# Azure AI Document Intelligence client library for Python
Azure AI Document Intelligence ([previously known as Form Recognizer][service-rename]) is a cloud service that uses machine learning to analyze text and structured data from your documents. It includes the following main features:
- Layout - Extract content and structure (ex. words, selection marks, tables) from documents.
- Document - Analyze key-value pairs in addition to general layout from documents.
- Read - Read page information from documents.
- Prebuilt - Extract common field values from select document types (ex. receipts, invoices, business cards, ID documents, U.S. W-2 tax documents, among others) using prebuilt models.
- Custom - Build custom models from your own data to extract tailored field values in addition to general layout from documents.
- Classifiers - Build custom classification models that combine layout and language features to accurately detect and identify documents you process within your application.
- Add-on capabilities - Extract barcodes/QR codes, formulas, font/style, etc. or enable high resolution mode for large documents with optional parameters.
[Source code][python-di-src]
| [Package (PyPI)][python-di-pypi]
| [API reference documentation][python-di-ref-docs]
| [Product documentation][python-di-product-docs]
| [Samples][python-di-samples]
## _Disclaimer_
_The API version 2024-02-29-preview is currently only available in some Azure regions, the available regions can be found from [here][python-di-available-regions]._
## Getting started
### Installating the package
```bash
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][migration-guide] for detailed instructions on how to update application.
#### Prequisites
- Python 3.8 or later is required to use this package.
- You need an [Azure subscription][azure_sub] to use this package.
- An existing Azure AI Document Intelligence instance.
#### Create a Cognitive Services or Document Intelligence resource
Document Intelligence supports both [multi-service and single-service access][cognitive_resource_portal]. 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](#create-the-client-with-an-azure-active-directory-credential).
You can create either resource using:
* Option 1: [Azure Portal][cognitive_resource_portal].
* Option 2: [Azure CLI][cognitive_resource_cli].
Below is an example of how you can create a Document Intelligence resource using the CLI:
```PowerShell
# 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>
```
```PowerShell
# 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][cognitive_resource_cli].
### Authenticate the client
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.
#### Get the endpoint
You can find the endpoint for your Document Intelligence resource using the
[Azure Portal][azure_portal_get_endpoint]
or [Azure CLI][azure_cli_endpoint_lookup]:
```bash
# 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][regional_endpoints]. 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][cognitive_resource_portal].
#### Get the API key
The API key can be found in the [Azure Portal][azure_portal] or by running the following Azure CLI command:
```bash
az cognitiveservices account keys list --name "<resource-name>" --resource-group "<resource-group-name>"
```
#### Create the client with AzureKeyCredential
To use an [API key][cognitive_authentication_api_key] as the `credential` parameter,
pass the key as a string into an instance of [AzureKeyCredential][azure-key-credential].
```python
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)
```
#### Create the client with an Azure Active Directory 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][azure_identity] library.
Note that regional endpoints do not support AAD authentication. Create a [custom subdomain][custom_subdomain]
name for your resource in order to use this type of authentication.
To use the [DefaultAzureCredential][default_azure_credential] 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][register_aad_app] 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`.
```python
"""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)
```
## Key concepts
### DocumentIntelligenceClient
`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][di-models].
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](#examples "Examples").
More information about analyzing documents, including supported features, locales, and document types can be found in the [service documentation][di-models].
### DocumentIntelligenceAdministrationClient
`DocumentIntelligenceAdministrationClient` provides operations for:
- Building custom models to analyze specific fields you specify by labeling your custom documents. A `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][di-build-model] for a more detailed explanation.
- Creating a composed model from a collection of existing models.
- Managing models created in your account.
- Listing operations or getting a specific model operation created within the last 24 hours.
- Copying a custom model from one Document Intelligence resource to another.
- Build and manage a custom classification model to classify the documents you process within your application.
Please note that models can also be built using a graphical user interface such as [Document Intelligence Studio][di-studio].
Sample code snippets are provided to illustrate using a DocumentIntelligenceAdministrationClient [here](#examples "Examples").
### Long-running operations
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](#examples "Examples").
## Examples
The following section provides several code snippets covering some of the most common Document Intelligence tasks, including:
* [Extract Layout](#extract-layout "Extract Layout")
* [Using the General Document Model](#using-the-general-document-model "Using the General Document Model")
* [Using Prebuilt Models](#using-prebuilt-models "Using Prebuilt Models")
* [Build a Custom Model](#build-a-custom-model "Build a custom model")
* [Analyze Documents Using a Custom Model](#analyze-documents-using-a-custom-model "Analyze Documents Using a Custom Model")
* [Manage Your Models](#manage-your-models "Manage Your Models")
* [Add-on capabilities](#add-on-capabilities "Add-on Capabilities")
### Extract Layout
Extract text, selection marks, text styles, and table structures, along with their bounding region coordinates, from documents.
<!-- SNIPPET:sample_analyze_layout.extract_layout -->
```python
from azure.core.credentials import AzureKeyCredential
from azure.ai.documentintelligence import DocumentIntelligenceClient
from azure.ai.documentintelligence.models import 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, 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 = get_words(page, line)
print(
f"...Line # {line_idx} has word count {len(words)} and text '{line.content}' "
f"within bounding polygon '{line.polygon}'"
)
for word in words:
print(f"......Word '{word.content}' has a confidence of {word.confidence}")
if page.selection_marks:
for selection_mark in page.selection_marks:
print(
f"Selection mark is '{selection_mark.state}' within bounding polygon "
f"'{selection_mark.polygon}' and has a confidence of {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 " 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 {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 '{region.polygon}'")
print("----------------------------------------")
```
<!-- END SNIPPET -->
### Using the General Document Model
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:
<!-- SNIPPET:sample_analyze_general_documents.analyze_general_documents -->
```python
from azure.core.credentials import AzureKeyCredential
from azure.ai.documentintelligence import DocumentIntelligenceClient
from azure.ai.documentintelligence.models import DocumentAnalysisFeature, 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,
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"'{kv_pair.key.bounding_regions}' bounding regions")
if kv_pair.value:
print(
f"Value '{kv_pair.value.content}' found within "
f"'{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 = get_words(page.words, line)
print(
f"...Line #{line_idx} has {len(words)} words and text '{line.content}' within "
f"bounding polygon '{line.polygon}'"
)
for word in words:
print(f"......Word '{word.content}' has a confidence of {word.confidence}")
if page.selection_marks:
for selection_mark in page.selection_marks:
print(
f"Selection mark is '{selection_mark.state}' within bounding polygon "
f"'{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 {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 '{region.polygon}'\n"
)
print("----------------------------------------")
```
<!-- END SNIPPET -->
- Read more about the features provided by the `prebuilt-document` model [here][service_prebuilt_document].
