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.
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 (
Optional
[str
]) – unique reference name within a flow, a unique one will be generated when not providedcompatibility_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_v40.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='${workflow.input.submission_bootstrap_http_config}', abs_auth_type='${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 (
Optional
[str
]) – unique identifier within a flow, a unique one will be generated when not providedlayout_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 return42
- 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 objectcases (
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 provideddedupe_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 passedNone
, no changes to the deletion date will be made. Defaults to workflow_input(Settings.CollationRetentionPeriod)refresh_retention_period (
Union
[bool
,str
]) – IfTrue
, (re)applies Retention Period parameter to all cases passed into the block. IfFalse
, 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 objectapi_params (
Any
) –
Parameters with defaults:
- Parameters:
reference_name (
Optional
[str
]) – unique identifier within a flow, a unique one will be generated when not providedrotation_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 objectapi_params (
Any
) –
Parameters with defaults:
- Parameters:
reference_name (
Optional
[str
]) – unique identifier within a flow, a unique one will be generated when not providedlayout_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 (
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_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='${workflow.input.manual_field_id_enabled}', field_id_target_accuracy='${workflow.input.field_id_target_accuracy}', table_id_target_accuracy='${workflow.input.table_id_target_accuracy}')
Bases:
Block
Machine identification automatically identify fields and tables in the submission
Mandatory parameters:
- Parameters:
submission (
Any
) – Submission objectapi_params (
Any
) –
Parameters with defaults:
- Parameters:
layout_release_uuid (
Any
) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)reference_name (
Optional
[str
]) – unique identifier within a flow, a unique one will be generated when not providedmanual_field_id_enabled (
str
) – Enables manual identification when applicable, defaults to workflow_input(Settings.ManualFieldIdEnabled)field_id_target_accuracy (
str
) – Field ID Target Accuracy, defaults to workflow_input(Settings.FieldIdTargetAccuracy)table_id_target_accuracy (
str
) – Table ID Target Accuracy, defaults to workflow_input(Settings.TableIdTargetAccuracy)
Example usage:
machine_identification = MachineIdentificationBlock( reference_name='machine_identification', submission=manual_classification.output('submission'), api_params=submission_bootstrap.output('result.api_params'), )
- IDENTIFIER = 'MACHINE_IDENTIFICATION_7'
- class flows_sdk.implementations.idp_v40.idp_blocks.ManualIdentificationBlock(submission, api_params, reference_name=None, task_restrictions='${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='${workflow.input.notification_workflow_identification}')
Bases:
Block
Manual identification allows keyers to complete field identification or table identification tasks, where they draw bounding boxes around the contents of certain fields, table columns or table rows. This identification process ensures that the system will be able to transcribe the correct content in the upcoming transcription process
Mandatory parameters:
- Parameters:
submission (
Any
) – Submission objectapi_params (
Any
) –
Parameters with defaults:
- Parameters:
reference_name (
Optional
[str
]) – unique identifier within a flow, a unique one will be generated when not providedtask_restrictions (
Union
[List
[Any
],str
,None
]) – Defines what users can access Supervision tasks created by this block, defaults to workflow_input(Settings.ManualFieldIdTaskRestrictions)manual_field_id_enabled (
str
) – Enables manual identification when applicable, defaults to workflow_input(Settings.ManualFieldIdEnabled)layout_release_uuid (
str
) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)notification_workflow (
str
) – Notification flow name to run when the submission enters Manual Identification, defaults to workflow_inputSettings.ManualFieldIdNotificationWorkflow
Example usage:
