azure-ai-documentintelligence
Advanced tools
Microsoft Azure AI Document Intelligence Client Library for Python
Azure AI Document Intelligence (previously known as Form Recognizer) is a cloud service that uses machine learning to analyze text and structured data from your documents. It includes the following main features:
Source code | Package (PyPI) | API reference documentation | Product documentation | Samples
The latest service API is currently only available in some Azure regions, the available regions can be found from here.
python -m pip install azure-ai-documentintelligence
This table shows the relationship between SDK versions and supported API service versions:
| SDK version | Supported API service version |
|---|---|
| 1.0.0b1 | 2023-10-31-preview |
| 1.0.0b2 | 2024-02-29-preview |
Older API versions are supported in azure-ai-formrecognizer, please see the Migration Guide for detailed instructions on how to update application.
Document Intelligence supports both multi-service and single-service access. Create a Cognitive Services resource if you plan to access multiple cognitive services under a single endpoint/key. For Document Intelligence access only, create a Document Intelligence resource. Please note that you will need a single-service resource if you intend to use Azure Active Directory authentication.
You can create either resource using:
Below is an example of how you can create a Document Intelligence resource using the CLI:
# Create a new resource group to hold the Document Intelligence resource
# if using an existing resource group, skip this step
az group create --name <your-resource-name> --location <location>
# Create the Document Intelligence resource
az cognitiveservices account create \
--name <your-resource-name> \
--resource-group <your-resource-group-name> \
--kind FormRecognizer \
--sku <sku> \
--location <location> \
--yes
For more information about creating the resource or how to get the location and sku information see here.
In order to interact with the Document Intelligence service, you will need to create an instance of a client. An endpoint and credential are necessary to instantiate the client object.
You can find the endpoint for your Document Intelligence resource using the Azure Portal or Azure CLI:
# Get the endpoint for the Document Intelligence resource
az cognitiveservices account show --name "resource-name" --resource-group "resource-group-name" --query "properties.endpoint"
Either a regional endpoint or a custom subdomain can be used for authentication. They are formatted as follows:
Regional endpoint: https://<region>.api.cognitive.microsoft.com/
Custom subdomain: https://<resource-name>.cognitiveservices.azure.com/
A regional endpoint is the same for every resource in a region. A complete list of supported regional endpoints can be consulted here. Please note that regional endpoints do not support AAD authentication.
A custom subdomain, on the other hand, is a name that is unique to the Document Intelligence resource. They can only be used by single-service resources.
The API key can be found in the Azure Portal or by running the following Azure CLI command:
az cognitiveservices account keys list --name "<resource-name>" --resource-group "<resource-group-name>"
To use an API key as the credential parameter,
pass the key as a string into an instance of AzureKeyCredential.
from azure.core.credentials import AzureKeyCredential
from azure.ai.documentintelligence import DocumentIntelligenceClient
endpoint = "https://<my-custom-subdomain>.cognitiveservices.azure.com/"
credential = AzureKeyCredential("<api_key>")
document_intelligence_client = DocumentIntelligenceClient(endpoint, credential)
AzureKeyCredential authentication is used in the examples in this getting started guide, but you can also
authenticate with Azure Active Directory using the azure-identity library.
Note that regional endpoints do not support AAD authentication. Create a custom subdomain
name for your resource in order to use this type of authentication.
To use the DefaultAzureCredential type shown below, or other credential types provided
with the Azure SDK, please install the azure-identity package:
pip install azure-identity
You will also need to register a new AAD application and grant access to Document Intelligence by assigning the "Cognitive Services User" role to your service principal.
Once completed, set the values of the client ID, tenant ID, and client secret of the AAD application as environment variables:
AZURE_CLIENT_ID, AZURE_TENANT_ID, AZURE_CLIENT_SECRET.
