# This is an automatically generated code sample.
# To make this code sample work in your Oracle Cloud tenancy,
# please replace the values for any parameters whose current values do not fit
# your use case (such as resource IDs, strings containing ‘EXAMPLE’ or ‘unique_id’, and
# boolean, number, and enum parameters with values not fitting your use case).
from datetime import datetime
import oci
# Create a default config using DEFAULT profile in default location
# Refer to
# https://docs.cloud.oracle.com/en-us/iaas/Content/API/Concepts/sdkconfig.htm#SDK_and_CLI_Configuration_File
# for more info
config = oci.config.from_file()
# Initialize service client with default config file
ai_document_client = oci.ai_document.AIServiceDocumentClient(config)
# Send the request to service, some parameters are not required, see API
# doc for more info
analyze_document_response = ai_document_client.analyze_document(
analyze_document_details=oci.ai_document.models.AnalyzeDocumentDetails(
features=[
oci.ai_document.models.DocumentElementsExtractionFeature(
feature_type="DOCUMENT_ELEMENTS_EXTRACTION",
model_id="ocid1.test.oc1..<unique_ID>EXAMPLE-modelId-Value")],
document=oci.ai_document.models.InlineDocumentDetails(
source="INLINE",
data="pgDWu90EiVeVNs9uGE5H",
page_range=["EXAMPLE--Value"]),
compartment_id="ocid1.test.oc1..<unique_ID>EXAMPLE-compartmentId-Value",
output_location=oci.ai_document.models.OutputLocation(
namespace_name="EXAMPLE-namespaceName-Value",
bucket_name="EXAMPLE-bucketName-Value",
prefix="EXAMPLE-prefix-Value"),
language="EXAMPLE-language-Value",
document_type="PAYSLIP",
ocr_data=oci.ai_document.models.AnalyzeDocumentResult(
document_metadata=oci.ai_document.models.DocumentMetadata(
page_count=921,
mime_type="EXAMPLE-mimeType-Value"),
pages=[
oci.ai_document.models.Page(
page_number=64,
dimensions=oci.ai_document.models.Dimensions(
width=3369.32,
height=9824.681,
unit="PIXEL"),
detected_document_types=[
oci.ai_document.models.DetectedDocumentType(
document_type="EXAMPLE-documentType-Value",
confidence=0.57072455,
document_id="ocid1.test.oc1..<unique_ID>EXAMPLE-documentId-Value")],
detected_languages=[
oci.ai_document.models.DetectedLanguage(
language="EXAMPLE-language-Value",
confidence=0.34501714)],
words=[
oci.ai_document.models.Word(
text="EXAMPLE-text-Value",
confidence=0.9973894,
bounding_polygon=oci.ai_document.models.BoundingPolygon(
normalized_vertices=[
oci.ai_document.models.NormalizedVertex(
x=0.50961673,
y=0.80474406)]))],
lines=[
oci.ai_document.models.Line(
text="EXAMPLE-text-Value",
confidence=0.339808,
bounding_polygon=oci.ai_document.models.BoundingPolygon(
normalized_vertices=[
oci.ai_document.models.NormalizedVertex(
x=0.045131147,
y=0.4193164)]),
word_indexes=[919])],
tables=[
oci.ai_document.models.Table(
row_count=244,
column_count=417,
header_rows=[
oci.ai_document.models.TableRow(
cells=[
oci.ai_document.models.Cell(
text="EXAMPLE-text-Value",
row_index=817,
column_index=931,
confidence=0.9510738,
bounding_polygon=oci.ai_document.models.BoundingPolygon(
normalized_vertices=[
oci.ai_document.models.NormalizedVertex(
x=0.20636326,
y=0.9804424)]),
word_indexes=[110])])],
confidence=0.7567968,
bounding_polygon=oci.ai_document.models.BoundingPolygon(
normalized_vertices=[
oci.ai_document.models.NormalizedVertex(
x=0.16993767,
y=0.2521472)]))],
document_fields=[
oci.ai_document.models.DocumentField(
field_type="LINE_ITEM",
field_value=oci.ai_document.models.ValueDate(
value_type="DATE",
confidence=0.50920236,
bounding_polygon=oci.ai_document.models.BoundingPolygon(
normalized_vertices=[
oci.ai_document.models.NormalizedVertex(
x=0.36996943,
y=0.82883954)]),
word_indexes=[862],
value=datetime.strptime(
"2013-07-24T19:23:50.712Z",
"%Y-%m-%dT%H:%M:%S.%fZ"),
text="EXAMPLE-text-Value",
normalized_value="EXAMPLE-normalizedValue-Value",
normalized_confidence=0.67663234),
field_label=oci.ai_document.models.FieldLabel(
name="EXAMPLE-name-Value",
confidence=0.5289005),
field_name=oci.ai_document.models.FieldName(
name="EXAMPLE-name-Value",
confidence=0.7253601,
bounding_polygon=oci.ai_document.models.BoundingPolygon(
normalized_vertices=[
oci.ai_document.models.NormalizedVertex(
x=0.571634,
y=0.74919754)]),
word_indexes=[192]))],
signatures=[
oci.ai_document.models.Signature(
confidence=0.81502527,
bounding_polygon=oci.ai_document.models.BoundingPolygon(
normalized_vertices=[
oci.ai_document.models.NormalizedVertex(
x=0.17849636,
y=0.83037513)]))],
bar_codes=[
oci.ai_document.models.BarCode(
confidence=0.9311609,
value="EXAMPLE-value-Value",
bounding_polygon=oci.ai_document.models.BoundingPolygon(
normalized_vertices=[
oci.ai_document.models.NormalizedVertex(
x=0.05867535,
y=0.44432253)]),
code_type="EXAMPLE-codeType-Value")],
selection_marks=[
oci.ai_document.models.SelectionMark(
state="SELECTED",
confidence=0.12940931,
bounding_polygon=oci.ai_document.models.BoundingPolygon(
normalized_vertices=[
oci.ai_document.models.NormalizedVertex(
x=0.6239295,
y=0.31811917)]))])],
detected_document_types=[
oci.ai_document.models.DetectedDocumentType(
document_type="EXAMPLE-documentType-Value",
confidence=0.53643167,
document_id="ocid1.test.oc1..<unique_ID>EXAMPLE-documentId-Value")],
detected_languages=[
oci.ai_document.models.DetectedLanguage(
language="EXAMPLE-language-Value",
confidence=0.053895116)],
document_classification_model_version="EXAMPLE-documentClassificationModelVersion-Value",
language_classification_model_version="EXAMPLE-languageClassificationModelVersion-Value",
text_extraction_model_version="EXAMPLE-textExtractionModelVersion-Value",
key_value_extraction_model_version="EXAMPLE-keyValueExtractionModelVersion-Value",
table_extraction_model_version="EXAMPLE-tableExtractionModelVersion-Value",
signature_extraction_model_version="EXAMPLE-signatureExtractionModelVersion-Value",
bar_code_extraction_model_version="EXAMPLE-barCodeExtractionModelVersion-Value",
errors=[
oci.ai_document.models.ProcessingError(
code="EXAMPLE-code-Value",
message="EXAMPLE-message-Value")],
searchable_pdf="jm7p3TNVDMJ4m2NfDwbn")),
if_match="EXAMPLE-ifMatch-Value",
opc_request_id="J1GBG1IXHDZMNREAKWJS<unique_ID>")
# Get the data from response
print(analyze_document_response.data)