manual_identification = ManualIdentificationBlock( reference_name='manual_identification', submission=machine_identification.output('submission'), api_params=submission_bootstrap.output('result.api_params'), task_restrictions=idp_wf_config.manual_identification_config.task_restrictions, )
- IDENTIFIER = 'MANUAL_IDENTIFICATION_5'
- class flows_sdk.implementations.idp_v40.idp_blocks.MachineTranscriptionBlock(submission, api_params, reference_name=None, layout_release_uuid='${workflow.input.layout_release_uuid}', transcription_model='${workflow.input.transcription_model}', finetuning_only_trained_layouts='${workflow.input.finetuning_only_trained_layouts}', 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 objectapi_params (
Any
) –
Parameters with defaults:
- Parameters:
reference_name (
Optional
[str
]) – unique identifier within a flow, a unique one will be generated when not providedlayout_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 )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 objectapi_params (
Any
) –
Parameters with defaults:
- Parameters:
reference_name (
Optional
[str
]) – unique identifier within a flow, a unique one will be generated when not providedsupervision_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='${workflow.input.notification_workflow_flexible_extraction}')
Bases:
Block
Flexible extraction manually transcribes fields marked for review
Mandatory parameters:
- Parameters:
submission (
Any
) – Submission objectapi_params (
Any
) –
Parameters with defaults:
- Parameters:
reference_name (
Optional
[str
]) – unique identifier within a flow, a unique one will be generated when not providedsupervision_transcription_masking (
Union
[bool
,str
,None
]) – Prevents users from inputting invalid characters during the Flexible Extraction task, defaults to workflow_input(Settings.FlexibleExtractionSupervisionTranscriptionMasking)task_restrictions (
Union
[List
[Any
],str
,None
]) – Defines what users can access Supervision tasks created by this block, defaults workflow_input(Settings.FlexibleExtractionTaskRestrictions)layout_release_uuid (
str
) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)notification_workflow (
str
) – Notification flow name to run when the submission enters Flexible Extraction, defaults to workflow_input(Settings.FlexibleExtractionNotificationWorkflow)
Example usage:
flexible_extraction = FlexibleExtractionBlock( reference_name='flexible_extraction', submission=manual_transcription.output('submission'), api_params=submission_bootstrap.output('result.api_params'), supervision_transcription_masking=( idp_wf_config.flexible_extraction_config.supervision_transcription_masking ), task_restrictions=idp_wf_config.flexible_extraction_config.task_restrictions, )
- IDENTIFIER = 'FLEXIBLE_EXTRACTION_4'
- class flows_sdk.implementations.idp_v40.idp_blocks.ReprocessingBlock(reference_name, submission, parent_flow='${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, 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.
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”.
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 thesubmission
object and apayload
to be passed in.Mandatory parameters:
- Parameters:
submission (
str
) – Submission objectpayload (
Optional
[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 providednlc_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.com/#retrieving-transformed-submissions.
Mandatory parameters:
- Parameters:
submission (
str
) – Submission objecttransformed_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 objecttask_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 providedsupervision_transcription_masking (
Union
[str
,bool
]) – Prevents users from inputting invalid characters during the Custom Supervision task, defaults to Truetask_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_NAMEtitle (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 (
Optional
[str
]) – unique identifier within a flow, a unique one will be generated when not providedapp_metadata (
Optional
[List
[str
]]) – used for capturing information about the flowlanguage_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 section of the documentation https://hyperscience.zendesk.com/hc/en-us/articles/24808469909133-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, 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 11document_rendering_width (
Union
[str
,float
]) – the width of the document to be generated. Default is 8.5document_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.