### Using Prebuilt Models
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:
<!-- SNIPPET:sample_analyze_receipts.analyze_receipts -->
```python
from azure.core.credentials import AzureKeyCredential
from azure.ai.documentintelligence import DocumentIntelligenceClient
from azure.ai.documentintelligence.models import 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-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("--------------------------------------")
```
<!-- END SNIPPET -->
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][di-models].
### Build a Custom Model
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][di-build-training-set].
<!-- SNIPPET:sample_manage_models.build_model -->
```python
# 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:")
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]}"
)
```
<!-- END SNIPPET -->
### Analyze Documents Using a Custom Model
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.
<!-- SNIPPET:sample_analyze_custom_documents.analyze_custom_documents -->
```python
from azure.core.credentials import AzureKeyCredential
from azure.ai.documentintelligence import DocumentIntelligenceClient
from azure.ai.documentintelligence.models import AnalyzeResult
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"
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():
# "MaintenanceLog" is the table field name which you labeled. Table cell information store as array in document field.
if (
field_name == "MaintenanceLog"
and 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 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)
print("------------------------------------")
```
<!-- END SNIPPET -->
Additionally, a document URL can also be used to analyze documents using the `begin_analyze_document` method.
<!-- SNIPPET:sample_analyze_receipts_from_url.analyze_receipts_from_url -->
```python
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()
```
<!-- END SNIPPET -->
### Manage Your Models
Manage the custom models attached to your account.
<!-- SNIPPET:sample_manage_models.build_model -->
```python
# 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:")
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]}"
)
```
<!-- END SNIPPET -->
<!-- SNIPPET:sample_manage_models.get_resource_info -->
```python
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"
)
neural_models = account_details.custom_neural_document_model_builds
print(
f"The quota limit for custom neural document models is {neural_models.quota} and the resource has"
f"used {neural_models.used}. The resource quota will reset on {neural_models.quota_reset_date_time}"
)
```
<!-- END SNIPPET -->
<!-- SNIPPET:sample_manage_models.list_models -->
```python
# 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}")
```
<!-- END SNIPPET -->
<!-- SNIPPET:sample_manage_models.get_model -->
```python
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}")
```
<!-- END SNIPPET -->
<!-- SNIPPET:sample_manage_models.delete_model -->
```python
# 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}")
```
<!-- END SNIPPET -->
### Add-on Capabilities
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:
- [barcode/QR code][addon_barcodes_sample]
- [formula][addon_formulas_sample]
- [font/style][addon_fonts_sample]
- [high resolution mode][addon_highres_sample]
- [language][addon_languages_sample]
- [query fields][query_fields_sample]
Note that some add-on capabilities will incur additional charges. See pricing: https://azure.microsoft.com/pricing/details/ai-document-intelligence/.
## Troubleshooting
### General
Document Intelligence client library will raise exceptions defined in [Azure Core][azure_core_exceptions].
Error codes and messages raised by the Document Intelligence service can be found in the [service documentation][di-errors].
### Logging
This library uses the standard
[logging][python_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][sdk_logging_docs].
### Optional Configuration
Optional keyword arguments can be passed in at the client and per-operation level.
The azure-core [reference documentation][azure_core_ref_docs]
describes available configurations for retries, logging, transport protocols, and more.
## Next steps
### More sample code
See the [Sample README][sample_readme] for several code snippets illustrating common patterns used in the Document Intelligence Python API.
### Additional documentation
For more extensive documentation on Azure AI Document Intelligence, see the [Document Intelligence documentation][python-di-product-docs] on docs.microsoft.com.
## Contributing
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][code_of_conduct]. For more information,
see the Code of Conduct FAQ or contact opencode@microsoft.com with any
additional questions or comments.
<!-- LINKS -->
[code_of_conduct]: https://opensource.microsoft.com/codeofconduct/
[default_azure_credential]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/identity/azure-identity#defaultazurecredential
[azure_sub]: https://azure.microsoft.com/free/
[python-di-src]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/documentintelligence/azure-ai-documentintelligence/azure/ai/documentintelligence
[python-di-pypi]: https://pypi.org/project/azure-ai-documentintelligence/
[python-di-product-docs]: https://learn.microsoft.com/azure/ai-services/document-intelligence/overview?view=doc-intel-4.0.0&viewFallbackFrom=form-recog-3.0.0
[python-di-ref-docs]: https://aka.ms/azsdk/python/documentintelligence/docs
[python-di-samples]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/documentintelligence/azure-ai-documentintelligence/samples
[python-di-available-regions]: https://aka.ms/azsdk/documentintelligence/available-regions
[azure_portal]: https://ms.portal.azure.com/
[regional_endpoints]: https://azure.microsoft.com/global-infrastructure/services/?products=form-recognizer
[cognitive_resource_portal]: https://ms.portal.azure.com/#create/Microsoft.CognitiveServicesFormRecognizer
[cognitive_resource_cli]: https://docs.microsoft.com/azure/cognitive-services/cognitive-services-apis-create-account-cli?tabs=windows
[azure-key-credential]: https://aka.ms/azsdk/python/core/azurekeycredential
[di-studio]: https://documentintelligence.ai.azure.com/studio
[di-build-model]: https://aka.ms/azsdk/documentintelligence/buildmodel
[di-build-training-set]: https://aka.ms/azsdk/documentintelligence/buildtrainingset
[di-models]: https://aka.ms/azsdk/documentintelligence/models
[di-errors]: https://aka.ms/azsdk/documentintelligence/errors
[azure_core_ref_docs]: https://aka.ms/azsdk/python/core/docs
[azure_core_exceptions]: https://aka.ms/azsdk/python/core/docs#module-azure.core.exceptions
