.structured_signature_target_accuracy}', structured_signature_confidence_threshold='${workflow.input.structured_signature_threshold}', structured_signature_acceptable_confidence='${workflow.input.signature_min_leg_threshold}', semi_structured_checkbox_target_accuracy='${workflow.input.semi_structured_checkbox_target_accuracy}', semi_structured_checkbox_confidence_threshold='${workflow.input.semi_structured_checkbox_threshold}', semi_structured_checkbox_acceptable_confidence='${workflow.input.semi_structured_checkbox_min_leg_threshold}', field_target_accuracies='${workflow.input.field_transcription_target_accuracies}', column_target_accuracies='${workflow.input.column_transcription_target_accuracies}', always_supervise_blank_cells='${workflow.input.manual_transcription_always_supervise_blank_cells}', manual_transcription_enabled='${workflow.input.manual_transcription_enabled}')

Bases: Block

Machine transcription automatically transcribes the content of your submission

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• transcription_model (str) – The transcription model that will be used for this flow, defaults to workflow_input(Settings.TranscriptionModel)

• finetuning_only_trained_layouts (str) – defaults to workflow_input( Settings.FinetuningOnlyTrainedLayouts )

• force_normalization_errors_to_sv (str) – defaults to workflow_input( Settings.ForceNormalizationErrorsToSV )

• force_required_blank_entries_to_sv (str) – defaults to workflow_input( Settings.ForceRequiredEntriesToSV )

• transcription_overrides (str) – defaults to workflow_input( Settings.TranscriptionOverrides )

• structured_text_target_accuracy (str) – defaults to workflow_input( Settings.StructuredTextTargetAccuracy )

• structured_text_confidence_threshold (str) – defaults to workflow_input( Settings.StructuredTextThreshold )

• structured_text_acceptable_confidence (str) – defaults to workflow_input( Settings.StructuredTextMinLegThreshold )

• semi_structured_text_target_accuracy (str) – defaults to workflow_input( Settings.SemiStructuredTextTargetAccuracy )

• semi_structured_text_confidence_threshold (str) – defaults to workflow_input( Settings.SemiStructuredTextThreshold )

• semi_structured_text_acceptable_confidence (str) – defaults to workflow_input( Settings.SemiStructuredTextMinLegThreshold )

• structured_checkbox_target_accuracy (str) – defaults to workflow_input( Settings.StructuredCheckboxTargetAccuracy )

• structured_checkbox_confidence_threshold (str) – defaults to workflow_input( Settings.StructuredCheckboxThreshold )

• structured_checkbox_acceptable_confidence (str) – defaults to workflow_input( Settings.StructuredCheckboxMinLegThreshold )

• structured_signature_target_accuracy (str) – defaults to workflow_input( Settings.StructuredSignatureTargetAccuracy )

• structured_signature_confidence_threshold (str) – defaults to workflow_input( Settings.StructuredSignatureThreshold )

• structured_signature_acceptable_confidence (str) – defaults to workflow_input( Settings.StructuredSignatureMinLegThreshold )

• semi_structured_checkbox_target_accuracy (str) – defaults to workflow_input( Settings.SemiStructuredCheckboxTargetAccuracy )

• semi_structured_checkbox_confidence_threshold (str) – defaults to workflow_input( Settings.SemiStructuredCheckboxThreshold )

• semi_structured_checkbox_acceptable_confidence (str) – defaults to workflow_input( Settings.SemiStructuredCheckboxMinLegThreshold )

• field_target_accuracies (str) – defaults to workflow_input( Settings.FieldTargetAccuracies )

• column_target_accuracies (str) – Deprecated since R40.1 and will be removed in R43 defaults to workflow_input( Settings.ColumnTargetAccuracies )

• always_supervise_blank_cells (str | bool | None) – Available from V41.2; if enabled, always send blank cells in transcribed tables to be supervised manually, regardless of confidence defaults to workflow_input(Settings.ManualTranscriptionAlwaysSuperviseBlankCells)

• manual_transcription_enabled (str | None) – Available from V41.2; enables manual transcription when applicable, defaults to workflow_input(Settings.ManualTranscriptionEnabled)

Example usage:

machine_transcription = MachineTranscriptionBlock(
    reference_name='machine_transcription',
    submission=manual_identification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
)

IDENTIFIER = 'MACHINE_TRANSCRIPTION_9'

class flows_sdk.implementations.idp_v41.idp_blocks.ManualTranscriptionBlock(submission, api_params, reference_name=None, supervision_transcription_masking='${workflow.input.manual_transcription_supervision_transcription_masking}', table_output_manual_review='${workflow.input.manual_transcription_table_output_manual_review}', always_supervise_blank_cells='${workflow.input.manual_transcription_always_supervise_blank_cells}', task_restrictions='${workflow.input.manual_transcription_task_restrictions}', manual_transcription_enabled='${workflow.input.manual_transcription_enabled}', notification_workflow='${workflow.input.notification_workflow_transcription}', title='Manual Transcription', description='Manually transcribe fields that could not be automatically transcribed')

Bases: Block

Manual transcription lets your keyers manually enter the text found in fields or tables that could not be automatically transcribed

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (bool | str) – Prevents users from inputting invalid characters during Supervision Transcription tasks, defaults to workflow_input(Settings.ManualTranscriptionSupervisionTranscriptionMasking)

• table_output_manual_review (bool | str) – Always generates a table transcription task if the layout contains a table. If disabled, a table transcription task will only be generated if one or more cells have transcribed values below the defined thresholds, defaults to workflow_input(Settings.ManualTranscriptionTableOutputManualReview)

• always_supervise_blank_cells (bool | str) – If enabled, always send blank cells in transcribed tables to be supervised manually, regardless of confidence defaults to workflow_input(Settings.ManualTranscriptionAlwaysSuperviseBlankCells)

• task_restrictions (list[Any] | str | None) – Defines what users can access Supervision tasks for submission from a particular source or submissions matching a specific layout, defaults to workflow_input(Settings.ManualTranscriptionTaskRestrictions)

• manual_transcription_enabled (str) – Enables manual transcription when applicable, defaults to workflow_input(Settings.ManualTranscriptionEnabled)

• notification_workflow (str) – Notification flow name to run when the submission enters Manual Transcription, defaults to workflow_input(Settings.ManualTranscriptionNotificationWorkflow)

Example usage:

manual_transcription = ManualTranscriptionBlock(
    reference_name='manual_transcription',
    submission=machine_transcription.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    supervision_transcription_masking=(
        idp_wf_config.manual_transcription_config.supervision_transcription_masking
    ),
    table_output_manual_review=(
        idp_wf_config.manual_transcription_config.table_output_manual_review
    ),
    task_restrictions=idp_wf_config.manual_transcription_config.task_restrictions,
)

IDENTIFIER = 'MANUAL_TRANSCRIPTION_5'

class flows_sdk.implementations.idp_v41.idp_blocks.FlexibleExtractionBlock(submission, api_params, reference_name=None, supervision_transcription_masking='${workflow.input.flexible_extraction_supervision_transcription_masking}', task_restrictions='${workflow.input.flexible_extraction_task_restrictions}', layout_release_uuid='${workflow.input.layout_release_uuid}', notification_workflow='${workflow.input.notification_workflow_flexible_extraction}')

Bases: Block

Flexible extraction manually transcribes fields marked for review

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (bool | str | None) – Prevents users from inputting invalid characters during the Flexible Extraction task, defaults to workflow_input(Settings.FlexibleExtractionSupervisionTranscriptionMasking)

• task_restrictions (list[Any] | str | None) – Defines what users can access Supervision tasks created by this block, defaults workflow_input(Settings.FlexibleExtractionTaskRestrictions)

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• notification_workflow (str) – Notification flow name to run when the submission enters Flexible Extraction, defaults to workflow_input(Settings.FlexibleExtractionNotificationWorkflow)

Example usage:

flexible_extraction = FlexibleExtractionBlock(
    reference_name='flexible_extraction',
    submission=manual_transcription.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    supervision_transcription_masking=(
        idp_wf_config.flexible_extraction_config.supervision_transcription_masking
    ),
    task_restrictions=idp_wf_config.flexible_extraction_config.task_restrictions,
)

IDENTIFIER = 'FLEXIBLE_EXTRACTION_4'

class flows_sdk.implementations.idp_v41.idp_blocks.ReprocessingBlock(reference_name, submission, parent_flow='${workflow.definition.identifier}', idp_wf_config='${workflow.input}', trigger='${workflow.input.$trigger}', reprocessing_enabled='${workflow.input.reprocessing_enabled}')

Bases: Block

Reprocessing block enables a second extraction attempt if documents were initially confidently mis-classified by the machine. When an end-user specifies that a document layout is incorrect during Manual Identification, Manual Transcription, or Flexible Extraction, the flow will reach the Reprocessing block which will mark correctly classified documents as “complete” status. Misclassified documents with “reprocess” status will be marked with status “reprocessing” and processed in a second execution of the flow. This block should be placed before the SubmissionCompleteBlock to enable reprocessing.

How the Reprocessing block works

The Reprocessing block examines whether any documents within the submission have been marked with an incorrect layout, and if so, redirects the submission to the classification step for proper classification. It does this by recursively calling the same flow or subflow with the same inputs and documents, but with the addition of a status key for each document to denote which were marked with an incorrect layout during supervision.

Note

The Reprocessing block works in tandem with the “Mark Incorrect Layout” option presented to users during Manual Identification, Manual Transcription, and Flexible Extraction supervision interfaces introduced in R38. Include the Reprocessing block in all custom flows in order to take advantage of this feature. The feature will remain selectable in supervision interfaces even if the Reprocessing block is not included in the flow.

The initial flow run will pause execution at the Reprocessing block until the recursive flow has completed, at which point the initial flow will resume to finish its execution. If an error occurs during the execution of the recursive flow, that error will be raised to the Reprocessing block of the initial flow.

Note

The recursive flow run is linked with the same correlation_id as the initial flow run and the submission. Users can click the “View related flow runs” link on the left sidebar of the initial flow run to view any recursive flow runs.

As expected, recursively calling the same flow may cause some blocks to run again, resulting in unnecessary work being performed, or work which doesn’t gracefully handle duplications (non-idempotent blocks). To mitigate this, certain Hyperscience-provided blocks - including Submission Initialization, Machine Collation, and Machine Classification - detect when the submission object already contains the output from its block to avoid re-execution. It’s a best practice to build custom blocks such that they are idempotent and reentrant; if that’s not feasible, you can add similar logic to custom blocks which do not depend on the layout for the documents being processed. Common methods for handling this include:

• Iterating through each status key within the submission.document[] collection to see if any documents are set to “reprocess” or “reprocessing”.

• Inspecting if the submission object already contains a key or value explicitly defined by the custom code block.

Recommended Placement for the Reprocessing Block

• The Reprocessing block should not be placed before any supervision blocks which allow users to mark a document with an incorrect layout. These supervision blocks include Manual Identification, Manual Transcription and Flexible Extraction.

• The Reprocessing block should always be placed before the SubmissionCompete Block.

• The Reprocessing block can be used with flows which include multiple branches and conditionals; however, it’s recommended to ensure the Reprocessing block is executed to honor user’s selection of marking layouts incorrectly when supervising documents.

• In general, any custom logic that is to be performed after data has been extracted from documents should be placed after the Reprocessing block. To avoid duplicate executions of this custom logic, it is recommended to separate the document processing blocks - inclusive of the Reprocessing block - from the custom logic. This can be handled in different ways:

  • Split the flow into separate subflows, where the Reprocessing block is in a subflow that does not contain the custom logic.

  • Add a decision to bypass the custom logic if the Reprocessing block was triggered. With this method, the recursive flow run will execute the custom logic, and the initial flow run will bypass the logic.

Mandatory parameters:

Parameters:

submission (Any) – Submission object

IDENTIFIER = 'REPROCESSING_2'

class flows_sdk.implementations.idp_v41.idp_blocks.SubmissionCompleteBlock(submission, is_reprocessing=False, reference_name=None, nlc_qa_sampling_ratio='${workflow.input.semi_qa_sample_rate}', field_id_qa_enabled='${workflow.input.field_id_qa_enabled}', field_id_qa_sampling_ratio='${workflow.input.field_id_qa_sample_rate}', table_id_qa_enabled='${workflow.input.table_id_qa_enabled}', table_id_qa_sampling_ratio='${workflow.input.table_id_qa_sample_rate}', transcription_qa_enabled='${workflow.input.qa}', transcription_structured_entry_qa_sample_rate='${workflow.input.structured_entry_qa_sample_rate}', transcription_structured_signature_qa_sample_rate='${workflow.input.structured_signature_qa_sample_rate}', transcription_structured_checkbox_qa_sample_rate='${workflow.input.structured_checkbox_qa_sample_rate}', transcription_semi_structured_qa_sample_rate='${workflow.input.semi_structured_qa_sample_rate}', table_cell_transcription_qa_enabled='${workflow.input.table_cell_transcription_qa_enabled}', table_cell_transcription_qa_sample_rate='${workflow.input.table_cell_transcription_qa_sample_rate}', auto_qa_sampling_rate_enabled='${workflow.input.auto_qa_sampling_rate_enabled}', exclude_auto_transcribe_fields_from_qa='${workflow.input.exclude_auto_transcribe_fields_from_qa}', payload=None)

Bases: Block

Submission complete block finalizes submission processing and updates reporting data. This block should be called at the END of every flow that uses blocks included in the flows_sdk.implementations.idp_v34 library. The block requires both the submission object and a payload to be passed in.

Mandatory parameters:

Parameters:

• submission (str) – Submission object

• payload (Any) – Object to pass to downstream systems

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• nlc_qa_sampling_ratio (str | int) – Defines the percentage of documents sampled for Classification QA, defaults to workflow_input(Settings.SemiQaSampleRate)

• field_id_qa_enabled (str | bool) – defaults to workflow_input(Settings.FieldIdQaEnabled)

• field_id_qa_sampling_ratio (str | int) – Defines the percentage of documents sampled for Identification QA, defaults to workflow_input(Settings.FieldIdQaSampleRate)

• table_id_qa_enabled (str | bool) – Allows users to verify the location of table cells, defaults to workflow_input(Settings.TableIdQaEnabled)

• table_id_qa_sampling_ratio (str | int) – Defines the percentage of tables the system samples for QA, defaults to workflow_input(Settings.TableIdQaSampleRate)

• transcription_qa_enabled (str | bool) – defaults to workflow_input(Settings.TranscriptionQaEnabled)

• transcription_qa_sampling_ratio – Defines the percentage of fields sampled for Transcription QA, defaults to workflow_input(Settings.TranscriptionQaSampleRate)

• table_cell_transcription_qa_enabled (str | bool) – defaults to workflow_input(Settings.TableCellTranscriptionQaEnabled)

• table_cell_transcription_qa_sample_rate (str | int) – Defines the percentage of cells the system samples for QA. This value is likely to be lower than “Transcription QA Sample Rate” since there are more table cells than fields on any given page, defaults to workflow_input(Settings.TableCellTranscriptionQaSampleRate)

• exclude_auto_transcribe_fields_from_qa (str | bool) – Defines fields that are marked as auto-transcribe in layout should be excluded from QA or not. defaults to workflow_input(Settings.ExcludeAutoTranscribeFieldsFromQA)

• is_reprocessing (str | bool) – boolean to handle execution when reprocessing defaults to False

Example usage:

submission_complete = SubmissionCompleteBlock(
    reference_name='complete_submission',
    submission=flexible_extraction.output('submission')
)

IDENTIFIER = 'SUBMISSION_COMPLETE_4'

class flows_sdk.implementations.idp_v41.idp_blocks.SetTransformedOutputBlock(submission, transformed_output, reference_name=None)

Bases: Block

Set Transformed Output block sets the transformed output for the provided submission. This can later be used to send the transformed data downstream using output blocks. For more information see https://docs.hyperscience.ai/#retrieving-transformed-submissions.

Mandatory parameters:

Parameters:

• submission (str) – Submission object

• transformed_output (str) – Object that will be set as the transformed output for this submission

IDENTIFIER = 'SET_TRANSFORMED_OUTPUT'

class flows_sdk.implementations.idp_v41.idp_blocks.IdpCustomSupervisionBlock(submission, task_purpose, supervision_template, data=None, reference_name=None, supervision_transcription_masking=True, task_restrictions=None, page_ids=None, notification_workflow='IDP_SUBMISSION_NOTIFY', chat_flow_identifier=None, title='IDP Custom Supervision', description='IDP Custom Supervision')

Bases: Block

An IDP wrapper for the Custom Supervision block. It has the same functionality, but handles reading/writing data from the IDP database.

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• task_purpose (str) – The custom task name that’s given to all supervision tasks that run through a particular instance of a custom supervision block. This can be used to filter tasks in the task queue.

• supervision_template (Any) – JSON used for configuration of the Custom Supervision Task.

Parameters with defaults:

Parameters:

• data (dict[str, Any] | None) – Optional JSON structure that extends the data used to populate the supervision_template. This is intended to append to the data already gathered by the IDP wrapper. If there are any conflicting keys, the keys in this input will take precedent.

• reference_name (str | None) – Unique identifier within a flow. A unique one will be generated when not provided.

• supervision_transcription_masking (str | bool) – Prevents users from inputting invalid characters during the Custom Supervision task. Defaults to True.

• task_restrictions (str | list[Any] | None) – Defines what users can access Supervision tasks created by this block. Defaults to [].

• page_ids (str | list[int] | None) – If left empty, the IDP wrapper will, by default, pull in all pages in the submission. Adding a list of page IDs will override the default list of pages. Defaults to [].

• notification_workflow (str) – Notification flow name to run when the submission enters Custom Supervision. Defaults to IDP_SUBMISSION_NOTIFY_NAME.

• chat_flow_identifier (str | None) – Available from V41.2; optional chat flow identifier to enable Document Chat.

• title (Optional[str]) – UI-visible title of the block. Defaults to ‘IDP Custom Supervision’.

• description (Optional[str]) – Description of the block. Useful for documentation and shown in Flow Studio. Defaults to ‘IDP Custom Supervision’.

Example usage:

idp_custom_supervision = IdpCustomSupervisionBlock(
    reference_name='idp_custom_supervision',
    submission=manual_transcription.output('submission'),
    task_purpose='make_custom_decision',
    supervision_template=[
        {
            'name': 'three_column_template',
            'version': '1.0',
            'thumbnail': {
                'group_by_document': True,
                'group_by_case': True,
            },
            'action': [
                {
                    'name': 'my_tab',
                    'display': 'SSN Info',
                    'input': [
                        {
                            'name': 'ssn_field',
                            'type': 'transcription',
                            'data_identifier': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                            'title': 'SSN',
                        },
                    ],
                    'ui': {
                        'groups': [
                            {
                                'title': 'My Group',
                                'fields': ['ssn_field'],
                            },
                        ],
                    },
                },
            ],
        },
    ],
    data={
        'documents': [
            {
                'id': 2,
                'layout_uuid': 'e31c3cbf-dcea-44fd-a052-902463c6040f',
                'layout_name': 'layout_name',
                'page_count': 2,
                'read_only': False,
            }
        ]
    },
    supervision_transcription_masking=False,
)

IDENTIFIER = 'IDP_CUSTOM_SUPERVISION_2'

class flows_sdk.implementations.idp_v41.idp_blocks.IDPFullPageTranscriptionBlock(submission, reference_name=None, title='Full Page Transcription (Submission)', description='Transcribes the documents included in a submission', app_metadata=None, language_family=None)

Bases: Block

IDP Full Page Transcription Block machine-transcribes the documents contained in a submission

Mandatory parameters:

Parameters:

submission (str) – submission object that includes the raw documents that need to be transcribed

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• app_metadata (list[str] | None) – used for capturing information about the flow

• language_family (str | None) – language family to use when instantiating the underlying transcription model. If not provided, the system will default to latin. Language families are single word lowercase strings, e.g., latin, arabic, korean, chinese. You can read more about language families in the “Supported Languages” article in the application documentation: https://help.hyperscience.ai/v41/docs/supported-languages.

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# IDPFullPageTranscriptionBlock transcribes the documents contained in the submission object
idp_fpt_block = IDPFullPageTranscriptionBlock(
    reference_name='idp_fpt',
    submission=submission_bootstrap.output(),
)
# reference to the output of IDPFullPageTranscriptionBlock which can be passed as input to
# the next block. For more information on output() and output references, please take a
# look at class Block and its output() method
output_ref = idp_fpt_block.output()

IDENTIFIER = 'FULL_PAGE_TRANSCRIPTION_SUBMISSION_2'

class flows_sdk.implementations.idp_v41.idp_blocks.DocumentRendererBlock(submission, is_reprocessing=False, title='Document Renderer', description='Renders a document into a PDF', reference_name=None, document_rendering_enabled='${workflow.input.document_rendering_enabled}', document_rendering_mime_type='${workflow.input.document_rendering_mime_type}', document_rendering_unit='${workflow.input.document_rendering_unit}', document_rendering_height='${workflow.input.document_rendering_height}', document_rendering_width='${workflow.input.document_rendering_width}', document_rendering_quality='${workflow.input.document_rendering_quality}')

Bases: Block

Document File Generator Block renders a document into a PDF and stores it in the Blob storage.

This block should be placed ALWAYS after a Reprocessing block (if there is any) so that it is not possible to have more changes in the documents’ organisation.

Mandatory parameters:

Parameters:

submission (str) – submission object that includes the raw documents that need to be rendered.

Optional parameters:

Parameters:

• document_rendering_enabled (bool | str) – a boolean that enables or disables the document rendering. Default is False.

• document_rendering_mime_type (str) – the mime type of the document to be generated. Default is ‘application/pdf’

• document_rendering_unit (str) – the unit of the document to be generated. Default is ‘inches’

• document_rendering_height (str | float) – the height of the document to be generated. Default is 11

• document_rendering_width (str | float) – the width of the document to be generated. Default is 8.5

• document_rendering_quality (str | int) – Quality of pages (images) appended to the resulting document as a percentage (recommended 50%). Higher quality results in a larger file size for the generated PDF.

• is_reprocessing (str | bool) – boolean to handle execution when reprocessing defaults to False

Example usage:

# Use the output of the reprocessing block.
reprocessing = ReprocessingBlock(reference_name='reprocessing')

document_renderer_block = DocumentRendererBlock(
    reference_name='document_renderer',
    submission=reprocessing.output(submission),
    document_rendering_enabled=True,
    document_rendering_unit='in',
    document_rendering_width=8.5,
    document_rendering_height=11,
    document_rendering_quality=50,
    is_reprocessing=reprocessing.output('is_reprocessing')
)

output_ref = document_renderer_block.output()

Example content of output_ref:

{
    'document_files': {
        [
            {
                'document_id': "1",
                'blob_uuid': 'f42fc9af-b551-4403-aba6-d0eba2c41973',
                'mime_type': 'application/pdf',
                'height_in_mm': 279.4,
                'width_in_mm': 215.9,
                'quality': 50,
            }
        ]
    },
    'submission': {
        ...
    }
}

IDENTIFIER = 'DOCUMENT_RENDERER'

class flows_sdk.implementations.idp_v41.idp_blocks.IDPImageCorrectionBlock(submission, reference_name=None, rotation_correction_enabled=True, mobile_processing_enabled=False, language_family=None, title='Image Correction (Submission)', description='Rotate and de-skew documents included in a submission', app_metadata=None)

Bases: Block

IDP Image Correction Block rotates and de-skews tilted/skewed images so that they are more transcription-friendly

Mandatory parameters:

Parameters:

submission (str) – submission object that includes the raw documents that need to be image-corrected

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• rotation_correction_enabled (bool) – Identifies and corrects the orientation of images. Enabled by default.

• mobile_processing_enabled (bool) – Improves the machine readability of photo captured documents. Disabled by default.

• language_family (str | None) – language family to use when performing image correction. If not provided, the system will default to latin. Language families are single word lowercase strings, e.g., latin, arabic, korean, chinese. You can read more about language families in the “Supported Languages” article in the application documentation: https://help.hyperscience.ai/v41/docs/supported-languages.

• app_metadata (list[str] | None) – used for capturing information about the flow

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# IDPImageCorrectionBlock takes in the submission object and outputs in the same format with
# its images
idp_image_correct_block = IDPImageCorrectionBlock(
    reference_name='idp_image_correct',
    submission=submission_bootstrap.output(),
)
# reference to the output of IDPImageCorrectionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look at
# class Block and its output() method
output_ref = idp_image_correct_block.output()

IDENTIFIER = 'IMAGE_CORRECTION_SUBMISSION'

Additional Blocks (v41)

class flows_sdk.implementations.idp_v41.additional_blocks.JSONOutputsBlock(inputs, blocks=None)

Bases: Outputs

Output block allows users to send JSON data extracted by an IDP flow to other systems for downstream processing Mandatory parameters:

Parameters:

inputs (dict[str, Any]) – Used by the UI to automatically pre-populate the input of newly added blocks. Extended in this class to include 'enabled': True to visualize an enabled/disabled trigger that is controllable via Flow Studio.

Usage when parameters are to be defined in Flow Studio:

outputs = JSONOutputBlock(
    inputs={'result': }
)

class flows_sdk.implementations.idp_v41.additional_blocks.ImageCorrectionBlock(pages, reference_name=None, rotation_correction_enabled=True, mobile_processing_enabled=False, language_family=None, title='Image Correction (Pages)', description='Rotate and de-skew documents', app_metadata=None)

Bases: Block

Image Correction Block rotates and de-skews tilted/skewed images so that they are more transcription-friendly Mandatory parameters:

Parameters:

pages (str) – list of page object that includes the raw documents that need to be image-corrected

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• rotation_correction_enabled (bool) – Identifies and corrects the orientation of images. Enabled by default.

• mobile_processing_enabled (bool) – Improves the machine readability of photo captured documents. Disabled by default.

• language_family (str | None) – language family to use when performing image correction. If not provided, the system will default to latin. Language families are single word lowercase strings, e.g., latin, arabic, korean, chinese. You can read more about language families in the “Supported Languages” article in the application documentation: https://help.hyperscience.ai/v41/docs/supported-languages.

• app_metadata (list[str] | None) – used for capturing information about the flow

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# ImageCorrectionBlock takes in the submission object and outputs in the same format with
# its images
image_correct_block = ImageCorrectionBlock(
    reference_name='image_correct',
    pages=submission_bootstrap.output(submission.unassigned_pages),
)
# reference to the output of IDPImageCorrectionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look
# at class Block and its output() method
output_ref = image_correct_block.output()

IDENTIFIER = 'IMAGE_CORRECTION_PAGES'

class flows_sdk.implementations.idp_v41.additional_blocks.FullPageTranscriptionBlock(pages, reference_name=None, title='Full Page Transcription (Pages)', description='Transcribes documents', language_family=None, app_metadata=None)

Bases: Block

Full Page Transcription Block machine-transcribes the documents contained in a submission

Mandatory parameters:

Parameters:

pages (str) – list of pages that includes the raw documents that need to be transcribed

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• language_family (str | None) – language family to use when instantiating the underlying transcription model. If not provided, the system will default to latin. Language families are single word lowercase strings, e.g., latin, arabic, korean, chinese. You can read more about language families in the “Supported Languages” article in the application documentation: https://help.hyperscience.ai/v41/docs/supported-languages.

• app_metadata (list[str] | None) – used for capturing information about the flow

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# FullPageTranscriptionBlock transcribes the documents contained in the submission object
fpt_block = FullPageTranscriptionBlock(
    reference_name='fpt',
    submission=submission_bootstrap.output(submission.unassigned_pages),
)
# reference to the output of FullPageTranscriptionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look at
# class Block and its output() method
output_ref = fpt_block.output()

IDENTIFIER = 'FULL_PAGE_TRANSCRIPTION_PAGES_2'

class flows_sdk.implementations.idp_v41.additional_blocks.QASchedulerBlock(flow_identifier, submission, qa_config, is_reprocessing=False)

Bases: Block

Available from V41.2

Schedules Quality Assurance (QA) tasks for a specific workflow.

This class serves as a specialized block within a flow for scheduling Quality Assurance tasks. It ensures the proper configuration and scheduling processes for a given workflow instance. It leverages provided data and parameters such as identification of the workflow, submission details, QA configuration, and optional reprocessing cases.

Mandatory parameters:

Parameters:

• flow_identifier (str) – A unique string identifying the flow to associate this QA scheduler with.

• submission (dict[str, Any] | str) – A dictionary containing submission details. Will be used as payload to trigger the target flow to be QA’d.

• qa_config (dict[str, Any] | str) – A dictionary containing QA configuration. Will be used as payload to trigger the target flow to be QA’d.

Optional parameters:

Parameters:

is_reprocessing (bool | str | None) – An optional value indicating whether this block is part of a reprocessing workflow. Defaults to False.

Example usage:

qa_scheduler = QASchedulerBlock(
    flow_identifier='my_qa_flow',
    submission={'submission_id': 12345, 'data': 'example_data'},
    qa_config={'sample_rate': 0.1, 'language_family': 'latin'},
    is_reprocessing=False,
)

# reference to the output of QASchedulerBlock which can be passed
 as input to the next block.
# For more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = qa_scheduler.output()

IDENTIFIER = 'QA_SCHEDULER'

class flows_sdk.implementations.idp_v41.additional_blocks.FullPageTranscriptionQABlock(submission, sample_rate, language_family, split_text_segments, min_characters_in_segment=4, task_restrictions=None)

Bases: Block

Available from V41.2

Full Page Transcription QA Block performs Quality Assurance on the output of Full Page Transcription Block. It samples a percentage of segments for QA and allows users to split segments into smaller tokens for easier QA.

Mandatory parameters:

Parameters:

• submission (dict[str, Any] | str) – Submission object

• sample_rate (int | str) – Defines the percentage of segments the system samples for Quality Assurance

• language_family (str) – Specifies the language family for the class

• split_text_segments (bool | str) – Enabling this option splits segments into smaller, more manageable tokens for easier QA

Parameters with defaults:

Parameters:

• min_characters_in_segment (integer) – Minimum number of characters in a token. Defaults to 4. Minimum value is 1, maximum is 100.

• task_restrictions (Optional[TaskRestrictions]) – Defines what users can access tasks created by this block. Defaults to None, which is treated as an empty list.

Example usage:

full_page_transcription_qa = FullPageTranscriptionQABlock(
    reference_name='full_page_transcription_qa',
    submission=manual_transcription.output('submission'),
    sample_rate=10,
    language_family='latin',
    split_text_segments=True,
    min_characters_in_segment=4,
    task_restrictions=[],
)

# reference to the output of FullPageTranscriptionQABlock which can be passed
as input to the
# next block. For more information on output() and output references, please take a look
# at class Block and its output() method
output_ref = full_page_transcription_qa.output()

IDENTIFIER = 'FULL_PAGE_TRANSCRIPTION_QA'

class flows_sdk.implementations.idp_v41.additional_blocks.CustomSupervisionBlock(submission, task_purpose, data, supervision_template, reference_name=None, supervision_transcription_masking=True, task_restrictions=None, notification_workflow='IDP_SUBMISSION_NOTIFY', chat_flow_identifier=None, title='Custom Supervision', description='Custom Supervision')

Bases: Block

The CustomSupervisionBlock uses a JSON schema as a form builder to create customer-driven UIs for Supervision tasks.

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• task_purpose (str) – the custom task name that’s given to all supervision tasks that run through a particular instance of a custom supervision block. This can be used to filter tasks in the task queue.

• data (Any) – JSON structure that supplies the data used to populate the supervision_template

• supervision_template (Any) – JSON used for configuration of the Custom Supervision Task.

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (bool) – Prevents users from inputting invalid characters during the Custom Supervision task, defaults to True

• task_restrictions (list[Any] | None) – Defines what users can access Supervision tasks created by this block, defaults to []

• notification_workflow (str) – Notification flow name to run when the submission enters Custom Supervision, defaults to IDP_SUBMISSION_NOTIFY_NAME

• chat_flow_identifier (str | None) – Available from V41.2; optional chat flow identifier to be populated to enable Document Chat in Custom Supervision

• title (Optional[str]) – UI-visible title of the block. Defaults to ‘IDP Custom Supervision’

• description (Optional[str]) – Description of the block. Both useful for documentation purpose and visible to users in the Flow studio. Defaults to ‘IDP Custom Supervision’

Example usage:

custom_supervision = CustomSupervisionBlock(
    reference_name='custom_supervision',
    submission=manual_transcription.output('submission'),
    task_purpose='make_custom_decision',
    supervision_template=[
        {
            'name': 'three_column_template',
            'version': '1.0',
            'thumbnail': {
                'group_by_document': True,
                'group_by_case': True,
            },
            'action': [
                {
                    'name': 'my_tab',
                    'display': 'SSN Info',
                    'input': [
                        {
                            'name': 'ssn_field',
                            'type': 'transcription',
                            'data_identifier': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                            'title': 'SSN',
                        },
                    ],
                    'ui': {
                        'groups': [
                            {
                                'title': 'My Group',
                                'fields': ['ssn_field'],
                            },
                        ],
                    },
                },
            ],
        },
    ],
    data={
        'fields': [
            {
                'id': 967,
                'uuid': 'fe8d4e7a-a82c-448c-85a1-0a3cbbe53b61',
                'value': '111-22-3333',
                'page_id': 15,
                'layout_field_id': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                'validation_overridden': False,
                'bounding_box': [
                    0.09360783305186686,
                    0.25792617589433436,
                    0.6358913805295097,
                    0.28862414740388187,
                ],
                'occurrence_index': 1,
            },
        ],
        'template_fields': [
            {
                'uuid': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                'notes': '',
                'type': 'entry',
                'data_type_uuid': 'b600a4e4-758f-4c4b-a2f4-cbb5dd870a0c',
                'name': 'SSN',
            },
        ],
        'pages': [
            {
                'id': 15,
                'file_page_number': 1,
                'document_page_number': 1,
                'image_url': '/image/2ed3d989-b64a-44bd-a9fb-6e0dcc5ccdb8',
                'form_id': 2,
                'submission_id': 1,
                'external_case_ids': [],
                'read_only': False,
                'filename': '1 page.pdf',
                'form_page_id': 1
            },
        ],
        'documents': [
            {
                'id': 2,
                'layout_uuid': 'e31c3cbf-dcea-44fd-a052-902463c6040f',
                'layout_name': 'layout_name',
                'page_count': 2,
                'read_only': False,
            },
        ],
        'cases': [],
        'selected_choices': [],
    },
    supervision_transcription_masking=False,
)

IDENTIFIER = 'CUSTOM_SUPERVISION_2'

class flows_sdk.implementations.idp_v41.additional_blocks.SoapRequestBlock(method, reference_name=None, endpoint='', body_namespace=None, headers_namespace=None, headers=None, params=None, use_wsdl=False, wsdl_uri='', wsdl_service_name='', wsdl_port_name='', title='HTTP REST Block', description='HTTP REST block')

Bases: Block

Soap Block allows user to perform SOAP requests Mandatory parameters:

Parameters:

• method (str) – SOAP method to invoke

• endpoint (str) – absolute url including schema to HTTP endpoint for this request

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• body_namespace (str | None) – SOAP request body namespace url

• headers_namespace (str | None) – SOAP request headers namespace url

• headers (dict[Any, Any] | None) – SOAP headers to use

• params (dict[Any, Any] | None) – key-value pair used as query parameters in the request to be inserted as SOAP body

Example usage:

# minimum viable instantiation, the rest of the fields can later be added in the UI
soap_block = SoapRequestBlock(
    reference_name='soap_request',
    title='Soap Request',
    description='Perform soap request',
    method='ExampleMethod',
)
soap_block = SoapRequestBlock(
    reference_name='soap_request',
    title='Soap Request',
    description='Perform soap request',
    method='ExampleMethod',
    endpoint='http://example.org/abc.wso',
    body_namespace='http://www.example.org/abc.countryinfo',
    params={'example': 'stuff'},
    headers={}
)
# reference to the output of SoapRequestBlock which can be passed as input to the next block
# for more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = soap_block.output()

IDENTIFIER = 'SOAP_REQ'

class flows_sdk.implementations.idp_v41.additional_blocks.HttpRestBlock(method, endpoint, reference_name=None, headers=None, params=None, payload=None, json=None, timeout=600, authorization_type=None, handled_error_codes=None, auth_params=None, title='HTTP REST Block', description='HTTP REST block')

Bases: BaseHttpRestBlock

Http Rest Block allows user to perform HTTP requests Mandatory parameters:

Parameters:

• method (str) – HTTP method to use

• endpoint (str) – absolute url including schema to HTTP endpoint for this request

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• headers (dict[str, str] | None) – HTTP headers to use

• params (dict[str, Any] | None) – key-value pair used as query parameters in the request

• payload (dict[str, str] | None) – key-value pair to be used as x-www-form-urlencoded data. Mutually exclusive with json

• json (dict[str, Any] | None) – key-value pair to be used as json data. Mutually exclusive with payload

• timeout (int | None) – How long (in seconds) to keep the connection open if no bytes are received from the endpoint. Set to 0 to wait indefinitely (not recommended).

• authorization_type (str | None) – authorization type (none, http_header, oauth_2_client_credentials)

• handled_error_codes (list[int | str] | None) – specify HTTP error codes that will not fail the task, allowing for wildcards with the ‘x’ char. Example: [400, “5xx”]

• auth_params (None | HttpHeaderAuthorizationParams | OAuth2AuthorizationParams) – authorization parameters to be passed when the authorization_type is set to “http_header” or “oauth_2_client_credentials”

Example usage:

# minimum viable instantiation, the rest of the fields can later be added in the UI
http_rest_block = HttpRestBlock(
    reference_name='http_request',
    title='Http Request',
    description='Perform http request',
    method='GET',
)
http_rest_block = HttpRestBlock(
    reference_name='http_request',
    title='Http Request',
    description='Perform http request',
    method='GET',
    endpoint='https://sheets.googleapis.com/v4/spreadsheets/example',
)
# reference to the output of HttpRestBlock which can be passed as input to the next block
# for more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = http_rest_block.output()

class flows_sdk.implementations.idp_v41.additional_blocks.HyperscienceRestApiBlock(method, app_endpoint, reference_name=None, headers=None, params=None, payload=None, json=None, handled_error_codes=None, timeout=600, title='Hyperscience HTTP REST Block', description='Hyperscience HTTP REST block')

Bases: BaseHttpRestBlock

Make Http requests to the Hyperscience platform APIs. This block will automatically take care of prepending the base url and will include the appropriate authentication headers with the request. Mandatory parameters:

Parameters:

• method (str) – HTTP method to use

• app_endpoint (str) – relative url, beginning with a forward slash, to an HTTP endpoint served by the Hypersciecne platform. For reference on the available endpoints, check out https://docs.hyperscience.ai/

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• headers (dict[str, str] | None) – HTTP headers to use

• params (dict[str, Any] | None) – key-value pair used as query parameters in the request

• payload (dict[str, str] | None) – key-value pair to be used as x-www-form-urlencoded data. Mutually exclusive with json

• json (dict[str, Any] | None) – key-value pair to be used as json data. Mutually exclusive with payload

• handled_error_codes (list[int | str] | None) – specify HTTP error codes that will not fail the task, allowing for wildcards with the ‘x’ char. Example: [400, “5xx”]

• timeout (int | None) – How long (in seconds) to keep the connection open if no bytes are received from the endpoint. Set to 0 to wait indefinitely (not recommended).

Example usage:

hs_rest_block = HyperscienceRestApiBlock(
    reference_name='http_request',
    app_endpoint='/api/healthcheck',
    method='GET',
)

class flows_sdk.implementations.idp_v41.additional_blocks.DatabaseAccessBlock(reference_name=None, db_type='', database='', host='', username='', password='', query='', port=None, options=None, timeout=None, query_params=None, title='DB Block', description='DB access block')

Bases: Block

Database Block allows user to access and perform queries on the specified database Mandatory parameters:

Parameters:

• db_type (str) – database type (mssql, oracle, postgres)

• host (str) – URL/IP address of the server database is hosted on

• database (str) – database name

• username (str) – database username

• password (str) – database password

• query (str) – parameterized query

Optional parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• port (int | None) – database port number

• options (dict[Any, Any] | None) – dictionary of additional connection string options

• query_params (dict[Any, Any] | None) – dictionary of values for query placeholders

• timeout (int | None) – timeout in seconds (mssql and postgres only)

Example usage:

# minimum viable instantiation, the rest of the fields can later be added in the UI
db_lookup = DatabaseAccessBlock(
    reference_name='db_lookup',
    title='Database Lookup',
    description='Perform database lookup',
)
# note that storing password in plain text form is not recommended
# password can later be entered in the UI or defined as a flow secret.
db_lookup = DatabaseAccessBlock(
    reference_name='db_lookup',
    title='Database Lookup',
    description='Perform database lookup',
    db_type='mssql',
    database='cars',
    host='example.com',
    username='user',
    # we recommend skipping the password field or defining it as a system secret
    # e.g. system_secret('{your_secret_identifier}') (both allow for secret input via. UI)
    password='pw'
    port=1433,
    timeout=200,
    query='SELECT * from CAR',
)
# reference to the output of DBAccessBlock which can be passed as input to the next block
# for more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = db_lookup.output()
# content of output_ref:
{
    "result": [
        {
            "id": 1,
            "brand": "Aston Martin",
            "model": "Vanquish"
        },
        {
            "id": 2,
            "brand": "Jaguar",
            "model": "XE 2018"
        }
    ]
}

Database output for the same query (SELECT * FROM CAR)

IDENTIFIER = 'DB_ACCESS'

class flows_sdk.implementations.idp_v41.additional_blocks.LLMInstallBlock(model_name='ORCA', cloudsmith_key='', user_consent=False, reference_name=None, title='Install LLM/VLM', description='Sets up the necessary LLM or VLM in the HS instance.')

Bases: Block

The LLM Install Block sets up the necessary LLM or VLM in the HS instance. It begins by verifying whether the model is already present and, if not, retrieves it from S3 for SaaS instances or Cloudsmith for on-premises instances.

Mandatory parameters:

Parameters:

• model_name (str) – Name of the LLM model to install (Choose from the available models)

• cloudsmith_key (str) – Cloudsmith API key (Only when running on on-premise instance)

• user_consent (bool) – User consent (The block will fail if set to False)

The block outputs the detailed model name and model UUID of the chosen LLM.

Example usage:

llm_install = LLMInstallBlock(
    model_name='ORCA',
    cloudsmith_key='cloudsmith_key',
    user_consent=True
)

Example output:

{
    "model": "orca",
    "model_uuid": "14554188-cf8e-4f10-9057-d1df2f710072"
}

IDENTIFIER = 'LLM_INSTALL'

class flows_sdk.implementations.idp_v41.additional_blocks.LlamaBlock(model_id, prompt, reference_name=None, consent=False, tokenize_only=False, predict_extra_kwargs=None, title='Llama Text Generation', description='Text generation using the Llama 2 model')

Bases: Block

Text generation using the Llama 2 model by Meta.

Deprecated from version 41.1

This block utilizes Llama 2 by Meta. Llama 2 is licensed under the LLAMA 2 Community License (https://ai.meta.com/llama/license/). By using the Llama Block, you acknowledge that you have received a copy of the LLAMA 2 Community License.

Note: This block is still experimental and not ready for production use.

Mandatory parameters:

Parameters:

• model_id (str) – UUID, identifier of the asset with the ML model. Model assets are not part of the Hypersicence bundle and must first be downloaded and imported into the system before they can be used. Please contact your Hyperscience representative to discuss the usecase and the appropriate model and to get instructions on downloading the models. Once downloaded, here are the available model asset UUIDs: b3da05c7-061c-4a58-af6d-4c905d2e618f - Llama 2, 13B, 8-bit quantized

• prompt (str) – The prompt to generate text from. Use a CodeBlock ahead of the LlamaBlock in order to build the prompt. The context window of the model is limited to 4096 tokens. If the prompt has more tokens, the block would return an error. The total input and output length also cannot exceed 4096 tokens, which means that if the input is too long, the output may be truncated. Also see tokenize_only for a more detailed explanation of how to handle longer texts.

Optional parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• consent (bool | None) – Has the business user read and acknowledged the Llama 2 legal notice. Unless the flow would be used in CI automation, this should be set to False so that a business user can read and acknowledge the notice in the Hyperscience UI. Setting this to True in the Python definition of the flow is also treated as acknowledgement of the notice on behalf of the entity that will be using the flow.

• tokenize_only (bool | None) – In the context of LLMs, tokens correspond roughly to words, partial words and punctuation. A rough way to compute the number of tokens in a text is number_of_tokens = 1.25 * number_of_words. The Llama block works with a context window of 4096 tokens. If a longer prompt is passed in, it would return an error. Also, if the prompt is shorter, but still close to 4096, the output may be truncated. Since the length of the prompt in tokens cannot be known ahead of time, sometimes the flow may need to make dynamic decisions whether to pass the text to the Llama block, or to split it into multiple chunks, or to use some alternative method for processing it (e.g. a custom supervision manual task). The tokenize_only option enables flows to take such dynamic decisions based on the length of the prompt in tokens. The default is false - it instructs the block to run the ML model inference over the prompt, i.e. to generate a text response. When true is passed, the model will simply tokenize the prompt without running inference and return the list of tokens - this will work regardless of the length of the prompt. A subsequent Decision task can then route longer prompts to an alternative path in the flow, while routing shorter prompts to the LlamaBlock for actual text generation.

• predict_extra_kwargs (dict[Any, Any] | None) – Extra keyword arguments used for model inference. (Used to fine-tune the model prediction. Not recommended to change this unless you know what you are doing)

Example usage:

llama = LlamaBlock(
    reference_name='llama',
    model_id='b3da05c7-061c-4a58-af6d-4c905d2e618f',
    prompt='${previous_block.output.llama_prompt}',
)

IDENTIFIER = 'LLAMA'

class flows_sdk.implementations.idp_v41.additional_blocks.EntityRecognitionBlock(pages, regex_map=None, keyword_map=None, enhanced_output=False, ner_enabled=True, ced_enabled=True, signature_enabled=True, reference_name=None, title='Entity Recognition', description='Recognizes entities of different types in documents')

Bases: Block

Entity Recognition Block detects entities of different types within the documents contained in a submission

This block requires the output of the Full Page Transcription Block as input data.

Mandatory parameters:

Parameters:

• pages (str) – list of pages that includes the raw documents with the transcribed information from the FPT block

• regex_map (dict[str, str] | None) – a map that specifies different regex patterns that the block should look for, a default one is present

• keyword_map (dict[str, list[str]] | None) – a map that specifies different keyword patterns that the block should look for, a default one is present

Optional Parameters:

Parameters:

• enhanced_output (bool) – a boolean flag that enables toggling between simple and enhanced output formats. Default value is False.

• ner_enabled (bool) – a boolean flag that enables or disables Named Entity Recognition. Default value is True.

• ced_enabled (bool) – a boolean flag that enables or disables Custom Entity Detection. Default value is True.

• signature_enabled (bool) – a boolean flag that enables or disables Signature Detection. Default value is True.

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')

# EntityRecognitionBlock detects entities within the documents contained in the submission
entity_recognition_block = EntityRecognitionBlock(
    reference_name='entity_recognition',
    pages=submission_bootstrap.output(submission.unassigned_pages),
    regex_map=DEFAULT_PII_REGEX_MAP,
    keyword_map=DEFAULT_PII_KEYWORD_MAP,
    enhanced_output=False,
)

# reference to the output of EntityRecognitionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look at
# class Block and its output() method
output_ref = entity_recognition_block.output()

Example content of output_ref:

{
    "documents": [
        {
            "file_uuid": "da2c6117-43c4-4b30-84c4-cdc59cc3fa4f",
            "entities": [
                {
                    "text": "John Smith",
                    "type": "Person Name",
                    "positions": [
                        [0.5, 0.1, 0.55, 0.13],
                        [0.6, 0.2, 0.65, 0.23],
                        [0.7, 0.3, 0.75, 0.33],
                    ],
                    "page_ids": [0, 0, 1],
                    "origin": "NER",
                },
                {
                    "text": "<SIGNED>",
                    "type": "signature",
                    "positions": [[0.701171875, 0.732421875, 0.92578125, 0.8681640625]],
                    "page_ids": [0],
                    "origin": "Signature",
                },
            ]
        },
        {
            "file_uuid": "4aa38f84-e5af-4cdc-ba5c-edd60f0a6b89",
            "entities": [
                ...
            ]
        }
    ],
    "redaction_annotations": {
        "ner": [ ... ],
        "ced": [ ... ],
    }
}

IDENTIFIER = 'ENTITY_RECOGNITION'

class flows_sdk.implementations.idp_v41.additional_blocks.FileFilterBlock(trigger='${workflow.input.$trigger}', filter_type='${workflow.input.file_filter_filter_type}', minimum_image_width='${workflow.input.file_filter_minimum_image_width}', minimum_image_height='${workflow.input.file_filter_minimum_image_height}', minimum_file_size_kb='${workflow.input.file_filter_minimum_file_size_kb}', file_extensions_action='${workflow.input.file_filter_file_extensions_action}', file_extensions='${workflow.input.file_filter_file_extensions}', file_extensions_other='${workflow.input.file_filter_file_extensions_other}', reference_name=None, title='File Filter', description='Filters out unwanted files from the input of a flow')

Bases: Block

Available from V40.2

File Filter Block filters out unwanted files from the input of a flow

This block is meant to be placed at the start of the flow/subflow - BEFORE the SubmissionBootstrapBlock, not after it. It requires the output of a connector (e.g. Email Listener) or an API submission as input data, and its output is meant to be consumed by a SubmissionBootstrapBlock.

Mandatory parameters:

This block has no strictly required parameters - all of its filters are optional.

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• trigger (str) – the trigger input from a connector - if not set explicitly, it will default to the workflow input trigger (which is where this value is normally found)

• filter_type (str) – a discrete value, either “ALL_FILES” or “IMAGES_ONLY”. Determines what kind of files the filters will be applied to. Default value is “ALL_FILES”. If set to “IMAGES_ONLY”, all filtering options will be applied only to images, other files will pass through.

• minimum_image_width (str | None) – image width in pixels - half of the image dimension filter. Any image with less width than configured here will be filtered out. Defaults to None (will do nothing).

• minimum_image_height (str | None) – image height in pixels - second half of the image dimension filter. Any image with less height than configured here will be filtered out. Defaults to None (will do nothing).

• minimum_file_size_kb (str | None) – represents the file size filter. Any file whose disk size is less than this number (in KB) will be filtered out. May be fractional. Defaults to None (will do nothing).

• file_extensions_action (str) – a discrete value, either “ALLOW”, “DENY” or “NONE”. Represents the function of the file extension filter. “ALLOW” means we only allow the configured file extensions. “DENY” means we only deny the configured file extensions. “NONE” means no file extension filtering takes place. Defaults to “NONE”

• file_extensions (str | None) – a list of strings, representing the file extensions used for file extension filtering. Expected if file_extensions_action is not “NONE”. Otherwise, defaults to None (will do nothing).

• file_extensions_other (str | None) – a comma separated list of file extensions used for file extension filtering. Meant for adding extra extensions that are not available in the pre-defined list. Optional, defaults to None (will do nothing). However, it is expected in case the file_extensions parameter contains “other”.

Example usage:

# File Filter Block filters out unwanted files from the input of a flow
file_filter_block = FileFilterBlock(
    # trigger: str = workflow_input(Inputs.Trigger),  # usually not necessary to set this
    reference_name='file_filter',
    filter_type='ALL_FILES',
    minimum_image_width=500,
    minimum_image_height=500,
    minimum_file_size_kb=2.5,
    file_extensions_action='ALLOW',
    file_extensions=['pdf', 'png'],
    file_extensions_other='jpg,svg,txt',
)

# reference to the output of FileFilterBlock which can be passed as input to the
# next block (a SubmissionBootstrapBlock)
output_ref = file_filter_block.output('result.filtered_trigger')
submission_bootstrap = SubmissionBootstrapBlock(trigger=output_ref)

Example content of output_ref:

{
    "payload": {
        "blob_storage_files": [
            {
                "uuid": "db58606e-32f9-42c9-a0bc-ed9848ff247a",
            }
        ],
        "filtered_files": [
            {
                "uuid": "db58606e-32f9-42c9-a0bc-ed9848ff246e",
                "filtered_by": [
                    "file_size_filter"
                ]
            },
        ]
    }
}

IDENTIFIER = 'FILE_FILTER'

Trigger Blocks (v41)

Note

“Triggers” are referred to as “Input Blocks” in our application documentation.

Warning

Flows are built to allow full definition of triggers either in code or manually in the Flow Studio. We recommend defining triggers manually in the Flow Studio. Instructions on how to do so are in our application documentation.

To enable trigger definition via the Flow Studio UI, you need to instantiate the IDPTriggers() convenience class and pass it to the triggers property of the Flow

return Flow(
   ...
   triggers=IDPTriggers()
)

If instead you wish to define specific triggers in Python, use the trigger definitions below.

Folder Listener (v41)

Identifier: FOLDER_TRIGGER

Deprecated in favor of the Universal Folder Listener

Parameters:

Name	Type	Default Value	Title	Description
folder	Path	N/A	Folder to scan for submissions	N/A
file_extensions	string	N/A	File extensions	List of file extensions to monitor for (e.g.: ‘png, jpg, pdf’)
has_meta	boolean	False	Enable metadata	Select this if a metadata file is to be expected along with document files (in XXX_index.txt file)
poll_interval	integer	10	Poll interval	Poll interval in seconds
warmup_interval	integer	15	Warm-up interval	Seconds to wait past document last modified time before processing it
folder_cleanup_delay	integer	86400	Folder cleanup delay	Seconds to wait before cleaning up subfolders
api_params	object	{}	API Parameters	N/A

folder_listener = IOBlock(
    identifier='FOLDER_TRIGGER',
    reference_name='folder_trigger',
    title='Folder listener',
    enabled=True,
    input={
        'folder': '/var/www/forms/forms/folder/',
        'file_extensions': 'png, pdf, jpg',
    },
)

triggers = IDPTriggers(blocks=[folder_listener])

S3 Listener (v41)

Identifier: S3_TRIGGER Parameters:

Name	Type	Default Value	Title	Description
bucket_uri	string	N/A	S3 source URI	S3 URI, where the files will be uploaded (e.g. s3://hs_bucket/prefix1/)
archive_bucket_uri	string	N/A	S3 archive URI	S3 URI to move the processed files to (e.g. s3://hs_bucket/prefix1/)
file_extensions	array	[]	File extensions	Types of file extensions for which to monitor
other_file_extensions	string	N/A	Other file extensions	Comma separated list of file extensions for which to monitor (e.g. ‘png, jpg, pdf’)
has_meta	boolean	false	Include submission level parameters	Select this to ingest JSON files along with document files and submission s3 prefixes. These JSON files can contain information such as metadata, cases and external_id. These JSON file names should match the related files or s3 prefixes (e.g., XXX.jpg.json for XXX.jpg)
aws_access_key_id	string	N/A	AWS ACCESS KEY ID	Access Key used for authentication
aws_secret_access_key	password	N/A	SECRET ACCESS KEY	Secret Key used for authentication
use_ec2_instance_credentials	boolean	true	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used
poll_interval	integer	10	Polling interval (in seconds)	How often the S3 Listener will check the S3 bucket for submissions
warmup_interval	integer	15	Warm-up interval (in seconds)	How long the S3 Listener will wait to process the document after it was first detected
api_params	object	{}	API Parameters	N/A

s3_listener = IOBlock(
    identifier='S3_TRIGGER',
    reference_name='s3_trigger',
    title='S3 Listener',
    enabled=True,
    input={
        'bucket_uri': 's3://hs_bucket/prefix1/',
        'archive_bucket_uri': 's3://hs_bucket/prefix2/',
        'file_extensions': ['png', 'pdf', 'jpg'],
        'aws_access_key_id': 'admin',
        'aws_secret_access_key': system_secret('s3_secret_access_key'),
        'use_ec2_instance_credentials': False,
    },
)
triggers = IDPTriggers(blocks=[s3_listener])

Email Listener (v41)

Identifier: IMAP_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
provider [1]	string	imap	Email Provider	N/A
poll_interval	integer	60	Polling interval in seconds	The frequency at which the connection will monitor the email folder for submissions
folder	Path	N/A	Folder to scan for emails	The email folder to retrieve submissions from (e.g., “inbox”)
email_body_treatment	string	“ignore”	Email body treatment	What to do with the body of an email that is ingested. Available options are “process” and “ignore”.
post_process_action	string	“move”	Post process action	What to do with the email after it is processed. Available options are “move” and “delete”.
post_process_move_folder	string	“”	Post process archive folder	Folder to move emails to once they are processed. It will be included only if post_process_action is set to “move”.
api_params	object	{}	API Parameters	N/A
render_headers [2]	array	[]	Headers to include	Headers to render at the top of the email body. Headers will be included only if email_body_treatment is set to “process”.
raw_email_treatment [3]	string	“ignore”	Original email file	If stored, the copy will be found under “raw_email” in the block output. Storing a copy will use additional storage.
attachment_header_treatment [3]	string	“ignore”	Attachment headers	If extracted, the attachment headers will be found under “attachment_metadata” in the block output.

[1] one of:

imap
graph

[2] any of:

To
From
Subject
Date

[3] only available in specific Hyperscience versions:

• version 41: since patch 41.0.24

• version 41.2: since patch 41.2.15

• version 42: since patch 42.0.8

• version 42.1: since patch 42.1.2

• all versions past (and including) 42.2

Additional inputs when "imap" is the selected provider:

Name	Type	Default Value	Title	Description
host	string	N/A	IMAP server address	N/A
port	integer	993	Port Number	N/A
ssl	boolean	True	Use SSL connection	N/A
username	string	N/A	Username	N/A
password	Password	N/A	Password	N/A

Additional inputs when "graph" is the selected provider:

Name	Type	Default Value	Title	Description
client_id	string	N/A	Client id	Application (client) ID for the corresponding application registered with the Microsoft identity platform.
tenant_id	string	N/A	Tenant id	Also called directory ID. Unique identifier of the tenant in which the corresponding application is registered with.
graph_cloud_endpoint [1]	string	Global	Microsoft Graph Cloud Endpoint	Determines base URL used for accessing cloud services through Microsoft Graph.
azure_ad_endpoint [2]	string	AZURE_PUBLIC_CLOUD	Azure AD Endpoint	Determines base URL for the Azure Active Directory (Azure AD) endpoint to acquire token for each national cloud.
client_secret	Password	N/A	Client secret	Client secret for the corresponding application registered with the Microsoft identity platform.

[1] one of:

Global
US_GOV
US_DoD
Germany
China

[2] one of:

AZURE_PUBLIC_CLOUD
AZURE_CHINA
AZURE_GERMANY
AZURE_GOVERNMENT

imap_trigger = IOBlock(
    identifier='IMAP_TRIGGER',
    reference_name='imap_trigger',
    title='IMAP trigger',
    enabled=True,
    input={
        'host': 'example@mail.com',
        'folder': '/var/www/forms/forms/imap/',
        'username': 'admin',
        'password': 'pass',
    }
)

triggers = IDPTriggers(blocks=[imap_trigger])

Box Folder Listener (v41)

Identifier: BOX_FOLDER_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
source_folder_id	string	N/A	Folder to scan for submissions	Use the Box Folder ID found in the URL
target_folder_id	string	N/A	Folder to move completed files	Use the Box Folder ID found in the URL
file_extensions	array	[]	File extensions	Types of file extensions for which to monitor
custom_file_extensions	string	N/A	Other file extension types	Comma separated list of file extensions for which to monitor (e.g. ‘png, jpg, pdf’)
poll_interval	integer	10	Polling interval (in seconds)	How often the connector will check the base folder for submissions
warmup_interval	integer	15	Warm-up interval (in seconds)	How long the connector will wait to process the document after it was last modified
public_key_id	string	N/A	Public-Key ID	ID of the Public-key used for authentication with Box
private_key	Password	N/A	Private-Key	Private key used for authentication with Box
passphrase	Password	N/A	Passphrase	Passphrase used for authentication with Box
client_id	string	N/A	Client ID	Client Id used for authentication with Box
client_secret	Password	N/A	Client Secret	Client Secret used for authentication with Box
enterprise_id	string	N/A	Enterprise ID	Enterprise Id used for authentication with Box
api_params	object	{}	API Parameters	N/A

box_folder_trigger = IOBlock(
    identifier='BOX_FOLDER_TRIGGER',
    reference_name='box_folder_trigger',
    title='Box folder',
    enabled=True,
    input={
        'file_extensions': ['png', 'pdf', 'jpg'],
        'source_folder_id': '24',
        'target_folder_id': '42',
        'public_key_id': 'admin',
        'private_key': 'secret',
        'passphrase': 'password',
        'client_id': 'admin',
        'client_secret': 'secret',
        'enterprise_id': 'admin',
    }
)

triggers = IDPTriggers(blocks=[box_folder_trigger])

Cron Listener (v41)

Identifier: CRON_TRIGGER

Inputs:

Name	Type	Default Value	Title	Description
cron_spec	string	"*/5 * * * *"	Cron specification	N/A
time_zone	string	“US/Eastern”	Time Zone	N/A
api_params	object	{}	API Parameters	N/A

cron_trigger = IOBlock(
    identifier='CRON_TRIGGER',
    reference_name='cron_trigger',
    title='Cron Trigger',
    enabled=True,
    input={
        'cron_spec': '0 10 * * *',
        'time_zone': 'Europe/Sofia',
    }
)

triggers = IDPTriggers(blocks=[cron_trigger])

Salesforce Listener (v41)

Identifier: SALESFORCE_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
channel_name	string	N/A	Channel Name	Configured in the Hyperscience app in Salesforce.
username	string	N/A	Associated Username	Username associated with the Salesforce Connected App.
consumer_key	Password	N/A	Consumer Key	Consumer key used for authentication with Salesforce. Paired with the Salesforce instance from which it was issued.
private_key	Password	N/A	Private Key	Private key used for authentication with Salesforce.
sandbox_environment	boolean	false	Private Key	Enable if the Salesforce environment is a sandbox.
api_params	object	{}	API Parameters	N/A

Message Queue Listener (v41)

Identifier: MQ_LISTENER

Inputs:

Name	Type	Default Value	Title	Description
MQ_TYPE	string	“ACTIVE_MQ”	Message Queue type	Type of the message queue to connect with. Valid values are “ACTIVE_MQ”, “AMAZON_SQS”, “IBM_MQ” and “RABBIT_MQ”
MQ_USERNAME	string	null	Username/Access Key ID	N/A
MQ_PASSWORD	Password	null	Password/Secret Access Key	N/A
MQ_QUEUE_NAME	string	null	Queue Name	Name of the queue (topic) to connect to.
MQ_HOST	string	null	Host Name	N/A
MQ_PORT	integer	5672 when “RABBIT_MQ”, 1414 when “IBM_MQ”, otherwise null	Port Number	N/A
api_params	object	{}	API Parameters	N/A

Additional inputs when "AMAZON_SQS" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_REGION	string	null	AWS Region	N/A
MQ_QUEUE_URL	string	null	Queue URL	N/A
MQ_USE_EC2_INSTANCE_CREDENTIALS	boolean	True	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used.

NOTE: MQ_USERNAME can be used to provide Access Key ID and MQ_PASSWORD to provide Secret Access Key.

Additional inputs when "IBM_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_QUEUE_MANAGER	string	null	Queue Manager (IBM MQ)	N/A
MQ_SSL_CIPHER_SUITE [1]	string	null	SSL cipher suite	SSL cipher suite to use if SSL connection is used.
MQ_CHANNEL	string	null	Channel	N/A
MQ_NO_AUTH_REQUIRED	boolean	false	No auth credentials required	N/A
MQ_MQCSP_AUTHENTICATION_MODE	boolean	false	MQCSP authentication mode	N/A

NOTE: MQ_NO_AUTH_REQUIRED is True, MQ_USERNAME and MQ_PASSWORD are optional.

[1] one of:

"NULL_MD5"
"NULL_SHA"
"RC4_MD5_EXPORT"
"RC4_MD5_US"
"RC4_SHA_US"
"RC2_MD5_EXPORT"
"DES_SHA_EXPORT"
"RC4_56_SHA_EXPORT1024"
"DES_SHA_EXPORT1024"
"TRIPLE_DES_SHA_US"
"TLS_RSA_WITH_NULL_SHA256"
"TLS_RSA_WITH_AES_128_CBC_SHA"
"TLS_RSA_WITH_AES_128_CBC_SHA256"
"TLS_RSA_WITH_AES_256_CBC_SHA"
"TLS_RSA_WITH_AES_256_CBC_SHA256"
"AES_SHA_US"
"TLS_RSA_WITH_DES_CBC_SHA"
"TLS_RSA_WITH_3DES_EDE_CBC_SHA"
"TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256"
"FIPS_WITH_DES_CBC_SHA"
"FIPS_WITH_3DES_EDE_CBC_SHA"

Additional inputs when "RABBIT_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_VIRTUAL_HOST	string	null	Virtual Host (RABBIT MQ)	N/A
MQ_EXCHANGE	string	“”	Exchange	N/A
MQ_ROUTING_KEY	string	“”	Routing key	N/A
MQ_CONNECTION_TYPE	string	“TCP”	Connection Type	“TCP or SSL”

rabbit_mq_listener = IOBlock(
    identifier='MQ_LISTENER',
    reference_name='mq_listener',
    title='RabbitMQ Listener',
    enabled=True,
    input={
        'MQ_TYPE': 'RABBIT_MQ',
        'MQ_QUEUE_NAME': 'some_queue_name',
        'MQ_HOST': 'somehost.com',
        'MQ_USERNAME': 'foo',
        'MQ_PASSWORD': system_secret('rabbit_mq_1_password'),
    }
)

triggers = IDPTriggers(blocks=[rabbit_mq_listener])

Universal Folder Listener (v41)

Identifier: UNIVERSAL_FOLDER_TRIGGER

Inputs:

Name	Type	Default Value	Title	Description
folder	string	N/A	Folder to scan for submissions	Enter the path relative to the base folder. Leave blank to monitor the base folder
file_extensions	array	[]	File extensions	Types of file extensions for which to monitor
other_file_extensions	string	N/A	Other file extensions	Comma separated list of file extensions for which to monitor (e.g. ‘png, jpg, pdf’)
has_meta	boolean	false	Include submission level parameters	Select this to ingest JSON files along with document files and submission folders. These JSON files can contain information such as metadata, cases and external_id. These JSON file names should match the related files or folders (e.g., XXX.jpg.json for XXX.jpg)
poll_interval	integer	10	Polling interval (in seconds)	How often the Folder Listener will check the base folder for submissions
warmup_interval	integer	15	Warm-up interval (in seconds)	How long the Folder Listener will wait to process the document after it was last modified
folder_cleanup_delay	number	24	Empty folder cleanup delay (in hours)	How often the Folder Listener will remove empty folders from the base folder
api_params	object	{}	API Parameters	N/A

universal_folder_listener = IOBlock(
    identifier='UNIVERSAL_FOLDER_TRIGGER',
    reference_name='universal_folder_trigger',
    title='Universal Folder Listener',
    enabled=True,
    input={
        'folder': '/var/www/forms/forms/folder/',
        'file_extensions': 'png, pdf, jpg',
    }
)

triggers = IDPTriggers(blocks=[universal_folder_listener])

Kafka Listener (v41)

Identifier: KAFKA_TRIGGER

Inputs:

Name	Type	Default Value	Title	Description
bootstrap_servers	string	N/A	Bootstrap Servers	A Comma separated list of host:port pairs to use for establishing the initial connection to the cluster
group_id	string	N/A	Group Id	A unique string that identifies the consumer group this consumer belongs to. BLOCK_THREADS_KAFKA_TRIGGER should be used to scale up or down the number of consumers (default: number of available cores)
topics	string	N/A	Topics	Comma separated list of topic names that the block will subscribe to
consumer_config_json	json	N/A	Consumer Config	JSON formatted string containing key-value pairs of Kafka consumer configurations described in the official Kafka documentation
api_params	object	{}	API Parameters	N/A
consume_timeout	integer	-1	Consume Timeout	Maximum time, in seconds, that the consumer will wait for new messages. If messages are received within the specified time, they are processed immediately. However, if no messages are received within that specified time, consumption cycle will be restarted internally to continue fetching messages. This ensures continuous message consumption (default: infinite)
consume_num_messages	integer	1	Consume Num Messages	Sets the maximum number of messages the consumer fetches in a single consumption cycle.If the number of available messages is less than the specified, and timeout is reached, messages will be processed. Consumption cycle will be restarted after the current finishes

kafka_listener = IOBlock(
    identifier='KAFKA_TRIGGER',
    reference_name='kafka_trigger',
    title='Kafka Listener',
    enabled=True,
    input={
        'folder': '/var/www/forms/forms/folder/',
        'file_extensions': 'png, pdf, jpg',
         'bootstrap_servers': 'example.com',
         'group_id': 'test_group',
         'topics': 'test_topic1, test_topic2',
         'consumer_config_json': {},
         'number_of_workers': 1,
         'consume_timeout': 5,
         'consume_num_messages': 10
    }
)

triggers = IDPTriggers(blocks=[kafka_listener])

Output Blocks (v41)

Warning

Flows pass information to downstream systems using the IDPOutputsBlock. This block is specially built to allow full definition either in code or manually in the Flow Studio. We recommend defining outputs manually in the Flow Studio.

Instructions on how to do so are in our application documentation. Simply include an IDPOutputsBlock in your Python flow definition wherever you wish to send information to downstream systems.

class flows_sdk.implementations.idp_v41.idp_blocks.IDPOutputsBlock(inputs, blocks=None)

Bases: Outputs

Output block allows users to send data extracted by an IDP flow to other systems for downstream processing

Mandatory parameters:

Parameters:

inputs (dict[str, Any]) – Used by the UI to automatically pre-populate the input of newly added blocks. Extended in this class to include 'enabled': True to visualize an enabled/disabled trigger that is controllable via Flow Studio.

Usage when parameters are to be defined in Flow Studio:

outputs = IDPOutputBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')}
)

If instead you wish to define specific outputs in Python, use the outputs definitions below.

HTTP Notifier Output (v41)

The HTTP Notifier (REST) output connection will POST results from the system to a specified HTTP endpoint.

Identifier: COB_HTTP_EXPORT

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
endpoint	string	N/A	Endpoint URL	URL that output notifications will be posted to
endpoint_secrets	string	N/A	Endpoint URL secrets	Endpoint URL Secrets, format: (one per line) secret_key_name=secret_value
timeout	int	600	Timeout (seconds)	How long (in seconds) to keep the connection open if no bytes are received from the endpoint. Set to 0 to wait indefinitely.
authorization_type [2]	string	“none”	Authorization type	Type of authorization
authorization_header_name	string	null	Authorization header name	Authorization header name to be set in the notification request
authorization_header	Password	null	Authorization header value	Authorization header to be set in the notification request
auth_url	string	null	OAuth2 authorization URL	The endpoint for the authorization server
client_id	string	null	Client ID	The client identifier issued to the client during the application registration process
client_secret	Password	null	Client Secret	The client secret issued to the client during the application registration process
scope	string	null	Scope	Scope to request to oauth server
audience	string	null	Audience	Resource service URL where token will be valid
additional_oauth_parameters	json	{}	Additional Oauth Request Parameters	Additional parameters to send when requesting token
ssl_cert_source [3]	string	null	Source	N/A
ssl_cert_path	string	null	Trusted Certificate(s) path	Enter the path relative to the base folder
ssl_cert_value	MultilineText	null	Trusted Certificate(s)	N/A
additional_notification_http_headers	json	null	Additional Notification Request Headers	Key value pairs in json format which will be sent via the request as http headers

[1] one of:

"v5"
"transformed"

[2] one of:

"none"
"http_header"
"oauth_2_client_credentials"

[3] one of:

"env_var"
"ca_bundle_path"
"certificate_value"

cob_http_export = IOBlock(
    identifier='COB_HTTP_EXPORT',
    reference_name='cob_http_export',
    title='HTTP Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'SUBMISSION',
        'endpoint': 'example.com',
        'authorization_type': 'none',
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_http_export],
)

Message Queue Notifier Output (v41)

The Message Queue Notifier Output can configure connections to ActiveMQ, Amazon SQS, IBM MQ, and RabbitMQ message queues.

Identifier: COB_MQ_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
MQ_TYPE	string	“ACTIVE_MQ”	Message Queue type	Type of the message queue to connect with. Valid values are “ACTIVE_MQ”, “AMAZON_SQS”, “IBM_MQ” and “RABBIT_MQ”
MQ_USERNAME	string	null	Username/Access Key ID	N/A
MQ_PASSWORD	Password	null	Password/Secret Access Key	N/A
MQ_QUEUE_NAME	string	null	Queue Name	Name of the queue (topic) to connect to. It applies only to “ACTIVE_MQ”, “IBM_MQ” and “RABBIT_MQ” MQ_TYPEs.
MQ_HOST	string	null	Host Name	N/A
MQ_PORT	integer	5672 when “RABBIT_MQ”, 1414 when “IBM_MQ”, otherwise null	Port Number	N/A

[1] one of:

"v5"
"transformed"

Additional inputs when "AMAZON_SQS" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_REGION	string	null	AWS Region	N/A
MQ_QUEUE_URL	string	null	Queue URL	N/A
MQ_USE_EC2_INSTANCE_CREDENTIALS	boolean	True	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used.
MQ_MESSAGE_GROUP_ID	string	null	Group ID for FIFO queues	N/A
MQ_MESSAGE_METADATA	string	null	Additional SQS Metadata	Input additional metadata key value pairs in the format of { “key”: {“TYPE”: “String”, “VALUE”: “”}, “key2”: {“TYPE”: “Number”, “VALUE”: “12345”}

NOTE: MQ_USERNAME can be used to provide Access Key ID and MQ_PASSWORD to provide Secret Access Key.

Additional inputs when "IBM_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_QUEUE_MANAGER	string	null	Queue Manager (IBM MQ)	N/A
MQ_SSL_CIPHER_SUITE [1]	string	null	SSL cipher suite	SSL cipher suite to use if SSL connection is used.
MQ_CHANNEL	string	null	Channel	N/A
MQ_NO_AUTH_REQUIRED	boolean	false	Channel	N/A
MQ_MQCSP_AUTHENTICATION_MODE	boolean	false	Channel	N/A

NOTE: MQ_NO_AUTH_REQUIRED is True, MQ_USERNAME and MQ_PASSWORD are optional.

[1] one of:

"NULL_MD5"
"NULL_SHA"
"RC4_MD5_EXPORT"
"RC4_MD5_US"
"RC4_SHA_US"
"RC2_MD5_EXPORT"
"DES_SHA_EXPORT"
"RC4_56_SHA_EXPORT1024"
"DES_SHA_EXPORT1024"
"TRIPLE_DES_SHA_US"
"TLS_RSA_WITH_NULL_SHA256"
"TLS_RSA_WITH_AES_128_CBC_SHA"
"TLS_RSA_WITH_AES_128_CBC_SHA256"
"TLS_RSA_WITH_AES_256_CBC_SHA"
"TLS_RSA_WITH_AES_256_CBC_SHA256"
"AES_SHA_US"
"TLS_RSA_WITH_DES_CBC_SHA"
"TLS_RSA_WITH_3DES_EDE_CBC_SHA"
"TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256"
"FIPS_WITH_DES_CBC_SHA"
"FIPS_WITH_3DES_EDE_CBC_SHA"

Additional inputs when "RABBIT_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_VIRTUAL_HOST	string	null	Virtual Host (RABBIT MQ)	N/A
MQ_EXCHANGE	string	“”	Exchange	N/A
MQ_ROUTING_KEY	string	“”	Routing key	N/A
MQ_CONNECTION_TYPE	string	“TCP”	Connection Type	“TCP or SSL”

mq_notifier = IOBlock(
    identifier='COB_MQ_NOTIFIER',
    reference_name='mq_notifier',
    title='MQ Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'SUBMISSION',
        'MQ_USERNAME': 'admin',
        'MQ_PASSWORD': 'pass',
        'MQ_QUEUE_NAME': 'queue',
        'MQ_HOST': 'host',
    },
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[mq_notifier],
)

Box Notifier Output (v41)

Provides an out-of-the-box integration into Box systems.

Identifier: COB_BOX_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
public_key_id	string	N/A	Public-Key ID	ID of the Public-key used for authentication with Box
private_key	Password	N/A	Private-Key	Private key used for authentication with Box
passphrase	Password	N/A	Passphrase	Passphrase used for authentication with Box
client_id	string	N/A	Client ID	Client Id used for authentication with Box
client_secret	Password	N/A	Client Secret	Client Secret used for authentication with Box
enterprise_id	string	N/A	Enterprise ID	Enterprise Id used for authentication with Box
template_key	string	“”	Box Metadata Template Key	Enter the key of the Box Metadata template that you would like to map the Hyperscience metadata to.
static_output_fields_to_map	array	[]	Static Metadata Fields	Specify the Hyperscience fields you want to store in Box metadata.
submission_id	string	“”	Key for Mapping Submission ID	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_id	string	“”	Key for Mapping Document ID	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
submission_state	string	“”	Key for Mapping Submission State	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_state	string	“”	Key for Mapping Document State	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
submission_exceptions	string	“”	Key for Mapping Submission Exceptions	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_exceptions	string	“”	Key for Mapping Document Exceptions	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_layout_uuid	string	“”	Key for Mapping Document Layout Uuid	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_layout_name	string	“”	Key for Mapping Document Layout Name	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
start_time	string	“”	Key for Mapping Start Time	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
complete_time	string	“”	Key for Mapping Complete Time	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_fields_template_mappings	json	{}	JSON for Additional Metadata Fields	Specify additional metadata fields using their key value. Please consult Box Integration setup manual for JSON template and instructions.

[1] one of:

"v5"
"transformed"

box_notifier = IOBlock(
    identifier='COB_BOX_NOTIFIER',
    reference_name='box_notifier',
    title='Box Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'private_key': 'password',
        'passphrase': 'password',
        'public_key_id': 'admin',
        'client_id': 'admin',
        'client_secret': 'secret',
        'enterprise_id': 'admin',
        'template_key': 'key',
    },
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[box_notifier],
)

UiPath Notifier Output (v41)

Provides an out-of-the-box integration into UiPath systems.

Identifier: COB_EXPORT_UIPATH

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
auth_method [2]	string	“basic_auth”	Authentication method	N/A
url	string	N/A	URL	Base URL of the uipath instance
app_id	string	N/A	App ID	App ID provided by UiPath
app_secret	Password	N/A	App Secret	App Secret provided by UiPath
oauth_token_endpoint	string	N/A	OAuth token endpoint	Endpoint to retrieve the token. Using https://cloud.uipath.com/identity_/connect/token by default
organization_unit_id	string	N/A	Organization unit id	Organization unit id
tenant	string	N/A	Tenant	Tenant
username	string	N/A	Username	Username
password	Password	N/A	Password	Password
queue	string	N/A	Queue	Queue

[1] one of:

"v5"
"transformed"

[2] one of:

"basic_auth"
"oauth"

cob_export_uipath = IOBlock(
    identifier='COB_EXPORT_UIPATH',
    reference_name='cob_export_uipath',
    title='CCB Export UiPath',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'FLAT_SUBMISSION',
        'url': 'example.com',
        'tenant': 'tenant',
        'username': 'admin',
        'password': 'pass',
        'queue': 'queue',
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_export_uipath],
)

Salesforce Notifier Output (v41)

With the Salesforce Notifier Block, you can configure your flow to send extracted information to Salesforce. The Salesforce Notifier Block can use extracted information to look up, and then update any number of fields on a Salesforce object. The Salesforce Notifier Block is available in both SaaS and on-premise versions of the Hyperscience application.

Identifier: COB_SALESFORCE_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
username	string	N/A	Associated Username	Username associated with the Salesforce Connected App
consumer_key	password	N/A	Consumer Key	Consumer key used for authentication with Salesforce. Paired with the Salesforce instance from which it was issued.
private_key	password	N/A	Private Key	Private key used for authentication with Salesforce.
sandbox_environment	boolean	false	Private Key	Enable if the Salesforce environment is a sandbox.
object_api_name	string	N/A	Object API Name	API name of the object in Salesforce to lookup and update.
new_record_on_lookup_failure	boolean	false	Create new record on lookup failure	When enabled, it will create a new record if the lookup fails to find a record using the lookup parameters. When disabled, lookup will fail if it does not find a record using the lookup parameters.
object_lookup_mapping	json	N/A	Lookup Field Mapping	Specify which Salesforce fields (using their API name) to be queried with which extracted values from the document for the lookup using key-value format. For example: “DOCUMENT_FIELD_NAME”: “OBJECT_FIELD_API_NAME”.
document_fields_mapping	json	N/A	Document Field Mappings	Specify which Salesforce fields (using their API name) you want to update with which extracted data from Hyperscience using key-value format. For example: “DOCUMENT_FIELD_NAME”: “OBJECT_FIELD_API_NAME”.”
overwrite_existing_field_data	boolean	false	Overwrite existing field data	If this is checked and a selected field has data in it, Hyperscience will overwrite that data. If this is unchecked, existing data will not be overwritten and the operation will fail.

[1] one of:

"v5"
"transformed"

cob_salesforce_notifier = IOBlock(
    identifier='COB_SALESFORCE_NOTIFIER',
    reference_name='cob_salesforce_notifier',
    title='Salesforce Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'username': 'salesforce_user',
        'consumer_key': 'salesforce_consumer_key',
        'private_key': 'salesforce_private_key',
        'object_api_name': 'salesforce_object_api_name',
        'object_lookup_mapping': {"LAYOUT_VARIATION_XYZ_FIELD_NAME_1": "SF_FIELD_API_NAME_1", "LAYOUT_VARIATION_XYZ_FIELD_NAME_2": "SF_FIELD_API_NAME_2"},
        'document_fields_mapping': {"LAYOUT_VARIATION_XYZ_FIELD_NAME_3": "SF_FIELD_API_NAME_3", "LAYOUT_VARIATION_XYZ_FIELD_NAME_4": "SF_FIELD_API_NAME_4"}
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_salesforce_notifier],
)

S3 Notifier Output (v41)

With the S3 Notifier Block, you can configure your flow to send extracted information to an S3 bucket.

Identifier: COB_S3_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
bucket_uri	string	N/A	S3 URI	S3 URI, where the data should be stored (e.g. s3://hs_bucket/prefix1/prefix2/)
aws_access_key_id	string	N/A	AWS ACCESS KEY ID	Access Key used for authentication
aws_secret_access_key	password	N/A	SECRET ACCESS KEY	Secret Key used for authentication
use_ec2_instance_credentials	boolean	true	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used

[1] one of:

"v5"
"transformed"

cob_s3_notifier = IOBlock(
    identifier='COB_S3_NOTIFIER',
    reference_name='cob_s3_notifier',
    title='S3 Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'FLAT_SUBMISSION',
        'bucket_uri': 's3://hs_bucket/prefix1/prefix2/',
        'aws_access_key_id': 'aws_access',
        'aws_secret_access_key': system_secret('aws_secret_access_key'),
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_s3_notifier],
)

Kafka Notifier Output (v41)

With the Kafka Notifier Block, you can configure your flow to send extracted information to a kafka topic.

Identifier: COB_KAFKA_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
bootstrap_servers	string	N/A	Bootstrap Servers	A list of Kafka brokers to connect to. For example: “kafka1:9092,kafka2:9092”
topic	string	N/A	Topic	The Kafka topic where your data will be sent
client_id	string	N/A	SECRET ACCESS KEY	An identifier for your application when connecting to Kafka, typically used for tracking and logging purposes
producer_config_json	json	true	Producer Config (JSON)	Configuration settings for the Kafka producer in JSON format. See https://kafka.apache.org/documentation/#producerconfigs for more information
synchronous_publish	boolean	true	Sync Publish	If true, the publish call will block until the message is sent successfully or an error is raised. If false, the publish call will return immediately after enqueueing the message
message_key	string	null	Message Key	The key associated with the message to be sent. This is used to determine which partition the message is sent to. If null, the message will be sent to a random partition

[1] one of:

"v5"
"transformed"

cob_kafka_notifier = IOBlock(
    identifier='COB_KAFKA_NOTIFIER',
    reference_name='cob_kafka_notifier',
    title='Kafka Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'FLAT_SUBMISSION',
        'bootstrap_servers': 'kafka1:9092,kafka2:9092',
        'topic': 'topic',
        'client_id': 'client_id',
        'producer_config_json': {},
        'synchronous_publish': True,
        'message_key': 'key'
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_kafka_notifier],
)

Helper Classes and Functions (v41)

The following classes are used to easily instantiate and manage a flow’s settings.

flows_sdk.implementations.idp_v41.idp_values.get_idp_wf_inputs(idp_wf_config)

A helper function that maps the values provided by an IdpWorkflowConfig to their corresponding IDP keys.

Parameters:

idp_wf_config (IdpWorkflowConfig) – with static values to be filled.

Return type:

dict[str, Any]

Returns:

a dictionary with filled static values, compatible with IDP flows.

Example usage with default values:

flow = Flow(
    input=get_idp_wf_inputs(get_idp_wf_config()),
    ...
)

Example usage:

Example usage when instantiating an IDP flow with custom parameters::

flow = Flow(
    input=get_idp_wf_inputs({an instance of IdpWorkflowConfig}),
    ...
)

flows_sdk.implementations.idp_v41.idp_values.get_idp_wf_config()

A helper function that instantiates an IdpWorkflowConfig pre-filled with default values. IdpWorkflowConfig is the container class for defining flow-level settings.

Return type:

IdpWorkflowConfig

Returns:

a valid, fully constructed IdpWorkflowConfig instance

Example usage when instantiating an IDP flow:

flow = Flow(
    input=get_idp_wf_inputs(get_idp_wf_config()),
    ...
)

class flows_sdk.implementations.idp_v41.idp_values.IdpWorkflowConfig

Convenience dataclass outlining all flow-level config values for IDP.

class flows_sdk.implementations.idp_v41.idp_values.IDPTriggers(blocks=None)

An adapter class making it easier to instantiate flows_sdk.flows.Triggers for IDP flows. In particular, provides defaults for some fields (e.g. title, description, reference_name) and defines api_params_manifest with IDP specific parameters.

Parameters:

blocks (Sequence[IOBlock] | None) – flows_sdk.blocks.IOBlock s to be included as triggers for an IDP Flow. Optional, defaults to an empty list.

Example usage when instantiating an IDP flow:

folder_listener = IOBlock(...)
flow = Flow(
    triggers=IDPTriggers([folder_listener]),
    ...
)

class flows_sdk.implementations.idp_v41.idp_values.IDPWrapperManifest(flow_identifier)

An adapter class making it easier to instantiate flows_sdk.flows.Manifest for IDP wrapper flows. In particular, provides defaults for some fields (e.g. roles) but more importantly, defines all flows_sdk.flows.Parameter that construct the Flow inputs and their UI representation (left-hand side menu in Flow studio).