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 ) 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 (
Optional
[str
]) – unique identifier within a flow, a unique one will be generated when not providedrotation_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 (
Optional
[str
]) – 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 section of the documentation https://hyperscience.zendesk.com/hc/en-us/articles/24808469909133-Supported-Languagesapp_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 (
Optional
[str
]) – unique identifier within a flow, a unique one will be generated when not providedrotation_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 (
Optional
[str
]) – 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 section of the documentation https://hyperscience.zendesk.com/hc/en-us/articles/24808469909133-Supported-Languagesapp_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 (
Optional
[str
]) – unique identifier within a flow, a unique one will be generated when not providedlanguage_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 section of the documentation https://hyperscience.zendesk.com/hc/en-us/articles/24808469909133-Supported-Languagesapp_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 objecttask_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_templatesupervision_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 providedsupervision_transcription_masking (
bool
) – Prevents users from inputting invalid characters during the Custom Supervision task, defaults to Truetask_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_NAMEtitle (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 invokeendpoint (
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 providedbody_namespace (
Optional
[str
]) – SOAP request body namespace urlheaders_namespace (
Optional
[str
]) – SOAP request headers namespace urlheaders (
Optional
[Dict
[Any
,Any
]]) – SOAP headers to useparams (
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 useendpoint (
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 providedheaders (
Optional
[Dict
[str
,str
]]) – HTTP headers to useparams (
Optional
[Dict
[str
,Any
]]) – key-value pair used as query parameters in the requestpayload (
Optional
[Dict
[str
,str
]]) – key-value pair to be used as x-www-form-urlencoded data. Mutually exclusive with jsonjson (
Optional
[Dict
[str
,Any
]]) – key-value pair to be used as json data. Mutually exclusive with payloadauthorization_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_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 useapp_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.com/
Optional Parameters:
- Parameters:
reference_name (
Optional
[str
]) – unique identifier within a flow, a unique one will be generated when not providedheaders (
Optional
[Dict
[str
,str
]]) – HTTP headers to useparams (
Optional
[Dict
[str
,Any
]]) – key-value pair used as query parameters in the requestpayload (
Optional
[Dict
[str
,str
]]) – key-value pair to be used as x-www-form-urlencoded data. Mutually exclusive with jsonjson (
Optional
[Dict
[str
,Any
]]) – key-value pair to be used as json data. Mutually exclusive with payloadhandled_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 ondatabase (
str
) – database nameusername (
str
) – database usernamepassword (
str
) – database passwordquery (
str
) – parameterized query
Optional parameters:
- Parameters:
reference_name (
Optional
[str
]) – unique identifier within a flow, a unique one will be generated when not providedport (
Optional
[int
]) – database port numberoptions (
Optional
[Dict
[Any
,Any
]]) – dictionary of additional connection string optionsquery_params (
Optional
[Dict
[Any
,Any
]]) – dictionary of values for query placeholderstimeout (
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_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 quantizedprompt (
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 providedconsent (
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_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 blockregex_map (
Optional
[Dict
[str
,str
]]) – a map that specifies different regex patterns that the block should look for, a default one is presentkeyword_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 (
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 (v40)
Note
“Triggers” are referred to as “Input Blocks” in our non-technical 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 non-technical 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
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 |
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 |
[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 |
|
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 non-technical 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_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”: “ |
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:
- 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(rotation_correction_enabled, document_grouping_logic, qa_config, manual_document_organization_enabled, transcription_automation_training, transcription_training_legibility_period, transcription_model, manual_transcription_enabled, finetuning_only_trained_layouts, flex_confidence_boosting_enabled, improved_transcription_threshold_accuracy, structured_text, semi_structured_text, semi_structured_table, structured_checkbox, structured_signature, semi_structured_checkbox, field_id_target_accuracy, table_id_target_accuracy, manual_field_id_enabled, machine_classification_config, manual_transcription_config, manual_identification_config, flexible_extraction_config, document_rendering, notification_workflow_initialization=None, notification_workflow_classification=None, notification_workflow_identification=None, notification_workflow_transcription=None, notification_workflow_flexible_extraction=None, field_transcription_target_accuracies=None, column_transcription_target_accuracies=None)
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 allflows_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 forflows_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 allflows_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 forflows_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'), ... )
V39.1
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 providedcompatibility_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 providedlayout_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 return42
- 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 objectcases (
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 provideddedupe_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 passedNone
, no changes to the deletion date will be made. Defaults to workflow_input(Settings.CollationRetentionPeriod)refresh_retention_period (
Union
[bool
,str
]) – IfTrue
, (re)applies Retention Period parameter to all cases passed into the block. IfFalse
, 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 objectapi_params (
Any
) –
Parameters with defaults:
- Parameters:
reference_name (
Optional
[str
]) – unique identifier within a flow, a unique one will be generated when not providedrotation_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 objectapi_params (
Any
) –
Parameters with defaults:
- Parameters:
reference_name (
Optional
[str
]) – unique identifier within a flow, a unique one will be generated when not providedlayout_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 objectapi_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 providedmanual_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 objectapi_params (
Any
) –
Parameters with defaults:
- Parameters:
reference_name (
Optional
[str
]) – unique identifier within a flow, a unique one will be generated when not providedtask_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 objectapi_params (
Any
) –
Parameters with defaults:
- Parameters:
reference_name (
Optional
[str
]) – unique identifier within a flow, a unique one will be generated when not providedlayout_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 objectapi_params (
Any
) –
Parameters with defaults:
- Parameters:
reference_name (
Optional
[str
]) – unique identifier within a flow, a unique one will be generated when not providedsupervision_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 objectapi_params (
Any
) –
Parameters with defaults:
- Parameters:
reference_name (
Optional
[str
]) – unique identifier within a flow, a unique one will be generated when not providedsupervision_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 thesubmission
object and apayload
to be passed in.Mandatory parameters:
- Parameters:
submission (
str
) – Submission objectpayload (
Optional
[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 providednlc_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.com/#retrieving-transformed-submissions.