[python_logging]: https://docs.python.org/3/library/logging.html
[azure_cli_endpoint_lookup]: https://docs.microsoft.com/cli/azure/cognitiveservices/account?view=azure-cli-latest#az-cognitiveservices-account-show
[azure_portal_get_endpoint]: https://docs.microsoft.com/azure/cognitive-services/cognitive-services-apis-create-account?tabs=multiservice%2Cwindows#get-the-keys-for-your-resource
[cognitive_authentication_api_key]: https://docs.microsoft.com/azure/cognitive-services/cognitive-services-apis-create-account?tabs=multiservice%2Cwindows#get-the-keys-for-your-resource
[register_aad_app]: https://docs.microsoft.com/azure/cognitive-services/authentication#assign-a-role-to-a-service-principal
[custom_subdomain]: https://docs.microsoft.com/azure/cognitive-services/authentication#create-a-resource-with-a-custom-subdomain
[azure_identity]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/identity/azure-identity
[sdk_logging_docs]: https://docs.microsoft.com/azure/developer/python/sdk/azure-sdk-logging
[migration-guide]: https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/documentintelligence/azure-ai-documentintelligence/MIGRATION_GUIDE.md
[sample_readme]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/documentintelligence/azure-ai-documentintelligence/samples
[addon_barcodes_sample]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/documentintelligence/azure-ai-documentintelligence/samples/sample_analyze_addon_barcodes.py
[addon_fonts_sample]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/documentintelligence/azure-ai-documentintelligence/samples/sample_analyze_addon_fonts.py
[addon_formulas_sample]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/documentintelligence/azure-ai-documentintelligence/samples/sample_analyze_addon_formulas.py
[addon_highres_sample]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/documentintelligence/azure-ai-documentintelligence/samples/sample_analyze_addon_highres.py
[addon_languages_sample]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/documentintelligence/azure-ai-documentintelligence/samples/sample_analyze_addon_languages.py
[query_fields_sample]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/documentintelligence/azure-ai-documentintelligence/samples/sample_analyze_addon_query_fields.py
[service-rename]: https://techcommunity.microsoft.com/t5/azure-ai-services-blog/azure-form-recognizer-is-now-azure-ai-document-intelligence-with/ba-p/3875765
[service_prebuilt_document]: https://docs.microsoft.com/azure/ai-services/document-intelligence/concept-general-document#general-document-features
Raw data
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"requires_python": ">=3.8",
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"keywords": "azure, azure sdk",
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"description": "# Azure AI Document Intelligence client library for Python\n\nAzure AI Document Intelligence ([previously known as Form Recognizer][service-rename]) is a cloud service that uses machine learning to analyze text and structured data from your documents. It includes the following main features:\n\n- Layout - Extract content and structure (ex. words, selection marks, tables) from documents.\n- Document - Analyze key-value pairs in addition to general layout from documents.\n- Read - Read page information from documents.\n- Prebuilt - Extract common field values from select document types (ex. receipts, invoices, business cards, ID documents, U.S. W-2 tax documents, among others) using prebuilt models.\n- Custom - Build custom models from your own data to extract tailored field values in addition to general layout from documents.\n- Classifiers - Build custom classification models that combine layout and language features to accurately detect and identify documents you process within your application.\n- Add-on capabilities - Extract barcodes/QR codes, formulas, font/style, etc. or enable high resolution mode for large documents with optional parameters.\n\n[Source code][python-di-src]\n| [Package (PyPI)][python-di-pypi]\n| [API reference documentation][python-di-ref-docs]\n| [Product documentation][python-di-product-docs]\n| [Samples][python-di-samples]\n\n## _Disclaimer_\n\n_The API version 2024-02-29-preview is currently only available in some Azure regions, the available regions can be found from [here][python-di-available-regions]._\n\n## Getting started\n\n### Installating the package\n\n```bash\npython -m pip install azure-ai-documentintelligence\n```\n\nThis table shows the relationship between SDK versions and supported API service versions:\n|SDK version|Supported API service version\n|-|-\n|1.0.0b1 | 2023-10-31-preview\n|1.0.0b2 | 2024-02-29-preview\n\nOlder API versions are supported in `azure-ai-formrecognizer`, please see the [Migration Guide][migration-guide] for detailed instructions on how to update application.\n\n#### Prequisites\n\n- Python 3.8 or later is required to use this package.\n- You need an [Azure subscription][azure_sub] to use this package.\n- An existing Azure AI Document Intelligence instance.\n\n#### Create a Cognitive Services or Document Intelligence resource\n\nDocument Intelligence supports both [multi-service and single-service access][cognitive_resource_portal]. 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](#create-the-client-with-an-azure-active-directory-credential).\n\nYou can create either resource using: \n\n* Option 1: [Azure Portal][cognitive_resource_portal].\n* Option 2: [Azure CLI][cognitive_resource_cli].\n\nBelow is an example of how you can create a Document Intelligence resource using the CLI:\n\n```PowerShell\n# Create a new resource group to hold the Document Intelligence resource\n# if using an existing resource group, skip this step\naz group create --name <your-resource-name> --location <location>\n```\n\n```PowerShell\n# Create the Document Intelligence resource\naz cognitiveservices account create \\\n --name <your-resource-name> \\\n --resource-group <your-resource-group-name> \\\n --kind FormRecognizer \\\n --sku <sku> \\\n --location <location> \\\n --yes\n```\n\nFor more information about creating the resource or how to get the location and sku information see [here][cognitive_resource_cli].\n\n### Authenticate the client\n\nIn order to interact with the Document Intelligence service, you will need to create an instance of a client.\nAn **endpoint** and **credential** are necessary to instantiate the client object.