"""DefaultAzureCredential will use the values from these environment
variables: AZURE_CLIENT_ID, AZURE_TENANT_ID, AZURE_CLIENT_SECRET
"""
from azure.ai.documentintelligence import DocumentIntelligenceClient
from azure.identity import DefaultAzureCredential
endpoint = os.environ["DOCUMENTINTELLIGENCE_ENDPOINT"]
credential = DefaultAzureCredential()
document_intelligence_client = DocumentIntelligenceClient(endpoint, credential)
DocumentIntelligenceClient provides operations for analyzing input documents using prebuilt and custom models through the begin_analyze_document API.
Use the model_id parameter to select the type of model for analysis. See a full list of supported models here.
The DocumentIntelligenceClient also provides operations for classifying documents through the begin_classify_document API.
Custom classification models can classify each page in an input file to identify the document(s) within and can also identify multiple documents or multiple instances of a single document within an input file.
Sample code snippets are provided to illustrate using a DocumentIntelligenceClient here. More information about analyzing documents, including supported features, locales, and document types can be found in the service documentation.
DocumentIntelligenceAdministrationClient provides operations for:
DocumentModelDetails is returned indicating the document type(s) the model can analyze, as well as the estimated confidence for each field. See the service documentation for a more detailed explanation.Please note that models can also be built using a graphical user interface such as Document Intelligence Studio.
Sample code snippets are provided to illustrate using a DocumentIntelligenceAdministrationClient here.
Long-running operations are operations which consist of an initial request sent to the service to start an operation, followed by polling the service at intervals to determine whether the operation has completed or failed, and if it has succeeded, to get the result.
Methods that analyze documents, build models, or copy/compose models are modeled as long-running operations.
The client exposes a begin_<method-name> method that returns an LROPoller or AsyncLROPoller. Callers should wait
for the operation to complete by calling result() on the poller object returned from the begin_<method-name> method.
Sample code snippets are provided to illustrate using long-running operations below.
The following section provides several code snippets covering some of the most common Document Intelligence tasks, including:
Extract text, selection marks, text styles, and table structures, along with their bounding region coordinates, from documents.
from azure.core.credentials import AzureKeyCredential
from azure.ai.documentintelligence import DocumentIntelligenceClient
from azure.ai.documentintelligence.models import AnalyzeResult
def _in_span(word, spans):
for span in spans:
if word.span.offset >= span.offset and (word.span.offset + word.span.length) <= (span.offset + span.length):
return True
return False
def _format_polygon(polygon):
if not polygon:
return "N/A"
return ", ".join([f"[{polygon[i]}, {polygon[i + 1]}]" for i in range(0, len(polygon), 2)])
endpoint = os.environ["DOCUMENTINTELLIGENCE_ENDPOINT"]
key = os.environ["DOCUMENTINTELLIGENCE_API_KEY"]
document_intelligence_client = DocumentIntelligenceClient(endpoint=endpoint, credential=AzureKeyCredential(key))
with open(path_to_sample_documents, "rb") as f:
poller = document_intelligence_client.begin_analyze_document(
"prebuilt-layout", analyze_request=f, content_type="application/octet-stream"
)
result: AnalyzeResult = poller.result()
if result.styles and any([style.is_handwritten for style in result.styles]):
print("Document contains handwritten content")
else:
print("Document does not contain handwritten content")
for page in result.pages:
print(f"----Analyzing layout from page #{page.page_number}----")
print(f"Page has width: {page.width} and height: {page.height}, measured with unit: {page.unit}")
if page.lines:
for line_idx, line in enumerate(page.lines):
words = []
if page.words:
for word in page.words:
print(f"......Word '{word.content}' has a confidence of {word.confidence}")
if _in_span(word, line.spans):
words.append(word)
print(
f"...Line # {line_idx} has word count {len(words)} and text '{line.content}' "
f"within bounding polygon '{_format_polygon(line.polygon)}'"
)
if page.selection_marks:
for selection_mark in page.selection_marks:
print(
f"Selection mark is '{selection_mark.state}' within bounding polygon "
f"'{_format_polygon(selection_mark.polygon)}' and has a confidence of {selection_mark.confidence}"
)
if result.paragraphs:
print(f"----Detected #{len(result.paragraphs)} paragraphs in the document----")