Mandatory parameters:
- Parameters:
submission (
str
) – Submission objecttransformed_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 objecttask_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 providedsupervision_transcription_masking (
Union
[str
,bool
]) – Prevents users from inputting invalid characters during the Custom Supervision task, defaults to Truetask_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_NAMEtitle (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 providedapp_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 providedapp_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 providedapp_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 providedapp_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 objecttask_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_templatesupervision_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 providedsupervision_transcription_masking (
bool
) – Prevents users from inputting invalid characters during the Custom Supervision task, defaults to Truetask_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_NAMEtitle (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 invokeendpoint (
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 providedbody_namespace (
Optional
[str
]) – SOAP request body namespace urlheaders_namespace (
Optional
[str
]) – SOAP request headers namespace urlheaders (
Optional
[Dict
[Any
,Any
]]) – SOAP headers to useparams (
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 useendpoint (
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 providedheaders (
Optional
[Dict
[str
,str
]]) – HTTP headers to useparams (
Optional
[Dict
[str
,Any
]]) – key-value pair used as query parameters in the requestpayload (
Optional
[Dict
[str
,str
]]) – key-value pair to be used as x-www-form-urlencoded data. Mutually exclusive with jsonjson (
Optional
[Dict
[str
,Any
]]) – key-value pair to be used as json data. Mutually exclusive with payloadauthorization_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 useapp_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.com/
Optional Parameters:
- Parameters:
reference_name (
Optional
[str
]) – unique identifier within a flow, a unique one will be generated when not providedheaders (
Optional
[Dict
[str
,str
]]) – HTTP headers to useparams (
Optional
[Dict
[str
,Any
]]) – key-value pair used as query parameters in the requestpayload (
Optional
[Dict
[str
,str
]]) – key-value pair to be used as x-www-form-urlencoded data. Mutually exclusive with jsonjson (
Optional
[Dict
[str
,Any
]]) – key-value pair to be used as json data. Mutually exclusive with payloadhandled_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 ondatabase (
str
) – database nameusername (
str
) – database usernamepassword (
str
) – database passwordquery (
str
) – parameterized query
Optional parameters:
- Parameters:
reference_name (
Optional
[str
]) – unique identifier within a flow, a unique one will be generated when not providedport (
Optional
[int
]) – database port numberoptions (
Optional
[Dict
[Any
,Any
]]) – dictionary of additional connection string optionsquery_params (
Optional
[Dict
[Any
,Any
]]) – dictionary of values for query placeholderstimeout (
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 quantizedprompt (
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 providedconsent (
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 presentkeyword_map (
Optional
[Dict
[str
,List
[str
]]]) – a map that specifies different keyword patterns that the block should look for, a default one is presentenhanced_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 non-technical 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 non-technical 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
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 |
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 |
[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 |
|
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 non-technical 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_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”: “ |
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:
- 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(rotation_correction_enabled, document_grouping_logic, qa_config, manual_document_organization_enabled, transcription_automation_training, transcription_training_legibility_period, transcription_model, manual_transcription_enabled, finetuning_only_trained_layouts, flex_confidence_boosting_enabled, improved_transcription_threshold_accuracy, structured_text, semi_structured_text, semi_structured_table, structured_checkbox, structured_signature, semi_structured_checkbox, field_id_target_accuracy, table_id_target_accuracy, manual_field_id_enabled, machine_classification_config, manual_transcription_config, manual_identification_config, flexible_extraction_config, notification_workflow_initialization=None, notification_workflow_classification=None, notification_workflow_identification=None, notification_workflow_transcription=None, notification_workflow_flexible_extraction=None)