\n\n#### Get the endpoint\n\nYou can find the endpoint for your Document Intelligence resource using the\n[Azure Portal][azure_portal_get_endpoint]\nor [Azure CLI][azure_cli_endpoint_lookup]:\n\n```bash\n# Get the endpoint for the Document Intelligence resource\naz cognitiveservices account show --name \"resource-name\" --resource-group \"resource-group-name\" --query \"properties.endpoint\"\n```\n\nEither a regional endpoint or a custom subdomain can be used for authentication. They are formatted as follows:\n\n```\nRegional endpoint: https://<region>.api.cognitive.microsoft.com/\nCustom subdomain: https://<resource-name>.cognitiveservices.azure.com/\n```\n\nA regional endpoint is the same for every resource in a region. A complete list of supported regional endpoints can be consulted [here][regional_endpoints]. Please note that regional endpoints do not support AAD authentication.\n\nA 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][cognitive_resource_portal].\n\n#### Get the API key\n\nThe API key can be found in the [Azure Portal][azure_portal] or by running the following Azure CLI command:\n\n```bash\naz cognitiveservices account keys list --name \"<resource-name>\" --resource-group \"<resource-group-name>\"\n```\n\n#### Create the client with AzureKeyCredential\n\nTo use an [API key][cognitive_authentication_api_key] as the `credential` parameter,\npass the key as a string into an instance of [AzureKeyCredential][azure-key-credential].\n\n```python\nfrom azure.core.credentials import AzureKeyCredential\nfrom azure.ai.documentintelligence import DocumentIntelligenceClient\n\nendpoint = \"https://<my-custom-subdomain>.cognitiveservices.azure.com/\"\ncredential = AzureKeyCredential(\"<api_key>\")\ndocument_intelligence_client = DocumentIntelligenceClient(endpoint, credential)\n```\n\n#### Create the client with an Azure Active Directory credential\n\n`AzureKeyCredential` authentication is used in the examples in this getting started guide, but you can also\nauthenticate with Azure Active Directory using the [azure-identity][azure_identity] library.\nNote that regional endpoints do not support AAD authentication. Create a [custom subdomain][custom_subdomain]\nname for your resource in order to use this type of authentication.\n\nTo use the [DefaultAzureCredential][default_azure_credential] type shown below, or other credential types provided\nwith the Azure SDK, please install the `azure-identity` package:\n\n```pip install azure-identity```\n\nYou will also need to [register a new AAD application and grant access][register_aad_app] to Document Intelligence by assigning the `\"Cognitive Services User\"` role to your service principal.\n\nOnce completed, set the values of the client ID, tenant ID, and client secret of the AAD application as environment variables:\n`AZURE_CLIENT_ID`, `AZURE_TENANT_ID`, `AZURE_CLIENT_SECRET`.\n\n```python\n\"\"\"DefaultAzureCredential will use the values from these environment\nvariables: AZURE_CLIENT_ID, AZURE_TENANT_ID, AZURE_CLIENT_SECRET\n\"\"\"\nfrom azure.ai.documentintelligence import DocumentIntelligenceClient\nfrom azure.identity import DefaultAzureCredential\n\nendpoint = os.environ[\"DOCUMENTINTELLIGENCE_ENDPOINT\"]\ncredential = DefaultAzureCredential()\n\ndocument_intelligence_client = DocumentIntelligenceClient(endpoint, credential)\n```\n\n## Key concepts\n\n### DocumentIntelligenceClient\n\n`DocumentIntelligenceClient` provides operations for analyzing input documents using prebuilt and custom models through the `begin_analyze_document` API.\nUse the `model_id` parameter to select the type of model for analysis. See a full list of supported models [here][di-models]. \nThe `DocumentIntelligenceClient` also provides operations for classifying documents through the `begin_classify_document` API. \nCustom 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.\n\nSample code snippets are provided to illustrate using a DocumentIntelligenceClient [here](#examples \"Examples\").\nMore information about analyzing documents, including supported features, locales, and document types can be found in the [service documentation][di-models].\n\n### DocumentIntelligenceAdministrationClient\n\n`DocumentIntelligenceAdministrationClient` provides operations for:\n\n- Building custom models to analyze specific fields you specify by labeling your custom documents. A `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][di-build-model] for a more detailed explanation.\n- Creating a composed model from a collection of existing models.\n- Managing models created in your account.\n- Listing operations or getting a specific model operation created within the last 24 hours.\n- Copying a custom model from one Document Intelligence resource to another.\n- Build and manage a custom classification model to classify the documents you process within your application.\n\nPlease note that models can also be built using a graphical user interface such as [Document Intelligence Studio][di-studio].\n\nSample code snippets are provided to illustrate using a DocumentIntelligenceAdministrationClient [here](#examples \"Examples\").\n\n### Long-running operations\n\nLong-running operations are operations which consist of an initial request sent to the service to start an operation,\nfollowed by polling the service at intervals to determine whether the operation has completed or failed, and if it has\nsucceeded, to get the result.\n\nMethods that analyze documents, build models, or copy/compose models are modeled as long-running operations.\nThe client exposes a `begin_<method-name>` method that returns an `LROPoller` or `AsyncLROPoller`. Callers should wait\nfor the operation to complete by calling `result()` on the poller object returned from the `begin_<method-name>` method.\nSample code snippets are provided to illustrate using long-running operations [below](#examples \"Examples\").\n\n## Examples\n\nThe following section provides several code snippets covering some of the most common Document Intelligence tasks, including:\n\n* [Extract Layout](#extract-layout \"Extract Layout\")\n* [Using the General Document Model](#using-the-general-document-model \"Using the General Document Model\")\n* [Using Prebuilt Models](#using-prebuilt-models \"Using Prebuilt Models\")\n* [Build a Custom Model](#build-a-custom-model \"Build a custom model\")\n* [Analyze Documents Using a Custom Model](#analyze-documents-using-a-custom-model \"Analyze Documents Using a Custom Model\")\n* [Manage Your Models](#manage-your-models \"Manage Your Models\")\n* [Add-on capabilities](#add-on-capabilities \"Add-on Capabilities\")\n\n### Extract Layout\n\nExtract text, selection marks, text styles, and table structures, along with their bounding region coordinates, from documents.