# Sort all paragraphs by span's offset to read in the right order.
result.paragraphs.sort(key=lambda p: (p.spans.sort(key=lambda s: s.offset), p.spans[0].offset))
print("-----Print sorted paragraphs-----")
for paragraph in result.paragraphs:
if not paragraph.bounding_regions:
print(f"Found paragraph with role: '{paragraph.role}' within N/A bounding region")
else:
print(f"Found paragraph with role: '{paragraph.role}' within")
print(
", ".join(
f" Page #{region.page_number}: {_format_polygon(region.polygon)} bounding region"
for region in paragraph.bounding_regions
)
)
print(f"...with content: '{paragraph.content}'")
print(f"...with offset: {paragraph.spans[0].offset} and length: {paragraph.spans[0].length}")
if result.tables:
for table_idx, table in enumerate(result.tables):
print(f"Table # {table_idx} has {table.row_count} rows and " f"{table.column_count} columns")
if table.bounding_regions:
for region in table.bounding_regions:
print(
f"Table # {table_idx} location on page: {region.page_number} is {_format_polygon(region.polygon)}"
)
for cell in table.cells:
print(f"...Cell[{cell.row_index}][{cell.column_index}] has text '{cell.content}'")
if cell.bounding_regions:
for region in cell.bounding_regions:
print(
f"...content on page {region.page_number} is within bounding polygon '{_format_polygon(region.polygon)}'"
)
print("----------------------------------------")
Extract figures from the document as cropped images.
from azure.core.credentials import AzureKeyCredential
from azure.ai.documentintelligence import DocumentIntelligenceClient
from azure.ai.documentintelligence.models import AnalyzeOutputOption, AnalyzeResult
endpoint = os.environ["DOCUMENTINTELLIGENCE_ENDPOINT"]
key = os.environ["DOCUMENTINTELLIGENCE_API_KEY"]
document_intelligence_client = DocumentIntelligenceClient(endpoint=endpoint, credential=AzureKeyCredential(key))
with open(path_to_sample_documents, "rb") as f:
poller = document_intelligence_client.begin_analyze_document(
"prebuilt-layout",
analyze_request=f,
output=[AnalyzeOutputOption.FIGURES],
content_type="application/octet-stream",
)
result: AnalyzeResult = poller.result()
operation_id = poller.details["operation_id"]
if result.figures:
for figure in result.figures:
if figure.id:
response = document_intelligence_client.get_analyze_result_figure(
model_id=result.model_id, result_id=operation_id, figure_id=figure.id
)
with open(f"{figure.id}.png", "wb") as writer:
writer.writelines(response)
else:
print("No figures found.")
Convert an analog PDF into a PDF with embedded text. Such text can enable text search within the PDF or allow the PDF to be used in LLM chat scenarios.
Note: For now, this feature is only supported by prebuilt-read. All other models will return error.
from azure.core.credentials import AzureKeyCredential
from azure.ai.documentintelligence import DocumentIntelligenceClient
from azure.ai.documentintelligence.models import AnalyzeOutputOption, AnalyzeResult
endpoint = os.environ["DOCUMENTINTELLIGENCE_ENDPOINT"]
key = os.environ["DOCUMENTINTELLIGENCE_API_KEY"]
document_intelligence_client = DocumentIntelligenceClient(endpoint=endpoint, credential=AzureKeyCredential(key))
with open(path_to_sample_documents, "rb") as f:
poller = document_intelligence_client.begin_analyze_document(
"prebuilt-read",
analyze_request=f,
output=[AnalyzeOutputOption.PDF],
content_type="application/octet-stream",
)
result: AnalyzeResult = poller.result()
operation_id = poller.details["operation_id"]
response = document_intelligence_client.get_analyze_result_pdf(model_id=result.model_id, result_id=operation_id)
with open("analyze_result.pdf", "wb") as writer:
writer.writelines(response)
Analyze key-value pairs, tables, styles, and selection marks from documents using the general document model provided by the Document Intelligence service.