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 allflows_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 forflows_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 allflows_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 forflows_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 providedcompatibility_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 providedlayout_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 return42
- 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 objectcases (
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 provideddedupe_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 passedNone
, no changes to the deletion date will be made. Defaults to workflow_input(Settings.CollationRetentionPeriod)refresh_retention_period (
Union
[bool
,str
]) – IfTrue
, (re)applies Retention Period parameter to all cases passed into the block. IfFalse
, 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 objectapi_params (
Any
) –
Parameters with defaults:
- Parameters:
reference_name (
Optional
[str
]) – unique identifier within a flow, a unique one will be generated when not providedrotation_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 objectapi_params (
Any
) –
Parameters with defaults:
- Parameters:
reference_name (
Optional
[str
]) – unique identifier within a flow, a unique one will be generated when not providedlayout_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='${workflow.input.table_id_target_accuracy}')
Bases:
Block
Machine identification automatically identify fields and tables in the submission
Mandatory parameters:
- Parameters:
submission (
Any
) – Submission objectapi_params (
Any
) –
Parameters with defaults:
- Parameters:
layout_release_uuid (
Any
) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)reference_name (
Optional
[str
]) – unique identifier within a flow, a unique one will be generated when not providedmanual_field_id_enabled (
str
) – Enables manual identification when applicable, defaults to workflow_input(Settings.ManualFieldIdEnabled)field_id_target_accuracy (
str
) – Field ID Target Accuracy, defaults to workflow_input(Settings.FieldIdTargetAccuracy)table_id_target_accuracy (
str
) – Table ID Target Accuracy, defaults to workflow_input(Settings.TableIdTargetAccuracy)
Example usage:
machine_identification = MachineIdentificationBlock( reference_name='machine_identification', submission=manual_classification.output('submission'), api_params=submission_bootstrap.output('result.api_params'), )
- IDENTIFIER = 'MACHINE_IDENTIFICATION_7'
- class flows_sdk.implementations.idp_v39.idp_blocks.ManualIdentificationBlock(submission, api_params, reference_name=None, task_restrictions='${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='${workflow.input.notification_workflow_identification}')
Bases:
Block
Manual identification allows keyers to complete field identification or table identification tasks, where they draw bounding boxes around the contents of certain fields, table columns or table rows. This identification process ensures that the system will be able to transcribe the correct content in the upcoming transcription process
Mandatory parameters:
- Parameters:
submission (
Any
) – Submission objectapi_params (
Any
) –
Parameters with defaults:
- Parameters:
reference_name (
Optional
[str
]) – unique identifier within a flow, a unique one will be generated when not providedtask_restrictions (
Union
[List
[Any
],str
,None
]) – Defines what users can access Supervision tasks created by this block, defaults to workflow_input(Settings.ManualFieldIdTaskRestrictions)manual_field_id_enabled (
str
) – Enables manual identification when applicable, defaults to workflow_input(Settings.ManualFieldIdEnabled)layout_release_uuid (
str
) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)notification_workflow (
str
) – Notification flow name to run when the submission enters Manual Identification, defaults to workflow_inputSettings.ManualFieldIdNotificationWorkflow
Example usage:
manual_identification = ManualIdentificationBlock( reference_name='manual_identification', submission=machine_identification.output('submission'), api_params=submission_bootstrap.output('result.api_params'), task_restrictions=idp_wf_config.manual_identification_config.task_restrictions, )
- IDENTIFIER = 'MANUAL_IDENTIFICATION_5'
- class flows_sdk.implementations.idp_v39.idp_blocks.MachineTranscriptionBlock(submission, api_params, reference_name=None, layout_release_uuid='${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 objectapi_params (
Any
) –
Parameters with defaults:
- Parameters:
reference_name (
Optional
[str