\n\n<!-- SNIPPET:sample_analyze_layout.extract_layout -->\n\n```python\nfrom azure.core.credentials import AzureKeyCredential\nfrom azure.ai.documentintelligence import DocumentIntelligenceClient\nfrom azure.ai.documentintelligence.models import AnalyzeResult\n\nendpoint = os.environ[\"DOCUMENTINTELLIGENCE_ENDPOINT\"]\nkey = os.environ[\"DOCUMENTINTELLIGENCE_API_KEY\"]\n\ndocument_intelligence_client = DocumentIntelligenceClient(endpoint=endpoint, credential=AzureKeyCredential(key))\nwith open(path_to_sample_documents, \"rb\") as f:\n poller = document_intelligence_client.begin_analyze_document(\n \"prebuilt-layout\", analyze_request=f, content_type=\"application/octet-stream\"\n )\nresult: AnalyzeResult = poller.result()\n\nif result.styles and any([style.is_handwritten for style in result.styles]):\n print(\"Document contains handwritten content\")\nelse:\n print(\"Document does not contain handwritten content\")\n\nfor page in result.pages:\n print(f\"----Analyzing layout from page #{page.page_number}----\")\n print(f\"Page has width: {page.width} and height: {page.height}, measured with unit: {page.unit}\")\n\n if page.lines:\n for line_idx, line in enumerate(page.lines):\n words = get_words(page, line)\n print(\n f\"...Line # {line_idx} has word count {len(words)} and text '{line.content}' \"\n f\"within bounding polygon '{line.polygon}'\"\n )\n\n for word in words:\n print(f\"......Word '{word.content}' has a confidence of {word.confidence}\")\n\n if page.selection_marks:\n for selection_mark in page.selection_marks:\n print(\n f\"Selection mark is '{selection_mark.state}' within bounding polygon \"\n f\"'{selection_mark.polygon}' and has a confidence of {selection_mark.confidence}\"\n )\n\nif result.tables:\n for table_idx, table in enumerate(result.tables):\n print(f\"Table # {table_idx} has {table.row_count} rows and \" f\"{table.column_count} columns\")\n if table.bounding_regions:\n for region in table.bounding_regions:\n print(f\"Table # {table_idx} location on page: {region.page_number} is {region.polygon}\")\n for cell in table.cells:\n print(f\"...Cell[{cell.row_index}][{cell.column_index}] has text '{cell.content}'\")\n if cell.bounding_regions:\n for region in cell.bounding_regions:\n print(f\"...content on page {region.page_number} is within bounding polygon '{region.polygon}'\")\n\nprint(\"----------------------------------------\")\n```\n\n<!-- END SNIPPET -->\n\n### Using the General Document Model\n\nAnalyze key-value pairs, tables, styles, and selection marks from documents using the general document model provided by the Document Intelligence service.\nSelect the General Document Model by passing `model_id=\"prebuilt-document\"` into the `begin_analyze_document` method:\n\n<!-- SNIPPET:sample_analyze_general_documents.analyze_general_documents -->\n\n```python\nfrom azure.core.credentials import AzureKeyCredential\nfrom azure.ai.documentintelligence import DocumentIntelligenceClient\nfrom azure.ai.documentintelligence.models import DocumentAnalysisFeature, AnalyzeResult\n\nendpoint = os.environ[\"DOCUMENTINTELLIGENCE_ENDPOINT\"]\nkey = os.environ[\"DOCUMENTINTELLIGENCE_API_KEY\"]\n\ndocument_intelligence_client = DocumentIntelligenceClient(endpoint=endpoint, credential=AzureKeyCredential(key))\nwith open(path_to_sample_documents, \"rb\") as f:\n poller = document_intelligence_client.begin_analyze_document(\n \"prebuilt-layout\",\n analyze_request=f,\n features=[DocumentAnalysisFeature.KEY_VALUE_PAIRS],\n content_type=\"application/octet-stream\",\n )\nresult: AnalyzeResult = poller.result()\n\nif result.styles:\n for style in result.styles:\n if style.is_handwritten:\n print(\"Document contains handwritten content: \")\n print(\",\".join([result.content[span.offset : span.offset + span.length] for span in style.spans]))\n\nprint(\"----Key-value pairs found in document----\")\nif result.key_value_pairs:\n for kv_pair in result.key_value_pairs:\n if kv_pair.key:\n print(f\"Key '{kv_pair.key.content}' found within \" f\"'{kv_pair.key.bounding_regions}' bounding regions\")\n if kv_pair.value:\n print(\n f\"Value '{kv_pair.value.content}' found within \"\n f\"'{kv_pair.value.bounding_regions}' bounding regions\\n\"\n )\n\nfor page in result.pages:\n print(f\"----Analyzing document from page #{page.page_number}----\")\n print(f\"Page has width: {page.width} and height: {page.height}, measured with unit: {page.unit}\")\n\n if page.lines:\n for line_idx, line in enumerate(page.lines):\n words = get_words(page.words, line)\n print(\n f\"...Line #{line_idx} has {len(words)} words and text '{line.content}' within \"\n f\"bounding polygon '{line.polygon}'\"\n )\n\n for word in words:\n print(f\"......Word '{word.content}' has a confidence of {word.confidence}\")\n\n if page.selection_marks:\n for selection_mark in page.selection_marks:\n print(\n f\"Selection mark is '{selection_mark.state}' within bounding polygon \"\n f\"'{selection_mark.polygon}' and has a confidence of \"\n f\"{selection_mark.confidence}\"\n )\n\nif result.tables:\n for table_idx, table in enumerate(result.tables):\n print(f\"Table # {table_idx} has {table.row_count} rows and {table.column_count} columns\")\n if table.bounding_regions:\n for region in table.bounding_regions:\n print(f\"Table # {table_idx} location on page: {region.page_number} is {region.polygon}\")\n for cell in table.cells:\n print(f\"...Cell[{cell.row_index}][{cell.column_index}] has text '{cell.content}'\")\n if cell.bounding_regions:\n for region in cell.bounding_regions:\n print(\n f\"...content on page {region.page_number} is within bounding polygon '{region.polygon}'\\n\"\n )\nprint(\"----------------------------------------\")\n```\n\n<!-- END SNIPPET -->\n\n- Read more about the features provided by the `prebuilt-document` model [here][service_prebuilt_document].\n\n### Using Prebuilt Models\n\nExtract 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.\n\nFor 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:\n\n<!