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Regenerate DI for API 2024-11-30 (Azure#38529)
* Regen * Remove generated samples and tests * Customize on batch analyze operation * Create sample_analyze_batch_documents.py * Regen * Align new names in _patch.py * Regen * Update batch analyze samples * Default content-type when analyze request is a stream * Fix typo * Update docstring in patch * Update README.md * Fix mypy pylint * Update MIGRATION_GUIDE.md * Update CHANGELOG.md * body rename * black * update emitter version * fix changelog entry * fix content type patch * fix content type logic + some patch body renames * test fixes * rename body params * regen with latest python emitter * changelog update * more renaming fixes * required body fix * fix tests * update docs * test fix * update samples * fix link * fix async patch * pylint * formatting * long line * update tests * revert content type change * content type wip * test updates * fix tests * fix tests * fix assert * update tests * update batch tests * update assets.json * skip assert * update assert * revert recorded_variables change * patch fix * pylint * spelling * skip assert * pylint * set bodiless matcher * update recordings * skip test --------- Co-authored-by: Catalina Peralta <[email protected]>
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@@ -30,10 +30,9 @@ python -m pip install azure-ai-documentintelligence | |
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| This table shows the relationship between SDK versions and supported API service versions: | ||
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| |SDK version|Supported API service version| | ||
| |-|-| | ||
| |1.0.0b1 | 2023-10-31-preview| | ||
| |1.0.0b2 | 2024-02-29-preview| | ||
| | SDK version | Supported API service version | | ||
| | ----------- | ----------------------------- | | ||
| | 1.0.0 | 2024-11-30 | | ||
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| Older API versions are supported in `azure-ai-formrecognizer`, please see the [Migration Guide][migration-guide] for detailed instructions on how to update application. | ||
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@@ -47,10 +46,10 @@ Older API versions are supported in `azure-ai-formrecognizer`, please see the [M | |
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| 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). | ||
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| You can create either resource using: | ||
| You can create either resource using: | ||
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| * Option 1: [Azure Portal][cognitive_resource_portal]. | ||
| * Option 2: [Azure CLI][cognitive_resource_cli]. | ||
| - Option 1: [Azure Portal][cognitive_resource_portal]. | ||
| - Option 2: [Azure CLI][cognitive_resource_cli]. | ||
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| Below is an example of how you can create a Document Intelligence resource using the CLI: | ||
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@@ -132,9 +131,11 @@ 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: | ||
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| ```pip install azure-identity``` | ||
| ``` | ||
| pip install azure-identity | ||
| ``` | ||
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| 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. | ||
| You will also need to [register a new AAD application and grant access][register_aad_app] to Document Intelligence by assigning the [Cognitive Services Data Reader][entra_auth_role] role to your service principal. | ||
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| 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`. | ||
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@@ -157,8 +158,8 @@ document_intelligence_client = DocumentIntelligenceClient(endpoint, credential) | |
| ### DocumentIntelligenceClient | ||
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| `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. | ||
| 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. | ||
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| Sample code snippets are provided to illustrate using a DocumentIntelligenceClient [here](#examples "Examples"). | ||
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@@ -194,16 +195,16 @@ Sample code snippets are provided to illustrate using long-running operations [b | |
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| The following section provides several code snippets covering some of the most common Document Intelligence tasks, including: | ||
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| * [Extract Layout](#extract-layout "Extract Layout") | ||
| * [Extract Figures from Documents](#extract-figures-from-documents "Extract Figures from Documents") | ||
| * [Analyze Documents Result in PDF](#analyze-documents-result-in-pdf "Analyze Documents Result in PDF") | ||
| * [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") | ||
| * [Get Raw JSON Result](#get-raw-json-result "Get Raw JSON Result") | ||
| - [Extract Layout](#extract-layout "Extract Layout") | ||
| - [Extract Figures from Documents](#extract-figures-from-documents "Extract Figures from Documents") | ||
| - [Analyze Documents Result in PDF](#analyze-documents-result-in-pdf "Analyze Documents Result in PDF") | ||
| - [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") | ||
| - [Get Raw JSON Result](#get-raw-json-result "Get Raw JSON Result") | ||
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| ### Extract Layout | ||
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@@ -233,7 +234,7 @@ 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" | ||
| "prebuilt-layout", body=f | ||
| ) | ||
| result: AnalyzeResult = poller.result() | ||
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@@ -325,9 +326,8 @@ document_intelligence_client = DocumentIntelligenceClient(endpoint=endpoint, cre | |
| with open(path_to_sample_documents, "rb") as f: | ||
| poller = document_intelligence_client.begin_analyze_document( | ||
| "prebuilt-layout", | ||