]) – unique identifier within a flow, a unique one will be generated when not providedlayout_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 objectapi_params (
Any
) –
Parameters with defaults:
- Parameters:
reference_name (
Optional
[str
]) – unique identifier within a flow, a unique one will be generated when not providedsupervision_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='${workflow.input.notification_workflow_flexible_extraction}')
Bases:
Block
Flexible extraction manually transcribes fields marked for review
Mandatory parameters:
- Parameters:
submission (
Any
) – Submission objectapi_params (
Any
) –
Parameters with defaults:
- Parameters:
reference_name (
Optional
[str
]) – unique identifier within a flow, a unique one will be generated when not providedsupervision_transcription_masking (
Union
[bool
,str
,None
]) – Prevents users from inputting invalid characters during the Flexible Extraction task, defaults to workflow_input(Settings.FlexibleExtractionSupervisionTranscriptionMasking)task_restrictions (
Union
[List
[Any
],str
,None
]) – Defines what users can access Supervision tasks created by this block, defaults workflow_input(Settings.FlexibleExtractionTaskRestrictions)layout_release_uuid (
str
) – Layout Release UUID at submission creation time, defaults to workflow_input(Settings.LayoutReleaseUuid)notification_workflow (
str
) – Notification flow name to run when the submission enters Flexible Extraction, defaults to workflow_input(Settings.FlexibleExtractionNotificationWorkflow)
Example usage:
flexible_extraction = FlexibleExtractionBlock( reference_name='flexible_extraction', submission=manual_transcription.output('submission'), api_params=submission_bootstrap.output('result.api_params'), supervision_transcription_masking=( idp_wf_config.flexible_extraction_config.supervision_transcription_masking ), task_restrictions=idp_wf_config.flexible_extraction_config.task_restrictions, )
- IDENTIFIER = 'FLEXIBLE_EXTRACTION_4'
- class flows_sdk.implementations.idp_v39.idp_blocks.ReprocessingBlock(reference_name, submission, parent_flow='${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 thesubmission
object and apayload
to be passed in.Mandatory parameters:
- Parameters:
submission (
str
) – Submission objectpayload (
Optional
[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 providednlc_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.com/#retrieving-transformed-submissions.
Mandatory parameters:
- Parameters:
submission (
str
) – Submission objecttransformed_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 objecttask_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 providedsupervision_transcription_masking (
Union
[str
,bool
]) – Prevents users from inputting invalid characters during the Custom Supervision task, defaults to Truetask_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_NAMEtitle (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 providedapp_metadata (
Optional
[List
[str
]]) – used for capturing information about the flowlanguage_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 section of the documentation https://hyperscience.zendesk.com/hc/en-us/articles/24808469909133-Supported-Languages Note: This field was included 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 providedapp_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 providedapp_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 providedlanguage_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 section of the documentation https://hyperscience.zendesk.com/hc/en-us/articles/24808469909133-Supported-Languages Note: This field was included in v39.0.9app_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 objecttask_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_templatesupervision_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 providedsupervision_transcription_masking (
bool
) – Prevents users from inputting invalid characters during the Custom Supervision task, defaults to Truetask_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_NAMEtitle (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 invokeendpoint (
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 providedbody_namespace (
Optional
[str
]) – SOAP request body namespace urlheaders_namespace (
Optional
[str
]) – SOAP request headers namespace urlheaders (
Optional
[Dict
[Any
,Any
]]) – SOAP headers to useparams (
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 useendpoint (
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 providedheaders (
Optional
[Dict
[str
,str
]]) – HTTP headers to useparams (
Optional
[Dict
[str
,Any
]]) – key-value pair used as query parameters in the requestpayload (
Optional
[Dict
[str
,str
]]) – key-value pair to be used as x-www-form-urlencoded data. Mutually exclusive with jsonjson (
Optional
[Dict
[str
,Any
]]) – key-value pair to be used as json data. Mutually exclusive with payloadauthorization_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, js