Parameters:

flow_identifier (str) – A system-wide unique identifier for flows_sdk.flows.Flow

Example usage when instantiating an IDP flow:

flow = Flow(
    manifest=IDPWrapperManifest(flow_identifier='FLOW_IDENTIFIER'),
    ...
)

class flows_sdk.implementations.idp_v41.idp_values.IDPCoreManifest(flow_identifier, roles=None)

An adapter class making it easier to instantiate flows_sdk.flows.Manifest for IDP Core flows. In particular, provides defaults for some fields (e.g. roles, output) but more importantly, defines all flows_sdk.flows.Parameter that construct the Flow inputs and their UI representation (left-hand side menu in Flow studio).

Parameters:

• flow_identifier (str) – A system-wide unique identifier for flows_sdk.flows.Flow

• roles (list[str] | None) – ‘read_only’, ‘supporting’ and ‘idp_core’ are the default roles of every subflow that can replace the default IDP_CORE subflow inside the top-level IDP flows. ‘supporting’ and ‘idp_core’ are mandatory roles that are necessary for all IDP Core flows. ‘supporting’ ensures that the subflow will be auto-deployed together with the top-level IDP flow. ‘idp_core’ ensures that this subflow can be selected in the UI in dropdowns which allow picking an alternative subflow to the default IDP_CORE subflow. If you are implementing a subflow that is not supposed to be a drop-in replacement for IDP_CORE, you will have to subclass or implement your own version of IDPCoreManifest class in order to remove the idp_core role.

Example usage when instantiating an IDP flow:

flow = Flow(
    manifest=IDPCoreManifest(flow_identifier='FLOW_IDENTIFIER'),
    ...
)

V40

Processing Blocks (v40)

Core Blocks are powerful processing blocks necessary to build intelligent document processing solutions on Hyperscience. They include both manual and machine processing blocks.

Note

Classes in the idp_v40 implementation have parameters with ambiguous typing. For example, a field of type integer could also be represented as a string reference (e.g. that integer gets provided at runtime). The types introduced in the example are therefore incomplete and are likely to be revisioned in a future version.

class flows_sdk.implementations.idp_v40.idp_blocks.IDPCoreBlock(idp_wf_config, reference_name='__idp_core__', title='Start Document Processing Subflow', description='Calls the Document Processing Subflow with the specified settings.', compatibility_spec=CompatibilitySpec(filter_roles=['idp_core'], filter_schema=None, label=None), identifier='IDP_CORE_V40', additional_inputs=None)

Bases: Block

IDP core block includes the steps to process the passed documents into machine-readable output.

Mandatory parameters:

Parameters:

idp_wf_config (IdpWorkflowConfig) – IdpWorkflowConfig object with all flow-level configuration settings for IDP

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique reference name within a flow, a unique one will be generated when not provided

• compatibility_spec (CompatibilitySpec | None) – defines a filter for flows that could be selected in the UI in place of “IDP Core” for the block. Defaults to flows with the role ‘idp_core’. The user will be able to pick any flow that matches the compatibility_spec. This serves to make upgrading to a new compatible version of “IDP Core” (e.g. IDP_CORE_V39) easier in future releases.

• identifier (str) – The identifier of the flow. Defaults to the IDP_CORE_VXX identifier of the out-of-the-box flow that comes with the system.

• additional_inputs (Optional[Dict[str, Any]]) – Dictionary containing key-value pairs that will be added to the inputs of the block. If a key is contained in the default inputs, its value will be overwritten by the value provided here. You generally do not need to provide additional_inputs, unless you are creating a custom derivative of the IDP_CODE_Vxx flow and are modifying or extending the list of inputs it accepts.

Example usage:

idp_core = IDPCoreBlock(idp_wf_config=idp_wf_config)

REFERENCE_NAME = '__idp_core__'

class flows_sdk.implementations.idp_v40.idp_blocks.SubmissionBootstrapBlock(reference_name=None, trigger='${workflow.input.$trigger}', layout_release_uuid='${workflow.input.layout_release_uuid}', s3_config='${workflow.input.submission_bootstrap_s3_config}', s3_endpoint_url='${workflow.input.submission_bootstrap_s3_endpoint_url}', ocs_config='${workflow.input.submission_bootstrap_ocs_config}', http_config='${workflow.input.submission_bootstrap_http_config}', abs_auth_type='${workflow.input.submission_bootstrap_abs_auth_type}', abs_account_url='${workflow.input.submission_bootstrap_abs_account_url}', abs_tenant_id='${workflow.input.submission_bootstrap_abs_tenant_id}', abs_client_id='${workflow.input.submission_bootstrap_abs_client_id}', abs_client_secret='${workflow.input.submission_bootstrap_abs_client_secret}', abs_authority_host='${workflow.input.submission_bootstrap_abs_authority_host}', abs_account_name='${workflow.input.submission_bootstrap_abs_account_name}', abs_account_key='${workflow.input.submission_bootstrap_abs_account_key}', notification_workflow='${workflow.input.notification_workflow_initialization}', workflow_uuid='${workflow.input.workflow_uuid}', workflow_name='${workflow.input.workflow_name}', workflow_version='${workflow.input.workflow_version}', submission='${workflow.input.submission}')

Bases: Block

Submission bootstrap block initializes the submission object and prepares external images or other submission data if needed. This block should be called at the START of every flow that uses blocks included in the library.

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• trigger (str) – the trigger input from a connector - if not set explicitly, it will default to the workflow input trigger (which is where this value is normally found)

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• s3_config (str) – Specify Amazon S3 credentials for submission retrieval store. Expects a json expression: {"aws_access_key_id": "X", "aws_secret_access_key": "Y"}, defaults to workflow_input(Settings.SubmissionBootstrapS3Config)

• s3_endpoint_url (str) – Endpoint URL for S3 submission retrieval store, defaults to workflow_input(Settings.SubmissionBootstrapS3EndpointUrl)

• ocs_config (str) – OCS Configuration for downloading submission data, defaults to workflow_input(Settings.SubmissionBootstrapOCSConfig)

• http_config (str | None) – An optional system secret field expressing the HTTP Configuration for downloading submission data. The content is expected to be a json formatted as: {"username": "X", "password": "Y", "ssl_cert": "CA bundle filename"}, defaults to workflow_input(Settings.SubmissionBootstrapHTTPConfig)

• notification_workflow (str | None) – Notification flow name to run when the submission is initialized, defaults to workflow_input(Settings.SubmissionBootstrapNotificationWorkflow)

• workflow_uuid (str) – UUID of the triggered workflow version, defaults to workflow_input(Inputs.WorkflowUuid)

• workflow_name (str) – Name of the triggered workflow, defaults to workflow_input(Inputs.WorkflowName)

• workflow_version (str) – Version of the triggered workflow, defaults to workflow_input(Inputs.WorkflowVersion)

• submission (Any) – Submission object

Example usage:

submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')

IDENTIFIER = 'SUBMISSION_BOOTSTRAP_2'

output(key=None)

Submission bootstrap ends with a CodeBlock, which nests the actual outputs under the ‘result’ key. This method overrides the default output in the same manner that flows_sdk.blocks.Routing.CodeBlock does for convenience.

Parameters:

key (str | None) –

Optionally provide a key to directly get a nested property. When not provided, the entire ‘result’ will be returned. For example, we have:

{
    "result": {
        "a": {
            "b": 42
        }
    }
}

• skipping the key output()` will return ``{"a": {"b": 42}}

• calling with output("a") will result in {"b": 42}

• calling with output("a.b") will return 42

Return type:

str

Returns:

the value under the provided key

class flows_sdk.implementations.idp_v40.idp_blocks.MachineCollationBlock(submission, cases, dedupe_files='${workflow.input.machine_collation_dedupe_files}', refresh_retention_period='${workflow.input.machine_collation_refresh_retention_period}', remove_from_cases='${workflow.input.machine_collation_remove_from_cases}', retention_period='${workflow.input.machine_collation_retention_period}', reference_name=None)

Bases: Block

Machine collation block groups files, documents and pages (from the submission) into cases.

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• cases (Any) –

  This parameter accepts an array of JSON objects that contain information on how Cases should be created. There are two ways to collate a case: by the filenames specified in the current Submission, or by the ids of the Documents or Pages submitted in previous submissions.

  This parameter follows the given format:

  {
      'cases': [
          'external_case_id': 'HS-1',
          'filename': 'file1.pdf',
          'documents': [42],
          'pages': [43]
      ]
  }
  

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• dedupe_files (Union[bool, str]) – Enabling this setting will replace case data from repeated file names within the same case. Cases will retain data from the most recently submitted version of a particular file. Note that this setting does not delete the old data, it just removes it from the case. Keep in mind this option is only relevant when adding to cases. Defaults to workflow_input(Settings.CollationDedupeFiles)

• remove_from_cases (Any | None) –

  This parameter accepts an array of JSON objects that contains information on how documents or pages should be removed from a pre-existing case. Currently, the only way to de-collate from a case is by the ids of the Documents or Pages within a given case, defaults to workflow_input(Settings.CollationRemoveFromCases)

  This parameter follows the given format:

  {
      'remove_from_cases': [
          'external_case_id': 'HS-1',
          'documents': [42],
          'pages': [43]
      ]
  }
  

• retention_period (Union[int, str, None]) – The number of days to retain cases after they are updated by this block. If passed None, no changes to the deletion date will be made. Defaults to workflow_input(Settings.CollationRetentionPeriod)

• refresh_retention_period (Union[bool, str]) – If True, (re)applies Retention Period parameter to all cases passed into the block. If False, only updates those without a pre-existing deletion date. Defaults to workflow_input(Settings.CollationRefreshRetentionPeriod)

Example usage:

machine_collation = MachineCollationBlock(
    reference_name='machine_collation',
    submission=submission_bootstrap.output('result.submission'),
    cases=submission_bootstrap.output('result.api_params.cases'),
    dedupe_files=True,
    remove_from_cases=custom_supervision.output('remove_from_cases')
)

IDENTIFIER = 'MACHINE_COLLATION_2'

class flows_sdk.implementations.idp_v40.idp_blocks.MachineClassificationBlock(submission, api_params, reference_name=None, rotation_correction_enabled='${workflow.input.machine_classification_rotation_correction_enabled}', mobile_processing_enabled='${workflow.input.machine_classification_mobile_processing_enabled}', file_transcription_enabled='${workflow.input.machine_classification_file_transcription_enabled}', layout_release_uuid='${workflow.input.layout_release_uuid}', vpc_registration_threshold='${workflow.input.structured_layout_match_threshold}', nlc_enabled='${workflow.input.semi_structured_classification}', nlc_target_accuracy='${workflow.input.semi_target_accuracy}', nlc_doc_grouping_logic='${workflow.input.semi_doc_grouping_logic}', layout_id_dealbreaker='${workflow.input.layout_id_dealbreaker}', layout_id_dealbreaker_bypass='${workflow.input.layout_id_dealbreaker_bypass}')

Bases: Block

Machine classification block automatically matches documents to structured, semi-structured or additional layouts

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• rotation_correction_enabled (Union[bool, str]) – Identifies and corrects the orientation of semi-structured images, defaults to workflow_input(Settings.RotationCorrectionEnabled)

• mobile_processing_enabled (Union[bool, str]) – Improves the machine readability of photo captured documents, defaults to workflow_input(Settings.MobileProcessingEnabled)

• file_transcription_enabled (Union[bool, str]) – Improves the transcription speed of pdf files. Make sure that the “Image Correction” option is disabled and the pdfs you submit do not contain any rotated images defaults to workflow_input(Settings.FileTranscriptionEnabled)

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• vpc_registration_threshold (str) – Structured pages above this threshold will be automatically matched to a layout variation, defaults to workflow_input(Settings.StructuredLayoutMatchThreshold)

• nlc_enabled (str) – Enables workflow to manage a model for automated classification of semi-structured and additional layout variations, defaults to workflow_input(Settings.SemiStructuredClassification)

• nlc_target_accuracy (str) – defaults to workflow_input(Settings.SemiTargetAccuracy)

• nlc_doc_grouping_logic (str) – Logic to handle multiple pages matched to the same layout variationin a given submission, defaults to workflow_input(Settings.SemiDocGroupingLogic)

Example usage:

machine_classification = MachineClassificationBlock(
    reference_name='machine_classification',
    submission=case_collation.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    rotation_correction_enabled=idp_wf_config.rotation_correction_enabled,
)

IDENTIFIER = 'MACHINE_CLASSIFICATION_7'

class flows_sdk.implementations.idp_v40.idp_blocks.ManualClassificationBlock(submission, api_params, reference_name=None, layout_release_uuid='${workflow.input.layout_release_uuid}', manual_nlc_enabled='${workflow.input.manual_nlc_enabled}', task_restrictions='${workflow.input.manual_classification_task_restrictions}', notification_workflow='${workflow.input.notification_workflow_classification}', vpc_registration_threshold='${workflow.input.structured_layout_match_threshold}')

Bases: Block

Manual classification block allows keyers to manually match submissions to their layouts. Keyers may perform manual classification if machine classification cannot automatically match a submission to a layout with high confidence

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• manual_nlc_enabled (str) – Enables manual classification when applicable, defaults to workflow_input(Settings.ManualNlcEnabled)

• task_restrictions (Union[List[Any], str, None]) – Defines what users can access Supervision tasks created by this block, defaults to workflow_input(Settings.ManualClassificationTaskRestrictions)

• notification_workflow (str) – Notification flow name to run when the submission enters Manual Classification, defaults to workflow_input(Settings.ManualClassificationNotificationWorkflow)

• nlc_enabled – Enables workflow to manage a model for automated classification of semi-structured and additional layout variations, defaults to workflow_input(Settings.SemiStructuredClassification)

• nlc_target_accuracy – Classification Target Accuracy, defaults to workflow_input(Settings.SemiTargetAccuracy)

• mobile_processing_enabled – Improves the machine readability of photo captured document, defaults to workflow_input(Settings.MobileProcessingEnabled)

• vpc_registration_threshold (str | None) – Structured pages above this threshold will be automatically matched to a layout variation when performing light vpc registration in the doc-org task, defaults to workflow_input(Settings.StructuredLayoutMatchThreshold)

Example usage:

manual_classification = ManualClassificationBlock(
    reference_name='manual_classification',
    submission=machine_classification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
)

IDENTIFIER = 'MANUAL_CLASSIFICATION_4'

class flows_sdk.implementations.idp_v40.idp_blocks.MachineIdentificationBlock(submission, api_params, layout_release_uuid='${workflow.input.layout_release_uuid}', reference_name=None, manual_field_id_enabled='${workflow.input.manual_field_id_enabled}', force_required_missing_entries_to_sv='${workflow.input.force_required_entries_to_sv}', field_id_target_accuracy='${workflow.input.field_id_target_accuracy}', table_id_target_accuracy='${workflow.input.table_id_target_accuracy}')

Bases: Block

Machine identification automatically identify fields and tables in the submission

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• layout_release_uuid (Any) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• manual_field_id_enabled (str) – Enables manual identification when applicable, defaults to workflow_input(Settings.ManualFieldIdEnabled)

• force_required_missing_entries_to_sv (str) – Available from V40.2; defaults to workflow_input( Settings.ForceRequiredEntriesToSV )

• field_id_target_accuracy (str) – Field ID Target Accuracy, defaults to workflow_input(Settings.FieldIdTargetAccuracy)

• table_id_target_accuracy (str) – Table ID Target Accuracy, defaults to workflow_input(Settings.TableIdTargetAccuracy)

Example usage:

machine_identification = MachineIdentificationBlock(
    reference_name='machine_identification',
    submission=manual_classification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
)

IDENTIFIER = 'MACHINE_IDENTIFICATION_7'

class flows_sdk.implementations.idp_v40.idp_blocks.ManualIdentificationBlock(submission, api_params, reference_name=None, task_restrictions='${workflow.input.manual_identification_task_restrictions}', manual_field_id_enabled='${workflow.input.manual_field_id_enabled}', layout_release_uuid='${workflow.input.layout_release_uuid}', notification_workflow='${workflow.input.notification_workflow_identification}')

Bases: Block

Manual identification allows keyers to complete field identification or table identification tasks, where they draw bounding boxes around the contents of certain fields, table columns or table rows. This identification process ensures that the system will be able to transcribe the correct content in the upcoming transcription process

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• task_restrictions (Union[List[Any], str, None]) – Defines what users can access Supervision tasks created by this block, defaults to workflow_input(Settings.ManualFieldIdTaskRestrictions)

• manual_field_id_enabled (str) – Enables manual identification when applicable, defaults to workflow_input(Settings.ManualFieldIdEnabled)

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• notification_workflow (str) – Notification flow name to run when the submission enters Manual Identification, defaults to workflow_inputSettings.ManualFieldIdNotificationWorkflow

Example usage:

manual_identification = ManualIdentificationBlock(
    reference_name='manual_identification',
    submission=machine_identification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    task_restrictions=idp_wf_config.manual_identification_config.task_restrictions,
)

IDENTIFIER = 'MANUAL_IDENTIFICATION_5'

class flows_sdk.implementations.idp_v40.idp_blocks.MachineTranscriptionBlock(submission, api_params, reference_name=None, layout_release_uuid='${workflow.input.layout_release_uuid}', transcription_model='${workflow.input.transcription_model}', finetuning_only_trained_layouts='${workflow.input.finetuning_only_trained_layouts}', force_normalization_errors_to_sv='${workflow.input.force_normalization_errors_to_sv}', force_required_blank_entries_to_sv='${workflow.input.force_required_entries_to_sv}', transcription_overrides='${workflow.input.transcription_overrides}', structured_text_target_accuracy='${workflow.input.structured_text_target_accuracy}', structured_text_confidence_threshold='${workflow.input.structured_text_threshold}', structured_text_acceptable_confidence='${workflow.input.structured_min_leg_threshold}', semi_structured_text_target_accuracy='${workflow.input.semi_structured_text_target_accuracy}', semi_structured_text_confidence_threshold='${workflow.input.semi_structured_text_threshold}', semi_structured_text_acceptable_confidence='${workflow.input.semi_structured_min_leg_threshold}', semi_structured_table_target_accuracy='${workflow.input.semi_structured_table_target_accuracy}', semi_structured_table_confidence_threshold='${workflow.input.semi_structured_table_threshold}', semi_structured_table_acceptable_confidence='${workflow.input.semi_structured_table_min_leg_threshold}', structured_checkbox_target_accuracy='${workflow.input.structured_checkbox_target_accuracy}', structured_checkbox_confidence_threshold='${workflow.input.structured_checkbox_threshold}', structured_checkbox_acceptable_confidence='${workflow.input.checkbox_min_leg_threshold}', structured_signature_target_accuracy='${workflow.input.structured_signature_target_accuracy}', structured_signature_confidence_threshold='${workflow.input.structured_signature_threshold}', structured_signature_acceptable_confidence='${workflow.input.signature_min_leg_threshold}', semi_structured_checkbox_target_accuracy='${workflow.input.semi_structured_checkbox_target_accuracy}', semi_structured_checkbox_confidence_threshold='${workflow.input.semi_structured_checkbox_threshold}', semi_structured_checkbox_acceptable_confidence='${workflow.input.semi_structured_checkbox_min_leg_threshold}', field_target_accuracies='${workflow.input.field_transcription_target_accuracies}', column_target_accuracies='${workflow.input.column_transcription_target_accuracies}')

Bases: Block

Machine transcription automatically transcribes the content of your submission

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• transcription_model (str) – The transcription model that will be used for this flow, defaults to workflow_input(Settings.TranscriptionModel)

• finetuning_only_trained_layouts (str) – defaults to workflow_input( Settings.FinetuningOnlyTrainedLayouts )

• force_normalization_errors_to_sv (str) – Available from V40.2; defaults to workflow_input( Settings.ForceNormalizationErrorsToSV )

• force_required_blank_entries_to_sv (str) – Available from V40.2; defaults to workflow_input( Settings.ForceRequiredEntriesToSV )

• transcription_overrides (str) – defaults to workflow_input( Settings.TranscriptionOverrides )

• structured_text_target_accuracy (str) – defaults to workflow_input( Settings.StructuredTextTargetAccuracy )

• structured_text_confidence_threshold (str) – defaults to workflow_input( Settings.StructuredTextThreshold )

• structured_text_acceptable_confidence (str) – defaults to workflow_input( Settings.StructuredTextMinLegThreshold )

• semi_structured_text_target_accuracy (str) – defaults to workflow_input( Settings.SemiStructuredTextTargetAccuracy )

• semi_structured_text_confidence_threshold (str) – defaults to workflow_input( Settings.SemiStructuredTextThreshold )

• semi_structured_text_acceptable_confidence (str) – defaults to workflow_input( Settings.SemiStructuredTextMinLegThreshold )

• structured_checkbox_target_accuracy (str) – defaults to workflow_input( Settings.StructuredCheckboxTargetAccuracy )

• structured_checkbox_confidence_threshold (str) – defaults to workflow_input( Settings.StructuredCheckboxThreshold )

• structured_checkbox_acceptable_confidence (str) – defaults to workflow_input( Settings.StructuredCheckboxMinLegThreshold )

• structured_signature_target_accuracy (str) – defaults to workflow_input( Settings.StructuredSignatureTargetAccuracy )

• structured_signature_confidence_threshold (str) – defaults to workflow_input( Settings.StructuredSignatureThreshold )

• structured_signature_acceptable_confidence (str) – defaults to workflow_input( Settings.StructuredSignatureMinLegThreshold )

• semi_structured_checkbox_target_accuracy (str) – defaults to workflow_input( Settings.SemiStructuredCheckboxTargetAccuracy )

• semi_structured_checkbox_confidence_threshold (str) – defaults to workflow_input( Settings.SemiStructuredCheckboxThreshold )

• semi_structured_checkbox_acceptable_confidence (str) – defaults to workflow_input( Settings.SemiStructuredCheckboxMinLegThreshold )

• field_target_accuracies (str) – defaults to workflow_input( Settings.FieldTargetAccuracies )

• column_target_accuracies (str) – defaults to workflow_input( Settings.ColumnTargetAccuracies )

Example usage:

machine_transcription = MachineTranscriptionBlock(
    reference_name='machine_transcription',
    submission=manual_identification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
)

IDENTIFIER = 'MACHINE_TRANSCRIPTION_9'

class flows_sdk.implementations.idp_v40.idp_blocks.ManualTranscriptionBlock(submission, api_params, reference_name=None, supervision_transcription_masking='${workflow.input.manual_transcription_supervision_transcription_masking}', table_output_manual_review='${workflow.input.manual_transcription_table_output_manual_review}', always_supervise_blank_cells='${workflow.input.manual_transcription_always_supervise_blank_cells}', task_restrictions='${workflow.input.manual_transcription_task_restrictions}', manual_transcription_enabled='${workflow.input.manual_transcription_enabled}', notification_workflow='${workflow.input.notification_workflow_transcription}', title='Manual Transcription', description='Manually transcribe fields that could not be automatically transcribed')

Bases: Block

Manual transcription lets your keyers manually enter the text found in fields or tables that could not be automatically transcribed

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (Union[bool, str]) – Prevents users from inputting invalid characters during Supervision Transcription tasks, defaults to workflow_input(Settings.ManualTranscriptionSupervisionTranscriptionMasking)

• table_output_manual_review (Union[bool, str]) – Always generates a table transcription task if the layout contains a table. If disabled, a table transcription task will only be generated if one or more cells have transcribed values below the defined thresholds, defaults to workflow_input(Settings.ManualTranscriptionTableOutputManualReview)

• always_supervise_blank_cells (Union[bool, str]) – If enabled, always send blank cells in transcribed tables to be supervised manually, regardless of confidence defaults to workflow_input(Settings.ManualTranscriptionAlwaysSuperviseBlankCells)

• task_restrictions (Union[List[Any], str, None]) – Defines what users can access Supervision tasks for submission from a particular source or submissions matching a specific layout, defaults to workflow_input(Settings.ManualTranscriptionTaskRestrictions)

• manual_transcription_enabled (str) – Enables manual transcription when applicable, defaults to workflow_input(Settings.ManualTranscriptionEnabled)

• notification_workflow (str) – Notification flow name to run when the submission enters Manual Transcription, defaults to workflow_input(Settings.ManualTranscriptionNotificationWorkflow)

Example usage:

manual_transcription = ManualTranscriptionBlock(
    reference_name='manual_transcription',
    submission=machine_transcription.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    supervision_transcription_masking=(
        idp_wf_config.manual_transcription_config.supervision_transcription_masking
    ),
    table_output_manual_review=(
        idp_wf_config.manual_transcription_config.table_output_manual_review
    ),
    task_restrictions=idp_wf_config.manual_transcription_config.task_restrictions,
)

IDENTIFIER = 'MANUAL_TRANSCRIPTION_5'

class flows_sdk.implementations.idp_v40.idp_blocks.FlexibleExtractionBlock(submission, api_params, reference_name=None, supervision_transcription_masking='${workflow.input.flexible_extraction_supervision_transcription_masking}', task_restrictions='${workflow.input.flexible_extraction_task_restrictions}', layout_release_uuid='${workflow.input.layout_release_uuid}', notification_workflow='${workflow.input.notification_workflow_flexible_extraction}')

Bases: Block

Flexible extraction manually transcribes fields marked for review

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (Union[bool, str, None]) – Prevents users from inputting invalid characters during the Flexible Extraction task, defaults to workflow_input(Settings.FlexibleExtractionSupervisionTranscriptionMasking)

• task_restrictions (Union[List[Any], str, None]) – Defines what users can access Supervision tasks created by this block, defaults workflow_input(Settings.FlexibleExtractionTaskRestrictions)

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• notification_workflow (str) – Notification flow name to run when the submission enters Flexible Extraction, defaults to workflow_input(Settings.FlexibleExtractionNotificationWorkflow)

Example usage:

flexible_extraction = FlexibleExtractionBlock(
    reference_name='flexible_extraction',
    submission=manual_transcription.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    supervision_transcription_masking=(
        idp_wf_config.flexible_extraction_config.supervision_transcription_masking
    ),
    task_restrictions=idp_wf_config.flexible_extraction_config.task_restrictions,
)

IDENTIFIER = 'FLEXIBLE_EXTRACTION_4'

class flows_sdk.implementations.idp_v40.idp_blocks.ReprocessingBlock(reference_name, submission, parent_flow='${workflow.definition.identifier}', idp_wf_config='${workflow.input}', trigger='${workflow.input.$trigger}')

Bases: Block

Reprocessing block enables a second extraction attempt if documents were initially confidently mis-classified by the machine. When an end-user specifies that a document layout is incorrect during Manual Identification, Manual Transcription, or Flexible Extraction, the flow will reach the Reprocessing block which will mark correctly classified documents as “complete” status. Misclassified documents with “reprocess” status will be marked with status “reprocessing” and processed in a second execution of the flow. This block should be placed before the SubmissionCompleteBlock to enable reprocessing.

How the Reprocessing block works

The Reprocessing block examines whether any documents within the submission have been marked with an incorrect layout, and if so, redirects the submission to the classification step for proper classification. It does this by recursively calling the same flow or subflow with the same inputs and documents, but with the addition of a status key for each document to denote which were marked with an incorrect layout during supervision.

Note

The Reprocessing block works in tandem with the “Mark Incorrect Layout” option presented to users during Manual Identification, Manual Transcription, and Flexible Extraction supervision interfaces introduced in R38. Include the Reprocessing block in all custom flows in order to take advantage of this feature. The feature will remain selectable in supervision interfaces even if the Reprocessing block is not included in the flow.

The initial flow run will pause execution at the Reprocessing block until the recursive flow has completed, at which point the initial flow will resume to finish its execution. If an error occurs during the execution of the recursive flow, that error will be raised to the Reprocessing block of the initial flow.

Note

The recursive flow run is linked with the same correlation_id as the initial flow run and the submission. Users can click the “View related flow runs” link on the left sidebar of the initial flow run to view any recursive flow runs.

As expected, recursively calling the same flow may cause some blocks to run again, resulting in unnecessary work being performed, or work which doesn’t gracefully handle duplications (non-idempotent blocks). To mitigate this, certain Hyperscience-provided blocks - including Submission Initialization, Machine Collation, and Machine Classification - detect when the submission object already contains the output from its block to avoid re-execution. It’s a best practice to build custom blocks such that they are idempotent and reentrant; if that’s not feasible, you can add similar logic to custom blocks which do not depend on the layout for the documents being processed. Common methods for handling this include:

• Iterating through each status key within the submission.document[] collection to see if any documents are set to “reprocess” or “reprocessing”.

• Inspecting if the submission object already contains a key or value explicitly defined by the custom code block.

Recommended Placement for the Reprocessing Block

• The Reprocessing block should not be placed before any supervision blocks which allow users to mark a document with an incorrect layout. These supervision blocks include Manual Identification, Manual Transcription and Flexible Extraction.

• The Reprocessing block should always be placed before the SubmissionCompete Block.

• The Reprocessing block can be used with flows which include multiple branches and conditionals; however, it’s recommended to ensure the Reprocessing block is executed to honor user’s selection of marking layouts incorrectly when supervising documents.

• In general, any custom logic that is to be performed after data has been extracted from documents should be placed after the Reprocessing block. To avoid duplicate executions of this custom logic, it is recommended to separate the document processing blocks - inclusive of the Reprocessing block - from the custom logic. This can be handled in different ways:

  • Split the flow into separate subflows, where the Reprocessing block is in a subflow that does not contain the custom logic.

  • Add a decision to bypass the custom logic if the Reprocessing block was triggered. With this method, the recursive flow run will execute the custom logic, and the initial flow run will bypass the logic.

Mandatory parameters:

Parameters:

submission (Any) – Submission object

IDENTIFIER = 'REPROCESSING_2'

class flows_sdk.implementations.idp_v40.idp_blocks.SubmissionCompleteBlock(submission, is_reprocessing=False, reference_name=None, nlc_qa_sampling_ratio='${workflow.input.semi_qa_sample_rate}', field_id_qa_enabled='${workflow.input.field_id_qa_enabled}', field_id_qa_sampling_ratio='${workflow.input.field_id_qa_sample_rate}', table_id_qa_enabled='${workflow.input.table_id_qa_enabled}', table_id_qa_sampling_ratio='${workflow.input.table_id_qa_sample_rate}', transcription_qa_enabled='${workflow.input.qa}', transcription_structured_entry_qa_sample_rate='${workflow.input.structured_entry_qa_sample_rate}', transcription_structured_signature_qa_sample_rate='${workflow.input.structured_signature_qa_sample_rate}', transcription_structured_checkbox_qa_sample_rate='${workflow.input.structured_checkbox_qa_sample_rate}', transcription_semi_structured_qa_sample_rate='${workflow.input.semi_structured_qa_sample_rate}', table_cell_transcription_qa_enabled='${workflow.input.table_cell_transcription_qa_enabled}', table_cell_transcription_qa_sample_rate='${workflow.input.table_cell_transcription_qa_sample_rate}', auto_qa_sampling_rate_enabled='${workflow.input.auto_qa_sampling_rate_enabled}', exclude_auto_transcribe_fields_from_qa='${workflow.input.exclude_auto_transcribe_fields_from_qa}', payload=None)

Bases: Block

Submission complete block finalizes submission processing and updates reporting data. This block should be called at the END of every flow that uses blocks included in the flows_sdk.implementations.idp_v34 library. The block requires both the submission object and a payload to be passed in.

Mandatory parameters:

Parameters:

• submission (str) – Submission object

• payload (Any) – Object to pass to downstream systems

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• nlc_qa_sampling_ratio (Union[str, int]) – Defines the percentage of documents sampled for Classification QA, defaults to workflow_input(Settings.SemiQaSampleRate)

• field_id_qa_enabled (Union[str, bool]) – defaults to workflow_input(Settings.FieldIdQaEnabled)

• field_id_qa_sampling_ratio (Union[str, int]) – Defines the percentage of documents sampled for Identification QA, defaults to workflow_input(Settings.FieldIdQaSampleRate)

• table_id_qa_enabled (Union[str, bool]) – Allows users to verify the location of table cells, defaults to workflow_input(Settings.TableIdQaEnabled)

• table_id_qa_sampling_ratio (Union[str, int]) – Defines the percentage of tables the system samples for QA, defaults to workflow_input(Settings.TableIdQaSampleRate)

• transcription_qa_enabled (Union[str, bool]) – defaults to workflow_input(Settings.TranscriptionQaEnabled)

• transcription_qa_sampling_ratio – Defines the percentage of fields sampled for Transcription QA, defaults to workflow_input(Settings.TranscriptionQaSampleRate)

• table_cell_transcription_qa_enabled (Union[str, bool]) – defaults to workflow_input(Settings.TableCellTranscriptionQaEnabled)

• table_cell_transcription_qa_sample_rate (Union[str, int]) – Defines the percentage of cells the system samples for QA. This value is likely to be lower than “Transcription QA Sample Rate” since there are more table cells than fields on any given page, defaults to workflow_input(Settings.TableCellTranscriptionQaSampleRate)

• exclude_auto_transcribe_fields_from_qa (Union[str, bool]) – Defines fields that are marked as auto-transcribe in layout should be excluded from QA or not. defaults to workflow_input(Settings.ExcludeAutoTranscribeFieldsFromQA)

• is_reprocessing (Union[str, bool]) – boolean to handle execution when reprocessing defaults to False

Example usage:

submission_complete = SubmissionCompleteBlock(
    reference_name='complete_submission',
    submission=flexible_extraction.output('submission')
)

IDENTIFIER = 'SUBMISSION_COMPLETE_4'

class flows_sdk.implementations.idp_v40.idp_blocks.SetTransformedOutputBlock(submission, transformed_output, reference_name=None)

Bases: Block

Set Transformed Output block sets the transformed output for the provided submission. This can later be used to send the transformed data downstream using output blocks. For more information see https://docs.hyperscience.ai/#retrieving-transformed-submissions.

Mandatory parameters:

Parameters:

• submission (str) – Submission object

• transformed_output (str) – Object that will be set as the transformed output for this submission

IDENTIFIER = 'SET_TRANSFORMED_OUTPUT'

class flows_sdk.implementations.idp_v40.idp_blocks.IdpCustomSupervisionBlock(submission, task_purpose, supervision_template, reference_name=None, supervision_transcription_masking=True, task_restrictions=None, page_ids=None, notification_workflow='IDP_SUBMISSION_NOTIFY', title='IDP Custom Supervision', description='IDP Custom Supervision')

Bases: Block

An IDP wrapper for the Custom Supervision block. It has the same functionality, but handles reading/writing data from the IDP database.

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• task_purpose (str) – the custom task name that’s given to all supervision tasks that run through a particular instance of a custom supervision block. This can be used to filter tasks in the task queue.

• supervision_template (Any) – JSON used for configuration of the Custom Supervision Task.

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (Union[str, bool]) – Prevents users from inputting invalid characters during the Custom Supervision task, defaults to True

• task_restrictions (Union[str, List[Any], None]) – Defines what users can access Supervision tasks created by this block, defaults to []

• page_ids (Union[str, List[int], None]) – This is an optional parameter. If left empty, the IDP wrapper will, by default, pull in all pages in the submission. Adding a list of page IDs will override the default list of pages. defaults to []

• notification_workflow (str) – Notification flow name to run when the submission enters Custom Supervision (which is called within the IDP Custom Supervision block), defaults to IDP_SUBMISSION_NOTIFY_NAME

• title (Optional[str]) – UI-visible title of the block. Defaults to ‘IDP Custom Supervision’

• description (Optional[str]) – Description of the block. Both useful for documentation purpose and visible to users in the Flow studio. Defaults to ‘IDP Custom Supervision’

Example usage:

idp_custom_supervision = IdpCustomSupervisionBlock(
    reference_name='idp_custom_supervision',
    submission=manual_transcription.output('submission'),
    task_purpose='make_custom_decision',
    supervision_template=[
        {
            'name': 'three_column_template',
            'version': '1.0',
            'thumbnail': {
                'group_by_document': True,
                'group_by_case': True,
            },
            'action': [
                {
                    'name': 'my_tab',
                    'display': 'SSN Info',
                    'input': [
                        {
                            'name': 'ssn_field',
                            'type': 'transcription',
                            'data_identifier': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                            'title': 'SSN',
                        },
                    ],
                    'ui': {
                        'groups': [
                            {
                                'title': 'My Group',
                                'fields': ['ssn_field'],
                            },
                        ],
                    },
                },
            ],
        },
    ],
    supervision_transcription_masking=False,
)

IDENTIFIER = 'IDP_CUSTOM_SUPERVISION_2'

class flows_sdk.implementations.idp_v40.idp_blocks.IDPFullPageTranscriptionBlock(submission, reference_name=None, title='Full Page Transcription (Submission)', description='Transcribes the documents included in a submission', app_metadata=None, language_family=None)

Bases: Block

IDP Full Page Transcription Block machine-transcribes the documents contained in a submission

Mandatory parameters:

Parameters:

submission (str) – submission object that includes the raw documents that need to be transcribed

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• app_metadata (Optional[List[str]]) – used for capturing information about the flow

• language_family (str | None) – language family to use when instantiating the underlying transcription model. If not provided, the system will default to latin. Language families are single word lowercase strings, e.g., latin, arabic, korean, chinese. You can read more about language families in the “Supported Languages” article in the application documentation: https://help.hyperscience.ai/v40/docs/supported-languages.

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# IDPFullPageTranscriptionBlock transcribes the documents contained in the submission object
idp_fpt_block = IDPFullPageTranscriptionBlock(
    reference_name='idp_fpt',
    submission=submission_bootstrap.output(),
)
# reference to the output of IDPFullPageTranscriptionBlock which can be passed as input to
# the next block. For more information on output() and output references, please take a
# look at class Block and its output() method
output_ref = idp_fpt_block.output()

IDENTIFIER = 'FULL_PAGE_TRANSCRIPTION_SUBMISSION_2'

class flows_sdk.implementations.idp_v40.idp_blocks.DocumentRendererBlock(submission, is_reprocessing=False, title='Document Renderer', description='Renders a document into a PDF', reference_name=None, document_rendering_enabled='${workflow.input.document_rendering_enabled}', document_rendering_mime_type='${workflow.input.document_rendering_mime_type}', document_rendering_unit='${workflow.input.document_rendering_unit}', document_rendering_height='${workflow.input.document_rendering_height}', document_rendering_width='${workflow.input.document_rendering_width}', document_rendering_quality='${workflow.input.document_rendering_quality}')

Bases: Block

Document File Generator Block renders a document into a PDF and stores it in the Blob storage.

This block should be placed ALWAYS after a Reprocessing block (if there is any) so that it is not possible to have more changes in the documents’ organisation.

Mandatory parameters:

Parameters:

submission (str) – submission object that includes the raw documents that need to be rendered.

Optional parameters:

Parameters:

• document_rendering_enabled (Union[bool, str]) – a boolean that enables or disables the document rendering. Default is False.

• document_rendering_mime_type (str) – the mime type of the document to be generated. Default is ‘application/pdf’

• document_rendering_unit (str) – the unit of the document to be generated. Default is ‘inches’

• document_rendering_height (Union[str, float]) – the height of the document to be generated. Default is 11

• document_rendering_width (Union[str, float]) – the width of the document to be generated. Default is 8.5

• document_rendering_quality (Union[str, int]) – Quality of pages (images) appended to the resulting document as a percentage (recommended 50%). Higher quality results in a larger file size for the generated PDF.

• is_reprocessing (Union[str, bool]) – boolean to handle execution when reprocessing defaults to False

Example usage:

# Use the output of the reprocessing block.
reprocessing = ReprocessingBlock(reference_name='reprocessing')

document_renderer_block = DocumentRendererBlock(
    reference_name='document_renderer',
    submission=reprocessing.output(submission),
    document_rendering_enabled=True,
    document_rendering_unit='in',
    document_rendering_width=8.5,
    document_rendering_height=11,
    document_rendering_quality=50
    is_reprocessing=reprocessing.output('is_reprocessing')
)

output_ref = document_renderer_block.output()

Example content of output_ref:

{
    'document_files': {
        [
            {
                'document_id': "1",
                'blob_uuid': 'f42fc9af-b551-4403-aba6-d0eba2c41973',
                'mime_type': 'application/pdf',
                'height_in_mm': 279.4,
                'width_in_mm': 215.9,
                'quality': 50,
            }
        ]
    },
    'submission': {
        ...
    }
}

IDENTIFIER = 'DOCUMENT_RENDERER'

class flows_sdk.implementations.idp_v40.idp_blocks.IDPImageCorrectionBlock(submission, reference_name=None, rotation_correction_enabled=True, mobile_processing_enabled=False, language_family=None, title='Image Correction (Submission)', description='Rotate and de-skew documents included in a submission', app_metadata=None)

Bases: Block

IDP Image Correction Block rotates and de-skews tilted/skewed images so that they are more transcription-friendly

Mandatory parameters:

Parameters:

submission (str) – submission object that includes the raw documents that need to be image-corrected

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• rotation_correction_enabled (bool) – Identifies and corrects the orientation of images. Enabled by default.

• mobile_processing_enabled (bool) – Improves the machine readability of photo captured documents. Disabled by default.

• language_family (str | None) – language family to use when performing image correction. If not provided, the system will default to latin. Language families are single word lowercase strings, e.g., latin, arabic, korean, chinese. You can read more about language families in the “Supported Languages” article in the application documentation: https://help.hyperscience.ai/v40/docs/supported-languages.

• app_metadata (Optional[List[str]]) – used for capturing information about the flow

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# IDPImageCorrectionBlock takes in the submission object and outputs in the same format with
# its images
idp_image_correct_block = IDPImageCorrectionBlock(
    reference_name='idp_image_correct',
    submission=submission_bootstrap.output(),
)
# reference to the output of IDPImageCorrectionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look at
# class Block and its output() method
output_ref = idp_image_correct_block.output()

IDENTIFIER = 'IMAGE_CORRECTION_SUBMISSION'

Additional Blocks (v40)

class flows_sdk.implementations.idp_v40.additional_blocks.JSONOutputsBlock(inputs, blocks=None)

Bases: Outputs

Output block allows users to send JSON data extracted by an IDP flow to other systems for downstream processing Mandatory parameters:

Parameters:

inputs (Dict[str, Any]) – Used by the UI to automatically pre-populate the input of newly added blocks. Extended in this class to include 'enabled': True to visualize an enabled/disabled trigger that is controllable via Flow Studio.

Usage when parameters are to be defined in Flow Studio:

outputs = JSONOutputBlock(
    inputs={'result': }
)

class flows_sdk.implementations.idp_v40.additional_blocks.ImageCorrectionBlock(pages, reference_name=None, rotation_correction_enabled=True, mobile_processing_enabled=False, language_family=None, title='Image Correction (Pages)', description='Rotate and de-skew documents', app_metadata=None)

Bases: Block

Image Correction Block rotates and de-skews tilted/skewed images so that they are more transcription-friendly Mandatory parameters:

Parameters:

pages (str) – list of page object that includes the raw documents that need to be image-corrected

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• rotation_correction_enabled (bool) – Identifies and corrects the orientation of images. Enabled by default.

• mobile_processing_enabled (bool) – Improves the machine readability of photo captured documents. Disabled by default.

• language_family (str | None) – language family to use when performing image correction. If not provided, the system will default to latin. Language families are single word lowercase strings, e.g., latin, arabic, korean, chinese. You can read more about language families in the “Supported Languages” article in the application documentation: https://help.hyperscience.ai/v40/docs/supported-languages.

• app_metadata (Optional[List[str]]) – used for capturing information about the flow

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# ImageCorrectionBlock takes in the submission object and outputs in the same format with
# its images
image_correct_block = ImageCorrectionBlock(
    reference_name='image_correct',
    pages=submission_bootstrap.output(submission.unassigned_pages),
)
# reference to the output of IDPImageCorrectionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look
# at class Block and its output() method
output_ref = image_correct_block.output()

IDENTIFIER = 'IMAGE_CORRECTION_PAGES'

class flows_sdk.implementations.idp_v40.additional_blocks.FullPageTranscriptionBlock(pages, reference_name=None, title='Full Page Transcription (Pages)', description='Transcribes documents', language_family=None, app_metadata=None)

Bases: Block

Full Page Transcription Block machine-transcribes the documents contained in a submission

Mandatory parameters:

Parameters:

pages (str) – list of pages that includes the raw documents that need to be transcribed

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• language_family (str | None) – language family to use when instantiating the underlying transcription model. If not provided, the system will default to latin. Language families are single word lowercase strings, e.g., latin, arabic, korean, chinese. You can read more about language families in the “Supported Languages” article in the application documentation: https://help.hyperscience.ai/v40/docs/supported-languages.

• app_metadata (Optional[List[str]]) – used for capturing information about the flow

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# FullPageTranscriptionBlock transcribes the documents contained in the submission object
fpt_block = FullPageTranscriptionBlock(
    reference_name='fpt',
    submission=submission_bootstrap.output(submission.unassigned_pages),
)
# reference to the output of FullPageTranscriptionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look at
# class Block and its output() method
output_ref = fpt_block.output()

IDENTIFIER = 'FULL_PAGE_TRANSCRIPTION_PAGES_2'

class flows_sdk.implementations.idp_v40.additional_blocks.CustomSupervisionBlock(submission, task_purpose, data, supervision_template, reference_name=None, supervision_transcription_masking=True, task_restrictions=None, notification_workflow='IDP_SUBMISSION_NOTIFY', title='Custom Supervision', description='Custom Supervision')

Bases: Block

The CustomSupervisionBlock uses a JSON schema as a form builder to create customer-driven UIs for Supervision tasks.

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• task_purpose (str) – the custom task name that’s given to all supervision tasks that run through a particular instance of a custom supervision block. This can be used to filter tasks in the task queue.

• data (Any) – JSON structure that supplies the data used to populated the supervision_template

• supervision_template (Any) – JSON used for configuration of the Custom Supervision Task.

Parameters with defaults:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (bool) – Prevents users from inputting invalid characters during the Custom Supervision task, defaults to True

• task_restrictions (Optional[List[Any]]) – Defines what users can access Supervision tasks created by this block, defaults to []

• notification_workflow (str) – Notification flow name to run when the submission enters Custom Supervision, defaults to IDP_SUBMISSION_NOTIFY_NAME

• title (Optional[str]) – UI-visible title of the block. Defaults to ‘IDP Custom Supervision’

• description (Optional[str]) – Description of the block. Both useful for documentation purpose and visible to users in the Flow studio. Defaults to ‘IDP Custom Supervision’

Example usage:

custom_supervision = CustomSupervisionBlock(
    reference_name='custom_supervision',
    submission=manual_transcription.output('submission'),
    task_purpose='make_custom_decision',
    supervision_template=[
        {
            'name': 'three_column_template',
            'version': '1.0',
            'thumbnail': {
                'group_by_document': True,
                'group_by_case': True,
            },
            'action': [
                {
                    'name': 'my_tab',
                    'display': 'SSN Info',
                    'input': [
                        {
                            'name': 'ssn_field',
                            'type': 'transcription',
                            'data_identifier': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                            'title': 'SSN',
                        },
                    ],
                    'ui': {
                        'groups': [
                            {
                                'title': 'My Group',
                                'fields': ['ssn_field'],
                            },
                        ],
                    },
                },
            ],
        },
    ],
    data={
        'fields': [
            {
                'id': 967,
                'uuid': 'fe8d4e7a-a82c-448c-85a1-0a3cbbe53b61',
                'value': '111-22-3333',
                'page_id': 15,
                'layout_field_id': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                'validation_overridden': False,
                'bounding_box': [
                    0.09360783305186686,
                    0.25792617589433436,
                    0.6358913805295097,
                    0.28862414740388187,
                ],
                'occurrence_index': 1,
            },
        ],
        'template_fields': [
            {
                'uuid': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                'notes': '',
                'type': 'entry',
                'data_type_uuid': 'b600a4e4-758f-4c4b-a2f4-cbb5dd870a0c',
                'name': 'SSN',
            },
        ],
        'pages': [
            {
                'id': 15,
                'file_page_number': 1,
                'document_page_number': 1,
                'image_url': '/image/2ed3d989-b64a-44bd-a9fb-6e0dcc5ccdb8',
                'form_id': 2,
                'submission_id': 1,
                'external_case_ids': [],
                'read_only': False,
                'filename': '1 page.pdf',
                'form_page_id': 1
            },
        ],
        'documents': [
            {
                'id': 2,
                'layout_uuid': 'e31c3cbf-dcea-44fd-a052-902463c6040f',
                'layout_name': 'layout_name',
                'page_count': 2,
                'read_only': False,
            },
        ],
        'cases': [],
        'selected_choices': [],
    },
    supervision_transcription_masking=False,
)

IDENTIFIER = 'CUSTOM_SUPERVISION_2'

class flows_sdk.implementations.idp_v40.additional_blocks.SoapRequestBlock(method, reference_name=None, endpoint='', body_namespace=None, headers_namespace=None, headers=None, params=None, use_wsdl=False, wsdl_uri='', wsdl_service_name='', wsdl_port_name='', title='HTTP REST Block', description='HTTP REST block')

Bases: Block

Soap Block allows user to perform SOAP requests Mandatory parameters:

Parameters:

• method (str) – SOAP method to invoke

• endpoint (str) – absolute url including schema to HTTP endpoint for this request

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• body_namespace (str | None) – SOAP request body namespace url

• headers_namespace (str | None) – SOAP request headers namespace url

• headers (Optional[Dict[Any, Any]]) – SOAP headers to use

• params (Optional[Dict[Any, Any]]) – key-value pair used as query parameters in the request to be inserted as SOAP body

Example usage:

# minimum viable instantiation, the rest of the fields can later be added in the UI
soap_block = SoapRequestBlock(
    reference_name='soap_request',
    title='Soap Request',
    description='Perform soap request',
    method='ExampleMethod',
)
soap_block = SoapRequestBlock(
    reference_name='soap_request',
    title='Soap Request',
    description='Perform soap request',
    method='ExampleMethod',
    endpoint='http://example.org/abc.wso',
    body_namespace='http://www.example.org/abc.countryinfo',
    params={'example': 'stuff'},
    headers={}
)
# reference to the output of SoapRequestBlock which can be passed as input to the next block
# for more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = soap_block.output()

IDENTIFIER = 'SOAP_REQ'

class flows_sdk.implementations.idp_v40.additional_blocks.HttpRestBlock(method, endpoint, reference_name=None, headers=None, params=None, payload=None, json=None, authorization_type=None, handled_error_codes=None, auth_params=None, title='HTTP REST Block', description='HTTP REST block')

Bases: BaseHttpRestBlock

Http Rest Block allows user to perform HTTP requests Mandatory parameters:

Parameters:

• method (str) – HTTP method to use

• endpoint (str) – absolute url including schema to HTTP endpoint for this request

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• headers (Optional[Dict[str, str]]) – HTTP headers to use

• params (Optional[Dict[str, Any]]) – key-value pair used as query parameters in the request

• payload (Optional[Dict[str, str]]) – key-value pair to be used as x-www-form-urlencoded data. Mutually exclusive with json

• json (Optional[Dict[str, Any]]) – key-value pair to be used as json data. Mutually exclusive with payload

• authorization_type (str | None) – authorization type (none, http_header, oauth_2_client_credentials)

• handled_error_codes (Optional[List[Union[int, str]]]) – specify HTTP error codes that will not fail the task, allowing for wildcards with the ‘x’ char. Example: [400, “5xx”]

• auth_params (Union[HttpHeaderAuthorizationParams, OAuth2AuthorizationParams, None]) – authorization parameters to be passed when the authorization_type is set to “http_header” or “oauth_2_client_credentials”

Example usage:

# minimum viable instantiation, the rest of the fields can later be added in the UI
http_rest_block = HttpRestBlock(
    reference_name='http_request',
    title='Http Request',
    description='Perform http request',
    method='GET',
)
http_rest_block = HttpRestBlock(
    reference_name='http_request',
    title='Http Request',
    description='Perform http request',
    method='GET',
    endpoint='https://sheets.googleapis.com/v4/spreadsheets/example',
)
# reference to the output of HttpRestBlock which can be passed as input to the next block
# for more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = http_rest_block.output()

class flows_sdk.implementations.idp_v40.additional_blocks.HyperscienceRestApiBlock(method, app_endpoint, reference_name=None, headers=None, params=None, payload=None, json=None, handled_error_codes=None, title='Hyperscience HTTP REST Block', description='Hyperscience HTTP REST block')

Bases: BaseHttpRestBlock

Make HTTP requests to the Hyperscience platform APIs. This block will automatically take care of prepending the base url and will include the appropriate authentication headers with the request.

Mandatory parameters:

Parameters:

• method (str) – HTTP method to use, e.g. ‘GET’, ‘POST’, etc.

• app_endpoint (str) – relative url, beginning with a forward slash, to an HTTP endpoint served by the Hyperscience platform. For reference on the available endpoints, check out https://docs.hyperscience.ai/. Absolute URLs that include a hostname are allowed only if the flow has received them as part of the response of a previous API request that the flow has made. Do not hardcode the hostname of the Hyperscience instance in your flow definitions as they will not work when you run the flows on another instance, or if the hostname changes. For making requests to external systems, use the HttpRestBlock or a library like “requests”.

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• headers (Optional[Dict[str, str]]) – HTTP headers to use

• params (Optional[Dict[str, Any]]) – key-value pair used as query parameters in the request

• payload (Optional[Dict[str, str]]) – key-value pair to be used as x-www-form-urlencoded data. Mutually exclusive with json

• json (Optional[Dict[str, Any]]) – key-value pair to be used as json data. Mutually exclusive with payload

• handled_error_codes (Optional[List[Union[int, str]]]) – specify HTTP error codes that will not fail the task, allowing for wildcards with the ‘x’ char. Example: [400, “5xx”]

Example usage:

hs_rest_block = HyperscienceRestApiBlock(
    reference_name='http_request',
    app_endpoint='/api/healthcheck',
    method='GET',
)

class flows_sdk.implementations.idp_v40.additional_blocks.DatabaseAccessBlock(reference_name=None, db_type='', database='', host='', username='', password='', query='', port=None, options=None, timeout=None, query_params=None, title='DB Block', description='DB access block')

Bases: Block

Database Block allows user to access and perform queries on the specified database Mandatory parameters:

Parameters:

• db_type (str) – database type (mssql, oracle, postgres)

• host (str) – URL/IP address of the server database is hosted on

• database (str) – database name

• username (str) – database username

• password (str) – database password

• query (str) – parameterized query

Optional parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• port (int | None) – database port number

• options (Optional[Dict[Any, Any]]) – dictionary of additional connection string options

• query_params (Optional[Dict[Any, Any]]) – dictionary of values for query placeholders

• timeout (int | None) – timeout in seconds (mssql and postgres only)

Example usage:

# minimum viable instantiation, the rest of the fields can later be added in the UI
db_lookup = DatabaseAccessBlock(
    reference_name='db_lookup',
    title='Database Lookup',
    description='Perform database lookup',
)
# note that storing password in plain text form is not recommended
# password can later be entered in the UI or defined as a flow secret.
db_lookup = DatabaseAccessBlock(
    reference_name='db_lookup',
    title='Database Lookup',
    description='Perform database lookup',
    db_type='mssql',
    database='cars',
    host='example.com',
    username='user',
    # we recommend skipping the password field or defining it as a system secret
    # e.g. system_secret('{your_secret_identifier}') (both allow for secret input via. UI)
    password='pw'
    port=1433,
    timeout=200,
    query='SELECT * from CAR',
)
# reference to the output of DBAccessBlock which can be passed as input to the next block
# for more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = db_lookup.output()
# content of output_ref:
{
    "result": [
        {
            "id": 1,
            "brand": "Aston Martin",
            "model": "Vanquish"
        },
        {
            "id": 2,
            "brand": "Jaguar",
            "model": "XE 2018"
        }
    ]
}

Database output for the same query (SELECT * FROM CAR)

IDENTIFIER = 'DB_ACCESS'

class flows_sdk.implementations.idp_v40.additional_blocks.LlamaBlock(model_id, prompt, reference_name=None, consent=False, tokenize_only=False, predict_extra_kwargs=None, title='Llama Text Generation', description='Text generation using the Llama 2 model')

Bases: Block

Text generation using the Llama 2 model by Meta.

This block utilizes Llama 2 by Meta. Llama 2 is licensed under the LLAMA 2 Community License (https://ai.meta.com/llama/license/). By using the Llama Block, you acknowledge that you have received a copy of the LLAMA 2 Community License.

Note: This block is still experimental and not ready for production use.

Mandatory parameters:

Parameters:

• model_id (str) – UUID, identifier of the asset with the ML model. Model assets are not part of the Hypersicence bundle and must first be downloaded and imported into the system before they can be used. Please contact your Hyperscience representative to discuss the usecase and the appropriate model and to get instructions on downloading the models. Once downloaded, here are the available model asset UUIDs: b3da05c7-061c-4a58-af6d-4c905d2e618f - Llama 2, 13B, 8-bit quantized

• prompt (str) – The prompt to generate text from. Use a CodeBlock ahead of the LlamaBlock in order to build the prompt. The context window of the model is limited to 4096 tokens. If the prompt has more tokens, the block would return an error. The total input and output length also cannot exceed 4096 tokens, which means that if the input is too long, the output may be truncated. Also see tokenize_only for a more detailed explanation of how to handle longer texts.

Optional parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• consent (bool | None) – Has the business user read and acknowledged the Llama 2 legal notice. Unless the flow would be used in CI automation, this should be set to False so that a business user can read and acknowledge the notice in the Hyperscience UI. Setting this to True in the Python definition of the flow is also treated as acknowledgement of the notice on behalf of the entity that will be using the flow.

• tokenize_only (bool | None) – In the context of LLMs, tokens correspond roughly to words, partial words and punctuation. A rough way to compute the number of tokens in a text is number_of_tokens = 1.25 * number_of_words. The Llama block works with a context window of 4096 tokens. If a longer prompt is passed in, it would return an error. Also, if the prompt is shorter, but still close to 4096, the output may be truncated. Since the length of the prompt in tokens cannot be known ahead of time, sometimes the flow may need to make dynamic decisions whether to pass the text to the Llama block, or to split it into multiple chunks, or to use some alternative method for processing it (e.g. a custom supervision manual task). The tokenize_only option enables flows to take such dynamic decisions based on the length of the prompt in tokens. The default is false - it instructs the block to run the ML model inference over the prompt, i.e. to generate a text response. When true is passed, the model will simply tokenize the prompt without running inference and return the list of tokens - this will work regardless of the length of the prompt. A subsequent Decision task can then route longer prompts to an alternative path in the flow, while routing shorter prompts to the LlamaBlock for actual text generation.

• predict_extra_kwargs (Optional[Dict[Any, Any]]) – Extra keyword arguments used for model inference. (Used to fine-tune the model prediction. Not recommended to change this unless you know what you are doing)

Example usage:

llama = LlamaBlock(
    reference_name='llama',
    model_id='b3da05c7-061c-4a58-af6d-4c905d2e618f',
    prompt='${previous_block.output.llama_prompt}',
)

IDENTIFIER = 'LLAMA'

class flows_sdk.implementations.idp_v40.additional_blocks.EntityRecognitionBlock(pages, regex_map=None, keyword_map=None, enhanced_output=False, ner_enabled=True, ced_enabled=True, signature_enabled=True, reference_name=None, title='Entity Recognition', description='Recognizes entities of different types in documents')

Bases: Block

Entity Recognition Block detects entities of different types within the documents contained in a submission

This block requires the output of the Full Page Transcription Block as input data.

Mandatory parameters:

Parameters:

• pages (str) – list of pages that includes the raw documents with the transcribed information from the FPT block

• regex_map (Optional[Dict[str, str]]) – a map that specifies different regex patterns that the block should look for, a default one is present

• keyword_map (Optional[Dict[str, List[str]]]) – a map that specifies different keyword patterns that the block should look for, a default one is present

Optional Parameters:

Parameters:

• enhanced_output (bool) – a boolean flag that enables toggling between simple and enhanced output formats. Default value is False.

• ner_enabled (bool) – a boolean flag that enables or disables Named Entity Recognition. Default value is True.

• ced_enabled (bool) – a boolean flag that enables or disables Custom Entity Detection. Default value is True.

• signature_enabled (bool) – a boolean flag that enables or disables Signature Detection. Default value is True.

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')

# EntityRecognitionBlock detects entities within the documents contained in the submission
entity_recognition_block = EntityRecognitionBlock(
    reference_name='entity_recognition',
    pages=submission_bootstrap.output(submission.unassigned_pages),
    regex_map=DEFAULT_PII_REGEX_MAP,
    keyword_map=DEFAULT_PII_KEYWORD_MAP,
    enhanced_output=False,
)

# reference to the output of EntityRecognitionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look at
# class Block and its output() method
output_ref = entity_recognition_block.output()

Example content of output_ref:

{
    "documents": [
        {
            "file_uuid": "da2c6117-43c4-4b30-84c4-cdc59cc3fa4f",
            "entities": [
                {
                    "text": "John Smith",
                    "type": "Person Name",
                    "positions": [
                        [0.5, 0.1, 0.55, 0.13],
                        [0.6, 0.2, 0.65, 0.23],
                        [0.7, 0.3, 0.75, 0.33],
                    ],
                    "page_ids": [0, 0, 1],
                    "origin": "NER",
                },
                {
                    "text": "<SIGNED>",
                    "type": "signature",
                    "positions": [[0.701171875, 0.732421875, 0.92578125, 0.8681640625]],
                    "page_ids": [0],
                    "origin": "Signature",
                },
            ]
        },
        {
            "file_uuid": "4aa38f84-e5af-4cdc-ba5c-edd60f0a6b89",
            "entities": [
                ...
            ]
        }
    ],
    "redaction_annotations": {
        "ner": [ ... ],
        "ced": [ ... ],
    }
}

IDENTIFIER = 'ENTITY_RECOGNITION'

class flows_sdk.implementations.idp_v40.additional_blocks.FileFilterBlock(trigger='${workflow.input.$trigger}', filter_type='ALL_FILES', minimum_image_width=None, minimum_image_height=None, minimum_file_size_kb=None, file_extensions_action='NONE', file_extensions=None, file_extensions_other=None, reference_name=None, title='File Filter', description='Filters out unwanted files from the input of a flow')

Bases: Block

Available from V40.2

File Filter Block filters out unwanted files from the input of a flow

This block is meant to be placed at the start of the flow/subflow - BEFORE the SubmissionBootstrapBlock, not after it. It requires the output of a connector (e.g. Email Listener) or an API submission as input data, and its output is meant to be consumed by a SubmissionBootstrapBlock.

Mandatory parameters:

This block has no strictly required parameters - all of its filters are optional.

Optional Parameters:

Parameters:

• reference_name (str | None) – unique identifier within a flow, a unique one will be generated when not provided

• trigger (str) – the trigger input from a connector - if not set explicitly, it will default to the workflow input trigger (which is where this value is normally found)

• filter_type (Literal['ALL_FILES', 'IMAGES_ONLY']) – a discrete value, either “ALL_FILES” or “IMAGES_ONLY”. Determines what kind of files the filters will be applied to. Default value is “ALL_FILES”. If set to “IMAGES_ONLY”, all filtering options will be applied only to images, other files will pass through.

• minimum_image_width (int | None) – image width in pixels - half of the image dimension filter. Any image with less width than configured here will be filtered out. Defaults to None (will do nothing).

• minimum_image_height (int | None) – image height in pixels - second half of the image dimension filter. Any image with less height than configured here will be filtered out. Defaults to None (will do nothing).

• minimum_file_size_kb (float | None) – represents the file size filter. Any file whose disk size is less than this number (in KB) will be filtered out. May be fractional. Defaults to None (will do nothing).

• file_extensions_action (Literal['NONE', 'ALLOW', 'DENY']) – a discrete value, either “ALLOW”, “DENY” or “NONE”. Represents the function of the file extension filter. “ALLOW” means we only allow the configured file extensions. “DENY” means we only deny the configured file extensions. “NONE” means no file extension filtering takes place. Defaults to “NONE”

• file_extensions (list | None) – a list of strings, representing the file extensions used for file extension filtering. Expected if file_extensions_action is not “NONE”. Otherwise, defaults to None (will do nothing).

• file_extensions_other (str | None) – a comma separated list of file extensions used for file extension filtering. Meant for adding extra extensions that are not available in the pre-defined list. Optional, defaults to None (will do nothing). However, it is expected in case the file_extensions parameter contains “other”.

Example usage:

# File Filter Block filters out unwanted files from the input of a flow
file_filter_block = FileFilterBlock(
    # trigger: str = workflow_input(Inputs.Trigger),  # usually not necessary to set this
    reference_name='file_filter',
    filter_type='ALL_FILES',
    minimum_image_width=500,
    minimum_image_height=500,
    minimum_file_size_kb=2.5,
    file_extensions_action='ALLOW',
    file_extensions=['pdf', 'png'],
    file_extensions_other='jpg,svg,txt',
)

# reference to the output of FileFilterBlock which can be passed as input to the
# next block (a SubmissionBootstrapBlock)
output_ref = file_filter_block.output('result.filtered_trigger')
submission_bootstrap = SubmissionBootstrapBlock(trigger=output_ref)

Example content of output_ref:

{
    "payload": {
        "blob_storage_files": [
            {
                "uuid": "db58606e-32f9-42c9-a0bc-ed9848ff247a",
            }
        ],
        "filtered_files": [
            {
                "uuid": "db58606e-32f9-42c9-a0bc-ed9848ff246e",
                "filtered_by": [
                    "file_size_filter"
                ]
            },
        ]
    }
}

IDENTIFIER = 'FILE_FILTER'

Trigger Blocks (v40)

Note

“Triggers” are referred to as “Input Blocks” in our application documentation.

Warning

Flows are built to allow full definition of triggers either in code or manually in the Flow Studio. We recommend defining triggers manually in the Flow Studio. Instructions on how to do so are in our application documentation.

To enable trigger definition via the Flow Studio UI, you need to instantiate the IDPTriggers() convenience class and pass it to the triggers property of the Flow

return Flow(
   ...
   triggers=IDPTriggers()
)

If instead you wish to define specific triggers in Python, use the trigger definitions below.

Folder Listener (v40)

Identifier: FOLDER_TRIGGER

Deprecated in favor of the Universal Folder Listener

Parameters:

Name	Type	Default Value	Title	Description
folder	Path	N/A	Folder to scan for submissions	N/A
file_extensions	string	N/A	File extensions	List of file extensions to monitor for (e.g.: ‘png, jpg, pdf’)
has_meta	boolean	False	Enable metadata	Select this if a metadata file is to be expected along with document files (in XXX_index.txt file)
poll_interval	integer	10	Poll interval	Poll interval in seconds
warmup_interval	integer	15	Warm-up interval	Seconds to wait past document last modified time before processing it
folder_cleanup_delay	integer	86400	Folder cleanup delay	Seconds to wait before cleaning up subfolders
api_params	object	{}	API Parameters	N/A

folder_listener = IOBlock(
    identifier='FOLDER_TRIGGER',
    reference_name='folder_trigger',
    title='Folder listener',
    enabled=True,
    input={
        'folder': '/var/www/forms/forms/folder/',
        'file_extensions': 'png, pdf, jpg',
    },
)

triggers = IDPTriggers(blocks=[folder_listener])

S3 Listener (v40)

Identifier: S3_TRIGGER Parameters:

Name	Type	Default Value	Title	Description
bucket_uri	string	N/A	S3 source URI	S3 URI, where the files will be uploaded (e.g. s3://hs_bucket/prefix1/)
archive_bucket_uri	string	N/A	S3 archive URI	S3 URI to move the processed files to (e.g. s3://hs_bucket/prefix1/)
file_extensions	array	[]	File extensions	Types of file extensions for which to monitor
other_file_extensions	string	N/A	Other file extensions	Comma separated list of file extensions for which to monitor (e.g. ‘png, jpg, pdf’)
has_meta	boolean	false	Include submission level parameters	Select this to ingest JSON files along with document files and submission s3 prefixes. These JSON files can contain information such as metadata, cases and external_id. These JSON file names should match the related files or s3 prefixes (e.g., XXX.jpg.json for XXX.jpg)
aws_access_key_id	string	N/A	AWS ACCESS KEY ID	Access Key used for authentication
aws_secret_access_key	password	N/A	SECRET ACCESS KEY	Secret Key used for authentication
use_ec2_instance_credentials	boolean	true	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used
poll_interval	integer	10	Polling interval (in seconds)	How often the S3 Listener will check the S3 bucket for submissions
warmup_interval	integer	15	Warm-up interval (in seconds)	How long the S3 Listener will wait to process the document after it was first detected
api_params	object	{}	API Parameters	N/A

s3_listener = IOBlock(
    identifier='S3_TRIGGER',
    reference_name='s3_trigger',
    title='S3 Listener',
    enabled=True,
    input={
        'bucket_uri': 's3://hs_bucket/prefix1/',
        'archive_bucket_uri': 's3://hs_bucket/prefix2/',
        'file_extensions': ['png', 'pdf', 'jpg'],
        'aws_access_key_id': 'admin',
        'aws_secret_access_key': system_secret('s3_secret_access_key'),
        'use_ec2_instance_credentials': False,
    },
)
triggers = IDPTriggers(blocks=[s3_listener])

Email Listener (v40)

Identifier: IMAP_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
provider [1]	string	imap	Email Provider	N/A
poll_interval	integer	60	Polling interval in seconds	The frequency at which the connection will monitor the email folder for submissions
folder	Path	N/A	Folder to scan for emails	The email folder to retrieve submissions from (e.g., “inbox”)
email_body_treatment	string	“ignore”	Email body treatment	What to do with the body of an email that is ingested. Available options are “process” and “ignore”.
post_process_action	string	“move”	Post process action	What to do with the email after it is processed. Available options are “move” and “delete”.
post_process_move_folder	string	“”	Post process archive folder	Folder to move emails to once they are processed. It will be included only if post_process_action is set to “move”.
api_params	object	{}	API Parameters	N/A
render_headers [2]	array	[]	Headers to include	Headers to render at the top of the email body. Headers will be included only if email_body_treatment is set to “process”.

[1] one of:

imap
graph

[2] any of:

To
From
Subject
Date

Additional inputs when "imap" is the selected provider:

Name	Type	Default Value	Title	Description
host	string	N/A	IMAP server address	N/A
port	integer	993	Port Number	N/A
ssl	boolean	True	Use SSL connection	N/A
username	string	N/A	Username	N/A
password	Password	N/A	Password	N/A

Additional inputs when "graph" is the selected provider:

Name	Type	Default Value	Title	Description
client_id	string	N/A	Client id	Application (client) ID for the corresponding application registered with the Microsoft identity platform.
tenant_id	string	N/A	Tenant id	Also called directory ID. Unique identifier of the tenant in which the corresponding application is registered with.
graph_cloud_endpoint [1]	string	Global	Microsoft Graph Cloud Endpoint	Determines base URL used for accessing cloud services through Microsoft Graph.
azure_ad_endpoint [2]	string	AZURE_PUBLIC_CLOUD	Azure AD Endpoint	Determines base URL for the Azure Active Directory (Azure AD) endpoint to acquire token for each national cloud.
client_secret	Password	N/A	Client secret	Client secret for the corresponding application registered with the Microsoft identity platform.

[1] one of:

Global
US_GOV
US_DoD
Germany
China

[2] one of:

AZURE_PUBLIC_CLOUD
AZURE_CHINA
AZURE_GERMANY
AZURE_GOVERNMENT

imap_trigger = IOBlock(
    identifier='IMAP_TRIGGER',
    reference_name='imap_trigger',
    title='IMAP trigger',
    enabled=True,
    input={
        'host': 'example@mail.com',
        'folder': '/var/www/forms/forms/imap/',
        'username': 'admin',
        'password': 'pass',
    }
)

triggers = IDPTriggers(blocks=[imap_trigger])

Box Folder Listener (v40)

Identifier: BOX_FOLDER_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
source_folder_id	string	N/A	Folder to scan for submissions	Use the Box Folder ID found in the URL
target_folder_id	string	N/A	Folder to move completed files	Use the Box Folder ID found in the URL
file_extensions	array	[]	File extensions	Types of file extensions for which to monitor
custom_file_extensions	string	N/A	Other file extension types	Comma separated list of file extensions for which to monitor (e.g. ‘png, jpg, pdf’)
poll_interval	integer	10	Polling interval (in seconds)	How often the connector will check the base folder for submissions
warmup_interval	integer	15	Warm-up interval (in seconds)	How long the connector will wait to process the document after it was last modified
public_key_id	string	N/A	Public-Key ID	ID of the Public-key used for authentication with Box
private_key	Password	N/A	Private-Key	Private key used for authentication with Box
passphrase	Password	N/A	Passphrase	Passphrase used for authentication with Box
client_id	string	N/A	Client ID	Client Id used for authentication with Box
client_secret	Password	N/A	Client Secret	Client Secret used for authentication with Box
enterprise_id	string	N/A	Enterprise ID	Enterprise Id used for authentication with Box
api_params	object	{}	API Parameters	N/A

box_folder_trigger = IOBlock(
    identifier='BOX_FOLDER_TRIGGER',
    reference_name='box_folder_trigger',
    title='Box folder',
    enabled=True,
    input={
        'file_extensions': ['png', 'pdf', 'jpg'],
        'source_folder_id': '24',
        'target_folder_id': '42',
        'public_key_id': 'admin',
        'private_key': 'secret',
        'passphrase': 'password',
        'client_id': 'admin',
        'client_secret': 'secret',
        'enterprise_id': 'admin',
    }
)

triggers = IDPTriggers(blocks=[box_folder_trigger])

Cron Listener (v40)

Identifier: CRON_TRIGGER

Inputs:

Name	Type	Default Value	Title	Description
cron_spec	string	"*/5 * * * *"	Cron specification	N/A
time_zone	string	“US/Eastern”	Time Zone	N/A
api_params	object	{}	API Parameters	N/A

cron_trigger = IOBlock(
    identifier='CRON_TRIGGER',
    reference_name='cron_trigger',
    title='Cron Trigger',
    enabled=True,
    input={
        'cron_spec': '0 10 * * *',
        'time_zone': 'Europe/Sofia',
    }
)

triggers = IDPTriggers(blocks=[cron_trigger])

Salesforce Listener (v40)

Identifier: SALESFORCE_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
channel_name	string	N/A	Channel Name	Configured in the Hyperscience app in Salesforce.
username	string	N/A	Associated Username	Username associated with the Salesforce Connected App.
consumer_key	Password	N/A	Consumer Key	Consumer key used for authentication with Salesforce. Paired with the Salesforce instance from which it was issued.
private_key	Password	N/A	Private Key	Private key used for authentication with Salesforce.
sandbox_environment	boolean	false	Private Key	Enable if the Salesforce environment is a sandbox.
api_params	object	{}	API Parameters	N/A

Message Queue Listener (v40)

Identifier: MQ_LISTENER

Inputs:

Name	Type	Default Value	Title	Description
MQ_TYPE	string	“ACTIVE_MQ”	Message Queue type	Type of the message queue to connect with. Valid values are “ACTIVE_MQ”, “AMAZON_SQS”, “IBM_MQ” and “RABBIT_MQ”
MQ_USERNAME	string	null	Username/Access Key ID	N/A
MQ_PASSWORD	Password	null	Password/Secret Access Key	N/A
MQ_QUEUE_NAME	string	null	Queue Name	Name of the queue (topic) to connect to.
MQ_HOST	string	null	Host Name	N/A
MQ_PORT	integer	5672 when “RABBIT_MQ”, 1414 when “IBM_MQ”, otherwise null	Port Number	N/A
api_params	object	{}	API Parameters	N/A

Additional inputs when "AMAZON_SQS" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_REGION	string	null	AWS Region	N/A
MQ_QUEUE_URL	string	null	Queue URL	N/A
MQ_USE_EC2_INSTANCE_CREDENTIALS	boolean	True	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used.

NOTE: MQ_USERNAME can be used to provide Access Key ID and MQ_PASSWORD to provide Secret Access Key.

Additional inputs when "IBM_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_QUEUE_MANAGER	string	null	Queue Manager (IBM MQ)	N/A
MQ_SSL_CIPHER_SUITE [1]	string	null	SSL cipher suite	SSL cipher suite to use if SSL connection is used.
MQ_CHANNEL	string	null	Channel	N/A
MQ_NO_AUTH_REQUIRED	boolean	false	No auth credentials required	N/A
MQ_MQCSP_AUTHENTICATION_MODE	boolean	false	MQCSP authentication mode	N/A

NOTE: MQ_NO_AUTH_REQUIRED is True, MQ_USERNAME and MQ_PASSWORD are optional.

[1] one of:

"NULL_MD5"
"NULL_SHA"
"RC4_MD5_EXPORT"
"RC4_MD5_US"
"RC4_SHA_US"
"RC2_MD5_EXPORT"
"DES_SHA_EXPORT"
"RC4_56_SHA_EXPORT1024"
"DES_SHA_EXPORT1024"
"TRIPLE_DES_SHA_US"
"TLS_RSA_WITH_NULL_SHA256"
"TLS_RSA_WITH_AES_128_CBC_SHA"
"TLS_RSA_WITH_AES_128_CBC_SHA256"
"TLS_RSA_WITH_AES_256_CBC_SHA"
"TLS_RSA_WITH_AES_256_CBC_SHA256"
"AES_SHA_US"
"TLS_RSA_WITH_DES_CBC_SHA"
"TLS_RSA_WITH_3DES_EDE_CBC_SHA"
"TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256"
"FIPS_WITH_DES_CBC_SHA"
"FIPS_WITH_3DES_EDE_CBC_SHA"

Additional inputs when "RABBIT_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_VIRTUAL_HOST	string	null	Virtual Host (RABBIT MQ)	N/A
MQ_EXCHANGE	string	“”	Exchange	N/A
MQ_ROUTING_KEY	string	“”	Routing key	N/A
MQ_CONNECTION_TYPE	string	“TCP”	Connection Type	“TCP or SSL”

rabbit_mq_listener = IOBlock(
    identifier='MQ_LISTENER',
    reference_name='mq_listener',
    title='RabbitMQ Listener',
    enabled=True,
    input={
        'MQ_TYPE': 'RABBIT_MQ',
        'MQ_QUEUE_NAME': 'some_queue_name',
        'MQ_HOST': 'somehost.com',
        'MQ_USERNAME': 'foo',
        'MQ_PASSWORD': system_secret('rabbit_mq_1_password'),
    }
)

triggers = IDPTriggers(blocks=[rabbit_mq_listener])

Universal Folder Listener (v40)

Identifier: UNIVERSAL_FOLDER_TRIGGER

Inputs:

Name	Type	Default Value	Title	Description
folder	string	N/A	Folder to scan for submissions	Enter the path relative to the base folder. Leave blank to monitor the base folder
file_extensions	array	[]	File extensions	Types of file extensions for which to monitor
other_file_extensions	string	N/A	Other file extensions	Comma separated list of file extensions for which to monitor (e.g. ‘png, jpg, pdf’)
has_meta	boolean	false	Include submission level parameters	Select this to ingest JSON files along with document files and submission folders. These JSON files can contain information such as metadata, cases and external_id. These JSON file names should match the related files or folders (e.g., XXX.jpg.json for XXX.jpg)
poll_interval	integer	10	Polling interval (in seconds)	How often the Folder Listener will check the base folder for submissions
warmup_interval	integer	15	Warm-up interval (in seconds)	How long the Folder Listener will wait to process the document after it was last modified
folder_cleanup_delay	number	24	Empty folder cleanup delay (in hours)	How often the Folder Listener will remove empty folders from the base folder
api_params	object	{}	API Parameters	N/A

universal_folder_listener = IOBlock(
    identifier='UNIVERSAL_FOLDER_TRIGGER',
    reference_name='universal_folder_trigger',
    title='Universal Folder Listener',
    enabled=True,
    input={
        'folder': '/var/www/forms/forms/folder/',
        'file_extensions': 'png, pdf, jpg',
    }
)

triggers = IDPTriggers(blocks=[universal_folder_listener])

Kafka Listener (v40)

Identifier: KAFKA_TRIGGER

Inputs:

Name	Type	Default Value	Title	Description
bootstrap_servers	string	N/A	Bootstrap Servers	A Comma separated list of host:port pairs to use for establishing the initial connection to the cluster
group_id	string	N/A	Group Id	A unique string that identifies the consumer group this consumer belongs to. BLOCK_THREADS_KAFKA_TRIGGER should be used to scale up or down the number of consumers (default: number of available cores)
topics	string	N/A	Topics	Comma separated list of topic names that the block will subscribe to
consumer_config_json	json	N/A	Consumer Config	JSON formatted string containing key-value pairs of Kafka consumer configurations described in the official Kafka documentation
api_params	object	{}	API Parameters	N/A
consume_timeout	integer	-1	Consume Timeout	Maximum time, in seconds, that the consumer will wait for new messages. If messages are received within the specified time, they are processed immediately. However, if no messages are received within that specified time, consumption cycle will be restarted internally to continue fetching messages. This ensures continuous message consumption (default: infinite)
consume_num_messages	integer	1	Consume Num Messages	Sets the maximum number of messages the consumer fetches in a single consumption cycle.If the number of available messages is less than the specified, and timeout is reached, messages will be processed. Consumption cycle will be restarted after the current finishes

kafka_listener = IOBlock(
    identifier='KAFKA_TRIGGER',
    reference_name='kafka_trigger',
    title='Kafka Listener',
    enabled=True,
    input={
        'folder': '/var/www/forms/forms/folder/',
        'file_extensions': 'png, pdf, jpg',
         'bootstrap_servers': 'example.com',
         'group_id': 'test_group',
         'topics': 'test_topic1, test_topic2',
         'consumer_config_json': {},
         'number_of_workers': 1,
         'consume_timeout': 5,
         'consume_num_messages': 10
    }
)

triggers = IDPTriggers(blocks=[kafka_listener])

Output Blocks (v40)

Warning

Flows pass information to downstream systems using the IDPOutputsBlock. This block is specially built to allow full definition either in code or manually in the Flow Studio. We recommend defining outputs manually in the Flow Studio.

Instructions on how to do so are in our application documentation. Simply include an IDPOutputsBlock in your Python flow definition wherever you wish to send information to downstream systems.

class flows_sdk.implementations.idp_v40.idp_blocks.IDPOutputsBlock(inputs, blocks=None)

Bases: Outputs

Output block allows users to send data extracted by an IDP flow to other systems for downstream processing

Mandatory parameters:

Parameters:

inputs (Dict[str, Any]) – Used by the UI to automatically pre-populate the input of newly added blocks. Extended in this class to include 'enabled': True to visualize an enabled/disabled trigger that is controllable via Flow Studio.

Usage when parameters are to be defined in Flow Studio:

outputs = IDPOutputBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')}
)

If instead you wish to define specific outputs in Python, use the outputs definitions below.

HTTP Notifier Output (v40)

The HTTP Notifier (REST) output connection will POST results from the system to a specified HTTP endpoint.

Identifier: COB_HTTP_EXPORT

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
endpoint	string	N/A	Endpoint URL	URL that output notifications will be posted to
endpoint_secrets	string	N/A	Endpoint URL secrets	Endpoint URL Secrets, format: (one per line) secret_key_name=secret_value
timeout	int	600	Timeout (seconds)	How long (in seconds) to keep the connection open if no bytes are received from the endpoint. Set to 0 to wait indefinitely.
authorization_type [2]	string	“none”	Authorization type	Type of authorization
authorization_header_name	string	null	Authorization header name	Authorization header name to be set in the notification request
authorization_header	Password	null	Authorization header value	Authorization header to be set in the notification request
auth_url	string	null	OAuth2 authorization URL	The endpoint for the authorization server
client_id	string	null	Client ID	The client identifier issued to the client during the application registration process
client_secret	Password	null	Client Secret	The client secret issued to the client during the application registration process
scope	string	null	Scope	Scope to request to oauth server
audience	string	null	Audience	Resource service URL where token will be valid
additional_oauth_parameters	json	{}	Additional Oauth Request Parameters	Additional parameters to send when requesting token
ssl_cert_source [3]	string	null	Source	N/A
ssl_cert_path	string	null	Trusted Certificate(s) path	Enter the path relative to the base folder
ssl_cert_value	MultilineText	null	Trusted Certificate(s)	N/A
additional_notification_http_headers	json	null	Additional Notification Request Headers	Key value pairs in json format which will be sent via the request as http headers

[1] one of:

"v5"
"transformed"

[2] one of:

"none"
"http_header"
"oauth_2_client_credentials"

[3] one of:

"env_var"
"ca_bundle_path"
"certificate_value"

cob_http_export = IOBlock(
    identifier='COB_HTTP_EXPORT',
    reference_name='cob_http_export',
    title='HTTP Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'SUBMISSION',
        'endpoint': 'example.com',
        'authorization_type': 'none',
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_http_export],
)

Message Queue Notifier Output (v40)

The Message Queue Notifier Output can configure connections to ActiveMQ, Amazon SQS, IBM MQ, and RabbitMQ message queues.

Identifier: COB_MQ_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
MQ_TYPE	string	“ACTIVE_MQ”	Message Queue type	Type of the message queue to connect with. Valid values are “ACTIVE_MQ”, “AMAZON_SQS”, “IBM_MQ” and “RABBIT_MQ”
MQ_USERNAME	string	null	Username/Access Key ID	N/A
MQ_PASSWORD	Password	null	Password/Secret Access Key	N/A
MQ_QUEUE_NAME	string	null	Queue Name	Name of the queue (topic) to connect to. It applies only to “ACTIVE_MQ”, “IBM_MQ” and “RABBIT_MQ” MQ_TYPEs.
MQ_HOST	string	null	Host Name	N/A
MQ_PORT	integer	5672 when “RABBIT_MQ”, 1414 when “IBM_MQ”, otherwise null	Port Number	N/A

[1] one of:

"v5"
"transformed"

Additional inputs when "AMAZON_SQS" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_REGION	string	null	AWS Region	N/A
MQ_QUEUE_URL	string	null	Queue URL	N/A
MQ_USE_EC2_INSTANCE_CREDENTIALS	boolean	True	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used.
MQ_MESSAGE_GROUP_ID	string	null	Group ID for FIFO queues	N/A
MQ_MESSAGE_METADATA	string	null	Additional SQS Metadata	Input additional metadata key value pairs in the format of { “key”: {“TYPE”: “String”, “VALUE”: “”}, “key2”: {“TYPE”: “Number”, “VALUE”: “12345”}

NOTE: MQ_USERNAME can be used to provide Access Key ID and MQ_PASSWORD to provide Secret Access Key.

Additional inputs when "IBM_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_QUEUE_MANAGER	string	null	Queue Manager (IBM MQ)	N/A
MQ_SSL_CIPHER_SUITE [1]	string	null	SSL cipher suite	SSL cipher suite to use if SSL connection is used.
MQ_CHANNEL	string	null	Channel	N/A
MQ_NO_AUTH_REQUIRED	boolean	false	Channel	N/A
MQ_MQCSP_AUTHENTICATION_MODE	boolean	false	Channel	N/A

NOTE: MQ_NO_AUTH_REQUIRED is True, MQ_USERNAME and MQ_PASSWORD are optional.