-- SNIPPET:sample_analyze_receipts.analyze_receipts -->\n\n```python\nfrom azure.core.credentials import AzureKeyCredential\nfrom azure.ai.documentintelligence import DocumentIntelligenceClient\nfrom azure.ai.documentintelligence.models import AnalyzeResult\n\nendpoint = os.environ[\"DOCUMENTINTELLIGENCE_ENDPOINT\"]\nkey = os.environ[\"DOCUMENTINTELLIGENCE_API_KEY\"]\n\ndocument_intelligence_client = DocumentIntelligenceClient(endpoint=endpoint, credential=AzureKeyCredential(key))\nwith open(path_to_sample_documents, \"rb\") as f:\n poller = document_intelligence_client.begin_analyze_document(\n \"prebuilt-receipt\", analyze_request=f, locale=\"en-US\", content_type=\"application/octet-stream\"\n )\nreceipts: AnalyzeResult = poller.result()\n\nif receipts.documents:\n for idx, receipt in enumerate(receipts.documents):\n print(f\"--------Analysis of receipt #{idx + 1}--------\")\n print(f\"Receipt type: {receipt.doc_type if receipt.doc_type else 'N/A'}\")\n if receipt.fields:\n merchant_name = receipt.fields.get(\"MerchantName\")\n if merchant_name:\n print(\n f\"Merchant Name: {merchant_name.get('valueString')} has confidence: \"\n f\"{merchant_name.confidence}\"\n )\n transaction_date = receipt.fields.get(\"TransactionDate\")\n if transaction_date:\n print(\n f\"Transaction Date: {transaction_date.get('valueDate')} has confidence: \"\n f\"{transaction_date.confidence}\"\n )\n items = receipt.fields.get(\"Items\")\n if items:\n print(\"Receipt items:\")\n for idx, item in enumerate(items.get(\"valueArray\")):\n print(f\"...Item #{idx + 1}\")\n item_description = item.get(\"valueObject\").get(\"Description\")\n if item_description:\n print(\n f\"......Item Description: {item_description.get('valueString')} has confidence: \"\n f\"{item_description.confidence}\"\n )\n item_quantity = item.get(\"valueObject\").get(\"Quantity\")\n if item_quantity:\n print(\n f\"......Item Quantity: {item_quantity.get('valueString')} has confidence: \"\n f\"{item_quantity.confidence}\"\n )\n item_total_price = item.get(\"valueObject\").get(\"TotalPrice\")\n if item_total_price:\n print(\n f\"......Total Item Price: {format_price(item_total_price.get('valueCurrency'))} has confidence: \"\n f\"{item_total_price.confidence}\"\n )\n subtotal = receipt.fields.get(\"Subtotal\")\n if subtotal:\n print(\n f\"Subtotal: {format_price(subtotal.get('valueCurrency'))} has confidence: {subtotal.confidence}\"\n )\n tax = receipt.fields.get(\"TotalTax\")\n if tax:\n print(f\"Total tax: {format_price(tax.get('valueCurrency'))} has confidence: {tax.confidence}\")\n tip = receipt.fields.get(\"Tip\")\n if tip:\n print(f\"Tip: {format_price(tip.get('valueCurrency'))} has confidence: {tip.confidence}\")\n total = receipt.fields.get(\"Total\")\n if total:\n print(f\"Total: {format_price(total.get('valueCurrency'))} has confidence: {total.confidence}\")\n print(\"--------------------------------------\")\n```\n\n<!-- END SNIPPET -->\n\nYou 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][di-models].\n\n### Build a Custom Model\n\nBuild 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.\nProvide a container SAS URL to your Azure Storage Blob container where you're storing the training documents.\n\nMore details on setting up a container and required file structure can be found in the [service documentation][di-build-training-set].\n\n<!-- SNIPPET:sample_manage_models.build_model -->\n\n```python\n# Let's build a model to use for this sample\nimport uuid\nfrom azure.ai.documentintelligence import DocumentIntelligenceAdministrationClient\nfrom azure.ai.documentintelligence.models import (\n DocumentBuildMode,\n BuildDocumentModelRequest,\n AzureBlobContentSource,\n DocumentModelDetails,\n)\nfrom azure.core.credentials import AzureKeyCredential\n\nendpoint = os.environ[\"DOCUMENTINTELLIGENCE_ENDPOINT\"]\nkey = os.environ[\"DOCUMENTINTELLIGENCE_API_KEY\"]\ncontainer_sas_url = os.environ[\"DOCUMENTINTELLIGENCE_STORAGE_CONTAINER_SAS_URL\"]\n\ndocument_intelligence_admin_client = DocumentIntelligenceAdministrationClient(endpoint, AzureKeyCredential(key))\npoller = document_intelligence_admin_client.begin_build_document_model(\n BuildDocumentModelRequest(\n model_id=str(uuid.uuid4()),\n build_mode=DocumentBuildMode.TEMPLATE,\n azure_blob_source=AzureBlobContentSource(container_url=container_sas_url),\n description=\"my model description\",\n )\n)\nmodel: DocumentModelDetails = poller.result()\n\nprint(f\"Model ID: {model.model_id}\")\nprint(f\"Description: {model.description}\")\nprint(f\"Model created on: {model.created_date_time}\")\nprint(f\"Model expires on: {model.expiration_date_time}\")\nif model.doc_types:\n print(\"Doc types the model can recognize:\")\n for name, doc_type in model.doc_types.items():\n print(f\"Doc Type: '{name}' built with '{doc_type.build_mode}' mode which has the following fields:\")\n for field_name, field in doc_type.field_schema.items():\n if doc_type.field_confidence:\n print(\n f\"Field: '{field_name}' has type '{field['type']}' and confidence score \"\n f\"{doc_type.field_confidence[field_name]}\"\n )\n```\n\n<!-- END SNIPPET -->\n\n### Analyze Documents Using a Custom Model\n\nAnalyze document fields, tables, selection marks, and more. These models are trained with your own data, so they're tailored to your documents.\nFor best results, you should only analyze documents of the same document type that the custom model was built with.\n\n<!-- SNIPPET:sample_analyze_custom_documents.analyze_custom_documents -->\n\n```python\nfrom azure.core.credentials import AzureKeyCredential\nfrom azure.ai.documentintelligence import DocumentIntelligenceClient\nfrom azure.ai.documentintelligence.models import AnalyzeResult\n\nendpoint = os.environ[\"DOCUMENTINTELLIGENCE_ENDPOINT\"]\nkey = os.environ[\"DOCUMENTINTELLIGENCE_API_KEY\"]\nmodel_id = os.getenv(\"CUSTOM_BUILT_MODEL_ID\", custom_model_id)\n\ndocument_intelligence_client = DocumentIntelligenceClient(endpoint=endpoint, credential=AzureKeyCredential(key))\n\n# Make sure your document's type is included in the list of document types the custom model can analyze\nwith open(path_to_sample_documents, \"rb\") as f:\n poller = document_intelligence_client.begin_analyze_document(\n model_id=model_id, analyze_request=f, content_type=\"application/octet-stream\"\n )\nresult: AnalyzeResult = poller.result()\n\nif result.documents:\n for idx, document in enumerate(result.documents):\n print(f\"--------Analyzing document #{idx + 1}--------\")\n print(f\"Document has type {document.doc_type}\")\n print(f\"Document has document type confidence {document.confidence}\")\n print(f\"Document was analyzed with model with ID {result.model_id}\")\n if document.fields:\n for name, field in document.fields.items():\n field_value = field.get(\"valueString\") if field.get(\"valueString\") else field.content\n print(\n f\"......found field of type '{field.type}' with value '{field_value}' and with confidence {field.confidence}\"\n )\n\n # Extract table cell values\n SYMBOL_OF_TABLE_TYPE = \"array\"\n KEY_OF_VALUE_OBJECT = \"valueObject\"\n KEY_OF_CELL_CONTENT = \"content\"\n\n for doc in result.documents:\n if not doc.fields is None:\n for field_name, field_value in doc.fields.items():\n # \"MaintenanceLog\" is the table field name which you labeled. Table cell information store as array in document field.