Class Document (0.7.0a0)

Document(
    shards: List[google.cloud.documentai_v1.types.document.Document],
    gcs_bucket_name: Optional[str] = None,
    gcs_prefix: Optional[str] = None,
    gcs_input_uri: Optional[str] = None,
)

Represents a wrapped Document.

This class hides away the complexities of using Document protobuf response outputted by BatchProcessDocuments or ProcessDocument methods and implements convenient methods for searching and extracting information within the Document.

Optional. The name of the gcs bucket.

Format: gs://{bucket_name}/{optional_folder}/{target_folder}/ where gcs_bucket_name=bucket.

:type: Optional[str]

(List[Entity]): A list of Entities in the Document.

Attributes
Name	Description
`gcs_prefix`	`Optional[str]` Optional. The prefix of the json files in the target_folder. Format: `gs://{bucket_name}/{optional_folder}/{target_folder}/` where gcs_prefix=`{optional_folder}/{target_folder}`. For more information please take a look at https://cloud.google.com/storage/docs/json_api/v1/objects/list .
`pages`	`Optional[str]` (List[Page]): A list of Pages in the Document.

Methods

convert_document_to_annotate_file_response

convert_document_to_annotate_file_response()

Convert OCR data from Document.proto to AnnotateFileResponse.proto for Vision API.

Returns
Type	Description
`AnnotateFileResponse`	Proto with TextAnnotations.

entities_to_bigquery

entities_to_bigquery(
    dataset_name: str, table_name: str, project_id: Optional[str] = None
)

Adds extracted entities to a BigQuery table.

Parameters
Name	Description
`dataset_name`	`str` Required. Name of the BigQuery dataset.
`table_name`	`str` Required. Name of the BigQuery table.
`project_id`	`Optional[str]` Optional. Project ID containing the BigQuery table. If not passed, falls back to the default inferred from the environment.

Returns
Type	Description
`bigquery.job.LoadJob`	The BigQuery LoadJob for adding the entities.

entities_to_dict

entities_to_dict()

Returns Dictionary of entities in document.

Returns
Type	Description
`Dict`	The Dict of the entities indexed by type.

export_images

export_images(
    output_path: str, output_file_prefix: str, output_file_extension: str
)

Exports images from Document to files.

Parameters
Name	Description
`output_path`	`str` Required. The path to the output directory.
`output_file_prefix`	`str` Required. The output file name prefix.
`output_file_extension`	`str` Required. The output file extension. Format: `png`, `jpg`, etc.

Returns
Type	Description
`List[str]`	A list of output image file names. Format: `{output_path}/{output_file_prefix}_{index}_{Entity.type_}.{output_file_extension}`

form_fields_to_bigquery

form_fields_to_bigquery(
    dataset_name: str, table_name: str, project_id: Optional[str] = None
)

Adds extracted form fields to a BigQuery table.

Parameters
Name	Description
`dataset_name`	`str` Required. Name of the BigQuery dataset.
`table_name`	`str` Required. Name of the BigQuery table.
`project_id`	`Optional[str]` Optional. Project ID containing the BigQuery table. If not passed, falls back to the default inferred from the environment.

Returns
Type	Description
`bigquery.job.LoadJob`	The BigQuery LoadJob for adding the form fields.

form_fields_to_dict

form_fields_to_dict()

Returns Dictionary of form fields in document.

Returns
Type	Description
`Dict`	The Dict of the form fields indexed by type.

from_batch_process_metadata

from_batch_process_metadata(
    metadata: google.cloud.documentai_v1.types.document_processor_service.BatchProcessMetadata,
)

Loads Documents from Cloud Storage, using the output from BatchProcessMetadata.

.. code-block:: python

    from google.cloud import documentai

    operation = client.batch_process_documents(request)
    operation.result(timeout=timeout)
    metadata = documentai.BatchProcessMetadata(operation.metadata)

Parameter
Name	Description
`metadata`	`documentai.BatchProcessMetadata` Required. The operation metadata after a `batch_process_documents()` operation completes.