[1] one of:

"NULL_MD5"
"NULL_SHA"
"RC4_MD5_EXPORT"
"RC4_MD5_US"
"RC4_SHA_US"
"RC2_MD5_EXPORT"
"DES_SHA_EXPORT"
"RC4_56_SHA_EXPORT1024"
"DES_SHA_EXPORT1024"
"TRIPLE_DES_SHA_US"
"TLS_RSA_WITH_NULL_SHA256"
"TLS_RSA_WITH_AES_128_CBC_SHA"
"TLS_RSA_WITH_AES_128_CBC_SHA256"
"TLS_RSA_WITH_AES_256_CBC_SHA"
"TLS_RSA_WITH_AES_256_CBC_SHA256"
"AES_SHA_US"
"TLS_RSA_WITH_DES_CBC_SHA"
"TLS_RSA_WITH_3DES_EDE_CBC_SHA"
"TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256"
"FIPS_WITH_DES_CBC_SHA"
"FIPS_WITH_3DES_EDE_CBC_SHA"

Additional inputs when "RABBIT_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_VIRTUAL_HOST	string	null	Virtual Host (RABBIT MQ)	N/A
MQ_EXCHANGE	string	“”	Exchange	N/A
MQ_ROUTING_KEY	string	“”	Routing key	N/A
MQ_CONNECTION_TYPE	string	“TCP”	Connection Type	“TCP or SSL”

mq_notifier = IOBlock(
    identifier='COB_MQ_NOTIFIER',
    reference_name='mq_notifier',
    title='MQ Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'SUBMISSION',
        'MQ_USERNAME': 'admin',
        'MQ_PASSWORD': 'pass',
        'MQ_QUEUE_NAME': 'queue',
        'MQ_HOST': 'host',
    },
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[mq_notifier],
)

Box Notifier Output (v40)

Provides an out-of-the-box integration into Box systems.

Identifier: COB_BOX_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
public_key_id	string	N/A	Public-Key ID	ID of the Public-key used for authentication with Box
private_key	Password	N/A	Private-Key	Private key used for authentication with Box
passphrase	Password	N/A	Passphrase	Passphrase used for authentication with Box
client_id	string	N/A	Client ID	Client Id used for authentication with Box
client_secret	Password	N/A	Client Secret	Client Secret used for authentication with Box
enterprise_id	string	N/A	Enterprise ID	Enterprise Id used for authentication with Box
template_key	string	“”	Box Metadata Template Key	Enter the key of the Box Metadata template that you would like to map the Hyperscience metadata to.
static_output_fields_to_map	array	[]	Static Metadata Fields	Specify the Hyperscience fields you want to store in Box metadata.
submission_id	string	“”	Key for Mapping Submission ID	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_id	string	“”	Key for Mapping Document ID	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
submission_state	string	“”	Key for Mapping Submission State	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_state	string	“”	Key for Mapping Document State	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
submission_exceptions	string	“”	Key for Mapping Submission Exceptions	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_exceptions	string	“”	Key for Mapping Document Exceptions	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_layout_uuid	string	“”	Key for Mapping Document Layout Uuid	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_layout_name	string	“”	Key for Mapping Document Layout Name	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
start_time	string	“”	Key for Mapping Start Time	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
complete_time	string	“”	Key for Mapping Complete Time	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_fields_template_mappings	json	{}	JSON for Additional Metadata Fields	Specify additional metadata fields using their key value. Please consult Box Integration setup manual for JSON template and instructions.

[1] one of:

"v5"
"transformed"

box_notifier = IOBlock(
    identifier='COB_BOX_NOTIFIER',
    reference_name='box_notifier',
    title='Box Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'private_key': 'password',
        'passphrase': 'password',
        'public_key_id': 'admin',
        'client_id': 'admin',
        'client_secret': 'secret',
        'enterprise_id': 'admin',
        'template_key': 'key',
    },
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[box_notifier],
)

UiPath Notifier Output (v40)

Provides an out-of-the-box integration into UiPath systems.

Identifier: COB_EXPORT_UIPATH

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
auth_method [2]	string	“basic_auth”	Authentication method	N/A
url	string	N/A	URL	Base URL of the uipath instance
app_id	string	N/A	App ID	App ID provided by UiPath
app_secret	Password	N/A	App Secret	App Secret provided by UiPath
oauth_token_endpoint	string	N/A	OAuth token endpoint	Endpoint to retrieve the token. Using https://cloud.uipath.com/identity_/connect/token by default
organization_unit_id	string	N/A	Organization unit id	Organization unit id
tenant	string	N/A	Tenant	Tenant
username	string	N/A	Username	Username
password	Password	N/A	Password	Password
queue	string	N/A	Queue	Queue

[1] one of:

"v5"
"transformed"

[2] one of:

"basic_auth"
"oauth"

cob_export_uipath = IOBlock(
    identifier='COB_EXPORT_UIPATH',
    reference_name='cob_export_uipath',
    title='CCB Export UiPath',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'FLAT_SUBMISSION',
        'url': 'example.com',
        'tenant': 'tenant',
        'username': 'admin',
        'password': 'pass',
        'queue': 'queue',
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_export_uipath],
)

Salesforce Notifier Output (v40)

With the Salesforce Notifier Block, you can configure your flow to send extracted information to Salesforce. The Salesforce Notifier Block can use extracted information to look up, and then update any number of fields on a Salesforce object. The Salesforce Notifier Block is available in both SaaS and on-premise versions of the Hyperscience application.

Identifier: COB_SALESFORCE_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
username	string	N/A	Associated Username	Username associated with the Salesforce Connected App
consumer_key	password	N/A	Consumer Key	Consumer key used for authentication with Salesforce. Paired with the Salesforce instance from which it was issued.
private_key	password	N/A	Private Key	Private key used for authentication with Salesforce.
sandbox_environment	boolean	false	Private Key	Enable if the Salesforce environment is a sandbox.
object_api_name	string	N/A	Object API Name	API name of the object in Salesforce to lookup and update.
new_record_on_lookup_failure	boolean	false	Create new record on lookup failure	When enabled, it will create a new record if the lookup fails to find a record using the lookup parameters. When disabled, lookup will fail if it does not find a record using the lookup parameters.
object_lookup_mapping	json	N/A	Lookup Field Mapping	Specify which Salesforce fields (using their API name) to be queried with which extracted values from the document for the lookup using key-value format. For example: “DOCUMENT_FIELD_NAME”: “OBJECT_FIELD_API_NAME”.
document_fields_mapping	json	N/A	Document Field Mappings	Specify which Salesforce fields (using their API name) you want to update with which extracted data from Hyperscience using key-value format. For example: “DOCUMENT_FIELD_NAME”: “OBJECT_FIELD_API_NAME”.”
overwrite_existing_field_data	boolean	false	Overwrite existing field data	If this is checked and a selected field has data in it, Hyperscience will overwrite that data. If this is unchecked, existing data will not be overwritten and the operation will fail.

[1] one of:

"v5"
"transformed"

cob_salesforce_notifier = IOBlock(
    identifier='COB_SALESFORCE_NOTIFIER',
    reference_name='cob_salesforce_notifier',
    title='Salesforce Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'username': 'salesforce_user',
        'consumer_key': 'salesforce_consumer_key',
        'private_key': 'salesforce_private_key',
        'object_api_name': 'salesforce_object_api_name',
        'object_lookup_mapping': {"LAYOUT_VARIATION_XYZ_FIELD_NAME_1": "SF_FIELD_API_NAME_1", "LAYOUT_VARIATION_XYZ_FIELD_NAME_2": "SF_FIELD_API_NAME_2"},
        'document_fields_mapping': {"LAYOUT_VARIATION_XYZ_FIELD_NAME_3": "SF_FIELD_API_NAME_3", "LAYOUT_VARIATION_XYZ_FIELD_NAME_4": "SF_FIELD_API_NAME_4"}
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_salesforce_notifier],
)

S3 Notifier Output (v40)

With the S3 Notifier Block, you can configure your flow to send extracted information to an S3 bucket.

Identifier: COB_S3_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
bucket_uri	string	N/A	S3 URI	S3 URI, where the data should be stored (e.g. s3://hs_bucket/prefix1/prefix2/)
aws_access_key_id	string	N/A	AWS ACCESS KEY ID	Access Key used for authentication
aws_secret_access_key	password	N/A	SECRET ACCESS KEY	Secret Key used for authentication
use_ec2_instance_credentials	boolean	true	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used

[1] one of:

"v5"
"transformed"

cob_s3_notifier = IOBlock(
    identifier='COB_S3_NOTIFIER',
    reference_name='cob_s3_notifier',
    title='S3 Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'FLAT_SUBMISSION',
        'bucket_uri': 's3://hs_bucket/prefix1/prefix2/',
        'aws_access_key_id': 'aws_access',
        'aws_secret_access_key': system_secret('aws_secret_access_key'),
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_s3_notifier],
)

Kafka Notifier Output (v40)

With the Kafka Notifier Block, you can configure your flow to send extracted information to a kafka topic.

Identifier: COB_KAFKA_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
bootstrap_servers	string	N/A	Bootstrap Servers	A list of Kafka brokers to connect to. For example: “kafka1:9092,kafka2:9092”
topic	string	N/A	Topic	The Kafka topic where your data will be sent
client_id	string	N/A	SECRET ACCESS KEY	An identifier for your application when connecting to Kafka, typically used for tracking and logging purposes
producer_config_json	json	true	Producer Config (JSON)	Configuration settings for the Kafka producer in JSON format. See https://kafka.apache.org/documentation/#producerconfigs for more information
synchronous_publish	boolean	true	Sync Publish	If true, the publish call will block until the message is sent successfully or an error is raised. If false, the publish call will return immediately after enqueueing the message
message_key	string	null	Message Key	The key associated with the message to be sent. This is used to determine which partition the message is sent to. If null, the message will be sent to a random partition

[1] one of:

"v5"
"transformed"

cob_kafka_notifier = IOBlock(
    identifier='COB_KAFKA_NOTIFIER',
    reference_name='cob_kafka_notifier',
    title='Kafka Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'FLAT_SUBMISSION',
        'bootstrap_servers': 'kafka1:9092,kafka2:9092',
        'topic': 'topic',
        'client_id': 'client_id',
        'producer_config_json': {},
        'synchronous_publish': True,
        'message_key': 'key'
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_kafka_notifier],
)

Helper Classes and Functions (v40)

The following classes are used to easily instantiate and manage a flow’s settings.

flows_sdk.implementations.idp_v40.idp_values.get_idp_wf_inputs(idp_wf_config)

A helper function that maps the values provided by an IdpWorkflowConfig to their corresponding IDP keys.

Parameters:

idp_wf_config (IdpWorkflowConfig) – with static values to be filled.

Return type:

Dict[str, Any]

Returns:

a dictionary with filled static values, compatible with IDP flows.

Example usage with default values:

flow = Flow(
    input=get_idp_wf_inputs(get_idp_wf_config()),
    ...
)

Example usage:

Example usage when instantiating an IDP flow with custom parameters::

flow = Flow(
    input=get_idp_wf_inputs({an instance of IdpWorkflowConfig}),
    ...
)

flows_sdk.implementations.idp_v40.idp_values.get_idp_wf_config()

A helper function that instantiates an IdpWorkflowConfig pre-filled with default values. IdpWorkflowConfig is the container class for defining flow-level settings.

Return type:

IdpWorkflowConfig

Returns:

a valid, fully constructed IdpWorkflowConfig instance

Example usage when instantiating an IDP flow:

flow = Flow(
    input=get_idp_wf_inputs(get_idp_wf_config()),
    ...
)

class flows_sdk.implementations.idp_v40.idp_values.IdpWorkflowConfig

Convenience dataclass outlining all flow-level config values for IDP.

class flows_sdk.implementations.idp_v40.idp_values.IDPTriggers(blocks=None)

An adapter class making it easier to instantiate flows_sdk.flows.Triggers for IDP flows. In particular, provides defaults for some fields (e.g. title, description, reference_name) and defines api_params_manifest with IDP specific parameters.

Parameters:

blocks (Optional[Sequence[IOBlock]]) – flows_sdk.blocks.IOBlock s to be included as triggers for an IDP Flow. Optional, defaults to an empty list.

Example usage when instantiating an IDP flow:

folder_listener = IOBlock(...)
flow = Flow(
    triggers=IDPTriggers([folder_listener]),
    ...
)

class flows_sdk.implementations.idp_v40.idp_values.IDPWrapperManifest(flow_identifier)

An adapter class making it easier to instantiate flows_sdk.flows.Manifest for IDP wrapper flows. In particular, provides defaults for some fields (e.g. roles) but more importantly, defines all flows_sdk.flows.Parameter that construct the Flow inputs and their UI representation (left-hand side menu in Flow studio).

Parameters:

flow_identifier (str) – A system-wide unique identifier for flows_sdk.flows.Flow

Example usage when instantiating an IDP flow:

flow = Flow(
    manifest=IDPWrapperManifest(flow_identifier='FLOW_IDENTIFIER'),
    ...
)

class flows_sdk.implementations.idp_v40.idp_values.IDPCoreManifest(flow_identifier, roles=None)

An adapter class making it easier to instantiate flows_sdk.flows.Manifest for IDP Core flows. In particular, provides defaults for some fields (e.g. roles, output) but more importantly, defines all flows_sdk.flows.Parameter that construct the Flow inputs and their UI representation (left-hand side menu in Flow studio).

Parameters:

• flow_identifier (str) – A system-wide unique identifier for flows_sdk.flows.Flow

• roles (Optional[List[str]]) – ‘read_only’, ‘supporting’ and ‘idp_core’ are the default roles of every subflow that can replace the default IDP_CORE subflow inside the top-level IDP flows. ‘supporting’ and ‘idp_core’ are mandatory roles that are necessary for all IDP Core flows. ‘supporting’ ensures that the subflow will be auto-deployed together with the top-level IDP flow. ‘idp_core’ ensures that this subflow can be selected in the UI in dropdowns which allow picking an alternative subflow to the default IDP_CORE subflow. If you are implementing a subflow that is not supposed to be a drop-in replacement for IDP_CORE, you will have to subclass or implement your own version of IDPCoreManifest class in order to remove the idp_core role.

Example usage when instantiating an IDP flow:

flow = Flow(
    manifest=IDPCoreManifest(flow_identifier='FLOW_IDENTIFIER'),
    ...
)

---

Unsupported Versions

Warning

The following Hyperscience versions (V39.1 and below) are deprecated and no longer actively supported. Please upgrade to V40 or later for the latest features and bug fixes.

V39.1

Processing Blocks (v39.1)

Core Blocks are powerful processing blocks necessary to build intelligent document processing solutions on Hyperscience. They include both manual and machine processing blocks.

Note

Classes in the idp_v39_1 implementation have parameters with ambiguous typing. For example, a field of type integer could also be represented as a string reference (e.g. that integer gets provided at runtime). The types introduced in the example are therefore incomplete and are likely to be revisioned in a future version.

class flows_sdk.implementations.idp_v39_1.idp_blocks.IDPCoreBlock(idp_wf_config, reference_name='__idp_core__', title='Start Document Processing Subflow', description='Calls the Document Processing Subflow with the specified settings.', compatibility_spec=CompatibilitySpec(filter_roles=['idp_core'], filter_schema=None, label=None), identifier='IDP_CORE_V39_1', additional_inputs=None)

Bases: Block

IDP core block includes the steps to process the passed documents into machine-readable output.

Mandatory parameters:

Parameters:

idp_wf_config (IdpWorkflowConfig) – IdpWorkflowConfig object with all flow-level configuration settings for IDP

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique reference name within a flow, a unique one will be generated when not provided

• compatibility_spec (Optional[CompatibilitySpec]) – defines a filter for flows that could be selected in the UI in place of “IDP Core” for the block. Defaults to flows with the role ‘idp_core’. The user will be able to pick any flow that matches the compatibility_spec. This serves to make upgrading to a new compatible version of “IDP Core” (e.g. IDP_CORE_V39) easier in future releases.

• identifier (str) – The identifier of the flow. Defaults to the IDP_CORE_VXX identifier of the out-of-the-box flow that comes with the system.

• additional_inputs (Optional[Dict[str, Any]]) – Dictionary containing key-value pairs that will be added to the inputs of the block. If a key is contained in the default inputs, its value will be overwritten by the value provided here. You generally do not need to provide additional_inputs, unless you are creating a custom derivative of the IDP_CODE_Vxx flow and are modifying or extending the list of inputs it accepts.

Example usage:

idp_core = IDPCoreBlock(idp_wf_config=idp_wf_config)

REFERENCE_NAME = '__idp_core__'

class flows_sdk.implementations.idp_v39_1.idp_blocks.SubmissionBootstrapBlock(reference_name=None, layout_release_uuid='${workflow.input.layout_release_uuid}', s3_config='${workflow.input.submission_bootstrap_s3_config}', s3_endpoint_url='${workflow.input.submission_bootstrap_s3_endpoint_url}', ocs_config='${workflow.input.submission_bootstrap_ocs_config}', http_config='${workflow.input.submission_bootstrap_http_config}', notification_workflow='${workflow.input.notification_workflow_initialization}', workflow_uuid='${workflow.input.workflow_uuid}', workflow_name='${workflow.input.workflow_name}', workflow_version='${workflow.input.workflow_version}', submission='${workflow.input.submission}')

Bases: Block

Submission bootstrap block initializes the submission object and prepares external images or other submission data if needed. This block should be called at the START of every flow that uses blocks included in the library.

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• s3_config (str) – Specify Amazon S3 credentials for submission retrieval store. Expects a json expression: {"aws_access_key_id": "X", "aws_secret_access_key": "Y"}, defaults to workflow_input(Settings.SubmissionBootstrapS3Config)

• s3_endpoint_url (str) – Endpoint URL for S3 submission retrieval store, defaults to workflow_input(Settings.SubmissionBootstrapS3EndpointUrl)

• ocs_config (str) – OCS Configuration for downloading submission data, defaults to workflow_input(Settings.SubmissionBootstrapOCSConfig)

• http_config (Optional[str]) – An optional system secret field expressing the HTTP Configuration for downloading submission data. The content is expected to be a json formatted as: {"username": "X", "password": "Y", "ssl_cert": "CA bundle filename"}, defaults to workflow_input(Settings.SubmissionBootstrapHTTPConfig)

• notification_workflow (Optional[str]) – Notification flow name to run when the submission is initialized, defaults to workflow_input(Settings.SubmissionBootstrapNotificationWorkflow)

• workflow_uuid (str) – UUID of the triggered workflow version, defaults to workflow_input(Inputs.WorkflowUuid)

• workflow_name (str) – Name of the triggered workflow, defaults to workflow_input(Inputs.WorkflowName)

• workflow_version (str) – Version of the triggered workflow, defaults to workflow_input(Inputs.WorkflowVersion)

• submission (Any) – Submission object

Example usage:

submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')

IDENTIFIER = 'SUBMISSION_BOOTSTRAP_2'

output(key=None)

Submission bootstrap ends with a CodeBlock, which nests the actual outputs under the ‘result’ key. This method overrides the default output in the same manner that flows_sdk.blocks.Routing.CodeBlock does for convenience.

Parameters:

key (Optional[str]) –

Optionally provide a key to directly get a nested property. When not provided, the entire ‘result’ will be returned. For example, we have:

{
    "result": {
        "a": {
            "b": 42
        }
    }
}

• skipping the key output()` will return ``{"a": {"b": 42}}

• calling with output("a") will result in {"b": 42}

• calling with output("a.b") will return 42

Return type:

str

Returns:

the value under the provided key

class flows_sdk.implementations.idp_v39_1.idp_blocks.MachineCollationBlock(submission, cases, dedupe_files='${workflow.input.machine_collation_dedupe_files}', refresh_retention_period='${workflow.input.machine_collation_refresh_retention_period}', remove_from_cases='${workflow.input.machine_collation_remove_from_cases}', retention_period='${workflow.input.machine_collation_retention_period}', reference_name=None)

Bases: Block

Machine collation block groups files, documents and pages (from the submission) into cases.

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• cases (Any) –

  This parameter accepts an array of JSON objects that contain information on how Cases should be created. There are two ways to collate a case: by the filenames specified in the current Submission, or by the ids of the Documents or Pages submitted in previous submissions.

  This parameter follows the given format:

  {
      'cases': [
          'external_case_id': 'HS-1',
          'filename': 'file1.pdf',
          'documents': [42],
          'pages': [43]
      ]
  }
  

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• dedupe_files (Union[bool, str]) – Enabling this setting will replace case data from repeated file names within the same case. Cases will retain data from the most recently submitted version of a particular file. Note that this setting does not delete the old data, it just removes it from the case. Keep in mind this option is only relevant when adding to cases. Defaults to workflow_input(Settings.CollationDedupeFiles)

• remove_from_cases (Optional[Any]) –

  This parameter accepts an array of JSON objects that contains information on how documents or pages should be removed from a pre-existing case. Currently, the only way to de-collate from a case is by the ids of the Documents or Pages within a given case, defaults to workflow_input(Settings.CollationRemoveFromCases)

  This parameter follows the given format:

  {
      'remove_from_cases': [
          'external_case_id': 'HS-1',
          'documents': [42],
          'pages': [43]
      ]
  }
  

• retention_period (Union[int, str, None]) – The number of days to retain cases after they are updated by this block. If passed None, no changes to the deletion date will be made. Defaults to workflow_input(Settings.CollationRetentionPeriod)

• refresh_retention_period (Union[bool, str]) – If True, (re)applies Retention Period parameter to all cases passed into the block. If False, only updates those without a pre-existing deletion date. Defaults to workflow_input(Settings.CollationRefreshRetentionPeriod)

Example usage:

machine_collation = MachineCollationBlock(
    reference_name='machine_collation',
    submission=submission_bootstrap.output('result.submission'),
    cases=submission_bootstrap.output('result.api_params.cases'),
    dedupe_files=True,
    remove_from_cases=custom_supervision.output('remove_from_cases')
)

IDENTIFIER = 'MACHINE_COLLATION_2'

class flows_sdk.implementations.idp_v39_1.idp_blocks.MachineClassificationBlock(submission, api_params, reference_name=None, rotation_correction_enabled='${workflow.input.machine_classification_rotation_correction_enabled}', mobile_processing_enabled='${workflow.input.machine_classification_mobile_processing_enabled}', file_transcription_enabled='${workflow.input.machine_classification_file_transcription_enabled}', layout_release_uuid='${workflow.input.layout_release_uuid}', vpc_registration_threshold='${workflow.input.structured_layout_match_threshold}', nlc_enabled='${workflow.input.semi_structured_classification}', nlc_target_accuracy='${workflow.input.semi_target_accuracy}', nlc_doc_grouping_logic='${workflow.input.semi_doc_grouping_logic}')

Bases: Block

Machine classification block automatically matches documents to structured, semi-structured or additional layouts

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• rotation_correction_enabled (Union[bool, str]) – Identifies and corrects the orientation of semi-structured images, defaults to workflow_input(Settings.RotationCorrectionEnabled)

• mobile_processing_enabled (Union[bool, str]) – Improves the machine readability of photo captured documents, defaults to workflow_input(Settings.MobileProcessingEnabled)

• file_transcription_enabled (Union[bool, str]) – Improves the transcription speed of pdf files. Make sure that the “Image Correction” option is disabled and the pdfs you submit do not contain any rotated images defaults to workflow_input(Settings.FileTranscriptionEnabled)

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• vpc_registration_threshold (str) – Structured pages above this threshold will be automatically matched to a layout variation, defaults to workflow_input(Settings.StructuredLayoutMatchThreshold)

• nlc_enabled (str) – Enables workflow to manage a model for automated classification of semi-structured and additional layout variations, defaults to workflow_input(Settings.SemiStructuredClassification)

• nlc_target_accuracy (str) – defaults to workflow_input(Settings.SemiTargetAccuracy)

• nlc_doc_grouping_logic (str) – Logic to handle multiple pages matched to the same layout variationin a given submission, defaults to workflow_input(Settings.SemiDocGroupingLogic)

Example usage:

machine_classification = MachineClassificationBlock(
    reference_name='machine_classification',
    submission=case_collation.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    rotation_correction_enabled=idp_wf_config.rotation_correction_enabled,
)

IDENTIFIER = 'MACHINE_CLASSIFICATION_6'

class flows_sdk.implementations.idp_v39_1.idp_blocks.ManualClassificationBlock(submission, api_params, reference_name=None, layout_release_uuid='${workflow.input.layout_release_uuid}', manual_nlc_enabled='${workflow.input.manual_nlc_enabled}', task_restrictions='${workflow.input.manual_classification_task_restrictions}', notification_workflow='${workflow.input.notification_workflow_classification}', nlc_enabled='${workflow.input.semi_structured_classification}', nlc_target_accuracy='${workflow.input.semi_target_accuracy}', mobile_processing_enabled='${workflow.input.machine_classification_mobile_processing_enabled}', vpc_registration_threshold='${workflow.input.structured_layout_match_threshold}')

Bases: Block

Manual classification block allows keyers to manually match submissions to their layouts. Keyers may perform manual classification if machine classification cannot automatically match a submission to a layout with high confidence

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• manual_nlc_enabled (str) – Enables manual classification when applicable, defaults to workflow_input(Settings.ManualNlcEnabled)

• task_restrictions (Union[List[Any], str, None]) – Defines what users can access Supervision tasks created by this block, defaults to workflow_input(Settings.ManualClassificationTaskRestrictions)

• notification_workflow (str) – Notification flow name to run when the submission enters Manual Classification, defaults to workflow_input(Settings.ManualClassificationNotificationWorkflow)

• nlc_enabled (Union[bool, str, None]) – Enables workflow to manage a model for automated classification of semi-structured and additional layout variations, defaults to workflow_input(Settings.SemiStructuredClassification)

• nlc_target_accuracy (Union[float, str, None]) – Classification Target Accuracy, defaults to workflow_input(Settings.SemiTargetAccuracy)

• mobile_processing_enabled (Union[bool, str, None]) – Improves the machine readability of photo captured document, defaults to workflow_input(Settings.MobileProcessingEnabled)

• vpc_registration_threshold (Optional[str]) – Structured pages above this threshold will be automatically matched to a layout variation when performing light vpc registration in the doc-org task, defaults to workflow_input(Settings.StructuredLayoutMatchThreshold)

Example usage:

manual_classification = ManualClassificationBlock(
    reference_name='manual_classification',
    submission=machine_classification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
)

IDENTIFIER = 'MANUAL_CLASSIFICATION_3'

class flows_sdk.implementations.idp_v39_1.idp_blocks.MachineIdentificationBlock(submission, api_params, layout_release_uuid='${workflow.input.layout_release_uuid}', reference_name=None, manual_field_id_enabled='${workflow.input.manual_field_id_enabled}', field_id_target_accuracy='${workflow.input.field_id_target_accuracy}', table_id_target_accuracy='${workflow.input.table_id_target_accuracy}')

Bases: Block

Machine identification automatically identify fields and tables in the submission

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• layout_release_uuid (Any) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• manual_field_id_enabled (str) – Enables manual identification when applicable, defaults to workflow_input(Settings.ManualFieldIdEnabled)

• field_id_target_accuracy (str) – Field ID Target Accuracy, defaults to workflow_input(Settings.FieldIdTargetAccuracy)

• table_id_target_accuracy (str) – Table ID Target Accuracy, defaults to workflow_input(Settings.TableIdTargetAccuracy)

Example usage:

machine_identification = MachineIdentificationBlock(
    reference_name='machine_identification',
    submission=manual_classification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
)

IDENTIFIER = 'MACHINE_IDENTIFICATION_7'

class flows_sdk.implementations.idp_v39_1.idp_blocks.ManualIdentificationBlock(submission, api_params, reference_name=None, task_restrictions='${workflow.input.manual_identification_task_restrictions}', manual_field_id_enabled='${workflow.input.manual_field_id_enabled}', layout_release_uuid='${workflow.input.layout_release_uuid}', notification_workflow='${workflow.input.notification_workflow_identification}')

Bases: Block

Manual identification allows keyers to complete field identification or table identification tasks, where they draw bounding boxes around the contents of certain fields, table columns or table rows. This identification process ensures that the system will be able to transcribe the correct content in the upcoming transcription process

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• task_restrictions (Union[List[Any], str, None]) – Defines what users can access Supervision tasks created by this block, defaults to workflow_input(Settings.ManualFieldIdTaskRestrictions)

• manual_field_id_enabled (str) – Enables manual identification when applicable, defaults to workflow_input(Settings.ManualFieldIdEnabled)

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• notification_workflow (str) – Notification flow name to run when the submission enters Manual Identification, defaults to workflow_inputSettings.ManualFieldIdNotificationWorkflow

Example usage:

manual_identification = ManualIdentificationBlock(
    reference_name='manual_identification',
    submission=machine_identification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    task_restrictions=idp_wf_config.manual_identification_config.task_restrictions,
)

IDENTIFIER = 'MANUAL_IDENTIFICATION_5'

class flows_sdk.implementations.idp_v39_1.idp_blocks.MachineTranscriptionBlock(submission, api_params, reference_name=None, layout_release_uuid='${workflow.input.layout_release_uuid}', transcription_model='${workflow.input.transcription_model}', finetuning_only_trained_layouts='${workflow.input.finetuning_only_trained_layouts}', transcription_overrides='${workflow.input.transcription_overrides}', structured_text_target_accuracy='${workflow.input.structured_text_target_accuracy}', structured_text_confidence_threshold='${workflow.input.structured_text_threshold}', structured_text_acceptable_confidence='${workflow.input.structured_min_leg_threshold}', semi_structured_text_target_accuracy='${workflow.input.semi_structured_text_target_accuracy}', semi_structured_text_confidence_threshold='${workflow.input.semi_structured_text_threshold}', semi_structured_text_acceptable_confidence='${workflow.input.semi_structured_min_leg_threshold}', semi_structured_table_target_accuracy='${workflow.input.semi_structured_table_target_accuracy}', semi_structured_table_confidence_threshold='${workflow.input.semi_structured_table_threshold}', semi_structured_table_acceptable_confidence='${workflow.input.semi_structured_table_min_leg_threshold}', structured_checkbox_target_accuracy='${workflow.input.structured_checkbox_target_accuracy}', structured_checkbox_confidence_threshold='${workflow.input.structured_checkbox_threshold}', structured_checkbox_acceptable_confidence='${workflow.input.checkbox_min_leg_threshold}', structured_signature_target_accuracy='${workflow.input.structured_signature_target_accuracy}', structured_signature_confidence_threshold='${workflow.input.structured_signature_threshold}', structured_signature_acceptable_confidence='${workflow.input.signature_min_leg_threshold}', semi_structured_checkbox_target_accuracy='${workflow.input.semi_structured_checkbox_target_accuracy}', semi_structured_checkbox_confidence_threshold='${workflow.input.semi_structured_checkbox_threshold}', semi_structured_checkbox_acceptable_confidence='${workflow.input.semi_structured_checkbox_min_leg_threshold}')

Bases: Block

Machine transcription automatically transcribes the content of your submission

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• transcription_model (str) – The transcription model that will be used for this flow, defaults to workflow_input(Settings.TranscriptionModel)

• finetuning_only_trained_layouts (str) – defaults to workflow_input( Settings.FinetuningOnlyTrainedLayouts )

• transcription_overrides (str) – defaults to workflow_input( Settings.TranscriptionOverrides )

• structured_text_target_accuracy (str) – defaults to workflow_input( Settings.StructuredTextTargetAccuracy )

• structured_text_confidence_threshold (str) – defaults to workflow_input( Settings.StructuredTextThreshold )

• structured_text_acceptable_confidence (str) – defaults to workflow_input( Settings.StructuredTextMinLegThreshold )

• semi_structured_text_target_accuracy (str) – defaults to workflow_input( Settings.SemiStructuredTextTargetAccuracy )

• semi_structured_text_confidence_threshold (str) – defaults to workflow_input( Settings.SemiStructuredTextThreshold )

• semi_structured_text_acceptable_confidence (str) – defaults to workflow_input( Settings.SemiStructuredTextMinLegThreshold )

• structured_checkbox_target_accuracy (str) – defaults to workflow_input( Settings.StructuredCheckboxTargetAccuracy )

• structured_checkbox_confidence_threshold (str) – defaults to workflow_input( Settings.StructuredCheckboxThreshold )

• structured_checkbox_acceptable_confidence (str) – defaults to workflow_input( Settings.StructuredCheckboxMinLegThreshold )

• structured_signature_target_accuracy (str) – defaults to workflow_input( Settings.StructuredSignatureTargetAccuracy )

• structured_signature_confidence_threshold (str) – defaults to workflow_input( Settings.StructuredSignatureThreshold )

• structured_signature_acceptable_confidence (str) – defaults to workflow_input( Settings.StructuredSignatureMinLegThreshold )

• semi_structured_checkbox_target_accuracy (str) – defaults to workflow_input( Settings.SemiStructuredCheckboxTargetAccuracy )

• semi_structured_checkbox_confidence_threshold (str) – defaults to workflow_input( Settings.SemiStructuredCheckboxThreshold )

• semi_structured_checkbox_acceptable_confidence (str) – defaults to workflow_input( Settings.SemiStructuredCheckboxMinLegThreshold )

Example usage:

machine_transcription = MachineTranscriptionBlock(
    reference_name='machine_transcription',
    submission=manual_identification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
)

IDENTIFIER = 'MACHINE_TRANSCRIPTION_9'

class flows_sdk.implementations.idp_v39_1.idp_blocks.ManualTranscriptionBlock(submission, api_params, reference_name=None, supervision_transcription_masking='${workflow.input.manual_transcription_supervision_transcription_masking}', table_output_manual_review='${workflow.input.manual_transcription_table_output_manual_review}', always_supervise_blank_cells='${workflow.input.manual_transcription_always_supervise_blank_cells}', task_restrictions='${workflow.input.manual_transcription_task_restrictions}', manual_transcription_enabled='${workflow.input.manual_transcription_enabled}', notification_workflow='${workflow.input.notification_workflow_transcription}', title='Manual Transcription', description='Manually transcribe fields that could not be automatically transcribed')

Bases: Block

Manual transcription lets your keyers manually enter the text found in fields or tables that could not be automatically transcribed

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (Union[bool, str]) – Prevents users from inputting invalid characters during Supervision Transcription tasks, defaults to workflow_input(Settings.ManualTranscriptionSupervisionTranscriptionMasking)

• table_output_manual_review (Union[bool, str]) – Always generates a table transcription task if the layout contains a table. If disabled, a table transcription task will only be generated if one or more cells have transcribed values below the defined thresholds, defaults to workflow_input(Settings.ManualTranscriptionTableOutputManualReview)

• always_supervise_blank_cells (Union[bool, str]) – If enabled, always send blank cells in transcribed tables to be supervised manually, regardless of confidence defaults to workflow_input(Settings.ManualTranscriptionAlwaysSuperviseBlankCells)

• task_restrictions (Union[List[Any], str, None]) – Defines what users can access Supervision tasks for submission from a particular source or submissions matching a specific layout, defaults to workflow_input(Settings.ManualTranscriptionTaskRestrictions)

• manual_transcription_enabled (str) – Enables manual transcription when applicable, defaults to workflow_input(Settings.ManualTranscriptionEnabled)

• notification_workflow (str) – Notification flow name to run when the submission enters Manual Transcription, defaults to workflow_input(Settings.ManualTranscriptionNotificationWorkflow)

Example usage:

manual_transcription = ManualTranscriptionBlock(
    reference_name='manual_transcription',
    submission=machine_transcription.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    supervision_transcription_masking=(
        idp_wf_config.manual_transcription_config.supervision_transcription_masking
    ),
    table_output_manual_review=(
        idp_wf_config.manual_transcription_config.table_output_manual_review
    ),
    task_restrictions=idp_wf_config.manual_transcription_config.task_restrictions,
)

IDENTIFIER = 'MANUAL_TRANSCRIPTION_5'

class flows_sdk.implementations.idp_v39_1.idp_blocks.FlexibleExtractionBlock(submission, api_params, reference_name=None, supervision_transcription_masking='${workflow.input.flexible_extraction_supervision_transcription_masking}', task_restrictions='${workflow.input.flexible_extraction_task_restrictions}', layout_release_uuid='${workflow.input.layout_release_uuid}', notification_workflow='${workflow.input.notification_workflow_flexible_extraction}')

Bases: Block

Flexible extraction manually transcribes fields marked for review

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (Union[bool, str, None]) – Prevents users from inputting invalid characters during the Flexible Extraction task, defaults to workflow_input(Settings.FlexibleExtractionSupervisionTranscriptionMasking)

• task_restrictions (Union[List[Any], str, None]) – Defines what users can access Supervision tasks created by this block, defaults workflow_input(Settings.FlexibleExtractionTaskRestrictions)

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• notification_workflow (str) – Notification flow name to run when the submission enters Flexible Extraction, defaults to workflow_input(Settings.FlexibleExtractionNotificationWorkflow)

Example usage:

flexible_extraction = FlexibleExtractionBlock(
    reference_name='flexible_extraction',
    submission=manual_transcription.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    supervision_transcription_masking=(
        idp_wf_config.flexible_extraction_config.supervision_transcription_masking
    ),
    task_restrictions=idp_wf_config.flexible_extraction_config.task_restrictions,
)

IDENTIFIER = 'FLEXIBLE_EXTRACTION_4'

class flows_sdk.implementations.idp_v39_1.idp_blocks.ReprocessingBlock(reference_name, submission, parent_flow='${workflow.definition.identifier}', idp_wf_config='${workflow.input}', trigger='${workflow.input.$trigger}')

Bases: Block

Reprocessing block enables a second extraction attempt if documents were initially confidently mis-classified by the machine. When an end-user specifies that a document layout is incorrect during Manual Identification, Manual Transcription, or Flexible Extraction, the flow will reach the Reprocessing block which will mark correctly classified documents as “complete” status, and misclassified documents with “reprocess” status will be processed in a second execution of the flow. This block should be placed before the SubmissionCompleteBlock to enable reprocessing.

Mandatory parameters:

Parameters:

submission (Any) – Submission object

IDENTIFIER = 'REPROCESSING'

class flows_sdk.implementations.idp_v39_1.idp_blocks.SubmissionCompleteBlock(submission, is_reprocessing=False, reference_name=None, nlc_qa_sampling_ratio='${workflow.input.semi_qa_sample_rate}', field_id_qa_enabled='${workflow.input.field_id_qa_enabled}', field_id_qa_sampling_ratio='${workflow.input.field_id_qa_sample_rate}', table_id_qa_enabled='${workflow.input.table_id_qa_enabled}', table_id_qa_sampling_ratio='${workflow.input.table_id_qa_sample_rate}', transcription_qa_enabled='${workflow.input.qa}', transcription_structured_entry_qa_sample_rate='${workflow.input.structured_entry_qa_sample_rate}', transcription_structured_signature_qa_sample_rate='${workflow.input.structured_signature_qa_sample_rate}', transcription_structured_checkbox_qa_sample_rate='${workflow.input.structured_checkbox_qa_sample_rate}', transcription_semi_structured_qa_sample_rate='${workflow.input.semi_structured_qa_sample_rate}', table_cell_transcription_qa_enabled='${workflow.input.table_cell_transcription_qa_enabled}', table_cell_transcription_qa_sample_rate='${workflow.input.table_cell_transcription_qa_sample_rate}', auto_qa_sampling_rate_enabled='${workflow.input.auto_qa_sampling_rate_enabled}', exclude_auto_transcribe_fields_from_qa='${workflow.input.exclude_auto_transcribe_fields_from_qa}', payload=None)

Bases: Block

Submission complete block finalizes submission processing and updates reporting data. This block should be called at the END of every flow that uses blocks included in the flows_sdk.implementations.idp_v34 library. The block requires both the submission object and a payload to be passed in.

Mandatory parameters:

Parameters:

• submission (str) – Submission object

• payload (Any) – Object to pass to downstream systems

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• nlc_qa_sampling_ratio (Union[str, int]) – Defines the percentage of documents sampled for Classification QA, defaults to workflow_input(Settings.SemiQaSampleRate)

• field_id_qa_enabled (Union[str, bool]) – defaults to workflow_input(Settings.FieldIdQaEnabled)

• field_id_qa_sampling_ratio (Union[str, int]) – Defines the percentage of documents sampled for Identification QA, defaults to workflow_input(Settings.FieldIdQaSampleRate)

• table_id_qa_enabled (Union[str, bool]) – Allows users to verify the location of table cells, defaults to workflow_input(Settings.TableIdQaEnabled)

• table_id_qa_sampling_ratio (Union[str, int]) – Defines the percentage of tables the system samples for QA, defaults to workflow_input(Settings.TableIdQaSampleRate)

• transcription_qa_enabled (Union[str, bool]) – defaults to workflow_input(Settings.TranscriptionQaEnabled)

• transcription_qa_sampling_ratio – Defines the percentage of fields sampled for Transcription QA, defaults to workflow_input(Settings.TranscriptionQaSampleRate)

• table_cell_transcription_qa_enabled (Union[str, bool]) – defaults to workflow_input(Settings.TableCellTranscriptionQaEnabled)

• table_cell_transcription_qa_sample_rate (Union[str, int]) – Defines the percentage of cells the system samples for QA. This value is likely to be lower than “Transcription QA Sample Rate” since there are more table cells than fields on any given page, defaults to workflow_input(Settings.TableCellTranscriptionQaSampleRate)

• exclude_auto_transcribe_fields_from_qa (Union[str, bool]) – Defines fields that are marked as auto-transcribe in layout should be excluded from QA or not. defaults to workflow_input(Settings.ExcludeAutoTranscribeFieldsFromQA)

• is_reprocessing (Union[str, bool]) – boolean to handle execution when reprocessing defaults to False

Example usage:

submission_complete = SubmissionCompleteBlock(
    reference_name='complete_submission',
    submission=flexible_extraction.output('submission')
)

IDENTIFIER = 'SUBMISSION_COMPLETE_4'

class flows_sdk.implementations.idp_v39_1.idp_blocks.SetTransformedOutputBlock(submission, transformed_output, reference_name=None)

Bases: Block

Set Transformed Output block sets the transformed output for the provided submission. This can later be used to send the transformed data downstream using output blocks. For more information see https://docs.hyperscience.ai/#retrieving-transformed-submissions.

Mandatory parameters:

Parameters:

• submission (str) – Submission object

• transformed_output (str) – Object that will be set as the transformed output for this submission

IDENTIFIER = 'SET_TRANSFORMED_OUTPUT'

class flows_sdk.implementations.idp_v39_1.idp_blocks.IdpCustomSupervisionBlock(submission, task_purpose, supervision_template, reference_name=None, supervision_transcription_masking=True, task_restrictions=None, page_ids=None, notification_workflow='IDP_SUBMISSION_NOTIFY_V39_1', title='IDP Custom Supervision', description='IDP Custom Supervision')

Bases: Block

An IDP wrapper for the Custom Supervision block. It has the same functionality, but handles reading/writing data from the IDP database.

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• task_purpose (str) – the custom task name that’s given to all supervision tasks that run through a particular instance of a custom supervision block. This can be used to filter tasks in the task queue.

• supervision_template (Any) – JSON used for configuration of the Custom Supervision Task.

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (Union[str, bool]) – Prevents users from inputting invalid characters during the Custom Supervision task, defaults to True

• task_restrictions (Union[str, List[Any], None]) – Defines what users can access Supervision tasks created by this block, defaults to []

• page_ids (Union[str, List[int], None]) – This is an optional parameter. If left empty, the IDP wrapper will, by default, pull in all pages in the submission. Adding a list of page IDs will override the default list of pages. defaults to []

• notification_workflow (str) – Notification flow name to run when the submission enters Custom Supervision (which is called within the IDP Custom Supervision block), defaults to IDP_SUBMISSION_NOTIFY_NAME

• title (Optional[str]) – UI-visible title of the block. Defaults to ‘IDP Custom Supervision’

• description (Optional[str]) – Description of the block. Both useful for documentation purpose and visible to users in the Flow studio. Defaults to ‘IDP Custom Supervision’

Example usage:

idp_custom_supervision = IdpCustomSupervisionBlock(
    reference_name='idp_custom_supervision',
    submission=manual_transcription.output('submission'),
    task_purpose='make_custom_decision',
    supervision_template=[
        {
            'name': 'three_column_template',
            'version': '1.0',
            'thumbnail': {
                'group_by_document': True,
                'group_by_case': True,
            },
            'action': [
                {
                    'name': 'my_tab',
                    'display': 'SSN Info',
                    'input': [
                        {
                            'name': 'ssn_field',
                            'type': 'transcription',
                            'data_identifier': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                            'title': 'SSN',
                        },
                    ],
                    'ui': {
                        'groups': [
                            {
                                'title': 'My Group',
                                'fields': ['ssn_field'],
                            },
                        ],
                    },
                },
            ],
        },
    ],
    supervision_transcription_masking=False,
)

IDENTIFIER = 'IDP_CUSTOM_SUPERVISION_2'

class flows_sdk.implementations.idp_v39_1.idp_blocks.IDPFullPageTranscriptionBlock(submission, reference_name=None, title='Full Page Transcription (Submission)', description='Transcribes the documents included in a submission', app_metadata=None)

Bases: Block

IDP Full Page Transcription Block machine-transcribes the documents contained in a submission

Mandatory parameters:

Parameters:

submission (str) – submission object that includes the raw documents that need to be transcribed

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• app_metadata (Optional[List[str]]) – used for capturing information about the flow

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# IDPFullPageTranscriptionBlock transcribes the documents contained in the submission object
idp_fpt_block = IDPFullPageTranscriptionBlock(
    reference_name='idp_fpt',
    submission=submission_bootstrap.output(),
)
# reference to the output of IDPFullPageTranscriptionBlock which can be passed as input to
# the next block. For more information on output() and output references, please take a
# look at class Block and its output() method
output_ref = idp_fpt_block.output()

IDENTIFIER = 'FULL_PAGE_TRANSCRIPTION_SUBMISSION_2'

class flows_sdk.implementations.idp_v39_1.idp_blocks.IDPImageCorrectionBlock(submission, reference_name=None, rotation_correction_enabled=True, mobile_processing_enabled=False, title='Image Correction (Submission)', description='Rotate and de-skew documents included in a submission', app_metadata=None)

Bases: Block

IDP Image Correction Block rotates and de-skews tilted/skewed images so that they are more transcription-friendly

Mandatory parameters:

Parameters:

submission (str) – submission object that includes the raw documents that need to be image-corrected

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• app_metadata (Optional[List[str]]) – used for capturing information about the flow

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# IDPImageCorrectionBlock takes in the submission object and outputs in the same format with
# its images
idp_image_correct_block = IDPImageCorrectionBlock(
    reference_name='idp_image_correct',
    submission=submission_bootstrap.output(),
)
# reference to the output of IDPImageCorrectionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look at
# class Block and its output() method
output_ref = idp_image_correct_block.output()

IDENTIFIER = 'IMAGE_CORRECTION_SUBMISSION'

Additional Blocks (v39.1)

class flows_sdk.implementations.idp_v39_1.additional_blocks.JSONOutputsBlock(inputs, blocks=None)

Bases: Outputs

Output block allows users to send JSON data extracted by an IDP flow to other systems for downstream processing Mandatory parameters:

Parameters:

inputs (Dict[str, Any]) – Used by the UI to automatically pre-populate the input of newly added blocks. Extended in this class to include 'enabled': True to visualize an enabled/disabled trigger that is controllable via Flow Studio.

Usage when parameters are to be defined in Flow Studio:

outputs = JSONOutputBlock(
    inputs={'result': }
)

class flows_sdk.implementations.idp_v39_1.additional_blocks.ImageCorrectionBlock(pages, reference_name=None, rotation_correction_enabled=True, mobile_processing_enabled=False, title='Image Correction (Pages)', description='Rotate and de-skew documents', app_metadata=None)

Bases: Block

Image Correction Block rotates and de-skews tilted/skewed images so that they are more transcription-friendly Mandatory parameters:

Parameters:

pages (str) – list of page object that includes the raw documents that need to be image-corrected

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• app_metadata (Optional[List[str]]) – used for capturing information about the flow

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# ImageCorrectionBlock takes in the submission object and outputs in the same format with
# its images
image_correct_block = ImageCorrectionBlock(
    reference_name='image_correct',
    pages=submission_bootstrap.output(submission.unassigned_pages),
)
# reference to the output of IDPImageCorrectionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look
# at class Block and its output() method
output_ref = image_correct_block.output()

IDENTIFIER = 'IMAGE_CORRECTION_PAGES'

class flows_sdk.implementations.idp_v39_1.additional_blocks.FullPageTranscriptionBlock(pages, reference_name=None, title='Full Page Transcription (Pages)', description='Transcribes documents', app_metadata=None)

Bases: Block

Full Page Transcription Block machine-transcribes the documents contained in a submission

Mandatory parameters:

Parameters:

pages (str) – list of pages that includes the raw documents that need to be transcribed

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• app_metadata (Optional[List[str]]) – used for capturing information about the flow

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# FullPageTranscriptionBlock transcribes the documents contained in the submission object
fpt_block = FullPageTranscriptionBlock(
    reference_name='fpt',
    submission=submission_bootstrap.output(submission.unassigned_pages),
)
# reference to the output of FullPageTranscriptionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look at
# class Block and its output() method
output_ref = fpt_block.output()

IDENTIFIER = 'FULL_PAGE_TRANSCRIPTION_PAGES_2'

class flows_sdk.implementations.idp_v39_1.additional_blocks.CustomSupervisionBlock(submission, task_purpose, data, supervision_template, reference_name=None, supervision_transcription_masking=True, task_restrictions=None, notification_workflow='IDP_SUBMISSION_NOTIFY_V39_1', title='Custom Supervision', description='Custom Supervision')

Bases: Block

The CustomSupervisionBlock uses a JSON schema as a form builder to create customer-driven UIs for Supervision tasks.

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• task_purpose (str) – the custom task name that’s given to all supervision tasks that run through a particular instance of a custom supervision block. This can be used to filter tasks in the task queue.

• data (Any) – JSON structure that supplies the data used to populated the supervision_template

• supervision_template (Any) – JSON used for configuration of the Custom Supervision Task.

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (bool) – Prevents users from inputting invalid characters during the Custom Supervision task, defaults to True

• task_restrictions (Optional[List[Any]]) – Defines what users can access Supervision tasks created by this block, defaults to []

• notification_workflow (str) – Notification flow name to run when the submission enters Custom Supervision, defaults to IDP_SUBMISSION_NOTIFY_NAME

• title (Optional[str]) – UI-visible title of the block. Defaults to ‘IDP Custom Supervision’

• description (Optional[str]) – Description of the block. Both useful for documentation purpose and visible to users in the Flow studio. Defaults to ‘IDP Custom Supervision’

Example usage:

custom_supervision = CustomSupervisionBlock(
    reference_name='custom_supervision',
    submission=manual_transcription.output('submission'),
    task_purpose='make_custom_decision',
    supervision_template=[
        {
            'name': 'three_column_template',
            'version': '1.0',
            'thumbnail': {
                'group_by_document': True,
                'group_by_case': True,
            },
            'action': [
                {
                    'name': 'my_tab',
                    'display': 'SSN Info',
                    'input': [
                        {
                            'name': 'ssn_field',
                            'type': 'transcription',
                            'data_identifier': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                            'title': 'SSN',
                        },
                    ],
                    'ui': {
                        'groups': [
                            {
                                'title': 'My Group',
                                'fields': ['ssn_field'],
                            },
                        ],
                    },
                },
            ],
        },
    ],
    data={
        'fields': [
            {
                'id': 967,
                'uuid': 'fe8d4e7a-a82c-448c-85a1-0a3cbbe53b61',
                'value': '111-22-3333',
                'page_id': 15,
                'layout_field_id': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                'validation_overridden': False,
                'bounding_box': [
                    0.09360783305186686,
                    0.25792617589433436,
                    0.6358913805295097,
                    0.28862414740388187,
                ],
                'occurrence_index': 1,
            },
        ],
        'template_fields': [
            {
                'uuid': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                'notes': '',
                'type': 'entry',
                'data_type_uuid': 'b600a4e4-758f-4c4b-a2f4-cbb5dd870a0c',
                'name': 'SSN',
            },
        ],
        'pages': [
            {
                'id': 15,
                'file_page_number': 1,
                'document_page_number': 1,
                'image_url': '/image/2ed3d989-b64a-44bd-a9fb-6e0dcc5ccdb8',
                'form_id': 2,
                'submission_id': 1,
                'external_case_ids': [],
                'read_only': False,
                'filename': '1 page.pdf',
                'form_page_id': 1
            },
        ],
        'documents': [
            {
                'id': 2,
                'layout_uuid': 'e31c3cbf-dcea-44fd-a052-902463c6040f',
                'layout_name': 'layout_name',
                'page_count': 2,
                'read_only': False,
            },
        ],
        'cases': [],
        'selected_choices': [],
    },
    supervision_transcription_masking=False,
)

IDENTIFIER = 'CUSTOM_SUPERVISION_2'

class flows_sdk.implementations.idp_v39_1.additional_blocks.SoapRequestBlock(method, reference_name=None, endpoint='', body_namespace=None, headers_namespace=None, headers=None, params=None, use_wsdl=False, wsdl_uri='', wsdl_service_name='', wsdl_port_name='', title='HTTP REST Block', description='HTTP REST block')

Bases: Block

Soap Block allows user to perform SOAP requests Mandatory parameters:

Parameters:

• method (str) – SOAP method to invoke

• endpoint (str) – absolute url including schema to HTTP endpoint for this request

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• body_namespace (Optional[str]) – SOAP request body namespace url

• headers_namespace (Optional[str]) – SOAP request headers namespace url

• headers (Optional[Dict[Any, Any]]) – SOAP headers to use

• params (Optional[Dict[Any, Any]]) – key-value pair used as query parameters in the request to be inserted as SOAP body

Example usage:

# minimum viable instantiation, the rest of the fields can later be added in the UI
soap_block = SoapRequestBlock(
    reference_name='soap_request',
    title='Soap Request',
    description='Perform soap request',
    method='ExampleMethod',
)
soap_block = SoapRequestBlock(
    reference_name='soap_request',
    title='Soap Request',
    description='Perform soap request',
    method='ExampleMethod',
    endpoint='http://example.org/abc.wso',
    body_namespace='http://www.example.org/abc.countryinfo',
    params={'example': 'stuff'},
    headers={}
)
# reference to the output of SoapRequestBlock which can be passed as input to the next block
# for more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = soap_block.output()

IDENTIFIER = 'SOAP_REQ'

class flows_sdk.implementations.idp_v39_1.additional_blocks.HttpRestBlock(method, endpoint, reference_name=None, headers=None, params=None, payload=None, json=None, authorization_type=None, handled_error_codes=None, auth_params=None, title='HTTP REST Block', description='HTTP REST block')

Bases: BaseHttpRestBlock

Http Rest Block allows user to perform HTTP requests Mandatory parameters:

Parameters:

• method (str) – HTTP method to use

• endpoint (str) – absolute url including schema to HTTP endpoint for this request

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• headers (Optional[Dict[str, str]]) – HTTP headers to use

• params (Optional[Dict[str, Any]]) – key-value pair used as query parameters in the request

• payload (Optional[Dict[str, str]]) – key-value pair to be used as x-www-form-urlencoded data. Mutually exclusive with json

• json (Optional[Dict[str, Any]]) – key-value pair to be used as json data. Mutually exclusive with payload

• authorization_type (Optional[str]) – authorization type (none, http_header, oauth_2_client_credentials)

• handled_error_codes (Optional[List[Union[int, str]]]) – specify HTTP error codes that will not fail the task, allowing for wildcards with the ‘x’ char. Example: [400, “5xx”]

• auth_params (Union[HttpHeaderAuthorizationParams, OAuth2AuthorizationParams, None]) – authorization parameters to be passed when the authorization_type is set to “http_header” or “oauth_2_client_credentials”

Example usage:

# minimum viable instantiation, the rest of the fields can later be added in the UI
http_rest_block = HttpRestBlock(
    reference_name='http_request',
    title='Http Request',
    description='Perform http request',
    method='GET',
)
http_rest_block = HttpRestBlock(
    reference_name='http_request',
    title='Http Request',
    description='Perform http request',
    method='GET',
    endpoint='https://sheets.googleapis.com/v4/spreadsheets/example',
)
# reference to the output of HttpRestBlock which can be passed as input to the next block
# for more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = http_rest_block.output()

class flows_sdk.implementations.idp_v39_1.additional_blocks.HyperscienceRestApiBlock(method, app_endpoint, reference_name=None, headers=None, params=None, payload=None, json=None, handled_error_codes=None, title='Hyperscience HTTP REST Block', description='Hyperscience HTTP REST block')

Bases: BaseHttpRestBlock

Make HTTP requests to the Hyperscience platform APIs. This block will automatically take care of prepending the base url and will include the appropriate authentication headers with the request.

Mandatory parameters:

Parameters:

• method (str) – HTTP method to use, e.g. ‘GET’, ‘POST’, etc.

• app_endpoint (str) – relative url, beginning with a forward slash, to an HTTP endpoint served by the Hyperscience platform. For reference on the available endpoints, check out https://docs.hyperscience.ai/. Absolute URLs that include a hostname are allowed only if the flow has received them as part of the response of a previous API request that the flow has made. Do not hardcode the hostname of the Hyperscience instance in your flow definitions as they will not work when you run the flows on another instance, or if the hostname changes. For making requests to external systems, use the HttpRestBlock or a library like “requests”.

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• headers (Optional[Dict[str, str]]) – HTTP headers to use

• params (Optional[Dict[str, Any]]) – key-value pair used as query parameters in the request

• payload (Optional[Dict[str, str]]) – key-value pair to be used as x-www-form-urlencoded data. Mutually exclusive with json

• json (Optional[Dict[str, Any]]) – key-value pair to be used as json data. Mutually exclusive with payload

• handled_error_codes (Optional[List[Union[int, str]]]) – specify HTTP error codes that will not fail the task, allowing for wildcards with the ‘x’ char. Example: [400, “5xx”]

Example usage:

hs_rest_block = HyperscienceRestApiBlock(
    reference_name='http_request',
    app_endpoint='/api/healthcheck',
    method='GET',
)

class flows_sdk.implementations.idp_v39_1.additional_blocks.DatabaseAccessBlock(reference_name=None, db_type='', database='', host='', username='', password='', query='', port=None, options=None, timeout=None, query_params=None, title='DB Block', description='DB access block')

Bases: Block

Database Block allows user to access and perform queries on the specified database Mandatory parameters:

Parameters:

• db_type (str) – database type (mssql, oracle, postgres)

• host (str) – URL/IP address of the server database is hosted on

• database (str) – database name

• username (str) – database username

• password (str) – database password

• query (str) – parameterized query

Optional parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• port (Optional[int]) – database port number

• options (Optional[Dict[Any, Any]]) – dictionary of additional connection string options

• query_params (Optional[Dict[Any, Any]]) – dictionary of values for query placeholders

• timeout (Optional[int]) – timeout in seconds (mssql and postgres only)

Example usage:

# minimum viable instantiation, the rest of the fields can later be added in the UI
db_lookup = DatabaseAccessBlock(
    reference_name='db_lookup',
    title='Database Lookup',
    description='Perform database lookup',
)
# note that storing password in plain text form is not recommended
# password can later be entered in the UI or defined as a flow secret.
db_lookup = DatabaseAccessBlock(
    reference_name='db_lookup',
    title='Database Lookup',
    description='Perform database lookup',
    db_type='mssql',
    database='cars',
    host='example.com',
    username='user',
    # we recommend skipping the password field or defining it as a system secret
    # e.g. system_secret('{your_secret_identifier}') (both allow for secret input via. UI)
    password='pw'
    port=1433,
    timeout=200,
    query='SELECT * from CAR',
)
# reference to the output of DBAccessBlock which can be passed as input to the next block
# for more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = db_lookup.output()
# content of output_ref:
{
    "result": [
        {
            "id": 1,
            "brand": "Aston Martin",
            "model": "Vanquish"
        },
        {
            "id": 2,
            "brand": "Jaguar",
            "model": "XE 2018"
        }
    ]
}

Database output for the same query (SELECT * FROM CAR)

IDENTIFIER = 'DB_ACCESS'

class flows_sdk.implementations.idp_v39_1.additional_blocks.LlamaBlock(model_id, prompt, reference_name=None, consent=False, tokenize_only=False, predict_extra_kwargs=None, title='Llama Text Generation', description='Text generation using the Llama 2 model')

Bases: Block

Text generation using the Llama 2 model by Meta.

This block utilizes Llama 2 by Meta. Llama 2 is licensed under the LLAMA 2 Community License (https://ai.meta.com/llama/license/). By using the Llama Block, you acknowledge that you have received a copy of the LLAMA 2 Community License.

Note: This block is still experimental and not ready for production use.

Mandatory parameters:

Parameters:

• model_id (str) – UUID, identifier of the asset with the ML model. Model assets are not part of the Hypersicence bundle and must first be downloaded and imported into the system before they can be used. Please contact your Hyperscience representative to discuss the usecase and the appropriate model and to get instructions on downloading the models. Once downloaded, here are the available model asset UUIDs: b3da05c7-061c-4a58-af6d-4c905d2e618f - Llama 2, 13B, 8-bit quantized

• prompt (str) – The prompt to generate text from. Use a CodeBlock ahead of the LlamaBlock in order to build the prompt. The context window of the model is limited to 4096 tokens. If the prompt has more tokens, the block would return an error. The total input and output length also cannot exceed 4096 tokens, which means that if the input is too long, the output may be truncated. Also see tokenize_only for a more detailed explanation of how to handle longer texts.

Optional parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• consent (Optional[bool]) – Has the business user read and acknowledged the Llama 2 legal notice. Unless the flow would be used in CI automation, this should be set to False so that a business user can read and acknowledge the notice in the Hyperscience UI. Setting this to True in the Python definition of the flow is also treated as acknowledgement of the notice on behalf of the entity that will be using the flow.

• tokenize_only (Optional[bool]) – In the context of LLMs, tokens correspond roughly to words, partial words and punctuation. A rough way to compute the number of tokens in a text is number_of_tokens = 1.25 * number_of_words. The Llama block works with a context window of 4096 tokens. If a longer prompt is passed in, it would return an error. Also, if the prompt is shorter, but still close to 4096, the output may be truncated. Since the length of the prompt in tokens cannot be known ahead of time, sometimes the flow may need to make dynamic decisions whether to pass the text to the Llama block, or to split it into multiple chunks, or to use some alternative method for processing it (e.g. a custom supervision manual task). The tokenize_only option enables flows to take such dynamic decisions based on the length of the prompt in tokens. The default is false - it instructs the block to run the ML model inference over the prompt, i.e. to generate a text response. When true is passed, the model will simply tokenize the prompt without running inference and return the list of tokens - this will work regardless of the length of the prompt. A subsequent Decision task can then route longer prompts to an alternative path in the flow, while routing shorter prompts to the LlamaBlock for actual text generation.

• predict_extra_kwargs (Optional[Dict[Any, Any]]) – Extra keyword arguments used for model inference. (Used to fine-tune the model prediction. Not recommended to change this unless you know what you are doing)

Example usage:

llama = LlamaBlock(
    reference_name='llama',
    model_id='b3da05c7-061c-4a58-af6d-4c905d2e618f',
    prompt='${previous_block.output.llama_prompt}',
)

IDENTIFIER = 'LLAMA'

class flows_sdk.implementations.idp_v39_1.additional_blocks.EntityRecognitionBlock(pages, regex_map=None, keyword_map=None, enhanced_output=False, reference_name=None, title='Entity Recognition', description='Recognizes entities of different types in documents')

Bases: Block

Entity Recognition Block detects entities of different types within the documents contained in a submission

Mandatory parameters:

Parameters:

pages (str) – list of pages that includes the raw documents that need to be transcribed

Optional Parameters:

Parameters:

• regex_map (Optional[Dict[str, str]]) – a map that specifies different regex patterns that the block should look for, a default one is present

• keyword_map (Optional[Dict[str, List[str]]]) – a map that specifies different keyword patterns that the block should look for, a default one is present

• enhanced_output (bool) – a boolean flag that enables toggling between simple and enhanced output formats. Default value is False.

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')

# EntityRecognitionBlock detects entities within the documents contained in the submission
entity_recognition_block = EntityRecognitionBlock(
    reference_name='entity_recognition',
    pages=submission_bootstrap.output(submission.unassigned_pages),
    regex_map=DEFAULT_PII_REGEX_MAP,
    keyword_map=DEFAULT_PII_KEYWORD_MAP,
    enhanced_output=False,
)

# reference to the output of EntityRecognitionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look at
# class Block and its output() method
output_ref = entity_recognition_block.output()

Example content of output_ref:

{
    "documents": [
        {
            "file_uuid": "da2c6117-43c4-4b30-84c4-cdc59cc3fa4f",
            "entities": [
                {
                    "text": "John Smith",
                    "type": "Person Name",
                    "positions": [
                        [0.5, 0.1, 0.55, 0.13],
                        [0.6, 0.2, 0.65, 0.23],
                        [0.7, 0.3, 0.75, 0.33],
                    ],
                    "page_ids": [0, 0, 1],
                    "origin": "NER",
                },
                {
                    "text": "<SIGNED>",
                    "type": "signature",
                    "positions": [[0.701171875, 0.732421875, 0.92578125, 0.8681640625]],
                    "page_ids": [0],
                    "origin": "Signature",
                },
            ]
        },
        {
            "file_uuid": "4aa38f84-e5af-4cdc-ba5c-edd60f0a6b89",
            "entities": [
                ...
            ]
        }
    ],
    "redaction_annotations": {
        "ner": [ ... ],
        "ced": [ ... ],
    }
}

IDENTIFIER = 'ENTITY_RECOGNITION'

Trigger Blocks (v39.1)

Note

“Triggers” are referred to as “Input Blocks” in our application documentation.

Warning

Flows are built to allow full definition of triggers either in code or manually in the Flow Studio. We recommend defining triggers manually in the Flow Studio. Instructions on how to do so are in our application documentation.

To enable trigger definition via the Flow Studio UI, you need to instantiate the IDPTriggers() convenience class and pass it to the triggers property of the Flow

return Flow(
   ...
   triggers=IDPTriggers()
)

If instead you wish to define specific triggers in Python, use the trigger definitions below.

Folder Listener (v39.1)

Identifier: FOLDER_TRIGGER

Deprecated in favor of the Universal Folder Listener

Parameters:

Name	Type	Default Value	Title	Description
folder	Path	N/A	Folder to scan for submissions	N/A
file_extensions	string	N/A	File extensions	List of file extensions to monitor for (e.g.: ‘png, jpg, pdf’)
has_meta	boolean	False	Enable metadata	Select this if a metadata file is to be expected along with document files (in XXX_index.txt file)
poll_interval	integer	10	Poll interval	Poll interval in seconds
warmup_interval	integer	15	Warm-up interval	Seconds to wait past document last modified time before processing it
folder_cleanup_delay	integer	86400	Folder cleanup delay	Seconds to wait before cleaning up subfolders
api_params	object	{}	API Parameters	N/A

folder_listener = IOBlock(
    identifier='FOLDER_TRIGGER',
    reference_name='folder_trigger',
    title='Folder listener',
    enabled=True,
    input={
        'folder': '/var/www/forms/forms/folder/',
        'file_extensions': 'png, pdf, jpg',
    },
)

triggers = IDPTriggers(blocks=[folder_listener])

S3 Listener (v39.1)

Identifier: S3_TRIGGER Parameters:

Name	Type	Default Value	Title	Description
bucket_uri	string	N/A	S3 source URI	S3 URI, where the files will be uploaded (e.g. s3://hs_bucket/prefix1/)
archive_bucket_uri	string	N/A	S3 archive URI	S3 URI to move the processed files to (e.g. s3://hs_bucket/prefix1/)
file_extensions	array	[]	File extensions	Types of file extensions for which to monitor
other_file_extensions	string	N/A	Other file extensions	Comma separated list of file extensions for which to monitor (e.g. ‘png, jpg, pdf’)
has_meta	boolean	false	Include submission level parameters	Select this to ingest JSON files along with document files and submission s3 prefixes. These JSON files can contain information such as metadata, cases and external_id. These JSON file names should match the related files or s3 prefixes (e.g., XXX.jpg.json for XXX.jpg)
aws_access_key_id	string	N/A	AWS ACCESS KEY ID	Access Key used for authentication
aws_secret_access_key	password	N/A	SECRET ACCESS KEY	Secret Key used for authentication
use_ec2_instance_credentials	boolean	true	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used
poll_interval	integer	10	Polling interval (in seconds)	How often the S3 Listener will check the S3 bucket for submissions
warmup_interval	integer	15	Warm-up interval (in seconds)	How long the S3 Listener will wait to process the document after it was first detected
api_params	object	{}	API Parameters	N/A

s3_listener = IOBlock(
    identifier='S3_TRIGGER',
    reference_name='s3_trigger',
    title='S3 Listener',
    enabled=True,
    input={
        'bucket_uri': 's3://hs_bucket/prefix1/',
        'archive_bucket_uri': 's3://hs_bucket/prefix2/',
        'file_extensions': ['png', 'pdf', 'jpg'],
        'aws_access_key_id': 'admin',
        'aws_secret_access_key': system_secret('s3_secret_access_key'),
        'use_ec2_instance_credentials': False,
    },
)
triggers = IDPTriggers(blocks=[s3_listener])

Email Listener (v39.1)

Identifier: IMAP_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
provider [1]	string	imap	Email Provider	N/A
poll_interval	integer	60	Polling interval in seconds	The frequency at which the connection will monitor the email folder for submissions
folder	Path	N/A	Folder to scan for emails	The email folder to retrieve submissions from (e.g., “inbox”)
email_body_treatment	string	“ignore”	Email body treatment	What to do with the body of an email that is ingested. Available options are “process” and “ignore”.
post_process_action	string	“move”	Post process action	What to do with the email after it is processed. Available options are “move” and “delete”.
post_process_move_folder	string	“”	Post process archive folder	Folder to move emails to once they are processed. It will be included only if post_process_action is set to “move”.
api_params	object	{}	API Parameters	N/A
render_headers [2]	array	[]	Headers to include	Headers to render at the top of the email body. Headers will be included only if email_body_treatment is set to “process”.

[1] one of:

imap
graph

[2] any of:

To
From
Subject
Date

Additional inputs when "imap" is the selected provider:

Name	Type	Default Value	Title	Description
host	string	N/A	IMAP server address	N/A
port	integer	993	Port Number	N/A
ssl	boolean	True	Use SSL connection	N/A
username	string	N/A	Username	N/A
password	Password	N/A	Password	N/A

Additional inputs when "graph" is the selected provider:

Name	Type	Default Value	Title	Description
client_id	string	N/A	Client id	Application (client) ID for the corresponding application registered with the Microsoft identity platform.
tenant_id	string	N/A	Tenant id	Also called directory ID. Unique identifier of the tenant in which the corresponding application is registered with.
graph_cloud_endpoint [1]	string	Global	Microsoft Graph Cloud Endpoint	Determines base URL used for accessing cloud services through Microsoft Graph.
azure_ad_endpoint [2]	string	AZURE_PUBLIC_CLOUD	Azure AD Endpoint	Determines base URL for the Azure Active Directory (Azure AD) endpoint to acquire token for each national cloud.
client_secret	Password	N/A	Client secret	Client secret for the corresponding application registered with the Microsoft identity platform.

[1] one of:

Global
US_GOV
US_DoD
Germany
China

[2] one of:

AZURE_PUBLIC_CLOUD
AZURE_CHINA
AZURE_GERMANY
AZURE_GOVERNMENT

imap_trigger = IOBlock(
    identifier='IMAP_TRIGGER',
    reference_name='imap_trigger',
    title='IMAP trigger',
    enabled=True,
    input={
        'host': 'example@mail.com',
        'folder': '/var/www/forms/forms/imap/',
        'username': 'admin',
        'password': 'pass',
    }
)

triggers = IDPTriggers(blocks=[imap_trigger])

Box Folder Listener (v39.1)

Identifier: BOX_FOLDER_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
source_folder_id	string	N/A	Folder to scan for submissions	Use the Box Folder ID found in the URL
target_folder_id	string	N/A	Folder to move completed files	Use the Box Folder ID found in the URL
file_extensions	array	[]	File extensions	Types of file extensions for which to monitor
custom_file_extensions	string	N/A	Other file extension types	Comma separated list of file extensions for which to monitor (e.g. ‘png, jpg, pdf’)
poll_interval	integer	10	Polling interval (in seconds)	How often the connector will check the base folder for submissions
warmup_interval	integer	15	Warm-up interval (in seconds)	How long the connector will wait to process the document after it was last modified
public_key_id	string	N/A	Public-Key ID	ID of the Public-key used for authentication with Box
private_key	Password	N/A	Private-Key	Private key used for authentication with Box
passphrase	Password	N/A	Passphrase	Passphrase used for authentication with Box
client_id	string	N/A	Client ID	Client Id used for authentication with Box
client_secret	Password	N/A	Client Secret	Client Secret used for authentication with Box
enterprise_id	string	N/A	Enterprise ID	Enterprise Id used for authentication with Box
api_params	object	{}	API Parameters	N/A

box_folder_trigger = IOBlock(
    identifier='BOX_FOLDER_TRIGGER',
    reference_name='box_folder_trigger',
    title='Box folder',
    enabled=True,
    input={
        'file_extensions': ['png', 'pdf', 'jpg'],
        'source_folder_id': '24',
        'target_folder_id': '42',
        'public_key_id': 'admin',
        'private_key': 'secret',
        'passphrase': 'password',
        'client_id': 'admin',
        'client_secret': 'secret',
        'enterprise_id': 'admin',
    }
)

triggers = IDPTriggers(blocks=[box_folder_trigger])

Cron Listener (v39.1)

Identifier: CRON_TRIGGER

Inputs:

Name	Type	Default Value	Title	Description
cron_spec	string	"*/5 * * * *"	Cron specification	N/A
time_zone	string	“US/Eastern”	Time Zone	N/A
api_params	object	{}	API Parameters	N/A

cron_trigger = IOBlock(
    identifier='CRON_TRIGGER',
    reference_name='cron_trigger',
    title='Cron Trigger',
    enabled=True,
    input={
        'cron_spec': '0 10 * * *',
        'time_zone': 'Europe/Sofia',
    }
)

triggers = IDPTriggers(blocks=[cron_trigger])

Salesforce Listener (v39.1)

Identifier: SALESFORCE_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
channel_name	string	N/A	Channel Name	Configured in the Hyperscience app in Salesforce.
username	string	N/A	Associated Username	Username associated with the Salesforce Connected App.
consumer_key	Password	N/A	Consumer Key	Consumer key used for authentication with Salesforce. Paired with the Salesforce instance from which it was issued.
private_key	Password	N/A	Private Key	Private key used for authentication with Salesforce.
sandbox_environment	boolean	false	Private Key	Enable if the Salesforce environment is a sandbox.
api_params	object	{}	API Parameters	N/A

Message Queue Listener (v39.1)

Identifier: MQ_LISTENER

Inputs:

Name	Type	Default Value	Title	Description
MQ_TYPE	string	“ACTIVE_MQ”	Message Queue type	Type of the message queue to connect with. Valid values are “ACTIVE_MQ”, “AMAZON_SQS”, “IBM_MQ” and “RABBIT_MQ”
MQ_USERNAME	string	null	Username/Access Key ID	N/A
MQ_PASSWORD	Password	null	Password/Secret Access Key	N/A
MQ_QUEUE_NAME	string	null	Queue Name	Name of the queue (topic) to connect to.
MQ_HOST	string	null	Host Name	N/A
MQ_PORT	integer	5672 when “RABBIT_MQ”, 1414 when “IBM_MQ”, otherwise null	Port Number	N/A
api_params	object	{}	API Parameters	N/A

Additional inputs when "AMAZON_SQS" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_REGION	string	null	AWS Region	N/A
MQ_QUEUE_URL	string	null	Queue URL	N/A
MQ_USE_EC2_INSTANCE_CREDENTIALS	boolean	True	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used.

NOTE: MQ_USERNAME can be used to provide Access Key ID and MQ_PASSWORD to provide Secret Access Key.

Additional inputs when "IBM_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_QUEUE_MANAGER	string	null	Queue Manager (IBM MQ)	N/A
MQ_SSL_CIPHER_SUITE [1]	string	null	SSL cipher suite	SSL cipher suite to use if SSL connection is used.
MQ_CHANNEL	string	null	Channel	N/A
MQ_NO_AUTH_REQUIRED	boolean	false	No auth credentials required	N/A
MQ_MQCSP_AUTHENTICATION_MODE	boolean	false	MQCSP authentication mode	N/A

NOTE: MQ_NO_AUTH_REQUIRED is True, MQ_USERNAME and MQ_PASSWORD are optional.

[1] one of:

"NULL_MD5"
"NULL_SHA"
"RC4_MD5_EXPORT"
"RC4_MD5_US"
"RC4_SHA_US"
"RC2_MD5_EXPORT"
"DES_SHA_EXPORT"
"RC4_56_SHA_EXPORT1024"
"DES_SHA_EXPORT1024"
"TRIPLE_DES_SHA_US"
"TLS_RSA_WITH_NULL_SHA256"
"TLS_RSA_WITH_AES_128_CBC_SHA"
"TLS_RSA_WITH_AES_128_CBC_SHA256"
"TLS_RSA_WITH_AES_256_CBC_SHA"
"TLS_RSA_WITH_AES_256_CBC_SHA256"
"AES_SHA_US"
"TLS_RSA_WITH_DES_CBC_SHA"
"TLS_RSA_WITH_3DES_EDE_CBC_SHA"
"TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256"
"FIPS_WITH_DES_CBC_SHA"
"FIPS_WITH_3DES_EDE_CBC_SHA"

Additional inputs when "RABBIT_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_VIRTUAL_HOST	string	null	Virtual Host (RABBIT MQ)	N/A
MQ_EXCHANGE	string	“”	Exchange	N/A
MQ_ROUTING_KEY	string	“”	Routing key	N/A
MQ_CONNECTION_TYPE	string	“TCP”	Connection Type	“TCP or SSL”

rabbit_mq_listener = IOBlock(
    identifier='MQ_LISTENER',
    reference_name='mq_listener',
    title='RabbitMQ Listener',
    enabled=True,
    input={
        'MQ_TYPE': 'RABBIT_MQ',
        'MQ_QUEUE_NAME': 'some_queue_name',
        'MQ_HOST': 'somehost.com',
        'MQ_USERNAME': 'foo',
        'MQ_PASSWORD': system_secret('rabbit_mq_1_password'),
    }
)

triggers = IDPTriggers(blocks=[rabbit_mq_listener])

Universal Folder Listener (v39.1)

Identifier: UNIVERSAL_FOLDER_TRIGGER

Inputs:

Name	Type	Default Value	Title	Description
folder	string	N/A	Folder to scan for submissions	Enter the path relative to the base folder. Leave blank to monitor the base folder
file_extensions	array	[]	File extensions	Types of file extensions for which to monitor
other_file_extensions	string	N/A	Other file extensions	Comma separated list of file extensions for which to monitor (e.g. ‘png, jpg, pdf’)
has_meta	boolean	false	Include submission level parameters	Select this to ingest JSON files along with document files and submission folders. These JSON files can contain information such as metadata, cases and external_id. These JSON file names should match the related files or folders (e.g., XXX.jpg.json for XXX.jpg)
poll_interval	integer	10	Polling interval (in seconds)	How often the Folder Listener will check the base folder for submissions
warmup_interval	integer	15	Warm-up interval (in seconds)	How long the Folder Listener will wait to process the document after it was last modified
folder_cleanup_delay	number	24	Empty folder cleanup delay (in hours)	How often the Folder Listener will remove empty folders from the base folder
api_params	object	{}	API Parameters	N/A

universal_folder_listener = IOBlock(
    identifier='UNIVERSAL_FOLDER_TRIGGER',
    reference_name='universal_folder_trigger',
    title='Universal Folder Listener',
    enabled=True,
    input={
        'folder': '/var/www/forms/forms/folder/',
        'file_extensions': 'png, pdf, jpg',
    }
)

triggers = IDPTriggers(blocks=[universal_folder_listener])

Kafka Listener (v39.1)

Identifier: KAFKA_TRIGGER

Inputs:

Name	Type	Default Value	Title	Description
bootstrap_servers	string	N/A	Bootstrap Servers	A Comma separated list of host:port pairs to use for establishing the initial connection to the cluster
group_id	string	N/A	Group Id	A unique string that identifies the consumer group this consumer belongs to. BLOCK_THREADS_KAFKA_TRIGGER should be used to scale up or down the number of consumers (default: number of available cores)
topics	string	N/A	Topics	Comma separated list of topic names that the block will subscribe to
consumer_config_json	json	N/A	Consumer Config	JSON formatted string containing key-value pairs of Kafka consumer configurations described in the official Kafka documentation
api_params	object	{}	API Parameters	N/A
consume_timeout	integer	-1	Consume Timeout	Maximum time, in seconds, that the consumer will wait for new messages. If messages are received within the specified time, they are processed immediately. However, if no messages are received within that specified time, consumption cycle will be restarted internally to continue fetching messages. This ensures continuous message consumption (default: infinite)
consume_num_messages	integer	1	Consume Num Messages	Sets the maximum number of messages the consumer fetches in a single consumption cycle.If the number of available messages is less than the specified, and timeout is reached, messages will be processed. Consumption cycle will be restarted after the current finishes

kafka_listener = IOBlock(
    identifier='KAFKA_TRIGGER',
    reference_name='kafka_trigger',
    title='Kafka Listener',
    enabled=True,
    input={
        'folder': '/var/www/forms/forms/folder/',
        'file_extensions': 'png, pdf, jpg',
         'bootstrap_servers': 'example.com',
         'group_id': 'test_group',
         'topics': 'test_topic1, test_topic2',
         'consumer_config_json': {},
         'number_of_workers': 1,
         'consume_timeout': 5,
         'consume_num_messages': 10
    }
)

triggers = IDPTriggers(blocks=[kafka_listener])

Output Blocks (v39.1)

Warning

Flows pass information to downstream systems using the IDPOutputsBlock. This block is specially built to allow full definition either in code or manually in the Flow Studio. We recommend defining outputs manually in the Flow Studio.

Instructions on how to do so are in our application documentation. Simply include an IDPOutputsBlock in your Python flow definition wherever you wish to send information to downstream systems.

class flows_sdk.implementations.idp_v39_1.idp_blocks.IDPOutputsBlock(inputs, blocks=None)

Bases: Outputs

Output block allows users to send data extracted by an IDP flow to other systems for downstream processing

Mandatory parameters:

Parameters:

inputs (Dict[str, Any]) – Used by the UI to automatically pre-populate the input of newly added blocks. Extended in this class to include 'enabled': True to visualize an enabled/disabled trigger that is controllable via Flow Studio.

Usage when parameters are to be defined in Flow Studio:

outputs = IDPOutputBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')}
)

If instead you wish to define specific outputs in Python, use the outputs definitions below.

HTTP Notifier Output (v39.1)

The HTTP Notifier (REST) output connection will POST results from the system to a specified HTTP endpoint.

Identifier: COB_HTTP_EXPORT

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
endpoint	string	N/A	Endpoint URL	URL that output notifications will be posted to
endpoint_secrets	string	N/A	Endpoint URL secrets	Endpoint URL Secrets, format: (one per line) secret_key_name=secret_value
timeout	int	600	Timeout (seconds)	How long (in seconds) to keep the connection open if no bytes are received from the endpoint. Set to 0 to wait indefinitely.
authorization_type [2]	string	“none”	Authorization type	Type of authorization
authorization_header_name	string	null	Authorization header name	Authorization header name to be set in the notification request
authorization_header	Password	null	Authorization header value	Authorization header to be set in the notification request
auth_url	string	null	OAuth2 authorization URL	The endpoint for the authorization server
client_id	string	null	Client ID	The client identifier issued to the client during the application registration process
client_secret	Password	null	Client Secret	The client secret issued to the client during the application registration process
scope	string	null	Scope	Scope to request to oauth server
audience	string	null	Audience	Resource service URL where token will be valid
additional_oauth_parameters	json	{}	Additional Oauth Request Parameters	Additional parameters to send when requesting token
ssl_cert_source [3]	string	null	Source	N/A
ssl_cert_path	string	null	Trusted Certificate(s) path	Enter the path relative to the base folder
ssl_cert_value	MultilineText	null	Trusted Certificate(s)	N/A
additional_notification_http_headers	json	null	Additional Notification Request Headers	Key value pairs in json format which will be sent via the request as http headers

[1] one of:

"v5"
"transformed"

[2] one of:

"none"
"http_header"
"oauth_2_client_credentials"

[3] one of:

"env_var"
"ca_bundle_path"
"certificate_value"

cob_http_export = IOBlock(
    identifier='COB_HTTP_EXPORT',
    reference_name='cob_http_export',
    title='HTTP Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'SUBMISSION',
        'endpoint': 'example.com',
        'authorization_type': 'none',
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_http_export],
)

Message Queue Notifier Output (v39.1)

The Message Queue Notifier Output can configure connections to ActiveMQ, Amazon SQS, IBM MQ, and RabbitMQ message queues.

Identifier: COB_MQ_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
MQ_TYPE	string	“ACTIVE_MQ”	Message Queue type	Type of the message queue to connect with. Valid values are “ACTIVE_MQ”, “AMAZON_SQS”, “IBM_MQ” and “RABBIT_MQ”
MQ_USERNAME	string	null	Username/Access Key ID	N/A
MQ_PASSWORD	Password	null	Password/Secret Access Key	N/A
MQ_QUEUE_NAME	string	null	Queue Name	Name of the queue (topic) to connect to. It applies only to “ACTIVE_MQ”, “IBM_MQ” and “RABBIT_MQ” MQ_TYPEs.
MQ_HOST	string	null	Host Name	N/A
MQ_PORT	integer	5672 when “RABBIT_MQ”, 1414 when “IBM_MQ”, otherwise null	Port Number	N/A

[1] one of:

"v5"
"transformed"

Additional inputs when "AMAZON_SQS" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_REGION	string	null	AWS Region	N/A
MQ_QUEUE_URL	string	null	Queue URL	N/A
MQ_USE_EC2_INSTANCE_CREDENTIALS	boolean	True	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used.
MQ_MESSAGE_GROUP_ID	string	null	Group ID for FIFO queues	N/A
MQ_MESSAGE_METADATA	string	null	Additional SQS Metadata	Input additional metadata key value pairs in the format of { “key”: {“TYPE”: “String”, “VALUE”: “”}, “key2”: {“TYPE”: “Number”, “VALUE”: “12345”}

NOTE: MQ_USERNAME can be used to provide Access Key ID and MQ_PASSWORD to provide Secret Access Key.

Additional inputs when "IBM_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_QUEUE_MANAGER	string	null	Queue Manager (IBM MQ)	N/A
MQ_SSL_CIPHER_SUITE [1]	string	null	SSL cipher suite	SSL cipher suite to use if SSL connection is used.
MQ_CHANNEL	string	null	Channel	N/A
MQ_NO_AUTH_REQUIRED	boolean	false	Channel	N/A
MQ_MQCSP_AUTHENTICATION_MODE	boolean	false	Channel	N/A

NOTE: MQ_NO_AUTH_REQUIRED is True, MQ_USERNAME and MQ_PASSWORD are optional.