\n if (\n field_name == \"MaintenanceLog\"\n and field_value.type == SYMBOL_OF_TABLE_TYPE\n and field_value.value_array\n ):\n col_names = []\n sample_obj = field_value.value_array[0]\n if KEY_OF_VALUE_OBJECT in sample_obj:\n col_names = list(sample_obj[KEY_OF_VALUE_OBJECT].keys())\n print(\"----Extracting Table Cell Values----\")\n table_rows = []\n for obj in field_value.value_array:\n if KEY_OF_VALUE_OBJECT in obj:\n value_obj = obj[KEY_OF_VALUE_OBJECT]\n extract_value_by_col_name = lambda key: (\n value_obj[key].get(KEY_OF_CELL_CONTENT)\n if key in value_obj and KEY_OF_CELL_CONTENT in value_obj[key]\n else \"None\"\n )\n row_data = list(map(extract_value_by_col_name, col_names))\n table_rows.append(row_data)\n print_table(col_names, table_rows)\n\nprint(\"------------------------------------\")\n```\n\n<!-- END SNIPPET -->\n\nAdditionally, a document URL can also be used to analyze documents using the `begin_analyze_document` method.\n\n<!-- SNIPPET:sample_analyze_receipts_from_url.analyze_receipts_from_url -->\n\n```python\nfrom azure.core.credentials import AzureKeyCredential\nfrom azure.ai.documentintelligence import DocumentIntelligenceClient\nfrom azure.ai.documentintelligence.models import AnalyzeDocumentRequest, AnalyzeResult\n\nendpoint = os.environ[\"DOCUMENTINTELLIGENCE_ENDPOINT\"]\nkey = os.environ[\"DOCUMENTINTELLIGENCE_API_KEY\"]\n\ndocument_intelligence_client = DocumentIntelligenceClient(endpoint=endpoint, credential=AzureKeyCredential(key))\nurl = \"https://raw.githubusercontent.com/Azure/azure-sdk-for-python/main/sdk/documentintelligence/azure-ai-documentintelligence/samples/sample_forms/receipt/contoso-receipt.png\"\npoller = document_intelligence_client.begin_analyze_document(\n \"prebuilt-receipt\", AnalyzeDocumentRequest(url_source=url)\n)\nreceipts: AnalyzeResult = poller.result()\n```\n\n<!-- END SNIPPET -->\n\n### Manage Your Models\n\nManage the custom models attached to your account.\n\n<!-- SNIPPET:sample_manage_models.build_model -->\n\n```python\n# Let's build a model to use for this sample\nimport uuid\nfrom azure.ai.documentintelligence import DocumentIntelligenceAdministrationClient\nfrom azure.ai.documentintelligence.models import (\n DocumentBuildMode,\n BuildDocumentModelRequest,\n AzureBlobContentSource,\n DocumentModelDetails,\n)\nfrom azure.core.credentials import AzureKeyCredential\n\nendpoint = os.environ[\"DOCUMENTINTELLIGENCE_ENDPOINT\"]\nkey = os.environ[\"DOCUMENTINTELLIGENCE_API_KEY\"]\ncontainer_sas_url = os.environ[\"DOCUMENTINTELLIGENCE_STORAGE_CONTAINER_SAS_URL\"]\n\ndocument_intelligence_admin_client = DocumentIntelligenceAdministrationClient(endpoint, AzureKeyCredential(key))\npoller = document_intelligence_admin_client.begin_build_document_model(\n BuildDocumentModelRequest(\n model_id=str(uuid.uuid4()),\n build_mode=DocumentBuildMode.TEMPLATE,\n azure_blob_source=AzureBlobContentSource(container_url=container_sas_url),\n description=\"my model description\",\n )\n)\nmodel: DocumentModelDetails = poller.result()\n\nprint(f\"Model ID: {model.model_id}\")\nprint(f\"Description: {model.description}\")\nprint(f\"Model created on: {model.created_date_time}\")\nprint(f\"Model expires on: {model.expiration_date_time}\")\nif model.doc_types:\n print(\"Doc types the model can recognize:\")\n for name, doc_type in model.doc_types.items():\n print(f\"Doc Type: '{name}' built with '{doc_type.build_mode}' mode which has the following fields:\")\n for field_name, field in doc_type.field_schema.items():\n if doc_type.field_confidence:\n print(\n f\"Field: '{field_name}' has type '{field['type']}' and confidence score \"\n f\"{doc_type.field_confidence[field_name]}\"\n )\n```\n\n<!-- END SNIPPET -->\n\n<!-- SNIPPET:sample_manage_models.get_resource_info -->\n\n```python\naccount_details = document_intelligence_admin_client.get_resource_info()\nprint(\n f\"Our resource has {account_details.custom_document_models.count} custom models, \"\n f\"and we can have at most {account_details.custom_document_models.limit} custom models\"\n)\nneural_models = account_details.custom_neural_document_model_builds\nprint(\n f\"The quota limit for custom neural document models is {neural_models.quota} and the resource has\"\n f\"used {neural_models.used}. The resource quota will reset on {neural_models.quota_reset_date_time}\"\n)\n```\n\n<!-- END SNIPPET -->\n\n<!-- SNIPPET:sample_manage_models.list_models -->\n\n```python\n# Next, we get a paged list of all of our custom models\nmodels = document_intelligence_admin_client.list_models()\n\nprint(\"We have the following 'ready' models with IDs and descriptions:\")\nfor model in models:\n print(f\"{model.model_id} | {model.description}\")\n```\n\n<!-- END SNIPPET -->\n\n<!-- SNIPPET:sample_manage_models.get_model -->\n\n```python\nmy_model = document_intelligence_admin_client.get_model(model_id=model.model_id)\nprint(f\"\\nModel ID: {my_model.model_id}\")\nprint(f\"Description: {my_model.description}\")\nprint(f\"Model created on: {my_model.created_date_time}\")\nprint(f\"Model expires on: {my_model.expiration_date_time}\")\nif my_model.warnings:\n print(\"Warnings encountered while building the model:\")\n for warning in my_model.warnings:\n print(f\"warning code: {warning.code}, message: {warning.message}, target of the error: {warning.target}\")\n```\n\n<!-- END SNIPPET -->\n\n<!-- SNIPPET:sample_manage_models.delete_model -->\n\n```python\n# Finally, we will delete this model by ID\ndocument_intelligence_admin_client.delete_model(model_id=my_model.model_id)\n\nfrom azure.core.exceptions import ResourceNotFoundError\n\ntry:\n document_intelligence_admin_client.get_model(model_id=my_model.model_id)\nexcept ResourceNotFoundError:\n print(f\"Successfully deleted model with ID {my_model.model_id}\")\n```\n\n<!-- END SNIPPET -->\n\n### Add-on Capabilities\nDocument Intelligence supports more sophisticated analysis capabilities. These optional features can be enabled and disabled depending on the scenario of the document extraction.\n\nThe following add-on capabilities are available in this SDK:\n- [barcode/QR code][addon_barcodes_sample]\n- [formula][addon_formulas_sample]\n- [font/style][addon_fonts_sample]\n- [high resolution mode][addon_highres_sample]\n- [language][addon_languages_sample]\n- [query fields][query_fields_sample]\n\nNote that some add-on capabilities will incur additional charges. See pricing: https://azure.microsoft.com/pricing/details/ai-document-intelligence/.\n\n## Troubleshooting\n\n### General\n\nDocument Intelligence client library will raise exceptions defined in [Azure Core][azure_core_exceptions].