Select the General Document Model by passing model_id="prebuilt-document" into the begin_analyze_document method:
from azure.core.credentials import AzureKeyCredential
from azure.ai.documentintelligence import DocumentIntelligenceClient
from azure.ai.documentintelligence.models import DocumentAnalysisFeature, AnalyzeResult
def _in_span(word, spans):
for span in spans:
if word.span.offset >= span.offset and (word.span.offset + word.span.length) <= (span.offset + span.length):
return True
return False
def _format_bounding_region(bounding_regions):
if not bounding_regions:
return "N/A"
return ", ".join(
f"Page #{region.page_number}: {_format_polygon(region.polygon)}" for region in bounding_regions
)
def _format_polygon(polygon):
if not polygon:
return "N/A"
return ", ".join([f"[{polygon[i]}, {polygon[i + 1]}]" for i in range(0, len(polygon), 2)])
endpoint = os.environ["DOCUMENTINTELLIGENCE_ENDPOINT"]
key = os.environ["DOCUMENTINTELLIGENCE_API_KEY"]
document_intelligence_client = DocumentIntelligenceClient(endpoint=endpoint, credential=AzureKeyCredential(key))
with open(path_to_sample_documents, "rb") as f:
poller = document_intelligence_client.begin_analyze_document(
"prebuilt-layout",
analyze_request=f,
features=[DocumentAnalysisFeature.KEY_VALUE_PAIRS],
content_type="application/octet-stream",
)
result: AnalyzeResult = poller.result()
if result.styles:
for style in result.styles:
if style.is_handwritten:
print("Document contains handwritten content: ")
print(",".join([result.content[span.offset : span.offset + span.length] for span in style.spans]))
print("----Key-value pairs found in document----")
if result.key_value_pairs:
for kv_pair in result.key_value_pairs:
if kv_pair.key:
print(
f"Key '{kv_pair.key.content}' found within "
f"'{_format_bounding_region(kv_pair.key.bounding_regions)}' bounding regions"
)
if kv_pair.value:
print(
f"Value '{kv_pair.value.content}' found within "
f"'{_format_bounding_region(kv_pair.value.bounding_regions)}' bounding regions\n"
)
for page in result.pages:
print(f"----Analyzing document from page #{page.page_number}----")
print(f"Page has width: {page.width} and height: {page.height}, measured with unit: {page.unit}")
if page.lines:
for line_idx, line in enumerate(page.lines):
words = []
if page.words:
for word in page.words:
print(f"......Word '{word.content}' has a confidence of {word.confidence}")
if _in_span(word, line.spans):
words.append(word)
print(
f"...Line #{line_idx} has {len(words)} words and text '{line.content}' within "
f"bounding polygon '{_format_polygon(line.polygon)}'"
)
if page.selection_marks:
for selection_mark in page.selection_marks:
print(
f"Selection mark is '{selection_mark.state}' within bounding polygon "
f"'{_format_polygon(selection_mark.polygon)}' and has a confidence of "
f"{selection_mark.confidence}"
)
if result.tables:
for table_idx, table in enumerate(result.tables):
print(f"Table # {table_idx} has {table.row_count} rows and {table.column_count} columns")
if table.bounding_regions:
for region in table.bounding_regions:
print(
f"Table # {table_idx} location on page: {region.page_number} is {_format_polygon(region.polygon)}"
)
for cell in table.cells:
print(f"...Cell[{cell.row_index}][{cell.column_index}] has text '{cell.content}'")
if cell.bounding_regions:
for region in cell.bounding_regions:
print(
f"...content on page {region.page_number} is within bounding polygon '{_format_polygon(region.polygon)}'\n"
)
print("----------------------------------------")
prebuilt-document model here.Extract fields from select document types such as receipts, invoices, business cards, identity documents, and U.S. W-2 tax documents using prebuilt models provided by the Document Intelligence service.