[1] one of:

"NULL_MD5"
"NULL_SHA"
"RC4_MD5_EXPORT"
"RC4_MD5_US"
"RC4_SHA_US"
"RC2_MD5_EXPORT"
"DES_SHA_EXPORT"
"RC4_56_SHA_EXPORT1024"
"DES_SHA_EXPORT1024"
"TRIPLE_DES_SHA_US"
"TLS_RSA_WITH_NULL_SHA256"
"TLS_RSA_WITH_AES_128_CBC_SHA"
"TLS_RSA_WITH_AES_128_CBC_SHA256"
"TLS_RSA_WITH_AES_256_CBC_SHA"
"TLS_RSA_WITH_AES_256_CBC_SHA256"
"AES_SHA_US"
"TLS_RSA_WITH_DES_CBC_SHA"
"TLS_RSA_WITH_3DES_EDE_CBC_SHA"
"TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256"
"FIPS_WITH_DES_CBC_SHA"
"FIPS_WITH_3DES_EDE_CBC_SHA"

Additional inputs when "RABBIT_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_VIRTUAL_HOST	string	null	Virtual Host (RABBIT MQ)	N/A
MQ_EXCHANGE	string	“”	Exchange	N/A
MQ_ROUTING_KEY	string	“”	Routing key	N/A
MQ_CONNECTION_TYPE	string	“TCP”	Connection Type	“TCP or SSL”

mq_notifier = IOBlock(
    identifier='COB_MQ_NOTIFIER',
    reference_name='mq_notifier',
    title='MQ Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'SUBMISSION',
        'MQ_USERNAME': 'admin',
        'MQ_PASSWORD': 'pass',
        'MQ_QUEUE_NAME': 'queue',
        'MQ_HOST': 'host',
    },
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[mq_notifier],
)

Box Notifier Output (v39.1)

Provides an out-of-the-box integration into Box systems.

Identifier: COB_BOX_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
public_key_id	string	N/A	Public-Key ID	ID of the Public-key used for authentication with Box
private_key	Password	N/A	Private-Key	Private key used for authentication with Box
passphrase	Password	N/A	Passphrase	Passphrase used for authentication with Box
client_id	string	N/A	Client ID	Client Id used for authentication with Box
client_secret	Password	N/A	Client Secret	Client Secret used for authentication with Box
enterprise_id	string	N/A	Enterprise ID	Enterprise Id used for authentication with Box
template_key	string	“”	Box Metadata Template Key	Enter the key of the Box Metadata template that you would like to map the Hyperscience metadata to.
static_output_fields_to_map	array	[]	Static Metadata Fields	Specify the Hyperscience fields you want to store in Box metadata.
submission_id	string	“”	Key for Mapping Submission ID	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_id	string	“”	Key for Mapping Document ID	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
submission_state	string	“”	Key for Mapping Submission State	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_state	string	“”	Key for Mapping Document State	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
submission_exceptions	string	“”	Key for Mapping Submission Exceptions	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_exceptions	string	“”	Key for Mapping Document Exceptions	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_layout_uuid	string	“”	Key for Mapping Document Layout Uuid	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_layout_name	string	“”	Key for Mapping Document Layout Name	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
start_time	string	“”	Key for Mapping Start Time	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
complete_time	string	“”	Key for Mapping Complete Time	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_fields_template_mappings	json	{}	JSON for Additional Metadata Fields	Specify additional metadata fields using their key value. Please consult Box Integration setup manual for JSON template and instructions.

[1] one of:

"v5"
"transformed"

box_notifier = IOBlock(
    identifier='COB_BOX_NOTIFIER',
    reference_name='box_notifier',
    title='Box Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'private_key': 'password',
        'passphrase': 'password',
        'public_key_id': 'admin',
        'client_id': 'admin',
        'client_secret': 'secret',
        'enterprise_id': 'admin',
        'template_key': 'key',
    },
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[box_notifier],
)

UiPath Notifier Output (v39.1)

Provides an out-of-the-box integration into UiPath systems.

Identifier: COB_EXPORT_UIPATH

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
auth_method [2]	string	“basic_auth”	Authentication method	N/A
url	string	N/A	URL	Base URL of the uipath instance
app_id	string	N/A	App ID	App ID provided by UiPath
app_secret	Password	N/A	App Secret	App Secret provided by UiPath
oauth_token_endpoint	string	N/A	OAuth token endpoint	Endpoint to retrieve the token. Using https://cloud.uipath.com/identity_/connect/token by default
organization_unit_id	string	N/A	Organization unit id	Organization unit id
tenant	string	N/A	Tenant	Tenant
username	string	N/A	Username	Username
password	Password	N/A	Password	Password
queue	string	N/A	Queue	Queue

[1] one of:

"v5"
"transformed"

[2] one of:

"basic_auth"
"oauth"

cob_export_uipath = IOBlock(
    identifier='COB_EXPORT_UIPATH',
    reference_name='cob_export_uipath',
    title='CCB Export UiPath',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'FLAT_SUBMISSION',
        'url': 'example.com',
        'tenant': 'tenant',
        'username': 'admin',
        'password': 'pass',
        'queue': 'queue',
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_export_uipath],
)

Salesforce Notifier Output (v39.1)

With the Salesforce Notifier Block, you can configure your flow to send extracted information to Salesforce. The Salesforce Notifier Block can use extracted information to look up, and then update any number of fields on a Salesforce object. The Salesforce Notifier Block is available in both SaaS and on-premise versions of the Hyperscience application.

Identifier: COB_SALESFORCE_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
username	string	N/A	Associated Username	Username associated with the Salesforce Connected App
consumer_key	password	N/A	Consumer Key	Consumer key used for authentication with Salesforce. Paired with the Salesforce instance from which it was issued.
private_key	password	N/A	Private Key	Private key used for authentication with Salesforce.
sandbox_environment	boolean	false	Private Key	Enable if the Salesforce environment is a sandbox.
object_api_name	string	N/A	Object API Name	API name of the object in Salesforce to lookup and update.
new_record_on_lookup_failure	boolean	false	Create new record on lookup failure	When enabled, it will create a new record if the lookup fails to find a record using the lookup parameters. When disabled, lookup will fail if it does not find a record using the lookup parameters.
object_lookup_mapping	json	N/A	Lookup Field Mapping	Specify which Salesforce fields (using their API name) to be queried with which extracted values from the document for the lookup using key-value format. For example: “DOCUMENT_FIELD_NAME”: “OBJECT_FIELD_API_NAME”.
document_fields_mapping	json	N/A	Document Field Mappings	Specify which Salesforce fields (using their API name) you want to update with which extracted data from Hyperscience using key-value format. For example: “DOCUMENT_FIELD_NAME”: “OBJECT_FIELD_API_NAME”.”
overwrite_existing_field_data	boolean	false	Overwrite existing field data	If this is checked and a selected field has data in it, Hyperscience will overwrite that data. If this is unchecked, existing data will not be overwritten and the operation will fail.

[1] one of:

"v5"
"transformed"

cob_salesforce_notifier = IOBlock(
    identifier='COB_SALESFORCE_NOTIFIER',
    reference_name='cob_salesforce_notifier',
    title='Salesforce Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'username': 'salesforce_user',
        'consumer_key': 'salesforce_consumer_key',
        'private_key': 'salesforce_private_key',
        'object_api_name': 'salesforce_object_api_name',
        'object_lookup_mapping': {"LAYOUT_VARIATION_XYZ_FIELD_NAME_1": "SF_FIELD_API_NAME_1", "LAYOUT_VARIATION_XYZ_FIELD_NAME_2": "SF_FIELD_API_NAME_2"},
        'document_fields_mapping': {"LAYOUT_VARIATION_XYZ_FIELD_NAME_3": "SF_FIELD_API_NAME_3", "LAYOUT_VARIATION_XYZ_FIELD_NAME_4": "SF_FIELD_API_NAME_4"}
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_salesforce_notifier],
)

S3 Notifier Output (v39.1)

With the S3 Notifier Block, you can configure your flow to send extracted information to an S3 bucket.

Identifier: COB_S3_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
bucket_uri	string	N/A	S3 URI	S3 URI, where the data should be stored (e.g. s3://hs_bucket/prefix1/prefix2/)
aws_access_key_id	string	N/A	AWS ACCESS KEY ID	Access Key used for authentication
aws_secret_access_key	password	N/A	SECRET ACCESS KEY	Secret Key used for authentication
use_ec2_instance_credentials	boolean	true	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used

[1] one of:

"v5"
"transformed"

cob_s3_notifier = IOBlock(
    identifier='COB_S3_NOTIFIER',
    reference_name='cob_s3_notifier',
    title='S3 Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'FLAT_SUBMISSION',
        'bucket_uri': 's3://hs_bucket/prefix1/prefix2/',
        'aws_access_key_id': 'aws_access',
        'aws_secret_access_key': system_secret('aws_secret_access_key'),
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_s3_notifier],
)

Kafka Notifier Output (v39.1)

With the Kafka Notifier Block, you can configure your flow to send extracted information to a kafka topic.

Identifier: COB_KAFKA_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
bootstrap_servers	string	N/A	Bootstrap Servers	A list of Kafka brokers to connect to. For example: “kafka1:9092,kafka2:9092”
topic	string	N/A	Topic	The Kafka topic where your data will be sent
client_id	string	N/A	SECRET ACCESS KEY	An identifier for your application when connecting to Kafka, typically used for tracking and logging purposes
producer_config_json	json	true	Producer Config (JSON)	Configuration settings for the Kafka producer in JSON format. See https://kafka.apache.org/documentation/#producerconfigs for more information
synchronous_publish	boolean	true	Sync Publish	If true, the publish call will block until the message is sent successfully or an error is raised. If false, the publish call will return immediately after enqueueing the message
message_key	string	null	Message Key	The key associated with the message to be sent. This is used to determine which partition the message is sent to. If null, the message will be sent to a random partition

[1] one of:

"v5"
"transformed"

cob_kafka_notifier = IOBlock(
    identifier='COB_KAFKA_NOTIFIER',
    reference_name='cob_kafka_notifier',
    title='Kafka Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'FLAT_SUBMISSION',
        'bootstrap_servers': 'kafka1:9092,kafka2:9092',
        'topic': 'topic',
        'client_id': 'client_id',
        'producer_config_json': {},
        'synchronous_publish': True,
        'message_key': 'key'
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_kafka_notifier],
)

Helper Classes and Functions (v39.1)

The following classes are used to easily instantiate and manage a flow’s settings.

flows_sdk.implementations.idp_v39_1.idp_values.get_idp_wf_inputs(idp_wf_config)

A helper function that maps the values provided by an IdpWorkflowConfig to their corresponding IDP keys.

Parameters:

idp_wf_config (IdpWorkflowConfig) – with static values to be filled.

Return type:

Dict[str, Any]

Returns:

a dictionary with filled static values, compatible with IDP flows.

Example usage with default values:

flow = Flow(
    input=get_idp_wf_inputs(get_idp_wf_config()),
    ...
)

Example usage:

Example usage when instantiating an IDP flow with custom parameters::

flow = Flow(
    input=get_idp_wf_inputs({an instance of IdpWorkflowConfig}),
    ...
)

flows_sdk.implementations.idp_v39_1.idp_values.get_idp_wf_config()

A helper function that instantiates an IdpWorkflowConfig pre-filled with default values. IdpWorkflowConfig is the container class for defining flow-level settings.

Return type:

IdpWorkflowConfig

Returns:

a valid, fully constructed IdpWorkflowConfig instance

Example usage when instantiating an IDP flow:

flow = Flow(
    input=get_idp_wf_inputs(get_idp_wf_config()),
    ...
)

class flows_sdk.implementations.idp_v39_1.idp_values.IdpWorkflowConfig

Convenience dataclass outlining all flow-level config values for IDP.

class flows_sdk.implementations.idp_v39_1.idp_values.IDPTriggers(blocks=None)

An adapter class making it easier to instantiate flows_sdk.flows.Triggers for IDP flows. In particular, provides defaults for some fields (e.g. title, description, reference_name) and defines api_params_manifest with IDP specific parameters.

Parameters:

blocks (Optional[Sequence[IOBlock]]) – flows_sdk.blocks.IOBlock s to be included as triggers for an IDP Flow. Optional, defaults to an empty list.

Example usage when instantiating an IDP flow:

folder_listener = IOBlock(...)
flow = Flow(
    triggers=IDPTriggers([folder_listener]),
    ...
)

class flows_sdk.implementations.idp_v39_1.idp_values.IDPWrapperManifest(flow_identifier)

An adapter class making it easier to instantiate flows_sdk.flows.Manifest for IDP wrapper flows. In particular, provides defaults for some fields (e.g. roles) but more importantly, defines all flows_sdk.flows.Parameter that construct the Flow inputs and their UI representation (left-hand side menu in Flow studio).

Parameters:

flow_identifier (str) – A system-wide unique identifier for flows_sdk.flows.Flow

Example usage when instantiating an IDP flow:

flow = Flow(
    manifest=IDPWrapperManifest(flow_identifier='FLOW_IDENTIFIER'),
    ...
)

class flows_sdk.implementations.idp_v39_1.idp_values.IDPCoreManifest(flow_identifier, roles=None)

An adapter class making it easier to instantiate flows_sdk.flows.Manifest for IDP Core flows. In particular, provides defaults for some fields (e.g. roles, output) but more importantly, defines all flows_sdk.flows.Parameter that construct the Flow inputs and their UI representation (left-hand side menu in Flow studio).

Parameters:

• flow_identifier (str) – A system-wide unique identifier for flows_sdk.flows.Flow

• roles (Optional[List[str]]) – ‘read_only’, ‘supporting’ and ‘idp_core’ are the default roles of every subflow that can replace the default IDP_CORE subflow inside the top-level IDP flows. ‘supporting’ and ‘idp_core’ are mandatory roles that are necessary for all IDP Core flows. ‘supporting’ ensures that the subflow will be auto-deployed together with the top-level IDP flow. ‘idp_core’ ensures that this subflow can be selected in the UI in dropdowns which allow picking an alternative subflow to the default IDP_CORE subflow. If you are implementing a subflow that is not supposed to be a drop-in replacement for IDP_CORE, you will have to subclass or implement your own version of IDPCoreManifest class in order to remove the idp_core role.

Example usage when instantiating an IDP flow:

flow = Flow(
    manifest=IDPCoreManifest(flow_identifier='FLOW_IDENTIFIER'),
    ...
)

V39

Processing Blocks (v39)

Core Blocks are powerful processing blocks necessary to build intelligent document processing solutions on Hyperscience. They include both manual and machine processing blocks.

Note

Classes in the idp_v39 implementation have parameters with ambiguous typing. For example, a field of type integer could also be represented as a string reference (e.g. that integer gets provided at runtime). The types introduced in the example are therefore incomplete and are likely to be revisioned in a future version.

class flows_sdk.implementations.idp_v39.idp_blocks.IDPCoreBlock(idp_wf_config, reference_name='__idp_core__', title='Start Document Processing Subflow', description='Calls the Document Processing Subflow with the specified settings.', compatibility_spec=CompatibilitySpec(filter_roles=['idp_core'], filter_schema=None, label=None), identifier='IDP_CORE_V39', additional_inputs=None)

Bases: Block

IDP core block includes the steps to process the passed documents into machine-readable output.

Mandatory parameters:

Parameters:

idp_wf_config (IdpWorkflowConfig) – IdpWorkflowConfig object with all flow-level configuration settings for IDP

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique reference name within a flow, a unique one will be generated when not provided

• compatibility_spec (Optional[CompatibilitySpec]) – defines a filter for flows that could be selected in the UI in place of “IDP Core” for the block. Defaults to flows with the role ‘idp_core’. The user will be able to pick any flow that matches the compatibility_spec. This serves to make upgrading to a new compatible version of “IDP Core” (e.g. IDP_CORE_V39) easier in future releases.

• identifier (str) – The identifier of the flow. Defaults to the IDP_CORE_VXX identifier of the out-of-the-box flow that comes with the system.

• additional_inputs (Optional[Dict[str, Any]]) – Dictionary containing key-value pairs that will be added to the inputs of the block. If a key is contained in the default inputs, its value will be overwritten by the value provided here. You generally do not need to provide additional_inputs, unless you are creating a custom derivative of the IDP_CODE_Vxx flow and are modifying or extending the list of inputs it accepts.

Example usage:

idp_core = IDPCoreBlock(idp_wf_config=idp_wf_config)

REFERENCE_NAME = '__idp_core__'

class flows_sdk.implementations.idp_v39.idp_blocks.SubmissionBootstrapBlock(reference_name=None, layout_release_uuid='${workflow.input.layout_release_uuid}', s3_config='${workflow.input.submission_bootstrap_s3_config}', s3_endpoint_url='${workflow.input.submission_bootstrap_s3_endpoint_url}', ocs_config='${workflow.input.submission_bootstrap_ocs_config}', http_config='${workflow.input.submission_bootstrap_http_config}', notification_workflow='${workflow.input.notification_workflow_initialization}', workflow_uuid='${workflow.input.workflow_uuid}', workflow_name='${workflow.input.workflow_name}', workflow_version='${workflow.input.workflow_version}', submission='${workflow.input.submission}')

Bases: Block

Submission bootstrap block initializes the submission object and prepares external images or other submission data if needed. This block should be called at the START of every flow that uses blocks included in the library.

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• s3_config (str) – Specify Amazon S3 credentials for submission retrieval store. Expects a json expression: {"aws_access_key_id": "X", "aws_secret_access_key": "Y"}, defaults to workflow_input(Settings.SubmissionBootstrapS3Config)

• s3_endpoint_url (str) – Endpoint URL for S3 submission retrieval store, defaults to workflow_input(Settings.SubmissionBootstrapS3EndpointUrl)

• ocs_config (str) – OCS Configuration for downloading submission data, defaults to workflow_input(Settings.SubmissionBootstrapOCSConfig)

• http_config (Optional[str]) – An optional system secret field expressing the HTTP Configuration for downloading submission data. The content is expected to be a json formatted as: {"username": "X", "password": "Y", "ssl_cert": "CA bundle filename"}, defaults to workflow_input(Settings.SubmissionBootstrapHTTPConfig)

• notification_workflow (Optional[str]) – Notification flow name to run when the submission is initialized, defaults to workflow_input(Settings.SubmissionBootstrapNotificationWorkflow)

• workflow_uuid (str) – UUID of the triggered workflow version, defaults to workflow_input(Inputs.WorkflowUuid)

• workflow_name (str) – Name of the triggered workflow, defaults to workflow_input(Inputs.WorkflowName)

• workflow_version (str) – Version of the triggered workflow, defaults to workflow_input(Inputs.WorkflowVersion)

• submission (Any) – Submission object

Example usage:

submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')

IDENTIFIER = 'SUBMISSION_BOOTSTRAP_2'

output(key=None)

Submission bootstrap ends with a CodeBlock, which nests the actual outputs under the ‘result’ key. This method overrides the default output in the same manner that flows_sdk.blocks.Routing.CodeBlock does for convenience.

Parameters:

key (Optional[str]) –

Optionally provide a key to directly get a nested property. When not provided, the entire ‘result’ will be returned. For example, we have:

{
    "result": {
        "a": {
            "b": 42
        }
    }
}

• skipping the key output()` will return ``{"a": {"b": 42}}

• calling with output("a") will result in {"b": 42}

• calling with output("a.b") will return 42

Return type:

str

Returns:

the value under the provided key

class flows_sdk.implementations.idp_v39.idp_blocks.MachineCollationBlock(submission, cases, dedupe_files='${workflow.input.machine_collation_dedupe_files}', refresh_retention_period='${workflow.input.machine_collation_refresh_retention_period}', remove_from_cases='${workflow.input.machine_collation_remove_from_cases}', retention_period='${workflow.input.machine_collation_retention_period}', reference_name=None)

Bases: Block

Machine collation block groups files, documents and pages (from the submission) into cases.

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• cases (Any) –

  This parameter accepts an array of JSON objects that contain information on how Cases should be created. There are two ways to collate a case: by the filenames specified in the current Submission, or by the ids of the Documents or Pages submitted in previous submissions.

  This parameter follows the given format:

  {
      'cases': [
          'external_case_id': 'HS-1',
          'filename': 'file1.pdf',
          'documents': [42],
          'pages': [43]
      ]
  }
  

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• dedupe_files (Union[bool, str]) – Enabling this setting will replace case data from repeated file names within the same case. Cases will retain data from the most recently submitted version of a particular file. Note that this setting does not delete the old data, it just removes it from the case. Keep in mind this option is only relevant when adding to cases. Defaults to workflow_input(Settings.CollationDedupeFiles)

• remove_from_cases (Optional[Any]) –

  This parameter accepts an array of JSON objects that contains information on how documents or pages should be removed from a pre-existing case. Currently, the only way to de-collate from a case is by the ids of the Documents or Pages within a given case, defaults to workflow_input(Settings.CollationRemoveFromCases)

  This parameter follows the given format:

  {
      'remove_from_cases': [
          'external_case_id': 'HS-1',
          'documents': [42],
          'pages': [43]
      ]
  }
  

• retention_period (Union[int, str, None]) – The number of days to retain cases after they are updated by this block. If passed None, no changes to the deletion date will be made. Defaults to workflow_input(Settings.CollationRetentionPeriod)

• refresh_retention_period (Union[bool, str]) – If True, (re)applies Retention Period parameter to all cases passed into the block. If False, only updates those without a pre-existing deletion date. Defaults to workflow_input(Settings.CollationRefreshRetentionPeriod)

Example usage:

machine_collation = MachineCollationBlock(
    reference_name='machine_collation',
    submission=submission_bootstrap.output('result.submission'),
    cases=submission_bootstrap.output('result.api_params.cases'),
    dedupe_files=True,
    remove_from_cases=custom_supervision.output('remove_from_cases')
)

IDENTIFIER = 'MACHINE_COLLATION_2'

class flows_sdk.implementations.idp_v39.idp_blocks.MachineClassificationBlock(submission, api_params, reference_name=None, rotation_correction_enabled='${workflow.input.machine_classification_rotation_correction_enabled}', mobile_processing_enabled='${workflow.input.machine_classification_mobile_processing_enabled}', file_transcription_enabled='${workflow.input.machine_classification_file_transcription_enabled}', layout_release_uuid='${workflow.input.layout_release_uuid}', vpc_registration_threshold='${workflow.input.structured_layout_match_threshold}', nlc_enabled='${workflow.input.semi_structured_classification}', nlc_target_accuracy='${workflow.input.semi_target_accuracy}', nlc_doc_grouping_logic='${workflow.input.semi_doc_grouping_logic}')

Bases: Block

Machine classification block automatically matches documents to structured, semi-structured or additional layouts

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• rotation_correction_enabled (Union[bool, str]) – Identifies and corrects the orientation of semi-structured images, defaults to workflow_input(Settings.RotationCorrectionEnabled)

• mobile_processing_enabled (Union[bool, str]) – Improves the machine readability of photo captured documents, defaults to workflow_input(Settings.MobileProcessingEnabled)

• file_transcription_enabled (Union[bool, str]) – Improves the transcription speed of pdf files. Make sure that the “Image Correction” option is disabled and the pdfs you submit do not contain any rotated images defaults to workflow_input(Settings.FileTranscriptionEnabled)

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• vpc_registration_threshold (str) – Structured pages above this threshold will be automatically matched to a layout variation, defaults to workflow_input(Settings.StructuredLayoutMatchThreshold)

• nlc_enabled (str) – Enables workflow to manage a model for automated classification of semi-structured and additional layout variations, defaults to workflow_input(Settings.SemiStructuredClassification)

• nlc_target_accuracy (str) – defaults to workflow_input(Settings.SemiTargetAccuracy)

• nlc_doc_grouping_logic (str) – Logic to handle multiple pages matched to the same layout variationin a given submission, defaults to workflow_input(Settings.SemiDocGroupingLogic)

Example usage:

machine_classification = MachineClassificationBlock(
    reference_name='machine_classification',
    submission=case_collation.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    rotation_correction_enabled=idp_wf_config.rotation_correction_enabled,
)

IDENTIFIER = 'MACHINE_CLASSIFICATION_6'

class flows_sdk.implementations.idp_v39.idp_blocks.ManualClassificationBlock(submission, api_params, reference_name=None, layout_release_uuid='${workflow.input.layout_release_uuid}', manual_nlc_enabled='${workflow.input.manual_nlc_enabled}', task_restrictions='${workflow.input.manual_classification_task_restrictions}', notification_workflow='${workflow.input.notification_workflow_classification}', nlc_enabled='${workflow.input.semi_structured_classification}', nlc_target_accuracy='${workflow.input.semi_target_accuracy}', mobile_processing_enabled='${workflow.input.machine_classification_mobile_processing_enabled}', vpc_registration_threshold='${workflow.input.structured_layout_match_threshold}')

Bases: Block

Manual classification block allows keyers to manually match submissions to their layouts. Keyers may perform manual classification if machine classification cannot automatically match a submission to a layout with high confidence

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• manual_nlc_enabled (str) – Enables manual classification when applicable, defaults to workflow_input(Settings.ManualNlcEnabled)

• task_restrictions (Union[List[Any], str, None]) – Defines what users can access Supervision tasks created by this block, defaults to workflow_input(Settings.ManualClassificationTaskRestrictions)

• notification_workflow (str) – Notification flow name to run when the submission enters Manual Classification, defaults to workflow_input(Settings.ManualClassificationNotificationWorkflow)

• nlc_enabled (Union[bool, str, None]) – Enables workflow to manage a model for automated classification of semi-structured and additional layout variations, defaults to workflow_input(Settings.SemiStructuredClassification)

• nlc_target_accuracy (Union[float, str, None]) – Classification Target Accuracy, defaults to workflow_input(Settings.SemiTargetAccuracy)

• mobile_processing_enabled (Union[bool, str, None]) – Improves the machine readability of photo captured document, defaults to workflow_input(Settings.MobileProcessingEnabled)

• vpc_registration_threshold (Optional[str]) – Structured pages above this threshold will be automatically matched to a layout variation when performing light vpc registration in the doc-org task, defaults to workflow_input(Settings.StructuredLayoutMatchThreshold)

Example usage:

manual_classification = ManualClassificationBlock(
    reference_name='manual_classification',
    submission=machine_classification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
)

IDENTIFIER = 'MANUAL_CLASSIFICATION_3'

class flows_sdk.implementations.idp_v39.idp_blocks.MachineIdentificationBlock(submission, api_params, layout_release_uuid='${workflow.input.layout_release_uuid}', reference_name=None, manual_field_id_enabled='${workflow.input.manual_field_id_enabled}', field_id_target_accuracy='${workflow.input.field_id_target_accuracy}', table_id_target_accuracy='${workflow.input.table_id_target_accuracy}')

Bases: Block

Machine identification automatically identify fields and tables in the submission

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• layout_release_uuid (Any) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• manual_field_id_enabled (str) – Enables manual identification when applicable, defaults to workflow_input(Settings.ManualFieldIdEnabled)

• field_id_target_accuracy (str) – Field ID Target Accuracy, defaults to workflow_input(Settings.FieldIdTargetAccuracy)

• table_id_target_accuracy (str) – Table ID Target Accuracy, defaults to workflow_input(Settings.TableIdTargetAccuracy)

Example usage:

machine_identification = MachineIdentificationBlock(
    reference_name='machine_identification',
    submission=manual_classification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
)

IDENTIFIER = 'MACHINE_IDENTIFICATION_7'

class flows_sdk.implementations.idp_v39.idp_blocks.ManualIdentificationBlock(submission, api_params, reference_name=None, task_restrictions='${workflow.input.manual_identification_task_restrictions}', manual_field_id_enabled='${workflow.input.manual_field_id_enabled}', layout_release_uuid='${workflow.input.layout_release_uuid}', notification_workflow='${workflow.input.notification_workflow_identification}')

Bases: Block

Manual identification allows keyers to complete field identification or table identification tasks, where they draw bounding boxes around the contents of certain fields, table columns or table rows. This identification process ensures that the system will be able to transcribe the correct content in the upcoming transcription process

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• task_restrictions (Union[List[Any], str, None]) – Defines what users can access Supervision tasks created by this block, defaults to workflow_input(Settings.ManualFieldIdTaskRestrictions)

• manual_field_id_enabled (str) – Enables manual identification when applicable, defaults to workflow_input(Settings.ManualFieldIdEnabled)

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• notification_workflow (str) – Notification flow name to run when the submission enters Manual Identification, defaults to workflow_inputSettings.ManualFieldIdNotificationWorkflow

Example usage:

manual_identification = ManualIdentificationBlock(
    reference_name='manual_identification',
    submission=machine_identification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    task_restrictions=idp_wf_config.manual_identification_config.task_restrictions,
)

IDENTIFIER = 'MANUAL_IDENTIFICATION_5'

class flows_sdk.implementations.idp_v39.idp_blocks.MachineTranscriptionBlock(submission, api_params, reference_name=None, layout_release_uuid='${workflow.input.layout_release_uuid}', transcription_model='${workflow.input.transcription_model}', finetuning_only_trained_layouts='${workflow.input.finetuning_only_trained_layouts}', transcription_overrides='${workflow.input.transcription_overrides}', structured_text_target_accuracy='${workflow.input.structured_text_target_accuracy}', structured_text_confidence_threshold='${workflow.input.structured_text_threshold}', structured_text_acceptable_confidence='${workflow.input.structured_min_leg_threshold}', semi_structured_text_target_accuracy='${workflow.input.semi_structured_text_target_accuracy}', semi_structured_text_confidence_threshold='${workflow.input.semi_structured_text_threshold}', semi_structured_text_acceptable_confidence='${workflow.input.semi_structured_min_leg_threshold}', semi_structured_table_target_accuracy='${workflow.input.semi_structured_table_target_accuracy}', semi_structured_table_confidence_threshold='${workflow.input.semi_structured_table_threshold}', semi_structured_table_acceptable_confidence='${workflow.input.semi_structured_table_min_leg_threshold}', structured_checkbox_target_accuracy='${workflow.input.structured_checkbox_target_accuracy}', structured_checkbox_confidence_threshold='${workflow.input.structured_checkbox_threshold}', structured_checkbox_acceptable_confidence='${workflow.input.checkbox_min_leg_threshold}', structured_signature_target_accuracy='${workflow.input.structured_signature_target_accuracy}', structured_signature_confidence_threshold='${workflow.input.structured_signature_threshold}', structured_signature_acceptable_confidence='${workflow.input.signature_min_leg_threshold}', semi_structured_checkbox_target_accuracy='${workflow.input.semi_structured_checkbox_target_accuracy}', semi_structured_checkbox_confidence_threshold='${workflow.input.semi_structured_checkbox_threshold}', semi_structured_checkbox_acceptable_confidence='${workflow.input.semi_structured_checkbox_min_leg_threshold}')

Bases: Block

Machine transcription automatically transcribes the content of your submission

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• transcription_model (str) – The transcription model that will be used for this flow, defaults to workflow_input(Settings.TranscriptionModel)

• finetuning_only_trained_layouts (str) – defaults to workflow_input( Settings.FinetuningOnlyTrainedLayouts )

• transcription_overrides (str) – defaults to workflow_input( Settings.TranscriptionOverrides )

• structured_text_target_accuracy (str) – defaults to workflow_input( Settings.StructuredTextTargetAccuracy )

• structured_text_confidence_threshold (str) – defaults to workflow_input( Settings.StructuredTextThreshold )

• structured_text_acceptable_confidence (str) – defaults to workflow_input( Settings.StructuredTextMinLegThreshold )

• semi_structured_text_target_accuracy (str) – defaults to workflow_input( Settings.SemiStructuredTextTargetAccuracy )

• semi_structured_text_confidence_threshold (str) – defaults to workflow_input( Settings.SemiStructuredTextThreshold )

• semi_structured_text_acceptable_confidence (str) – defaults to workflow_input( Settings.SemiStructuredTextMinLegThreshold )

• structured_checkbox_target_accuracy (str) – defaults to workflow_input( Settings.StructuredCheckboxTargetAccuracy )

• structured_checkbox_confidence_threshold (str) – defaults to workflow_input( Settings.StructuredCheckboxThreshold )

• structured_checkbox_acceptable_confidence (str) – defaults to workflow_input( Settings.StructuredCheckboxMinLegThreshold )

• structured_signature_target_accuracy (str) – defaults to workflow_input( Settings.StructuredSignatureTargetAccuracy )

• structured_signature_confidence_threshold (str) – defaults to workflow_input( Settings.StructuredSignatureThreshold )

• structured_signature_acceptable_confidence (str) – defaults to workflow_input( Settings.StructuredSignatureMinLegThreshold )

• semi_structured_checkbox_target_accuracy (str) – defaults to workflow_input( Settings.SemiStructuredCheckboxTargetAccuracy )

• semi_structured_checkbox_confidence_threshold (str) – defaults to workflow_input( Settings.SemiStructuredCheckboxThreshold )

• semi_structured_checkbox_acceptable_confidence (str) – defaults to workflow_input( Settings.SemiStructuredCheckboxMinLegThreshold )

Example usage:

machine_transcription = MachineTranscriptionBlock(
    reference_name='machine_transcription',
    submission=manual_identification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
)

IDENTIFIER = 'MACHINE_TRANSCRIPTION_9'

class flows_sdk.implementations.idp_v39.idp_blocks.ManualTranscriptionBlock(submission, api_params, reference_name=None, supervision_transcription_masking='${workflow.input.manual_transcription_supervision_transcription_masking}', table_output_manual_review='${workflow.input.manual_transcription_table_output_manual_review}', always_supervise_blank_cells='${workflow.input.manual_transcription_always_supervise_blank_cells}', task_restrictions='${workflow.input.manual_transcription_task_restrictions}', manual_transcription_enabled='${workflow.input.manual_transcription_enabled}', notification_workflow='${workflow.input.notification_workflow_transcription}', title='Manual Transcription', description='Manually transcribe fields that could not be automatically transcribed')

Bases: Block

Manual transcription lets your keyers manually enter the text found in fields or tables that could not be automatically transcribed

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (Union[bool, str]) – Prevents users from inputting invalid characters during Supervision Transcription tasks, defaults to workflow_input(Settings.ManualTranscriptionSupervisionTranscriptionMasking)

• table_output_manual_review (Union[bool, str]) – Always generates a table transcription task if the layout contains a table. If disabled, a table transcription task will only be generated if one or more cells have transcribed values below the defined thresholds, defaults to workflow_input(Settings.ManualTranscriptionTableOutputManualReview)

• always_supervise_blank_cells (Union[bool, str]) – If enabled, always send blank cells in transcribed tables to be supervised manually, regardless of confidence defaults to workflow_input(Settings.ManualTranscriptionAlwaysSuperviseBlankCells)

• task_restrictions (Union[List[Any], str, None]) – Defines what users can access Supervision tasks for submission from a particular source or submissions matching a specific layout, defaults to workflow_input(Settings.ManualTranscriptionTaskRestrictions)

• manual_transcription_enabled (str) – Enables manual transcription when applicable, defaults to workflow_input(Settings.ManualTranscriptionEnabled)

• notification_workflow (str) – Notification flow name to run when the submission enters Manual Transcription, defaults to workflow_input(Settings.ManualTranscriptionNotificationWorkflow)

Example usage:

manual_transcription = ManualTranscriptionBlock(
    reference_name='manual_transcription',
    submission=machine_transcription.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    supervision_transcription_masking=(
        idp_wf_config.manual_transcription_config.supervision_transcription_masking
    ),
    table_output_manual_review=(
        idp_wf_config.manual_transcription_config.table_output_manual_review
    ),
    task_restrictions=idp_wf_config.manual_transcription_config.task_restrictions,
)

IDENTIFIER = 'MANUAL_TRANSCRIPTION_5'

class flows_sdk.implementations.idp_v39.idp_blocks.FlexibleExtractionBlock(submission, api_params, reference_name=None, supervision_transcription_masking='${workflow.input.flexible_extraction_supervision_transcription_masking}', task_restrictions='${workflow.input.flexible_extraction_task_restrictions}', layout_release_uuid='${workflow.input.layout_release_uuid}', notification_workflow='${workflow.input.notification_workflow_flexible_extraction}')

Bases: Block

Flexible extraction manually transcribes fields marked for review

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (Union[bool, str, None]) – Prevents users from inputting invalid characters during the Flexible Extraction task, defaults to workflow_input(Settings.FlexibleExtractionSupervisionTranscriptionMasking)

• task_restrictions (Union[List[Any], str, None]) – Defines what users can access Supervision tasks created by this block, defaults workflow_input(Settings.FlexibleExtractionTaskRestrictions)

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• notification_workflow (str) – Notification flow name to run when the submission enters Flexible Extraction, defaults to workflow_input(Settings.FlexibleExtractionNotificationWorkflow)

Example usage:

flexible_extraction = FlexibleExtractionBlock(
    reference_name='flexible_extraction',
    submission=manual_transcription.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    supervision_transcription_masking=(
        idp_wf_config.flexible_extraction_config.supervision_transcription_masking
    ),
    task_restrictions=idp_wf_config.flexible_extraction_config.task_restrictions,
)

IDENTIFIER = 'FLEXIBLE_EXTRACTION_4'

class flows_sdk.implementations.idp_v39.idp_blocks.ReprocessingBlock(reference_name, submission, parent_flow='${workflow.definition.identifier}', idp_wf_config='${workflow.input}', trigger='${workflow.input.$trigger}')

Bases: Block

Reprocessing block enables a second extraction attempt if documents were initially confidently mis-classified by the machine. When an end-user specifies that a document layout is incorrect during Manual Identification, Manual Transcription, or Flexible Extraction, the flow will reach the Reprocessing block which will mark correctly classified documents as “complete” status, and misclassified documents with “reprocess” status will be processed in a second execution of the flow. This block should be placed before the SubmissionCompleteBlock to enable reprocessing.

Mandatory parameters:

Parameters:

submission (Any) – Submission object

IDENTIFIER = 'REPROCESSING'

class flows_sdk.implementations.idp_v39.idp_blocks.SubmissionCompleteBlock(submission, is_reprocessing=False, reference_name=None, nlc_qa_sampling_ratio='${workflow.input.semi_qa_sample_rate}', field_id_qa_enabled='${workflow.input.field_id_qa_enabled}', field_id_qa_sampling_ratio='${workflow.input.field_id_qa_sample_rate}', table_id_qa_enabled='${workflow.input.table_id_qa_enabled}', table_id_qa_sampling_ratio='${workflow.input.table_id_qa_sample_rate}', transcription_qa_enabled='${workflow.input.qa}', transcription_structured_entry_qa_sample_rate='${workflow.input.structured_entry_qa_sample_rate}', transcription_structured_signature_qa_sample_rate='${workflow.input.structured_signature_qa_sample_rate}', transcription_structured_checkbox_qa_sample_rate='${workflow.input.structured_checkbox_qa_sample_rate}', transcription_semi_structured_qa_sample_rate='${workflow.input.semi_structured_qa_sample_rate}', table_cell_transcription_qa_enabled='${workflow.input.table_cell_transcription_qa_enabled}', table_cell_transcription_qa_sample_rate='${workflow.input.table_cell_transcription_qa_sample_rate}', auto_qa_sampling_rate_enabled='${workflow.input.auto_qa_sampling_rate_enabled}', exclude_auto_transcribe_fields_from_qa='${workflow.input.exclude_auto_transcribe_fields_from_qa}', payload=None)

Bases: Block

Submission complete block finalizes submission processing and updates reporting data. This block should be called at the END of every flow that uses blocks included in the flows_sdk.implementations.idp_v34 library. The block requires both the submission object and a payload to be passed in.

Mandatory parameters:

Parameters:

• submission (str) – Submission object

• payload (Any) – Object to pass to downstream systems

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• nlc_qa_sampling_ratio (Union[str, int]) – Defines the percentage of documents sampled for Classification QA, defaults to workflow_input(Settings.SemiQaSampleRate)

• field_id_qa_enabled (Union[str, bool]) – defaults to workflow_input(Settings.FieldIdQaEnabled)

• field_id_qa_sampling_ratio (Union[str, int]) – Defines the percentage of documents sampled for Identification QA, defaults to workflow_input(Settings.FieldIdQaSampleRate)

• table_id_qa_enabled (Union[str, bool]) – Allows users to verify the location of table cells, defaults to workflow_input(Settings.TableIdQaEnabled)

• table_id_qa_sampling_ratio (Union[str, int]) – Defines the percentage of tables the system samples for QA, defaults to workflow_input(Settings.TableIdQaSampleRate)

• transcription_qa_enabled (Union[str, bool]) – defaults to workflow_input(Settings.TranscriptionQaEnabled)

• transcription_qa_sampling_ratio – Defines the percentage of fields sampled for Transcription QA, defaults to workflow_input(Settings.TranscriptionQaSampleRate)

• table_cell_transcription_qa_enabled (Union[str, bool]) – defaults to workflow_input(Settings.TableCellTranscriptionQaEnabled)

• table_cell_transcription_qa_sample_rate (Union[str, int]) – Defines the percentage of cells the system samples for QA. This value is likely to be lower than “Transcription QA Sample Rate” since there are more table cells than fields on any given page, defaults to workflow_input(Settings.TableCellTranscriptionQaSampleRate)

• exclude_auto_transcribe_fields_from_qa (Union[str, bool]) – Defines fields that are marked as auto-transcribe in layout should be excluded from QA or not. defaults to workflow_input(Settings.ExcludeAutoTranscribeFieldsFromQA)

• is_reprocessing (Union[str, bool]) – boolean to handle execution when reprocessing defaults to False

Example usage:

submission_complete = SubmissionCompleteBlock(
    reference_name='complete_submission',
    submission=flexible_extraction.output('submission')
)

IDENTIFIER = 'SUBMISSION_COMPLETE_4'

class flows_sdk.implementations.idp_v39.idp_blocks.SetTransformedOutputBlock(submission, transformed_output, reference_name=None)

Bases: Block

Set Transformed Output block sets the transformed output for the provided submission. This can later be used to send the transformed data downstream using output blocks. For more information see https://docs.hyperscience.ai/#retrieving-transformed-submissions.

Mandatory parameters:

Parameters:

• submission (str) – Submission object

• transformed_output (str) – Object that will be set as the transformed output for this submission

IDENTIFIER = 'SET_TRANSFORMED_OUTPUT'

class flows_sdk.implementations.idp_v39.idp_blocks.IdpCustomSupervisionBlock(submission, task_purpose, supervision_template, reference_name=None, supervision_transcription_masking=True, task_restrictions=None, page_ids=None, notification_workflow='IDP_SUBMISSION_NOTIFY_V39', title='IDP Custom Supervision', description='IDP Custom Supervision')

Bases: Block

An IDP wrapper for the Custom Supervision block. It has the same functionality, but handles reading/writing data from the IDP database.

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• task_purpose (str) – the custom task name that’s given to all supervision tasks that run through a particular instance of a custom supervision block. This can be used to filter tasks in the task queue.

• supervision_template (Any) – JSON used for configuration of the Custom Supervision Task.

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (Union[str, bool]) – Prevents users from inputting invalid characters during the Custom Supervision task, defaults to True

• task_restrictions (Union[str, List[Any], None]) – Defines what users can access Supervision tasks created by this block, defaults to []

• page_ids (Union[str, List[int], None]) – This is an optional parameter. If left empty, the IDP wrapper will, by default, pull in all pages in the submission. Adding a list of page IDs will override the default list of pages. defaults to []

• notification_workflow (str) – Notification flow name to run when the submission enters Custom Supervision (which is called within the IDP Custom Supervision block), defaults to IDP_SUBMISSION_NOTIFY_NAME

• title (Optional[str]) – UI-visible title of the block. Defaults to ‘IDP Custom Supervision’

• description (Optional[str]) – Description of the block. Both useful for documentation purpose and visible to users in the Flow studio. Defaults to ‘IDP Custom Supervision’

Example usage:

idp_custom_supervision = IdpCustomSupervisionBlock(
    reference_name='idp_custom_supervision',
    submission=manual_transcription.output('submission'),
    task_purpose='make_custom_decision',
    supervision_template=[
        {
            'name': 'three_column_template',
            'version': '1.0',
            'thumbnail': {
                'group_by_document': True,
                'group_by_case': True,
            },
            'action': [
                {
                    'name': 'my_tab',
                    'display': 'SSN Info',
                    'input': [
                        {
                            'name': 'ssn_field',
                            'type': 'transcription',
                            'data_identifier': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                            'title': 'SSN',
                        },
                    ],
                    'ui': {
                        'groups': [
                            {
                                'title': 'My Group',
                                'fields': ['ssn_field'],
                            },
                        ],
                    },
                },
            ],
        },
    ],
    supervision_transcription_masking=False,
)

IDENTIFIER = 'IDP_CUSTOM_SUPERVISION_2'

class flows_sdk.implementations.idp_v39.idp_blocks.IDPFullPageTranscriptionBlock(submission, reference_name=None, title='Full Page Transcription (Submission)', description='Transcribes the documents included in a submission', app_metadata=None, language_family=None)

Bases: Block

IDP Full Page Transcription Block machine-transcribes the documents contained in a submission

Mandatory parameters:

Parameters:

submission (str) – submission object that includes the raw documents that need to be transcribed

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• app_metadata (Optional[List[str]]) – used for capturing information about the flow

• language_family (Optional[str]) – language family to use when instantiating the underlying transcription model. If not provided, the system will default to latin. Language families are single word lowercase strings, e.g., latin, arabic, korean, chinese. You can read more about language families in the “Supported Languages” article in the application documentation: https://help.hyperscience.ai/v39/docs/supported-languages. Note: This field was added in v39.0.9.

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# IDPFullPageTranscriptionBlock transcribes the documents contained in the submission object
idp_fpt_block = IDPFullPageTranscriptionBlock(
    reference_name='idp_fpt',
    submission=submission_bootstrap.output(),
)
# reference to the output of IDPFullPageTranscriptionBlock which can be passed as input to
# the next block. For more information on output() and output references, please take a
# look at class Block and its output() method
output_ref = idp_fpt_block.output()

IDENTIFIER = 'FULL_PAGE_TRANSCRIPTION_SUBMISSION_2'

class flows_sdk.implementations.idp_v39.idp_blocks.IDPImageCorrectionBlock(submission, reference_name=None, rotation_correction_enabled=True, mobile_processing_enabled=False, title='Image Correction (Submission)', description='Rotate and de-skew documents included in a submission', app_metadata=None)

Bases: Block

IDP Image Correction Block rotates and de-skews tilted/skewed images so that they are more transcription-friendly

Mandatory parameters:

Parameters:

submission (str) – submission object that includes the raw documents that need to be image-corrected

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• app_metadata (Optional[List[str]]) – used for capturing information about the flow

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# IDPImageCorrectionBlock takes in the submission object and outputs in the same format with
# its images
idp_image_correct_block = IDPImageCorrectionBlock(
    reference_name='idp_image_correct',
    submission=submission_bootstrap.output(),
)
# reference to the output of IDPImageCorrectionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look at
# class Block and its output() method
output_ref = idp_image_correct_block.output()

IDENTIFIER = 'IMAGE_CORRECTION_SUBMISSION'

Additional Blocks (v39)

class flows_sdk.implementations.idp_v39.additional_blocks.JSONOutputsBlock(inputs, blocks=None)

Bases: Outputs

Output block allows users to send JSON data extracted by an IDP flow to other systems for downstream processing Mandatory parameters:

Parameters:

inputs (Dict[str, Any]) – Used by the UI to automatically pre-populate the input of newly added blocks. Extended in this class to include 'enabled': True to visualize an enabled/disabled trigger that is controllable via Flow Studio.

Usage when parameters are to be defined in Flow Studio:

outputs = JSONOutputBlock(
    inputs={'result': }
)

class flows_sdk.implementations.idp_v39.additional_blocks.ImageCorrectionBlock(pages, reference_name=None, rotation_correction_enabled=True, mobile_processing_enabled=False, title='Image Correction (Pages)', description='Rotate and de-skew documents', app_metadata=None)

Bases: Block

Image Correction Block rotates and de-skews tilted/skewed images so that they are more transcription-friendly Mandatory parameters:

Parameters:

pages (str) – list of page object that includes the raw documents that need to be image-corrected

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• app_metadata (Optional[List[str]]) – used for capturing information about the flow

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# ImageCorrectionBlock takes in the submission object and outputs in the same format with
# its images
image_correct_block = ImageCorrectionBlock(
    reference_name='image_correct',
    pages=submission_bootstrap.output(submission.unassigned_pages),
)
# reference to the output of IDPImageCorrectionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look
# at class Block and its output() method
output_ref = image_correct_block.output()

IDENTIFIER = 'IMAGE_CORRECTION_PAGES'

class flows_sdk.implementations.idp_v39.additional_blocks.FullPageTranscriptionBlock(pages, reference_name=None, title='Full Page Transcription (Pages)', description='Transcribes documents', language_family=None, app_metadata=None)

Bases: Block

Full Page Transcription Block machine-transcribes the documents contained in a submission

Mandatory parameters:

Parameters:

pages (str) – list of pages that includes the raw documents that need to be transcribed

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• language_family (Optional[str]) – language family to use when instantiating the underlying transcription model. If not provided, the system will default to latin. Language families are single word lowercase strings, e.g., latin, arabic, korean, chinese. You can read more about language families in the “Supported Languages” article in the application documentation: https://help.hyperscience.ai/v39/docs/supported-languages. Note: This field was added in v39.0.9.

• app_metadata (Optional[List[str]]) – used for capturing information about the flow

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# FullPageTranscriptionBlock transcribes the documents contained in the submission object
fpt_block = FullPageTranscriptionBlock(
    reference_name='fpt',
    submission=submission_bootstrap.output(submission.unassigned_pages),
)
# reference to the output of FullPageTranscriptionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look at
# class Block and its output() method
output_ref = fpt_block.output()

IDENTIFIER = 'FULL_PAGE_TRANSCRIPTION_PAGES_2'

class flows_sdk.implementations.idp_v39.additional_blocks.CustomSupervisionBlock(submission, task_purpose, data, supervision_template, reference_name=None, supervision_transcription_masking=True, task_restrictions=None, notification_workflow='IDP_SUBMISSION_NOTIFY_V39', title='Custom Supervision', description='Custom Supervision')

Bases: Block

The CustomSupervisionBlock uses a JSON schema as a form builder to create customer-driven UIs for Supervision tasks.

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• task_purpose (str) – the custom task name that’s given to all supervision tasks that run through a particular instance of a custom supervision block. This can be used to filter tasks in the task queue.

• data (Any) – JSON structure that supplies the data used to populated the supervision_template

• supervision_template (Any) – JSON used for configuration of the Custom Supervision Task.

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (bool) – Prevents users from inputting invalid characters during the Custom Supervision task, defaults to True

• task_restrictions (Optional[List[Any]]) – Defines what users can access Supervision tasks created by this block, defaults to []

• notification_workflow (str) – Notification flow name to run when the submission enters Custom Supervision, defaults to IDP_SUBMISSION_NOTIFY_NAME

• title (Optional[str]) – UI-visible title of the block. Defaults to ‘IDP Custom Supervision’

• description (Optional[str]) – Description of the block. Both useful for documentation purpose and visible to users in the Flow studio. Defaults to ‘IDP Custom Supervision’

Example usage:

custom_supervision = CustomSupervisionBlock(
    reference_name='custom_supervision',
    submission=manual_transcription.output('submission'),
    task_purpose='make_custom_decision',
    supervision_template=[
        {
            'name': 'three_column_template',
            'version': '1.0',
            'thumbnail': {
                'group_by_document': True,
                'group_by_case': True,
            },
            'action': [
                {
                    'name': 'my_tab',
                    'display': 'SSN Info',
                    'input': [
                        {
                            'name': 'ssn_field',
                            'type': 'transcription',
                            'data_identifier': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                            'title': 'SSN',
                        },
                    ],
                    'ui': {
                        'groups': [
                            {
                                'title': 'My Group',
                                'fields': ['ssn_field'],
                            },
                        ],
                    },
                },
            ],
        },
    ],
    data={
        'fields': [
            {
                'id': 967,
                'uuid': 'fe8d4e7a-a82c-448c-85a1-0a3cbbe53b61',
                'value': '111-22-3333',
                'page_id': 15,
                'layout_field_id': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                'validation_overridden': False,
                'bounding_box': [
                    0.09360783305186686,
                    0.25792617589433436,
                    0.6358913805295097,
                    0.28862414740388187,
                ],
                'occurrence_index': 1,
            },
        ],
        'template_fields': [
            {
                'uuid': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                'notes': '',
                'type': 'entry',
                'data_type_uuid': 'b600a4e4-758f-4c4b-a2f4-cbb5dd870a0c',
                'name': 'SSN',
            },
        ],
        'pages': [
            {
                'id': 15,
                'file_page_number': 1,
                'document_page_number': 1,
                'image_url': '/image/2ed3d989-b64a-44bd-a9fb-6e0dcc5ccdb8',
                'form_id': 2,
                'submission_id': 1,
                'external_case_ids': [],
                'read_only': False,
                'filename': '1 page.pdf',
                'form_page_id': 1
            },
        ],
        'documents': [
            {
                'id': 2,
                'layout_uuid': 'e31c3cbf-dcea-44fd-a052-902463c6040f',
                'layout_name': 'layout_name',
                'page_count': 2,
                'read_only': False,
            },
        ],
        'cases': [],
        'selected_choices': [],
    },
    supervision_transcription_masking=False,
)

IDENTIFIER = 'CUSTOM_SUPERVISION_2'

class flows_sdk.implementations.idp_v39.additional_blocks.SoapRequestBlock(method, reference_name=None, endpoint='', body_namespace=None, headers_namespace=None, headers=None, params=None, use_wsdl=False, wsdl_uri='', wsdl_service_name='', wsdl_port_name='', title='HTTP REST Block', description='HTTP REST block')

Bases: Block

Soap Block allows user to perform SOAP requests Mandatory parameters:

Parameters:

• method (str) – SOAP method to invoke

• endpoint (str) – absolute url including schema to HTTP endpoint for this request

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• body_namespace (Optional[str]) – SOAP request body namespace url

• headers_namespace (Optional[str]) – SOAP request headers namespace url

• headers (Optional[Dict[Any, Any]]) – SOAP headers to use

• params (Optional[Dict[Any, Any]]) – key-value pair used as query parameters in the request to be inserted as SOAP body

Example usage:

# minimum viable instantiation, the rest of the fields can later be added in the UI
soap_block = SoapRequestBlock(
    reference_name='soap_request',
    title='Soap Request',
    description='Perform soap request',
    method='ExampleMethod',
)
soap_block = SoapRequestBlock(
    reference_name='soap_request',
    title='Soap Request',
    description='Perform soap request',
    method='ExampleMethod',
    endpoint='http://example.org/abc.wso',
    body_namespace='http://www.example.org/abc.countryinfo',
    params={'example': 'stuff'},
    headers={}
)
# reference to the output of SoapRequestBlock which can be passed as input to the next block
# for more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = soap_block.output()

IDENTIFIER = 'SOAP_REQ'

class flows_sdk.implementations.idp_v39.additional_blocks.HttpRestBlock(method, endpoint, reference_name=None, headers=None, params=None, payload=None, json=None, authorization_type=None, handled_error_codes=None, auth_params=None, title='HTTP REST Block', description='HTTP REST block')

Bases: BaseHttpRestBlock

Http Rest Block allows user to perform HTTP requests Mandatory parameters:

Parameters:

• method (str) – HTTP method to use

• endpoint (str) – absolute url including schema to HTTP endpoint for this request

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• headers (Optional[Dict[str, str]]) – HTTP headers to use

• params (Optional[Dict[str, Any]]) – key-value pair used as query parameters in the request

• payload (Optional[Dict[str, str]]) – key-value pair to be used as x-www-form-urlencoded data. Mutually exclusive with json

• json (Optional[Dict[str, Any]]) – key-value pair to be used as json data. Mutually exclusive with payload

• authorization_type (Optional[str]) – authorization type (none, http_header, oauth_2_client_credentials)

• handled_error_codes (Optional[List[Union[int, str]]]) – specify HTTP error codes that will not fail the task, allowing for wildcards with the ‘x’ char. Example: [400, “5xx”]

• auth_params (Union[HttpHeaderAuthorizationParams, OAuth2AuthorizationParams, None]) – authorization parameters to be passed when the authorization_type is set to “http_header” or “oauth_2_client_credentials”

Example usage:

# minimum viable instantiation, the rest of the fields can later be added in the UI
http_rest_block = HttpRestBlock(
    reference_name='http_request',
    title='Http Request',
    description='Perform http request',
    method='GET',
)
http_rest_block = HttpRestBlock(
    reference_name='http_request',
    title='Http Request',
    description='Perform http request',
    method='GET',
    endpoint='https://sheets.googleapis.com/v4/spreadsheets/example',
)
# reference to the output of HttpRestBlock which can be passed as input to the next block
# for more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = http_rest_block.output()

class flows_sdk.implementations.idp_v39.additional_blocks.HyperscienceRestApiBlock(method, app_endpoint, reference_name=None, headers=None, params=None, payload=None, json=None, handled_error_codes=None, title='Hyperscience HTTP REST Block', description='Hyperscience HTTP REST block')

Bases: BaseHttpRestBlock

Make HTTP requests to the Hyperscience platform APIs. This block will automatically take care of prepending the base url and will include the appropriate authentication headers with the request.

Mandatory parameters:

Parameters:

• method (str) – HTTP method to use, e.g. ‘GET’, ‘POST’, etc.

• app_endpoint (str) – relative url, beginning with a forward slash, to an HTTP endpoint served by the Hyperscience platform. For reference on the available endpoints, check out https://docs.hyperscience.ai/. Absolute URLs that include a hostname are allowed only if the flow has received them as part of the response of a previous API request that the flow has made. Do not hardcode the hostname of the Hyperscience instance in your flow definitions as they will not work when you run the flows on another instance, or if the hostname changes. For making requests to external systems, use the HttpRestBlock or a library like “requests”.

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• headers (Optional[Dict[str, str]]) – HTTP headers to use

• params (Optional[Dict[str, Any]]) – key-value pair used as query parameters in the request

• payload (Optional[Dict[str, str]]) – key-value pair to be used as x-www-form-urlencoded data. Mutually exclusive with json

• json (Optional[Dict[str, Any]]) – key-value pair to be used as json data. Mutually exclusive with payload

• handled_error_codes (Optional[List[Union[int, str]]]) – specify HTTP error codes that will not fail the task, allowing for wildcards with the ‘x’ char. Example: [400, “5xx”]

Example usage:

hs_rest_block = HyperscienceRestApiBlock(
    reference_name='http_request',
    app_endpoint='/api/healthcheck',
    method='GET',
)

class flows_sdk.implementations.idp_v39.additional_blocks.DatabaseAccessBlock(reference_name=None, db_type='', database='', host='', username='', password='', query='', port=None, options=None, timeout=None, query_params=None, title='DB Block', description='DB access block')

Bases: Block

Database Block allows user to access and perform queries on the specified database Mandatory parameters:

Parameters:

• db_type (str) – database type (mssql, oracle, postgres)

• host (str) – URL/IP address of the server database is hosted on

• database (str) – database name

• username (str) – database username

• password (str) – database password

• query (str) – parameterized query

Optional parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• port (Optional[int]) – database port number

• options (Optional[Dict[Any, Any]]) – dictionary of additional connection string options

• query_params (Optional[Dict[Any, Any]]) – dictionary of values for query placeholders

• timeout (Optional[int]) – timeout in seconds (mssql and postgres only)

Example usage:

# minimum viable instantiation, the rest of the fields can later be added in the UI
db_lookup = DatabaseAccessBlock(
    reference_name='db_lookup',
    title='Database Lookup',
    description='Perform database lookup',
)
# note that storing password in plain text form is not recommended
# password can later be entered in the UI or defined as a flow secret.
db_lookup = DatabaseAccessBlock(
    reference_name='db_lookup',
    title='Database Lookup',
    description='Perform database lookup',
    db_type='mssql',
    database='cars',
    host='example.com',
    username='user',
    # we recommend skipping the password field or defining it as a system secret
    # e.g. system_secret('{your_secret_identifier}') (both allow for secret input via. UI)
    password='pw'
    port=1433,
    timeout=200,
    query='SELECT * from CAR',
)
# reference to the output of DBAccessBlock which can be passed as input to the next block
# for more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = db_lookup.output()
# content of output_ref:
{
    "result": [
        {
            "id": 1,
            "brand": "Aston Martin",
            "model": "Vanquish"
        },
        {
            "id": 2,
            "brand": "Jaguar",
            "model": "XE 2018"
        }
    ]
}

Database output for the same query (SELECT * FROM CAR)

IDENTIFIER = 'DB_ACCESS'

class flows_sdk.implementations.idp_v39.additional_blocks.LlamaBlock(model_id, prompt, reference_name=None, consent=False, tokenize_only=False, predict_extra_kwargs=None, title='Llama Text Generation', description='Text generation using the Llama 2 model')

Bases: Block

Text generation using the Llama 2 model by Meta.

This block utilizes Llama 2 by Meta. Llama 2 is licensed under the LLAMA 2 Community License (https://ai.meta.com/llama/license/). By using the Llama Block, you acknowledge that you have received a copy of the LLAMA 2 Community License.

Note: This block is still experimental and not ready for production use.

Mandatory parameters:

Parameters:

• model_id (str) – UUID, identifier of the asset with the ML model. Model assets are not part of the Hypersicence bundle and must first be downloaded and imported into the system before they can be used. Please contact your Hyperscience representative to discuss the usecase and the appropriate model and to get instructions on downloading the models. Once downloaded, here are the available model asset UUIDs: b3da05c7-061c-4a58-af6d-4c905d2e618f - Llama 2, 13B, 8-bit quantized

• prompt (str) – The prompt to generate text from. Use a CodeBlock ahead of the LlamaBlock in order to build the prompt. The context window of the model is limited to 4096 tokens. If the prompt has more tokens, the block would return an error. The total input and output length also cannot exceed 4096 tokens, which means that if the input is too long, the output may be truncated. Also see tokenize_only for a more detailed explanation of how to handle longer texts.

Optional parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• consent (Optional[bool]) – Has the business user read and acknowledged the Llama 2 legal notice. Unless the flow would be used in CI automation, this should be set to False so that a business user can read and acknowledge the notice in the Hyperscience UI. Setting this to True in the Python definition of the flow is also treated as acknowledgement of the notice on behalf of the entity that will be using the flow.

• tokenize_only (Optional[bool]) – In the context of LLMs, tokens correspond roughly to words, partial words and punctuation. A rough way to compute the number of tokens in a text is number_of_tokens = 1.25 * number_of_words. The Llama block works with a context window of 4096 tokens. If a longer prompt is passed in, it would return an error. Also, if the prompt is shorter, but still close to 4096, the output may be truncated. Since the length of the prompt in tokens cannot be known ahead of time, sometimes the flow may need to make dynamic decisions whether to pass the text to the Llama block, or to split it into multiple chunks, or to use some alternative method for processing it (e.g. a custom supervision manual task). The tokenize_only option enables flows to take such dynamic decisions based on the length of the prompt in tokens. The default is false - it instructs the block to run the ML model inference over the prompt, i.e. to generate a text response. When true is passed, the model will simply tokenize the prompt without running inference and return the list of tokens - this will work regardless of the length of the prompt. A subsequent Decision task can then route longer prompts to an alternative path in the flow, while routing shorter prompts to the LlamaBlock for actual text generation.

• predict_extra_kwargs (Optional[Dict[Any, Any]]) – Extra keyword arguments used for model inference. (Used to fine-tune the model prediction. Not recommended to change this unless you know what you are doing)

Example usage:

llama = LlamaBlock(
    reference_name='llama',
    model_id='b3da05c7-061c-4a58-af6d-4c905d2e618f',
    prompt='${previous_block.output.llama_prompt}',
)

IDENTIFIER = 'LLAMA'

class flows_sdk.implementations.idp_v39.additional_blocks.EntityRecognitionBlock(pages, regex_map=None, keyword_map=None, reference_name=None, title='Entity Recognition', description='Recognizes entities of different types in documents')

Bases: Block

Entity Recognition Block detects entities of different types within the documents contained in a submission

Mandatory parameters:

Parameters:

pages (str) – list of pages that includes the raw documents that need to be transcribed

Optional Parameters:

Parameters:

• regex_map (Optional[Dict[str, str]]) – a map that specifies different regex patterns that the block should look for, a default one is present

• keyword_map (Optional[Dict[str, List[str]]]) – a map that specifies different keyword patterns that the block should look for, a default one is present

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')

# EntityRecognitionBlock detects entities within the documents contained in the submission
entity_recognition_block = EntityRecognitionBlock(
    reference_name='entity_recognition',
    pages=submission_bootstrap.output(submission.unassigned_pages),
    regex_map=DEFAULT_PII_REGEX_MAP,
    keyword_map=DEFAULT_PII_KEYWORD_MAP,
)

# reference to the output of EntityRecognitionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look at
# class Block and its output() method
output_ref = entity_recognition_block.output()

Example content of output_ref:

{
    "documents": [
        {
            "file_uuid": "da2c6117-43c4-4b30-84c4-cdc59cc3fa4f",
            "entities": [
                {
                    "text": "John Smith",
                    "type": "Person Name",
                    "positions": [
                        [0.5, 0.1, 0.55, 0.13],
                        [0.6, 0.2, 0.65, 0.23],
                        [0.7, 0.3, 0.75, 0.33],
                    ],
                    "page_ids": [0, 0, 1],
                    "origin": "NER",
                },
                {
                    "text": "<SIGNED>",
                    "type": "signature",
                    "positions": [[0.701171875, 0.732421875, 0.92578125, 0.8681640625]],
                    "page_ids": [0],
                    "origin": "Signature",
                },
            ]
        },
        {
            "file_uuid": "4aa38f84-e5af-4cdc-ba5c-edd60f0a6b89",
            "entities": [
                ...
            ]
        }
    ]
}

IDENTIFIER = 'ENTITY_RECOGNITION'

Trigger Blocks (v39)

Note

“Triggers” are referred to as “Input Blocks” in our application documentation.

Warning

Flows are built to allow full definition of triggers either in code or manually in the Flow Studio. We recommend defining triggers manually in the Flow Studio. Instructions on how to do so are in our application documentation.

To enable trigger definition via the Flow Studio UI, you need to instantiate the IDPTriggers() convenience class and pass it to the triggers property of the Flow

return Flow(
   ...
   triggers=IDPTriggers()
)

If instead you wish to define specific triggers in Python, use the trigger definitions below.

Folder Listener (v39)

Identifier: FOLDER_TRIGGER

Deprecated in favor of the Universal Folder Listener

Parameters:

Name	Type	Default Value	Title	Description
folder	Path	N/A	Folder to scan for submissions	N/A
file_extensions	string	N/A	File extensions	List of file extensions to monitor for (e.g.: ‘png, jpg, pdf’)
has_meta	boolean	False	Enable metadata	Select this if a metadata file is to be expected along with document files (in XXX_index.txt file)
poll_interval	integer	10	Poll interval	Poll interval in seconds
warmup_interval	integer	15	Warm-up interval	Seconds to wait past document last modified time before processing it
folder_cleanup_delay	integer	86400	Folder cleanup delay	Seconds to wait before cleaning up subfolders
api_params	object	{}	API Parameters	N/A

folder_listener = IOBlock(
    identifier='FOLDER_TRIGGER',
    reference_name='folder_trigger',
    title='Folder listener',
    enabled=True,
    input={
        'folder': '/var/www/forms/forms/folder/',
        'file_extensions': 'png, pdf, jpg',
    },
)

triggers = IDPTriggers(blocks=[folder_listener])

S3 Listener (v39)

Identifier: S3_TRIGGER Parameters:

Name	Type	Default Value	Title	Description
bucket_uri	string	N/A	S3 source URI	S3 URI, where the files will be uploaded (e.g. s3://hs_bucket/prefix1/)
archive_bucket_uri	string	N/A	S3 archive URI	S3 URI to move the processed files to (e.g. s3://hs_bucket/prefix1/)
file_extensions	array	[]	File extensions	Types of file extensions for which to monitor
other_file_extensions	string	N/A	Other file extensions	Comma separated list of file extensions for which to monitor (e.g. ‘png, jpg, pdf’)
has_meta	boolean	false	Include submission level parameters	Select this to ingest JSON files along with document files and submission s3 prefixes. These JSON files can contain information such as metadata, cases and external_id. These JSON file names should match the related files or s3 prefixes (e.g., XXX.jpg.json for XXX.jpg)
aws_access_key_id	string	N/A	AWS ACCESS KEY ID	Access Key used for authentication
aws_secret_access_key	password	N/A	SECRET ACCESS KEY	Secret Key used for authentication
use_ec2_instance_credentials	boolean	true	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used
poll_interval	integer	10	Polling interval (in seconds)	How often the S3 Listener will check the S3 bucket for submissions
warmup_interval	integer	15	Warm-up interval (in seconds)	How long the S3 Listener will wait to process the document after it was first detected
api_params	object	{}	API Parameters	N/A

s3_listener = IOBlock(
    identifier='S3_TRIGGER',
    reference_name='s3_trigger',
    title='S3 Listener',
    enabled=True,
    input={
        'bucket_uri': 's3://hs_bucket/prefix1/',
        'archive_bucket_uri': 's3://hs_bucket/prefix2/',
        'file_extensions': ['png', 'pdf', 'jpg'],
        'aws_access_key_id': 'admin',
        'aws_secret_access_key': system_secret('s3_secret_access_key'),
        'use_ec2_instance_credentials': False,
    },
)
triggers = IDPTriggers(blocks=[s3_listener])

Email Listener (v39)

Identifier: IMAP_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
provider [1]	string	imap	Email Provider	N/A
poll_interval	integer	60	Polling interval in seconds	The frequency at which the connection will monitor the email folder for submissions
folder	Path	N/A	Folder to scan for emails	The email folder to retrieve submissions from (e.g., “inbox”)
email_body_treatment	string	“ignore”	Email body treatment	What to do with the body of an email that is ingested. Available options are “process” and “ignore”.
post_process_action	string	“move”	Post process action	What to do with the email after it is processed. Available options are “move” and “delete”.
post_process_move_folder	string	“”	Post process archive folder	Folder to move emails to once they are processed. It will be included only if post_process_action is set to “move”.
api_params	object	{}	API Parameters	N/A
render_headers [2]	array	[]	Headers to include	Headers to render at the top of the email body. Headers will be included only if email_body_treatment is set to “process”.

[1] one of:

imap
graph

[2] any of:

To
From
Subject
Date

Additional inputs when "imap" is the selected provider:

Name	Type	Default Value	Title	Description
host	string	N/A	IMAP server address	N/A
port	integer	993	Port Number	N/A
ssl	boolean	True	Use SSL connection	N/A
username	string	N/A	Username	N/A
password	Password	N/A	Password	N/A

Additional inputs when "graph" is the selected provider:

Name	Type	Default Value	Title	Description
client_id	string	N/A	Client id	Application (client) ID for the corresponding application registered with the Microsoft identity platform.
tenant_id	string	N/A	Tenant id	Also called directory ID. Unique identifier of the tenant in which the corresponding application is registered with.
graph_cloud_endpoint [1]	string	Global	Microsoft Graph Cloud Endpoint	Determines base URL used for accessing cloud services through Microsoft Graph.
azure_ad_endpoint [2]	string	AZURE_PUBLIC_CLOUD	Azure AD Endpoint	Determines base URL for the Azure Active Directory (Azure AD) endpoint to acquire token for each national cloud.
client_secret	Password	N/A	Client secret	Client secret for the corresponding application registered with the Microsoft identity platform.

[1] one of:

Global
US_GOV
US_DoD
Germany
China

[2] one of:

AZURE_PUBLIC_CLOUD
AZURE_CHINA
AZURE_GERMANY
AZURE_GOVERNMENT

imap_trigger = IOBlock(
    identifier='IMAP_TRIGGER',
    reference_name='imap_trigger',
    title='IMAP trigger',
    enabled=True,
    input={
        'host': 'example@mail.com',
        'folder': '/var/www/forms/forms/imap/',
        'username': 'admin',
        'password': 'pass',
    }
)

triggers = IDPTriggers(blocks=[imap_trigger])

Box Folder Listener (v39)

Identifier: BOX_FOLDER_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
source_folder_id	string	N/A	Folder to scan for submissions	Use the Box Folder ID found in the URL
target_folder_id	string	N/A	Folder to move completed files	Use the Box Folder ID found in the URL
file_extensions	array	[]	File extensions	Types of file extensions for which to monitor
custom_file_extensions	string	N/A	Other file extension types	Comma separated list of file extensions for which to monitor (e.g. ‘png, jpg, pdf’)
poll_interval	integer	10	Polling interval (in seconds)	How often the connector will check the base folder for submissions
warmup_interval	integer	15	Warm-up interval (in seconds)	How long the connector will wait to process the document after it was last modified
public_key_id	string	N/A	Public-Key ID	ID of the Public-key used for authentication with Box
private_key	Password	N/A	Private-Key	Private key used for authentication with Box
passphrase	Password	N/A	Passphrase	Passphrase used for authentication with Box
client_id	string	N/A	Client ID	Client Id used for authentication with Box
client_secret	Password	N/A	Client Secret	Client Secret used for authentication with Box
enterprise_id	string	N/A	Enterprise ID	Enterprise Id used for authentication with Box
api_params	object	{}	API Parameters	N/A

box_folder_trigger = IOBlock(
    identifier='BOX_FOLDER_TRIGGER',
    reference_name='box_folder_trigger',
    title='Box folder',
    enabled=True,
    input={
        'file_extensions': ['png', 'pdf', 'jpg'],
        'source_folder_id': '24',
        'target_folder_id': '42',
        'public_key_id': 'admin',
        'private_key': 'secret',
        'passphrase': 'password',
        'client_id': 'admin',
        'client_secret': 'secret',
        'enterprise_id': 'admin',
    }
)

triggers = IDPTriggers(blocks=[box_folder_trigger])

Cron Listener (v39)

Identifier: CRON_TRIGGER

Inputs:

Name	Type	Default Value	Title	Description
cron_spec	string	"*/5 * * * *"	Cron specification	N/A
time_zone	string	“US/Eastern”	Time Zone	N/A
api_params	object	{}	API Parameters	N/A

cron_trigger = IOBlock(
    identifier='CRON_TRIGGER',
    reference_name='cron_trigger',
    title='Cron Trigger',
    enabled=True,
    input={
        'cron_spec': '0 10 * * *',
        'time_zone': 'Europe/Sofia',
    }
)

triggers = IDPTriggers(blocks=[cron_trigger])

Salesforce Listener (v39)

Identifier: SALESFORCE_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
channel_name	string	N/A	Channel Name	Configured in the Hyperscience app in Salesforce.
username	string	N/A	Associated Username	Username associated with the Salesforce Connected App.
consumer_key	Password	N/A	Consumer Key	Consumer key used for authentication with Salesforce. Paired with the Salesforce instance from which it was issued.
private_key	Password	N/A	Private Key	Private key used for authentication with Salesforce.
sandbox_environment	boolean	false	Private Key	Enable if the Salesforce environment is a sandbox.
api_params	object	{}	API Parameters	N/A

Message Queue Listener (v39)

Identifier: MQ_LISTENER

Inputs:

Name	Type	Default Value	Title	Description
MQ_TYPE	string	“ACTIVE_MQ”	Message Queue type	Type of the message queue to connect with. Valid values are “ACTIVE_MQ”, “AMAZON_SQS”, “IBM_MQ” and “RABBIT_MQ”
MQ_USERNAME	string	null	Username/Access Key ID	N/A
MQ_PASSWORD	Password	null	Password/Secret Access Key	N/A
MQ_QUEUE_NAME	string	null	Queue Name	Name of the queue (topic) to connect to.
MQ_HOST	string	null	Host Name	N/A
MQ_PORT	integer	5672 when “RABBIT_MQ”, 1414 when “IBM_MQ”, otherwise null	Port Number	N/A
api_params	object	{}	API Parameters	N/A

Additional inputs when "AMAZON_SQS" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_REGION	string	null	AWS Region	N/A
MQ_QUEUE_URL	string	null	Queue URL	N/A
MQ_USE_EC2_INSTANCE_CREDENTIALS	boolean	True	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used.

NOTE: MQ_USERNAME can be used to provide Access Key ID and MQ_PASSWORD to provide Secret Access Key.

Additional inputs when "IBM_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_QUEUE_MANAGER	string	null	Queue Manager (IBM MQ)	N/A
MQ_SSL_CIPHER_SUITE [1]	string	null	SSL cipher suite	SSL cipher suite to use if SSL connection is used.
MQ_CHANNEL	string	null	Channel	N/A
MQ_NO_AUTH_REQUIRED	boolean	false	No auth credentials required	N/A
MQ_MQCSP_AUTHENTICATION_MODE	boolean	false	MQCSP authentication mode	N/A

NOTE: MQ_NO_AUTH_REQUIRED is True, MQ_USERNAME and MQ_PASSWORD are optional.

[1] one of:

"NULL_MD5"
"NULL_SHA"
"RC4_MD5_EXPORT"
"RC4_MD5_US"
"RC4_SHA_US"
"RC2_MD5_EXPORT"
"DES_SHA_EXPORT"
"RC4_56_SHA_EXPORT1024"
"DES_SHA_EXPORT1024"
"TRIPLE_DES_SHA_US"
"TLS_RSA_WITH_NULL_SHA256"
"TLS_RSA_WITH_AES_128_CBC_SHA"
"TLS_RSA_WITH_AES_128_CBC_SHA256"
"TLS_RSA_WITH_AES_256_CBC_SHA"
"TLS_RSA_WITH_AES_256_CBC_SHA256"
"AES_SHA_US"
"TLS_RSA_WITH_DES_CBC_SHA"
"TLS_RSA_WITH_3DES_EDE_CBC_SHA"
"TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256"
"FIPS_WITH_DES_CBC_SHA"
"FIPS_WITH_3DES_EDE_CBC_SHA"

Additional inputs when "RABBIT_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_VIRTUAL_HOST	string	null	Virtual Host (RABBIT MQ)	N/A
MQ_EXCHANGE	string	“”	Exchange	N/A
MQ_ROUTING_KEY	string	“”	Routing key	N/A
MQ_CONNECTION_TYPE	string	“TCP”	Connection Type	“TCP or SSL”

rabbit_mq_listener = IOBlock(
    identifier='MQ_LISTENER',
    reference_name='mq_listener',
    title='RabbitMQ Listener',
    enabled=True,
    input={
        'MQ_TYPE': 'RABBIT_MQ',
        'MQ_QUEUE_NAME': 'some_queue_name',
        'MQ_HOST': 'somehost.com',
        'MQ_USERNAME': 'foo',
        'MQ_PASSWORD': system_secret('rabbit_mq_1_password'),
    }
)

triggers = IDPTriggers(blocks=[rabbit_mq_listener])

Universal Folder Listener (v39)

Identifier: UNIVERSAL_FOLDER_TRIGGER

Inputs:

Name	Type	Default Value	Title	Description
folder	string	N/A	Folder to scan for submissions	Enter the path relative to the base folder. Leave blank to monitor the base folder
file_extensions	array	[]	File extensions	Types of file extensions for which to monitor
other_file_extensions	string	N/A	Other file extensions	Comma separated list of file extensions for which to monitor (e.g. ‘png, jpg, pdf’)
has_meta	boolean	false	Include submission level parameters	Select this to ingest JSON files along with document files and submission folders. These JSON files can contain information such as metadata, cases and external_id. These JSON file names should match the related files or folders (e.g., XXX.jpg.json for XXX.jpg)
poll_interval	integer	10	Polling interval (in seconds)	How often the Folder Listener will check the base folder for submissions
warmup_interval	integer	15	Warm-up interval (in seconds)	How long the Folder Listener will wait to process the document after it was last modified
folder_cleanup_delay	number	24	Empty folder cleanup delay (in hours)	How often the Folder Listener will remove empty folders from the base folder
api_params	object	{}	API Parameters	N/A

universal_folder_listener = IOBlock(
    identifier='UNIVERSAL_FOLDER_TRIGGER',
    reference_name='universal_folder_trigger',
    title='Universal Folder Listener',
    enabled=True,
    input={
        'folder': '/var/www/forms/forms/folder/',
        'file_extensions': 'png, pdf, jpg',
    }
)

triggers = IDPTriggers(blocks=[universal_folder_listener])

Kafka Listener (v39)

Identifier: KAFKA_TRIGGER

Inputs:

Name	Type	Default Value	Title	Description
bootstrap_servers	string	N/A	Bootstrap Servers	A Comma separated list of host:port pairs to use for establishing the initial connection to the cluster
group_id	string	N/A	Group Id	A unique string that identifies the consumer group this consumer belongs to. BLOCK_THREADS_KAFKA_TRIGGER should be used to scale up or down the number of consumers (default: number of available cores)
topics	string	N/A	Topics	Comma separated list of topic names that the block will subscribe to
consumer_config_json	json	N/A	Consumer Config	JSON formatted string containing key-value pairs of Kafka consumer configurations described in the official Kafka documentation
api_params	object	{}	API Parameters	N/A
consume_timeout	integer	-1	Consume Timeout	Maximum time, in seconds, that the consumer will wait for new messages. If messages are received within the specified time, they are processed immediately. However, if no messages are received within that specified time, consumption cycle will be restarted internally to continue fetching messages. This ensures continuous message consumption (default: infinite)
consume_num_messages	integer	1	Consume Num Messages	Sets the maximum number of messages the consumer fetches in a single consumption cycle.If the number of available messages is less than the specified, and timeout is reached, messages will be processed. Consumption cycle will be restarted after the current finishes

kafka_listener = IOBlock(
    identifier='KAFKA_TRIGGER',
    reference_name='kafka_trigger',
    title='Kafka Listener',
    enabled=True,
    input={
        'folder': '/var/www/forms/forms/folder/',
        'file_extensions': 'png, pdf, jpg',
         'bootstrap_servers': 'example.com',
         'group_id': 'test_group',
         'topics': 'test_topic1, test_topic2',
         'consumer_config_json': {},
         'number_of_workers': 1,
         'consume_timeout': 5,
         'consume_num_messages': 10
    }
)

triggers = IDPTriggers(blocks=[kafka_listener])

Output Blocks (v39)

Warning

Flows pass information to downstream systems using the IDPOutputsBlock. This block is specially built to allow full definition either in code or manually in the Flow Studio. We recommend defining outputs manually in the Flow Studio.

Instructions on how to do so are in our application documentation. Simply include an IDPOutputsBlock in your Python flow definition wherever you wish to send information to downstream systems.

class flows_sdk.implementations.idp_v39.idp_blocks.IDPOutputsBlock(inputs, blocks=None)

Bases: Outputs

Output block allows users to send data extracted by an IDP flow to other systems for downstream processing

Mandatory parameters:

Parameters:

inputs (Dict[str, Any]) – Used by the UI to automatically pre-populate the input of newly added blocks. Extended in this class to include 'enabled': True to visualize an enabled/disabled trigger that is controllable via Flow Studio.

Usage when parameters are to be defined in Flow Studio:

outputs = IDPOutputBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')}
)

If instead you wish to define specific outputs in Python, use the outputs definitions below.

HTTP Notifier Output (v39)

The HTTP Notifier (REST) output connection will POST results from the system to a specified HTTP endpoint.

Identifier: COB_HTTP_EXPORT

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
endpoint	string	N/A	Endpoint URL	URL that output notifications will be posted to
endpoint_secrets	string	N/A	Endpoint URL secrets	Endpoint URL Secrets, format: (one per line) secret_key_name=secret_value
timeout	int	600	Timeout (seconds)	How long (in seconds) to keep the connection open if no bytes are received from the endpoint. Set to 0 to wait indefinitely.
authorization_type [2]	string	“none”	Authorization type	Type of authorization
authorization_header_name	string	null	Authorization header name	Authorization header name to be set in the notification request
authorization_header	Password	null	Authorization header value	Authorization header to be set in the notification request
auth_url	string	null	OAuth2 authorization URL	The endpoint for the authorization server
client_id	string	null	Client ID	The client identifier issued to the client during the application registration process
client_secret	Password	null	Client Secret	The client secret issued to the client during the application registration process
scope	string	null	Scope	Scope to request to oauth server
audience	string	null	Audience	Resource service URL where token will be valid
additional_oauth_parameters	json	{}	Additional Oauth Request Parameters	Additional parameters to send when requesting token
ssl_cert_source [3]	string	null	Source	N/A
ssl_cert_path	string	null	Trusted Certificate(s) path	Enter the path relative to the base folder
ssl_cert_value	MultilineText	null	Trusted Certificate(s)	N/A
additional_notification_http_headers	json	null	Additional Notification Request Headers	Key value pairs in json format which will be sent via the request as http headers

[1] one of:

"v5"
"transformed"

[2] one of:

"none"
"http_header"
"oauth_2_client_credentials"

[3] one of:

"env_var"
"ca_bundle_path"
"certificate_value"

cob_http_export = IOBlock(
    identifier='COB_HTTP_EXPORT',
    reference_name='cob_http_export',
    title='HTTP Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'SUBMISSION',
        'endpoint': 'example.com',
        'authorization_type': 'none',
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_http_export],
)

Message Queue Notifier Output (v39)

The Message Queue Notifier Output can configure connections to ActiveMQ, Amazon SQS, IBM MQ, and RabbitMQ message queues.

Identifier: COB_MQ_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
MQ_TYPE	string	“ACTIVE_MQ”	Message Queue type	Type of the message queue to connect with. Valid values are “ACTIVE_MQ”, “AMAZON_SQS”, “IBM_MQ” and “RABBIT_MQ”
MQ_USERNAME	string	null	Username/Access Key ID	N/A
MQ_PASSWORD	Password	null	Password/Secret Access Key	N/A
MQ_QUEUE_NAME	string	null	Queue Name	Name of the queue (topic) to connect to. It applies only to “ACTIVE_MQ”, “IBM_MQ” and “RABBIT_MQ” MQ_TYPEs.
MQ_HOST	string	null	Host Name	N/A
MQ_PORT	integer	5672 when “RABBIT_MQ”, 1414 when “IBM_MQ”, otherwise null	Port Number	N/A

[1] one of:

"v5"
"transformed"

Additional inputs when "AMAZON_SQS" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_REGION	string	null	AWS Region	N/A
MQ_QUEUE_URL	string	null	Queue URL	N/A
MQ_USE_EC2_INSTANCE_CREDENTIALS	boolean	True	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used.
MQ_MESSAGE_GROUP_ID	string	null	Group ID for FIFO queues	N/A
MQ_MESSAGE_METADATA	string	null	Additional SQS Metadata	Input additional metadata key value pairs in the format of { “key”: {“TYPE”: “String”, “VALUE”: “”}, “key2”: {“TYPE”: “Number”, “VALUE”: “12345”}

NOTE: MQ_USERNAME can be used to provide Access Key ID and MQ_PASSWORD to provide Secret Access Key.