| analyze_request=f, | ||
| body=f, | ||
| output=[AnalyzeOutputOption.FIGURES], | ||
| content_type="application/octet-stream", | ||
| ) | ||
| result: AnalyzeResult = poller.result() | ||
| operation_id = poller.details["operation_id"] | ||
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@@ -367,9 +367,8 @@ document_intelligence_client = DocumentIntelligenceClient(endpoint=endpoint, cre | |
| with open(path_to_sample_documents, "rb") as f: | ||
| poller = document_intelligence_client.begin_analyze_document( | ||
| "prebuilt-read", | ||
| analyze_request=f, | ||
| body=f, | ||
| output=[AnalyzeOutputOption.PDF], | ||
| content_type="application/octet-stream", | ||
| ) | ||
| result: AnalyzeResult = poller.result() | ||
| operation_id = poller.details["operation_id"] | ||
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@@ -418,9 +417,8 @@ document_intelligence_client = DocumentIntelligenceClient(endpoint=endpoint, cre | |
| with open(path_to_sample_documents, "rb") as f: | ||
| poller = document_intelligence_client.begin_analyze_document( | ||
| "prebuilt-layout", | ||
| analyze_request=f, | ||
| body=f, | ||
| features=[DocumentAnalysisFeature.KEY_VALUE_PAIRS], | ||
| content_type="application/octet-stream", | ||
| ) | ||
| result: AnalyzeResult = poller.result() | ||
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@@ -513,7 +511,7 @@ 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" | ||
| "prebuilt-receipt", body=f, locale="en-US" | ||
| ) | ||
| receipts: AnalyzeResult = poller.result() | ||
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@@ -668,7 +666,7 @@ document_intelligence_client = DocumentIntelligenceClient(endpoint=endpoint, cre | |
| # 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" | ||
| model_id=model_id, body=f | ||
| ) | ||
| result: AnalyzeResult = poller.result() | ||
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@@ -827,10 +825,10 @@ if model.doc_types: | |
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| <!-- END SNIPPET --> | ||
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| <!-- SNIPPET:sample_manage_models.get_resource_info --> | ||
| <!-- SNIPPET:sample_manage_models.get_resource_details --> | ||
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| ```python | ||
| account_details = document_intelligence_admin_client.get_resource_info() | ||
| account_details = document_intelligence_admin_client.get_resource_details() | ||
| 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" | ||
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@@ -885,9 +883,11 @@ except ResourceNotFoundError: | |
| <!-- END SNIPPET --> | ||
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| ### Add-on Capabilities | ||
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| Document Intelligence supports more sophisticated analysis capabilities. These optional features can be enabled and disabled depending on the scenario of the document extraction. | ||
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| The following add-on capabilities are available in this SDK: | ||
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| - [barcode/QR code][addon_barcodes_sample] | ||
| - [formula][addon_formulas_sample] | ||
| - [font/style][addon_fonts_sample] | ||
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@@ -900,6 +900,7 @@ Note that some add-on capabilities will incur additional charges. See pricing: h | |
| ### Get Raw JSON Result | ||
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| Can get the HTTP response by passing parameter `raw_response_hook` to any client method. | ||
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| <!-- SNIPPET:sample_get_raw_response.raw_response_hook --> | ||
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| ```python | ||
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@@ -918,7 +919,7 @@ def callback(response): | |
| responses["status_code"] = response.http_response.status_code | ||
| responses["response_body"] = response.http_response.json() | ||
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| client.get_resource_info(raw_response_hook=callback) | ||
| client.get_resource_details(raw_response_hook=callback) | ||
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| print(f"Response status code is: {responses["status_code"]}") | ||
| response_body = responses["response_body"] | ||
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@@ -964,7 +965,6 @@ print( | |
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| <!-- END SNIPPET --> | ||
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| ## Troubleshooting | ||
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| ### General | ||
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@@ -1000,7 +1000,6 @@ See the [Sample README][sample_readme] for several code snippets illustrating co | |
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| For more extensive documentation on Azure AI Document Intelligence, see the [Document Intelligence documentation][python-di-product-docs] on docs.microsoft.com. | ||
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| ## Contributing | ||
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| This project welcomes contributions and suggestions. Most contributions require | ||
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@@ -1019,6 +1018,7 @@ see the Code of Conduct FAQ or contact [email protected] with any | |
| additional questions or comments. | ||
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| <!-- LINKS --> | ||
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| [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/ | ||
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@@ -1045,6 +1045,7 @@ additional questions or comments. | |
| [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 | ||
| [entra_auth_role]: https://learn.microsoft.com/azure/role-based-access-control/built-in-roles/ai-machine-learning#cognitive-services-data-reader | ||
| [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 | ||
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