For example, to analyze fields from a sales receipt, use the prebuilt receipt model provided by passing model_id="prebuilt-receipt" into the begin_analyze_document method:
from azure.core.credentials import AzureKeyCredential
from azure.ai.documentintelligence import DocumentIntelligenceClient
from azure.ai.documentintelligence.models import AnalyzeResult
def _format_price(price_dict):
return "".join([f"{p}" for p in price_dict.values()])
endpoint = os.environ["DOCUMENTINTELLIGENCE_ENDPOINT"]
key = os.environ["DOCUMENTINTELLIGENCE_API_KEY"]
document_intelligence_client = DocumentIntelligenceClient(endpoint=endpoint, credential=AzureKeyCredential(key))
with open(path_to_sample_documents, "rb") as f:
poller = document_intelligence_client.begin_analyze_document(
"prebuilt-receipt", analyze_request=f, locale="en-US", content_type="application/octet-stream"
)
receipts: AnalyzeResult = poller.result()
if receipts.documents:
for idx, receipt in enumerate(receipts.documents):
print(f"--------Analysis of receipt #{idx + 1}--------")
print(f"Receipt type: {receipt.doc_type if receipt.doc_type else 'N/A'}")
if receipt.fields:
merchant_name = receipt.fields.get("MerchantName")
if merchant_name:
print(
f"Merchant Name: {merchant_name.get('valueString')} has confidence: "
f"{merchant_name.confidence}"
)
transaction_date = receipt.fields.get("TransactionDate")
if transaction_date:
print(
f"Transaction Date: {transaction_date.get('valueDate')} has confidence: "
f"{transaction_date.confidence}"
)
items = receipt.fields.get("Items")
if items:
print("Receipt items:")
for idx, item in enumerate(items.get("valueArray")):
print(f"...Item #{idx + 1}")
item_description = item.get("valueObject").get("Description")
if item_description:
print(
f"......Item Description: {item_description.get('valueString')} has confidence: "
f"{item_description.confidence}"
)
item_quantity = item.get("valueObject").get("Quantity")
if item_quantity:
print(
f"......Item Quantity: {item_quantity.get('valueString')} has confidence: "
f"{item_quantity.confidence}"
)
item_total_price = item.get("valueObject").get("TotalPrice")
if item_total_price:
print(
f"......Total Item Price: {_format_price(item_total_price.get('valueCurrency'))} has confidence: "
f"{item_total_price.confidence}"
)
subtotal = receipt.fields.get("Subtotal")
if subtotal:
print(
f"Subtotal: {_format_price(subtotal.get('valueCurrency'))} has confidence: {subtotal.confidence}"
)
tax = receipt.fields.get("TotalTax")
if tax:
print(f"Total tax: {_format_price(tax.get('valueCurrency'))} has confidence: {tax.confidence}")
tip = receipt.fields.get("Tip")
if tip:
print(f"Tip: {_format_price(tip.get('valueCurrency'))} has confidence: {tip.confidence}")
total = receipt.fields.get("Total")
if total:
print(f"Total: {_format_price(total.get('valueCurrency'))} has confidence: {total.confidence}")
print("--------------------------------------")
You are not limited to receipts! There are a few prebuilt models to choose from, each of which has its own set of supported fields. See other supported prebuilt models here.
Build a custom model on your own document type. The resulting model can be used to analyze values from the types of documents it was trained on. Provide a container SAS URL to your Azure Storage Blob container where you're storing the training documents.
More details on setting up a container and required file structure can be found in the service documentation.