Additional inputs when "IBM_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_QUEUE_MANAGER	string	null	Queue Manager (IBM MQ)	N/A
MQ_SSL_CIPHER_SUITE [1]	string	null	SSL cipher suite	SSL cipher suite to use if SSL connection is used.
MQ_CHANNEL	string	null	Channel	N/A
MQ_NO_AUTH_REQUIRED	boolean	false	Channel	N/A
MQ_MQCSP_AUTHENTICATION_MODE	boolean	false	Channel	N/A

NOTE: MQ_NO_AUTH_REQUIRED is True, MQ_USERNAME and MQ_PASSWORD are optional.

[1] one of:

"NULL_MD5"
"NULL_SHA"
"RC4_MD5_EXPORT"
"RC4_MD5_US"
"RC4_SHA_US"
"RC2_MD5_EXPORT"
"DES_SHA_EXPORT"
"RC4_56_SHA_EXPORT1024"
"DES_SHA_EXPORT1024"
"TRIPLE_DES_SHA_US"
"TLS_RSA_WITH_NULL_SHA256"
"TLS_RSA_WITH_AES_128_CBC_SHA"
"TLS_RSA_WITH_AES_128_CBC_SHA256"
"TLS_RSA_WITH_AES_256_CBC_SHA"
"TLS_RSA_WITH_AES_256_CBC_SHA256"
"AES_SHA_US"
"TLS_RSA_WITH_DES_CBC_SHA"
"TLS_RSA_WITH_3DES_EDE_CBC_SHA"
"TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256"
"FIPS_WITH_DES_CBC_SHA"
"FIPS_WITH_3DES_EDE_CBC_SHA"

Additional inputs when "RABBIT_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_VIRTUAL_HOST	string	null	Virtual Host (RABBIT MQ)	N/A
MQ_EXCHANGE	string	“”	Exchange	N/A
MQ_ROUTING_KEY	string	“”	Routing key	N/A
MQ_CONNECTION_TYPE	string	“TCP”	Connection Type	“TCP or SSL”

mq_notifier = IOBlock(
    identifier='COB_MQ_NOTIFIER',
    reference_name='mq_notifier',
    title='MQ Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'SUBMISSION',
        'MQ_USERNAME': 'admin',
        'MQ_PASSWORD': 'pass',
        'MQ_QUEUE_NAME': 'queue',
        'MQ_HOST': 'host',
    },
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[mq_notifier],
)

Box Notifier Output (v39)

Provides an out-of-the-box integration into Box systems.

Identifier: COB_BOX_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
public_key_id	string	N/A	Public-Key ID	ID of the Public-key used for authentication with Box
private_key	Password	N/A	Private-Key	Private key used for authentication with Box
passphrase	Password	N/A	Passphrase	Passphrase used for authentication with Box
client_id	string	N/A	Client ID	Client Id used for authentication with Box
client_secret	Password	N/A	Client Secret	Client Secret used for authentication with Box
enterprise_id	string	N/A	Enterprise ID	Enterprise Id used for authentication with Box
template_key	string	“”	Box Metadata Template Key	Enter the key of the Box Metadata template that you would like to map the Hyperscience metadata to.
static_output_fields_to_map	array	[]	Static Metadata Fields	Specify the Hyperscience fields you want to store in Box metadata.
submission_id	string	“”	Key for Mapping Submission ID	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_id	string	“”	Key for Mapping Document ID	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
submission_state	string	“”	Key for Mapping Submission State	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_state	string	“”	Key for Mapping Document State	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
submission_exceptions	string	“”	Key for Mapping Submission Exceptions	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_exceptions	string	“”	Key for Mapping Document Exceptions	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_layout_uuid	string	“”	Key for Mapping Document Layout Uuid	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_layout_name	string	“”	Key for Mapping Document Layout Name	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
start_time	string	“”	Key for Mapping Start Time	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
complete_time	string	“”	Key for Mapping Complete Time	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_fields_template_mappings	json	{}	JSON for Additional Metadata Fields	Specify additional metadata fields using their key value. Please consult Box Integration setup manual for JSON template and instructions.

[1] one of:

"v5"
"transformed"

box_notifier = IOBlock(
    identifier='COB_BOX_NOTIFIER',
    reference_name='box_notifier',
    title='Box Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'private_key': 'password',
        'passphrase': 'password',
        'public_key_id': 'admin',
        'client_id': 'admin',
        'client_secret': 'secret',
        'enterprise_id': 'admin',
        'template_key': 'key',
    },
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[box_notifier],
)

UiPath Notifier Output (v39)

Provides an out-of-the-box integration into UiPath systems.

Identifier: COB_EXPORT_UIPATH

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
auth_method [2]	string	“basic_auth”	Authentication method	N/A
url	string	N/A	URL	Base URL of the uipath instance
app_id	string	N/A	App ID	App ID provided by UiPath
app_secret	Password	N/A	App Secret	App Secret provided by UiPath
oauth_token_endpoint	string	N/A	OAuth token endpoint	Endpoint to retrieve the token. Using https://cloud.uipath.com/identity_/connect/token by default
organization_unit_id	string	N/A	Organization unit id	Organization unit id
tenant	string	N/A	Tenant	Tenant
username	string	N/A	Username	Username
password	Password	N/A	Password	Password
queue	string	N/A	Queue	Queue

[1] one of:

"v5"
"transformed"

[2] one of:

"basic_auth"
"oauth"

cob_export_uipath = IOBlock(
    identifier='COB_EXPORT_UIPATH',
    reference_name='cob_export_uipath',
    title='CCB Export UiPath',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'FLAT_SUBMISSION',
        'url': 'example.com',
        'tenant': 'tenant',
        'username': 'admin',
        'password': 'pass',
        'queue': 'queue',
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_export_uipath],
)

Salesforce Notifier Output (v39)

With the Salesforce Notifier Block, you can configure your flow to send extracted information to Salesforce. The Salesforce Notifier Block can use extracted information to look up, and then update any number of fields on a Salesforce object. The Salesforce Notifier Block is available in both SaaS and on-premise versions of the Hyperscience application.

Identifier: COB_SALESFORCE_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
username	string	N/A	Associated Username	Username associated with the Salesforce Connected App
consumer_key	password	N/A	Consumer Key	Consumer key used for authentication with Salesforce. Paired with the Salesforce instance from which it was issued.
private_key	password	N/A	Private Key	Private key used for authentication with Salesforce.
sandbox_environment	boolean	false	Private Key	Enable if the Salesforce environment is a sandbox.
object_api_name	string	N/A	Object API Name	API name of the object in Salesforce to lookup and update.
new_record_on_lookup_failure	boolean	false	Create new record on lookup failure	When enabled, it will create a new record if the lookup fails to find a record using the lookup parameters. When disabled, lookup will fail if it does not find a record using the lookup parameters.
object_lookup_mapping	json	N/A	Lookup Field Mapping	Specify which Salesforce fields (using their API name) to be queried with which extracted values from the document for the lookup using key-value format. For example: “DOCUMENT_FIELD_NAME”: “OBJECT_FIELD_API_NAME”.
document_fields_mapping	json	N/A	Document Field Mappings	Specify which Salesforce fields (using their API name) you want to update with which extracted data from Hyperscience using key-value format. For example: “DOCUMENT_FIELD_NAME”: “OBJECT_FIELD_API_NAME”.”
overwrite_existing_field_data	boolean	false	Overwrite existing field data	If this is checked and a selected field has data in it, Hyperscience will overwrite that data. If this is unchecked, existing data will not be overwritten and the operation will fail.

[1] one of:

"v5"
"transformed"

cob_salesforce_notifier = IOBlock(
    identifier='COB_SALESFORCE_NOTIFIER',
    reference_name='cob_salesforce_notifier',
    title='Salesforce Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'username': 'salesforce_user',
        'consumer_key': 'salesforce_consumer_key',
        'private_key': 'salesforce_private_key',
        'object_api_name': 'salesforce_object_api_name',
        'object_lookup_mapping': {"LAYOUT_VARIATION_XYZ_FIELD_NAME_1": "SF_FIELD_API_NAME_1", "LAYOUT_VARIATION_XYZ_FIELD_NAME_2": "SF_FIELD_API_NAME_2"},
        'document_fields_mapping': {"LAYOUT_VARIATION_XYZ_FIELD_NAME_3": "SF_FIELD_API_NAME_3", "LAYOUT_VARIATION_XYZ_FIELD_NAME_4": "SF_FIELD_API_NAME_4"}
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_salesforce_notifier],
)

S3 Notifier Output (v39)

With the S3 Notifier Block, you can configure your flow to send extracted information to an S3 bucket.

Identifier: COB_S3_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
bucket_uri	string	N/A	S3 URI	S3 URI, where the data should be stored (e.g. s3://hs_bucket/prefix1/prefix2/)
aws_access_key_id	string	N/A	AWS ACCESS KEY ID	Access Key used for authentication
aws_secret_access_key	password	N/A	SECRET ACCESS KEY	Secret Key used for authentication
use_ec2_instance_credentials	boolean	true	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used

[1] one of:

"v5"
"transformed"

cob_s3_notifier = IOBlock(
    identifier='COB_S3_NOTIFIER',
    reference_name='cob_s3_notifier',
    title='S3 Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'FLAT_SUBMISSION',
        'bucket_uri': 's3://hs_bucket/prefix1/prefix2/',
        'aws_access_key_id': 'aws_access',
        'aws_secret_access_key': system_secret('aws_secret_access_key'),
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_s3_notifier],
)

Kafka Notifier Output (v39)

With the Kafka Notifier Block, you can configure your flow to send extracted information to a kafka topic.

Identifier: COB_KAFKA_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
bootstrap_servers	string	N/A	Bootstrap Servers	A list of Kafka brokers to connect to. For example: “kafka1:9092,kafka2:9092”
topic	string	N/A	Topic	The Kafka topic where your data will be sent
client_id	string	N/A	SECRET ACCESS KEY	An identifier for your application when connecting to Kafka, typically used for tracking and logging purposes
producer_config_json	json	true	Producer Config (JSON)	Configuration settings for the Kafka producer in JSON format. See https://kafka.apache.org/documentation/#producerconfigs for more information
synchronous_publish	boolean	true	Sync Publish	If true, the publish call will block until the message is sent successfully or an error is raised. If false, the publish call will return immediately after enqueueing the message
message_key	string	null	Message Key	The key associated with the message to be sent. This is used to determine which partition the message is sent to. If null, the message will be sent to a random partition

[1] one of:

"v5"
"transformed"

cob_kafka_notifier = IOBlock(
    identifier='COB_KAFKA_NOTIFIER',
    reference_name='cob_kafka_notifier',
    title='Kafka Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'FLAT_SUBMISSION',
        'bootstrap_servers': 'kafka1:9092,kafka2:9092',
        'topic': 'topic',
        'client_id': 'client_id',
        'producer_config_json': {},
        'synchronous_publish': True,
        'message_key': 'key'
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_kafka_notifier],
)

Helper Classes and Functions (v39)

The following classes are used to easily instantiate and manage a flow’s settings.

flows_sdk.implementations.idp_v39.idp_values.get_idp_wf_inputs(idp_wf_config)

A helper function that maps the values provided by an IdpWorkflowConfig to their corresponding IDP keys.

Parameters:

idp_wf_config (IdpWorkflowConfig) – with static values to be filled.

Return type:

Dict[str, Any]

Returns:

a dictionary with filled static values, compatible with IDP flows.

Example usage with default values:

flow = Flow(
    input=get_idp_wf_inputs(get_idp_wf_config()),
    ...
)

Example usage:

Example usage when instantiating an IDP flow with custom parameters::

flow = Flow(
    input=get_idp_wf_inputs({an instance of IdpWorkflowConfig}),
    ...
)

flows_sdk.implementations.idp_v39.idp_values.get_idp_wf_config()

A helper function that instantiates an IdpWorkflowConfig pre-filled with default values. IdpWorkflowConfig is the container class for defining flow-level settings.

Return type:

IdpWorkflowConfig

Returns:

a valid, fully constructed IdpWorkflowConfig instance

Example usage when instantiating an IDP flow:

flow = Flow(
    input=get_idp_wf_inputs(get_idp_wf_config()),
    ...
)

class flows_sdk.implementations.idp_v39.idp_values.IdpWorkflowConfig

Convenience dataclass outlining all flow-level config values for IDP.

class flows_sdk.implementations.idp_v39.idp_values.IDPTriggers(blocks=None)

An adapter class making it easier to instantiate flows_sdk.flows.Triggers for IDP flows. In particular, provides defaults for some fields (e.g. title, description, reference_name) and defines api_params_manifest with IDP specific parameters.

Parameters:

blocks (Optional[Sequence[IOBlock]]) – flows_sdk.blocks.IOBlock s to be included as triggers for an IDP Flow. Optional, defaults to an empty list.

Example usage when instantiating an IDP flow:

folder_listener = IOBlock(...)
flow = Flow(
    triggers=IDPTriggers([folder_listener]),
    ...
)

class flows_sdk.implementations.idp_v39.idp_values.IDPWrapperManifest(flow_identifier)

An adapter class making it easier to instantiate flows_sdk.flows.Manifest for IDP wrapper flows. In particular, provides defaults for some fields (e.g. roles) but more importantly, defines all flows_sdk.flows.Parameter that construct the Flow inputs and their UI representation (left-hand side menu in Flow studio).

Parameters:

flow_identifier (str) – A system-wide unique identifier for flows_sdk.flows.Flow

Example usage when instantiating an IDP flow:

flow = Flow(
    manifest=IDPWrapperManifest(flow_identifier='FLOW_IDENTIFIER'),
    ...
)

class flows_sdk.implementations.idp_v39.idp_values.IDPCoreManifest(flow_identifier, roles=None)

An adapter class making it easier to instantiate flows_sdk.flows.Manifest for IDP Core flows. In particular, provides defaults for some fields (e.g. roles, output) but more importantly, defines all flows_sdk.flows.Parameter that construct the Flow inputs and their UI representation (left-hand side menu in Flow studio).

Parameters:

• flow_identifier (str) – A system-wide unique identifier for flows_sdk.flows.Flow

• roles (Optional[List[str]]) – ‘read_only’, ‘supporting’ and ‘idp_core’ are the default roles of every subflow that can replace the default IDP_CORE subflow inside the top-level IDP flows. ‘supporting’ and ‘idp_core’ are mandatory roles that are necessary for all IDP Core flows. ‘supporting’ ensures that the subflow will be auto-deployed together with the top-level IDP flow. ‘idp_core’ ensures that this subflow can be selected in the UI in dropdowns which allow picking an alternative subflow to the default IDP_CORE subflow. If you are implementing a subflow that is not supposed to be a drop-in replacement for IDP_CORE, you will have to subclass or implement your own version of IDPCoreManifest class in order to remove the idp_core role.

Example usage when instantiating an IDP flow:

flow = Flow(
    manifest=IDPCoreManifest(flow_identifier='FLOW_IDENTIFIER'),
    ...
)

V38

Processing Blocks (v38)

Core Blocks are powerful processing blocks necessary to build intelligent document processing solutions on Hyperscience. They include both manual and machine processing blocks.

Note

Classes in the idp_v38 implementation have parameters with ambiguous typing. For example, a field of type integer could also be represented as a string reference (e.g. that integer gets provided at runtime). The types introduced in the example are therefore incomplete and are likely to be revisioned in a future version.

class flows_sdk.implementations.idp_v38.idp_blocks.IDPCoreBlock(idp_wf_config, reference_name='__idp_core__', title='Start Document Processing Subflow', description='Calls the Document Processing Subflow with the specified settings.', compatibility_spec=CompatibilitySpec(filter_roles=['idp_core'], filter_schema=None, label=None), identifier='IDP_CORE_V38', additional_inputs=None)

Bases: Block

IDP core block includes the steps to process the passed documents into machine-readable output.

Mandatory parameters:

Parameters:

idp_wf_config (IdpWorkflowConfig) – IdpWorkflowConfig object with all flow-level configuration settings for IDP

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• compatibility_spec (Optional[CompatibilitySpec]) – defines a filter for flows that could be selected in the UI in place of “IDP Core” for the block. Defaults to flows with the role ‘idp_core’. The user will be able to pick any flow that matches the compatibility_spec. This serves to make upgrading to a new compatible version of “IDP Core” (e.g. IDP_CORE_V39) easier in future releases.

• identifier (str) – The identifier of the flow. Defaults to the IDP_CORE_VXX identifier of the out-of-the-box flow that comes with the system.

• additional_inputs (Optional[Dict[str, Any]]) – Dictionary containing key-value pairs that will be added to the inputs of the block. If a key is contained in the default inputs, its value will be overwritten by the value provided here. You generally do not need to provide additional_inputs, unless you are creating a custom derivative of the IDP_CODE_Vxx flow and are modifying or extending the list of inputs it accepts.

Example usage:

idp_core = IDPCoreBlock(idp_wf_config=idp_wf_config)

REFERENCE_NAME = '__idp_core__'

class flows_sdk.implementations.idp_v38.idp_blocks.SubmissionBootstrapBlock(reference_name=None, layout_release_uuid='${workflow.input.layout_release_uuid}', s3_config='${workflow.input.submission_bootstrap_s3_config}', s3_endpoint_url='${workflow.input.submission_bootstrap_s3_endpoint_url}', ocs_config='${workflow.input.submission_bootstrap_ocs_config}', http_config='${workflow.input.submission_bootstrap_http_config}', notification_workflow='${workflow.input.notification_workflow_initialization}', workflow_uuid='${workflow.input.workflow_uuid}', workflow_name='${workflow.input.workflow_name}', workflow_version='${workflow.input.workflow_version}', submission='${workflow.input.submission}')

Bases: Block

Submission bootstrap block initializes the submission object and prepares external images or other submission data if needed. This block should be called at the START of every flow that uses blocks included in the library.

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• s3_config (str) – Specify Amazon S3 credentials for submission retrieval store. Expects a json expression: {"aws_access_key_id": "X", "aws_secret_access_key": "Y"}, defaults to system_secret(S3_SECRET_KEY)

• s3_endpoint_url (str) – Endpoint URL for S3 submission retrieval store, defaults to None

• ocs_config (str) – OCS Configuration for downloading submission data, defaults to system_secret(OCS_SECRET_KEY)

• http_config (Optional[str]) – An optional system secret field expressing the HTTP Configuration for downloading submission data. The content is expected to be a json formatted as: {"username": "X", "password": "Y", "ssl_cert": "CA bundle filename"}, defaults to None

• notification_workflow (str) – Notification flow name to run when the submission is initialized, defaults to IDP_SUBMISSION_NOTIFY_NAME

• workflow_uuid (str) – UUID of the triggered workflow version, defaults to workflow_input(Inputs.WorkflowUuid)

• workflow_name (str) – Name of the triggered workflow, defaults to workflow_input(Inputs.WorkflowName)

• workflow_version (str) – Version of the triggered workflow, defaults to workflow_input(Inputs.WorkflowVersion)

Example usage:

submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')

IDENTIFIER = 'SUBMISSION_BOOTSTRAP_2'

output(key=None)

Submission bootstrap ends with a CodeBlock, which nests the actual outputs under the ‘result’ key. This method overrides the default output in the same manner that flows_sdk.blocks.Routing.CodeBlock does for convenience.

Parameters:

key (Optional[str]) –

Optionally provide a key to directly get a nested property. When not provided, the entire ‘result’ will be returned. For example, we have:

{
    "result": {
        "a": {
            "b": 42
        }
    }
}

• skipping the key output()` will return ``{"a": {"b": 42}}

• calling with output("a") will result in {"b": 42}

• calling with output("a.b") will return 42

Return type:

str

Returns:

the value under the provided key

class flows_sdk.implementations.idp_v38.idp_blocks.MachineCollationBlock(submission, cases, dedupe_files='${workflow.input.machine_collation_dedupe_files}', refresh_retention_period='${workflow.input.machine_collation_refresh_retention_period}', remove_from_cases='${workflow.input.machine_collation_remove_from_cases}', retention_period='${workflow.input.machine_collation_retention_period}', reference_name=None)

Bases: Block

Machine collation block groups files, documents and pages (from the submission) into cases.

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• cases (Any) –

  This parameter accepts an array of JSON objects that contain information on how Cases should be created. There are two ways to collate a case: by the filenames specified in the current Submission, or by the ids of the Documents or Pages submitted in previous submissions.

  This parameter follows the given format:

  {
      'cases': [
          'external_case_id': 'HS-1',
          'filename': 'file1.pdf',
          'documents': [42],
          'pages': [43]
      ]
  }
  

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• dedupe_files (Union[bool, str]) – Enabling this setting will replace case data from repeated file names within the same case. Cases will retain data from the most recently submitted version of a particular file. Note that this setting does not delete the old data, it just removes it from the case. Keep in mind this option is only relevant when adding to cases

• remove_from_cases (Optional[Any]) –

  This parameter accepts an array of JSON objects that contains information on how documents or pages should be removed from a pre-existing case. Currently, the only way to de-collate from a case is by the ids of the Documents or Pages within a given case

  This parameter follows the given format:

  {
      'remove_from_cases': [
          'external_case_id': 'HS-1',
          'documents': [42],
          'pages': [43]
      ]
  }
  

• retention_period (Union[int, str, None]) – The number of days to retain cases after they are updated by this block. If passed None, no changes to the deletion date will be made. By default, this value is None

• refresh_retention_period (Union[bool, str]) – If True, (re)applies Retention Period parameter to all cases passed into the block. If False, only updates those without a pre-existing deletion date. By default, this value is True

Example usage:

machine_collation = MachineCollationBlock(
    reference_name='machine_collation',
    submission=submission_bootstrap.output('result.submission'),
    cases=submission_bootstrap.output('result.api_params.cases'),
    dedupe_files=True,
    remove_from_cases=custom_supervision.output('remove_from_cases')
)

IDENTIFIER = 'MACHINE_COLLATION_2'

class flows_sdk.implementations.idp_v38.idp_blocks.MachineClassificationBlock(submission, api_params, reference_name=None, rotation_correction_enabled='${workflow.input.machine_classification_rotation_correction_enabled}', mobile_processing_enabled='${workflow.input.machine_classification_mobile_processing_enabled}', file_transcription_enabled='${workflow.input.machine_classification_file_transcription_enabled}', layout_release_uuid='${workflow.input.layout_release_uuid}', vpc_registration_threshold='${workflow.input.structured_layout_match_threshold}', nlc_enabled='${workflow.input.semi_structured_classification}', nlc_target_accuracy='${workflow.input.semi_target_accuracy}', nlc_doc_grouping_logic='${workflow.input.semi_doc_grouping_logic}')

Bases: Block

Machine classification block automatically matches documents to structured, semi-structured or additional layouts

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• rotation_correction_enabled (Union[bool, str]) – Identifies and corrects the orientation of semi-structured images, defaults to True

• mobile_processing_enabled (Union[bool, str]) – Improves the machine readability of photo captured documents, defaults to False

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• vpc_registration_threshold (str) – Structured pages above this threshold will be automatically matched to a layout variation, defaults to workflow_input(Settings.StructuredLayoutMatchThreshold)

• nlc_enabled (str) – Enables workflow to manage a model for automated classification of semi-structured and additional layout variations, defaults to workflow_input(Settings.SemiStructuredClassification)

• nlc_target_accuracy (str) – defaults to workflow_input(Settings.SemiTargetAccuracy)

• nlc_doc_grouping_logic (str) – Logic to handle multiple pages matched to the same layout variationin a given submission, defaults to workflow_input(Settings.SemiDocGroupingLogic)

Example usage:

machine_classification = MachineClassificationBlock(
    reference_name='machine_classification',
    submission=case_collation.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    rotation_correction_enabled=idp_wf_config.rotation_correction_enabled,
)

IDENTIFIER = 'MACHINE_CLASSIFICATION_5'

class flows_sdk.implementations.idp_v38.idp_blocks.ManualClassificationBlock(submission, api_params, reference_name=None, layout_release_uuid='${workflow.input.layout_release_uuid}', manual_nlc_enabled='${workflow.input.manual_nlc_enabled}', task_restrictions='${workflow.input.manual_classification_task_restrictions}', notification_workflow='${workflow.input.notification_workflow_classification}', nlc_enabled='${workflow.input.semi_structured_classification}', nlc_target_accuracy='${workflow.input.semi_target_accuracy}', mobile_processing_enabled='${workflow.input.machine_classification_mobile_processing_enabled}')

Bases: Block

Manual classification block allows keyers to manually match submissions to their layouts. Keyers may perform manual classification if machine classification cannot automatically match a submission to a layout with high confidence

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• manual_nlc_enabled (str) – Enables manual classification when applicable, defaults to workflow_input(Settings.ManualNlcEnabled)

• task_restrictions (Union[List[Any], str, None]) – Defines what users can access Supervision tasks created by this block, defaults to None

• notification_workflow (str) – Notification flow name to run when the submission enters Manual Classification, defaults to IDP_SUBMISSION_NOTIFY_NAME

Example usage:

manual_classification = ManualClassificationBlock(
    reference_name='manual_classification',
    submission=machine_classification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
)

IDENTIFIER = 'MANUAL_CLASSIFICATION_2'

class flows_sdk.implementations.idp_v38.idp_blocks.MachineIdentificationBlock(submission, api_params, layout_release_uuid='${workflow.input.layout_release_uuid}', reference_name=None, manual_field_id_enabled='${workflow.input.manual_field_id_enabled}', field_id_target_accuracy='${workflow.input.field_id_target_accuracy}', table_id_target_accuracy='${workflow.input.table_id_target_accuracy}')

Bases: Block

Machine identification automatically identify fields and tables in the submission

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• manual_field_id_enabled (str) – Enables manual identification when applicable, defaults to workflow_input(Settings.ManualFieldIdEnabled)

• field_id_target_accuracy (str) – Field ID Target Accuracy, defaults to workflow_input(Settings.FieldIdTargetAccuracy)

• table_id_target_accuracy (str) – Table ID Target Accuracy, defaults to workflow_input(Settings.TableIdTargetAccuracy)

Example usage:

machine_identification = MachineIdentificationBlock(
    reference_name='machine_identification',
    submission=manual_classification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
)

IDENTIFIER = 'MACHINE_IDENTIFICATION_6'

class flows_sdk.implementations.idp_v38.idp_blocks.ManualIdentificationBlock(submission, api_params, reference_name=None, task_restrictions='${workflow.input.manual_identification_task_restrictions}', manual_field_id_enabled='${workflow.input.manual_field_id_enabled}', layout_release_uuid='${workflow.input.layout_release_uuid}', notification_workflow='${workflow.input.notification_workflow_identification}')

Bases: Block

Manual identification allows keyers to complete field identification or table identification tasks, where they draw bounding boxes around the contents of certain fields, table columns or table rows. This identification process ensures that the system will be able to transcribe the correct content in the upcoming transcription process

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• task_restrictions (Union[List[Any], str, None]) – Defines what users can access Supervision tasks created by this block, defaults to []

• manual_field_id_enabled (str) – Enables manual identification when applicable, defaults to workflow_input(Settings.ManualFieldIdEnabled)

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• notification_workflow (str) – Notification flow name to run when the submission enters Manual Identification, defaults to DEFAULT_IDP_SUBMISSION_NOTIFY_IDENTIFIER

Example usage:

manual_identification = ManualIdentificationBlock(
    reference_name='manual_identification',
    submission=machine_identification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    task_restrictions=idp_wf_config.manual_identification_config.task_restrictions,
)

IDENTIFIER = 'MANUAL_IDENTIFICATION_5'

class flows_sdk.implementations.idp_v38.idp_blocks.MachineTranscriptionBlock(submission, api_params, reference_name=None, layout_release_uuid='${workflow.input.layout_release_uuid}', transcription_model='${workflow.input.transcription_model}', finetuning_only_trained_layouts='${workflow.input.finetuning_only_trained_layouts}', transcription_overrides='${workflow.input.transcription_overrides}', structured_text_target_accuracy='${workflow.input.structured_text_target_accuracy}', structured_text_confidence_threshold='${workflow.input.structured_text_threshold}', structured_text_acceptable_confidence='${workflow.input.structured_min_leg_threshold}', semi_structured_text_target_accuracy='${workflow.input.semi_structured_text_target_accuracy}', semi_structured_text_confidence_threshold='${workflow.input.semi_structured_text_threshold}', semi_structured_text_acceptable_confidence='${workflow.input.semi_structured_min_leg_threshold}', semi_structured_table_target_accuracy='${workflow.input.semi_structured_table_target_accuracy}', semi_structured_table_confidence_threshold='${workflow.input.semi_structured_table_threshold}', semi_structured_table_acceptable_confidence='${workflow.input.semi_structured_table_min_leg_threshold}', structured_checkbox_target_accuracy='${workflow.input.structured_checkbox_target_accuracy}', structured_checkbox_confidence_threshold='${workflow.input.structured_checkbox_threshold}', structured_checkbox_acceptable_confidence='${workflow.input.checkbox_min_leg_threshold}', structured_signature_target_accuracy='${workflow.input.structured_signature_target_accuracy}', structured_signature_confidence_threshold='${workflow.input.structured_signature_threshold}', structured_signature_acceptable_confidence='${workflow.input.signature_min_leg_threshold}', semi_structured_checkbox_target_accuracy='${workflow.input.semi_structured_checkbox_target_accuracy}', semi_structured_checkbox_confidence_threshold='${workflow.input.semi_structured_checkbox_threshold}', semi_structured_checkbox_acceptable_confidence='${workflow.input.semi_structured_checkbox_min_leg_threshold}')

Bases: Block

Machine transcription automatically transcribes the content of your submission

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• transcription_model (str) – The transcription model that will be used for this flow, defaults to workflow_input(Settings.TranscriptionModel)

• finetuning_only_trained_layouts (str) – defaults to workflow_input( Settings.FinetuningOnlyTrainedLayouts )

• transcription_overrides (str) – defaults to workflow_input( Settings.TranscriptionOverrides )

• structured_text_target_accuracy (str) – defaults to workflow_input( Settings.StructuredTextTargetAccuracy )

• structured_text_confidence_threshold (str) – defaults to workflow_input( Settings.StructuredTextThreshold )

• structured_text_acceptable_confidence (str) – defaults to workflow_input( Settings.StructuredTextMinLegThreshold )

• semi_structured_text_target_accuracy (str) – defaults to workflow_input( Settings.SemiStructuredTextTargetAccuracy )

• semi_structured_text_confidence_threshold (str) – defaults to workflow_input( Settings.SemiStructuredTextThreshold )

• semi_structured_text_acceptable_confidence (str) – defaults to workflow_input( Settings.SemiStructuredTextMinLegThreshold )

• structured_checkbox_target_accuracy (str) – defaults to workflow_input( Settings.StructuredCheckboxTargetAccuracy )

• structured_checkbox_confidence_threshold (str) – defaults to workflow_input( Settings.StructuredCheckboxThreshold )

• structured_checkbox_acceptable_confidence (str) – defaults to workflow_input( Settings.StructuredCheckboxMinLegThreshold )

• structured_signature_target_accuracy (str) – defaults to workflow_input( Settings.StructuredSignatureTargetAccuracy )

• structured_signature_confidence_threshold (str) – defaults to workflow_input( Settings.StructuredSignatureThreshold )

• structured_signature_acceptable_confidence (str) – defaults to workflow_input( Settings.StructuredSignatureMinLegThreshold )

• semi_structured_checkbox_target_accuracy (str) – defaults to workflow_input( Settings.SemiStructuredCheckboxTargetAccuracy )

• semi_structured_checkbox_confidence_threshold (str) – defaults to workflow_input( Settings.SemiStructuredCheckboxThreshold )

• semi_structured_checkbox_acceptable_confidence (str) – defaults to workflow_input( Settings.SemiStructuredCheckboxMinLegThreshold )

Example usage:

machine_transcription = MachineTranscriptionBlock(
    reference_name='machine_transcription',
    submission=manual_identification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
)

IDENTIFIER = 'MACHINE_TRANSCRIPTION_9'

class flows_sdk.implementations.idp_v38.idp_blocks.ManualTranscriptionBlock(submission, api_params, reference_name=None, supervision_transcription_masking='${workflow.input.manual_transcription_supervision_transcription_masking}', table_output_manual_review='${workflow.input.manual_transcription_table_output_manual_review}', always_supervise_blank_cells='${workflow.input.manual_transcription_always_supervise_blank_cells}', task_restrictions='${workflow.input.manual_transcription_task_restrictions}', manual_transcription_enabled='${workflow.input.manual_transcription_enabled}', notification_workflow='${workflow.input.notification_workflow_transcription}', title='Manual Transcription', description='Manually transcribe fields that could not be automatically transcribed')

Bases: Block

Manual transcription lets your keyers manually enter the text found in fields or tables that could not be automatically transcribed

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (Union[bool, str]) – Prevents users from inputting invalid characters during Supervision Transcription tasks, defaults to True

• table_output_manual_review (Union[bool, str]) – Always generates a table transcription task if the layout contains a table. If disabled, a table transcription task will only be generated if one or more cells have transcribed values below the defined thresholds, defaults to False

• task_restrictions (Union[List[Any], str, None]) – Defines what users can access Supervision tasks for submission from a particular source or submissions matching a specific layout, defaults to []

• manual_transcription_enabled (str) – Enables manual transcription when applicable, defaults to workflow_input(Settings.ManualTranscriptionEnabled)

• notification_workflow (str) – Notification flow name to run when the submission enters Manual Transcription, defaults to DEFAULT_IDP_SUBMISSION_NOTIFY_IDENTIFIER

Example usage:

manual_transcription = ManualTranscriptionBlock(
    reference_name='manual_transcription',
    submission=machine_transcription.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    supervision_transcription_masking=(
        idp_wf_config.manual_transcription_config.supervision_transcription_masking
    ),
    table_output_manual_review=(
        idp_wf_config.manual_transcription_config.table_output_manual_review
    ),
    task_restrictions=idp_wf_config.manual_transcription_config.task_restrictions,
)

IDENTIFIER = 'MANUAL_TRANSCRIPTION_5'

class flows_sdk.implementations.idp_v38.idp_blocks.FlexibleExtractionBlock(submission, api_params, reference_name=None, supervision_transcription_masking='${workflow.input.flexible_extraction_supervision_transcription_masking}', task_restrictions='${workflow.input.flexible_extraction_task_restrictions}', layout_release_uuid='${workflow.input.layout_release_uuid}', notification_workflow='${workflow.input.notification_workflow_flexible_extraction}')

Bases: Block

Flexible extraction manually transcribes fields marked for review

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (Union[bool, str, None]) – Prevents users from inputting invalid characters during the Flexible Extraction task, defaults to True

• task_restrictions (Union[List[Any], str, None]) – Defines what users can access Supervision tasks created by this block, defaults to []

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• notification_workflow (str) – Notification flow name to run when the submission enters Flexible Extraction, defaults to DEFAULT_IDP_SUBMISSION_NOTIFY_IDENTIFIER

Example usage:

flexible_extraction = FlexibleExtractionBlock(
    reference_name='flexible_extraction',
    submission=manual_transcription.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    supervision_transcription_masking=(
        idp_wf_config.flexible_extraction_config.supervision_transcription_masking
    ),
    task_restrictions=idp_wf_config.flexible_extraction_config.task_restrictions,
)

IDENTIFIER = 'FLEXIBLE_EXTRACTION_4'

class flows_sdk.implementations.idp_v38.idp_blocks.ReprocessingBlock(reference_name, submission, parent_flow='${workflow.definition.identifier}', idp_wf_config=None, trigger='${workflow.input.$trigger}')

Bases: Block

Reprocessing block enables a second extraction attempt if documents were initially confidently mis-classified by the machine. When an end-user specifies that a document layout is incorrect during Manual Identification, Manual Transcription, or Flexible Extraction, the flow will reach the Reprocessing block which will mark correctly classified documents as “complete” status, and misclassified documents with “reprocess” status will be processed in a second execution of the flow. This block should be placed before the SubmissionCompleteBlock to enable reprocessing.

Mandatory parameters:

Parameters:

submission (Any) – Submission object

IDENTIFIER = 'REPROCESSING'

class flows_sdk.implementations.idp_v38.idp_blocks.SubmissionCompleteBlock(submission, is_reprocessing=False, reference_name=None, nlc_qa_sampling_ratio='${workflow.input.semi_qa_sample_rate}', field_id_qa_enabled='${workflow.input.field_id_qa_enabled}', field_id_qa_sampling_ratio='${workflow.input.field_id_qa_sample_rate}', table_id_qa_enabled='${workflow.input.table_id_qa_enabled}', table_id_qa_sampling_ratio='${workflow.input.table_id_qa_sample_rate}', transcription_qa_enabled='${workflow.input.qa}', transcription_structured_entry_qa_sample_rate='${workflow.input.structured_entry_qa_sample_rate}', transcription_structured_signature_qa_sample_rate='${workflow.input.structured_signature_qa_sample_rate}', transcription_structured_checkbox_qa_sample_rate='${workflow.input.structured_checkbox_qa_sample_rate}', transcription_semi_structured_qa_sample_rate='${workflow.input.semi_structured_qa_sample_rate}', table_cell_transcription_qa_enabled='${workflow.input.table_cell_transcription_qa_enabled}', table_cell_transcription_qa_sample_rate='${workflow.input.table_cell_transcription_qa_sample_rate}', auto_qa_sampling_rate_enabled='${workflow.input.auto_qa_sampling_rate_enabled}', exclude_auto_transcribe_fields_from_qa='${workflow.input.exclude_auto_transcribe_fields_from_qa}', payload=None)

Bases: Block

Submission complete block finalizes submission processing and updates reporting data. This block should be called at the END of every flow that uses blocks included in the flows_sdk.implementations.idp_v34 library. The block requires both the submission object and a payload to be passed in.

Mandatory parameters:

Parameters:

• submission (str) – Submission object

• payload (Any) – Object to pass to downstream systems

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• nlc_qa_sampling_ratio (Union[str, int]) – Defines the percentage of documents sampled for Classification QA, defaults to workflow_input(Settings.SemiQaSampleRate)

• field_id_qa_enabled (Union[str, bool]) – defaults to workflow_input(Settings.FieldIdQaEnabled)

• field_id_qa_sampling_ratio (Union[str, int]) – Defines the percentage of documents sampled for Identification QA, defaults to workflow_input(Settings.FieldIdQaSampleRate)

• table_id_qa_enabled (Union[str, bool]) – Allows users to verify the location of table cells, defaults to workflow_input(Settings.TableIdQaEnabled)

• table_id_qa_sampling_ratio (Union[str, int]) – Defines the percentage of tables the system samples for QA, defaults to workflow_input(Settings.TableIdQaSampleRate)

• transcription_qa_enabled (Union[str, bool]) – defaults to workflow_input(Settings.TranscriptionQaEnabled)

• transcription_qa_sampling_ratio – Defines the percentage of fields sampled for Transcription QA, defaults to workflow_input(Settings.TranscriptionQaSampleRate)

• table_cell_transcription_qa_enabled (Union[str, bool]) – defaults to workflow_input(Settings.TableCellTranscriptionQaEnabled)

• table_cell_transcription_qa_sample_rate (Union[str, int]) – Defines the percentage of cells the system samples for QA. This value is likely to be lower than “Transcription QA Sample Rate” since there are more table cells than fields on any given page, defaults to workflow_input(Settings.TableCellTranscriptionQaSampleRate)

• exclude_auto_transcribe_fields_from_qa (Union[str, bool]) – Defines fields that are marked as auto-transcribe in layout should be excluded from QA or not. defaults to workflow_input(Settings.ExcludeAutoTranscribeFieldsFromQA)

• is_reprocessing (Union[str, bool]) – boolean to handle execution when reprocessing defaults to False

Example usage:

submission_complete = SubmissionCompleteBlock(
    reference_name='complete_submission',
    submission=flexible_extraction.output('submission')
)

IDENTIFIER = 'SUBMISSION_COMPLETE_4'

class flows_sdk.implementations.idp_v38.idp_blocks.SetTransformedOutputBlock(submission, transformed_output, reference_name=None)

Bases: Block

Set Transformed Output block sets the transformed output for the provided submission. This can later be used to send the transformed data downstream using output blocks. For more information see https://docs.hyperscience.ai/#retrieving-transformed-submissions.

Mandatory parameters:

Parameters:

• submission (str) – Submission object

• transformed_output (str) – Object that will be set as the transformed output for this submission

IDENTIFIER = 'SET_TRANSFORMED_OUTPUT'

class flows_sdk.implementations.idp_v38.idp_blocks.IdpCustomSupervisionBlock(submission, task_purpose, supervision_template, reference_name=None, supervision_transcription_masking=True, task_restrictions=None, page_ids=None, notification_workflow='IDP_SUBMISSION_NOTIFY_V38', title='IDP Custom Supervision', description='IDP Custom Supervision')

Bases: Block

An IDP wrapper for the Custom Supervision block. It has the same functionality, but handles reading/writing data from the IDP database.

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• task_purpose (str) – the custom task name that’s given to all supervision tasks that run through a particular instance of a custom supervision block. This can be used to filter tasks in the task queue.

• supervision_template (Any) – JSON used for configuration of the Custom Supervision Task.

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (bool) – Prevents users from inputting invalid characters during the Custom Supervision task, defaults to True

• task_restrictions (Optional[List[Any]]) – Defines what users can access Supervision tasks created by this block, defaults to []

• page_ids (Optional[List[int]]) – This is an optional parameter. If left empty, the IDP wrapper will, by default, pull in all pages in the submission. Adding a list of page IDs will override the default list of pages. defaults to []

• notification_workflow (str) – Notification flow name to run when the submission enters Custom Supervision (which is called within the IDP Custom Supervision block), defaults to DEFAULT_IDP_SUBMISSION_NOTIFY_IDENTIFIER

• title (Optional[str]) – UI-visible title of the block. Defaults to ‘IDP Custom Supervision’

• description (Optional[str]) – Description of the block. Both useful for documentation purpose and visible to users in the Flow studio. Defaults to ‘IDP Custom Supervision’

Example usage:

idp_custom_supervision = IdpCustomSupervisionBlock(
    reference_name='idp_custom_supervision',
    submission=manual_transcription.output('submission'),
    task_purpose='make_custom_decision',
    supervision_template=[
        {
            'name': 'three_column_template',
            'version': '1.0',
            'thumbnail': {
                'group_by_document': True,
                'group_by_case': True,
            },
            'action': [
                {
                    'name': 'my_tab',
                    'display': 'SSN Info',
                    'input': [
                        {
                            'name': 'ssn_field',
                            'type': 'transcription',
                            'data_identifier': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                            'title': 'SSN',
                        },
                    ],
                    'ui': {
                        'groups': [
                            {
                                'title': 'My Group',
                                'fields': ['ssn_field'],
                            },
                        ],
                    },
                },
            ],
        },
    ],
    supervision_transcription_masking=False,
)

IDENTIFIER = 'IDP_CUSTOM_SUPERVISION_2'

class flows_sdk.implementations.idp_v38.idp_blocks.IDPFullPageTranscriptionBlock(submission, reference_name=None, title='Full Page Transcription (Submission)', description='Transcribes the documents included in a submission', app_metadata=None)

Bases: Block

IDP Full Page Transcription Block machine-transcribes the unassigned pages in a submission

Mandatory parameters:

Parameters:

submission (str) – submission object that includes the raw documents that need to be transcribed

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• app_metadata (Optional[List[str]]) – used for capturing information about the flow

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# IDPFullPageTranscriptionBlock transcribes the documents contained in the submission object
idp_fpt_block = IDPFullPageTranscriptionBlock(
    reference_name='idp_fpt',
    submission=submission_bootstrap.output(),
)
# reference to the output of IDPFullPageTranscriptionBlock which can be passed as input to
# the next block. For more information on output() and output references, please take a
# look at class Block and its output() method
output_ref = idp_fpt_block.output()

IDENTIFIER = 'FULL_PAGE_TRANSCRIPTION_SUBMISSION'

class flows_sdk.implementations.idp_v38.idp_blocks.IDPImageCorrectionBlock(submission, reference_name=None, rotation_correction_enabled=True, mobile_processing_enabled=False, title='Image Correction (Submission)', description='Rotate and de-skew documents included in a submission', app_metadata=None)

Bases: Block

IDP Image Correction Block rotates and de-skews tilted/skewed images so that they are more transcription-friendly

Mandatory parameters:

Parameters:

submission (str) – submission object that includes the raw documents that need to be image-corrected

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• app_metadata (Optional[List[str]]) – used for capturing information about the flow

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# IDPImageCorrectionBlock takes in the submission object and outputs in the same format with
# its images
idp_image_correct_block = IDPImageCorrectionBlock(
    reference_name='idp_image_correct',
    submission=submission_bootstrap.output(),
)
# reference to the output of IDPImageCorrectionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look at
# class Block and its output() method
output_ref = idp_image_correct_block.output()

IDENTIFIER = 'IMAGE_CORRECTION_SUBMISSION'

Additional Blocks (v38)

class flows_sdk.implementations.idp_v38.additional_blocks.JSONOutputsBlock(inputs, blocks=None)

Bases: Outputs

Output block allows users to send JSON data extracted by an IDP flow to other systems for downstream processing Mandatory parameters:

Parameters:

inputs (Dict[str, Any]) – Used by the UI to automatically pre-populate the input of newly added blocks. Extended in this class to include 'enabled': True to visualize an enabled/disabled trigger that is controllable via Flow Studio.

Usage when parameters are to be defined in Flow Studio:

outputs = JSONOutputBlock(
    inputs={'result': }
)

class flows_sdk.implementations.idp_v38.additional_blocks.ImageCorrectionBlock(pages, reference_name=None, rotation_correction_enabled=True, mobile_processing_enabled=False, title='Image Correction (Pages)', description='Rotate and de-skew documents', app_metadata=None)

Bases: Block

Image Correction Block rotates and de-skews tilted/skewed images so that they are more transcription-friendly Mandatory parameters:

Parameters:

pages (str) – list of page object that includes the raw documents that need to be image-corrected

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• app_metadata (Optional[List[str]]) – used for capturing information about the flow

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# ImageCorrectionBlock takes in the submission object and outputs in the same format with
# its images
image_correct_block = ImageCorrectionBlock(
    reference_name='image_correct',
    pages=submission_bootstrap.output(submission.unassigned_pages),
)
# reference to the output of IDPImageCorrectionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look
# at class Block and its output() method
output_ref = image_correct_block.output()

IDENTIFIER = 'IMAGE_CORRECTION_PAGES'

class flows_sdk.implementations.idp_v38.additional_blocks.FullPageTranscriptionBlock(pages, reference_name=None, title='Full Page Transcription (Pages)', description='Transcribes documents', app_metadata=None)

Bases: Block

Full Page Transcription Block machine-transcribes the document pages passed to it Mandatory parameters:

Parameters:

pages (str) – list of pages that includes the raw documents that need to be transcribed

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• app_metadata (Optional[List[str]]) – used for capturing information about the flow

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# FullPageTranscriptionBlock transcribes the documents contained in the submission object
fpt_block = FullPageTranscriptionBlock(
    reference_name='fpt',
    submission=submission_bootstrap.output(submission.unassigned_pages),
)
# reference to the output of FullPageTranscriptionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look at
# class Block and its output() method
output_ref = fpt_block.output()

IDENTIFIER = 'FULL_PAGE_TRANSCRIPTION_PAGES'

class flows_sdk.implementations.idp_v38.additional_blocks.CustomSupervisionBlock(submission, task_purpose, data, supervision_template, reference_name=None, supervision_transcription_masking=True, task_restrictions=None, notification_workflow='IDP_SUBMISSION_NOTIFY_V38')

Bases: Block

The CustomSupervisionBlock uses a JSON schema as a form builder to create customer-driven UIs for Supervision tasks.

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• task_purpose (str) – the custom task name that’s given to all supervision tasks that run through a particular instance of a custom supervision block. This can be used to filter tasks in the task queue.

• data (Any) – JSON structure that supplies the data used to populated the supervision_template

• supervision_template (Any) – JSON used for configuration of the Custom Supervision Task.

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (bool) – Prevents users from inputting invalid characters during the Custom Supervision task, defaults to True

• task_restrictions (Optional[List[Any]]) – Defines what users can access Supervision tasks created by this block, defaults to []

• notification_workflow (Optional[str]) – Notification flow name to run when the submission enters Custom Supervision, defaults to IDP_SUBMISSION_NOTIFY_NAME

• title (Optional[str]) – UI-visible title of the block. Defaults to ‘IDP Custom Supervision’

• description (Optional[str]) – Description of the block. Both useful for documentation purpose and visible to users in the Flow studio. Defaults to ‘IDP Custom Supervision’

Example usage:

custom_supervision = CustomSupervisionBlock(
    reference_name='custom_supervision',
    submission=manual_transcription.output('submission'),
    task_purpose='make_custom_decision',
    supervision_template=[
        {
            'name': 'three_column_template',
            'version': '1.0',
            'thumbnail': {
                'group_by_document': True,
                'group_by_case': True,
            },
            'action': [
                {
                    'name': 'my_tab',
                    'display': 'SSN Info',
                    'input': [
                        {
                            'name': 'ssn_field',
                            'type': 'transcription',
                            'data_identifier': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                            'title': 'SSN',
                        },
                    ],
                    'ui': {
                        'groups': [
                            {
                                'title': 'My Group',
                                'fields': ['ssn_field'],
                            },
                        ],
                    },
                },
            ],
        },
    ],
    data={
        'fields': [
            {
                'id': 967,
                'uuid': 'fe8d4e7a-a82c-448c-85a1-0a3cbbe53b61',
                'value': '111-22-3333',
                'page_id': 15,
                'layout_field_id': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                'validation_overridden': False,
                'bounding_box': [
                    0.09360783305186686,
                    0.25792617589433436,
                    0.6358913805295097,
                    0.28862414740388187,
                ],
                'occurrence_index': 1,
            },
        ],
        'template_fields': [
            {
                'uuid': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                'notes': '',
                'type': 'entry',
                'data_type_uuid': 'b600a4e4-758f-4c4b-a2f4-cbb5dd870a0c',
                'name': 'SSN',
            },
        ],
        'pages': [
            {
                'id': 15,
                'file_page_number': 1,
                'document_page_number': 1,
                'image_url': '/image/2ed3d989-b64a-44bd-a9fb-6e0dcc5ccdb8',
                'form_id': 2,
                'submission_id': 1,
                'external_case_ids': [],
                'read_only': False,
                'filename': '1 page.pdf',
                'form_page_id': 1
            },
        ],
        'documents': [
            {
                'id': 2,
                'layout_uuid': 'e31c3cbf-dcea-44fd-a052-902463c6040f',
                'layout_name': 'layout_name',
                'page_count': 2,
                'read_only': False,
            },
        ],
        'cases': [],
        'selected_choices': [],
    },
    supervision_transcription_masking=False,
)

IDENTIFIER = 'CUSTOM_SUPERVISION_2'

class flows_sdk.implementations.idp_v38.additional_blocks.SoapRequestBlock(method, reference_name=None, endpoint='', body_namespace=None, headers_namespace=None, headers=None, params=None, title='HTTP REST Block', description='HTTP REST block')

Bases: Block

Soap Block allows user to perform SOAP requests Mandatory parameters:

Parameters:

• method (str) – SOAP method to invoke

• endpoint (str) – absolute url including schema to HTTP endpoint for this request

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• body_namespace (Optional[str]) – SOAP request body namespace url

• headers_namespace (Optional[str]) – SOAP request headers namespace url

• headers (Optional[Dict[Any, Any]]) – SOAP headers to use

• params (Optional[Dict[Any, Any]]) – key-value pair used as query parameters in the request to be inserted as SOAP body

Example usage:

# minimum viable instantiation, the rest of the fields can later be added in the UI
soap_block = SoapRequestBlock(
    reference_name='soap_request',
    title='Soap Request',
    description='Perform soap request',
    method='ExampleMethod',
)
soap_block = SoapRequestBlock(
    reference_name='soap_request',
    title='Soap Request',
    description='Perform soap request',
    method='ExampleMethod',
    endpoint='http://example.org/abc.wso',
    body_namespace='http://www.example.org/abc.countryinfo',
    params={'example': 'stuff'},
    headers={}
)
# reference to the output of SoapRequestBlock which can be passed as input to the next block
# for more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = soap_block.output()

IDENTIFIER = 'SOAP_REQ'

class flows_sdk.implementations.idp_v38.additional_blocks.HttpRestBlock(method, endpoint, reference_name=None, headers=None, params=None, payload=None, json=None, authorization_type=None, handled_error_codes=None, auth_params=None, title='HTTP REST Block', description='HTTP REST block')

Bases: BaseHttpRestBlock

Http Rest Block allows user to perform HTTP requests Mandatory parameters:

Parameters:

• method (str) – HTTP method to use

• endpoint (str) – absolute url including schema to HTTP endpoint for this request

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• headers (Optional[Dict[str, str]]) – HTTP headers to use

• params (Optional[Dict[str, Any]]) – key-value pair used as query parameters in the request

• payload (Optional[Dict[str, str]]) – key-value pair to be used as x-www-form-urlencoded data. Mutually exclusive with json

• json (Optional[Dict[str, Any]]) – key-value pair to be used as json data. Mutually exclusive with payload

• authorization_type (Optional[str]) – authorization type (none, http_header, oauth_2_client_credentials)

• handled_error_codes (Optional[List[Union[int, str]]]) – specify HTTP error codes that will not fail the task, allowing for wildcards with the ‘x’ char. Example: [400, “5xx”]

• auth_params (Union[HttpHeaderAuthorizationParams, OAuth2AuthorizationParams, None]) – authorization parameters to be passed when the authorization_type is set to “http_header” or “oauth_2_client_credentials”

Example usage:

# minimum viable instantiation, the rest of the fields can later be added in the UI
http_rest_block = HttpRestBlock(
    reference_name='http_request',
    title='Http Request',
    description='Perform http request',
    method='GET',
)
http_rest_block = HttpRestBlock(
    reference_name='http_request',
    title='Http Request',
    description='Perform http request',
    method='GET',
    endpoint='https://sheets.googleapis.com/v4/spreadsheets/example',
)
# reference to the output of HttpRestBlock which can be passed as input to the next block
# for more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = http_rest_block.output()

class flows_sdk.implementations.idp_v38.additional_blocks.HyperscienceRestApiBlock(method, app_endpoint, reference_name=None, headers=None, params=None, payload=None, json=None, handled_error_codes=None, title='Hyperscience HTTP REST Block', description='Hyperscience HTTP REST block')

Bases: BaseHttpRestBlock

Make HTTP requests to the Hyperscience platform APIs. This block will automatically take care of prepending the base url and will include the appropriate authentication headers with the request.

Mandatory parameters:

Parameters:

• method (str) – HTTP method to use, e.g. ‘GET’, ‘POST’, etc.

• app_endpoint (str) – relative url, beginning with a forward slash, to an HTTP endpoint served by the Hyperscience platform. For reference on the available endpoints, check out https://docs.hyperscience.ai/. Absolute URLs that include a hostname are allowed only if the flow has received them as part of the response of a previous API request that the flow has made. Do not hardcode the hostname of the Hyperscience instance in your flow definitions as they will not work when you run the flows on another instance, or if the hostname changes. For making requests to external systems, use the HttpRestBlock or a library like “requests”.

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• headers (Optional[Dict[str, str]]) – HTTP headers to use

• params (Optional[Dict[str, Any]]) – key-value pair used as query parameters in the request

• payload (Optional[Dict[str, str]]) – key-value pair to be used as x-www-form-urlencoded data. Mutually exclusive with json

• json (Optional[Dict[str, Any]]) – key-value pair to be used as json data. Mutually exclusive with payload

• handled_error_codes (Optional[List[Union[int, str]]]) – specify HTTP error codes that will not fail the task, allowing for wildcards with the ‘x’ char. Example: [400, “5xx”]

Example usage:

hs_rest_block = HyperscienceRestApiBlock(
    reference_name='http_request',
    app_endpoint='/api/healthcheck',
    method='GET',
)

class flows_sdk.implementations.idp_v38.additional_blocks.DatabaseAccessBlock(reference_name=None, db_type='', database='', host='', username='', password='', query='', port=None, options=None, timeout=None, query_params=None, title='DB Block', description='DB access block')

Bases: Block

Database Block allows user to access and perform queries on the specified database Mandatory parameters:

Parameters:

• db_type (str) – database type (mssql, oracle, postgres)

• host (str) – URL/IP address of the server database is hosted on

• database (str) – database name

• username (str) – database username

• password (str) – database password

• query (str) – parameterized query

Optional parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• port (Optional[int]) – database port number

• options (Optional[Dict[Any, Any]]) – dictionary of additional connection string options

• query_params (Optional[Dict[Any, Any]]) – dictionary of values for query placeholders

• timeout (Optional[int]) – timeout in seconds (mssql and postgres only)

Example usage:

# minimum viable instantiation, the rest of the fields can later be added in the UI
db_lookup = DatabaseAccessBlock(
    reference_name='db_lookup',
    title='Database Lookup',
    description='Perform database lookup',
)
# note that storing password in plain text form is not recommended
# password can later be entered in the UI or defined as a flow secret.
db_lookup = DatabaseAccessBlock(
    reference_name='db_lookup',
    title='Database Lookup',
    description='Perform database lookup',
    db_type='mssql',
    database='cars',
    host='example.com',
    username='user',
    # we recommend skipping the password field or defining it as a system secret
    # e.g. system_secret('{your_secret_identifier}') (both allow for secret input via. UI)
    password='pw'
    port=1433,
    timeout=200,
    query='SELECT * from CAR',
)
# reference to the output of DBAccessBlock which can be passed as input to the next block
# for more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = db_lookup.output()
# content of output_ref:
{
    "result": [
        {
            "id": 1,
            "brand": "Aston Martin",
            "model": "Vanquish"
        },
        {
            "id": 2,
            "brand": "Jaguar",
            "model": "XE 2018"
        }
    ]
}

Database output for the same query (SELECT * FROM CAR)

IDENTIFIER = 'DB_ACCESS'

class flows_sdk.implementations.idp_v38.additional_blocks.LlamaBlock(model_id, prompt, reference_name=None, consent=False, tokenize_only=False, predict_extra_kwargs=None, title='Llama Text Generation', description='Text generation using the Llama 2 model')

Bases: Block

Text generation using the Llama 2 model by Meta.

This block utilizes Llama 2 by Meta. Llama 2 is licensed under the LLAMA 2 Community License (https://ai.meta.com/llama/license/). By using the Llama Block, you acknowledge that you have received a copy of the LLAMA 2 Community License.

Note: This block is still experimental and not ready for production use.

Mandatory parameters:

Parameters:

• model_id (str) – UUID, identifier of the asset with the ML model. Model assets are not part of the Hypersicence bundle and must first be downloaded and imported into the system before they can be used. Please contact your Hyperscience representative to discuss the usecase and the appropriate model and to get instructions on downloading the models. Once downloaded, here are the available model asset UUIDs: b3da05c7-061c-4a58-af6d-4c905d2e618f - Llama 2, 13B, 8-bit quantized

• prompt (str) – The prompt to generate text from. Use a CodeBlock ahead of the LlamaBlock in order to build the prompt. The context window of the model is limited to 4096 tokens. If the prompt has more tokens, the block would return an error. The total input and output length also cannot exceed 4096 tokens, which means that if the input is too long, the output may be truncated. Also see tokenize_only for a more detailed explanation of how to handle longer texts.

Optional parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• consent (Optional[bool]) – Has the business user read and acknowledged the Llama 2 legal notice. Unless the flow would be used in CI automation, this should be set to False so that a business user can read and acknowledge the notice in the Hyperscience UI. Setting this to True in the Python definition of the flow is also treated as acknowledgement of the notice on behalf of the entity that will be using the flow.

• tokenize_only (Optional[bool]) – In the context of LLMs, tokens correspond roughly to words, partial words and punctuation. A rough way to compute the number of tokens in a text is number_of_tokens = 1.25 * number_of_words. The Llama block works with a context window of 4096 tokens. If a longer prompt is passed in, it would return an error. Also, if the prompt is shorter, but still close to 4096, the output may be truncated. Since the length of the prompt in tokens cannot be known ahead of time, sometimes the flow may need to make dynamic decisions whether to pass the text to the Llama block, or to split it into multiple chunks, or to use some alternative method for processing it (e.g. a custom supervision manual task). The tokenize_only option enables flows to take such dynamic decisions based on the length of the prompt in tokens. The default is false - it instructs the block to run the ML model inference over the prompt, i.e. to generate a text response. When true is passed, the model will simply tokenize the prompt without running inference and return the list of tokens - this will work regardless of the length of the prompt. A subsequent Decision task can then route longer prompts to an alternative path in the flow, while routing shorter prompts to the LlamaBlock for actual text generation.

• predict_extra_kwargs (Optional[Dict[Any, Any]]) – Extra keyword arguments used for model inference. (Used to fine-tune the model prediction. Not recommended to change this unless you know what you are doing)

Example usage:

llama = LlamaBlock(
    reference_name='llama',
    model_id='b3da05c7-061c-4a58-af6d-4c905d2e618f',
    prompt='${previous_block.output.llama_prompt}',
)

IDENTIFIER = 'LLAMA'

Trigger Blocks (v38)

Note

“Triggers” are referred to as “Input Blocks” in our application documentation.

Warning

Flows are built to allow full definition of triggers either in code or manually in the Flow Studio. We recommend defining triggers manually in the Flow Studio. Instructions on how to do so are in our application documentation.

To enable trigger definition via the Flow Studio UI, you need to instantiate the IDPTriggers() convenience class and pass it to the triggers property of the Flow

return Flow(
   ...
   triggers=IDPTriggers()
)

If instead you wish to define specific triggers in Python, use the trigger definitions below.

Folder Listener (v38)

Identifier: FOLDER_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
folder	Path	N/A	Folder to scan for submissions	N/A
file_extensions	string	N/A	File extensions	List of file extensions to monitor for (e.g.: ‘png, jpg, pdf’)
has_meta	boolean	False	Enable metadata	Select this if a metadata file is to be expected along with document files (in XXX_index.txt file)
poll_interval	integer	10	Poll interval	Poll interval in seconds
warmup_interval	integer	15	Warm-up interval	Seconds to wait past document last modified time before processing it
folder_cleanup_delay	integer	86400	Folder cleanup delay	Seconds to wait before cleaning up subfolders
api_params	object	{}	API Parameters	N/A

folder_listener = IOBlock(
    identifier='FOLDER_TRIGGER',
    reference_name='folder_trigger',
    title='Folder listener',
    enabled=True,
    input={
        'folder': '/var/www/forms/forms/folder/',
        'file_extensions': 'png, pdf, jpg',
    },
)

triggers = IDPTriggers(blocks=[folder_listener])

Email Listener (v38)

Identifier: IMAP_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
provider [1]	string	imap	Email Provider	N/A
poll_interval	integer	60	Polling interval in seconds	The frequency at which the connection will monitor the email folder for submissions
folder	Path	N/A	Folder to scan for emails	The email folder to retrieve submissions from (e.g., “inbox”)
email_body_treatment	string	“ignore”	Email body treatment	What to do with the body of an email that is ingested. Available options are “process” and “ignore”.
post_process_action	string	“move”	Post process action	What to do with the email after it is processed. Available options are “move” and “delete”.
post_process_move_folder	string	“”	Post process archive folder	Folder to move emails to once they are processed. It will be included only if post_process_action is set to “move”.
api_params	object	{}	API Parameters	N/A
render_headers [2]	array	[]	Headers to include	Headers to render at the top of the email body. Headers will be included only if email_body_treatment is set to “process”.

[1] one of:

imap
graph

[2] any of:

To
From
Subject
Date

Additional inputs when "imap" is the selected provider:

Name	Type	Default Value	Title	Description
host	string	N/A	IMAP server address	N/A
port	integer	993	Port Number	N/A
ssl	boolean	True	Use SSL connection	N/A
username	string	N/A	Username	N/A
password	Password	N/A	Password	N/A

Additional inputs when "graph" is the selected provider:

Name	Type	Default Value	Title	Description
client_id	string	N/A	Client id	Application (client) ID for the corresponding application registered with the Microsoft identity platform.
tenant_id	string	N/A	Tenant id	Also called directory ID. Unique identifier of the tenant in which the corresponding application is registered with.
graph_cloud_endpoint [1]	string	Global	Microsoft Graph Cloud Endpoint	Determines base URL used for accessing cloud services through Microsoft Graph.
azure_ad_endpoint [2]	string	AZURE_PUBLIC_CLOUD	Azure AD Endpoint	Determines base URL for the Azure Active Directory (Azure AD) endpoint to acquire token for each national cloud.
client_secret	Password	N/A	Client secret	Client secret for the corresponding application registered with the Microsoft identity platform.

[1] one of:

Global
US_GOV
US_DoD
Germany
China

[2] one of:

AZURE_PUBLIC_CLOUD
AZURE_CHINA
AZURE_GERMANY
AZURE_GOVERNMENT

imap_trigger = IOBlock(
    identifier='IMAP_TRIGGER',
    reference_name='imap_trigger',
    title='IMAP trigger',
    enabled=True,
    input={
        'host': 'example@mail.com',
        'folder': '/var/www/forms/forms/imap/',
        'username': 'admin',
        'password': 'pass',
    }
)

triggers = IDPTriggers(blocks=[imap_trigger])

Box Folder Listener (v38)

Identifier: BOX_FOLDER_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
source_folder_id	string	N/A	Folder to scan for submissions	Use the Box Folder ID found in the URL
target_folder_id	string	N/A	Folder to move completed files	Use the Box Folder ID found in the URL
file_extensions	array	[]	File extensions	Types of file extensions for which to monitor
custom_file_extensions	string	N/A	Other file extension types	Comma separated list of file extensions for which to monitor (e.g. ‘png, jpg, pdf’)
poll_interval	integer	10	Polling interval (in seconds)	How often the connector will check the base folder for submissions
warmup_interval	integer	15	Warm-up interval (in seconds)	How long the connector will wait to process the document after it was last modified
public_key_id	string	N/A	Public-Key ID	ID of the Public-key used for authentication with Box
private_key	Password	N/A	Private-Key	Private key used for authentication with Box
passphrase	Password	N/A	Passphrase	Passphrase used for authentication with Box
client_id	string	N/A	Client ID	Client Id used for authentication with Box
client_secret	Password	N/A	Client Secret	Client Secret used for authentication with Box
enterprise_id	string	N/A	Enterprise ID	Enterprise Id used for authentication with Box
api_params	object	{}	API Parameters	N/A

box_folder_trigger = IOBlock(
    identifier='BOX_FOLDER_TRIGGER',
    reference_name='box_folder_trigger',
    title='Box folder',
    enabled=True,
    input={
        'file_extensions': ['png', 'pdf', 'jpg'],
        'source_folder_id': '24',
        'target_folder_id': '42',
        'public_key_id': 'admin',
        'private_key': 'secret',
        'passphrase': 'password',
        'client_id': 'admin',
        'client_secret': 'secret',
        'enterprise_id': 'admin',
    }
)

triggers = IDPTriggers(blocks=[box_folder_trigger])

Cron Listener (v38)

Identifier: CRON_TRIGGER

Inputs:

Name	Type	Default Value	Title	Description
cron_spec	string	"*/5 * * * *"	Cron specification	N/A
time_zone	string	“US/Eastern”	Time Zone	N/A
api_params	object	{}	API Parameters	N/A

cron_trigger = IOBlock(
    identifier='CRON_TRIGGER',
    reference_name='cron_trigger',
    title='Cron Trigger',
    enabled=True,
    input={
        'cron_spec': '0 10 * * *',
        'time_zone': 'Europe/Sofia',
    }
)

triggers = IDPTriggers(blocks=[cron_trigger])

Salesforce Listener (v38)

Identifier: SALESFORCE_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
channel_name	string	N/A	Channel Name	Configured in the Hyperscience app in Salesforce.
username	string	N/A	Associated Username	Username associated with the Salesforce Connected App.
consumer_key	Password	N/A	Consumer Key	Consumer key used for authentication with Salesforce. Paired with the Salesforce instance from which it was issued.
private_key	Password	N/A	Private Key	Private key used for authentication with Salesforce.
sandbox_environment	boolean	false	Private Key	Enable if the Salesforce environment is a sandbox.
api_params	object	{}	API Parameters	N/A

Message Queue Listener (v38)

Identifier: MQ_LISTENER

Inputs:

Name	Type	Default Value	Title	Description
MQ_TYPE	string	“ACTIVE_MQ”	Message Queue type	Type of the message queue to connect with. Valid values are “ACTIVE_MQ”, “AMAZON_SQS”, “IBM_MQ” and “RABBIT_MQ”
MQ_USERNAME	string	null	Username/Access Key ID	N/A
MQ_PASSWORD	Password	null	Password/Secret Access Key	N/A
MQ_QUEUE_NAME	string	null	Queue Name	Name of the queue (topic) to connect to.
MQ_HOST	string	null	Host Name	N/A
MQ_PORT	integer	5672 when “RABBIT_MQ”, 1414 when “IBM_MQ”, otherwise null	Port Number	N/A
api_params	object	{}	API Parameters	N/A

Additional inputs when "AMAZON_SQS" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_REGION	string	null	AWS Region	N/A
MQ_QUEUE_URL	string	null	Queue URL	N/A
MQ_USE_EC2_INSTANCE_CREDENTIALS	boolean	True	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used.

NOTE: MQ_USERNAME can be used to provide Access Key ID and MQ_PASSWORD to provide Secret Access Key.

Additional inputs when "IBM_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_QUEUE_MANAGER	string	null	Queue Manager (IBM MQ)	N/A
MQ_SSL_CIPHER_SUITE [1]	string	null	SSL cipher suite	SSL cipher suite to use if SSL connection is used.
MQ_CHANNEL	string	null	Channel	N/A
MQ_NO_AUTH_REQUIRED	boolean	false	No auth credentials required	N/A
MQ_MQCSP_AUTHENTICATION_MODE	boolean	false	MQCSP authentication mode	N/A

NOTE: MQ_NO_AUTH_REQUIRED is True, MQ_USERNAME and MQ_PASSWORD are optional.

[1] one of:

"NULL_MD5"
"NULL_SHA"
"RC4_MD5_EXPORT"
"RC4_MD5_US"
"RC4_SHA_US"
"RC2_MD5_EXPORT"
"DES_SHA_EXPORT"
"RC4_56_SHA_EXPORT1024"
"DES_SHA_EXPORT1024"
"TRIPLE_DES_SHA_US"
"TLS_RSA_WITH_NULL_SHA256"
"TLS_RSA_WITH_AES_128_CBC_SHA"
"TLS_RSA_WITH_AES_128_CBC_SHA256"
"TLS_RSA_WITH_AES_256_CBC_SHA"
"TLS_RSA_WITH_AES_256_CBC_SHA256"
"AES_SHA_US"
"TLS_RSA_WITH_DES_CBC_SHA"
"TLS_RSA_WITH_3DES_EDE_CBC_SHA"
"TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256"
"FIPS_WITH_DES_CBC_SHA"
"FIPS_WITH_3DES_EDE_CBC_SHA"

Additional inputs when "RABBIT_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_VIRTUAL_HOST	string	null	Virtual Host (RABBIT MQ)	N/A
MQ_EXCHANGE	string	“”	Exchange	N/A
MQ_ROUTING_KEY	string	“”	Routing key	N/A
MQ_CONNECTION_TYPE	string	“TCP”	Connection Type	“TCP or SSL”

rabbit_mq_listener = IOBlock(
    identifier='MQ_LISTENER',
    reference_name='mq_listener',
    title='RabbitMQ Listener',
    enabled=True,
    input={
        'MQ_TYPE': 'RABBIT_MQ',
        'MQ_QUEUE_NAME': 'some_queue_name',
        'MQ_HOST': 'somehost.com',
        'MQ_USERNAME': 'foo',
        'MQ_PASSWORD': system_secret('rabbit_mq_1_password'),
    }
)

triggers = IDPTriggers(blocks=[rabbit_mq_listener])

Universal Folder Listener (v38)

Identifier: UNIVERSAL_FOLDER_TRIGGER

Inputs:

Name	Type	Default Value	Title	Description
folder	string	N/A	Folder to scan for submissions	Enter the path relative to the base folder. Leave blank to monitor the base folder
file_extensions	array	[]	File extensions	Types of file extensions for which to monitor
other_file_extensions	string	N/A	Other file extensions	Comma separated list of file extensions for which to monitor (e.g. ‘png, jpg, pdf’)
has_meta	boolean	false	Include submission level parameters	Select this to ingest JSON files along with document files and submission folders. These JSON files can contain information such as metadata, cases and external_id. These JSON file names should match the related files or folders (e.g., XXX.jpg.json for XXX.jpg)
poll_interval	integer	10	Polling interval (in seconds)	How often the Folder Listener will check the base folder for submissions
warmup_interval	integer	15	Warm-up interval (in seconds)	How long the Folder Listener will wait to process the document after it was last modified
folder_cleanup_delay	number	24	Empty folder cleanup delay (in hours)	How often the Folder Listener will remove empty folders from the base folder
api_params	object	{}	API Parameters	N/A

universal_folder_listener = IOBlock(
    identifier='UNIVERSAL_FOLDER_TRIGGER',
    reference_name='universal_folder_trigger',
    title='Universal Folder Listener',
    enabled=True,
    input={
        'folder': '/var/www/forms/forms/folder/',
        'file_extensions': 'png, pdf, jpg',
    }
)

triggers = IDPTriggers(blocks=[universal_folder_listener])

Kafka Listener (v38)

Identifier: KAFKA_TRIGGER

Inputs:

Name	Type	Default Value	Title	Description
bootstrap_servers	string	N/A	Bootstrap Servers	A Comma separated list of host:port pairs to use for establishing the initial connection to the cluster
group_id	string	N/A	Group Id	A unique string that identifies the consumer group this consumer belongs to. BLOCK_THREADS_KAFKA_TRIGGER should be used to scale up or down the number of consumers (default: number of available cores)
topics	string	N/A	Topics	Comma separated list of topic names that the block will subscribe to
consumer_config_json	json	N/A	Consumer Config	JSON formatted string containing key-value pairs of Kafka consumer configurations described in the official Kafka documentation
api_params	object	{}	API Parameters	N/A
consume_timeout	integer	-1	Consume Timeout	Maximum time, in seconds, that the consumer will wait for new messages. If messages are received within the specified time, they are processed immediately. However, if no messages are received within that specified time, consumption cycle will be restarted internally to continue fetching messages. This ensures continuous message consumption (default: infinite)
consume_num_messages	integer	1	Consume Num Messages	Sets the maximum number of messages the consumer fetches in a single consumption cycle.If the number of available messages is less than the specified, and timeout is reached, messages will be processed. Consumption cycle will be restarted after the current finishes

kafka_listener = IOBlock(
    identifier='KAFKA_TRIGGER',
    reference_name='kafka_trigger',
    title='Kafka Listener',
    enabled=True,
    input={
        'folder': '/var/www/forms/forms/folder/',
        'file_extensions': 'png, pdf, jpg',
         'bootstrap_servers': 'example.com',
         'group_id': 'test_group',
         'topics': 'test_topic1, test_topic2',
         'consumer_config_json': {},
         'number_of_workers': 1,
         'consume_timeout': 5,
         'consume_num_messages': 10
    }
)

triggers = IDPTriggers(blocks=[kafka_listener])

Output Blocks (v38)

Warning

Flows pass information to downstream systems using the IDPOutputsBlock. This block is specially built to allow full definition either in code or manually in the Flow Studio. We recommend defining outputs manually in the Flow Studio.

Instructions on how to do so are in our application documentation. Simply include an IDPOutputsBlock in your Python flow definition wherever you wish to send information to downstream systems.

class flows_sdk.implementations.idp_v38.idp_blocks.IDPOutputsBlock(inputs, blocks=None)

Bases: Outputs

Output block allows users to send data extracted by an IDP flow to other systems for downstream processing

Mandatory parameters:

Parameters:

inputs (Dict[str, Any]) – Used by the UI to automatically pre-populate the input of newly added blocks. Extended in this class to include 'enabled': True to visualize an enabled/disabled trigger that is controllable via Flow Studio.

Usage when parameters are to be defined in Flow Studio:

outputs = IDPOutputBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')}
)

If instead you wish to define specific outputs in Python, use the outputs definitions below.

HTTP Notifier Output (v38)

The HTTP Notifier (REST) output connection will POST results from the system to a specified HTTP endpoint.

Identifier: COB_HTTP_EXPORT

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
endpoint	string	N/A	Endpoint URL	URL that output notifications will be posted to
endpoint_secrets	string	N/A	Endpoint URL secrets	Endpoint URL Secrets, format: (one per line) secret_key_name=secret_value
timeout	int	600	Timeout (seconds)	How long (in seconds) to keep the connection open if no bytes are received from the endpoint. Set to 0 to wait indefinitely.
authorization_type [2]	string	“none”	Authorization type	Type of authorization
authorization_header_name	string	null	Authorization header name	Authorization header name to be set in the notification request
authorization_header	Password	null	Authorization header value	Authorization header to be set in the notification request
auth_url	string	null	OAuth2 authorization URL	The endpoint for the authorization server
client_id	string	null	Client ID	The client identifier issued to the client during the application registration process
client_secret	Password	null	Client Secret	The client secret issued to the client during the application registration process
scope	string	null	Scope	Scope to request to oauth server
audience	string	null	Audience	Resource service URL where token will be valid
additional_oauth_parameters	json	{}	Additional Oauth Request Parameters	Additional parameters to send when requesting token
ssl_cert_source [3]	string	null	Source	N/A
ssl_cert_path	string	null	Trusted Certificate(s) path	Enter the path relative to the base folder
ssl_cert_value	MultilineText	null	Trusted Certificate(s)	N/A
additional_notification_http_headers	json	null	Additional Notification Request Headers	Key value pairs in json format which will be sent via the request as http headers

[1] one of:

"v5"
"transformed"

[2] one of:

"none"
"http_header"
"oauth_2_client_credentials"

[3] one of:

"env_var"
"ca_bundle_path"
"certificate_value"

cob_http_export = IOBlock(
    identifier='COB_HTTP_EXPORT',
    reference_name='cob_http_export',
    title='HTTP Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'SUBMISSION',
        'endpoint': 'example.com',
        'authorization_type': 'none',
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_http_export],
)

Message Queue Notifier Output (v38)

The Message Queue Notifier Output can configure connections to ActiveMQ, Amazon SQS, IBM MQ, and RabbitMQ message queues.

Identifier: COB_MQ_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
MQ_TYPE	string	“ACTIVE_MQ”	Message Queue type	Type of the message queue to connect with. Valid values are “ACTIVE_MQ”, “AMAZON_SQS”, “IBM_MQ” and “RABBIT_MQ”
MQ_USERNAME	string	null	Username/Access Key ID	N/A
MQ_PASSWORD	Password	null	Password/Secret Access Key	N/A
MQ_QUEUE_NAME	string	null	Queue Name	Name of the queue (topic) to connect to. It applies only to “ACTIVE_MQ”, “IBM_MQ” and “RABBIT_MQ” MQ_TYPEs.
MQ_HOST	string	null	Host Name	N/A
MQ_PORT	integer	5672 when “RABBIT_MQ”, 1414 when “IBM_MQ”, otherwise null	Port Number	N/A

[1] one of:

"v5"
"transformed"

Additional inputs when "AMAZON_SQS" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_REGION	string	null	AWS Region	N/A
MQ_QUEUE_URL	string	null	Queue URL	N/A
MQ_USE_EC2_INSTANCE_CREDENTIALS	boolean	True	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used.
MQ_MESSAGE_GROUP_ID	string	null	Group ID for FIFO queues	N/A
MQ_MESSAGE_METADATA	string	null	Additional SQS Metadata	Input additional metadata key value pairs in the format of { “key”: {“TYPE”: “String”, “VALUE”: “”}, “key2”: {“TYPE”: “Number”, “VALUE”: “12345”}

NOTE: MQ_USERNAME can be used to provide Access Key ID and MQ_PASSWORD to provide Secret Access Key.

Additional inputs when "IBM_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_QUEUE_MANAGER	string	null	Queue Manager (IBM MQ)	N/A
MQ_SSL_CIPHER_SUITE [1]	string	null	SSL cipher suite	SSL cipher suite to use if SSL connection is used.
MQ_CHANNEL	string	null	Channel	N/A
MQ_NO_AUTH_REQUIRED	boolean	false	Channel	N/A
MQ_MQCSP_AUTHENTICATION_MODE	boolean	false	Channel	N/A

NOTE: MQ_NO_AUTH_REQUIRED is True, MQ_USERNAME and MQ_PASSWORD are optional.

[1] one of:

"NULL_MD5"
"NULL_SHA"
"RC4_MD5_EXPORT"
"RC4_MD5_US"
"RC4_SHA_US"
"RC2_MD5_EXPORT"
"DES_SHA_EXPORT"
"RC4_56_SHA_EXPORT1024"
"DES_SHA_EXPORT1024"
"TRIPLE_DES_SHA_US"
"TLS_RSA_WITH_NULL_SHA256"
"TLS_RSA_WITH_AES_128_CBC_SHA"
"TLS_RSA_WITH_AES_128_CBC_SHA256"
"TLS_RSA_WITH_AES_256_CBC_SHA"
"TLS_RSA_WITH_AES_256_CBC_SHA256"
"AES_SHA_US"
"TLS_RSA_WITH_DES_CBC_SHA"
"TLS_RSA_WITH_3DES_EDE_CBC_SHA"
"TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256"
"FIPS_WITH_DES_CBC_SHA"
"FIPS_WITH_3DES_EDE_CBC_SHA"

Additional inputs when "RABBIT_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_VIRTUAL_HOST	string	null	Virtual Host (RABBIT MQ)	N/A
MQ_EXCHANGE	string	“”	Exchange	N/A
MQ_ROUTING_KEY	string	“”	Routing key	N/A
MQ_CONNECTION_TYPE	string	“TCP”	Connection Type	“TCP or SSL”

mq_notifier = IOBlock(
    identifier='COB_MQ_NOTIFIER',
    reference_name='mq_notifier',
    title='MQ Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'SUBMISSION',
        'MQ_USERNAME': 'admin',
        'MQ_PASSWORD': 'pass',
        'MQ_QUEUE_NAME': 'queue',
        'MQ_HOST': 'host',
    },
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[mq_notifier],
)

Box Notifier Output (v38)

Provides an out-of-the-box integration into Box systems.

Identifier: COB_BOX_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
public_key_id	string	N/A	Public-Key ID	ID of the Public-key used for authentication with Box
private_key	Password	N/A	Private-Key	Private key used for authentication with Box
passphrase	Password	N/A	Passphrase	Passphrase used for authentication with Box
client_id	string	N/A	Client ID	Client Id used for authentication with Box
client_secret	Password	N/A	Client Secret	Client Secret used for authentication with Box
enterprise_id	string	N/A	Enterprise ID	Enterprise Id used for authentication with Box
template_key	string	“”	Box Metadata Template Key	Enter the key of the Box Metadata template that you would like to map the Hyperscience metadata to.
static_output_fields_to_map	array	[]	Static Metadata Fields	Specify the Hyperscience fields you want to store in Box metadata.
submission_id	string	“”	Key for Mapping Submission ID	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_id	string	“”	Key for Mapping Document ID	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
submission_state	string	“”	Key for Mapping Submission State	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_state	string	“”	Key for Mapping Document State	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
submission_exceptions	string	“”	Key for Mapping Submission Exceptions	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_exceptions	string	“”	Key for Mapping Document Exceptions	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_layout_uuid	string	“”	Key for Mapping Document Layout Uuid	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_layout_name	string	“”	Key for Mapping Document Layout Name	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
start_time	string	“”	Key for Mapping Start Time	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
complete_time	string	“”	Key for Mapping Complete Time	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_fields_template_mappings	json	{}	JSON for Additional Metadata Fields	Specify additional metadata fields using their key value. Please consult Box Integration setup manual for JSON template and instructions.

[1] one of:

"v5"
"transformed"

box_notifier = IOBlock(
    identifier='COB_BOX_NOTIFIER',
    reference_name='box_notifier',
    title='Box Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'private_key': 'password',
        'passphrase': 'password',
        'public_key_id': 'admin',
        'client_id': 'admin',
        'client_secret': 'secret',
        'enterprise_id': 'admin',
        'template_key': 'key',
    },
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[box_notifier],
)

UiPath Notifier Output (v38)

Provides an out-of-the-box integration into UiPath systems.

Identifier: COB_EXPORT_UIPATH

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
auth_method [2]	string	“basic_auth”	Authentication method	N/A
url	string	N/A	URL	Base URL of the uipath instance
app_id	string	N/A	App ID	App ID provided by UiPath
app_secret	Password	N/A	App Secret	App Secret provided by UiPath
oauth_token_endpoint	string	N/A	OAuth token endpoint	Endpoint to retrieve the token. Using https://cloud.uipath.com/identity_/connect/token by default
organization_unit_id	string	N/A	Organization unit id	Organization unit id
tenant	string	N/A	Tenant	Tenant
username	string	N/A	Username	Username
password	Password	N/A	Password	Password
queue	string	N/A	Queue	Queue

[1] one of:

"v5"
"transformed"

[2] one of:

"basic_auth"
"oauth"

cob_export_uipath = IOBlock(
    identifier='COB_EXPORT_UIPATH',
    reference_name='cob_export_uipath',
    title='CCB Export UiPath',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'FLAT_SUBMISSION',
        'url': 'example.com',
        'tenant': 'tenant',
        'username': 'admin',
        'password': 'pass',
        'queue': 'queue',
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_export_uipath],
)

Salesforce Notifier Output (v38)

With the Salesforce Notifier Block, you can configure your flow to send extracted information to Salesforce. The Salesforce Notifier Block can use extracted information to look up, and then update any number of fields on a Salesforce object. The Salesforce Notifier Block is available in both SaaS and on-premise versions of the Hyperscience application.

Identifier: COB_SALESFORCE_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version [1]	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
username	string	N/A	Associated Username	Username associated with the Salesforce Connected App
consumer_key	password	N/A	Consumer Key	Consumer key used for authentication with Salesforce. Paired with the Salesforce instance from which it was issued.
private_key	password	N/A	Private Key	Private key used for authentication with Salesforce.
sandbox_environment	boolean	false	Private Key	Enable if the Salesforce environment is a sandbox.
object_api_name	string	N/A	Object API Name	API name of the object in Salesforce to lookup and update.
new_record_on_lookup_failure	boolean	false	Create new record on lookup failure	When enabled, it will create a new record if the lookup fails to find a record using the lookup parameters. When disabled, lookup will fail if it does not find a record using the lookup parameters.
object_lookup_mapping	json	N/A	Lookup Field Mapping	Specify which Salesforce fields (using their API name) to be queried with which extracted values from the document for the lookup using key-value format. For example: “DOCUMENT_FIELD_NAME”: “OBJECT_FIELD_API_NAME”.
document_fields_mapping	json	N/A	Document Field Mappings	Specify which Salesforce fields (using their API name) you want to update with which extracted data from Hyperscience using key-value format. For example: “DOCUMENT_FIELD_NAME”: “OBJECT_FIELD_API_NAME”.”
overwrite_existing_field_data	boolean	false	Overwrite existing field data	If this is checked and a selected field has data in it, Hyperscience will overwrite that data. If this is unchecked, existing data will not be overwritten and the operation will fail.