\nError codes and messages raised by the Document Intelligence service can be found in the [service documentation][di-errors].\n\n### Logging\n\nThis library uses the standard\n[logging][python_logging] library for logging.\n\nBasic information about HTTP sessions (URLs, headers, etc.) is logged at `INFO` level.\n\nDetailed `DEBUG` level logging, including request/response bodies and **unredacted**\nheaders, can be enabled on the client or per-operation with the `logging_enable` keyword argument.\n\nSee full SDK logging documentation with examples [here][sdk_logging_docs].\n\n### Optional Configuration\n\nOptional keyword arguments can be passed in at the client and per-operation level.\nThe azure-core [reference documentation][azure_core_ref_docs]\ndescribes available configurations for retries, logging, transport protocols, and more.\n\n## Next steps\n\n### More sample code\n\nSee the [Sample README][sample_readme] for several code snippets illustrating common patterns used in the Document Intelligence Python API.\n\n### Additional documentation\n\nFor more extensive documentation on Azure AI Document Intelligence, see the [Document Intelligence documentation][python-di-product-docs] on docs.microsoft.com.\n\n\n## Contributing\n\nThis project welcomes contributions and suggestions. Most contributions require\nyou to agree to a Contributor License Agreement (CLA) declaring that you have\nthe right to, and actually do, grant us the rights to use your contribution.\nFor details, visit https://cla.microsoft.com.\n\nWhen you submit a pull request, a CLA-bot will automatically determine whether\nyou need to provide a CLA and decorate the PR appropriately (e.g., label,\ncomment). Simply follow the instructions provided by the bot. You will only\nneed to do this once across all repos using our CLA.\n\nThis project has adopted the\n[Microsoft Open Source Code of Conduct][code_of_conduct]. For more information,\nsee the Code of Conduct FAQ or contact opencode@microsoft.com with any\nadditional questions or comments.\n\n<!-- LINKS -->\n[code_of_conduct]: https://opensource.microsoft.com/codeofconduct/\n[default_azure_credential]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/identity/azure-identity#defaultazurecredential\n[azure_sub]: https://azure.microsoft.com/free/\n[python-di-src]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/documentintelligence/azure-ai-documentintelligence/azure/ai/documentintelligence\n[python-di-pypi]: https://pypi.org/project/azure-ai-documentintelligence/\n[python-di-product-docs]: https://learn.microsoft.com/azure/ai-services/document-intelligence/overview?view=doc-intel-4.0.0&viewFallbackFrom=form-recog-3.0.0\n[python-di-ref-docs]: https://aka.ms/azsdk/python/documentintelligence/docs\n[python-di-samples]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/documentintelligence/azure-ai-documentintelligence/samples\n[python-di-available-regions]: https://aka.ms/azsdk/documentintelligence/available-regions\n[azure_portal]: https://ms.portal.azure.com/\n[regional_endpoints]: https://azure.microsoft.com/global-infrastructure/services/?products=form-recognizer\n[cognitive_resource_portal]: https://ms.portal.azure.com/#create/Microsoft.CognitiveServicesFormRecognizer\n[cognitive_resource_cli]: https://docs.microsoft.com/azure/cognitive-services/cognitive-services-apis-create-account-cli?tabs=windows\n[azure-key-credential]: https://aka.ms/azsdk/python/core/azurekeycredential\n[di-studio]: https://documentintelligence.ai.azure.com/studio\n[di-build-model]: https://aka.ms/azsdk/documentintelligence/buildmodel\n[di-build-training-set]: https://aka.ms/azsdk/documentintelligence/buildtrainingset\n[di-models]: https://aka.ms/azsdk/documentintelligence/models\n[di-errors]: https://aka.ms/azsdk/documentintelligence/errors\n[azure_core_ref_docs]: https://aka.ms/azsdk/python/core/docs\n[azure_core_exceptions]: https://aka.ms/azsdk/python/core/docs#module-azure.core.exceptions\n[python_logging]: https://docs.python.org/3/library/logging.html\n[azure_cli_endpoint_lookup]: https://docs.microsoft.com/cli/azure/cognitiveservices/account?view=azure-cli-latest#az-cognitiveservices-account-show\n[azure_portal_get_endpoint]: https://docs.microsoft.com/azure/cognitive-services/cognitive-services-apis-create-account?tabs=multiservice%2Cwindows#get-the-keys-for-your-resource\n[cognitive_authentication_api_key]: https://docs.microsoft.com/azure/cognitive-services/cognitive-services-apis-create-account?tabs=multiservice%2Cwindows#get-the-keys-for-your-resource\n[register_aad_app]: https://docs.microsoft.com/azure/cognitive-services/authentication#assign-a-role-to-a-service-principal\n[custom_subdomain]: https://docs.microsoft.com/azure/cognitive-services/authentication#create-a-resource-with-a-custom-subdomain\n[azure_identity]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/identity/azure-identity\n[sdk_logging_docs]: https://docs.microsoft.com/azure/developer/python/sdk/azure-sdk-logging\n[migration-guide]: https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/documentintelligence/azure-ai-documentintelligence/MIGRATION_GUIDE.md\n[sample_readme]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/documentintelligence/azure-ai-documentintelligence/samples\n[addon_barcodes_sample]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/documentintelligence/azure-ai-documentintelligence/samples/sample_analyze_addon_barcodes.py\n[addon_fonts_sample]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/documentintelligence/azure-ai-documentintelligence/samples/sample_analyze_addon_fonts.py\n[addon_formulas_sample]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/documentintelligence/azure-ai-documentintelligence/samples/sample_analyze_addon_formulas.py\n[addon_highres_sample]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/documentintelligence/azure-ai-documentintelligence/samples/sample_analyze_addon_highres.py\n[addon_languages_sample]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/documentintelligence/azure-ai-documentintelligence/samples/sample_analyze_addon_languages.py\n[query_fields_sample]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/documentintelligence/azure-ai-documentintelligence/samples/sample_analyze_addon_query_fields.py\n[service-rename]: https://techcommunity.microsoft.com/t5/azure-ai-services-blog/azure-form-recognizer-is-now-azure-ai-document-intelligence-with/ba-p/3875765\n[service_prebuilt_document]: https://docs.microsoft.com/azure/ai-services/document-intelligence/concept-general-document#general-document-features\n",
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