# Let's build a model to use for this sample
import uuid
from azure.ai.documentintelligence import DocumentIntelligenceAdministrationClient
from azure.ai.documentintelligence.models import (
DocumentBuildMode,
BuildDocumentModelRequest,
AzureBlobContentSource,
DocumentModelDetails,
)
from azure.core.credentials import AzureKeyCredential
endpoint = os.environ["DOCUMENTINTELLIGENCE_ENDPOINT"]
key = os.environ["DOCUMENTINTELLIGENCE_API_KEY"]
container_sas_url = os.environ["DOCUMENTINTELLIGENCE_STORAGE_CONTAINER_SAS_URL"]
document_intelligence_admin_client = DocumentIntelligenceAdministrationClient(endpoint, AzureKeyCredential(key))
poller = document_intelligence_admin_client.begin_build_document_model(
BuildDocumentModelRequest(
model_id=str(uuid.uuid4()),
build_mode=DocumentBuildMode.TEMPLATE,
azure_blob_source=AzureBlobContentSource(container_url=container_sas_url),
description="my model description",
)
)
model: DocumentModelDetails = poller.result()
print(f"Model ID: {model.model_id}")
print(f"Description: {model.description}")
print(f"Model created on: {model.created_date_time}")
print(f"Model expires on: {model.expiration_date_time}")
if model.doc_types:
print("Doc types the model can recognize:")
for name, doc_type in model.doc_types.items():
print(f"Doc Type: '{name}' built with '{doc_type.build_mode}' mode which has the following fields:")
if doc_type.field_schema:
for field_name, field in doc_type.field_schema.items():
if doc_type.field_confidence:
print(
f"Field: '{field_name}' has type '{field['type']}' and confidence score "
f"{doc_type.field_confidence[field_name]}"
)
Analyze document fields, tables, selection marks, and more. These models are trained with your own data, so they're tailored to your documents. For best results, you should only analyze documents of the same document type that the custom model was built with.
from azure.core.credentials import AzureKeyCredential
from azure.ai.documentintelligence import DocumentIntelligenceClient
from azure.ai.documentintelligence.models import AnalyzeResult
def _print_table(header_names, table_data):
# Print a two-dimensional array like a table.
max_len_list = []
for i in range(len(header_names)):
col_values = list(map(lambda row: len(str(row[i])), table_data))
col_values.append(len(str(header_names[i])))
max_len_list.append(max(col_values))
row_format_str = "".join(map(lambda len: f"{{:<{len + 4}}}", max_len_list))
print(row_format_str.format(*header_names))
for row in table_data:
print(row_format_str.format(*row))
endpoint = os.environ["DOCUMENTINTELLIGENCE_ENDPOINT"]
key = os.environ["DOCUMENTINTELLIGENCE_API_KEY"]
model_id = os.getenv("CUSTOM_BUILT_MODEL_ID", custom_model_id)
document_intelligence_client = DocumentIntelligenceClient(endpoint=endpoint, credential=AzureKeyCredential(key))
# Make sure your document's type is included in the list of document types the custom model can analyze
with open(path_to_sample_documents, "rb") as f:
poller = document_intelligence_client.begin_analyze_document(
model_id=model_id, analyze_request=f, content_type="application/octet-stream"
)
result: AnalyzeResult = poller.result()
if result.documents:
for idx, document in enumerate(result.documents):
print(f"--------Analyzing document #{idx + 1}--------")
print(f"Document has type {document.doc_type}")
print(f"Document has document type confidence {document.confidence}")
print(f"Document was analyzed with model with ID {result.model_id}")
if document.fields:
for name, field in document.fields.items():
field_value = field.get("valueString") if field.get("valueString") else field.content
print(
f"......found field of type '{field.type}' with value '{field_value}' and with confidence {field.confidence}"
)
# Extract table cell values
SYMBOL_OF_TABLE_TYPE = "array"
SYMBOL_OF_OBJECT_TYPE = "object"
KEY_OF_VALUE_OBJECT = "valueObject"
KEY_OF_CELL_CONTENT = "content"
for doc in result.documents:
if not doc.fields is None:
for field_name, field_value in doc.fields.items():