[1] one of:

"v5"
"transformed"

cob_salesforce_notifier = IOBlock(
    identifier='COB_SALESFORCE_NOTIFIER',
    reference_name='cob_salesforce_notifier',
    title='Salesforce Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'username': 'salesforce_user',
        'consumer_key': 'salesforce_consumer_key',
        'private_key': 'salesforce_private_key',
        'object_api_name': 'salesforce_object_api_name',
        'object_lookup_mapping': {"LAYOUT_VARIATION_XYZ_FIELD_NAME_1": "SF_FIELD_API_NAME_1", "LAYOUT_VARIATION_XYZ_FIELD_NAME_2": "SF_FIELD_API_NAME_2"},
        'document_fields_mapping': {"LAYOUT_VARIATION_XYZ_FIELD_NAME_3": "SF_FIELD_API_NAME_3", "LAYOUT_VARIATION_XYZ_FIELD_NAME_4": "SF_FIELD_API_NAME_4"}
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_salesforce_notifier],
)

Helper Classes and Functions (v38)

The following classes are used to easily instantiate and manage a flow’s settings.

flows_sdk.implementations.idp_v38.idp_values.get_idp_wf_inputs(idp_wf_config)

A helper function that maps the values provided by an IdpWorkflowConfig to their corresponding IDP keys.

Parameters:

idp_wf_config (IdpWorkflowConfig) – with static values to be filled.

Return type:

Dict[str, Any]

Returns:

a dictionary with filled static values, compatible with IDP flows.

Example usage with default values:

flow = Flow(
    input=get_idp_wf_inputs(get_idp_wf_config()),
    ...
)

Example usage:

Example usage when instantiating an IDP flow with custom parameters::

flow = Flow(
    input=get_idp_wf_inputs({an instance of IdpWorkflowConfig}),
    ...
)

flows_sdk.implementations.idp_v38.idp_values.get_idp_wf_config()

A helper function that instantiates an IdpWorkflowConfig pre-filled with default values. IdpWorkflowConfig is the container class for defining flow-level settings.

Return type:

IdpWorkflowConfig

Returns:

a valid, fully constructed IdpWorkflowConfig instance

Example usage when instantiating an IDP flow:

flow = Flow(
    input=get_idp_wf_inputs(get_idp_wf_config()),
    ...
)

class flows_sdk.implementations.idp_v38.idp_values.IdpWorkflowConfig

Convenience dataclass outlining all flow-level config values for IDP.

class flows_sdk.implementations.idp_v38.idp_values.IDPTriggers(blocks=None)

An adapter class making it easier to instantiate flows_sdk.flows.Triggers for IDP flows. In particular, provides defaults for some fields (e.g. title, description, reference_name) and defines api_params_manifest with IDP specific parameters.

Parameters:

blocks (Optional[Sequence[IOBlock]]) – flows_sdk.blocks.IOBlock s to be included as triggers for an IDP Flow. Optional, defaults to an empty list.

Example usage when instantiating an IDP flow:

folder_listener = IOBlock(...)
flow = Flow(
    triggers=IDPTriggers([folder_listener]),
    ...
)

class flows_sdk.implementations.idp_v38.idp_values.IDPWrapperManifest(flow_identifier)

An adapter class making it easier to instantiate flows_sdk.flows.Manifest for IDP wrapper flows. In particular, provides defaults for some fields (e.g. roles) but more importantly, defines all flows_sdk.flows.Parameter that construct the Flow inputs and their UI representation (left-hand side menu in Flow studio).

Parameters:

flow_identifier (str) – A system-wide unique identifier for flows_sdk.flows.Flow

Example usage when instantiating an IDP flow:

flow = Flow(
    manifest=IDPWrapperManifest(flow_identifier='FLOW_IDENTIFIER'),
    ...
)

class flows_sdk.implementations.idp_v38.idp_values.IDPCoreManifest(flow_identifier, roles=None)

An adapter class making it easier to instantiate flows_sdk.flows.Manifest for IDP Core flows. In particular, provides defaults for some fields (e.g. roles, output) but more importantly, defines all flows_sdk.flows.Parameter that construct the Flow inputs and their UI representation (left-hand side menu in Flow studio).

Parameters:

• flow_identifier (str) – A system-wide unique identifier for flows_sdk.flows.Flow

• roles (Optional[List[str]]) – ‘read_only’, ‘supporting’ and ‘idp_core’ are the default roles of every subflow that can replace the default IDP_CORE subflow inside the top-level IDP flows. ‘supporting’ and ‘idp_core’ are mandatory roles that are necessary for all IDP Core flows. ‘supporting’ ensures that the subflow will be auto-deployed together with the top-level IDP flow. ‘idp_core’ ensures that this subflow can be selected in the UI in dropdowns which allow picking an alternative subflow to the default IDP_CORE subflow. If you are implementing a subflow that is not supposed to be a drop-in replacement for IDP_CORE, you will have to subclass or implement your own version of IDPCoreManifest class in order to remove the idp_core role.

Example usage when instantiating an IDP flow:

flow = Flow(
    manifest=IDPCoreManifest(flow_identifier='FLOW_IDENTIFIER'),
    ...
)

V37

Processing Blocks (v37)

Core Blocks are powerful processing blocks necessary to build intelligent document processing solutions on Hyperscience. They include both manual and machine processing blocks.

Note

Classes in the idp_v37 implementation have parameters with ambiguous typing. For example, a field of type integer could also be represented as a string reference (e.g. that integer gets provided at runtime). The types introduced in the example are therefore incomplete and are likely to be revisioned in a future version.

class flows_sdk.implementations.idp_v37.idp_blocks.SubmissionBootstrapBlock(reference_name=None, layout_release_uuid='${workflow.input.layout_release_uuid}', s3_config='${system.secrets.s3_downloader}', s3_endpoint_url=None, ocs_config='${system.secrets.ocs_downloader}', http_config=None, notification_workflow='IDP_SUBMISSION_NOTIFY_V37', workflow_uuid='${workflow.input.workflow_uuid}', workflow_name='${workflow.input.workflow_name}', workflow_version='${workflow.input.workflow_version}')

Bases: Block

Submission bootstrap block initializes the submission object and prepares external images or other submission data if needed. This block should be called at the START of every flow that uses blocks included in the library.

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• s3_config (str) – Specify Amazon S3 credentials for submission retrieval store. Expects a json expression: {"aws_access_key_id": "X", "aws_secret_access_key": "Y"}, defaults to system_secret(S3_SECRET_KEY)

• s3_endpoint_url (Optional[str]) – Endpoint URL for S3 submission retrieval store, defaults to None

• ocs_config (str) – OCS Configuration for downloading submission data, defaults to system_secret(OCS_SECRET_KEY)

• http_config (Optional[str]) – An optional system secret field expressing the HTTP Configuration for downloading submission data. The content is expected to be a json formatted as: {"username": "X", "password": "Y", "ssl_cert": "CA bundle filename"}, defaults to None

• notification_workflow (str) – Notification flow name to run when the submission is initialized, defaults to IDP_SUBMISSION_NOTIFY_NAME

• workflow_uuid (str) – UUID of the triggered workflow version, defaults to workflow_input(Inputs.WorkflowUuid)

• workflow_name (str) – Name of the triggered workflow, defaults to workflow_input(Inputs.WorkflowName)

• workflow_version (str) – Version of the triggered workflow, defaults to workflow_input(Inputs.WorkflowVersion)

Example usage:

submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')

IDENTIFIER = 'SUBMISSION_BOOTSTRAP_2'

output(key=None)

Submission bootstrap ends with a CodeBlock, which nests the actual outputs under the ‘result’ key. This method overrides the default output in the same manner that flows_sdk.blocks.Routing.CodeBlock does for convenience.

Parameters:

key (Optional[str]) –

Optionally provide a key to directly get a nested property. When not provided, the entire ‘result’ will be returned. For example, we have:

{
    "result": {
        "a": {
            "b": 42
        }
    }
}

• skipping the key output()` will return ``{"a": {"b": 42}}

• calling with output("a") will result in {"b": 42}

• calling with output("a.b") will return 42

Return type:

str

Returns:

the value under the provided key

class flows_sdk.implementations.idp_v37.idp_blocks.MachineCollationBlock(submission, cases, dedupe_files=False, refresh_retention_period=True, remove_from_cases=None, retention_period=None, reference_name=None)

Bases: Block

Machine collation block groups files, documents and pages (from the submission) into cases

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• cases (Any) –

  This parameter accepts an array of JSON objects that contain information on how Cases should be created. There are two ways to collate a case: by the filenames specified in the current Submission, or by the ids of the Documents or Pages submitted in previous submissions.

  This parameter follows the given format:

  {
      'cases': [
          'external_case_id': 'HS-1',
          'filename': 'file1.pdf',
          'documents': [42],
          'pages': [43]
      ]
  }
  

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• dedupe_files (bool) – Enabling this setting will replace case data from repeated file names within the same case. Cases will retain data from the most recently submitted version of a particular file. Note that this setting does not delete the old data, it just removes it from the case. Keep in mind this option is only relevant when adding to cases

• remove_from_cases (Optional[Any]) –

  This parameter accepts an array of JSON objects that contains information on how documents or pages should be removed from a pre-existing case. Currently, the only way to de-collate from a case is by the ids of the Documents or Pages within a given case

  This parameter follows the given format:

  {
      'remove_from_cases': [
          'external_case_id': 'HS-1',
          'documents': [42],
          'pages': [43]
      ]
  }
  

• retention_period (Optional[int]) – The number of days to retain cases after they are updated by this block. If passed None, no changes to the deletion date will be made. By default, this value is None

• refresh_retention_period (bool) – If True, (re)applies Retention Period parameter to all cases passed into the block. If False, only updates those without a pre-existing deletion date. By default, this value is True

Example usage:

machine_collation = MachineCollationBlock(
    reference_name='machine_collation',
    submission=submission_bootstrap.output('result.submission'),
    cases=submission_bootstrap.output('result.api_params.cases'),
    dedupe_files=True,
    remove_from_cases=custom_supervision.output('remove_from_cases')
)

IDENTIFIER = 'MACHINE_COLLATION_2'

class flows_sdk.implementations.idp_v37.idp_blocks.MachineClassificationBlock(submission, api_params, reference_name=None, rotation_correction_enabled=True, mobile_processing_enabled=False, layout_release_uuid='${workflow.input.layout_release_uuid}', vpc_registration_threshold='${workflow.input.structured_layout_match_threshold}', nlc_enabled='${workflow.input.semi_structured_classification}', nlc_target_accuracy='${workflow.input.semi_target_accuracy}', nlc_doc_grouping_logic='${workflow.input.semi_doc_grouping_logic}')

Bases: Block

Machine classification block automatically matches documents to structured, semi-structured or additional layouts

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• rotation_correction_enabled (bool) – Identifies and corrects the orientation of semi-structured images, defaults to True

• mobile_processing_enabled (bool) – Improves the machine readability of photo captured documents, defaults to False

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• vpc_registration_threshold (str) – Structured pages above this threshold will be automatically matched to a layout variation, defaults to workflow_input(Settings.StructuredLayoutMatchThreshold)

• nlc_enabled (str) – Enables workflow to manage a model for automated classification of semi-structured and additional layout variations, defaults to workflow_input(Settings.SemiStructuredClassification)

• nlc_target_accuracy (str) – defaults to workflow_input(Settings.SemiTargetAccuracy)

• nlc_doc_grouping_logic (str) – Logic to handle multiple pages matched to the same layout variationin a given submission, defaults to workflow_input(Settings.SemiDocGroupingLogic)

Example usage:

machine_classification = MachineClassificationBlock(
    reference_name='machine_classification',
    submission=case_collation.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    rotation_correction_enabled=idp_wf_config.rotation_correction_enabled,
)

IDENTIFIER = 'MACHINE_CLASSIFICATION_4'

class flows_sdk.implementations.idp_v37.idp_blocks.ManualClassificationBlock(submission, api_params, reference_name=None, layout_release_uuid='${workflow.input.layout_release_uuid}', manual_nlc_enabled='${workflow.input.manual_nlc_enabled}', task_restrictions=None, notification_workflow='IDP_SUBMISSION_NOTIFY_V37')

Bases: Block

Manual classification block allows keyers to manually match submissions to their layouts. Keyers may perform manual classification if machine classification cannot automatically match a submission to a layout with high confidence

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• manual_nlc_enabled (str) – Enables manual classification when applicable, defaults to workflow_input(Settings.ManualNlcEnabled)

• task_restrictions (Optional[List[Any]]) – Defines what users can access Supervision tasks created by this block, defaults to None

• notification_workflow (str) – Notification flow name to run when the submission enters Manual Classification, defaults to IDP_SUBMISSION_NOTIFY_NAME

Example usage:

manual_classification = ManualClassificationBlock(
    reference_name='manual_classification',
    submission=machine_classification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
)

IDENTIFIER = 'MANUAL_CLASSIFICATION_2'

class flows_sdk.implementations.idp_v37.idp_blocks.MachineIdentificationBlock(submission, api_params, layout_release_uuid='${workflow.input.layout_release_uuid}', reference_name=None, manual_field_id_enabled='${workflow.input.manual_field_id_enabled}', field_id_target_accuracy='${workflow.input.field_id_target_accuracy}', table_id_target_accuracy='${workflow.input.table_id_target_accuracy}')

Bases: Block

Machine identification automatically identify fields and tables in the submission

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• manual_field_id_enabled (str) – Enables manual identification when applicable, defaults to workflow_input(Settings.ManualFieldIdEnabled)

• field_id_target_accuracy (str) – Field ID Target Accuracy, defaults to workflow_input(Settings.FieldIdTargetAccuracy)

• table_id_target_accuracy (str) – Table ID Target Accuracy, defaults to workflow_input(Settings.TableIdTargetAccuracy)

Example usage:

machine_identification = MachineIdentificationBlock(
    reference_name='machine_identification',
    submission=manual_classification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
)

IDENTIFIER = 'MACHINE_IDENTIFICATION_5'

class flows_sdk.implementations.idp_v37.idp_blocks.ManualIdentificationBlock(submission, api_params, reference_name=None, task_restrictions=None, manual_field_id_enabled='${workflow.input.manual_field_id_enabled}', layout_release_uuid='${workflow.input.layout_release_uuid}', notification_workflow='IDP_SUBMISSION_NOTIFY_V37')

Bases: Block

Manual identification allows keyers to complete field identification or table identification tasks, where they draw bounding boxes around the contents of certain fields, table columns or table rows. This identification process ensures that the system will be able to transcribe the correct content in the upcoming transcription process

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• task_restrictions (Optional[List[Any]]) – Defines what users can access Supervision tasks created by this block, defaults to []

• manual_field_id_enabled (str) – Enables manual identification when applicable, defaults to workflow_input(Settings.ManualFieldIdEnabled)

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• notification_workflow (str) – Notification flow name to run when the submission enters Manual Identification, defaults to IDP_SUBMISSION_NOTIFY_NAME

Example usage:

manual_identification = ManualIdentificationBlock(
    reference_name='manual_identification',
    submission=machine_identification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    task_restrictions=idp_wf_config.manual_identification_config.task_restrictions,
)

IDENTIFIER = 'MANUAL_IDENTIFICATION_4'

class flows_sdk.implementations.idp_v37.idp_blocks.MachineTranscriptionBlock(submission, api_params, reference_name=None, layout_release_uuid='${workflow.input.layout_release_uuid}', transcription_model='${workflow.input.transcription_model}', finetuning_only_trained_layouts='${workflow.input.finetuning_only_trained_layouts}', transcription_overrides='${workflow.input.transcription_overrides}', structured_text_target_accuracy='${workflow.input.structured_text_target_accuracy}', structured_text_confidence_threshold='${workflow.input.structured_text_threshold}', structured_text_acceptable_confidence='${workflow.input.structured_min_leg_threshold}', semi_structured_text_target_accuracy='${workflow.input.semi_structured_text_target_accuracy}', semi_structured_text_confidence_threshold='${workflow.input.semi_structured_text_threshold}', semi_structured_text_acceptable_confidence='${workflow.input.semi_structured_min_leg_threshold}', semi_structured_table_target_accuracy='${workflow.input.semi_structured_table_target_accuracy}', semi_structured_table_confidence_threshold='${workflow.input.semi_structured_table_threshold}', semi_structured_table_acceptable_confidence='${workflow.input.semi_structured_table_min_leg_threshold}', structured_checkbox_target_accuracy='${workflow.input.structured_checkbox_target_accuracy}', structured_checkbox_confidence_threshold='${workflow.input.structured_checkbox_threshold}', structured_checkbox_acceptable_confidence='${workflow.input.checkbox_min_leg_threshold}', structured_signature_target_accuracy='${workflow.input.structured_signature_target_accuracy}', structured_signature_confidence_threshold='${workflow.input.structured_signature_threshold}', structured_signature_acceptable_confidence='${workflow.input.signature_min_leg_threshold}', semi_structured_checkbox_target_accuracy='${workflow.input.semi_structured_checkbox_target_accuracy}', semi_structured_checkbox_confidence_threshold='${workflow.input.semi_structured_checkbox_threshold}', semi_structured_checkbox_acceptable_confidence='${workflow.input.semi_structured_checkbox_min_leg_threshold}')

Bases: Block

Machine transcription automatically transcribes the content of your submission

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• transcription_model (str) – The transcription model that will be used for this flow, defaults to workflow_input(Settings.TranscriptionModel)

• finetuning_only_trained_layouts (str) – defaults to workflow_input( Settings.FinetuningOnlyTrainedLayouts )

• transcription_overrides (str) – defaults to workflow_input( Settings.TranscriptionOverrides )

• structured_text_target_accuracy (str) – defaults to workflow_input( Settings.StructuredTextTargetAccuracy )

• structured_text_confidence_threshold (str) – defaults to workflow_input( Settings.StructuredTextThreshold )

• structured_text_acceptable_confidence (str) – defaults to workflow_input( Settings.StructuredTextMinLegThreshold )

• semi_structured_text_target_accuracy (str) – defaults to workflow_input( Settings.SemiStructuredTextTargetAccuracy )

• semi_structured_text_confidence_threshold (str) – defaults to workflow_input( Settings.SemiStructuredTextThreshold )

• semi_structured_text_acceptable_confidence (str) – defaults to workflow_input( Settings.SemiStructuredTextMinLegThreshold )

• structured_checkbox_target_accuracy (str) – defaults to workflow_input( Settings.StructuredCheckboxTargetAccuracy )

• structured_checkbox_confidence_threshold (str) – defaults to workflow_input( Settings.StructuredCheckboxThreshold )

• structured_checkbox_acceptable_confidence (str) – defaults to workflow_input( Settings.StructuredCheckboxMinLegThreshold )

• structured_signature_target_accuracy (str) – defaults to workflow_input( Settings.StructuredSignatureTargetAccuracy )

• structured_signature_confidence_threshold (str) – defaults to workflow_input( Settings.StructuredSignatureThreshold )

• structured_signature_acceptable_confidence (str) – defaults to workflow_input( Settings.StructuredSignatureMinLegThreshold )

• semi_structured_checkbox_target_accuracy (str) – defaults to workflow_input( Settings.SemiStructuredCheckboxTargetAccuracy )

• semi_structured_checkbox_confidence_threshold (str) – defaults to workflow_input( Settings.SemiStructuredCheckboxThreshold )

• semi_structured_checkbox_acceptable_confidence (str) – defaults to workflow_input( Settings.SemiStructuredCheckboxMinLegThreshold )

Example usage:

machine_transcription = MachineTranscriptionBlock(
    reference_name='machine_transcription',
    submission=manual_identification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
)

IDENTIFIER = 'MACHINE_TRANSCRIPTION_8'

class flows_sdk.implementations.idp_v37.idp_blocks.ManualTranscriptionBlock(submission, api_params, reference_name=None, supervision_transcription_masking=True, table_output_manual_review=False, task_restrictions=None, manual_transcription_enabled='${workflow.input.manual_transcription_enabled}', always_supervise_blank_cells=True, notification_workflow='IDP_SUBMISSION_NOTIFY_V37')

Bases: Block

Manual transcription lets your keyers manually enter the text found in fields or tables that could not be automatically transcribed

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (bool) – Prevents users from inputting invalid characters during Supervision Transcription tasks, defaults to True

• table_output_manual_review (bool) – Always generates a table transcription task if the layout contains a table. If disabled, a table transcription task will only be generated if one or more cells have transcribed values below the defined thresholds, defaults to False

• task_restrictions (Optional[List[Any]]) – Defines what users can access Supervision tasks for submission from a particular source or submissions matching a specific layout, defaults to []

• manual_transcription_enabled (str) – Enables manual transcription when applicable, defaults to workflow_input(Settings.ManualTranscriptionEnabled)

• always_supervise_blank_cells (bool) – Generates a table transcription task even if all cells are high confidence but at least one of them is a blank cell. Defaults to True.

• notification_workflow (str) – Notification flow name to run when the submission enters Manual Transcription, defaults to IDP_SUBMISSION_NOTIFY_NAME

Example usage:

manual_transcription = ManualTranscriptionBlock(
    reference_name='manual_transcription',
    submission=machine_transcription.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    supervision_transcription_masking=(
        idp_wf_config.manual_transcription_config.supervision_transcription_masking
    ),
    table_output_manual_review=(
        idp_wf_config.manual_transcription_config.table_output_manual_review
    ),
    task_restrictions=idp_wf_config.manual_transcription_config.task_restrictions,
)

IDENTIFIER = 'MANUAL_TRANSCRIPTION_4'

class flows_sdk.implementations.idp_v37.idp_blocks.FlexibleExtractionBlock(submission, api_params, reference_name=None, supervision_transcription_masking=True, task_restrictions=None, layout_release_uuid='${workflow.input.layout_release_uuid}', notification_workflow='IDP_SUBMISSION_NOTIFY_V37')

Bases: Block

Flexible extraction manually transcribes fields marked for review

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (bool) – Prevents users from inputting invalid characters during the Flexible Extraction task, defaults to True

• task_restrictions (Optional[List[Any]]) – Defines what users can access Supervision tasks created by this block, defaults to []

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• notification_workflow (str) – Notification flow name to run when the submission enters Flexible Extraction, defaults to IDP_SUBMISSION_NOTIFY_NAME

Example usage:

flexible_extraction = FlexibleExtractionBlock(
    reference_name='flexible_extraction',
    submission=manual_transcription.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    supervision_transcription_masking=(
        idp_wf_config.flexible_extraction_config.supervision_transcription_masking
    ),
    task_restrictions=idp_wf_config.flexible_extraction_config.task_restrictions,
)

IDENTIFIER = 'FLEXIBLE_EXTRACTION_3'

class flows_sdk.implementations.idp_v37.idp_blocks.SubmissionCompleteBlock(submission, reference_name=None, nlc_qa_sampling_ratio='${workflow.input.semi_qa_sample_rate}', field_id_qa_enabled='${workflow.input.field_id_qa_enabled}', field_id_qa_sampling_ratio='${workflow.input.field_id_qa_sample_rate}', table_id_qa_enabled='${workflow.input.table_id_qa_enabled}', table_id_qa_sampling_ratio='${workflow.input.table_id_qa_sample_rate}', transcription_qa_enabled='${workflow.input.qa}', transcription_structured_entry_qa_sample_rate='${workflow.input.structured_entry_qa_sample_rate}', transcription_structured_signature_qa_sample_rate='${workflow.input.structured_signature_qa_sample_rate}', transcription_structured_checkbox_qa_sample_rate='${workflow.input.structured_checkbox_qa_sample_rate}', transcription_semi_structured_qa_sample_rate='${workflow.input.semi_structured_qa_sample_rate}', table_cell_transcription_qa_enabled='${workflow.input.table_cell_transcription_qa_enabled}', table_cell_transcription_qa_sample_rate='${workflow.input.table_cell_transcription_qa_sample_rate}', auto_qa_sampling_rate_enabled='${workflow.input.auto_qa_sampling_rate_enabled}', exclude_auto_transcribe_fields_from_qa='${workflow.input.exclude_auto_transcribe_fields_from_qa}', payload=None)

Bases: Block

Submission complete block finalizes submission processing and updates reporting data. This block should be called at the END of every flow that uses blocks included in the flows_sdk.implementations.idp_v34 library. The block requires both the submission object and a payload to be passed in.

Mandatory parameters:

Parameters:

• submission (str) – Submission object

• payload (Any) – Object to pass to downstream systems

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• nlc_qa_sampling_ratio (Union[str, int]) – Defines the percentage of documents sampled for Classification QA, defaults to workflow_input(Settings.SemiQaSampleRate)

• field_id_qa_enabled (Union[str, bool]) – defaults to workflow_input(Settings.FieldIdQaEnabled)

• field_id_qa_sampling_ratio (Union[str, int]) – Defines the percentage of documents sampled for Identification QA, defaults to workflow_input(Settings.FieldIdQaSampleRate)

• table_id_qa_enabled (Union[str, bool]) – Allows users to verify the location of table cells, defaults to workflow_input(Settings.TableIdQaEnabled)

• table_id_qa_sampling_ratio (Union[str, int]) – Defines the percentage of tables the system samples for QA, defaults to workflow_input(Settings.TableIdQaSampleRate)

• transcription_qa_enabled (Union[str, bool]) – defaults to workflow_input(Settings.TranscriptionQaEnabled)

• transcription_qa_sampling_ratio – Defines the percentage of fields sampled for Transcription QA, defaults to workflow_input(Settings.TranscriptionQaSampleRate)

• table_cell_transcription_qa_enabled (Union[str, bool]) – defaults to workflow_input(Settings.TableCellTranscriptionQaEnabled)

• table_cell_transcription_qa_sample_rate (Union[str, int]) – Defines the percentage of cells the system samples for QA. This value is likely to be lower than “Transcription QA Sample Rate” since there are more table cells than fields on any given page, defaults to workflow_input(Settings.TableCellTranscriptionQaSampleRate)

• exclude_auto_transcribe_fields_from_qa (Union[str, bool]) – Defines fields that are marked as auto-transcribe in layout should be excluded from QA or not. defaults to workflow_input(Settings.ExcludeAutoTranscribeFieldsFromQA)

Example usage:

submission_complete = SubmissionCompleteBlock(
    reference_name='complete_submission',
    submission=flexible_extraction.output('submission')
)

IDENTIFIER = 'SUBMISSION_COMPLETE_4'

class flows_sdk.implementations.idp_v37.idp_blocks.IdpCustomSupervisionBlock(submission, task_purpose, supervision_template, reference_name=None, supervision_transcription_masking=True, task_restrictions=None, page_ids=None, notification_workflow='IDP_SUBMISSION_NOTIFY_V37', title='IDP Custom Supervision', description='IDP Custom Supervision')

Bases: Block

An IDP wrapper for the Custom Supervision block. It has the same functionality, but handles reading/writing data from the IDP database.

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• task_purpose (str) – the custom task name that’s given to all supervision tasks that run through a particular instance of a custom supervision block. This can be used to filter tasks in the task queue.

• supervision_template (Any) – JSON used for configuration of the Custom Supervision Task.

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (bool) – Prevents users from inputting invalid characters during the Custom Supervision task, defaults to True

• task_restrictions (Optional[List[Any]]) – Defines what users can access Supervision tasks created by this block, defaults to []

• page_ids (Optional[List[int]]) – A list of page ids to include. This should not be used. The IDP wrapper should handle pulling in all pages in the submission. defaults to []

• notification_workflow (str) – Notification flow name to run when the submission enters Custom Supervision (which is called within the IDP Custom Supervision block), defaults to IDP_SUBMISSION_NOTIFY_NAME

• title (Optional[str]) – UI-visible title of the block. Defaults to ‘IDP Custom Supervision’

• description (Optional[str]) – Description of the block. Both useful for documentation purpose and visible to users in the Flow studio. Defaults to ‘IDP Custom Supervision’

Example usage:

idp_custom_supervision = IdpCustomSupervisionBlock(
    reference_name='idp_custom_supervision',
    submission=manual_transcription.output('submission'),
    task_purpose='make_custom_decision',
    supervision_template=[
        {
            'name': 'three_column_template',
            'version': '1.0',
            'thumbnail': {
                'group_by_document': True,
                'group_by_case': True,
            },
            'action': [
                {
                    'name': 'my_tab',
                    'display': 'SSN Info',
                    'input': [
                        {
                            'name': 'ssn_field',
                            'type': 'transcription',
                            'data_identifier': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                            'title': 'SSN',
                        },
                    ],
                    'ui': {
                        'groups': [
                            {
                                'title': 'My Group',
                                'fields': ['ssn_field'],
                            },
                        ],
                    },
                },
            ],
        },
    ],
    supervision_transcription_masking=False,
)

IDENTIFIER = 'IDP_CUSTOM_SUPERVISION_2'

class flows_sdk.implementations.idp_v37.idp_blocks.IDPFullPageTranscriptionBlock(submission, reference_name=None, title='Full Page Transcription (Submission)', description='Transcribes the documents included in a submission', app_metadata=None)

Bases: Block

IDP Full Page Transcription Block machine-transcribes the unassigned pages in a submission

Mandatory parameters:

Parameters:

submission (str) – submission object that includes the raw documents that need to be transcribed

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• app_metadata (Optional[List[str]]) – used for capturing information about the flow

Example usage:

# This is one way to obtain the submission object (using SUbmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# IDPFullPageTranscriptionBlock transcribes the documents contained in the submission object
idp_fpt_block = IDPFullPageTranscriptionBlock(
    reference_name='idp_fpt',
    submission=submission_bootstrap.output(),
)
# reference to the output of IDPFullPageTranscriptionBlock which can be passed as input to
# the next block. For more information on output() and output references, please take a
# look at class Block and its output() method
output_ref = idp_fpt_block.output()

IDENTIFIER = 'FULL_PAGE_TRANSCRIPTION_SUBMISSION'

class flows_sdk.implementations.idp_v37.idp_blocks.IDPImageCorrectionBlock(submission, reference_name=None, rotation_correction_enabled=True, mobile_processing_enabled=False, title='Image Correction (Submission)', description='Rotate and de-skew documents included in a submission', app_metadata=None)

Bases: Block

IDP Image Correction Block rotates and de-skews tilted/skewed images so that they are more transcription-friendly

Mandatory parameters:

Parameters:

submission (str) – submission object that includes the raw documents that need to be image-corrected

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• app_metadata (Optional[List[str]]) – used for capturing information about the flow

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# IDPImageCorrectionBlock takes in the submission object and outputs in the same format with
# its images
idp_image_correct_block = IDPImageCorrectionBlock(
    reference_name='idp_image_correct',
    submission=submission_bootstrap.output(),
)
# reference to the output of IDPImageCorrectionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look at
# class Block and its output() method
output_ref = idp_image_correct_block.output()

IDENTIFIER = 'IMAGE_CORRECTION_SUBMISSION'

class flows_sdk.implementations.idp_v37.additional_blocks.JSONOutputsBlock(inputs, blocks=None)

Bases: Outputs

Output block allows users to send JSON data extracted by an IDP flow to other systems for downstream processing

Mandatory parameters:

Parameters:

inputs (Dict[str, Any]) – Used by the UI to automatically pre-populate the input of newly added blocks. Extended in this class to include 'enabled': True to visualize an enabled/disabled trigger that is controllable via Flow Studio.

Usage when parameters are to be defined in Flow Studio:

outputs = JSONOutputBlock(
    inputs={'result': }
)

class flows_sdk.implementations.idp_v37.additional_blocks.ImageCorrectionBlock(pages, reference_name=None, rotation_correction_enabled=True, mobile_processing_enabled=False, title='Image Correction (Pages)', description='Rotate and de-skew documents', app_metadata=None)

Bases: Block

Image Correction Block rotates and de-skews tilted/skewed images so that they are more transcription-friendly

Mandatory parameters:

Parameters:

pages (str) – list of page object that includes the raw documents that need to be image-corrected

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• app_metadata (Optional[List[str]]) – used for capturing information about the flow

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# ImageCorrectionBlock takes in the submission object and outputs in the same format with
# its images
image_correct_block = ImageCorrectionBlock(
    reference_name='image_correct',
    pages=submission_bootstrap.output(submission.unassigned_pages),
)
# reference to the output of IDPImageCorrectionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look
# at class Block and its output() method
output_ref = image_correct_block.output()

IDENTIFIER = 'IMAGE_CORRECTION_PAGES'

class flows_sdk.implementations.idp_v37.additional_blocks.FullPageTranscriptionBlock(pages, reference_name=None, title='Full Page Transcription (Pages)', description='Transcribes documents', app_metadata=None)

Bases: Block

Full Page Transcription Block machine-transcribes the document pages passed to it

Mandatory parameters:

Parameters:

pages (str) – list of pages that includes the raw documents that need to be transcribed

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• app_metadata (Optional[List[str]]) – used for capturing information about the flow

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# FullPageTranscriptionBlock transcribes the documents contained in the submission object
fpt_block = FullPageTranscriptionBlock(
    reference_name='fpt',
    submission=submission_bootstrap.output(submission.unassigned_pages),
)
# reference to the output of FullPageTranscriptionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look at
# class Block and its output() method
output_ref = fpt_block.output()

IDENTIFIER = 'FULL_PAGE_TRANSCRIPTION_PAGES'

class flows_sdk.implementations.idp_v37.additional_blocks.CustomSupervisionBlock(submission, task_purpose, data, supervision_template, reference_name=None, supervision_transcription_masking=True, task_restrictions=None, notification_workflow='IDP_SUBMISSION_NOTIFY_V37')

Bases: Block

The CustomSupervisionBlock uses a JSON schema as a form builder to create customer-driven UIs for Supervision tasks.

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• task_purpose (str) – the custom task name that’s given to all supervision tasks that run through a particular instance of a custom supervision block. This can be used to filter tasks in the task queue.

• data (Any) – JSON structure that supplies the data used to populated the supervision_template

• supervision_template (Any) – JSON used for configuration of the Custom Supervision Task.

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (bool) – Prevents users from inputting invalid characters during the Custom Supervision task, defaults to True

• task_restrictions (Optional[List[Any]]) – Defines what users can access Supervision tasks created by this block, defaults to []

• notification_workflow (str) – Notification flow name to run when the submission enters Custom Supervision, defaults to IDP_SUBMISSION_NOTIFY_NAME

Example usage:

custom_supervision = CustomSupervisionBlock(
    reference_name='custom_supervision',
    submission=manual_transcription.output('submission'),
    task_purpose='make_custom_decision',
    supervision_template=[
        {
            'name': 'three_column_template',
            'version': '1.0',
            'thumbnail': {
                'group_by_document': True,
                'group_by_case': True,
            },
            'action': [
                {
                    'name': 'my_tab',
                    'display': 'SSN Info',
                    'input': [
                        {
                            'name': 'ssn_field',
                            'type': 'transcription',
                            'data_identifier': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                            'title': 'SSN',
                        },
                    ],
                    'ui': {
                        'groups': [
                            {
                                'title': 'My Group',
                                'fields': ['ssn_field'],
                            },
                        ],
                    },
                },
            ],
        },
    ],
    data={
        'fields': [
            {
                'id': 967,
                'uuid': 'fe8d4e7a-a82c-448c-85a1-0a3cbbe53b61',
                'value': '111-22-3333',
                'page_id': 1,
                'data_identifier': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                'validation_overridden': false,
                'bounding_box': [
                    0.09360783305186686,
                    0.25792617589433436,
                    0.6358913805295097,
                    0.28862414740388187,
                ],
                'occurence_index': 1,
            },
        ],
        'template_fields': [
            {
                'uuid': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                'notes': '',
                'type': 'entry',
                'data_type_uuid': 'b600a4e4-758f-4c4b-a2f4-cbb5dd870a0c',
                'name': 'SSN',
                'n_occurences': 1,
            },
        ],
        'pages': [
            {
                'id': 15,
                'file_page_number': 1,
                'document_page_number': 1,  // Optional
                'image_url': '/image/6e55fa86-a36b-4178-8db1-3a514621d4c1',
                'form_id': 2,
                'submission_id': 3,
                'external_case_ids': [
                    'HS-27',
                ],
                'read_only': False,
                'filename': 'filename.pdf',
            },
        ],
        'documents': [
            {
                'id': 2,
                'layout_uuid': 'e31c3cbf-dcea-44fd-a052-902463c6040f',
                'layout_name': 'layout_name',
                'page_count': 2,
                'read_only': false,
            },
        ],
        'cases': [],
        'selected_choices': [],
    },
    supervision_transcription_masking=False,
)

IDENTIFIER = 'CUSTOM_SUPERVISION_2'

class flows_sdk.implementations.idp_v37.additional_blocks.SoapRequestBlock(method, reference_name=None, endpoint='', body_namespace=None, headers_namespace=None, headers=None, params=None, title='HTTP REST Block', description='HTTP REST block')

Bases: Block

Soap Block allows user to perform SOAP requests

Mandatory parameters:

Parameters:

• method (str) – SOAP method to invoke

• endpoint (str) – absolute url including schema to HTTP endpoint for this request

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• body_namespace (Optional[str]) – SOAP request body namespace url

• headers_namespace (Optional[str]) – SOAP request headers namespace url

• headers (Optional[Dict[Any, Any]]) – SOAP headers to use

• params (Optional[Dict[Any, Any]]) – key-value pair used as query parameters in the request to be inserted as SOAP body

Example usage:

# minimum viable instantiation, the rest of the fields can later be added in the UI
soap_block = SoapRequestBlock(
    reference_name='soap_request',
    title='Soap Request',
    description='Perform soap request',
    method='ExampleMethod',
)
soap_block = SoapRequestBlock(
    reference_name='soap_request',
    title='Soap Request',
    description='Perform soap request',
    method='ExampleMethod',
    endpoint='http://example.org/abc.wso',
    body_namespace='http://www.example.org/abc.countryinfo',
    params={'example': 'stuff'},
    headers={}
)
# reference to the output of SoapRequestBlock which can be passed as input to the next block
# for more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = soap_block.output()

IDENTIFIER = 'SOAP_REQ'

class flows_sdk.implementations.idp_v37.additional_blocks.HttpRestBlock(method, endpoint, reference_name=None, headers=None, params=None, payload=None, json=None, authorization_type=None, handled_error_codes=None, auth_params=None, title='HTTP REST Block', description='HTTP REST block')

Bases: BaseHttpRestBlock

Http Rest Block allows user to perform HTTP requests

Mandatory parameters:

Parameters:

• method (str) – HTTP method to use

• endpoint (str) – absolute url including schema to HTTP endpoint for this request

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• headers (Optional[Dict[str, str]]) – HTTP headers to use

• params (Optional[Dict[str, Any]]) – key-value pair used as query parameters in the request

• payload (Optional[Dict[str, str]]) – key-value pair to be used as x-www-form-urlencoded data. Mutually exclusive with json

• json (Optional[Dict[str, Any]]) – key-value pair to be used as json data. Mutually exclusive with payload

• authorization_type (Optional[str]) – authorization type (none, http_header, oauth_2_client_credentials)

• handled_error_codes (Optional[List[Union[int, str]]]) – specify HTTP error codes that will not fail the task, allowing for wildcards with the ‘x’ char. Example: [400, “5xx”]

• auth_params (Union[HttpHeaderAuthorizationParams, OAuth2AuthorizationParams, None]) – authorization parameters to be passed when the authorization_type is set to “http_header” or “oauth_2_client_credentials”

Example usage:

# minimum viable instantiation, the rest of the fields can later be added in the UI
http_rest_block = HttpRestBlock(
    reference_name='http_request',
    title='Http Request',
    description='Perform http request',
    method='GET',
)
http_rest_block = HttpRestBlock(
    reference_name='http_request',
    title='Http Request',
    description='Perform http request',
    method='GET',
    endpoint='https://sheets.googleapis.com/v4/spreadsheets/example',
)
# reference to the output of HttpRestBlock which can be passed as input to the next block
# for more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = http_rest_block.output()

class flows_sdk.implementations.idp_v37.additional_blocks.HyperscienceRestApiBlock(method, app_endpoint, reference_name=None, headers=None, params=None, payload=None, json=None, handled_error_codes=None, title='Hyperscience HTTP REST Block', description='Hyperscience HTTP REST block')

Bases: BaseHttpRestBlock

Make HTTP requests to the Hyperscience platform APIs. This block will automatically take care of prepending the base url and will include the appropriate authentication headers with the request.

Mandatory parameters:

Parameters:

• method (str) – HTTP method to use, e.g. ‘GET’, ‘POST’, etc.

• app_endpoint (str) – relative url, beginning with a forward slash, to an HTTP endpoint served by the Hyperscience platform. For reference on the available endpoints, check out https://docs.hyperscience.ai/. Absolute URLs that include a hostname are allowed only if the flow has received them as part of the response of a previous API request that the flow has made. Do not hardcode the hostname of the Hyperscience instance in your flow definitions as they will not work when you run the flows on another instance, or if the hostname changes. For making requests to external systems, use the HttpRestBlock or a library like “requests”.

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• headers (Optional[Dict[str, str]]) – HTTP headers to use

• params (Optional[Dict[str, Any]]) – key-value pair used as query parameters in the request

• payload (Optional[Dict[str, str]]) – key-value pair to be used as x-www-form-urlencoded data. Mutually exclusive with json

• json (Optional[Dict[str, Any]]) – key-value pair to be used as json data. Mutually exclusive with payload

• handled_error_codes (Optional[List[Union[int, str]]]) – specify HTTP error codes that will not fail the task, allowing for wildcards with the ‘x’ char. Example: [400, “5xx”]

Example usage:

hs_rest_block = HyperscienceRestApiBlock(
    reference_name='http_request',
    app_endpoint='/api/healthcheck',
    method='GET',
)

class flows_sdk.implementations.idp_v37.additional_blocks.DatabaseAccessBlock(reference_name=None, db_type='', database='', host='', username='', password='', query='', port=None, options=None, timeout=None, query_params=None, title='DB Block', description='DB access block')

Bases: Block

Database Block allows user to access and perform queries on the specified database

Mandatory parameters:

Parameters:

• db_type (str) – database type (mssql, oracle, postgres)

• host (str) – URL/IP address of the server database is hosted on

• database (str) – database name

• username (str) – database username

• password (str) – database password

• query (str) – parameterized query

Optional parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• port (Optional[int]) – database port number

• options (Optional[Dict[Any, Any]]) – dictionary of additional connection string options

• query_params (Optional[Dict[Any, Any]]) – dictionary of values for query placeholders

• timeout (Optional[int]) – timeout in seconds (mssql and postgres only)

Example usage:

# minimum viable instantiation, the rest of the fields can later be added in the UI
db_lookup = DatabaseAccessBlock(
    reference_name='db_lookup',
    title='Database Lookup',
    description='Perform database lookup',
)
# note that storing password in plain text form is not recommended
# password can later be entered in the UI or defined as a flow secret.
db_lookup = DatabaseAccessBlock(
    reference_name='db_lookup',
    title='Database Lookup',
    description='Perform database lookup',
    db_type='mssql',
    database='cars',
    host='example.com',
    username='user',
    # we recommend skipping the password field or defining it as a system secret
    # e.g. system_secret('{your_secret_identifier}') (both allow for secret input via. UI)
    password='pw'
    port=1433,
    timeout=200,
    query='SELECT * from CAR',
)
# reference to the output of DBAccessBlock which can be passed as input to the next block
# for more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = db_lookup.output()
# content of output_ref:
{
    "result": [
        {
            "id": 1,
            "brand": "Aston Martin",
            "model": "Vanquish"
        },
        {
            "id": 2,
            "brand": "Jaguar",
            "model": "XE 2018"
        }
    ]
}

Database output for the same query (SELECT * FROM CAR)

IDENTIFIER = 'DB_ACCESS'

Trigger Blocks (v37)

Note

“Triggers” are referred to as “Input Blocks” in our application documentation.

Warning

Flows are built to allow full definition of triggers either in code or manually in the Flow Studio. We recommend defining triggers manually in the Flow Studio. Instructions on how to do so are in our application documentation.

To enable trigger definition via the Flow Studio UI, you need to instantiate the IDPTriggers() convenience class and pass it to the triggers property of the Flow

return Flow(
   ...
   triggers=IDPTriggers()
)

If instead you wish to define specific triggers in Python, use the trigger definitions below.

Folder Listener (v37)

Identifier: FOLDER_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
folder	Path	N/A	Folder to scan for submissions	N/A
file_extensions	string	N/A	File extensions	List of file extensions to monitor for (e.g.: ‘png, jpg, pdf’)
has_meta	boolean	False	Enable metadata	Select this if a metadata file is to be expected along with document files (in XXX_index.txt file)
poll_interval	integer	10	Poll interval	Poll interval in seconds
warmup_interval	integer	15	Warm-up interval	Seconds to wait past document last modified time before processing it
folder_cleanup_delay	integer	86400	Folder cleanup delay	Seconds to wait before cleaning up subfolders
api_params	object	{}	API Parameters	N/A

folder_listener = IOBlock(
    identifier='FOLDER_TRIGGER',
    reference_name='folder_trigger',
    title='Folder listener',
    enabled=True,
    input={
        'folder': '/var/www/forms/forms/folder/',
        'file_extensions': 'png, pdf, jpg',
    },
)

triggers = IDPTriggers(blocks=[folder_listener])

Email Listener (v37)

Identifier: IMAP_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
provider [1]	string	imap	Email Provider	N/A
poll_interval	integer	60	Polling interval in seconds	N/A
folder	Path	N/A	Folder to scan for emails	N/A
email_body_treatment	string	“ignore”	Email body treatment	What to do with the body of an email that is ingested. Available options are “process” and “ignore”.
post_process_action	string	“move”	Post process action	What to do with the email after it is processed. Available options are “move” and “delete”.
post_process_move_folder	string	“”	Post process archive folder	Folder to move emails to once they are processed. It will be included only if post_process_action is set to “move”.
api_params	object	{}	API Parameters	N/A
render_headers [2]	array	[]	Headers to include	Headers to render at the top of the email body. Headers will be included only if email_body_treatment is set to “process”.

[1] one of:

imap
graph

[2] any of:

To
From
Subject
Date

Additional inputs when "imap" is the selected provider:

Name	Type	Default Value	Title	Description
host	string	N/A	IMAP server address	N/A
port	integer	993	Port Number	N/A
ssl	boolean	True	Use SSL connection	N/A
username	string	N/A	Username	N/A
password	Password	N/A	Password	N/A

Additional inputs when "graph" is the selected provider:

Name	Type	Default Value	Title	Description
client_id	string	N/A	Client id	Application (client) ID for the corresponding application registered with the Microsoft identity platform.
tenant_id	string	N/A	Tenant id	Also called directory ID. Unique identifier of the tenant in which the corresponding application is registered with.
graph_cloud_endpoint [1]	string	Global	Microsoft Graph Cloud Endpoint	Determines base URL used for accessing cloud services through Microsoft Graph.
azure_ad_endpoint [2]	string	AZURE_PUBLIC_CLOUD	Azure AD Endpoint	Determines base URL for the Azure Active Directory (Azure AD) endpoint to acquire token for each national cloud.
client_secret	Password	N/A	Client secret	Client secret for the corresponding application registered with the Microsoft identity platform.

[1] one of:

Global
US_GOV
US_DoD
Germany
China

[2] one of:

AZURE_PUBLIC_CLOUD
AZURE_CHINA
AZURE_GERMANY
AZURE_GOVERNMENT

imap_trigger = IOBlock(
    identifier='IMAP_TRIGGER',
    reference_name='imap_trigger',
    title='IMAP trigger',
    enabled=True,
    input={
        'host': 'example@mail.com',
        'folder': '/var/www/forms/forms/imap/',
        'username': 'admin',
        'password': 'pass',
    }
)

triggers = IDPTriggers(blocks=[imap_trigger])

Box Folder Listener (v37)

Identifier: BOX_FOLDER_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
source_folder_id	integer	N/A	Folder to scan for submissions	Use the Box Folder ID found in the URL
target_folder_id	integer	N/A	Folder to move completed files	Use the Box Folder ID found in the URL
file_extensions	array	[]	File extensions	Types of file extensions for which to monitor
custom_file_extensions	string	N/A	Other file extension types	Comma separated list of file extensions for which to monitor (e.g. ‘png, jpg, pdf’)
poll_interval	integer	10	Polling interval (in seconds)	How often the connector will check the base folder for submissions
warmup_interval	integer	15	Warm-up interval (in seconds)	How long the connector will wait to process the document after it was last modified
public_key_id	string	N/A	Public-Key ID	ID of the Public-key used for authentication with Box
private_key	Password	N/A	Private-Key	Private key used for authentication with Box
passphrase	Password	N/A	Passphrase	Passphrase used for authentication with Box
client_id	string	N/A	Client ID	Client Id used for authentication with Box
client_secret	Password	N/A	Client Secret	Client Secret used for authentication with Box
enterprise_id	string	N/A	Enterprise ID	Enterprise Id used for authentication with Box
api_params	object	{}	API Parameters	N/A

box_folder_trigger = IOBlock(
    identifier='BOX_FOLDER_TRIGGER',
    reference_name='box_folder_trigger',
    title='Box folder',
    enabled=True,
    input={
        'file_extensions': ['png', 'pdf', 'jpg'],
        'source_folder_id': 24,
        'target_folder_id': 42,
        'public_key_id': 'admin',
        'private_key': 'secret',
        'passphrase': 'password',
        'client_id': 'admin',
        'client_secret': 'secret',
        'enterprise_id': 'admin',
    }
)

triggers = IDPTriggers(blocks=[box_folder_trigger])

Cron Listener (v37)

Identifier: CRON_TRIGGER

Inputs:

Name	Type	Default Value	Title	Description
cron_spec	string	"*/5 * * * *"	Cron specification	N/A
time_zone	string	“US/Eastern”	Time Zone	N/A
api_params	object	{}	API Parameters	N/A

cron_trigger = IOBlock(
    identifier='CRON_TRIGGER',
    reference_name='cron_trigger',
    title='Cron Trigger',
    enabled=True,
    input={
        'cron_spec': '0 10 * * *',
        'time_zone': 'Europe/Sofia',
    }
)

triggers = IDPTriggers(blocks=[cron_trigger])

Salesforce Listener (v37)

Identifier: SALESFORCE_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
channel_name	string	N/A	Channel Name	Configured in the Hyperscience app in Salesforce.
username	string	N/A	Associated Username	Username associated with the Salesforce Connected App.
consumer_key	Password	N/A	Consumer Key	Consumer key used for authentication with Salesforce. Paired with the Salesforce instance from which it was issued.
private_key	Password	N/A	Private Key	Private key used for authentication with Salesforce.
sandbox_environment	boolean	false	Private Key	Enable if the Salesforce environment is a sandbox.
api_params	object	{}	API Parameters	N/A

Message Queue Listener (v37)

Identifier: MQ_LISTENER

Inputs:

Name	Type	Default Value	Title	Description
MQ_TYPE	string	“ACTIVE_MQ”	Message Queue type	Type of the message queue to connect with. Valid values are “ACTIVE_MQ”, “AMAZON_SQS”, “IBM_MQ” and “RABBIT_MQ”
MQ_USERNAME	string	null	Username/Access Key ID	N/A
MQ_PASSWORD	Password	null	Password/Secret Access Key	N/A
MQ_QUEUE_NAME	string	null	Queue Name	Name of the queue (topic) to connect to.
MQ_HOST	string	null	Host Name	N/A
MQ_PORT	integer	1415	Port Number	N/A
api_params	object	{}	API Parameters	N/A

Additional inputs when "AMAZON_SQS" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_REGION	string	null	AWS Region	N/A
MQ_QUEUE_URL	string	null	Queue URL	N/A
MQ_USE_EC2_INSTANCE_CREDENTIALS	boolean	True	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used.

NOTE: MQ_USERNAME can be used to provide Access Key ID and MQ_PASSWORD to provide Secret Access Key.

Additional inputs when "IBM_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_QUEUE_MANAGER	string	null	Queue Manager (IBM MQ)	N/A
MQ_SSL_CIPHER_SUITE [1]	string	null	SSL cipher suite	SSL cipher suite to use if SSL connection is used.
MQ_CHANNEL	string	null	Channel	N/A
MQ_NO_AUTH_REQUIRED	boolean	false	Channel	N/A
MQ_MQCSP_AUTHENTICATION_MODE	boolean	false	Channel	N/A

NOTE: MQ_NO_AUTH_REQUIRED is True, MQ_USERNAME and MQ_PASSWORD are optional.

[1] one of:

"NULL_MD5"
"NULL_SHA"
"RC4_MD5_EXPORT"
"RC4_MD5_US"
"RC4_SHA_US"
"RC2_MD5_EXPORT"
"DES_SHA_EXPORT"
"RC4_56_SHA_EXPORT1024"
"DES_SHA_EXPORT1024"
"TRIPLE_DES_SHA_US"
"TLS_RSA_WITH_NULL_SHA256"
"TLS_RSA_WITH_AES_128_CBC_SHA"
"TLS_RSA_WITH_AES_128_CBC_SHA256"
"TLS_RSA_WITH_AES_256_CBC_SHA"
"TLS_RSA_WITH_AES_256_CBC_SHA256"
"AES_SHA_US"
"TLS_RSA_WITH_DES_CBC_SHA"
"TLS_RSA_WITH_3DES_EDE_CBC_SHA"
"TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256"
"FIPS_WITH_DES_CBC_SHA"
"FIPS_WITH_3DES_EDE_CBC_SHA"

Additional inputs when "RABBIT_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_VIRTUAL_HOST	string	null	Virtual Host (RABBIT MQ)	N/A
MQ_EXCHANGE	string	“”	Exchange	N/A
MQ_ROUTING_KEY	string	“”	Routing key	N/A
MQ_CONNECTION_TYPE	string	“TCP”	Connection Type	“TCP or SSL”

rabbit_mq_listener = IOBlock(
    identifier='MQ_LISTENER',
    reference_name='mq_listener',
    title='RabbitMQ Listener',
    enabled=True,
    input={
        'MQ_TYPE': 'RABBIT_MQ',
        'MQ_QUEUE_NAME': 'some_queue_name',
        'MQ_HOST': 'somehost.com',
        'MQ_USERNAME': 'foo',
        'MQ_PASSWORD': system_secret('rabbit_mq_1_password'),
    }
)

triggers = IDPTriggers(blocks=[rabbit_mq_listener])

Universal Folder Listener (v37)

Identifier: UNIVERSAL_FOLDER_TRIGGER

Inputs:

Name	Type	Default Value	Title	Description
folder	string	N/A	Folder to scan for submissions	Enter the path relative to the base folder. Leave blank to monitor the base folder
file_extensions	array	[]	File extensions	Types of file extensions for which to monitor
other_file_extensions	string	N/A	Other file extensions	Comma separated list of file extensions for which to monitor (e.g. ‘png, jpg, pdf’)
has_meta	boolean	false	Include submission level parameters	Select this to ingest JSON files along with document files and submission folders. These JSON files can contain information such as metadata, cases and external_id. These JSON file names should match the related files or folders (e.g., XXX.jpg.json for XXX.jpg)
poll_interval	integer	10	Polling interval (in seconds)	How often the Folder Listener will check the base folder for submissions
warmup_interval	integer	15	Warm-up interval (in seconds)	How long the Folder Listener will wait to process the document after it was last modified
folder_cleanup_delay	number	24	Empty folder cleanup delay (in hours)	How often the Folder Listener will remove empty folders from the base folder
api_params	object	{}	API Parameters	N/A

universal_folder_listener = IOBlock(
    identifier='UNIVERSAL_FOLDER_TRIGGER',
    reference_name='universal_folder_trigger',
    title='Universal Folder Listener',
    enabled=True,
    input={
        'folder': '/var/www/forms/forms/folder/',
        'file_extensions': 'png, pdf, jpg',
    }
)

triggers = IDPTriggers(blocks=[universal_folder_listener])

Output Blocks (v37)

Warning

Flows pass information to downstream systems using the IDPOutputsBlock. This block is specially built to allow full definition either in code or manually in the Flow Studio. We recommend defining outputs manually in the Flow Studio.

Instructions on how to do so are in our application documentation. Simply include an IDPOutputsBlock in your Python flow definition wherever you wish to send information to downstream systems.

class flows_sdk.implementations.idp_v37.idp_blocks.IDPOutputsBlock(inputs, blocks=None)

Bases: Outputs

Output block allows users to send data extracted by an IDP flow to other systems for downstream processing

Mandatory parameters:

Parameters:

inputs (Dict[str, Any]) – Used by the UI to automatically pre-populate the input of newly added blocks. Extended in this class to include 'enabled': True to visualize an enabled/disabled trigger that is controllable via Flow Studio.

Usage when parameters are to be defined in Flow Studio:

outputs = IDPOutputBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')}
)

If instead you wish to define specific outputs in Python, use the outputs definitions below.

HTTP Notifier Output (v37)

The HTTP Notifier (REST) output connection will POST results from the system to a specified HTTP endpoint.

Identifier: COB_HTTP_EXPORT

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
endpoint	string	N/A	Endpoint URL	URL that output notifications will be posted to
endpoint_secrets	string	N/A	Endpoint URL secrets	Endpoint URL Secrets, format: (one per line) secret_key_name=secret_value
timeout	int	600	Timeout (seconds)	How long (in seconds) to keep the connection open if no bytes are received from the endpoint. Set to 0 to wait indefinitely.
authorization_type [1]	string	“none”	Authorization type	Type of authorization
authorization_header_name	string	null	Authorization header name	Authorization header name to be set in the notification request
authorization_header	Password	null	Authorization header value	Authorization header to be set in the notification request
auth_url	string	null	OAuth2 authorization URL	The endpoint for the authorization server
client_id	string	null	Client ID	The client identifier issued to the client during the application registration process
client_secret	Password	null	Client Secret	The client secret issued to the client during the application registration process
scope	string	null	Scope	Scope to request to oauth server
audience	string	null	Audience	Resource service URL where token will be valid
additional_oauth_parameters	json	{}	Additional Oauth Request Parameters	Additional parameters to send when requesting token
ssl_cert_source [2]	string	null	Source	N/A
ssl_cert_path	string	null	Trusted Certificate(s) path	Enter the path relative to the base folder
ssl_cert_value	MultilineText	null	Trusted Certificate(s)	N/A

[1] one of:

"none"
"http_header"
"oauth_2_client_credentials"

[2] one of:

"env_var"
"ca_bundle_path"
"certificate_value"

cob_http_export = IOBlock(
    identifier='COB_HTTP_EXPORT',
    reference_name='cob_http_export',
    title='HTTP Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'SUBMISSION',
        'endpoint': 'example.com',
        'authorization_type': 'none',
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_http_export],
)

Message Queue Notifier Output (v37)

The Message Queue Notifier Output can configure connections to ActiveMQ, Amazon SQS, IBM MQ, and RabbitMQ message queues.

Identifier: COB_MQ_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
MQ_TYPE	string	“ACTIVE_MQ”	Message Queue type	Type of the message queue to connect with. Valid values are “ACTIVE_MQ”, “AMAZON_SQS”, “IBM_MQ” and “RABBIT_MQ”
MQ_USERNAME	string	null	Username/Access Key ID	N/A
MQ_PASSWORD	Password	null	Password/Secret Access Key	N/A
MQ_QUEUE_NAME	string	null	Queue Name	Name of the queue (topic) to connect to. It applies only to “ACTIVE_MQ”, “IBM_MQ” and “RABBIT_MQ” MQ_TYPEs.
MQ_HOST	string	null	Host Name	N/A
MQ_PORT	integer	1415	Port Number	N/A

Additional inputs when "AMAZON_SQS" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_REGION	string	null	AWS Region	N/A
MQ_QUEUE_URL	string	null	Queue URL	N/A
MQ_USE_EC2_INSTANCE_CREDENTIALS	boolean	True	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used.
MQ_MESSAGE_GROUP_ID	string	null	Group ID for FIFO queues	N/A
MQ_MESSAGE_METADATA	string	null	Additional SQS Metadata	Input additional metadata key value pairs in the format of { “key”: {“TYPE”: “String”, “VALUE”: “”}, “key2”: {“TYPE”: “Number”, “VALUE”: “12345”}

NOTE: MQ_USERNAME can be used to provide Access Key ID and MQ_PASSWORD to provide Secret Access Key.

Additional inputs when "IBM_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_QUEUE_MANAGER	string	null	Queue Manager (IBM MQ)	N/A
MQ_SSL_CIPHER_SUITE [1]	string	null	SSL cipher suite	SSL cipher suite to use if SSL connection is used.
MQ_CHANNEL	string	null	Channel	N/A
MQ_NO_AUTH_REQUIRED	boolean	false	Channel	N/A
MQ_MQCSP_AUTHENTICATION_MODE	boolean	false	Channel	N/A

NOTE: MQ_NO_AUTH_REQUIRED is True, MQ_USERNAME and MQ_PASSWORD are optional.

[1] one of:

"NULL_MD5"
"NULL_SHA"
"RC4_MD5_EXPORT"
"RC4_MD5_US"
"RC4_SHA_US"
"RC2_MD5_EXPORT"
"DES_SHA_EXPORT"
"RC4_56_SHA_EXPORT1024"
"DES_SHA_EXPORT1024"
"TRIPLE_DES_SHA_US"
"TLS_RSA_WITH_NULL_SHA256"
"TLS_RSA_WITH_AES_128_CBC_SHA"
"TLS_RSA_WITH_AES_128_CBC_SHA256"
"TLS_RSA_WITH_AES_256_CBC_SHA"
"TLS_RSA_WITH_AES_256_CBC_SHA256"
"AES_SHA_US"
"TLS_RSA_WITH_DES_CBC_SHA"
"TLS_RSA_WITH_3DES_EDE_CBC_SHA"
"TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256"
"FIPS_WITH_DES_CBC_SHA"
"FIPS_WITH_3DES_EDE_CBC_SHA"

Additional inputs when "RABBIT_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_VIRTUAL_HOST	string	null	Virtual Host (RABBIT MQ)	N/A
MQ_EXCHANGE	string	“”	Exchange	N/A
MQ_ROUTING_KEY	string	“”	Routing key	N/A
MQ_CONNECTION_TYPE	string	“TCP”	Connection Type	“TCP or SSL”

mq_notifier = IOBlock(
    identifier='COB_MQ_NOTIFIER',
    reference_name='mq_notifier',
    title='MQ Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'SUBMISSION',
        'MQ_USERNAME': 'admin',
        'MQ_PASSWORD': 'pass',
        'MQ_QUEUE_NAME': 'queue',
        'MQ_HOST': 'host',
    },
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[mq_notifier],
)

Box Notifier Output (v37)

Provides an out-of-the-box integration into Box systems.

Identifier: COB_BOX_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
public_key_id	string	N/A	Public-Key ID	ID of the Public-key used for authentication with Box
private_key	Password	N/A	Private-Key	Private key used for authentication with Box
passphrase	Password	N/A	Passphrase	Passphrase used for authentication with Box
client_id	string	N/A	Client ID	Client Id used for authentication with Box
client_secret	Password	N/A	Client Secret	Client Secret used for authentication with Box
enterprise_id	string	N/A	Enterprise ID	Enterprise Id used for authentication with Box
template_key	string	“”	Box Metadata Template Key	Enter the key of the Box Metadata template that you would like to map the Hyperscience metadata to.
static_output_fields_to_map	array	[]	Static Metadata Fields	Specify the Hyperscience fields you want to store in Box metadata.
submission_id	string	“”	Key for Mapping Submission ID	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_id	string	“”	Key for Mapping Document ID	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
submission_state	string	“”	Key for Mapping Submission State	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_state	string	“”	Key for Mapping Document State	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
submission_exceptions	string	“”	Key for Mapping Submission Exceptions	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_exceptions	string	“”	Key for Mapping Document Exceptions	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_layout_uuid	string	“”	Key for Mapping Document Layout Uuid	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_layout_name	string	“”	Key for Mapping Document Layout Name	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
start_time	string	“”	Key for Mapping Start Time	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
complete_time	string	“”	Key for Mapping Complete Time	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_fields_template_mappings	json	{}	JSON for Additional Metadata Fields	Specify additional metadata fields using their key value. Please consult Box Integration setup manual for JSON template and instructions.

box_notifier = IOBlock(
    identifier='COB_BOX_NOTIFIER',
    reference_name='box_notifier',
    title='Box Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'private_key': 'password',
        'passphrase': 'password',
        'public_key_id': 'admin',
        'client_id': 'admin',
        'client_secret': 'secret',
        'enterprise_id': 'admin',
        'template_key': 'key',
    },
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[box_notifier],
)

UiPath Notifier Output (v37)

Provides an out-of-the-box integration into UiPath systems.

Identifier: COB_EXPORT_UIPATH

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
url	string	N/A	URL	Base URL of the uipath instance
organization_unit_id	string	null	Organization unit id	Organization unit id
tenant	string	N/A	Tenant	Tenant
username	string	N/A	Username	Username
password	Password	N/A	Password	Password
queue	string	N/A	Queue	Queue

cob_export_uipath = IOBlock(
    identifier='COB_EXPORT_UIPATH',
    reference_name='cob_export_uipath',
    title='CCB Export UiPath',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'FLAT_SUBMISSION',
        'url': 'example.com',
        'tenant': 'tenant',
        'username': 'admin',
        'password': 'pass',
        'queue': 'queue',
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_export_uipath],
)

Salesforce Notifier Output (v37)

With the Salesforce Notifier Block, you can configure your flow to send extracted information to Salesforce. The Salesforce Notifier Block can use extracted information to look up, and then update any number of fields on a Salesforce object. The Salesforce Notifier Block is available in both SaaS and on-premise versions of the Hyperscience application.

Identifier: COB_SALESFORCE_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
username	string	N/A	Associated Username	Username associated with the Salesforce Connected App
consumer_key	password	N/A	Consumer Key	Consumer key used for authentication with Salesforce. Paired with the Salesforce instance from which it was issued.
private_key	password	N/A	Private Key	Private key used for authentication with Salesforce.
sandbox_environment	boolean	false	Private Key	Enable if the Salesforce environment is a sandbox.
object_api_name	string	N/A	Object API Name	API name of the object in Salesforce to lookup and update.
new_record_on_lookup_failure	boolean	false	Create new record on lookup failure	When enabled, it will create a new record if the lookup fails to find a record using the lookup parameters. When disabled, lookup will fail if it does not find a record using the lookup parameters.
object_lookup_mapping	json	N/A	Lookup Field Mapping	Specify which Salesforce fields (using their API name) to be queried with which extracted values from the document for the lookup using key-value format. For example: “DOCUMENT_FIELD_NAME”: “OBJECT_FIELD_API_NAME”.
document_fields_mapping	json	N/A	Document Field Mappings	Specify which Salesforce fields (using their API name) you want to update with which extracted data from Hyperscience using key-value format. For example: “DOCUMENT_FIELD_NAME”: “OBJECT_FIELD_API_NAME”.”
overwrite_existing_field_data	boolean	false	Overwrite existing field data	If this is checked and a selected field has data in it, Hyperscience will overwrite that data. If this is unchecked, existing data will not be overwritten and the operation will fail.

cob_salesforce_notifier = IOBlock(
    identifier='COB_SALESFORCE_NOTIFIER',
    reference_name='cob_salesforce_notifier',
    title='Salesforce Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'username': 'salesforce_user',
        'consumer_key': 'salesforce_consumer_key',
        'private_key': 'salesforce_private_key',
        'object_api_name': 'salesforce_object_api_name',
        'object_lookup_mapping': {"LAYOUT_VARIATION_XYZ_FIELD_NAME_1": "SF_FIELD_API_NAME_1", "LAYOUT_VARIATION_XYZ_FIELD_NAME_2": "SF_FIELD_API_NAME_2"},
        'document_fields_mapping': {"LAYOUT_VARIATION_XYZ_FIELD_NAME_3": "SF_FIELD_API_NAME_3", "LAYOUT_VARIATION_XYZ_FIELD_NAME_4": "SF_FIELD_API_NAME_4"}
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_salesforce_notifier],
)

Helper Classes and Functions (v37)

The following classes are used to easily instantiate and manage a flow’s settings.

flows_sdk.implementations.idp_v37.idp_values.get_idp_wf_inputs(idp_wf_config)

A helper function that maps the values provided by an IdpWorkflowConfig to their corresponding IDP keys. :type idp_wf_config: IdpWorkflowConfig :param idp_wf_config: with static values to be filled. :rtype: Dict[str, Any] :return: a dictionary with filled static values, compatible with IDP flows.

Example usage with default values:

flow = Flow(
    input=get_idp_wf_inputs(get_idp_wf_config()),
    ...
)

Example usage:

Example usage when instantiating an IDP flow with custom parameters::
flow = Flow(
    input=get_idp_wf_inputs({an instance of IdpWorkflowConfig}),
    ...
)

flows_sdk.implementations.idp_v37.idp_values.get_idp_wf_config()

A helper function that instantiates an IdpWorkflowConfig pre-filled with default values. IdpWorkflowConfig is the container class for defining flow-level settings. :rtype: IdpWorkflowConfig :return: a valid, fully constructed IdpWorkflowConfig instance

Example usage when instantiating an IDP flow:

flow = Flow(
    input=get_idp_wf_inputs(get_idp_wf_config()),
    ...
)

class flows_sdk.implementations.idp_v37.idp_values.IdpWorkflowConfig

Convenience dataclass outlining all flow-level config values for IDP.

class flows_sdk.implementations.idp_v37.idp_values.IDPTriggers(blocks=None)

An adapter class making it easier to instantiate flows_sdk.flows.Triggers for IDP flows. In particular, provides defaults for some fields (e.g. title, description, reference_name) and defines api_params_manifest with IDP specific parameters.

Parameters:

blocks (Optional[Sequence[IOBlock]]) – flows_sdk.blocks.IOBlock s to be included as triggers for an IDP Flow. Optional, defaults to an empty list.

Example usage when instantiating an IDP flow:

folder_listener = IOBlock(...)
flow = Flow(
    triggers=IDPTriggers([folder_listener]),
    ...
)

class flows_sdk.implementations.idp_v37.idp_values.IDPManifest(flow_identifier)

An adapter class making it easier to instantiate flows_sdk.flows.Manifest for IDP flows. In particular, provides defaults for some fields (e.g. roles, identifier) but more importantly, defines all py:class:flows_sdk.flows.Parameter that construct the Flow inputs and their UI representation (left-hand side menu in Flow studio).

Parameters:

flow_identifier (str) – A system-wide unique identifier for flows_sdk.flows.Flow

Example usage when instantiating an IDP flow:

flow = Flow(
    manifest=IDPManifest(flow_identifier='FLOW_IDENTIFIER'),
    ...
)

V36

Processing Blocks (v36)

Core Blocks are powerful processing blocks necessary to build intelligent document processing solutions on Hyperscience. They include both manual and machine processing blocks.

Note

Classes in the idp_v36 implementation have parameters with ambiguous typing. For example, a field of type integer could also be represented as a string reference (e.g. that integer gets provided at runtime). The types introduced in the example are therefore incomplete and are likely to be revisioned in a future version.

class flows_sdk.implementations.idp_v36.idp_blocks.SubmissionBootstrapBlock(reference_name=None, layout_release_uuid='${workflow.input.layout_release_uuid}', s3_config='${system.secrets.s3_downloader}', s3_endpoint_url=None, ocs_config='${system.secrets.ocs_downloader}', http_config=None, notification_workflow='IDP_SUBMISSION_NOTIFY_V36', workflow_uuid='${workflow.input.workflow_uuid}', workflow_name='${workflow.input.workflow_name}', workflow_version='${workflow.input.workflow_version}')

Bases: Block

Submission bootstrap block initializes the submission object and prepares external images or other submission data if needed.

This block should be called at the START of every flow that uses blocks included in the library.

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• s3_config (str) – Specify Amazon S3 credentials for submission retrieval store. Expects a json expression: {"aws_access_key_id": "X", "aws_secret_access_key": "Y"}, defaults to system_secret(S3_SECRET_KEY)

• s3_endpoint_url (Optional[str]) – Endpoint URL for S3 submission retrieval store, defaults to None

• ocs_config (str) – OCS Configuration for downloading submission data, defaults to system_secret(OCS_SECRET_KEY)

• http_config (Optional[str]) – An optional system secret field expressing the HTTP Configuration for downloading submission data. The content is expected to be a json formatted as: {"username": "X", "password": "Y", "ssl_cert": "CA bundle filename"}, defaults to None

• notification_workflow (str) – Notification flow name to run when the submission is initialized, defaults to IDP_SUBMISSION_NOTIFY_NAME

• workflow_uuid (str) – UUID of the triggered workflow version, defaults to workflow_input(Inputs.WorkflowUuid)

• workflow_name (str) – Name of the triggered workflow, defaults to workflow_input(Inputs.WorkflowName)

• workflow_version (str) – Version of the triggered workflow, defaults to workflow_input(Inputs.WorkflowVersion)

Example usage:

submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')

IDENTIFIER = 'SUBMISSION_BOOTSTRAP_2'

output(key=None)

Submission bootstrap ends with a CodeBlock, which nests the actual outputs under the ‘result’ key. This method overrides the default output in the same manner that flows_sdk.blocks.Routing.CodeBlock does for convenience.

Parameters:

key (Optional[str]) –

Optionally provide a key to directly get a nested property. When not provided, the entire ‘result’ will be returned.

For example, we have:

{
    "result": {
        "a": {
            "b": 42
        }
    }
}

• skipping the key output()` will return ``{"a": {"b": 42}}

• calling with output("a") will result in {"b": 42}

• calling with output("a.b") will return 42

Return type:

str

Returns:

the value under the provided key

class flows_sdk.implementations.idp_v36.idp_blocks.MachineCollationBlock(submission, cases, dedupe_files=False, refresh_retention_period=True, remove_from_cases=None, retention_period=None, reference_name=None)

Bases: Block

Machine collation block groups files, documents and pages (from the submission) into cases

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• cases (Any) –

  This parameter accepts an array of JSON objects that contain information on how Cases should be created. There are two ways to collate a case: by the filenames specified in the current Submission, or by the ids of the Documents or Pages submitted in previous submissions.

  This parameter follows the given format:

  {
      'cases': [
          'external_case_id': 'HS-1',
          'filename': 'file1.pdf',
          'documents': [42],
          'pages': [43]
      ]
  }
  

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• dedupe_files (bool) – Enabling this setting will replace case data from repeated file names within the same case. Cases will retain data from the most recently submitted version of a particular file. Note that this setting does not delete the old data, it just removes it from the case. Keep in mind this option is only relevant when adding to cases

• remove_from_cases (Optional[Any]) –

  This parameter accepts an array of JSON objects that contains information on how documents or pages should be removed from a pre-existing case. Currently, the only way to de-collate from a case is by the ids of the Documents or Pages within a given case

  This parameter follows the given format:

  {
      'remove_from_cases': [
          'external_case_id': 'HS-1',
          'documents': [42],
          'pages': [43]
      ]
  }
  

• retention_period (Optional[int]) – The number of days to retain cases after they are updated by this block. If passed None, no changes to the deletion date will be made. By default, this value is None

• refresh_retention_period (bool) – If True, (re)applies Retention Period parameter to all cases passed into the block. If False, only updates those without a pre-existing deletion date. By default, this value is True

Example usage:

machine_collation = MachineCollationBlock(
    reference_name='machine_collation',
    submission=submission_bootstrap.output('result.submission'),
    cases=submission_bootstrap.output('result.api_params.cases'),
    dedupe_files=True,
    remove_from_cases=custom_supervision.output('remove_from_cases')
)

IDENTIFIER = 'MACHINE_COLLATION_2'

class flows_sdk.implementations.idp_v36.idp_blocks.MachineClassificationBlock(submission, api_params, reference_name=None, rotation_correction_enabled=True, mobile_processing_enabled=False, layout_release_uuid='${workflow.input.layout_release_uuid}', vpc_registration_threshold='${workflow.input.structured_layout_match_threshold}', nlc_enabled='${workflow.input.semi_structured_classification}', nlc_target_accuracy='${workflow.input.semi_target_accuracy}', nlc_doc_grouping_logic='${workflow.input.semi_doc_grouping_logic}')

Bases: Block

Machine classification block automatically matches documents to structured, semi-structured or additional layouts

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• rotation_correction_enabled (bool) – Identifies and corrects the orientation of semi-structured images, defaults to True

• mobile_processing_enabled (bool) – Improves the machine readability of photo captured documents, defaults to False

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• vpc_registration_threshold (str) – Structured pages above this threshold will be automatically matched to a layout variation, defaults to workflow_input(Settings.StructuredLayoutMatchThreshold)

• nlc_enabled (str) – Enables workflow to manage a model for automated classification of semi-structured and additional layout variations, defaults to workflow_input(Settings.SemiStructuredClassification)

• nlc_target_accuracy (str) – defaults to workflow_input(Settings.SemiTargetAccuracy)

• nlc_doc_grouping_logic (str) – Logic to handle multiple pages matched to the same layout variationin a given submission, defaults to workflow_input(Settings.SemiDocGroupingLogic)

Example usage:

machine_classification = MachineClassificationBlock(
    reference_name='machine_classification',
    submission=case_collation.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    rotation_correction_enabled=idp_wf_config.rotation_correction_enabled,
)

IDENTIFIER = 'MACHINE_CLASSIFICATION_3'

class flows_sdk.implementations.idp_v36.idp_blocks.ManualClassificationBlock(submission, api_params, reference_name=None, layout_release_uuid='${workflow.input.layout_release_uuid}', manual_nlc_enabled='${workflow.input.manual_nlc_enabled}', task_restrictions=None, notification_workflow='IDP_SUBMISSION_NOTIFY_V36')

Bases: Block

Manual classification block allows keyers to manually match submissions to their layouts. Keyers may perform manual classification if machine classification cannot automatically match a submission to a layout with high confidence

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• manual_nlc_enabled (str) – Enables manual classification when applicable, defaults to workflow_input(Settings.ManualNlcEnabled)

• task_restrictions (Optional[List[Any]]) – Defines what users can access Supervision tasks created by this block, defaults to None

• notification_workflow (str) – Notification flow name to run when the submission enters Manual Classification, defaults to IDP_SUBMISSION_NOTIFY_NAME

Example usage:

manual_classification = ManualClassificationBlock(
    reference_name='manual_classification',
    submission=machine_classification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
)

IDENTIFIER = 'MANUAL_CLASSIFICATION_2'

class flows_sdk.implementations.idp_v36.idp_blocks.MachineIdentificationBlock(submission, api_params, layout_release_uuid='${workflow.input.layout_release_uuid}', reference_name=None, manual_field_id_enabled='${workflow.input.manual_field_id_enabled}', field_id_target_accuracy='${workflow.input.field_id_target_accuracy}', table_id_target_accuracy='${workflow.input.table_id_target_accuracy}')

Bases: Block

Machine identification automatically identify fields and tables in the submission

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• manual_field_id_enabled (str) – Enables manual identification when applicable, defaults to workflow_input(Settings.ManualFieldIdEnabled)

• field_id_target_accuracy (str) – Field ID Target Accuracy, defaults to workflow_input(Settings.FieldIdTargetAccuracy)

• table_id_target_accuracy (str) – Table ID Target Accuracy, defaults to workflow_input(Settings.TableIdTargetAccuracy)

Example usage:

machine_identification = MachineIdentificationBlock(
    reference_name='machine_identification',
    submission=manual_classification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
)

IDENTIFIER = 'MACHINE_IDENTIFICATION_4'

class flows_sdk.implementations.idp_v36.idp_blocks.ManualIdentificationBlock(submission, api_params, reference_name=None, task_restrictions=None, manual_field_id_enabled='${workflow.input.manual_field_id_enabled}', layout_release_uuid='${workflow.input.layout_release_uuid}', notification_workflow='IDP_SUBMISSION_NOTIFY_V36')

Bases: Block

Manual identification allows keyers to complete field identification or table identification tasks, where they draw bounding boxes around the contents of certain fields, table columns or table rows. This identification process ensures that the system will be able to transcribe the correct content in the upcoming transcription process

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• task_restrictions (Optional[List[Any]]) – Defines what users can access Supervision tasks created by this block, defaults to []

• manual_field_id_enabled (str) – Enables manual identification when applicable, defaults to workflow_input(Settings.ManualFieldIdEnabled)

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• notification_workflow (str) – Notification flow name to run when the submission enters Manual Identification, defaults to IDP_SUBMISSION_NOTIFY_NAME

Example usage:

manual_identification = ManualIdentificationBlock(
    reference_name='manual_identification',
    submission=machine_identification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    task_restrictions=idp_wf_config.manual_identification_config.task_restrictions,
)

IDENTIFIER = 'MANUAL_IDENTIFICATION_4'

class flows_sdk.implementations.idp_v36.idp_blocks.MachineTranscriptionBlock(submission, api_params, reference_name=None, layout_release_uuid='${workflow.input.layout_release_uuid}', transcription_model='${workflow.input.transcription_model}', finetuning_only_trained_layouts='${workflow.input.finetuning_only_trained_layouts}', transcription_overrides='${workflow.input.transcription_overrides}', structured_text_target_accuracy='${workflow.input.structured_text_target_accuracy}', structured_text_confidence_threshold='${workflow.input.structured_text_threshold}', structured_text_acceptable_confidence='${workflow.input.structured_min_leg_threshold}', semi_structured_text_target_accuracy='${workflow.input.semi_structured_text_target_accuracy}', semi_structured_text_confidence_threshold='${workflow.input.semi_structured_text_threshold}', semi_structured_text_acceptable_confidence='${workflow.input.semi_structured_min_leg_threshold}', semi_structured_table_target_accuracy='${workflow.input.semi_structured_table_target_accuracy}', semi_structured_table_confidence_threshold='${workflow.input.semi_structured_table_threshold}', semi_structured_table_acceptable_confidence='${workflow.input.semi_structured_table_min_leg_threshold}', structured_checkbox_target_accuracy='${workflow.input.structured_checkbox_target_accuracy}', structured_checkbox_confidence_threshold='${workflow.input.structured_checkbox_threshold}', structured_checkbox_acceptable_confidence='${workflow.input.checkbox_min_leg_threshold}', structured_signature_target_accuracy='${workflow.input.structured_signature_target_accuracy}', structured_signature_confidence_threshold='${workflow.input.structured_signature_threshold}', structured_signature_acceptable_confidence='${workflow.input.signature_min_leg_threshold}', semi_structured_checkbox_target_accuracy='${workflow.input.semi_structured_checkbox_target_accuracy}', semi_structured_checkbox_confidence_threshold='${workflow.input.semi_structured_checkbox_threshold}', semi_structured_checkbox_acceptable_confidence='${workflow.input.semi_structured_checkbox_min_leg_threshold}')

Bases: Block

Machine transcription automatically transcribes the content of your submission

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• transcription_model (str) – The transcription model that will be used for this flow, defaults to workflow_input(Settings.TranscriptionModel)

• finetuning_only_trained_layouts (str) – defaults to workflow_input( Settings.FinetuningOnlyTrainedLayouts )

• transcription_overrides (str) – defaults to workflow_input( Settings.TranscriptionOverrides )

• structured_text_target_accuracy (str) – defaults to workflow_input( Settings.StructuredTextTargetAccuracy )

• structured_text_confidence_threshold (str) – defaults to workflow_input( Settings.StructuredTextThreshold )

• structured_text_acceptable_confidence (str) – defaults to workflow_input( Settings.StructuredTextMinLegThreshold )

• semi_structured_text_target_accuracy (str) – defaults to workflow_input( Settings.SemiStructuredTextTargetAccuracy )

• semi_structured_text_confidence_threshold (str) – defaults to workflow_input( Settings.SemiStructuredTextThreshold )

• semi_structured_text_acceptable_confidence (str) – defaults to workflow_input( Settings.SemiStructuredTextMinLegThreshold )

• structured_checkbox_target_accuracy (str) – defaults to workflow_input( Settings.StructuredCheckboxTargetAccuracy )

• structured_checkbox_confidence_threshold (str) – defaults to workflow_input( Settings.StructuredCheckboxThreshold )

• structured_checkbox_acceptable_confidence (str) – defaults to workflow_input( Settings.StructuredCheckboxMinLegThreshold )

• structured_signature_target_accuracy (str) – defaults to workflow_input( Settings.StructuredSignatureTargetAccuracy )

• structured_signature_confidence_threshold (str) – defaults to workflow_input( Settings.StructuredSignatureThreshold )

• structured_signature_acceptable_confidence (str) – defaults to workflow_input( Settings.StructuredSignatureMinLegThreshold )

• semi_structured_checkbox_target_accuracy (str) – defaults to workflow_input( Settings.SemiStructuredCheckboxTargetAccuracy )

• semi_structured_checkbox_confidence_threshold (str) – defaults to workflow_input( Settings.SemiStructuredCheckboxThreshold )

• semi_structured_checkbox_acceptable_confidence (str) – defaults to workflow_input( Settings.SemiStructuredCheckboxMinLegThreshold )

Example usage:

machine_transcription = MachineTranscriptionBlock(
    reference_name='machine_transcription',
    submission=manual_identification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
)

IDENTIFIER = 'MACHINE_TRANSCRIPTION_7'

class flows_sdk.implementations.idp_v36.idp_blocks.ManualTranscriptionBlock(submission, api_params, reference_name=None, supervision_transcription_masking=True, table_output_manual_review=False, task_restrictions=None, manual_transcription_enabled='${workflow.input.manual_transcription_enabled}', notification_workflow='IDP_SUBMISSION_NOTIFY_V36')

Bases: Block

Manual transcription lets your keyers manually enter the text found in fields or tables that could not be automatically transcribed

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (bool) – Prevents users from inputting invalid characters during Supervision Transcription tasks, defaults to True

• table_output_manual_review (bool) – Always generates a table transcription task if the layout contains a table. If disabled, a table transcription task will only be generated if one or more cells have transcribed values below the defined thresholds, defaults to False

• task_restrictions (Optional[List[Any]]) – Defines what users can access Supervision tasks for submission from a particular source or submissions matching a specific layout, defaults to []

• manual_transcription_enabled (str) – Enables manual transcription when applicable, defaults to workflow_input(Settings.ManualTranscriptionEnabled)

• notification_workflow (str) – Notification flow name to run when the submission enters Manual Transcription, defaults to IDP_SUBMISSION_NOTIFY_NAME

Example usage:

manual_transcription = ManualTranscriptionBlock(
    reference_name='manual_transcription',
    submission=machine_transcription.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    supervision_transcription_masking=(
        idp_wf_config.manual_transcription_config.supervision_transcription_masking
    ),
    table_output_manual_review=(
        idp_wf_config.manual_transcription_config.table_output_manual_review
    ),
    task_restrictions=idp_wf_config.manual_transcription_config.task_restrictions,
)

IDENTIFIER = 'MANUAL_TRANSCRIPTION_4'

class flows_sdk.implementations.idp_v36.idp_blocks.FlexibleExtractionBlock(submission, api_params, reference_name=None, supervision_transcription_masking=True, task_restrictions=None, layout_release_uuid='${workflow.input.layout_release_uuid}', notification_workflow='IDP_SUBMISSION_NOTIFY_V36')

Bases: Block

Flexible extraction manually transcribes fields marked for review

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (bool) – Prevents users from inputting invalid characters during the Flexible Extraction task, defaults to True

• task_restrictions (Optional[List[Any]]) – Defines what users can access Supervision tasks created by this block, defaults to []

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• notification_workflow (str) – Notification flow name to run when the submission enters Flexible Extraction, defaults to IDP_SUBMISSION_NOTIFY_NAME

Example usage:

flexible_extraction = FlexibleExtractionBlock(
    reference_name='flexible_extraction',
    submission=manual_transcription.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    supervision_transcription_masking=(
        idp_wf_config.flexible_extraction_config.supervision_transcription_masking
    ),
    task_restrictions=idp_wf_config.flexible_extraction_config.task_restrictions,
)

IDENTIFIER = 'FLEXIBLE_EXTRACTION_3'

class flows_sdk.implementations.idp_v36.idp_blocks.SubmissionCompleteBlock(submission, reference_name=None, nlc_qa_sampling_ratio='${workflow.input.semi_qa_sample_rate}', field_id_qa_enabled='${workflow.input.field_id_qa_enabled}', field_id_qa_sampling_ratio='${workflow.input.field_id_qa_sample_rate}', table_id_qa_enabled='${workflow.input.table_id_qa_enabled}', table_id_qa_sampling_ratio='${workflow.input.table_id_qa_sample_rate}', transcription_qa_enabled='${workflow.input.qa}', transcription_structured_entry_qa_sample_rate='${workflow.input.structured_entry_qa_sample_rate}', transcription_structured_signature_qa_sample_rate='${workflow.input.structured_signature_qa_sample_rate}', transcription_structured_checkbox_qa_sample_rate='${workflow.input.structured_checkbox_qa_sample_rate}', transcription_semi_structured_qa_sample_rate='${workflow.input.semi_structured_qa_sample_rate}', table_cell_transcription_qa_enabled='${workflow.input.table_cell_transcription_qa_enabled}', table_cell_transcription_qa_sample_rate='${workflow.input.table_cell_transcription_qa_sample_rate}', auto_qa_sampling_rate_enabled='${workflow.input.auto_qa_sampling_rate_enabled}', exclude_auto_transcribe_fields_from_qa='${workflow.input.exclude_auto_transcribe_fields_from_qa}', payload=None)

Bases: Block

Submission complete block finalizes submission processing and updates reporting data. This block should be called at the END of every flow that uses blocks included in the flows_sdk.implementations.idp_v36 library.