Returns
Type	Description
`List[Document]`	A list of wrapped documents from gcs. Each document corresponds to an input file.

from_batch_process_operation

from_batch_process_operation(location: str, operation_name: str)

Loads Documents from Cloud Storage, using the operation name returned from batch_process_documents().

.. code-block:: python

    from google.cloud import documentai

    operation = client.batch_process_documents(request)
    operation_name = operation.operation.name

Parameters
Name	Description
`location`	`str` Required. The location of the processor used for `batch_process_documents()`.
`operation_name`	`str` Required. The fully qualified operation name for a `batch_process_documents()` operation.

Returns
Type	Description
`List[Document]`	A list of wrapped documents from gcs. Each document corresponds to an input file.

from_document_path

from_document_path(document_path: str)

Loads Document from local document_path.

.. code-block:: python

    from google.cloud.documentai_toolbox import document

    document_path = "/path/to/local/file.json
    wrapped_document = document.Document.from_document_path(document_path)

Parameter
Name	Description
`document_path`	`str` Required. The path to the document.json file.

Returns
Type	Description
`Document`	A document from local document_path.

from_documentai_document

from_documentai_document(
    documentai_document: google.cloud.documentai_v1.types.document.Document,
)

Loads Document from local documentai_document.

.. code-block:: python

    from google.cloud import documentai
    from google.cloud.documentai_toolbox import document

    documentai_document = client.process_documents(request).document
    wrapped_document = document.Document.from_documentai_document(documentai_document)

Parameter
Name	Description
`documentai_document`	`documentai.Document` Optional. The Document.proto response.

Returns
Type	Description
`Document`	A document from local documentai_document.

from_gcs

from_gcs(
    gcs_bucket_name: str, gcs_prefix: str, gcs_input_uri: Optional[str] = None
)

Loads Document from Cloud Storage.

Parameters
Name	Description
`gcs_bucket_name`	`str` Required. The gcs bucket. Format: Given `gs://{bucket_name}/{optional_folder}/{operation_id}/` where gcs_bucket_name=`{bucket_name}`.
`gcs_prefix`	`str` Required. The prefix to the location of the target folder. Format: Given `gs://{bucket_name}/{optional_folder}/{target_folder}` where gcs_prefix=`{optional_folder}/{target_folder}`.
`gcs_input_uri`	`str` Optional. The gcs uri to the original input file. Format: `gs://{bucket_name}/{optional_folder}/{target_folder}/{file_name}.pdf`

Returns
Type	Description
`Document`	A document from gcs.

get_entity_by_type

get_entity_by_type(target_type: str)

Returns the list of Entities of target_type.

Parameter
Name	Description
`target_type`	`str` Required. target_type.

Returns
Type	Description
`List[Entity]`	A list of Entity matching target_type.

get_form_field_by_name

get_form_field_by_name(target_field: str)

Returns the list of FormFields named target_field.

Parameter
Name	Description
`target_field`	`str` Required. Target field name.

Returns
Type	Description
`List[FormField]`	A list of FormField matching target_field.

search_pages

search_pages(target_string: Optional[str] = None, pattern: Optional[str] = None)

Returns the list of Pages containing target_string or text matching pattern.

Parameters
Name	Description
`target_string`	`Optional[str]` Optional. target str.
`pattern`	`Optional[str]` Optional. regex str.

Returns
Type	Description
`List[Page]`	A list of Pages.

split_pdf

split_pdf(pdf_path: str, output_path: str)

Splits local PDF file into multiple PDF files based on output from a Splitter/Classifier processor.

Parameters
Name	Description
`pdf_path`	`str` Required. The path to the PDF file.
`output_path`	`str` Required. The path to the output directory.

Returns
Type	Description
`List[str]`	A list of output pdf files.