# Dynamic Table cell information store as array in document field.
if field_value.type == SYMBOL_OF_TABLE_TYPE and field_value.value_array:
col_names = []
sample_obj = field_value.value_array[0]
if KEY_OF_VALUE_OBJECT in sample_obj:
col_names = list(sample_obj[KEY_OF_VALUE_OBJECT].keys())
print("----Extracting Dynamic Table Cell Values----")
table_rows = []
for obj in field_value.value_array:
if KEY_OF_VALUE_OBJECT in obj:
value_obj = obj[KEY_OF_VALUE_OBJECT]
extract_value_by_col_name = lambda key: (
value_obj[key].get(KEY_OF_CELL_CONTENT)
if key in value_obj and KEY_OF_CELL_CONTENT in value_obj[key]
else "None"
)
row_data = list(map(extract_value_by_col_name, col_names))
table_rows.append(row_data)
_print_table(col_names, table_rows)
elif (
field_value.type == SYMBOL_OF_OBJECT_TYPE
and KEY_OF_VALUE_OBJECT in field_value
and field_value[KEY_OF_VALUE_OBJECT] is not None
):
rows_by_columns = list(field_value[KEY_OF_VALUE_OBJECT].values())
is_fixed_table = all(
(
rows_of_column["type"] == SYMBOL_OF_OBJECT_TYPE
and Counter(list(rows_by_columns[0][KEY_OF_VALUE_OBJECT].keys()))
== Counter(list(rows_of_column[KEY_OF_VALUE_OBJECT].keys()))
)
for rows_of_column in rows_by_columns
)
# Fixed Table cell information store as object in document field.
if is_fixed_table:
print("----Extracting Fixed Table Cell Values----")
col_names = list(field_value[KEY_OF_VALUE_OBJECT].keys())
row_dict: dict = {}
for rows_of_column in rows_by_columns:
rows = rows_of_column[KEY_OF_VALUE_OBJECT]
for row_key in list(rows.keys()):
if row_key in row_dict:
row_dict[row_key].append(rows[row_key].get(KEY_OF_CELL_CONTENT))
else:
row_dict[row_key] = [
row_key,
rows[row_key].get(KEY_OF_CELL_CONTENT),
]
col_names.insert(0, "")
_print_table(col_names, list(row_dict.values()))
print("------------------------------------")
Additionally, a document URL can also be used to analyze documents using the begin_analyze_document method.
from azure.core.credentials import AzureKeyCredential
from azure.ai.documentintelligence import DocumentIntelligenceClient
from azure.ai.documentintelligence.models import AnalyzeDocumentRequest, AnalyzeResult
endpoint = os.environ["DOCUMENTINTELLIGENCE_ENDPOINT"]
key = os.environ["DOCUMENTINTELLIGENCE_API_KEY"]
document_intelligence_client = DocumentIntelligenceClient(endpoint=endpoint, credential=AzureKeyCredential(key))
url = "https://raw.githubusercontent.com/Azure/azure-sdk-for-python/main/sdk/documentintelligence/azure-ai-documentintelligence/samples/sample_forms/receipt/contoso-receipt.png"
poller = document_intelligence_client.begin_analyze_document(
"prebuilt-receipt", AnalyzeDocumentRequest(url_source=url)
)
receipts: AnalyzeResult = poller.result()
Manage the custom models attached to your account.