The block requires both the submission object and a payload to be passed in.

Mandatory parameters:

Parameters:

• submission (str) – Submission object

• payload (Any) – Object to pass to downstream systems

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• nlc_qa_sampling_ratio (Union[str, int]) – Defines the percentage of documents sampled for Classification QA, defaults to workflow_input(Settings.SemiQaSampleRate)

• field_id_qa_enabled (Union[str, bool]) – defaults to workflow_input(Settings.FieldIdQaEnabled)

• field_id_qa_sampling_ratio (Union[str, int]) – Defines the percentage of documents sampled for Identification QA, defaults to workflow_input(Settings.FieldIdQaSampleRate)

• table_id_qa_enabled (Union[str, bool]) – Allows users to verify the location of table cells, defaults to workflow_input(Settings.TableIdQaEnabled)

• table_id_qa_sampling_ratio (Union[str, int]) – Defines the percentage of tables the system samples for QA, defaults to workflow_input(Settings.TableIdQaSampleRate)

• transcription_qa_enabled (Union[str, bool]) – defaults to workflow_input(Settings.TranscriptionQaEnabled)

• transcription_qa_sampling_ratio – Defines the percentage of fields sampled for Transcription QA, defaults to workflow_input(Settings.TranscriptionQaSampleRate)

• table_cell_transcription_qa_enabled (Union[str, bool]) – defaults to workflow_input(Settings.TableCellTranscriptionQaEnabled)

• table_cell_transcription_qa_sample_rate (Union[str, int]) – Defines the percentage of cells the system samples for QA. This value is likely to be lower than “Transcription QA Sample Rate” since there are more table cells than fields on any given page, defaults to workflow_input(Settings.TableCellTranscriptionQaSampleRate)

• exclude_auto_transcribe_fields_from_qa (Union[str, bool]) – Defines fields that are marked as auto-transcribe in layout should be excluded from QA or not. defaults to workflow_input(Settings.ExcludeAutoTranscribeFieldsFromQA)

Example usage:

submission_complete = SubmissionCompleteBlock(
    reference_name='complete_submission',
    submission=flexible_extraction.output('submission')
)

IDENTIFIER = 'SUBMISSION_COMPLETE_4'

class flows_sdk.implementations.idp_v36.idp_blocks.IdpCustomSupervisionBlock(submission, task_purpose, supervision_template, reference_name=None, supervision_transcription_masking=True, task_restrictions=None, page_ids=None, notification_workflow='IDP_SUBMISSION_NOTIFY_V36', title='IDP Custom Supervision', description='IDP Custom Supervision')

Bases: Block

An IDP wrapper for the Custom Supervision block. It has the same functionality, but handles reading/writing data from the IDP database.

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• task_purpose (str) – the custom task name that’s given to all supervision tasks that run through a particular instance of a custom supervision block. This can be used to filter tasks in the task queue.

• supervision_template (Any) – JSON used for configuration of the Custom Supervision Task.

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (bool) – Prevents users from inputting invalid characters during the Custom Supervision task, defaults to True

• task_restrictions (Optional[List[Any]]) – Defines what users can access Supervision tasks created by this block, defaults to []

• page_ids (Optional[List[int]]) – A list of page ids to include. This should not be used. The IDP wrapper should handle pulling in all pages in the submission. defaults to []

• notification_workflow (str) – Notification flow name to run when the submission enters Custom Supervision (which is called within the IDP Custom Supervision block), defaults to IDP_SUBMISSION_NOTIFY_NAME

• title (Optional[str]) – UI-visible title of the block. Defaults to ‘IDP Custom Supervision’

• description (Optional[str]) – Description of the block. Both useful for documentation purpose and visible to users in the Flow studio. Defaults to ‘IDP Custom Supervision’

Example usage:

idp_custom_supervision = IdpCustomSupervisionBlock(
    reference_name='idp_custom_supervision',
    submission=manual_transcription.output('submission'),
    task_purpose='make_custom_decision',
    supervision_template=[
        {
            'name': 'three_column_template',
            'version': '1.0',
            'thumbnail': {
                'group_by_document': True,
                'group_by_case': True,
            },
            'action': [
                {
                    'name': 'my_tab',
                    'display': 'SSN Info',
                    'input': [
                        {
                            'name': 'ssn_field',
                            'type': 'transcription',
                            'data_identifier': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                            'title': 'SSN',
                        },
                    ],
                    'ui': {
                        'groups': [
                            {
                                'title': 'My Group',
                                'fields': ['ssn_field'],
                            },
                        ],
                    },
                },
            ],
        },
    ],
    supervision_transcription_masking=False,
)

IDENTIFIER = 'IDP_CUSTOM_SUPERVISION_2'

class flows_sdk.implementations.idp_v36.idp_blocks.IDPFullPageTranscriptionBlock(submission, reference_name=None, title='Full Page Transcription (Submission)', description='Transcribes the documents included in a submission', app_metadata=None)

Bases: Block

IDP Full Page Transcription Block machine-transcribes the unassigned pages in a submission

Mandatory parameters:

Parameters:

submission (str) – submission object that includes the raw documents that need to be transcribed

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• app_metadata (Optional[List[str]]) – used for capturing information about the flow

Example usage:

# This is one way to obtain the submission object (using SUbmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# IDPFullPageTranscriptionBlock transcribes the documents contained in the submission object
idp_fpt_block = IDPFullPageTranscriptionBlock(
    reference_name='idp_fpt',
    submission=submission_bootstrap.output(),
)
# reference to the output of IDPFullPageTranscriptionBlock which can be passed as input to
# the next block. For more information on output() and output references, please take a
# look at class Block and its output() method
output_ref = idp_fpt_block.output()

IDENTIFIER = 'FULL_PAGE_TRANSCRIPTION_SUBMISSION'

class flows_sdk.implementations.idp_v36.idp_blocks.IDPImageCorrectionBlock(submission, reference_name=None, rotation_correction_enabled=True, mobile_processing_enabled=False, title='Image Correction (Submission)', description='Rotate and de-skew documents included in a submission', app_metadata=None)

Bases: Block

IDP Image Correction Block rotates and de-skews tilted/skewed images so that they are more transcription-friendly

Mandatory parameters:

Parameters:

submission (str) – submission object that includes the raw documents that need to be image-corrected

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• app_metadata (Optional[List[str]]) – used for capturing information about the flow

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# IDPImageCorrectionBlock takes in the submission object and outputs in the same format with
# its images
idp_image_correct_block = IDPImageCorrectionBlock(
    reference_name='idp_image_correct',
    submission=submission_bootstrap.output(),
)
# reference to the output of IDPImageCorrectionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look at
# class Block and its output() method
output_ref = idp_image_correct_block.output()

IDENTIFIER = 'IMAGE_CORRECTION_SUBMISSION'

class flows_sdk.implementations.idp_v36.additional_blocks.ImageCorrectionBlock(pages, reference_name=None, rotation_correction_enabled=True, mobile_processing_enabled=False, title='Image Correction (Pages)', description='Rotate and de-skew documents', app_metadata=None)

Bases: Block

Image Correction Block rotates and de-skews tilted/skewed images so that they are more transcription-friendly

Mandatory parameters:

Parameters:

pages (str) – list of page object that includes the raw documents that need to be image-corrected

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• app_metadata (Optional[List[str]]) – used for capturing information about the flow

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# ImageCorrectionBlock takes in the submission object and outputs in the same format with
# its images
image_correct_block = ImageCorrectionBlock(
    reference_name='image_correct',
    pages=submission_bootstrap.output(submission.unassigned_pages),
)
# reference to the output of IDPImageCorrectionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look
# at class Block and its output() method
output_ref = image_correct_block.output()

IDENTIFIER = 'IMAGE_CORRECTION_PAGES'

class flows_sdk.implementations.idp_v36.additional_blocks.FullPageTranscriptionBlock(pages, reference_name=None, title='Full Page Transcription (Pages)', description='Transcribes documents', app_metadata=None)

Bases: Block

Full Page Transcription Block machine-transcribes the document pages passed to it

Mandatory parameters:

Parameters:

pages (str) – list of pages that includes the raw documents that need to be transcribed

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• app_metadata (Optional[List[str]]) – used for capturing information about the flow

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# FullPageTranscriptionBlock transcribes the documents contained in the submission object
fpt_block = FullPageTranscriptionBlock(
    reference_name='fpt',
    submission=submission_bootstrap.output(submission.unassigned_pages),
)
# reference to the output of FullPageTranscriptionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look at
# class Block and its output() method
output_ref = fpt_block.output()

IDENTIFIER = 'FULL_PAGE_TRANSCRIPTION_PAGES'

class flows_sdk.implementations.idp_v36.additional_blocks.CustomSupervisionBlock(submission, task_purpose, data, supervision_template, reference_name=None, supervision_transcription_masking=True, task_restrictions=None, notification_workflow='IDP_SUBMISSION_NOTIFY_V36')

Bases: Block

The CustomSupervisionBlock uses a JSON schema as a form builder to create customer-driven UIs for Supervision tasks.

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• task_purpose (str) – the custom task name that’s given to all supervision tasks that run through a particular instance of a custom supervision block. This can be used to filter tasks in the task queue.

• data (Any) – JSON structure that supplies the data used to populated the supervision_template

• supervision_template (Any) – JSON used for configuration of the Custom Supervision Task.

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (bool) – Prevents users from inputting invalid characters during the Custom Supervision task, defaults to True

• task_restrictions (Optional[List[Any]]) – Defines what users can access Supervision tasks created by this block, defaults to []

• notification_workflow (str) – Notification flow name to run when the submission enters Custom Supervision, defaults to IDP_SUBMISSION_NOTIFY_NAME

Example usage:

custom_supervision = CustomSupervisionBlock(
    reference_name='custom_supervision',
    submission=manual_transcription.output('submission'),
    task_purpose='make_custom_decision',
    supervision_template=[
        {
            'name': 'three_column_template',
            'version': '1.0',
            'thumbnail': {
                'group_by_document': True,
                'group_by_case': True,
            },
            'action': [
                {
                    'name': 'my_tab',
                    'display': 'SSN Info',
                    'input': [
                        {
                            'name': 'ssn_field',
                            'type': 'transcription',
                            'data_identifier': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                            'title': 'SSN',
                        },
                    ],
                    'ui': {
                        'groups': [
                            {
                                'title': 'My Group',
                                'fields': ['ssn_field'],
                            },
                        ],
                    },
                },
            ],
        },
    ],
    data={
        'fields': [
            {
                'id': 967,
                'uuid': 'fe8d4e7a-a82c-448c-85a1-0a3cbbe53b61',
                'value': '111-22-3333',
                'page_id': 1,
                'data_identifier': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                'validation_overridden': false,
                'bounding_box': [
                    0.09360783305186686,
                    0.25792617589433436,
                    0.6358913805295097,
                    0.28862414740388187,
                ],
                'occurence_index': 1,
            },
        ],
        'template_fields': [
            {
                'uuid': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                'notes': '',
                'type': 'entry',
                'data_type_uuid': 'b600a4e4-758f-4c4b-a2f4-cbb5dd870a0c',
                'name': 'SSN',
                'n_occurences': 1,
            },
        ],
        'pages': [
            {
                'id': 15,
                'file_page_number': 1,
                'document_page_number': 1,  // Optional
                'image_url': '/image/6e55fa86-a36b-4178-8db1-3a514621d4c1',
                'form_id': 2,
                'submission_id': 3,
                'external_case_ids': [
                    'HS-27',
                ],
                'read_only': False,
                'filename': 'filename.pdf',
            },
        ],
        'documents': [
            {
                'id': 2,
                'layout_uuid': 'e31c3cbf-dcea-44fd-a052-902463c6040f',
                'layout_name': 'layout_name',
                'page_count': 2,
                'read_only': false,
            },
        ],
        'cases': [],
        'selected_choices': [],
    },
    supervision_transcription_masking=False,
)

IDENTIFIER = 'CUSTOM_SUPERVISION_2'

class flows_sdk.implementations.idp_v36.additional_blocks.SoapRequestBlock(method, reference_name=None, endpoint='', body_namespace=None, headers_namespace=None, headers=None, params=None, title='HTTP REST Block', description='HTTP REST block')

Bases: Block

Soap Block allows user to perform SOAP requests

Mandatory parameters:

Parameters:

• method (str) – SOAP method to invoke

• endpoint (str) – absolute url including schema to HTTP endpoint for this request

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• body_namespace (Optional[str]) – SOAP request body namespace url

• headers_namespace (Optional[str]) – SOAP request headers namespace url

• headers (Optional[Dict[Any, Any]]) – SOAP headers to use

• params (Optional[Dict[Any, Any]]) – key-value pair used as query parameters in the request to be inserted as SOAP body

Example usage:

# minimum viable instantiation, the rest of the fields can later be added in the UI
soap_block = SoapRequestBlock(
    reference_name='soap_request',
    title='Soap Request',
    description='Perform soap request',
    method='ExampleMethod',
)
soap_block = SoapRequestBlock(
    reference_name='soap_request',
    title='Soap Request',
    description='Perform soap request',
    method='ExampleMethod',
    endpoint='http://example.org/abc.wso',
    body_namespace='http://www.example.org/abc.countryinfo',
    params={'example': 'stuff'},
    headers={}
)
# reference to the output of SoapRequestBlock which can be passed as input to the next block
# for more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = soap_block.output()

IDENTIFIER = 'SOAP_REQ'

class flows_sdk.implementations.idp_v36.additional_blocks.HttpRestBlock(method, endpoint, reference_name=None, headers=None, params=None, payload=None, json=None, authorization_type=None, handled_error_codes=None, title='HTTP REST Block', description='HTTP REST block', auth_params=None)

Bases: BaseHttpRestBlock

Http Rest Block allows user to perform HTTP requests

Mandatory parameters:

Parameters:

• method (str) – HTTP method to use

• endpoint (str) – absolute url including schema to HTTP endpoint for this request

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• headers (Optional[Dict[str, str]]) – HTTP headers to use

• params (Optional[Dict[str, Any]]) – key-value pair used as query parameters in the request

• payload (Optional[Dict[str, str]]) – key-value pair to be used as x-www-form-urlencoded data. Mutually exclusive with json

• json (Optional[Dict[str, Any]]) – key-value pair to be used as json data. Mutually exclusive with payload

• authorization_type (Optional[str]) – authorization type (none, http_header, oauth_2_client_credentials)

• handled_error_codes (Optional[List[Union[int, str]]]) – specify HTTP error codes that will not fail the task, allowing for wildcards with the ‘x’ char. Example: [400, “5xx”]

• auth_params (Union[HttpHeaderAuthorizationParams, OAuth2AuthorizationParams, None]) – authorization parameters to be passed when the authorization_type is set to “http_header” or “oauth_2_client_credentials”

Example usage:

# minimum viable instantiation, the rest of the fields can later be added in the UI
http_rest_block = HttpRestBlock(
    reference_name='http_request',
    title='Http Request',
    description='Perform http request',
    method='GET',
)
http_rest_block = HttpRestBlock(
    reference_name='http_request',
    title='Http Request',
    description='Perform http request',
    method='GET',
    endpoint='https://sheets.googleapis.com/v4/spreadsheets/example',
)
# reference to the output of HttpRestBlock which can be passed as input to the next block
# for more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = http_rest_block.output()

class flows_sdk.implementations.idp_v36.additional_blocks.HyperscienceRestApiBlock(method, app_endpoint, reference_name=None, headers=None, params=None, payload=None, json=None, handled_error_codes=None, title='Hyperscience HTTP REST Block', description='Hyperscience HTTP REST block')

Bases: BaseHttpRestBlock

Make HTTP requests to the Hyperscience platform APIs. This block will automatically take care of prepending the base url and will include the appropriate authentication headers with the request.

Mandatory parameters:

Parameters:

• method (str) – HTTP method to use, e.g. ‘GET’, ‘POST’, etc.

• app_endpoint (str) – relative url, beginning with a forward slash, to an HTTP endpoint served by the Hyperscience platform. For reference on the available endpoints, check out https://docs.hyperscience.ai/. Absolute URLs that include a hostname are allowed only if the flow has received them as part of the response of a previous API request that the flow has made. Do not hardcode the hostname of the Hyperscience instance in your flow definitions as they will not work when you run the flows on another instance, or if the hostname changes. For making requests to external systems, use the HttpRestBlock or a library like “requests”.

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• headers (Optional[Dict[str, str]]) – HTTP headers to use

• params (Optional[Dict[str, Any]]) – key-value pair used as query parameters in the request

• payload (Optional[Dict[str, str]]) – key-value pair to be used as x-www-form-urlencoded data. Mutually exclusive with json

• json (Optional[Dict[str, Any]]) – key-value pair to be used as json data. Mutually exclusive with payload

• handled_error_codes (Optional[List[Union[int, str]]]) – specify HTTP error codes that will not fail the task, allowing for wildcards with the ‘x’ char. Example: [400, “5xx”]

Example usage:

hs_rest_block = HyperscienceRestApiBlock(
    reference_name='http_request',
    app_endpoint='/api/healthcheck',
    method='GET',
)

class flows_sdk.implementations.idp_v36.additional_blocks.DatabaseAccessBlock(reference_name=None, db_type='', database='', host='', username='', password='', query='', port=None, options=None, timeout=None, query_params=None, title='DB Block', description='DB access block')

Bases: Block

Database Block allows user to access and perform queries on the specified database

Mandatory parameters:

Parameters:

• db_type (str) – database type (mssql, oracle, postgres)

• host (str) – URL/IP address of the server database is hosted on

• database (str) – database name

• username (str) – database username

• password (str) – database password

• query (str) – parameterized query

Optional parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• port (Optional[int]) – database port number

• options (Optional[Dict[Any, Any]]) – dictionary of additional connection string options

• query_params (Optional[Dict[Any, Any]]) – dictionary of values for query placeholders

• timeout (Optional[int]) – timeout in seconds (mssql and postgres only)

Example usage:

# minimum viable instantiation, the rest of the fields can later be added in the UI
db_lookup = DatabaseAccessBlock(
    reference_name='db_lookup',
    title='Database Lookup',
    description='Perform database lookup',
)
# note that storing password in plain text form is not recommended
# password can later be entered in the UI or defined as a flow secret.
db_lookup = DatabaseAccessBlock(
    reference_name='db_lookup',
    title='Database Lookup',
    description='Perform database lookup',
    db_type='mssql',
    database='cars',
    host='example.com',
    username='user',
    # we recommend skipping the password field or defining it as a system secret
    # e.g. system_secret('{your_secret_identifier}') (both allow for secret input via. UI)
    password='pw'
    port=1433,
    timeout=200,
    query='SELECT * from CAR',
)
# reference to the output of DBAccessBlock which can be passed as input to the next block
# for more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = db_lookup.output()
# content of output_ref:
{
    "result": [
        {
            "id": 1,
            "brand": "Aston Martin",
            "model": "Vanquish"
        },
        {
            "id": 2,
            "brand": "Jaguar",
            "model": "XE 2018"
        }
    ]
}

Database output for the same query (SELECT * FROM CAR)

IDENTIFIER = 'DB_ACCESS'

Trigger Blocks (v36)

Note

“Triggers” are referred to as “Input Blocks” in our application documentation.

Warning

Flows are built to allow full definition of triggers either in code or manually in the Flow Studio. We recommend defining triggers manually in the Flow Studio. Instructions on how to do so are in our application documentation.

To enable trigger definition via the Flow Studio UI, you need to instantiate the IDPTriggers() convenience class and pass it to the triggers property of the Flow

return Flow(
   ...
   triggers=IDPTriggers()
)

If instead you wish to define specific triggers in Python, use the trigger definitions below.

Folder Listener (v36)

Identifier: FOLDER_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
folder	Path	N/A	Folder to scan for submissions	N/A
file_extensions	string	N/A	File extensions	List of file extensions to monitor for (e.g.: ‘png, jpg, pdf’)
has_meta	boolean	False	Enable metadata	Select this if a metadata file is to be expected along with document files (in XXX_index.txt file)
poll_interval	integer	10	Poll interval	Poll interval in seconds
warmup_interval	integer	15	Warm-up interval	Seconds to wait past document last modified time before processing it
folder_cleanup_delay	integer	86400	Folder cleanup delay	Seconds to wait before cleaning up subfolders
api_params	object	{}	API Parameters	N/A

folder_listener = IOBlock(
    identifier='FOLDER_TRIGGER',
    reference_name='folder_trigger',
    title='Folder listener',
    enabled=True,
    input={
        'folder': '/var/www/forms/forms/folder/',
        'file_extensions': 'png, pdf, jpg',
    },
)

triggers = IDPTriggers(blocks=[folder_listener])

Kofax Folder Listener (v36)

Identifier: KOFAX_FOLDER_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
folder	Path	N/A	Folder to scan for submissions	N/A
file_extensions	string	N/A	File extensions	List of file extensions to monitor for (e.g.: ‘png, jpg, pdf’)
poll_interval	integer	10	Poll interval in seconds	N/A
warmup_interval	integer	15	Warm-up interval	Seconds to wait past document last modified time before processing it
folder_cleanup_delay	integer	86400	Folder cleanup delay	Seconds to wait before cleaning up subfolders
api_params	object	{}	API Parameters	N/A

kofax_folder_listener = IOBlock(
    identifier='KOFAX_FOLDER_TRIGGER',
    reference_name='kofax_folder_listener',
    title='Kofax Folder listener',
    enabled=True,
    input={
        'folder': '/var/www/forms/forms/kofax/',
        'file_extensions': 'png, pdf, jpg',
    },
)

triggers = IDPTriggers(blocks=[kofax_folder_listener])

Email Listener (v36)

Identifier: IMAP_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
provider [1]	string	imap	Email Provider	N/A
poll_interval	integer	60	Polling interval in seconds	N/A
folder	Path	N/A	Folder to scan for emails	N/A
email_body_treatment	string	“ignore”	Email body treatment	What to do with the body of an email that is ingested. Available options are “process” and “ignore”.
post_process_action	string	“move”	Post process action	What to do with the email after it is processed. Available options are “move” and “delete”.
post_process_move_folder	string	“”	Post process archive folder	Folder to move emails to once they are processed.
api_params	object	{}	API Parameters	N/A
render_headers [2]	array	[]	Headers to include	Headers to render at the top of the email body. Headers will be included only if email_body_treatment is set to “process”.

[1] one of:

imap
graph

[2] any of:

To
From
Subject
Date

Additional inputs when "imap" is the selected provider:

Name	Type	Default Value	Title	Description
host	string	N/A	IMAP server address	N/A
port	integer	993	Port Number	N/A
ssl	boolean	True	Use SSL connection	N/A
username	string	N/A	Username	N/A
password	Password	N/A	Password	N/A

Additional inputs when "graph" is the selected provider:

Name	Type	Default Value	Title	Description
client_id	string	N/A	Client id	Application (client) ID for the corresponding application registered with the Microsoft identity platform.
tenant_id	string	N/A	Tenant id	Also called directory ID. Unique identifier of the tenant in which the corresponding application is registered with.
graph_cloud_endpoint [1]	string	Global	Microsoft Graph Cloud Endpoint	Determines base URL used for accessing cloud services through Microsoft Graph.
azure_ad_endpoint [2]	string	AZURE_PUBLIC_CLOUD	Azure AD Endpoint	Determines base URL for the Azure Active Directory (Azure AD) endpoint to acquire token for each national cloud.
client_secret	Password	N/A	Client secret	Client secret for the corresponding application registered with the Microsoft identity platform.

[1] one of:

Global
US_GOV
US_DoD
Germany
China

[2] one of:

AZURE_PUBLIC_CLOUD
AZURE_CHINA
AZURE_GERMANY
AZURE_GOVERNMENT

imap_trigger = IOBlock(
    identifier='IMAP_TRIGGER',
    reference_name='imap_trigger',
    title='IMAP trigger',
    enabled=True,
    input={
        'host': 'example@mail.com',
        'folder': '/var/www/forms/forms/imap/',
        'username': 'admin',
        'password': 'pass',
    }
)

triggers = IDPTriggers(blocks=[imap_trigger])

Box Folder Listener (v36)

Identifier: BOX_FOLDER_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
source_folder_id	integer	N/A	Folder to scan for submissions	Use the Box Folder ID found in the URL
target_folder_id	integer	N/A	Folder to move completed files	Use the Box Folder ID found in the URL
file_extensions	array	[]	File extensions	Types of file extensions for which to monitor
custom_file_extensions	string	N/A	Other file extension types	Comma separated list of file extensions for which to monitor (e.g. ‘png, jpg, pdf’)
poll_interval	integer	10	Polling interval (in seconds)	How often the connector will check the base folder for submissions
warmup_interval	integer	15	Warm-up interval (in seconds)	How long the connector will wait to process the document after it was last modified
public_key_id	string	N/A	Public-Key ID	ID of the Public-key used for authentication with Box
private_key	Password	N/A	Private-Key	Private key used for authentication with Box
passphrase	Password	N/A	Passphrase	Passphrase used for authentication with Box
client_id	string	N/A	Client ID	Client Id used for authentication with Box
client_secret	Password	N/A	Client Secret	Client Secret used for authentication with Box
enterprise_id	string	N/A	Enterprise ID	Enterprise Id used for authentication with Box
api_params	object	{}	API Parameters	N/A

box_folder_trigger = IOBlock(
    identifier='BOX_FOLDER_TRIGGER',
    reference_name='box_folder_trigger',
    title='Box folder',
    enabled=True,
    input={
        'file_extensions': ['png', 'pdf', 'jpg'],
        'source_folder_id': 24,
        'target_folder_id': 42,
        'public_key_id': 'admin',
        'private_key': 'secret',
        'passphrase': 'password',
        'client_id': 'admin',
        'client_secret': 'secret',
        'enterprise_id': 'admin',
    }
)

triggers = IDPTriggers(blocks=[box_folder_trigger])

Cron Listener (v36)

Identifier: CRON_TRIGGER

Inputs:

Name	Type	Default Value	Title	Description
cron_spec	string	"*/5 * * * *"	Cron specification	N/A
time_zone	string	“US/Eastern”	Time Zone	N/A
api_params	object	{}	API Parameters	N/A

cron_trigger = IOBlock(
    identifier='CRON_TRIGGER',
    reference_name='cron_trigger',
    title='Cron Trigger',
    enabled=True,
    input={
        'cron_spec': '0 10 * * *',
        'time_zone': 'Europe/Sofia',
    }
)

triggers = IDPTriggers(blocks=[cron_trigger])

Salesforce Listener (v36)

Identifier: SALESFORCE_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
channel_name	string	N/A	Channel Name	Configured in the Hyperscience app in Salesforce.
username	string	N/A	Associated Username	Username associated with the Salesforce Connected App.
consumer_key	Password	N/A	Consumer Key	Consumer key used for authentication with Salesforce. Paired with the Salesforce instance from which it was issued.
private_key	Password	N/A	Private Key	Private key used for authentication with Salesforce.
sandbox_environment	boolean	false	Private Key	Enable if the Salesforce environment is a sandbox.
api_params	object	{}	API Parameters	N/A

Message Queue Listener (v36)

Identifier: MQ_LISTENER

Inputs:

Name	Type	Default Value	Title	Description
MQ_TYPE	string	“ACTIVE_MQ”	Message Queue type	Type of the message queue to connect with. Valid values are “ACTIVE_MQ”, “AMAZON_SQS”, “IBM_MQ” and “RABBIT_MQ”
MQ_USERNAME	string	null	Username/Access Key ID	N/A
MQ_PASSWORD	Password	null	Password/Secret Access Key	N/A
MQ_QUEUE_NAME	string	null	Queue Name	Name of the queue (topic) to connect to.
MQ_HOST	string	null	Host Name	N/A
MQ_PORT	integer	1415	Port Number	N/A
api_params	object	{}	API Parameters	N/A

Additional inputs when "AMAZON_SQS" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_REGION	string	null	AWS Region	N/A
MQ_QUEUE_URL	string	null	Queue URL	N/A
MQ_USE_EC2_INSTANCE_CREDENTIALS	boolean	True	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used.

NOTE: MQ_USERNAME can be used to provide Access Key ID and MQ_PASSWORD to provide Secret Access Key.

Additional inputs when "IBM_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_QUEUE_MANAGER	string	null	Queue Manager (IBM MQ)	N/A
MQ_SSL_CIPHER_SUITE [1]	string	null	SSL cipher suite	SSL cipher suite to use if SSL connection is used.
MQ_CHANNEL	string	null	Channel	N/A
MQ_NO_AUTH_REQUIRED	boolean	false	Channel	N/A
MQ_MQCSP_AUTHENTICATION_MODE	boolean	false	Channel	N/A

NOTE: MQ_NO_AUTH_REQUIRED is True, MQ_USERNAME and MQ_PASSWORD are optional.

[1] one of:

"NULL_MD5"
"NULL_SHA"
"RC4_MD5_EXPORT"
"RC4_MD5_US"
"RC4_SHA_US"
"RC2_MD5_EXPORT"
"DES_SHA_EXPORT"
"RC4_56_SHA_EXPORT1024"
"DES_SHA_EXPORT1024"
"TRIPLE_DES_SHA_US"
"TLS_RSA_WITH_NULL_SHA256"
"TLS_RSA_WITH_AES_128_CBC_SHA"
"TLS_RSA_WITH_AES_128_CBC_SHA256"
"TLS_RSA_WITH_AES_256_CBC_SHA"
"TLS_RSA_WITH_AES_256_CBC_SHA256"
"AES_SHA_US"
"TLS_RSA_WITH_DES_CBC_SHA"
"TLS_RSA_WITH_3DES_EDE_CBC_SHA"
"TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256"
"FIPS_WITH_DES_CBC_SHA"
"FIPS_WITH_3DES_EDE_CBC_SHA"

Additional inputs when "RABBIT_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_VIRTUAL_HOST	string	null	Virtual Host (RABBIT MQ)	N/A
MQ_EXCHANGE	string	“”	Exchange	N/A
MQ_ROUTING_KEY	string	“”	Routing key	N/A
MQ_CONNECTION_TYPE	string	“TCP”	Connection Type	“TCP or SSL”

rabbit_mq_listener = IOBlock(
    identifier='MQ_LISTENER',
    reference_name='mq_listener',
    title='RabbitMQ Listener',
    enabled=True,
    input={
        'MQ_TYPE': 'RABBIT_MQ',
        'MQ_QUEUE_NAME': 'some_queue_name',
        'MQ_HOST': 'somehost.com',
        'MQ_USERNAME': 'foo',
        'MQ_PASSWORD': system_secret('rabbit_mq_1_password'),
    }
)

triggers = IDPTriggers(blocks=[rabbit_mq_listener])

Output Blocks (v36)

Warning

Flows pass information to downstream systems using the IDPOutputsBlock. This block is specially built to allow full definition either in code or manually in the Flow Studio. We recommend defining outputs manually in the Flow Studio.

Instructions on how to do so are in our application documentation. Simply include an IDPOutputsBlock in your Python flow definition wherever you wish to send information to downstream systems.

class flows_sdk.implementations.idp_v36.idp_blocks.IDPOutputsBlock(inputs, blocks=None)

Bases: Outputs

Output block allows users to send data extracted by an IDP flow to other systems for downstream processing

Mandatory parameters:

Parameters:

inputs (Dict[str, Any]) – Used by the UI to automatically pre-populate the input of newly added blocks. Extended in this class to include 'enabled': True to visualize an enabled/disabled trigger that is controllable via Flow Studio.

Usage when parameters are to be defined in Flow Studio:

outputs = IDPOutputBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')}
)

If instead you wish to define specific outputs in Python, use the outputs definitions below.

HTTP Notifier Output (v36)

The HTTP Notifier (REST) output connection will POST results from the system to a specified HTTP endpoint.

Identifier: COB_HTTP_EXPORT

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
endpoint	string	N/A	Endpoint URL	URL that output notifications will be posted to
endpoint_secrets	string	N/A	Endpoint URL secrets	Endpoint URL Secrets, format: (one per line) secret_key_name=secret_value
timeout	int	600	Timeout (seconds)	How long (in seconds) to keep the connection open if no bytes are received from the endpoint. Set to 0 to wait indefinitely.
authorization_type [1]	string	“none”	Authorization type	Type of authorization
authorization_header_name	string	null	Authorization header name	Authorization header name to be set in the notification request
authorization_header	Password	null	Authorization header value	Authorization header to be set in the notification request
auth_url	string	null	OAuth2 authorization URL	The endpoint for the authorization server
client_id	string	null	Client ID	The client identifier issued to the client during the application registration process
client_secret	Password	null	Client Secret	The client secret issued to the client during the application registration process
scope	string	null	Scope	Scope to request to oauth server
audience	string	null	Audience	Resource service URL where token will be valid
additional_oauth_parameters	json	{}	Additional Oauth Request Parameters	Additional parameters to send when requesting token
ssl_cert_source [2]	string	null	Source	N/A
ssl_cert_path	string	null	Trusted Certificate(s) path	Enter the path relative to the base folder
ssl_cert_value	MultilineText	null	Trusted Certificate(s)	N/A

[1] one of:

"none"
"http_header"
"oauth_2_client_credentials"

[2] one of:

"env_var"
"ca_bundle_path"
"certificate_value"

cob_http_export = IOBlock(
    identifier='COB_HTTP_EXPORT',
    reference_name='cob_http_export',
    title='HTTP Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'SUBMISSION',
        'endpoint': 'example.com',
        'authorization_type': 'none',
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_http_export],
)

Message Queue Notifier Output (v36)

The Message Queue Notifier Output can configure connections to ActiveMQ, Amazon SQS, IBM MQ, and RabbitMQ message queues.

Identifier: COB_MQ_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
MQ_TYPE	string	“ACTIVE_MQ”	Message Queue type	Type of the message queue to connect with. Valid values are “ACTIVE_MQ”, “AMAZON_SQS”, “IBM_MQ” and “RABBIT_MQ”
MQ_USERNAME	string	null	Username/Access Key ID	N/A
MQ_PASSWORD	Password	null	Password/Secret Access Key	N/A
MQ_QUEUE_NAME	string	null	Queue Name	Name of the queue (topic) to connect to. It applies only to “ACTIVE_MQ”, “IBM_MQ” and “RABBIT_MQ” MQ_TYPEs.
MQ_HOST	string	null	Host Name	N/A
MQ_PORT	integer	1415	Port Number	N/A

Additional inputs when "AMAZON_SQS" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_REGION	string	null	AWS Region	N/A
MQ_QUEUE_URL	string	null	Queue URL	N/A
MQ_USE_EC2_INSTANCE_CREDENTIALS	boolean	True	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used.
MQ_MESSAGE_GROUP_ID	string	null	Group ID for FIFO queues	N/A
MQ_MESSAGE_METADATA	string	null	Additional SQS Metadata	Input additional metadata key value pairs in the format of { “key”: {“TYPE”: “String”, “VALUE”: “”}, “key2”: {“TYPE”: “Number”, “VALUE”: “12345”}

NOTE: MQ_USERNAME can be used to provide Access Key ID and MQ_PASSWORD to provide Secret Access Key.

Additional inputs when "IBM_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_QUEUE_MANAGER	string	null	Queue Manager (IBM MQ)	N/A
MQ_SSL_CIPHER_SUITE [1]	string	null	SSL cipher suite	SSL cipher suite to use if SSL connection is used.
MQ_CHANNEL	string	null	Channel	N/A
MQ_NO_AUTH_REQUIRED	boolean	false	Channel	N/A
MQ_MQCSP_AUTHENTICATION_MODE	boolean	false	Channel	N/A

NOTE: MQ_NO_AUTH_REQUIRED is True, MQ_USERNAME and MQ_PASSWORD are optional.

[1] one of:

"NULL_MD5"
"NULL_SHA"
"RC4_MD5_EXPORT"
"RC4_MD5_US"
"RC4_SHA_US"
"RC2_MD5_EXPORT"
"DES_SHA_EXPORT"
"RC4_56_SHA_EXPORT1024"
"DES_SHA_EXPORT1024"
"TRIPLE_DES_SHA_US"
"TLS_RSA_WITH_NULL_SHA256"
"TLS_RSA_WITH_AES_128_CBC_SHA"
"TLS_RSA_WITH_AES_128_CBC_SHA256"
"TLS_RSA_WITH_AES_256_CBC_SHA"
"TLS_RSA_WITH_AES_256_CBC_SHA256"
"AES_SHA_US"
"TLS_RSA_WITH_DES_CBC_SHA"
"TLS_RSA_WITH_3DES_EDE_CBC_SHA"
"TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256"
"FIPS_WITH_DES_CBC_SHA"
"FIPS_WITH_3DES_EDE_CBC_SHA"

Additional inputs when "RABBIT_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_VIRTUAL_HOST	string	null	Virtual Host (RABBIT MQ)	N/A
MQ_EXCHANGE	string	“”	Exchange	N/A
MQ_ROUTING_KEY	string	“”	Routing key	N/A
MQ_CONNECTION_TYPE	string	“TCP”	Connection Type	“TCP or SSL”

mq_notifier = IOBlock(
    identifier='COB_MQ_NOTIFIER',
    reference_name='mq_notifier',
    title='MQ Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'SUBMISSION',
        'MQ_USERNAME': 'admin',
        'MQ_PASSWORD': 'pass',
        'MQ_QUEUE_NAME': 'queue',
        'MQ_HOST': 'host',
    },
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[mq_notifier],
)

Box Notifier Output (v36)

Provides an out-of-the-box integration into Box systems.

Identifier: COB_BOX_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
public_key_id	string	N/A	Public-Key ID	ID of the Public-key used for authentication with Box
private_key	Password	N/A	Private-Key	Private key used for authentication with Box
passphrase	Password	N/A	Passphrase	Passphrase used for authentication with Box
client_id	string	N/A	Client ID	Client Id used for authentication with Box
client_secret	Password	N/A	Client Secret	Client Secret used for authentication with Box
enterprise_id	string	N/A	Enterprise ID	Enterprise Id used for authentication with Box
template_key	string	“”	Box Metadata Template Key	Enter the key of the Box Metadata template that you would like to map the Hyperscience metadata to.
static_output_fields_to_map	array	[]	Static Metadata Fields	Specify the Hyperscience fields you want to store in Box metadata.
submission_id	string	“”	Key for Mapping Submission ID	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_id	string	“”	Key for Mapping Document ID	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
submission_state	string	“”	Key for Mapping Submission State	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_state	string	“”	Key for Mapping Document State	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
submission_exceptions	string	“”	Key for Mapping Submission Exceptions	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_exceptions	string	“”	Key for Mapping Document Exceptions	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_layout_uuid	string	“”	Key for Mapping Document Layout Uuid	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_layout_name	string	“”	Key for Mapping Document Layout Name	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
start_time	string	“”	Key for Mapping Start Time	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
complete_time	string	“”	Key for Mapping Complete Time	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_fields_template_mappings	json	{}	JSON for Additional Metadata Fields	Specify additional metadata fields using their key value. Please consult Box Integration setup manual for JSON template and instructions.

box_notifier = IOBlock(
    identifier='COB_BOX_NOTIFIER',
    reference_name='box_notifier',
    title='Box Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'private_key': 'password',
        'passphrase': 'password',
        'public_key_id': 'admin',
        'client_id': 'admin',
        'client_secret': 'secret',
        'enterprise_id': 'admin',
        'template_key': 'key',
    },
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[box_notifier],
)

UiPath Notifier Output (v36)

Provides an out-of-the-box integration into UiPath systems.

Identifier: COB_EXPORT_UIPATH

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
url	string	N/A	URL	Base URL of the uipath instance
organization_unit_id	string	null	Organization unit id	Organization unit id
tenant	string	N/A	Tenant	Tenant
username	string	N/A	Username	Username
password	Password	N/A	Password	Password
queue	string	N/A	Queue	Queue

cob_export_uipath = IOBlock(
    identifier='COB_EXPORT_UIPATH',
    reference_name='cob_export_uipath',
    title='CCB Export UiPath',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'FLAT_SUBMISSION',
        'url': 'example.com',
        'tenant': 'tenant',
        'username': 'admin',
        'password': 'pass',
        'queue': 'queue',
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_export_uipath],
)

Salesforce Notifier Output (v36)

With the Salesforce Notifier Block, you can configure your flow to send extracted information to Salesforce. The Salesforce Notifier Block can use extracted information to look up, and then update any number of fields on a Salesforce object. The Salesforce Notifier Block is available in both SaaS and on-premise versions of the Hyperscience application.

Identifier: COB_SALESFORCE_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
username	string	N/A	Associated Username	Username associated with the Salesforce Connected App
consumer_key	password	N/A	Consumer Key	Consumer key used for authentication with Salesforce. Paired with the Salesforce instance from which it was issued.
private_key	password	N/A	Private Key	Private key used for authentication with Salesforce.
sandbox_environment	boolean	false	Private Key	Enable if the Salesforce environment is a sandbox.
object_api_name	string	N/A	Object API Name	API name of the object in Salesforce to lookup and update.
new_record_on_lookup_failure	boolean	false	Create new record on lookup failure	When enabled, it will create a new record if the lookup fails to find a record using the lookup parameters. When disabled, lookup will fail if it does not find a record using the lookup parameters.
object_lookup_mapping	json	N/A	Lookup Field Mapping	Specify which Salesforce fields (using their API name) to be queried with which extracted values from the document for the lookup using key-value format. For example: “DOCUMENT_FIELD_NAME”: “OBJECT_FIELD_API_NAME”.
document_fields_mapping	json	N/A	Document Field Mappings	Specify which Salesforce fields (using their API name) you want to update with which extracted data from Hyperscience using key-value format. For example: “DOCUMENT_FIELD_NAME”: “OBJECT_FIELD_API_NAME”.”
overwrite_existing_field_data	boolean	false	Overwrite existing field data	If this is checked and a selected field has data in it, Hyperscience will overwrite that data. If this is unchecked, existing data will not be overwritten and the operation will fail.

cob_salesforce_notifier = IOBlock(
    identifier='COB_SALESFORCE_NOTIFIER',
    reference_name='cob_salesforce_notifier',
    title='Salesforce Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'username': 'salesforce_user',
        'consumer_key': 'salesforce_consumer_key',
        'private_key': 'salesforce_private_key',
        'object_api_name': 'salesforce_object_api_name',
        'object_lookup_mapping': {"LAYOUT_VARIATION_XYZ_FIELD_NAME_1": "SF_FIELD_API_NAME_1", "LAYOUT_VARIATION_XYZ_FIELD_NAME_2": "SF_FIELD_API_NAME_2"},
        'document_fields_mapping': {"LAYOUT_VARIATION_XYZ_FIELD_NAME_3": "SF_FIELD_API_NAME_3", "LAYOUT_VARIATION_XYZ_FIELD_NAME_4": "SF_FIELD_API_NAME_4"}
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_salesforce_notifier],
)

Helper Classes and Functions (v36)

The following classes are used to easily instantiate and manage a flow’s settings.

flows_sdk.implementations.idp_v36.idp_values.get_idp_wf_inputs(idp_wf_config)

A helper function that maps the values provided by an IdpWorkflowConfig to their corresponding IDP keys.

Parameters:

idp_wf_config (IdpWorkflowConfig) – with static values to be filled.

Return type:

Dict[str, Any]

Returns:

a dictionary with filled static values, compatible with IDP flows.

Example usage with default values:

flow = Flow(
    input=get_idp_wf_inputs(get_idp_wf_config()),
    ...
)

Example usage:

Example usage when instantiating an IDP flow with custom parameters::
flow = Flow(
    input=get_idp_wf_inputs({an instance of IdpWorkflowConfig}),
    ...
)

flows_sdk.implementations.idp_v36.idp_values.get_idp_wf_config()

A helper function that instantiates an IdpWorkflowConfig pre-filled with default values. IdpWorkflowConfig is the container class for defining flow-level settings.

Return type:

IdpWorkflowConfig

Returns:

a valid, fully constructed IdpWorkflowConfig instance

Example usage when instantiating an IDP flow:

flow = Flow(
    input=get_idp_wf_inputs(get_idp_wf_config()),
    ...
)

class flows_sdk.implementations.idp_v36.idp_values.IdpWorkflowConfig

Convenience dataclass outlining all flow-level config values for IDP.

class flows_sdk.implementations.idp_v36.idp_values.IDPTriggers(blocks=None)

An adapter class making it easier to instantiate flows_sdk.flows.Triggers for IDP flows. In particular, provides defaults for some fields (e.g. title, description, reference_name) and defines api_params_manifest with IDP specific parameters.

Parameters:

blocks (Optional[Sequence[IOBlock]]) – flows_sdk.blocks.IOBlock s to be included as triggers for an IDP Flow. Optional, defaults to an empty list.

Example usage when instantiating an IDP flow:

folder_listener = IOBlock(...)
flow = Flow(
    triggers=IDPTriggers([folder_listener]),
    ...
)

class flows_sdk.implementations.idp_v36.idp_values.IDPManifest(flow_identifier)

An adapter class making it easier to instantiate flows_sdk.flows.Manifest for IDP flows. In particular, provides defaults for some fields (e.g. roles, identifier) but more importantly, defines all py:class:flows_sdk.flows.Parameter that construct the Flow inputs and their UI representation (left-hand side menu in Flow studio).

Parameters:

flow_identifier (str) – A system-wide unique identifier for flows_sdk.flows.Flow

Example usage when instantiating an IDP flow:

flow = Flow(
    manifest=IDPManifest(flow_identifier='FLOW_IDENTIFIER'),
    ...
)

V35

Processing Blocks (v35)

Core Blocks are powerful processing blocks necessary to build intelligent document processing solutions on Hyperscience. They include both manual and machine processing blocks.

Note

Classes in the idp_v35 implementation have parameters with ambiguous typing. For example, a field of type integer could also be represented as a string reference (e.g. that integer gets provided at runtime). The types introduced in the example are therefore incomplete and are likely to be revisioned in a future version.

class flows_sdk.implementations.idp_v35.idp_blocks.SubmissionBootstrapBlock(reference_name=None, layout_release_uuid='${workflow.input.layout_release_uuid}', s3_config='${system.secrets.s3_downloader}', s3_endpoint_url=None, ocs_config='${system.secrets.ocs_downloader}', http_config=None, notification_workflow='IDP_SUBMISSION_NOTIFY_V35', workflow_uuid='${workflow.input.workflow_uuid}', workflow_name='${workflow.input.workflow_name}', workflow_version='${workflow.input.workflow_version}')

Bases: Block

Submission bootstrap block initializes the submission object and prepares external images or other submission data if needed.

This block should be called at the START of every flow that uses blocks included in the library.

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• s3_config (str) – Specify Amazon S3 credentials for submission retrieval store. Expects a json expression: {"aws_access_key_id": "X", "aws_secret_access_key": "Y"}, defaults to system_secret(S3_SECRET_KEY)

• s3_endpoint_url (Optional[str]) – Endpoint URL for S3 submission retrieval store, defaults to None

• ocs_config (str) – OCS Configuration for downloading submission data, defaults to system_secret(OCS_SECRET_KEY)

• http_config (Optional[str]) – An optional system secret field expressing the HTTP Configuration for downloading submission data. The content is expected to be a json formatted as: {"username": "X", "password": "Y", "ssl_cert": "CA bundle filename"}, defaults to None

• notification_workflow (str) – Notification flow name to run when the submission is initialized, defaults to IDP_SUBMISSION_NOTIFY_NAME

• workflow_uuid (str) – UUID of the triggered workflow version, defaults to workflow_input(Inputs.WorkflowUuid)

• workflow_name (str) – Name of the triggered workflow, defaults to workflow_input(Inputs.WorkflowName)

• workflow_version (str) – Version of the triggered workflow, defaults to workflow_input(Inputs.WorkflowVersion)

Example usage:

submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')

output(key=None)

Submission bootstrap ends with a CodeBlock, which nests the actual outputs under the ‘result’ key. This method overrides the default output in the same manner that flows_sdk.blocks.Routing.CodeBlock does for convenience.

Parameters:

key (Optional[str]) –

Optionally provide a key to directly get a nested property. When not provided, the entire ‘result’ will be returned.

For example, we have:

{
    "result": {
        "a": {
            "b": 42
        }
    }
}

• skipping the key output()` will return ``{"a": {"b": 42}}

• calling with output("a") will result in {"b": 42}

• calling with output("a.b") will return 42

Return type:

str

Returns:

the value under the provided key

class flows_sdk.implementations.idp_v35.idp_blocks.MachineCollationBlock(submission, cases, dedupe_files=False, refresh_retention_period=True, remove_from_cases=None, retention_period=None, reference_name=None)

Bases: Block

Machine collation block groups files, documents and pages (from the submission) into cases

Mandatory parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow

• submission (Any) – Submission object

• cases (Any) –

  This parameter accepts an array of JSON objects that contain information on how Cases should be created. There are two ways to collate a case: by the filenames specified in the current Submission, or by the ids of the Documents or Pages submitted in previous submissions.

  This parameter follows the given format:

  {
      'cases': [
          'external_case_id': 'HS-1',
          'filename': 'file1.pdf',
          'documents': [42],
          'pages': [43]
      ]
  }
  

Parameters with defaults:

Parameters:

• dedupe_files (bool) – Enabling this setting will replace case data from repeated file names within the same case. Cases will retain data from the most recently submitted version of a particular file. Note that this setting does not delete the old data, it just removes it from the case. Keep in mind this option is only relevant when adding to cases

• remove_from_cases (Optional[Any]) –

  This parameter accepts an array of JSON objects that contains information on how documents or pages should be removed from a pre-existing case. Currently, the only way to de-collate from a case is by the ids of the Documents or Pages within a given case

  This parameter follows the given format:

  {
      'remove_from_cases': [
          'external_case_id': 'HS-1',
          'documents': [42],
          'pages': [43]
      ]
  }
  

• retention_period (Optional[int]) – The number of days to retain cases after they are updated by this block. If passed None, no changes to the deletion date will be made. By default, this value is None

• refresh_retention_period (bool) – If True, (re)applies Retention Period parameter to all cases passed into the block. If False, only updates those without a pre-existing deletion date. By default, this value is True

Example usage:

machine_collation = MachineCollationBlock(
    reference_name='machine_collation',
    submission=submission_bootstrap.output('result.submission'),
    cases=submission_bootstrap.output('result.api_params.cases'),
    dedupe_files=True,
    remove_from_cases=custom_supervision.output('remove_from_cases')
    retention_period=90,
    refresh_retention_period=True
)

class flows_sdk.implementations.idp_v35.idp_blocks.MachineClassificationBlock(submission, api_params, reference_name=None, rotation_correction_enabled=True, mobile_processing_enabled=False, layout_release_uuid='${workflow.input.layout_release_uuid}', vpc_registration_threshold='${workflow.input.structured_layout_match_threshold}', nlc_enabled='${workflow.input.semi_structured_classification}', nlc_target_accuracy='${workflow.input.semi_target_accuracy}', nlc_doc_grouping_logic='${workflow.input.semi_doc_grouping_logic}')

Bases: Block

Machine classification block automatically matches documents to structured, semi-structured or additional layouts

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• rotation_correction_enabled (bool) – Identifies and corrects the orientation of semi-structured images, defaults to True

• mobile_processing_enabled (bool) – Improves the machine readability of photo captured documents, defaults to False

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• vpc_registration_threshold (str) – Structured pages above this threshold will be automatically matched to a layout variation, defaults to workflow_input(Settings.StructuredLayoutMatchThreshold)

• nlc_enabled (str) – Enables workflow to manage a model for automated classification of semi-structured and additional layout variations, defaults to workflow_input(Settings.SemiStructuredClassification)

• nlc_target_accuracy (str) – defaults to workflow_input(Settings.SemiTargetAccuracy)

• nlc_doc_grouping_logic (str) – Logic to handle multiple pages matched to the same layout variationin a given submission, defaults to workflow_input(Settings.SemiDocGroupingLogic)

Example usage:

machine_classification = MachineClassificationBlock(
    reference_name='machine_classification',
    submission=case_collation.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    rotation_correction_enabled=idp_wf_config.rotation_correction_enabled,
)

class flows_sdk.implementations.idp_v35.idp_blocks.ManualClassificationBlock(submission, api_params, reference_name=None, layout_release_uuid='${workflow.input.layout_release_uuid}', manual_nlc_enabled='${workflow.input.manual_nlc_enabled}', task_restrictions=None, notification_workflow='IDP_SUBMISSION_NOTIFY_V35')

Bases: Block

Manual classification block allows keyers to manually match submissions to their layouts. Keyers may perform manual classification if machine classification cannot automatically match a submission to a layout with high confidence

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• manual_nlc_enabled (str) – Enables manual classification when applicable, defaults to workflow_input(Settings.ManualNlcEnabled)

• task_restrictions (Optional[List[Any]]) – Defines what users can access Supervision tasks created by this block, defaults to None

• notification_workflow (str) – Notification flow name to run when the submission enters Manual Classification, defaults to IDP_SUBMISSION_NOTIFY_NAME

Example usage:

manual_classification = ManualClassificationBlock(
    reference_name='manual_classification',
    submission=machine_classification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
)

class flows_sdk.implementations.idp_v35.idp_blocks.MachineIdentificationBlock(submission, api_params, layout_release_uuid='${workflow.input.layout_release_uuid}', reference_name=None, manual_field_id_enabled='${workflow.input.manual_field_id_enabled}', field_id_target_accuracy='${workflow.input.field_id_target_accuracy}', table_id_target_accuracy='${workflow.input.table_id_target_accuracy}')

Bases: Block

Machine identification automatically identify fields and tables in the submission

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• manual_field_id_enabled (str) – Enables manual identification when applicable, defaults to workflow_input(Settings.ManualFieldIdEnabled)

• field_id_target_accuracy (str) – Field ID Target Accuracy, defaults to workflow_input(Settings.FieldIdTargetAccuracy)

• table_id_target_accuracy (str) – Table ID Target Accuracy, defaults to workflow_input(Settings.TableIdTargetAccuracy)

Example usage:

machine_identification = MachineIdentificationBlock(
    reference_name='machine_identification',
    submission=manual_classification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
)

class flows_sdk.implementations.idp_v35.idp_blocks.ManualIdentificationBlock(submission, api_params, reference_name=None, task_restrictions=None, manual_field_id_enabled='${workflow.input.manual_field_id_enabled}', layout_release_uuid='${workflow.input.layout_release_uuid}', notification_workflow='IDP_SUBMISSION_NOTIFY_V35')

Bases: Block

Manual identification allows keyers to complete field identification or table identification tasks, where they draw bounding boxes around the contents of certain fields, table columns or table rows. This identification process ensures that the system will be able to transcribe the correct content in the upcoming transcription process

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• task_restrictions (Optional[List[Any]]) – Defines what users can access Supervision tasks created by this block, defaults to []

• manual_field_id_enabled (str) – Enables manual identification when applicable, defaults to workflow_input(Settings.ManualFieldIdEnabled)

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• notification_workflow (str) – Notification flow name to run when the submission enters Manual Identification, defaults to IDP_SUBMISSION_NOTIFY_NAME

Example usage:

manual_identification = ManualIdentificationBlock(
    reference_name='manual_identification',
    submission=machine_identification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    task_restrictions=idp_wf_config.manual_identification_config.task_restrictions,
)

class flows_sdk.implementations.idp_v35.idp_blocks.MachineTranscriptionBlock(submission, api_params, reference_name=None, layout_release_uuid='${workflow.input.layout_release_uuid}', transcription_model='${workflow.input.transcription_model}', finetuning_only_trained_layouts='${workflow.input.finetuning_only_trained_layouts}', transcription_overrides='${workflow.input.transcription_overrides}', structured_text_target_accuracy='${workflow.input.structured_text_target_accuracy}', structured_text_confidence_threshold='${workflow.input.structured_text_threshold}', structured_text_acceptable_confidence='${workflow.input.structured_min_leg_threshold}', semi_structured_text_target_accuracy='${workflow.input.semi_structured_text_target_accuracy}', semi_structured_text_confidence_threshold='${workflow.input.semi_structured_text_threshold}', semi_structured_text_acceptable_confidence='${workflow.input.semi_structured_min_leg_threshold}', semi_structured_table_target_accuracy='${workflow.input.semi_structured_table_target_accuracy}', semi_structured_table_confidence_threshold='${workflow.input.semi_structured_table_threshold}', semi_structured_table_acceptable_confidence='${workflow.input.semi_structured_table_min_leg_threshold}', structured_checkbox_target_accuracy='${workflow.input.structured_checkbox_target_accuracy}', structured_checkbox_confidence_threshold='${workflow.input.structured_checkbox_threshold}', structured_checkbox_acceptable_confidence='${workflow.input.checkbox_min_leg_threshold}', structured_signature_target_accuracy='${workflow.input.structured_signature_target_accuracy}', structured_signature_confidence_threshold='${workflow.input.structured_signature_threshold}', structured_signature_acceptable_confidence='${workflow.input.signature_min_leg_threshold}', semi_structured_checkbox_target_accuracy='${workflow.input.semi_structured_checkbox_target_accuracy}', semi_structured_checkbox_confidence_threshold='${workflow.input.semi_structured_checkbox_threshold}', semi_structured_checkbox_acceptable_confidence='${workflow.input.semi_structured_checkbox_min_leg_threshold}')

Bases: Block

Machine transcription automatically transcribes the content of your submission

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• transcription_model (str) – The transcription model that will be used for this flow, defaults to workflow_input(Settings.TranscriptionModel)

• finetuning_only_trained_layouts (str) – defaults to workflow_input( Settings.FinetuningOnlyTrainedLayouts )

• transcription_overrides (str) – defaults to workflow_input( Settings.TranscriptionOverrides )

• structured_text_target_accuracy (str) – defaults to workflow_input( Settings.StructuredTextTargetAccuracy )

• structured_text_confidence_threshold (str) – defaults to workflow_input( Settings.StructuredTextThreshold )

• structured_text_acceptable_confidence (str) – defaults to workflow_input( Settings.StructuredTextMinLegThreshold )

• semi_structured_text_target_accuracy (str) – defaults to workflow_input( Settings.SemiStructuredTextTargetAccuracy )

• semi_structured_text_confidence_threshold (str) – defaults to workflow_input( Settings.SemiStructuredTextThreshold )

• semi_structured_text_acceptable_confidence (str) – defaults to workflow_input( Settings.SemiStructuredTextMinLegThreshold )

• structured_checkbox_target_accuracy (str) – defaults to workflow_input( Settings.StructuredCheckboxTargetAccuracy )

• structured_checkbox_confidence_threshold (str) – defaults to workflow_input( Settings.StructuredCheckboxThreshold )

• structured_checkbox_acceptable_confidence (str) – defaults to workflow_input( Settings.StructuredCheckboxMinLegThreshold )

• structured_signature_target_accuracy (str) – defaults to workflow_input( Settings.StructuredSignatureTargetAccuracy )

• structured_signature_confidence_threshold (str) – defaults to workflow_input( Settings.StructuredSignatureThreshold )

• structured_signature_acceptable_confidence (str) – defaults to workflow_input( Settings.StructuredSignatureMinLegThreshold )

• semi_structured_checkbox_target_accuracy (str) – defaults to workflow_input( Settings.SemiStructuredCheckboxTargetAccuracy )

• semi_structured_checkbox_confidence_threshold (str) – defaults to workflow_input( Settings.SemiStructuredCheckboxThreshold )

• semi_structured_checkbox_acceptable_confidence (str) – defaults to workflow_input( Settings.SemiStructuredCheckboxMinLegThreshold )

Example usage:

machine_transcription = MachineTranscriptionBlock(
    reference_name='machine_transcription',
    submission=manual_identification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
)

class flows_sdk.implementations.idp_v35.idp_blocks.ManualTranscriptionBlock(submission, api_params, reference_name=None, supervision_transcription_masking=True, table_output_manual_review=False, task_restrictions=None, manual_transcription_enabled='${workflow.input.manual_transcription_enabled}', notification_workflow='IDP_SUBMISSION_NOTIFY_V35')

Bases: Block

Manual transcription lets your keyers manually enter the text found in fields or tables that could not be automatically transcribed

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (bool) – Prevents users from inputting invalid characters during Supervision Transcription tasks, defaults to True

• table_output_manual_review (bool) – Always generates a table transcription task if the layout contains a table. If disabled, a table transcription task will only be generated if one or more cells have transcribed values below the defined thresholds, defaults to False

• task_restrictions (Optional[List[Any]]) – Defines what users can access Supervision tasks for submission from a particular source or submissions matching a specific layout, defaults to []

• manual_transcription_enabled (str) – Enables manual transcription when applicable, defaults to workflow_input(Settings.ManualTranscriptionEnabled)

• notification_workflow (str) – Notification flow name to run when the submission enters Manual Transcription, defaults to IDP_SUBMISSION_NOTIFY_NAME

Example usage:

manual_transcription = ManualTranscriptionBlock(
    reference_name='manual_transcription',
    submission=machine_transcription.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    supervision_transcription_masking=(
        idp_wf_config.manual_transcription_config.supervision_transcription_masking
    ),
    table_output_manual_review=(
        idp_wf_config.manual_transcription_config.table_output_manual_review
    ),
    task_restrictions=idp_wf_config.manual_transcription_config.task_restrictions,
)

class flows_sdk.implementations.idp_v35.idp_blocks.FlexibleExtractionBlock(submission, api_params, reference_name=None, supervision_transcription_masking=True, task_restrictions=None, layout_release_uuid='${workflow.input.layout_release_uuid}', notification_workflow='IDP_SUBMISSION_NOTIFY_V35')

Bases: Block

Flexible extraction manually transcribes fields marked for review

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (bool) – Prevents users from inputting invalid characters during the Flexible Extraction task, defaults to True

• task_restrictions (Optional[List[Any]]) – Defines what users can access Supervision tasks created by this block, defaults to []

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• notification_workflow (str) – Notification flow name to run when the submission enters Flexible Extraction, defaults to IDP_SUBMISSION_NOTIFY_NAME

Example usage:

flexible_extraction = FlexibleExtractionBlock(
    reference_name='flexible_extraction',
    submission=manual_transcription.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    supervision_transcription_masking=(
        idp_wf_config.flexible_extraction_config.supervision_transcription_masking
    ),
    task_restrictions=idp_wf_config.flexible_extraction_config.task_restrictions,
)

class flows_sdk.implementations.idp_v35.idp_blocks.SubmissionCompleteBlock(submission, reference_name=None, nlc_qa_sampling_ratio='${workflow.input.semi_qa_sample_rate}', field_id_qa_enabled='${workflow.input.field_id_qa_enabled}', field_id_qa_sampling_ratio='${workflow.input.field_id_qa_sample_rate}', table_id_qa_enabled='${workflow.input.table_id_qa_enabled}', table_id_qa_sampling_ratio='${workflow.input.table_id_qa_sample_rate}', transcription_qa_enabled='${workflow.input.qa}', transcription_qa_sampling_ratio='${workflow.input.qa_sample_rate}', table_cell_transcription_qa_enabled='${workflow.input.table_cell_transcription_qa_enabled}', table_cell_transcription_qa_sample_rate='${workflow.input.table_cell_transcription_qa_sample_rate}', auto_qa_sampling_rate_enabled='${workflow.input.auto_qa_sampling_rate_enabled}', payload=None)

Bases: Block

Submission complete block finalizes submission processing and updates reporting data. This block should be called at the END of every flow that uses blocks included in the flows_sdk.implementations.idp_v35 library. The block requires both the submission object and a payload to be passed in.

Mandatory parameters:

Parameters:

• submission (str) – Submission object

• payload (Any) – Object to pass to downstream systems

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• nlc_qa_sampling_ratio (Union[str, int]) – Defines the percentage of documents sampled for Classification QA, defaults to workflow_input(Settings.SemiQaSampleRate)

• field_id_qa_enabled (Union[str, bool]) – defaults to workflow_input(Settings.FieldIdQaEnabled)

• field_id_qa_sampling_ratio (Union[str, int]) – Defines the percentage of documents sampled for Identification QA, defaults to workflow_input(Settings.FieldIdQaSampleRate)

• table_id_qa_enabled (Union[str, bool]) – Allows users to verify the location of table cells, defaults to workflow_input(Settings.TableIdQaEnabled)

• table_id_qa_sampling_ratio (Union[str, int]) – Defines the percentage of tables the system samples for QA, defaults to workflow_input(Settings.TableIdQaSampleRate)

• transcription_qa_enabled (Union[str, bool]) – defaults to workflow_input(Settings.TranscriptionQaEnabled)

• transcription_qa_sampling_ratio (Union[str, int]) – Defines the percentage of fields sampled for Transcription QA, defaults to workflow_input(Settings.TranscriptionQaSampleRate)

• table_cell_transcription_qa_enabled (Union[str, bool]) – defaults to workflow_input(Settings.TableCellTranscriptionQaEnabled)

• table_cell_transcription_qa_sample_rate (Union[str, int]) – Defines the percentage of cells the system samples for QA. This value is likely to be lower than “Transcription QA Sample Rate” since there are more table cells than fields on any given page, defaults to workflow_input(Settings.TableCellTranscriptionQaSampleRate)

Example usage:

submission_complete = SubmissionCompleteBlock(
    reference_name='complete_submission',
    submission=flexible_extraction.output('submission')
)

class flows_sdk.implementations.idp_v35.idp_blocks.DatabaseAccessBlock(reference_name=None, db_type='', database='', host='', username='', password='', query='', port=None, options=None, timeout=None, query_params=None, title='DB Block', description='DB access block')

Bases: Block

Database Block allows user to access and perform queries on the specified database

Mandatory parameters:

Parameters:

• db_type (str) – database type (mssql, oracle, postgres)

• host (str) – URL/IP address of the server database is hosted on

• database (str) – database name

• username (str) – database username

• password (str) – database password

• query (str) – parameterized query

Optional parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• port (Optional[int]) – database port number

• options (Optional[Dict[Any, Any]]) – dictionary of additional connection string options

• query_params (Optional[Dict[Any, Any]]) – dictionary of values for query placeholders

• timeout (Optional[int]) – timeout in seconds (mssql and postgres only)

Example usage:

# minimum viable instantiation, the rest of the fields can later be added in the UI
db_lookup = DatabaseAccessBlock(
    reference_name='db_lookup',
    title='Database Lookup',
    description='Perform database lookup',
)
# note that storing password in plain text form is not recommended
# password can later be entered in the UI or defined as a flow secret.
db_lookup = DatabaseAccessBlock(
    reference_name='db_lookup',
    title='Database Lookup',
    description='Perform database lookup',
    db_type='mssql',
    database='cars',
    host='example.com',
    username='user',
    # we recommend skipping the password field or defining it as a system secret
    # e.g. system_secret('{your_secret_identifier}') (both allow for secret input via. UI)
    password='pw'
    port=1433,
    timeout=200,
    query='SELECT * from CAR',
)
# reference to the output of DBAccessBlock which can be passed as input to the next block
# for more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = db_lookup.output()
# content of output_ref:
{
    "result": [
        {
            "id": 1,
            "brand": "Aston Martin",
            "model": "Vanquish"
        },
        {
            "id": 2,
            "brand": "Jaguar",
            "model": "XE 2018"
        }
    ]
}

Database output for the same query (SELECT * FROM CAR)

class flows_sdk.implementations.idp_v35.idp_blocks.HttpRestBlock(method, endpoint, reference_name=None, headers=None, params=None, payload=None, json=None, authorization_type=None, handled_error_codes=None, auth_params=None, title='HTTP REST Block', description='HTTP REST block')

Bases: BaseHttpRestBlock

Http Rest Block allows user to perform HTTP requests

Mandatory parameters:

Parameters:

• method (str) – HTTP method to use

• endpoint (str) – absolute url including schema to HTTP endpoint for this request

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• headers (Optional[Dict[str, str]]) – HTTP headers to use

• params (Optional[Dict[str, Any]]) – key-value pair used as query parameters in the request

• payload (Optional[Dict[str, str]]) – key-value pair to be used as x-www-form-urlencoded data. Mutually exclusive with json

• json (Optional[Dict[str, Any]]) – key-value pair to be used as json data. Mutually exclusive with payload

• authorization_type (Optional[str]) – authorization type (none, http_header, oauth_2_client_credentials)

• handled_error_codes (Optional[List[Union[int, str]]]) – specify HTTP error codes that will not fail the task, allowing for wildcards with the ‘x’ char. Example: [400, “5xx”]

• auth_params (Union[HttpHeaderAuthorizationParams, OAuth2AuthorizationParams, None]) – authorization parameters to be passed when the authorization_type is set to “http_header” or “oauth_2_client_credentials”

Example usage:

# minimum viable instantiation, the rest of the fields can later be added in the UI
http_rest_block = HttpRestBlock(
    reference_name='http_request',
    title='Http Request',
    description='Perform http request',
    method='GET',
)
http_rest_block = HttpRestBlock(
    reference_name='http_request',
    title='Http Request',
    description='Perform http request',
    method='GET',
    endpoint='https://sheets.googleapis.com/v4/spreadsheets/example',
)
# reference to the output of HttpRestBlock which can be passed as input to the next block
# for more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = http_rest_block.output()

class flows_sdk.implementations.idp_v35.idp_blocks.HyperscienceRestApiBlock(method, app_endpoint, reference_name=None, headers=None, params=None, payload=None, json=None, handled_error_codes=None, title='Hyperscience HTTP REST Block', description='Hyperscience HTTP REST block')

Bases: BaseHttpRestBlock

Make HTTP requests to the Hyperscience platform APIs. This block will automatically take care of prepending the base url and will include the appropriate authentication headers with the request.

Mandatory parameters:

Parameters:

• method (str) – HTTP method to use, e.g. ‘GET’, ‘POST’, etc.

• app_endpoint (str) – relative url, beginning with a forward slash, to an HTTP endpoint served by the Hyperscience platform. For reference on the available endpoints, check out https://docs.hyperscience.ai/. Absolute URLs that include a hostname are allowed only if the flow has received them as part of the response of a previous API request that the flow has made. Do not hardcode the hostname of the Hyperscience instance in your flow definitions as they will not work when you run the flows on another instance, or if the hostname changes. For making requests to external systems, use the HttpRestBlock or a library like “requests”.

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• headers (Optional[Dict[str, str]]) – HTTP headers to use

• params (Optional[Dict[str, Any]]) – key-value pair used as query parameters in the request

• payload (Optional[Dict[str, str]]) – key-value pair to be used as x-www-form-urlencoded data. Mutually exclusive with json

• json (Optional[Dict[str, Any]]) – key-value pair to be used as json data. Mutually exclusive with payload

• handled_error_codes (Optional[List[Union[int, str]]]) – specify HTTP error codes that will not fail the task, allowing for wildcards with the ‘x’ char. Example: [400, “5xx”]

Example usage:

hs_rest_block = HyperscienceRestApiBlock(
    reference_name='http_request',
    app_endpoint='/api/healthcheck',
    method='GET',
)

class flows_sdk.implementations.idp_v35.idp_blocks.SoapRequestBlock(method, reference_name=None, endpoint='', body_namespace=None, headers_namespace=None, headers=None, params=None, title='HTTP REST Block', description='HTTP REST block')

Bases: Block

Soap Block allows user to perform SOAP requests

Mandatory parameters:

Parameters:

• method (str) – SOAP method to invoke

• endpoint (str) – absolute url including schema to HTTP endpoint for this request

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• body_namespace (Optional[str]) – SOAP request body namespace url

• headers_namespace (Optional[str]) – SOAP request headers namespace url

• headers (Optional[Dict[Any, Any]]) – SOAP headers to use

• params (Optional[Dict[Any, Any]]) – key-value pair used as query parameters in the request to be inserted as SOAP body

Example usage:

# minimum viable instantiation, the rest of the fields can later be added in the UI
soap_block = SoapRequestBlock(
    reference_name='soap_request',
    title='Soap Request',
    description='Perform soap request',
    method='ExampleMethod',
)
soap_block = SoapRequestBlock(
    reference_name='soap_request',
    title='Soap Request',
    description='Perform soap request',
    method='ExampleMethod',
    endpoint='http://example.org/abc.wso',
    body_namespace='http://www.example.org/abc.countryinfo',
    params={'example': 'stuff'},
    headers={}
)
# reference to the output of SoapRequestBlock which can be passed as input to the next block
# for more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = soap_block.output()

class flows_sdk.implementations.idp_v35.idp_blocks.CustomSupervisionBlock(submission, task_purpose, data, supervision_template, reference_name=None, supervision_transcription_masking=True, task_restrictions=None, notification_workflow='IDP_SUBMISSION_NOTIFY_V35')

Bases: Block

The CustomSupervisionBlock uses a JSON schema as a form builder to create customer-driven UIs for Supervision tasks.

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• task_purpose (str) – the custom task name that’s given to all supervision tasks that run through a particular instance of a custom supervision block. This can be used to filter tasks in the task queue.

• data (Any) – JSON structure that supplies the data used to populated the supervision_template

• supervision_template (Any) – JSON used for configuration of the Custom Supervision Task.

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (bool) – Prevents users from inputting invalid characters during the Custom Supervision task, defaults to True

• task_restrictions (Optional[List[Any]]) – Defines what users can access Supervision tasks created by this block, defaults to []

• notification_workflow (str) – Notification flow name to run when the submission enters Custom Supervision, defaults to IDP_SUBMISSION_NOTIFY_NAME

Example usage:

custom_supervision = CustomSupervisionBlock(
    reference_name='custom_supervision',
    submission=manual_transcription.output('submission'),
    task_purpose='make_custom_decision',
    supervision_template=[
        {
            'name': 'three_column_template',
            'version': '1.0',
            'thumbnail': {
                'group_by_document': True,
                'group_by_case': True,
            },
            'action': [
                {
                    'name': 'my_tab',
                    'display': 'SSN Info',
                    'input': [
                        {
                            'name': 'ssn_field',
                            'type': 'transcription',
                            'data_identifier': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                            'title': 'SSN',
                        },
                    ],
                    'ui': {
                        'groups': [
                            {
                                'title': 'My Group',
                                'fields': ['ssn_field'],
                            },
                        ],
                    },
                },
            ],
        },
    ],
    data={
        'fields': [
            {
                'id': 967,
                'uuid': 'fe8d4e7a-a82c-448c-85a1-0a3cbbe53b61',
                'value': '111-22-3333',
                'page_id': 1,
                'data_identifier': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                'validation_overridden': false,
                'bounding_box': [
                    0.09360783305186686,
                    0.25792617589433436,
                    0.6358913805295097,
                    0.28862414740388187,
                ],
                'occurence_index': 1,
            },
        ],
        'template_fields': [
            {
                'uuid': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                'notes': '',
                'type': 'entry',
                'data_type_uuid': 'b600a4e4-758f-4c4b-a2f4-cbb5dd870a0c',
                'name': 'SSN',
                'n_occurences': 1,
            },
        ],
        'pages': [
            {
                'id': 15,
                'file_page_number': 1,
                'document_page_number': 1,  // Optional
                'image_url': '/image/6e55fa86-a36b-4178-8db1-3a514621d4c1',
                'form_id': 2,
                'submission_id': 3,
                'external_case_ids': [
                    'HS-27',
                ],
                'read_only': False,
                'filename': 'filename.pdf',
            },
        ],
        'documents': [
            {
                'id': 2,
                'layout_uuid': 'e31c3cbf-dcea-44fd-a052-902463c6040f',
                'layout_name': 'layout_name',
                'page_count': 2,
                'read_only': false,
            },
        ],
        'cases': [],
        'selected_choices': [],
    },
    supervision_transcription_masking=False,
)

class flows_sdk.implementations.idp_v35.idp_blocks.IdpCustomSupervisionBlock(submission, task_purpose, supervision_template, reference_name=None, supervision_transcription_masking=True, task_restrictions=None, page_ids=None, notification_workflow='IDP_SUBMISSION_NOTIFY_V35')

Bases: Block

An IDP wrapper for the Custom Supervision block. It has the same functionality, but handles reading/writing data from the IDP database.

Mandatory parameters:

Parameters:

• submission (Any) – Submission object

• task_purpose (str) – the custom task name that’s given to all supervision tasks that run through a particular instance of a custom supervision block. This can be used to filter tasks in the task queue.

• supervision_template (Any) – JSON used for configuration of the Custom Supervision Task.

Parameters with defaults:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• supervision_transcription_masking (bool) – Prevents users from inputting invalid characters during the Custom Supervision task, defaults to True

• task_restrictions (Optional[List[Any]]) – Defines what users can access Supervision tasks created by this block, defaults to []

• page_ids (Optional[List[int]]) – A list of page ids to include. This should not be used. The IDP wrapper should handle pulling in all pages in the submission. defaults to []

• notification_workflow (str) – Notification flow name to run when the submission enters Custom Supervision (which is called within the IDP Custom Supervision block), defaults to IDP_SUBMISSION_NOTIFY_NAME

Example usage:

idp_custom_supervision = IdpCustomSupervisionBlock(
    reference_name='idp_custom_supervision',
    submission=manual_transcription.output('submission'),
    task_purpose='make_custom_decision',
    supervision_template=[
        {
            'name': 'three_column_template',
            'version': '1.0',
            'thumbnail': {
                'group_by_document': True,
                'group_by_case': True,
            },
            'action': [
                {
                    'name': 'my_tab',
                    'display': 'SSN Info',
                    'input': [
                        {
                            'name': 'ssn_field',
                            'type': 'transcription',
                            'data_identifier': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                            'title': 'SSN',
                        },
                    ],
                    'ui': {
                        'groups': [
                            {
                                'title': 'My Group',
                                'fields': ['ssn_field'],
                            },
                        ],
                    },
                },
            ],
        },
    ],
    supervision_transcription_masking=False,
)

class flows_sdk.implementations.idp_v35.idp_blocks.IDPFullPageTranscriptionBlock(submission, reference_name=None, title='Full Page Transcription (Submission)', description='Transcribes the documents included in a submission', app_metadata=None)

Bases: Block

IDP Full Page Transcription Block machine-transcribes the unassigned pages in a submission

Mandatory parameters:

Parameters:

submission (str) – submission object that includes the raw documents that need to be transcribed

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• app_metadata (Optional[List[str]]) – used for capturing information about the flow

Example usage:

# This is one way to obtain the submission object (using SUbmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# IDPFullPageTranscriptionBlock transcribes the documents contained in the submission object
idp_fpt_block = IDPFullPageTranscriptionBlock(
    reference_name='idp_fpt',
    submission=submission_bootstrap.output(),
)
# reference to the output of IDPFullPageTranscriptionBlock which can be passed as input to
# the next block. For more information on output() and output references, please take a
# look at class Block and its output() method
output_ref = idp_fpt_block.output()

class flows_sdk.implementations.idp_v35.idp_blocks.IDPImageCorrectionBlock(submission, reference_name=None, rotation_correction_enabled=True, mobile_processing_enabled=False, title='Image Correction (Submission)', description='Rotate and de-skew documents included in a submission', app_metadata=None)

Bases: Block

IDP Image Correction Block rotates and de-skews tilted/skewed images so that they are more transcription-friendly

Mandatory parameters:

Parameters:

submission (str) – submission object that includes the raw documents that need to be image-corrected

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• app_metadata (Optional[List[str]]) – used for capturing information about the flow

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# IDPImageCorrectionBlock takes in the submission object and outputs in the same format with
# its images
idp_image_correct_block = IDPImageCorrectionBlock(
    reference_name='idp_image_correct',
    submission=submission_bootstrap.output(),
)
# reference to the output of IDPImageCorrectionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look at
# class Block and its output() method
output_ref = idp_image_correct_block.output()

class flows_sdk.implementations.idp_v35.idp_blocks.FullPageTranscriptionBlock(pages, reference_name=None, title='Full Page Transcription (Pages)', description='Transcribes documents', app_metadata=None)

Bases: Block

Full Page Transcription Block machine-transcribes the document pages passed to it

Mandatory parameters:

Parameters:

pages (str) – list of pages that includes the raw documents that need to be transcribed

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• app_metadata (Optional[List[str]]) – used for capturing information about the flow

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# FullPageTranscriptionBlock transcribes the documents contained in the submission object
fpt_block = FullPageTranscriptionBlock(
    reference_name='fpt',
    submission=submission_bootstrap.output(),
)
# reference to the output of FullPageTranscriptionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look at
# class Block and its output() method
output_ref = fpt_block.output()

class flows_sdk.implementations.idp_v35.idp_blocks.ImageCorrectionBlock(pages, reference_name=None, rotation_correction_enabled=True, mobile_processing_enabled=False, title='Image Correction (Pages)', description='Rotate and de-skew documents', app_metadata=None)

Bases: Block

Image Correction Block rotates and de-skews tilted/skewed images so that they are more transcription-friendly

Mandatory parameters:

Parameters:

pages (str) – list of page object that includes the raw documents that need to be image-corrected

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow, a unique one will be generated when not provided

• app_metadata (Optional[List[str]]) – used for capturing information about the flow

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# ImageCorrectionBlock takes in the submission object and outputs in the same format with
# its images
image_correct_block = ImageCorrectionBlock(
    reference_name='image_correct',
    pages=submission_bootstrap.output(submission.unassigned_pages),
)
# reference to the output of IDPImageCorrectionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look
# at class Block and its output() method
output_ref = image_correct_block.output()

Trigger Blocks (v35)

Note

“Triggers” are referred to as “Input Blocks” in our application documentation.

Warning

Flows are built to allow full definition of triggers either in code or manually in the Flow Studio. We recommend defining triggers manually in the Flow Studio. Instructions on how to do so are in our application documentation.

To define your triggers in the Flow Studio, simply use the IDPTriggers() convenience class:

return Flow(
   ...
   triggers=IDPTriggers()
)

If instead you wish to define specific triggers in Python, use the trigger definitions below.

Folder Listener (v35)

Identifier: FOLDER_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
folder	Path	N/A	Folder to scan for submissions	N/A
file_extensions	string	N/A	File extensions	List of file extensions to monitor for (e.g.: ‘png, jpg, pdf’)
has_meta	boolean	False	Enable metadata	Select this if a metadata file is to be expected along with document files (in XXX_index.txt file)
poll_interval	integer	10	Poll interval	Poll interval in seconds
warmup_interval	integer	15	Warm-up interval	Seconds to wait past document last modified time before processing it
folder_cleanup_delay	integer	86400	Folder cleanup delay	Seconds to wait before cleaning up subfolders
api_params	object	{}	API Parameters	N/A

folder_listener = IOBlock(
    identifier='FOLDER_TRIGGER',
    reference_name='folder_trigger',
    title='Folder listener',
    enabled=True,
    input={
        'folder': '/var/www/forms/forms/folder/',
        'file_extensions': 'png, pdf, jpg',
    },
)

triggers = IDPTriggers(blocks=[folder_listener])

Kofax Folder Listener (v35)

Identifier: KOFAX_FOLDER_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
folder	Path	N/A	Folder to scan for submissions	N/A
file_extensions	string	N/A	File extensions	List of file extensions to monitor for (e.g.: ‘png, jpg, pdf’)
poll_interval	integer	10	Poll interval in seconds	N/A
warmup_interval	integer	15	Warm-up interval	Seconds to wait past document last modified time before processing it
folder_cleanup_delay	integer	86400	Folder cleanup delay	Seconds to wait before cleaning up subfolders
api_params	object	{}	API Parameters	N/A

kofax_folder_listener = IOBlock(
    identifier='KOFAX_FOLDER_TRIGGER',
    reference_name='kofax_folder_listener',
    title='Kofax Folder listener',
    enabled=True,
    input={
        'folder': '/var/www/forms/forms/kofax/',
        'file_extensions': 'png, pdf, jpg',
    },
)

triggers = IDPTriggers(blocks=[kofax_folder_listener])

Email Listener (v35)

Identifier: IMAP_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
provider [1]	string	imap	Email Provider	N/A
poll_interval	integer	60	Polling interval in seconds	N/A
folder	Path	N/A	Folder to scan for emails	N/A
email_body_treatment	string	“ignore”	Email body treatment	What to do with the body of an email that is ingested. Available options are “process” and “ignore”.
post_process_action	string	“move”	Post process action	What to do with the email after it is processed. Available options are “move” and “delete”.
post_process_move_folder	string	“”	Post process archive folder	Folder to move emails to once they are processed.
api_params	object	{}	API Parameters	N/A
render_headers [2]	array	[]	Headers to include	Headers to render at the top of the email body. Headers will be included only if email_body_treatment is set to “process”.