# Let's build a model to use for this sample
import uuid
from azure.ai.documentintelligence import DocumentIntelligenceAdministrationClient
from azure.ai.documentintelligence.models import (
DocumentBuildMode,
BuildDocumentModelRequest,
AzureBlobContentSource,
DocumentModelDetails,
)
from azure.core.credentials import AzureKeyCredential
endpoint = os.environ["DOCUMENTINTELLIGENCE_ENDPOINT"]
key = os.environ["DOCUMENTINTELLIGENCE_API_KEY"]
container_sas_url = os.environ["DOCUMENTINTELLIGENCE_STORAGE_CONTAINER_SAS_URL"]
document_intelligence_admin_client = DocumentIntelligenceAdministrationClient(endpoint, AzureKeyCredential(key))
poller = document_intelligence_admin_client.begin_build_document_model(
BuildDocumentModelRequest(
model_id=str(uuid.uuid4()),
build_mode=DocumentBuildMode.TEMPLATE,
azure_blob_source=AzureBlobContentSource(container_url=container_sas_url),
description="my model description",
)
)
model: DocumentModelDetails = poller.result()
print(f"Model ID: {model.model_id}")
print(f"Description: {model.description}")
print(f"Model created on: {model.created_date_time}")
print(f"Model expires on: {model.expiration_date_time}")
if model.doc_types:
print("Doc types the model can recognize:")
for name, doc_type in model.doc_types.items():
print(f"Doc Type: '{name}' built with '{doc_type.build_mode}' mode which has the following fields:")
if doc_type.field_schema:
for field_name, field in doc_type.field_schema.items():
if doc_type.field_confidence:
print(
f"Field: '{field_name}' has type '{field['type']}' and confidence score "
f"{doc_type.field_confidence[field_name]}"
)
account_details = document_intelligence_admin_client.get_resource_info()
print(
f"Our resource has {account_details.custom_document_models.count} custom models, "
f"and we can have at most {account_details.custom_document_models.limit} custom models"
)
# Next, we get a paged list of all of our custom models
models = document_intelligence_admin_client.list_models()
print("We have the following 'ready' models with IDs and descriptions:")
for model in models:
print(f"{model.model_id} | {model.description}")
my_model = document_intelligence_admin_client.get_model(model_id=model.model_id)
print(f"\nModel ID: {my_model.model_id}")
print(f"Description: {my_model.description}")
print(f"Model created on: {my_model.created_date_time}")
print(f"Model expires on: {my_model.expiration_date_time}")
if my_model.warnings:
print("Warnings encountered while building the model:")
for warning in my_model.warnings:
print(f"warning code: {warning.code}, message: {warning.message}, target of the error: {warning.target}")
# Finally, we will delete this model by ID
document_intelligence_admin_client.delete_model(model_id=my_model.model_id)
from azure.core.exceptions import ResourceNotFoundError
try:
document_intelligence_admin_client.get_model(model_id=my_model.model_id)
except ResourceNotFoundError:
print(f"Successfully deleted model with ID {my_model.model_id}")
Document Intelligence supports more sophisticated analysis capabilities. These optional features can be enabled and disabled depending on the scenario of the document extraction.
The following add-on capabilities are available in this SDK:
Note that some add-on capabilities will incur additional charges. See pricing: https://azure.microsoft.com/pricing/details/ai-document-intelligence/.
Document Intelligence client library will raise exceptions defined in Azure Core. Error codes and messages raised by the Document Intelligence service can be found in the service documentation.
This library uses the standard logging library for logging.
Basic information about HTTP sessions (URLs, headers, etc.) is logged at INFO level.
Detailed DEBUG level logging, including request/response bodies and unredacted
headers, can be enabled on the client or per-operation with the logging_enable keyword argument.
See full SDK logging documentation with examples here.
Optional keyword arguments can be passed in at the client and per-operation level. The azure-core reference documentation describes available configurations for retries, logging, transport protocols, and more.
See the Sample README for several code snippets illustrating common patterns used in the Document Intelligence Python API.
For more extensive documentation on Azure AI Document Intelligence, see the Document Intelligence documentation on docs.microsoft.com.
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.
When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct. For more information, see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
FAQs
Microsoft Azure AI Document Intelligence Client Library for Python
We found that azure-ai-documentintelligence demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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