[1] one of:

imap
graph

[2] any of:

To
From
Subject
Date

Additional inputs when "imap" is the selected provider:

Name	Type	Default Value	Title	Description
host	string	N/A	IMAP server address	N/A
port	integer	993	Port Number	N/A
ssl	boolean	True	Use SSL connection	N/A
username	string	N/A	Username	N/A
password	Password	N/A	Password	N/A

Additional inputs when "graph" is the selected provider:

Name	Type	Default Value	Title	Description
client_id	string	N/A	Client id	Application (client) ID for the corresponding application registered with the Microsoft identity platform.
tenant_id	string	N/A	Tenant id	Also called directory ID. Unique identifier of the tenant in which the corresponding application is registered with.
graph_cloud_endpoint [1]	string	Global	Microsoft Graph Cloud Endpoint	Determines base URL used for accessing cloud services through Microsoft Graph.
azure_ad_endpoint [2]	string	AZURE_PUBLIC_CLOUD	Azure AD Endpoint	Determines base URL for the Azure Active Directory (Azure AD) endpoint to acquire token for each national cloud.
client_secret	Password	N/A	Client secret	Client secret for the corresponding application registered with the Microsoft identity platform.

[1] one of:

Global
US_GOV
US_DoD
Germany
China

[2] one of:

AZURE_PUBLIC_CLOUD
AZURE_CHINA
AZURE_GERMANY
AZURE_GOVERNMENT

imap_trigger = IOBlock(
    identifier='IMAP_TRIGGER',
    reference_name='imap_trigger',
    title='IMAP trigger',
    enabled=True,
    input={
        'host': 'example@mail.com',
        'folder': '/var/www/forms/forms/imap/',
        'username': 'admin',
        'password': 'pass',
    }
)

triggers = IDPTriggers(blocks=[imap_trigger])

Box Folder Listener (v35)

Identifier: BOX_FOLDER_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
source_folder_id	integer	N/A	Folder to scan for submissions	Use the Box Folder ID found in the URL
target_folder_id	integer	N/A	Folder to move completed files	Use the Box Folder ID found in the URL
file_extensions	array	[]	File extensions	Types of file extensions for which to monitor
custom_file_extensions	string	N/A	Other file extension types	Comma separated list of file extensions for which to monitor (e.g. ‘png, jpg, pdf’)
poll_interval	integer	10	Polling interval (in seconds)	How often the connector will check the base folder for submissions
warmup_interval	integer	15	Warm-up interval (in seconds)	How long the connector will wait to process the document after it was last modified
public_key_id	string	N/A	Public-Key ID	ID of the Public-key used for authentication with Box
private_key	Password	N/A	Private-Key	Private key used for authentication with Box
passphrase	Password	N/A	Passphrase	Passphrase used for authentication with Box
client_id	string	N/A	Client ID	Client Id used for authentication with Box
client_secret	Password	N/A	Client Secret	Client Secret used for authentication with Box
enterprise_id	string	N/A	Enterprise ID	Enterprise Id used for authentication with Box
api_params	object	{}	API Parameters	N/A

box_folder_trigger = IOBlock(
    identifier='BOX_FOLDER_TRIGGER',
    reference_name='box_folder_trigger',
    title='Box folder',
    enabled=True,
    input={
        'file_extensions': ['png', 'pdf', 'jpg'],
        'source_folder_id': 24,
        'target_folder_id': 42,
        'public_key_id': 'admin',
        'private_key': 'secret',
        'passphrase': 'password',
        'client_id': 'admin',
        'client_secret': 'secret',
        'enterprise_id': 'admin',
    }
)

triggers = IDPTriggers(blocks=[box_folder_trigger])

Cron Listener (v35)

Identifier: CRON_TRIGGER

Inputs:

Name	Type	Default Value	Title	Description
cron_spec	string	"*/5 * * * *"	Cron specification	N/A
time_zone	string	“US/Eastern”	Time Zone	N/A
api_params	object	{}	API Parameters	N/A

cron_trigger = IOBlock(
    identifier='CRON_TRIGGER',
    reference_name='cron_trigger',
    title='Cron Trigger',
    enabled=True,
    input={
        'cron_spec': '0 10 * * *',
        'time_zone': 'Europe/Sofia',
    }
)

triggers = IDPTriggers(blocks=[cron_trigger])

Salesforce Listener (v35)

Identifier: SALESFORCE_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
channel_name	string	N/A	Channel Name	Configured in the Hyperscience app in Salesforce.
username	string	N/A	Associated Username	Username associated with the Salesforce Connected App.
consumer_key	Password	N/A	Consumer Key	Consumer key used for authentication with Salesforce. Paired with the Salesforce instance from which it was issued.
private_key	Password	N/A	Private Key	Private key used for authentication with Salesforce.
sandbox_environment	boolean	false	Private Key	Enable if the Salesforce environment is a sandbox.
api_params	object	{}	API Parameters	N/A

Message Queue Listener (v35)

Identifier: MQ_LISTENER

Inputs:

Name	Type	Default Value	Title	Description
MQ_TYPE	string	“ACTIVE_MQ”	Message Queue type	Type of the message queue to connect with. Valid values are “ACTIVE_MQ”, “AMAZON_SQS”, “IBM_MQ” and “RABBIT_MQ”
MQ_USERNAME	string	null	Username/Access Key ID	N/A
MQ_PASSWORD	Password	null	Password/Secret Access Key	N/A
MQ_QUEUE_NAME	string	null	Queue Name	Name of the queue (topic) to connect to.
MQ_HOST	string	null	Host Name	N/A
MQ_PORT	integer	1415	Port Number	N/A
api_params	object	{}	API Parameters	N/A

Additional inputs when "AMAZON_SQS" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_REGION	string	null	AWS Region	N/A
MQ_QUEUE_URL	string	null	Queue URL	N/A
MQ_USE_EC2_INSTANCE_CREDENTIALS	boolean	True	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used.

NOTE: MQ_USERNAME can be used to provide Access Key ID and MQ_PASSWORD to provide Secret Access Key.

Additional inputs when "IBM_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_QUEUE_MANAGER	string	null	Queue Manager (IBM MQ)	N/A
MQ_SSL_CIPHER_SUITE [1]	string	null	SSL cipher suite	SSL cipher suite to use if SSL connection is used.
MQ_CHANNEL	string	null	Channel	N/A
MQ_NO_AUTH_REQUIRED	boolean	false	Channel	N/A

NOTE: MQ_NO_AUTH_REQUIRED is True, MQ_USERNAME and MQ_PASSWORD are optional.

[1] one of:

"NULL_MD5"
"NULL_SHA"
"RC4_MD5_EXPORT"
"RC4_MD5_US"
"RC4_SHA_US"
"RC2_MD5_EXPORT"
"DES_SHA_EXPORT"
"RC4_56_SHA_EXPORT1024"
"DES_SHA_EXPORT1024"
"TRIPLE_DES_SHA_US"
"TLS_RSA_WITH_NULL_SHA256"
"TLS_RSA_WITH_AES_128_CBC_SHA"
"TLS_RSA_WITH_AES_128_CBC_SHA256"
"TLS_RSA_WITH_AES_256_CBC_SHA"
"TLS_RSA_WITH_AES_256_CBC_SHA256"
"AES_SHA_US"
"TLS_RSA_WITH_DES_CBC_SHA"
"TLS_RSA_WITH_3DES_EDE_CBC_SHA"
"TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256"
"FIPS_WITH_DES_CBC_SHA"
"FIPS_WITH_3DES_EDE_CBC_SHA"

Additional inputs when "RABBIT_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_VIRTUAL_HOST	string	null	Virtual Host (RABBIT MQ)	N/A
MQ_EXCHANGE	string	“”	Exchange	N/A
MQ_ROUTING_KEY	string	“”	Routing key	N/A
MQ_CONNECTION_TYPE	string	“TCP”	Connection Type	“TCP or SSL”

rabbit_mq_listener = IOBlock(
    identifier='MQ_LISTENER',
    reference_name='mq_listener',
    title='RabbitMQ Listener',
    enabled=True,
    input={
        'MQ_TYPE': 'RABBIT_MQ',
        'MQ_QUEUE_NAME': 'some_queue_name',
        'MQ_HOST': 'somehost.com',
        'MQ_USERNAME': 'foo',
        'MQ_PASSWORD': system_secret('rabbit_mq_1_password'),
    }
)

triggers = IDPTriggers(blocks=[rabbit_mq_listener])

Output Blocks (v35)

Warning

Flows pass information to downstream systems using the IDPOutputsBlock. This block is specially built to allow full definition either in code or manually in the Flow Studio. We recommend defining outputs manually in the Flow Studio.

Instructions on how to do so are in our application documentation. Simply include an IDPOutputsBlock in your Python flow definition wherever you wish to send information to downstream systems.

class flows_sdk.implementations.idp_v35.idp_blocks.IDPOutputsBlock(inputs, blocks=None)

Bases: Outputs

Output block allows users to send data extracted by an IDP flow to other systems for downstream processing

Mandatory parameters:

Parameters:

inputs (Dict[str, Any]) – Used by the UI to automatically pre-populate the input of newly added blocks. Extended in this class to include 'enabled': True to visualize an enabled/disabled trigger that is controllable via Flow Studio.

Usage when parameters are to be defined in Flow Studio:

outputs = IDPOutputBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')}
)

If instead you wish to define specific outputs in Python, use the outputs definitions below.

HTTP Notifier Output (v35)

The HTTP Notifier (REST) output connection will POST results from the system to a specified HTTP endpoint.

Identifier: COB_HTTP_EXPORT

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
endpoint	string	N/A	Endpoint URL	URL that output notifications will be posted to
endpoint_secrets	string	N/A	Endpoint URL secrets	Endpoint URL Secrets, format: (one per line) secret_key_name=secret_value
timeout	int	600	Timeout (seconds)	How long (in seconds) to keep the connection open if no bytes are received from the endpoint. Set to 0 to wait indefinitely.
authorization_type [1]	string	“none”	Authorization type	Type of authorization
authorization_header_name	string	null	Authorization header name	Authorization header name to be set in the notification request
authorization_header	Password	null	Authorization header value	Authorization header to be set in the notification request
auth_url	string	null	OAuth2 authorization URL	The endpoint for the authorization server
client_id	string	null	Client ID	The client identifier issued to the client during the application registration process
client_secret	Password	null	Client Secret	The client secret issued to the client during the application registration process
scope	string	null	Scope	Scope to request to oauth server
audience	string	null	Audience	Resource service URL where token will be valid
additional_oauth_parameters	json	{}	Additional Oauth Request Parameters	Additional parameters to send when requesting token
ssl_cert_source [2]	string	null	Source	N/A
ssl_cert_path	string	null	Trusted Certificate(s) path	Enter the path relative to the base folder
ssl_cert_value	MultilineText	null	Trusted Certificate(s)	N/A

[1] one of:

"none"
"http_header"
"oauth_2_client_credentials"

[2] one of:

"env_var"
"ca_bundle_path"
"certificate_value"

cob_http_export = IOBlock(
    identifier='COB_HTTP_EXPORT',
    reference_name='cob_http_export',
    title='HTTP Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'SUBMISSION',
        'endpoint': 'example.com',
        'authorization_type': 'none',
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_http_export],
)

Message Queue Notifier Output (v35)

The Message Queue Notifier Output can configure connections to ActiveMQ, Amazon SQS, IBM MQ, and RabbitMQ message queues.

Identifier: COB_MQ_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
MQ_TYPE	string	“ACTIVE_MQ”	Message Queue type	Type of the message queue to connect with. Valid values are “ACTIVE_MQ”, “AMAZON_SQS”, “IBM_MQ” and “RABBIT_MQ”
MQ_USERNAME	string	null	Username/Access Key ID	N/A
MQ_PASSWORD	Password	null	Password/Secret Access Key	N/A
MQ_QUEUE_NAME	string	null	Queue Name	Name of the queue (topic) to connect to. It applies only to “ACTIVE_MQ”, “IBM_MQ” and “RABBIT_MQ” MQ_TYPEs.
MQ_HOST	string	null	Host Name	N/A
MQ_PORT	integer	1415	Port Number	N/A

Additional inputs when "AMAZON_SQS" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_REGION	string	null	AWS Region	N/A
MQ_QUEUE_URL	string	null	Queue URL	N/A
MQ_USE_EC2_INSTANCE_CREDENTIALS	boolean	True	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used.
MQ_MESSAGE_GROUP_ID	string	null	Group ID for FIFO queues	N/A
MQ_MESSAGE_METADATA	string	null	Additional SQS Metadata	Input additional metadata key value pairs in the format of { “key”: {“TYPE”: “String”, “VALUE”: “”}, “key2”: {“TYPE”: “Number”, “VALUE”: “12345”}

NOTE: MQ_USERNAME can be used to provide Access Key ID and MQ_PASSWORD to provide Secret Access Key.

Additional inputs when "IBM_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_QUEUE_MANAGER	string	null	Queue Manager (IBM MQ)	N/A
MQ_SSL_CIPHER_SUITE [1]	string	null	SSL cipher suite	SSL cipher suite to use if SSL connection is used.
MQ_CHANNEL	string	null	Channel	N/A
MQ_NO_AUTH_REQUIRED	boolean	false	Channel	N/A

NOTE: MQ_NO_AUTH_REQUIRED is True, MQ_USERNAME and MQ_PASSWORD are optional.

[1] one of:

"NULL_MD5"
"NULL_SHA"
"RC4_MD5_EXPORT"
"RC4_MD5_US"
"RC4_SHA_US"
"RC2_MD5_EXPORT"
"DES_SHA_EXPORT"
"RC4_56_SHA_EXPORT1024"
"DES_SHA_EXPORT1024"
"TRIPLE_DES_SHA_US"
"TLS_RSA_WITH_NULL_SHA256"
"TLS_RSA_WITH_AES_128_CBC_SHA"
"TLS_RSA_WITH_AES_128_CBC_SHA256"
"TLS_RSA_WITH_AES_256_CBC_SHA"
"TLS_RSA_WITH_AES_256_CBC_SHA256"
"AES_SHA_US"
"TLS_RSA_WITH_DES_CBC_SHA"
"TLS_RSA_WITH_3DES_EDE_CBC_SHA"
"TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256"
"FIPS_WITH_DES_CBC_SHA"
"FIPS_WITH_3DES_EDE_CBC_SHA"

Additional inputs when "RABBIT_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_VIRTUAL_HOST	string	null	Virtual Host (RABBIT MQ)	N/A
MQ_EXCHANGE	string	“”	Exchange	N/A
MQ_ROUTING_KEY	string	“”	Routing key	N/A
MQ_CONNECTION_TYPE	string	“TCP”	Connection Type	“TCP or SSL”

mq_notifier = IOBlock(
    identifier='COB_MQ_NOTIFIER',
    reference_name='mq_notifier',
    title='MQ Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'SUBMISSION',
        'MQ_USERNAME': 'admin',
        'MQ_PASSWORD': 'pass',
        'MQ_QUEUE_NAME': 'queue',
        'MQ_HOST': 'host',
    },
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[mq_notifier],
)

Box Notifier Output (v35)

Provides an out-of-the-box integration into Box systems.

Identifier: COB_BOX_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
public_key_id	string	N/A	Public-Key ID	ID of the Public-key used for authentication with Box
private_key	Password	N/A	Private-Key	Private key used for authentication with Box
passphrase	Password	N/A	Passphrase	Passphrase used for authentication with Box
client_id	string	N/A	Client ID	Client Id used for authentication with Box
client_secret	Password	N/A	Client Secret	Client Secret used for authentication with Box
enterprise_id	string	N/A	Enterprise ID	Enterprise Id used for authentication with Box
template_key	string	“”	Box Metadata Template Key	Enter the key of the Box Metadata template that you would like to map the Hyperscience metadata to.
static_output_fields_to_map	array	[]	Static Metadata Fields	Specify the Hyperscience fields you want to store in Box metadata.
submission_id	string	“”	Key for Mapping Submission ID	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_id	string	“”	Key for Mapping Document ID	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
submission_state	string	“”	Key for Mapping Submission State	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_state	string	“”	Key for Mapping Document State	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
submission_exceptions	string	“”	Key for Mapping Submission Exceptions	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_exceptions	string	“”	Key for Mapping Document Exceptions	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_layout_uuid	string	“”	Key for Mapping Document Layout Uuid	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_layout_name	string	“”	Key for Mapping Document Layout Name	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
start_time	string	“”	Key for Mapping Start Time	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
complete_time	string	“”	Key for Mapping Complete Time	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_fields_template_mappings	json	{}	JSON for Additional Metadata Fields	Specify additional metadata fields using their key value. Please consult Box Integration setup manual for JSON template and instructions.

box_notifier = IOBlock(
    identifier='COB_BOX_NOTIFIER',
    reference_name='box_notifier',
    title='Box Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'private_key': 'password',
        'passphrase': 'password',
        'public_key_id': 'admin',
        'client_id': 'admin',
        'client_secret': 'secret',
        'enterprise_id': 'admin',
        'template_key': 'key',
    },
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[box_notifier],
)

UiPath Notifier Output (v35)

Provides an out-of-the-box integration into UiPath systems.

Identifier: COB_EXPORT_UIPATH

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
url	string	N/A	URL	Base URL of the uipath instance
organization_unit_id	string	null	Organization unit id	Organization unit id
tenant	string	N/A	Tenant	Tenant
username	string	N/A	Username	Username
password	Password	N/A	Password	Password
queue	string	N/A	Queue	Queue

cob_export_uipath = IOBlock(
    identifier='COB_EXPORT_UIPATH',
    reference_name='cob_export_uipath',
    title='CCB Export UiPath',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'FLAT_SUBMISSION',
        'url': 'example.com',
        'tenant': 'tenant',
        'username': 'admin',
        'password': 'pass',
        'queue': 'queue',
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_export_uipath],
)

Salesforce Notifier Output (v35)

With the Salesforce Notifier Block, you can configure your flow to send extracted information to Salesforce. The Salesforce Notifier Block can use extracted information to look up, and then update any number of fields on a Salesforce object. The Salesforce Notifier Block is available in both SaaS and on-premise versions of the Hyperscience application.

Identifier: COB_SALESFORCE_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
username	string	N/A	Associated Username	Username associated with the Salesforce Connected App
consumer_key	password	N/A	Consumer Key	Consumer key used for authentication with Salesforce. Paired with the Salesforce instance from which it was issued.
private_key	password	N/A	Private Key	Private key used for authentication with Salesforce.
sandbox_environment	boolean	false	Private Key	Enable if the Salesforce environment is a sandbox.
object_api_name	string	N/A	Object API Name	API name of the object in Salesforce to lookup and update.
new_record_on_lookup_failure	boolean	false	Create new record on lookup failure	When enabled, it will create a new record if the lookup fails to find a record using the lookup parameters. When disabled, lookup will fail if it does not find a record using the lookup parameters.
object_lookup_mapping	json	N/A	Lookup Field Mapping	Specify which Salesforce fields (using their API name) to be queried with which extracted values from the document for the lookup using key-value format. For example: “DOCUMENT_FIELD_NAME”: “OBJECT_FIELD_API_NAME”.
document_fields_mapping	json	N/A	Document Field Mappings	Specify which Salesforce fields (using their API name) you want to update with which extracted data from Hyperscience using key-value format. For example: “DOCUMENT_FIELD_NAME”: “OBJECT_FIELD_API_NAME”.”
overwrite_existing_field_data	boolean	false	Overwrite existing field data	If this is checked and a selected field has data in it, Hyperscience will overwrite that data. If this is unchecked, existing data will not be overwritten and the operation will fail.

cob_salesforce_notifier = IOBlock(
    identifier='COB_SALESFORCE_NOTIFIER',
    reference_name='cob_salesforce_notifier',
    title='Salesforce Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'username': 'salesforce_user',
        'consumer_key': 'salesforce_consumer_key',
        'private_key': 'salesforce_private_key',
        'object_api_name': 'salesforce_object_api_name',
        'object_lookup_mapping': {"LAYOUT_VARIATION_XYZ_FIELD_NAME_1": "SF_FIELD_API_NAME_1", "LAYOUT_VARIATION_XYZ_FIELD_NAME_2": "SF_FIELD_API_NAME_2"},
        'document_fields_mapping': {"LAYOUT_VARIATION_XYZ_FIELD_NAME_3": "SF_FIELD_API_NAME_3", "LAYOUT_VARIATION_XYZ_FIELD_NAME_4": "SF_FIELD_API_NAME_4"}
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_salesforce_notifier],
)

Helper Classes and Functions (v35)

The following classes are used to easily instantiate and manage a flow’s settings.

flows_sdk.implementations.idp_v35.idp_values.get_idp_wf_inputs(idp_wf_config)

A helper function that maps the values provided by an IdpWorkflowConfig to their corresponding IDP keys.

Parameters:

idp_wf_config (IdpWorkflowConfig) – with static values to be filled.

Return type:

Dict[str, Any]

Returns:

a dictionary with filled static values, compatible with IDP flows.

Example usage with default values:

flow = Flow(
    input=get_idp_wf_inputs(get_idp_wf_config()),
    ...
)

Example usage:

Example usage when instantiating an IDP flow with custom parameters::
flow = Flow(
    input=get_idp_wf_inputs({an instance of IdpWorkflowConfig}),
    ...
)

flows_sdk.implementations.idp_v35.idp_values.get_idp_wf_config()

A helper function that instantiates an IdpWorkflowConfig pre-filled with default values. IdpWorkflowConfig is the container class for defining flow-level settings.

Return type:

IdpWorkflowConfig

Returns:

a valid, fully constructed IdpWorkflowConfig instance

Example usage when instantiating an IDP flow:

flow = Flow(
    input=get_idp_wf_inputs(get_idp_wf_config()),
    ...
)

class flows_sdk.implementations.idp_v35.idp_values.IdpWorkflowConfig

Convenience dataclass outlining all flow-level config values for IDP.

class flows_sdk.implementations.idp_v35.idp_values.IDPTriggers(blocks=None)

An adapter class making it easier to instantiate flows_sdk.flows.Triggers for IDP flows.

In particular, provides defaults for some fields (e.g. title, description, reference_name) and defines api_params_manifest with IDP specific parameters.

Parameters:

blocks (Optional[Sequence[IOBlock]]) – flows_sdk.blocks.IOBlock s to be included as triggers for an IDP Flow. Optional, defaults to an empty list.

Example usage when instantiating an IDP flow:

folder_listener = IOBlock(...)
flow = Flow(
    triggers=IDPTriggers([folder_listener]),
    ...
)

class flows_sdk.implementations.idp_v35.idp_values.IDPManifest(flow_identifier)

An adapter class making it easier to instantiate flows_sdk.flows.Manifest for IDP flows. In particular, provides defaults for some fields (e.g. roles, identifier) but more importantly, defines all py:class:flows_sdk.flows.Parameter that construct the Flow inputs and their UI representation (left-hand side menu in Flow studio).

Parameters:

flow_identifier (str) – A system-wide unique identifier for flows_sdk.flows.Flow

Example usage when instantiating an IDP flow:

flow = Flow(
    manifest=IDPManifest(flow_identifier='FLOW_IDENTIFIER'),
    ...
)

V34

Processing Blocks (v34)

Core Blocks are powerful processing blocks necessary to build intelligent document processing solutions on Hyperscience. They include both manual and machine processing blocks.

Note

Classes in the idp_v34 implementation have parameters with ambiguous typing. For example, a field of type integer could also be represented as a string reference (e.g. that integer gets provided at runtime). The types introduced in the example are therefore incomplete and are likely to be revisioned in a future version.

class flows_sdk.implementations.idp_v34.idp_blocks.SubmissionBootstrapBlock(reference_name=None, layout_release_uuid='${workflow.input.layout_release_uuid}', s3_config='${system.secrets.s3_downloader}', s3_endpoint_url=None, ocs_config='${system.secrets.ocs_downloader}', http_config=None, notification_workflow='IDP_SUBMISSION_NOTIFY_V34', workflow_uuid='${workflow.input.workflow_uuid}', workflow_name='${workflow.input.workflow_name}', workflow_version='${workflow.input.workflow_version}')

Bases: Block

Submission bootstrap block initializes the submission object and prepares external images or other submission data if needed.

This block should be called at the START of every flow that uses blocks included in the flows_sdk.implementations.idp_v34 library.

Mandatory parameters:

Parameters:

reference_name (Optional[str]) – unique identifier within a flow

Parameters with defaults:

Parameters:

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• s3_config (str) – Specify Amazon S3 credentials for submission retrieval store. Expects a json expression: {"aws_access_key_id": "X", "aws_secret_access_key": "Y"}, defaults to system_secret(S3_SECRET_KEY)

• s3_endpoint_url (Optional[str]) – Endpoint URL for S3 submission retrieval store, defaults to None

• ocs_config (str) – OCS Configuration for downloading submission data, defaults to system_secret(OCS_SECRET_KEY)

• http_config (Optional[str]) – An optional system secret field expressing the HTTP Configuration for downloading submission data. The content is expected to be a json formatted as: {"username": "X", "password": "Y", "ssl_cert": "CA bundle filename"}, defaults to None

• notification_workflow (str) – Notification flow name to run when the submission is initialized, defaults to IDP_SUBMISSION_NOTIFY_NAME

• workflow_uuid (str) – UUID of the triggered workflow version, defaults to workflow_input(Inputs.WorkflowUuid)

• workflow_name (str) – Name of the triggered workflow, defaults to workflow_input(Inputs.WorkflowName)

• workflow_version (str) – Version of the triggered workflow, defaults to workflow_input(Inputs.WorkflowVersion)

Example usage:

submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')

output(key=None)

Submission bootstrap ends with a CodeBlock, which nests the actual outputs under the ‘result’ key. This method overrides the default output in the same manner that flows_sdk.blocks.Routing.CodeBlock does for convenience.

Parameters:

key (Optional[str]) –

Optionally provide a key to directly get a nested property. When not provided, the entire ‘result’ will be returned.

For example, we have:

{
    "result": {
        "a": {
            "b": 42
        }
    }
}

• skipping the key output()` will return ``{"a": {"b": 42}}

• calling with output("a") will result in {"b": 42}

• calling with output("a.b") will return 42

Return type:

str

Returns:

the value under the provided key

class flows_sdk.implementations.idp_v34.idp_blocks.MachineCollationBlock(submission, cases, dedupe_files=False, remove_from_cases=None, reference_name=None)

Bases: Block

Machine collation block groups files, documents and pages (from the submission) into cases

Mandatory parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow

• submission (Any) – Submission object

• cases (Any) –

  This parameter accepts an array of JSON objects that contain information on how Cases should be created. There are two ways to collate a case: by the filenames specified in the current Submission, or by the ids of the Documents or Pages submitted in previous submissions.

  This parameter follows the given format:

  {
      'cases': [
          'external_case_id': 'HS-1',
          'filename': 'file1.pdf',
          'documents': [42],
          'pages': [43]
      ]
  }
  

Parameters with defaults:

Parameters:

• dedupe_files (bool) – Enabling this setting will replace case data from repeated file names within the same case. Cases will retain data from the most recently submitted version of a particular file. Note that this setting does not delete the old data, it just removes it from the case. Keep in mind this option is only relevant when adding to cases

• remove_from_cases (Optional[Any]) –

  This parameter accepts an array of JSON objects that contains information on how documents or pages should be removed from a pre-existing case. Currently, the only way to de-collate from a case is by the ids of the Documents or Pages within a given case

  This parameter follows the given format:

  {
      'remove_from_cases': [
          'external_case_id': 'HS-1',
          'documents': [42],
          'pages': [43]
      ]
  }
  

Example usage:

machine_collation = MachineCollationBlock(
    reference_name='machine_collation',
    submission=submission_bootstrap.output('result.submission'),
    cases=submission_bootstrap.output('result.api_params.cases'),
    dedupe_files=True,
    remove_from_cases=custom_supervision.output('remove_from_cases')
)

class flows_sdk.implementations.idp_v34.idp_blocks.MachineClassificationBlock(submission, api_params, reference_name=None, rotation_correction_enabled=True, mobile_processing_enabled=False, layout_release_uuid='${workflow.input.layout_release_uuid}', vpc_registration_threshold='${workflow.input.structured_layout_match_threshold}', nlc_enabled='${workflow.input.semi_structured_classification}', nlc_target_accuracy='${workflow.input.semi_target_accuracy}', nlc_doc_grouping_logic='${workflow.input.semi_doc_grouping_logic}')

Bases: Block

Machine classification block automatically matches documents to structured, semi-structured or additional layouts

Mandatory parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• rotation_correction_enabled (bool) – Identifies and corrects the orientation of semi-structured images, defaults to True

• mobile_processing_enabled (bool) – Improves the machine readability of photo captured documents, defaults to False

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• vpc_registration_threshold (str) – Structured pages above this threshold will be automatically matched to a layout variation, defaults to workflow_input(Settings.StructuredLayoutMatchThreshold)

• nlc_enabled (str) – Enables workflow to manage a model for automated classification of semi-structured and additional layout variations, defaults to workflow_input(Settings.SemiStructuredClassification)

• nlc_target_accuracy (str) – defaults to workflow_input(Settings.SemiTargetAccuracy)

• nlc_doc_grouping_logic (str) – Logic to handle multiple pages matched to the same layout variationin a given submission, defaults to workflow_input(Settings.SemiDocGroupingLogic)

Example usage:

machine_classification = MachineClassificationBlock(
    reference_name='machine_classification',
    submission=case_collation.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    rotation_correction_enabled=idp_wf_config.rotation_correction_enabled,
)

class flows_sdk.implementations.idp_v34.idp_blocks.ManualClassificationBlock(submission, api_params, reference_name=None, layout_release_uuid='${workflow.input.layout_release_uuid}', manual_nlc_enabled='${workflow.input.manual_nlc_enabled}', task_restrictions=None, notification_workflow='IDP_SUBMISSION_NOTIFY_V34')

Bases: Block

Manual classification block allows keyers to manually match submissions to their layouts. Keyers may perform manual classification if machine classification cannot automatically match a submission to a layout with high confidence

Mandatory parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• manual_nlc_enabled (str) – Enables manual classification when applicable, defaults to workflow_input(Settings.ManualNlcEnabled)

• task_restrictions (Optional[List[Any]]) – Defines what users can access Supervision tasks created by this block, defaults to None

• notification_workflow (str) – Notification flow name to run when the submission enters Manual Classification, defaults to IDP_SUBMISSION_NOTIFY_NAME

Example usage:

manual_classification = ManualClassificationBlock(
    reference_name='manual_classification',
    submission=machine_classification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
)

class flows_sdk.implementations.idp_v34.idp_blocks.MachineIdentificationBlock(submission, api_params, reference_name=None, manual_field_id_enabled='${workflow.input.manual_field_id_enabled}', field_id_target_accuracy='${workflow.input.field_id_target_accuracy}', table_id_target_accuracy='${workflow.input.table_id_target_accuracy}')

Bases: Block

Machine identification automatically identify fields and tables in the submission

Mandatory parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• manual_field_id_enabled (str) – Enables manual identification when applicable, defaults to workflow_input(Settings.ManualFieldIdEnabled)

• field_id_target_accuracy (str) – Field ID Target Accuracy, defaults to workflow_input(Settings.FieldIdTargetAccuracy)

• table_id_target_accuracy (str) – Table ID Target Accuracy, defaults to workflow_input(Settings.TableIdTargetAccuracy)

Example usage:

machine_identification = MachineIdentificationBlock(
    reference_name='machine_identification',
    submission=manual_classification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
)

class flows_sdk.implementations.idp_v34.idp_blocks.ManualIdentificationBlock(submission, api_params, reference_name=None, task_restrictions=None, manual_field_id_enabled='${workflow.input.manual_field_id_enabled}', layout_release_uuid='${workflow.input.layout_release_uuid}', notification_workflow='IDP_SUBMISSION_NOTIFY_V34')

Bases: Block

Manual identification allows keyers to complete field identification or table identification tasks, where they draw bounding boxes around the contents of certain fields, table columns or table rows. This identification process ensures that the system will be able to transcribe the correct content in the upcoming transcription process

Mandatory parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• task_restrictions (Optional[List[Any]]) – Defines what users can access Supervision tasks created by this block, defaults to []

• manual_field_id_enabled (str) – Enables manual identification when applicable, defaults to workflow_input(Settings.ManualFieldIdEnabled)

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• notification_workflow (str) – Notification flow name to run when the submission enters Manual Identification, defaults to IDP_SUBMISSION_NOTIFY_NAME

Example usage:

manual_identification = ManualIdentificationBlock(
    reference_name='manual_identification',
    submission=machine_identification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    task_restrictions=idp_wf_config.manual_identification_config.task_restrictions,
)

class flows_sdk.implementations.idp_v34.idp_blocks.MachineTranscriptionBlock(submission, api_params, reference_name=None, layout_release_uuid='${workflow.input.layout_release_uuid}', transcription_automation_training='${workflow.input.transcription_automation_training}', transcription_model='${workflow.input.transcription_model}', flex_confidence_boosting_enabled='${workflow.input.semi_transcription_conf_boost}', finetuning_only_trained_layouts='${workflow.input.finetuning_only_trained_layouts}', transcription_overrides='${workflow.input.transcription_overrides}', structured_text_target_accuracy='${workflow.input.structured_text_target_accuracy}', structured_text_confidence_threshold='${workflow.input.structured_text_threshold}', structured_text_acceptable_confidence='${workflow.input.structured_min_leg_threshold}', semi_structured_text_target_accuracy='${workflow.input.semi_structured_text_target_accuracy}', semi_structured_text_confidence_threshold='${workflow.input.semi_structured_text_threshold}', semi_structured_text_acceptable_confidence='${workflow.input.semi_structured_min_leg_threshold}', semi_structured_table_target_accuracy='${workflow.input.semi_structured_table_target_accuracy}', semi_structured_table_confidence_threshold='${workflow.input.semi_structured_table_threshold}', semi_structured_table_acceptable_confidence='${workflow.input.semi_structured_table_min_leg_threshold}', structured_checkbox_target_accuracy='${workflow.input.structured_checkbox_target_accuracy}', structured_checkbox_confidence_threshold='${workflow.input.structured_checkbox_threshold}', structured_checkbox_acceptable_confidence='${workflow.input.checkbox_min_leg_threshold}', structured_signature_target_accuracy='${workflow.input.structured_signature_target_accuracy}', structured_signature_confidence_threshold='${workflow.input.structured_signature_threshold}', structured_signature_acceptable_confidence='${workflow.input.signature_min_leg_threshold}', semi_structured_checkbox_target_accuracy='${workflow.input.semi_structured_checkbox_target_accuracy}', semi_structured_checkbox_confidence_threshold='${workflow.input.semi_structured_checkbox_threshold}', semi_structured_checkbox_acceptable_confidence='${workflow.input.semi_structured_checkbox_min_leg_threshold}')

Bases: Block

Machine transcription automatically transcribes the content of your submission

Mandatory parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• transcription_automation_training (str) – defaults to workflow_input( Settings.TranscriptionAutomationTraining )

• transcription_model (str) – The transcription model that will be used for this flow, defaults to workflow_input(Settings.TranscriptionModel)

• flex_confidence_boosting_enabled (str) – Boosts semi-structured transcription confidence for repeated values., defaults to workflow_input(Settings.SemiTranscriptionConfBoost)

• finetuning_only_trained_layouts (str) – defaults to workflow_input( Settings.FinetuningOnlyTrainedLayouts )

• transcription_overrides (str) – defaults to workflow_input( Settings.TranscriptionOverrides )

• structured_text_target_accuracy (str) – defaults to workflow_input( Settings.StructuredTextTargetAccuracy )

• structured_text_confidence_threshold (str) – defaults to workflow_input( Settings.StructuredTextThreshold )

• structured_text_acceptable_confidence (str) – defaults to workflow_input( Settings.StructuredTextMinLegThreshold )

• semi_structured_text_target_accuracy (str) – defaults to workflow_input( Settings.SemiStructuredTextTargetAccuracy )

• semi_structured_text_confidence_threshold (str) – defaults to workflow_input( Settings.SemiStructuredTextThreshold )

• semi_structured_text_acceptable_confidence (str) – defaults to workflow_input( Settings.SemiStructuredTextMinLegThreshold )

• structured_checkbox_target_accuracy (str) – defaults to workflow_input( Settings.StructuredCheckboxTargetAccuracy )

• structured_checkbox_confidence_threshold (str) – defaults to workflow_input( Settings.StructuredCheckboxThreshold )

• structured_checkbox_acceptable_confidence (str) – defaults to workflow_input( Settings.StructuredCheckboxMinLegThreshold )

• structured_signature_target_accuracy (str) – defaults to workflow_input( Settings.StructuredSignatureTargetAccuracy )

• structured_signature_confidence_threshold (str) – defaults to workflow_input( Settings.StructuredSignatureThreshold )

• structured_signature_acceptable_confidence (str) – defaults to workflow_input( Settings.StructuredSignatureMinLegThreshold )

• semi_structured_checkbox_target_accuracy (str) – defaults to workflow_input( Settings.SemiStructuredCheckboxTargetAccuracy )

• semi_structured_checkbox_confidence_threshold (str) – defaults to workflow_input( Settings.SemiStructuredCheckboxThreshold )

• semi_structured_checkbox_acceptable_confidence (str) – defaults to workflow_input( Settings.SemiStructuredCheckboxMinLegThreshold )

Example usage:

machine_transcription = MachineTranscriptionBlock(
    reference_name='machine_transcription',
    submission=manual_identification.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
)

class flows_sdk.implementations.idp_v34.idp_blocks.ManualTranscriptionBlock(submission, api_params, reference_name=None, supervision_transcription_masking=True, table_output_manual_review=False, task_restrictions=None, manual_transcription_enabled='${workflow.input.manual_transcription_enabled}', notification_workflow='IDP_SUBMISSION_NOTIFY_V34')

Bases: Block

Manual transcription lets your keyers manually enter the text found in fields or tables that could not be automatically transcribed

Mandatory parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• supervision_transcription_masking (bool) – Prevents users from inputting invalid characters during Supervision Transcription tasks, defaults to True

• table_output_manual_review (bool) – Always generates a table transcription task if the layout contains a table. If disabled, a table transcription task will only be generated if one or more cells have transcribed values below the defined thresholds, defaults to False

• task_restrictions (Optional[List[Any]]) – Defines what users can access Supervision tasks for submission from a particular source or submissions matching a specific layout, defaults to []

• manual_transcription_enabled (str) – Enables manual transcription when applicable, defaults to workflow_input(Settings.ManualTranscriptionEnabled)

• notification_workflow (str) – Notification flow name to run when the submission enters Manual Transcription, defaults to IDP_SUBMISSION_NOTIFY_NAME

Example usage:

manual_transcription = ManualTranscriptionBlock(
    reference_name='manual_transcription',
    submission=machine_transcription.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    supervision_transcription_masking=(
        idp_wf_config.manual_transcription_config.supervision_transcription_masking
    ),
    table_output_manual_review=(
        idp_wf_config.manual_transcription_config.table_output_manual_review
    ),
    task_restrictions=idp_wf_config.manual_transcription_config.task_restrictions,
)

class flows_sdk.implementations.idp_v34.idp_blocks.FlexibleExtractionBlock(submission, api_params, reference_name=None, supervision_transcription_masking=True, task_restrictions=None, layout_release_uuid='${workflow.input.layout_release_uuid}', notification_workflow='IDP_SUBMISSION_NOTIFY_V34')

Bases: Block

Flexible extraction manually transcribes fields marked for review

Mandatory parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow

• submission (Any) – Submission object

• api_params (Any)

Parameters with defaults:

Parameters:

• supervision_transcription_masking (bool) – Prevents users from inputting invalid characters during the Flexible Extraction task, defaults to True

• task_restrictions (Optional[List[Any]]) – Defines what users can access Supervision tasks created by this block, defaults to []

• layout_release_uuid (str) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)

• notification_workflow (str) – Notification flow name to run when the submission enters Flexible Extraction, defaults to IDP_SUBMISSION_NOTIFY_NAME

Example usage:

flexible_extraction = FlexibleExtractionBlock(
    reference_name='flexible_extraction',
    submission=manual_transcription.output('submission'),
    api_params=submission_bootstrap.output('result.api_params'),
    supervision_transcription_masking=(
        idp_wf_config.flexible_extraction_config.supervision_transcription_masking
    ),
    task_restrictions=idp_wf_config.flexible_extraction_config.task_restrictions,
)

class flows_sdk.implementations.idp_v34.idp_blocks.SubmissionCompleteBlock(submission, reference_name=None, nlc_qa_sampling_ratio='${workflow.input.semi_qa_sample_rate}', field_id_qa_enabled='${workflow.input.field_id_qa_enabled}', field_id_qa_sampling_ratio='${workflow.input.field_id_qa_sample_rate}', table_id_qa_enabled='${workflow.input.table_id_qa_enabled}', table_id_qa_sampling_ratio='${workflow.input.table_id_qa_sample_rate}', transcription_qa_enabled='${workflow.input.qa}', transcription_qa_sampling_ratio='${workflow.input.qa_sample_rate}', table_cell_transcription_qa_enabled='${workflow.input.table_cell_transcription_qa_enabled}', table_cell_transcription_qa_sample_rate='${workflow.input.table_cell_transcription_qa_sample_rate}', payload=None)

Bases: Block

Submission complete block finalizes submission processing and updates reporting data.

This block should be called at the END of every flow that uses blocks included in the flows_sdk.implementations.idp_v34 library. The block requires both the submission object and a payload to be passed in.

Mandatory parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow.

• submission (str) – Submission object

• payload (Any) – Object to pass to downstream systems

Parameters with defaults:

Parameters:

• nlc_qa_sampling_ratio (Union[str, int]) – Defines the percentage of documents sampled for Classification QA, defaults to workflow_input(Settings.SemiQaSampleRate)

• field_id_qa_enabled (Union[str, bool]) – defaults to workflow_input(Settings.FieldIdQaEnabled)

• field_id_qa_sampling_ratio (Union[str, int]) – Defines the percentage of documents sampled for Identification QA, defaults to workflow_input(Settings.FieldIdQaSampleRate)

• table_id_qa_enabled (Union[str, bool]) – Allows users to verify the location of table cells, defaults to workflow_input(Settings.TableIdQaEnabled)

• table_id_qa_sampling_ratio (Union[str, int]) – Defines the percentage of tables the system samples for QA, defaults to workflow_input(Settings.TableIdQaSampleRate)

• transcription_qa_enabled (Union[str, bool]) – defaults to workflow_input(Settings.TranscriptionQaEnabled)

• transcription_qa_sampling_ratio (Union[str, int]) – Defines the percentage of fields sampled for Transcription QA, defaults to workflow_input(Settings.TranscriptionQaSampleRate)

• table_cell_transcription_qa_enabled (Union[str, bool]) – defaults to workflow_input(Settings.TableCellTranscriptionQaEnabled)

• table_cell_transcription_qa_sample_rate (Union[str, int]) – Defines the percentage of cells the system samples for QA. This value is likely to be lower than “Transcription QA Sample Rate” since there are more table cells than fields on any given page, defaults to workflow_input(Settings.TableCellTranscriptionQaSampleRate)

Example usage:

submission_complete = SubmissionCompleteBlock(
    reference_name='complete_submission',
    submission=flexible_extraction.output('submission')
)

class flows_sdk.implementations.idp_v34.idp_blocks.DatabaseAccessBlock(reference_name=None, db_type='', database='', host='', username='', password='', query='', port=None, options=None, timeout=None, query_params=None, title='DB Block', description='DB access block')

Bases: Block

Database Block allows user to access and perform queries on the specified database

Mandatory parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow

• db_type (str) – database type (mssql, oracle, postgres)

• host (str) – URL/IP address of the server database is hosted on

• database (str) – database name

• username (str) – database username

• password (str) – database password

• query (str) – parameterized query

Optional parameters:

Parameters:

• port (Optional[int]) – database port number

• options (Optional[Dict[Any, Any]]) – dictionary of additional connection string options

• query_params (Optional[Dict[Any, Any]]) – dictionary of values for query placeholders

• timeout (Optional[int]) – timeout in seconds (mssql and postgres only)

Example usage:

# minimum viable instantiation, the rest of the fields can later be added in the UI
db_lookup = DatabaseAccessBlock(
    reference_name='db_lookup',
    title='Database Lookup',
    description='Perform database lookup',
)

# note that storing password in plain text form is not recommended
# password can later be entered in the UI or defined as a flow secret.
db_lookup = DatabaseAccessBlock(
    reference_name='db_lookup',
    title='Database Lookup',
    description='Perform database lookup',
    db_type='mssql',
    database='cars',
    host='example.com',
    username='user',
    # we recommend skipping the password field or defining it as a system secret
    # e.g. system_secret('{your_secret_identifier}') (both allow for secret input via. UI)
    password='pw'
    port=1433,
    timeout=200,
    query='SELECT * from CAR',
)

# reference to the output of DBAccessBlock which can be passed as input to the next block
# for more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = db_lookup.output()

# content of output_ref:
{
    "result": [
        {
            "id": 1,
            "brand": "Aston Martin",
            "model": "Vanquish"
        },
        {
            "id": 2,
            "brand": "Jaguar",
            "model": "XE 2018"
        }
    ]
}

Database output for the same query (SELECT * FROM CAR)

class flows_sdk.implementations.idp_v34.idp_blocks.HttpRestBlock(method, endpoint, reference_name=None, headers=None, params=None, payload=None, json=None, authorization_type=None, handled_error_codes=None, auth_params=None, title='HTTP REST Block', description='HTTP REST block')

Bases: BaseHttpRestBlock

Http Rest Block allows user to perform HTTP requests

Mandatory parameters:

Parameters:

• method (str) – HTTP method to use

• endpoint (str) – absolute url including schema to HTTP endpoint for this request

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow

• headers (Optional[Dict[str, str]]) – HTTP headers to use

• params (Optional[Dict[str, Any]]) – key-value pair used as query parameters in the request

• payload (Optional[Dict[str, str]]) – key-value pair to be used as x-www-form-urlencoded data. Mutually exclusive with json

• json (Optional[Dict[str, Any]]) – key-value pair to be used as json data. Mutually exclusive with payload

• authorization_type (Optional[str]) – authorization type (none, http_header, oauth_2_client_credentials)

• handled_error_codes (Optional[List[Union[int, str]]]) – specify HTTP error codes that will not fail the task, allowing for wildcards with the ‘x’ char. Example: [400, “5xx”]

• auth_params (Union[HttpHeaderAuthorizationParams, OAuth2AuthorizationParams, None]) – authorization parameters to be passed when the authorization_type is set to “http_header” or “oauth_2_client_credentials”

Example usage:

# minimum viable instantiation, the rest of the fields can later be added in the UI
http_rest_block = HttpRestBlock(
    reference_name='http_request',
    title='Http Request',
    description='Perform http request',
    method='GET',
)

http_rest_block = HttpRestBlock(
    reference_name='http_request',
    title='Http Request',
    description='Perform http request',
    method='GET',
    endpoint='https://sheets.googleapis.com/v4/spreadsheets/example',
)

# reference to the output of HttpRestBlock which can be passed as input to the next block
# for more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = http_rest_block.output()

class flows_sdk.implementations.idp_v34.idp_blocks.HyperscienceRestApiBlock(method, app_endpoint, reference_name=None, headers=None, params=None, payload=None, json=None, handled_error_codes=None, title='Hyperscience HTTP REST Block', description='Hyperscience HTTP REST block')

Bases: BaseHttpRestBlock

Make HTTP requests to the Hyperscience platform APIs. This block will automatically take care of prepending the base url and will include the appropriate authentication headers with the request.

Mandatory parameters:

Parameters:

• method (str) – HTTP method to use, e.g. ‘GET’, ‘POST’, etc.

• app_endpoint (str) – relative url, beginning with a forward slash, to an HTTP endpoint served by the Hyperscience platform. For reference on the available endpoints, check out https://docs.hyperscience.ai/. Absolute URLs that include a hostname are allowed only if the flow has received them as part of the response of a previous API request that the flow has made. Do not hardcode the hostname of the Hyperscience instance in your flow definitions as they will not work when you run the flows on another instance, or if the hostname changes. For making requests to external systems, use the HttpRestBlock or a library like “requests”.

Optional Parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow

• headers (Optional[Dict[str, str]]) – HTTP headers to use

• params (Optional[Dict[str, Any]]) – key-value pair used as query parameters in the request

• payload (Optional[Dict[str, str]]) – key-value pair to be used as x-www-form-urlencoded data. Mutually exclusive with json

• json (Optional[Dict[str, Any]]) – key-value pair to be used as json data. Mutually exclusive with payload

• handled_error_codes (Optional[List[Union[int, str]]]) – specify HTTP error codes that will not fail the task, allowing for wildcards with the ‘x’ char. Example: [400, “5xx”]

Example usage:

hs_rest_block = HyperscienceRestApiBlock(
    reference_name='http_request',
    app_endpoint='/api/healthcheck',
    method='GET',
)

class flows_sdk.implementations.idp_v34.idp_blocks.SoapRequestBlock(method, reference_name=None, endpoint='', body_namespace=None, headers_namespace=None, headers=None, params=None, title='HTTP REST Block', description='HTTP REST block')

Bases: Block

Soap Block allows user to perform SOAP requests

Mandatory parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow

• method (str) – SOAP method to invoke

• endpoint (str) – absolute url including schema to HTTP endpoint for this request

Optional Parameters:

Parameters:

• body_namespace (Optional[str]) – SOAP request body namespace url

• headers_namespace (Optional[str]) – SOAP request headers namespace url

• headers (Optional[Dict[Any, Any]]) – SOAP headers to use

• params (Optional[Dict[Any, Any]]) – key-value pair used as query parameters in the request to be inserted as SOAP body

Example usage:

# minimum viable instantiation, the rest of the fields can later be added in the UI
soap_block = SoapRequestBlock(
    reference_name='soap_request',
    title='Soap Request',
    description='Perform soap request',
    method='ExampleMethod',
)

soap_block = SoapRequestBlock(
    reference_name='soap_request',
    title='Soap Request',
    description='Perform soap request',
    method='ExampleMethod',
    endpoint='http://example.org/abc.wso',
    body_namespace='http://www.example.org/abc.countryinfo',
    params={'example': 'stuff'},
    headers={}
)

# reference to the output of SoapRequestBlock which can be passed as input to the next block
# for more information on output() and output references, please take a look at class Block
# and its output() method
output_ref = soap_block.output()

class flows_sdk.implementations.idp_v34.idp_blocks.CustomSupervisionBlock(submission, task_purpose, data, supervision_template, reference_name=None, supervision_transcription_masking=True, task_restrictions=None, notification_workflow='IDP_SUBMISSION_NOTIFY_V34')

Bases: Block

The CustomSupervisionBlock uses a JSON schema as a form builder to create customer-driven UIs for Supervision tasks.

Mandatory parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow

• submission (Any) – Submission object

• task_purpose (str) – the custom task name that’s given to all supervision tasks that run through a particular instance of a custom supervision block. This can be used to filter tasks in the task queue.

• data (Any) – JSON structure that supplies the data used to populated the supervision_template

• supervision_template (Any) – JSON used for configuration of the Custom Supervision Task.

Parameters with defaults:

Parameters:

• supervision_transcription_masking (bool) – Prevents users from inputting invalid characters during the Custom Supervision task, defaults to True

• task_restrictions (Optional[List[Any]]) – Defines what users can access Supervision tasks created by this block, defaults to []

• notification_workflow (str) – Notification flow name to run when the submission enters Custom Supervision, defaults to IDP_SUBMISSION_NOTIFY_NAME

Example usage:

custom_supervision = CustomSupervisionBlock(
    reference_name='custom_supervision',
    submission=manual_transcription.output('submission'),
    task_purpose='make_custom_decision',
    supervision_template=[
        {
            'name': 'three_column_template',
            'version': '1.0',
            'thumbnail': {
                'group_by_document': True,
                'group_by_case': True,
            },
            'action': [
                {
                    'name': 'my_tab',
                    'display': 'SSN Info',
                    'input': [
                        {
                            'name': 'ssn_field',
                            'type': 'transcription',
                            'data_identifier': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                            'title': 'SSN',
                        },
                    ],
                    'ui': {
                        'groups': [
                            {
                                'title': 'My Group',
                                'fields': ['ssn_field'],
                            },
                        ],
                    },
                },
            ],
        },
    ],
    data={
        'fields': [
            {
                'id': 967,
                'uuid': 'fe8d4e7a-a82c-448c-85a1-0a3cbbe53b61',
                'value': '111-22-3333',
                'page_id': 1,
                'data_identifier': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                'validation_overridden': false,
                'bounding_box': [
                    0.09360783305186686,
                    0.25792617589433436,
                    0.6358913805295097,
                    0.28862414740388187,
                ],
                'occurence_index': 1,
            },
        ],
        'template_fields': [
            {
                'uuid': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                'notes': '',
                'type': 'entry',
                'data_type_uuid': 'b600a4e4-758f-4c4b-a2f4-cbb5dd870a0c',
                'name': 'SSN',
                'n_occurences': 1,
            },
        ],
        'pages': [
            {
                'id': 15,
                'file_page_number': 1,
                'document_page_number': 1,  // Optional
                'image_url': '/image/6e55fa86-a36b-4178-8db1-3a514621d4c1',
                'form_id': 2,
                'submission_id': 3,
                'external_case_ids': [
                    'HS-27',
                ],
                'read_only': False,
                'filename': 'filename.pdf',
            },
        ],
        'documents': [
            {
                'id': 2,
                'layout_uuid': 'e31c3cbf-dcea-44fd-a052-902463c6040f',
                'layout_name': 'layout_name',
                'page_count': 2,
                'read_only': false,
            },
        ],
        'cases': [],
        'selected_choices': [],
    },
    supervision_transcription_masking=False,
)

class flows_sdk.implementations.idp_v34.idp_blocks.IdpCustomSupervisionBlock(submission, task_purpose, supervision_template, reference_name=None, supervision_transcription_masking=True, task_restrictions=None, page_ids=None, notification_workflow='IDP_SUBMISSION_NOTIFY_V34')

Bases: Block

An IDP wrapper for the Custom Supervision block. It has the same functionality, but handles reading/writing data from the IDP database.

Mandatory parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow

• submission (Any) – Submission object

• task_purpose (str) – the custom task name that’s given to all supervision tasks that run through a particular instance of a custom supervision block. This can be used to filter tasks in the task queue.

• supervision_template (Any) – JSON used for configuration of the Custom Supervision Task.

Parameters with defaults:

Parameters:

• supervision_transcription_masking (bool) – Prevents users from inputting invalid characters during the Custom Supervision task, defaults to True

• task_restrictions (Optional[List[Any]]) – Defines what users can access Supervision tasks created by this block, defaults to []

• page_ids (Optional[List[int]]) – A list of page ids to include. This should not be used. The IDP wrapper should handle pulling in all pages in the submission. defaults to []

• notification_workflow (str) – Notification flow name to run when the submission enters Custom Supervision (which is called within the IDP Custom Supervision block), defaults to IDP_SUBMISSION_NOTIFY_NAME

Example usage:

idp_custom_supervision = IdpCustomSupervisionBlock(
    reference_name='idp_custom_supervision',
    submission=manual_transcription.output('submission'),
    task_purpose='make_custom_decision',
    supervision_template=[
        {
            'name': 'three_column_template',
            'version': '1.0',
            'thumbnail': {
                'group_by_document': True,
                'group_by_case': True,
            },
            'action': [
                {
                    'name': 'my_tab',
                    'display': 'SSN Info',
                    'input': [
                        {
                            'name': 'ssn_field',
                            'type': 'transcription',
                            'data_identifier': '2afd7a67-ddee-484f-ab6b-2d292752e5ee',
                            'title': 'SSN',
                        },
                    ],
                    'ui': {
                        'groups': [
                            {
                                'title': 'My Group',
                                'fields': ['ssn_field'],
                            },
                        ],
                    },
                },
            ],
        },
    ],
    supervision_transcription_masking=False,
)

class flows_sdk.implementations.idp_v34.idp_blocks.IDPFullPageTranscriptionBlock(submission, reference_name=None, title='Full Page Transcription (Submission)', description='Transcribes the documents included in a submission', app_metadata=None)

Bases: Block

IDP Full Page Transcription Block machine-transcribes the unassigned pages in a submission

Mandatory parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow

• submission (str) – submission object that includes the raw documents that need to be transcribed

Optional Parameters:

Parameters:

app_metadata (Optional[List[str]]) – used for capturing information about the flow

Example usage:

# This is one way to obtain the submission object (using SUbmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# IDPFullPageTranscriptionBlock transcribes the documents contained in the submission object
idp_fpt_block = IDPFullPageTranscriptionBlock(
    reference_name='idp_fpt',
    submission=submission_bootstrap.output(),
)
# reference to the output of IDPFullPageTranscriptionBlock which can be passed as input to
# the next block. For more information on output() and output references, please take a
# look at class Block and its output() method
output_ref = idp_fpt_block.output()

class flows_sdk.implementations.idp_v34.idp_blocks.IDPImageCorrectionBlock(submission, reference_name=None, rotation_correction_enabled=True, mobile_processing_enabled=False, title='Image Correction (Submission)', description='Rotate and de-skew documents included in a submission', app_metadata=None)

Bases: Block

IDP Image Correction Block rotates and de-skews tilted/skewed images so that they are more transcription-friendly

Mandatory parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow

• submission (str) – submission object that includes the raw documents that need to be image-corrected

Optional Parameters:

Parameters:

app_metadata (Optional[List[str]]) – used for capturing information about the flow

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# IDPImageCorrectionBlock takes in the submission object and outputs in the same format with
# its images
idp_image_correct_block = IDPImageCorrectionBlock(
    reference_name='idp_image_correct',
    submission=submission_bootstrap.output(),
)
# reference to the output of IDPImageCorrectionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look at
# class Block and its output() method
output_ref = idp_image_correct_block.output()

class flows_sdk.implementations.idp_v34.idp_blocks.FullPageTranscriptionBlock(pages, reference_name=None, title='Full Page Transcription (Pages)', description='Transcribes documents', app_metadata=None)

Bases: Block

Full Page Transcription Block machine-transcribes the document pages passed to it

Mandatory parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow

• pages (str) – list of pages that includes the raw documents that need to be transcribed

Optional Parameters:

Parameters:

app_metadata (Optional[List[str]]) – used for capturing information about the flow

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# FullPageTranscriptionBlock transcribes the documents contained in the submission object
fpt_block = FullPageTranscriptionBlock(
    reference_name='fpt',
    submission=submission_bootstrap.output(),
)
# reference to the output of FullPageTranscriptionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look at
# class Block and its output() method
output_ref = fpt_block.output()

class flows_sdk.implementations.idp_v34.idp_blocks.ImageCorrectionBlock(pages, reference_name=None, rotation_correction_enabled=True, mobile_processing_enabled=False, title='Image Correction (Pages)', description='Rotate and de-skew documents', app_metadata=None)

Bases: Block

Image Correction Block rotates and de-skews tilted/skewed images so that they are more transcription-friendly

Mandatory parameters:

Parameters:

• reference_name (Optional[str]) – unique identifier within a flow

• pages (str) – list of page object that includes the raw documents that need to be image-corrected

Optional Parameters:

Parameters:

app_metadata (Optional[List[str]]) – used for capturing information about the flow

Example usage:

# This is one way to obtain the submission object (using SubmissionBootstrapBlock)
submission_bootstrap = SubmissionBootstrapBlock(reference_name='submission_bootstrap')
# ImageCorrectionBlock takes in the submission object and outputs in the same format with
# its images
image_correct_block = ImageCorrectionBlock(
    reference_name='image_correct',
    pages=submission_bootstrap.output(submission.unassigned_pages),
)
# reference to the output of IDPImageCorrectionBlock which can be passed as input to the
# next block. For more information on output() and output references, please take a look
# at class Block and its output() method
output_ref = image_correct_block.output()

Trigger Blocks (v34)

Note

“Triggers” are referred to as “Input Blocks” in our application documentation.

Warning

Flows are built to allow full definition of triggers either in code or manually in the Flow Studio. We recommend defining triggers manually in the Flow Studio. Instructions on how to do so are in our application documentation.

To define your triggers in the Flow Studio, simply use the IDPTriggers() convenience class:

return Flow(
   ...
   triggers=IDPTriggers()
)

If instead you wish to define specific triggers in Python, use the trigger definitions below.

Folder Listener (v34)

Identifier: FOLDER_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
folder	Path	N/A	Folder to scan for submissions	N/A
file_extensions	string	N/A	File extensions	List of file extensions to monitor for (e.g.: ‘png, jpg, pdf’)
has_meta	boolean	False	Enable metadata	Select this if a metadata file is to be expected along with document files (in XXX_index.txt file)
poll_interval	integer	10	Poll interval	Poll interval in seconds
warmup_interval	integer	15	Warm-up interval	Seconds to wait past document last modified time before processing it
folder_cleanup_delay	integer	86400	Folder cleanup delay	Seconds to wait before cleaning up subfolders
api_params	object	{}	API Parameters	N/A

folder_listener = IOBlock(
    identifier='FOLDER_TRIGGER',
    reference_name='folder_trigger',
    title='Folder listener',
    enabled=True,
    input={
        'folder': '/var/www/forms/forms/folder/',
        'file_extensions': 'png, pdf, jpg',
    },
)

triggers = IDPTriggers(blocks=[folder_listener])

Universal Folder Listener (v34)

Identifier: UNIVERSAL_FOLDER_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
folder	string	N/A	Folder to scan for submissions	Path relative to the base folder. Leave blank to monitor the base folder
file_extensions	string	N/A	File extensions	List of file extensions to monitor for
other_file_extensions	string	N/A	Other file extensions	Comma separated other file extensions to monitor for
has_meta	boolean	False	Include submission level parameters	Select this to ingest JSON files along with document files and submission folders. These JSON files can contain information such as metadata, cases and external_id. These JSON file names should match the related files or folders (e.g., XXX.jpg.json for XXX.jpg)
poll_interval	integer	10	Poll interval	Poll interval in seconds
warmup_interval	integer	15	Warm-up interval	Seconds to wait past document last modified time before processing it
folder_cleanup_delay	integer	24	Folder cleanup delay	How often the Folder Listener will remove empty folders from the base folder (in hours)
api_params	object	{}	API Parameters	N/A

folder_listener = IOBlock(
    identifier='UNIVERSAL_FOLDER_TRIGGER',
    reference_name='universal_folder_trigger',
    title='Universal Folder listener',
    enabled=True,
    input={
        'folder': 'folder',
        'file_extensions': ['png', 'pdf', 'jpg', 'other'],
        'other_file_extensions': 'msg, csv'
    },
)

triggers = IDPTriggers(blocks=[folder_listener])

Salesforce Listener (v34)

Identifier: SALESFORCE_TRIGGER

With the Salesforce Listener Block, you can configure your flow to ingest files attached to Salesforce Objects for processing within the Hyperscience application. The Salesforce Listener Block is available in both SaaS and on-premise versions of the Hyperscience application.

Parameters:

Name	Type	Default Value	Title	Description
channel_name	string	N/A	Channel Name	Configured in the Hyperscience app in Salesforce
username	string	N/A	Associated Username	Username associated with the Salesforce Connected App
consumer_key	password	N/A	Consumer Key	Consumer key used for authentication with Salesforce. Paired with the Salesforce instance from which it was issued
private_key	password	false	Sandbox Environment	Private key used for authentication with Salesforce
sandbox_environment	boolean	15	Warm-up interval	Enable if the Salesforce environment is a sandbox
api_params	object	{}	API Parameters	N/A

salesforce_listener = IOBlock(
    identifier='SALESFORCE_TRIGGER',
    reference_name='salesforce_trigger',
    title='Salesforce Listener',
    enabled=True,
    input={
        'channel_name': '/u/saleforce_streaming_channel',
        'username': 'salesforce_user',
        'consumer_key': 'salesforce_consumer_key',
        'private_key': 'salesforce_private_key'
    },
)

triggers = IDPTriggers(blocks=[salesforce_listener])

Email Listener (v34)

Identifier: IMAP_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
provider [1]	string	imap	Email Provider	N/A
poll_interval	integer	60	Polling interval in seconds	N/A
folder	Path	N/A	Folder to scan for emails	N/A
email_body_treatment	string	“ignore”	Email body treatment	What to do with the body of an email that is ingested. Available options are “process” and “ignore”.
post_process_action	string	“move”	Post process action	What to do with the email after it is processed. Available options are “move” and “delete”.
post_process_move_folder	string	“”	Post process archive folder	Folder to move emails to once they are processed.
api_params	object	{}	API Parameters	N/A
render_headers [2]	array	[]	Headers to include	Headers to render at the top of the email body. Headers will be included only if email_body_treatment is set to “process”.

[1] one of:

imap
graph

[2] any of:

To
From
Subject
Date

Additional inputs when "imap" is the selected provider:

Name	Type	Default Value	Title	Description
host	string	N/A	IMAP server address	N/A
port	integer	993	Port Number	N/A
ssl	boolean	True	Use SSL connection	N/A
username	string	N/A	Username	N/A
password	Password	N/A	Password	N/A

Additional inputs when "graph" is the selected provider:

Name	Type	Default Value	Title	Description
client_id	string	N/A	Client id	Application (client) ID for the corresponding application registered with the Microsoft identity platform.
tenant_id	string	N/A	Tenant id	Also called directory ID. Unique identifier of the tenant in which the corresponding application is registered with.
graph_cloud_endpoint [1]	string	Global	Microsoft Graph Cloud Endpoint	Determines base URL used for accessing cloud services through Microsoft Graph.
azure_ad_endpoint [2]	string	AZURE_PUBLIC_CLOUD	Azure AD Endpoint	Determines base URL for the Azure Active Directory (Azure AD) endpoint to acquire token for each national cloud.
client_secret	Password	N/A	Client secret	Client secret for the corresponding application registered with the Microsoft identity platform.

[1] one of:

Global
US_GOV
US_DoD
Germany
China

[2] one of:

AZURE_PUBLIC_CLOUD
AZURE_CHINA
AZURE_GERMANY
AZURE_GOVERNMENT

imap_trigger = IOBlock(
    identifier='IMAP_TRIGGER',
    reference_name='imap_trigger',
    title='IMAP trigger',
    enabled=True,
    input={
        'host': 'example@mail.com',
        'folder': '/var/www/forms/forms/imap/',
        'username': 'admin',
        'password': 'pass',
    }
)

triggers = IDPTriggers(blocks=[imap_trigger])

Box Folder Listener (v34)

Identifier: BOX_FOLDER_TRIGGER

Parameters:

Name	Type	Default Value	Title	Description
source_folder_id	integer	N/A	Folder to scan for submissions	Use the Box Folder ID found in the URL
target_folder_id	integer	N/A	Folder to move completed files	Use the Box Folder ID found in the URL
file_extensions	array	[]	File extensions	Types of file extensions for which to monitor
custom_file_extensions	string	N/A	Other file extension types	Comma separated list of file extensions for which to monitor (e.g. ‘png, jpg, pdf’)
poll_interval	integer	10	Polling interval (in seconds)	How often the connector will check the base folder for submissions
warmup_interval	integer	15	Warm-up interval (in seconds)	How long the connector will wait to process the document after it was last modified
public_key_id	string	N/A	Public-Key ID	ID of the Public-key used for authentication with Box
private_key	Password	N/A	Private-Key	Private key used for authentication with Box
passphrase	Password	N/A	Passphrase	Passphrase used for authentication with Box
client_id	string	N/A	Client ID	Client Id used for authentication with Box
client_secret	Password	N/A	Client Secret	Client Secret used for authentication with Box
enterprise_id	string	N/A	Enterprise ID	Enterprise Id used for authentication with Box
api_params	object	{}	API Parameters	N/A

box_folder_trigger = IOBlock(
    identifier='BOX_FOLDER_TRIGGER',
    reference_name='box_folder_trigger',
    title='Box folder',
    enabled=True,
    input={
        'file_extensions': ['png', 'pdf', 'jpg'],
        'source_folder_id': 24,
        'target_folder_id': 42,
        'public_key_id': 'admin',
        'private_key': 'secret',
        'passphrase': 'password',
        'client_id': 'admin',
        'client_secret': 'secret',
        'enterprise_id': 'admin',
    }
)

triggers = IDPTriggers(blocks=[box_folder_trigger])

Cron Listener (v34)

Identifier: CRON_TRIGGER

Inputs:

Name	Type	Default Value	Title	Description
cron_spec	string	"*/5 * * * *"	Cron specification	N/A
time_zone	string	“US/Eastern”	Time Zone	N/A
api_params	object	{}	API Parameters	N/A

cron_trigger = IOBlock(
    identifier='CRON_TRIGGER',
    reference_name='cron_trigger',
    title='Cron Trigger',
    enabled=True,
    input={
        'cron_spec': '0 10 * * *', 
        'time_zone': 'Europe/Sofia',
    }
)

triggers = IDPTriggers(blocks=[cron_trigger])

Message Queue Listener (v34)

Identifier: MQ_LISTENER

Inputs:

Name	Type	Default Value	Title	Description
MQ_TYPE	string	“ACTIVE_MQ”	Message Queue type	Type of the message queue to connect with. Valid values are “ACTIVE_MQ”, “AMAZON_SQS”, “IBM_MQ” and “RABBIT_MQ”
MQ_USERNAME	string	null	Username/Access Key ID	N/A
MQ_PASSWORD	Password	null	Password/Secret Access Key	N/A
MQ_QUEUE_NAME	string	null	Queue Name	Name of the queue (topic) to connect to.
MQ_HOST	string	null	Host Name	N/A
MQ_PORT	integer	1415	Port Number	N/A
api_params	object	{}	API Parameters	N/A

Additional inputs when "AMAZON_SQS" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_REGION	string	null	AWS Region	N/A
MQ_QUEUE_URL	string	null	Queue URL	N/A
MQ_USE_EC2_INSTANCE_CREDENTIALS	boolean	True	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used.

NOTE: MQ_USERNAME can be used to provide Access Key ID and MQ_PASSWORD to provide Secret Access Key.

Additional inputs when "IBM_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_QUEUE_MANAGER	string	null	Queue Manager (IBM MQ)	N/A
MQ_SSL_CIPHER_SUITE [1]	string	null	SSL cipher suite	SSL cipher suite to use if SSL connection is used.
MQ_CHANNEL	string	null	Channel	N/A
MQ_NO_AUTH_REQUIRED	boolean	false	Channel	N/A

NOTE: MQ_NO_AUTH_REQUIRED is True, MQ_USERNAME and MQ_PASSWORD are optional.

[1] one of:

"NULL_MD5"
"NULL_SHA"
"RC4_MD5_EXPORT"
"RC4_MD5_US"
"RC4_SHA_US"
"RC2_MD5_EXPORT"
"DES_SHA_EXPORT"
"RC4_56_SHA_EXPORT1024"
"DES_SHA_EXPORT1024"
"TRIPLE_DES_SHA_US"
"TLS_RSA_WITH_NULL_SHA256"
"TLS_RSA_WITH_AES_128_CBC_SHA"
"TLS_RSA_WITH_AES_128_CBC_SHA256"
"TLS_RSA_WITH_AES_256_CBC_SHA"
"TLS_RSA_WITH_AES_256_CBC_SHA256"
"AES_SHA_US"
"TLS_RSA_WITH_DES_CBC_SHA"
"TLS_RSA_WITH_3DES_EDE_CBC_SHA"
"TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256"
"FIPS_WITH_DES_CBC_SHA"
"FIPS_WITH_3DES_EDE_CBC_SHA"

Additional inputs when "RABBIT_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_VIRTUAL_HOST	string	null	Virtual Host (RABBIT MQ)	N/A
MQ_EXCHANGE	string	“”	Exchange	N/A
MQ_ROUTING_KEY	string	“”	Routing key	N/A
MQ_CONNECTION_TYPE	string	“TCP”	Connection Type	“TCP or SSL”

rabbit_mq_listener = IOBlock(
    identifier='MQ_LISTENER',
    reference_name='mq_listener',
    title='RabbitMQ Listener',
    enabled=True,
    input={
        'MQ_TYPE': 'RABBIT_MQ', 
        'MQ_QUEUE_NAME': 'some_queue_name',
        'MQ_HOST': 'somehost.com',
        'MQ_USERNAME': 'foo',
        'MQ_PASSWORD': system_secret('rabbit_mq_1_password'),
    }
)

triggers = IDPTriggers(blocks=[rabbit_mq_listener])

Output Blocks (v34)

Warning

Flows pass information to downstream systems using the IDPOutputsBlock. This block is specially built to allow full definition either in code or manually in the Flow Studio. We recommend defining outputs manually in the Flow Studio.

Instructions on how to do so are in our application documentation. Simply include an IDPOutputsBlock in your Python flow definition wherever you wish to send information to downstream systems.

class flows_sdk.implementations.idp_v34.idp_blocks.IDPOutputsBlock(inputs, blocks=None)

Bases: Outputs

Output block allows users to send data extracted by an IDP flow to other systems for downstream processing

Mandatory parameters:

Parameters:

inputs (Dict[str, Any]) – Used by the UI to automatically pre-populate the input of newly added blocks. Extended in this class to include 'enabled': True to visualize an enabled/disabled trigger that is controllable via Flow Studio.

Usage when parameters are to be defined in Flow Studio:

outputs = IDPOutputBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')}
)

If instead you wish to define specific outputs in Python, use the outputs definitions below.

HTTP Notifier Output (v34)

The HTTP Notifier (REST) output connection will POST results from the system to a specified HTTP endpoint.

Identifier: COB_HTTP_EXPORT

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
endpoint	string	N/A	Endpoint URL	URL that output notifications will be posted to
authorization_type	string	“none”	Authorization type	Type of authorization
authorization_header	Password	null	Authorization header	Authorization header to be set in the notification request
auth_url	string	null	OAuth2 authorization URL	The endpoint for the authorization server
client_id	string	null	Client ID	The client identifier issued to the client during the application registration process
client_secret	Password	null	Client Secret	The client secret issued to the client during the application registration process
audience	string	null	Audience	Resource service URL where token will be valid

cob_http_export = IOBlock(
    identifier='COB_HTTP_EXPORT',
    reference_name='cob_http_export',
    title='HTTP Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'SUBMISSION',
        'endpoint': 'example.com',
        'authorization_type': 'none',
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_http_export],
)

Message Queue Notifier Output (v34)

The Message Queue Notifier Output can configure connections to ActiveMQ, Amazon SQS, IBM MQ, and RabbitMQ message queues.

Identifier: COB_MQ_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
MQ_TYPE	string	“ACTIVE_MQ”	Message Queue type	Type of the message queue to connect with. Valid values are “ACTIVE_MQ”, “AMAZON_SQS”, “IBM_MQ” and “RABBIT_MQ”
MQ_USERNAME	string	null	Username/Access Key ID	N/A
MQ_PASSWORD	Password	null	Password/Secret Access Key	N/A
MQ_QUEUE_NAME	string	null	Queue Name	Name of the queue (topic) to connect to. It applies only to “ACTIVE_MQ”, “IBM_MQ” and “RABBIT_MQ” MQ_TYPEs.
MQ_HOST	string	null	Host Name	N/A
MQ_PORT	integer	1415	Port Number	N/A

Additional inputs when "AMAZON_SQS" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_REGION	string	null	AWS Region	N/A
MQ_QUEUE_URL	string	null	Queue URL	N/A
MQ_USE_EC2_INSTANCE_CREDENTIALS	boolean	True	Use AWS EC2 Instance IAM Role Credentials	If selected, credentials are obtained from the EC2 instance directly, and the AWS Access Key ID and Secret Key are not used.
MQ_MESSAGE_GROUP_ID	string	null	Group ID for FIFO queues	N/A
MQ_MESSAGE_METADATA	string	null	Additional SQS Metadata	Input additional metadata key value pairs in the format of { “key”: {“TYPE”: “String”, “VALUE”: “”}, “key2”: {“TYPE”: “Number”, “VALUE”: “12345”}

NOTE: MQ_USERNAME can be used to provide Access Key ID and MQ_PASSWORD to provide Secret Access Key.

Additional inputs when "IBM_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_QUEUE_MANAGER	string	null	Queue Manager (IBM MQ)	N/A
MQ_SSL_CIPHER_SUITE [1]	string	null	SSL cipher suite	SSL cipher suite to use if SSL connection is used.
MQ_CHANNEL	string	null	Channel	N/A
MQ_NO_AUTH_REQUIRED	boolean	false	Channel	N/A

NOTE: MQ_NO_AUTH_REQUIRED is True, MQ_USERNAME and MQ_PASSWORD are optional.

[1] one of:

"NULL_MD5"
"NULL_SHA"
"RC4_MD5_EXPORT"
"RC4_MD5_US"
"RC4_SHA_US"
"RC2_MD5_EXPORT"
"DES_SHA_EXPORT"
"RC4_56_SHA_EXPORT1024"
"DES_SHA_EXPORT1024"
"TRIPLE_DES_SHA_US"
"TLS_RSA_WITH_NULL_SHA256"
"TLS_RSA_WITH_AES_128_CBC_SHA"
"TLS_RSA_WITH_AES_128_CBC_SHA256"
"TLS_RSA_WITH_AES_256_CBC_SHA"
"TLS_RSA_WITH_AES_256_CBC_SHA256"
"AES_SHA_US"
"TLS_RSA_WITH_DES_CBC_SHA"
"TLS_RSA_WITH_3DES_EDE_CBC_SHA"
"TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256"
"FIPS_WITH_DES_CBC_SHA"
"FIPS_WITH_3DES_EDE_CBC_SHA"

Additional inputs when "RABBIT_MQ" is the selected MQ_TYPE:

Name	Type	Default Value	Title	Description
MQ_VIRTUAL_HOST	string	null	Virtual Host (RABBIT MQ)	N/A
MQ_EXCHANGE	string	“”	Exchange	N/A
MQ_ROUTING_KEY	string	“”	Routing key	N/A
MQ_CONNECTION_TYPE	string	“TCP”	Connection Type	“TCP or SSL”

mq_notifier = IOBlock(
    identifier='COB_MQ_NOTIFIER',
    reference_name='mq_notifier',
    title='MQ Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'SUBMISSION',
        'MQ_USERNAME': 'admin',
        'MQ_PASSWORD': 'pass',
        'MQ_QUEUE_NAME': 'queue',
        'MQ_HOST': 'host',
    },
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[mq_notifier],
)

Box Notifier Output (v34)

Provides an out-of-the-box integration into Box systems.

Identifier: COB_BOX_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
public_key_id	string	N/A	Public-Key ID	ID of the Public-key used for authentication with Box
private_key	Password	N/A	Private-Key	Private key used for authentication with Box
passphrase	Password	N/A	Passphrase	Passphrase used for authentication with Box
client_id	string	N/A	Client ID	Client Id used for authentication with Box
client_secret	Password	N/A	Client Secret	Client Secret used for authentication with Box
enterprise_id	string	N/A	Enterprise ID	Enterprise Id used for authentication with Box
template_key	string	“”	Box Metadata Template Key	Enter the key of the Box Metadata template that you would like to map the Hyperscience metadata to.
static_output_fields_to_map	array	[]	Static Metadata Fields	Specify the Hyperscience fields you want to store in Box metadata.
submission_id	string	“”	Key for Mapping Submission ID	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_id	string	“”	Key for Mapping Document ID	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
submission_state	string	“”	Key for Mapping Submission State	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_state	string	“”	Key for Mapping Document State	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
submission_exceptions	string	“”	Key for Mapping Submission Exceptions	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_exceptions	string	“”	Key for Mapping Document Exceptions	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_layout_uuid	string	“”	Key for Mapping Document Layout Uuid	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_layout_name	string	“”	Key for Mapping Document Layout Name	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
start_time	string	“”	Key for Mapping Start Time	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
complete_time	string	“”	Key for Mapping Complete Time	Specify a Box metadata field key from your chosen Box template. The Hyperscience field will be stored in this specified field.
document_fields_template_mappings	json	{}	JSON for Additional Metadata Fields	Specify additional metadata fields using their key value. Please consult Box Integration setup manual for JSON template and instructions.

box_notifier = IOBlock(
    identifier='COB_BOX_NOTIFIER',
    reference_name='box_notifier',
    title='Box Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'private_key': 'password',
        'passphrase': 'password',
        'public_key_id': 'admin',
        'client_id': 'admin',
        'client_secret': 'secret',
        'enterprise_id': 'admin',
        'template_key': 'key',
    },
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[box_notifier],
)

UiPath Notifier Output (v34)

Provides an out-of-the-box integration into UiPath systems.

Identifier: COB_EXPORT_UIPATH

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
url	string	N/A	URL	Base URL of the uipath instance
organization_unit_id	string	null	Organization unit id	Organization unit id
tenant	string	N/A	Tenant	Tenant
username	string	N/A	Username	Username
password	Password	N/A	Password	Password
queue	string	N/A	Queue	Queue

cob_export_uipath = IOBlock(
    identifier='COB_EXPORT_UIPATH',
    reference_name='cob_export_uipath',
    title='CCB Export UiPath',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'api_version': 'v5',
        'export_type': 'FLAT_SUBMISSION',
        'url': 'example.com',
        'tenant': 'tenant',
        'username': 'admin',
        'password': 'pass',
        'queue': 'queue',
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_export_uipath],
)

Salesforce Notifier Output (v34)

With the Salesforce Notifier Block, you can configure your flow to send extracted information to Salesforce. The Salesforce Notifier Block can use extracted information to look up, and then update any number of fields on a Salesforce object. The Salesforce Notifier Block is available in both SaaS and on-premise versions of the Hyperscience application.

Identifier: COB_SALESFORCE_NOTIFIER

Parameters:

Name	Type	Default Value	Title	Description
submission	Submission	N/A	Submission Object	N/A
enabled	boolean	True	Enabled	Enable or disable this output block
api_version	string	N/A	API Version	API version to be used for rendering payload
export_type	string	N/A	Export Type	Payload rendering choice. If “Flat” is chosen, a simplified version of the payload will be sent, as described in the “flat” parameter for submission retrieval API
export_routing_filter	string	null	Routing Filter	By default, status change notifications are sent for each submission processed by the system. You can filter what notifications are received through this connection by specifying an expression like has_layout_tag(‘some_layout_tag’) or has_source_routing_tag(‘some_source_routing_tag’)
username	string	N/A	Associated Username	Username associated with the Salesforce Connected App
consumer_key	password	N/A	Consumer Key	Consumer key used for authentication with Salesforce. Paired with the Salesforce instance from which it was issued.
private_key	password	N/A	Private Key	Private key used for authentication with Salesforce.
sandbox_environment	boolean	false	Private Key	Enable if the Salesforce environment is a sandbox.
object_api_name	string	N/A	Object API Name	API name of the object in Salesforce to lookup and update.
new_record_on_lookup_failure	boolean	false	Create new record on lookup failure	When enabled, it will create a new record if the lookup fails to find a record using the lookup parameters. When disabled, lookup will fail if it does not find a record using the lookup parameters.
object_lookup_mapping	json	N/A	Lookup Field Mapping	Specify which Salesforce fields (using their API name) to be queried with which extracted values from the document for the lookup using key-value format. For example: “DOCUMENT_FIELD_NAME”: “OBJECT_FIELD_API_NAME”.
document_fields_mapping	json	N/A	Document Field Mappings	Specify which Salesforce fields (using their API name) you want to update with which extracted data from Hyperscience using key-value format. For example: “DOCUMENT_FIELD_NAME”: “OBJECT_FIELD_API_NAME”.”
overwrite_existing_field_data	boolean	false	Overwrite existing field data	If this is checked and a selected field has data in it, Hyperscience will overwrite that data. If this is unchecked, existing data will not be overwritten and the operation will fail.

cob_salesforce_notifier = IOBlock(
    identifier='COB_SALESFORCE_NOTIFIER',
    reference_name='cob_salesforce_notifier',
    title='Salesforce Notifier Output Block',
    enabled=True,
    input={
        'submission': submission_bootstrap.output('result.submission'),
        'enabled': True,
        'username': 'salesforce_user',
        'consumer_key': 'salesforce_consumer_key',
        'private_key': 'salesforce_private_key',
        'object_api_name': 'salesforce_object_api_name',
        'object_lookup_mapping': {"LAYOUT_VARIATION_XYZ_FIELD_NAME_1": "SF_FIELD_API_NAME_1", "LAYOUT_VARIATION_XYZ_FIELD_NAME_2": "SF_FIELD_API_NAME_2"},
        'document_fields_mapping': {"LAYOUT_VARIATION_XYZ_FIELD_NAME_3": "SF_FIELD_API_NAME_3", "LAYOUT_VARIATION_XYZ_FIELD_NAME_4": "SF_FIELD_API_NAME_4"}
    }
)

outputs = IDPOutputsBlock(
    inputs={'submission': submission_bootstrap.output('result.submission')},
    blocks=[cob_salesforce_notifier],
)

Helper Classes and Functions (v34)

The following classes are used to easily instantiate and manage a flow’s settings.

flows_sdk.implementations.idp_v34.idp_values.get_idp_wf_inputs(idp_wf_config)

A helper function that maps the values provided by an IdpWorkflowConfig to their corresponding IDP keys.

Parameters:

idp_wf_config (IdpWorkflowConfig) – with static values to be filled.

Return type:

Dict[str, Any]

Returns:

a dictionary with filled static values, compatible with IDP flows.

Example usage with default values:

flow = Flow(
    input=get_idp_wf_inputs(get_idp_wf_config()),
    ...
)

Example usage:

Example usage when instantiating an IDP flow with custom parameters::

flow = Flow(
    input=get_idp_wf_inputs({an instance of IdpWorkflowConfig}),
    ...
)

flows_sdk.implementations.idp_v34.idp_values.get_idp_wf_config()

A helper function that instantiates an IdpWorkflowConfig pre-filled with default values. IdpWorkflowConfig is the container class for defining flow-level settings.

Return type:

IdpWorkflowConfig

Returns:

a valid, fully constructed IdpWorkflowConfig instance

Example usage when instantiating an IDP flow:

flow = Flow(
    input=get_idp_wf_inputs(get_idp_wf_config()),
    ...
)

class flows_sdk.implementations.idp_v34.idp_values.IdpWorkflowConfig

Convenience dataclass outlining all flow-level config values for IDP.

class flows_sdk.implementations.idp_v34.idp_values.IDPTriggers(blocks=None)

An adapter class making it easier to instantiate flows_sdk.flows.Triggers for IDP flows. In particular, provides defaults for some fields (e.g. title, description, reference_name) and defines api_params_manifest with IDP specific parameters.

Parameters:

blocks (Optional[Sequence[IOBlock]]) – flows_sdk.blocks.IOBlock s to be included as triggers for an IDP Flow. Optional, defaults to an empty list.

Example usage when instantiating an IDP flow:

folder_listener = IOBlock(...)
flow = Flow(
    triggers=IDPTriggers([folder_listener]),
    ...
)

class flows_sdk.implementations.idp_v34.idp_values.IDPManifest(flow_identifier)

An adapter class making it easier to instantiate flows_sdk.flows.Manifest for IDP flows. In particular, provides defaults for some fields (e.g. roles, identifier) but more importantly, defines all py:class:flows_sdk.flows.Parameter that construct the Flow inputs and their UI representation (left-hand side menu in Flow studio).

Parameters:

flow_identifier (str) – A system-wide unique identifier for flows_sdk.flows.Flow

Example usage when instantiating an IDP flow:

flow = Flow(
    manifest=IDPManifest(flow_identifier='FLOW_IDENTIFIER'),
    ...
)