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Copy file name to clipboardExpand all lines: notebooks/management.ipynb
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"metadata": {},
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"source": [
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"## Prerequisites\n",
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"1. Ensure your Azure AI service is configured following the [configuration steps](../README.md#configure-azure-ai-service-resource).\n",
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"1. Please ensure your Azure AI service is configured by following the [configuration steps](../README.md#configure-azure-ai-service-resource).\n",
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"2. Install the required packages to run this sample."
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]
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},
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"\n",
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"> The [AzureContentUnderstandingClient](../python/content_understanding_client.py) is a utility class that provides functions to interact with the Content Understanding API. Before the official release of the Content Understanding SDK, this client serves as a lightweight SDK.\n",
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"\n",
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"> Fill the constants **AZURE_AI_ENDPOINT**, **AZURE_AI_API_VERSION**, and **AZURE_AI_API_KEY** with your Azure AI Service details.\n",
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"> Please fill the constants **AZURE_AI_ENDPOINT**, **AZURE_AI_API_VERSION**, and **AZURE_AI_API_KEY** with your Azure AI Service details.\n",
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"\n",
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"> ⚠️ Important:\n",
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"Update the code below to match your Azure authentication method.\n",
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"Look for the `# IMPORTANT` comments and modify those sections accordingly.\n",
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"If you skip this step, the sample might not run correctly.\n",
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"Skipping this step might cause the sample not to run correctly.\n",
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"\n",
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"> ⚠️ Note: Using a subscription key works, but using Azure Active Directory (AAD) token-based authentication is more secure and highly recommended for production environments."
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"source": [
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"## Configure Model Deployments for Prebuilt Analyzers\n",
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"\n",
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"> **💡 Note:** This step is only required **once per Azure Content Understanding resource**, unless the GPT deployment has been changed. You can skip this section if:\n",
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"> **💡 Note:** This step is required **only once per Azure Content Understanding resource**, unless the GPT deployment has been changed.\n",
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"You can skip this section if:\n",
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"> - This configuration has already been run once for your resource, or\n",
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"> - Your administrator has already configured the model deployments for you\n",
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"> - Your administrator has already configured the model deployments for you.\n",
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"\n",
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"Before using prebuilt analyzers, you need to configure the default model deployment mappings. This tells Content Understanding which model deployments to use.\n",
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"Before using prebuilt analyzers, please configure the default model deployment mappings. This tells Content Understanding which model deployments to use.\n",
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"\n",
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"**Model Requirements:**\n",
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"- **GPT-4.1** - Required for most prebuilt analyzers (e.g., `prebuilt-invoice`, `prebuilt-receipt`, `prebuilt-idDocument`)\n",
"- **text-embedding-3-large** - Required for all prebuilt analyzers that use embeddings.\n",
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"\n",
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"**Prerequisites:**\n",
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"1. Deploy **GPT-4.1**, **GPT-4.1-mini**, and **text-embedding-3-large** models in Azure AI Foundry\n",
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"2. Set `GPT_4_1_DEPLOYMENT`, `GPT_4_1_MINI_DEPLOYMENT`, and `TEXT_EMBEDDING_3_LARGE_DEPLOYMENT` in your `.env` file with the deployment names"
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"1. Deploy **GPT-4.1**, **GPT-4.1-mini**, and **text-embedding-3-large** models in Azure AI Foundry.\n",
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"2. Set environment variables `GPT_4_1_DEPLOYMENT`, `GPT_4_1_MINI_DEPLOYMENT`, and `TEXT_EMBEDDING_3_LARGE_DEPLOYMENT` in your `.env` file with the respective deployment names."
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},
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{
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" print(f\" - {deployment}\")\n",
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" print(\"\\n Prebuilt analyzers require GPT-4.1, GPT-4.1-mini, and text-embedding-3-large deployments.\")\n",
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" print(\" Please:\")\n",
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" print(\" 1. Deploy all three models in Azure AI Foundry\")\n",
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" print(\" 1. Deploy all three models in Azure AI Foundry.\")\n",
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" print(\" 2. Add the following to notebooks/.env:\")\n",
"> **💡 Note:** This section demonstrates analyzer creation for learning purposes only. For actual invoice field extraction, we recommend using the **`prebuilt-invoice`** analyzer, which is optimized for invoice processing. See the `field_extraction.ipynb` notebook for examples of using prebuilt analyzers.\n",
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"> **💡 Note:** This section demonstrates analyzer creation for learning purposes only. For actual invoice field extraction, we recommend using the **`prebuilt-invoice`** analyzer, which is optimized for invoice processing. Please see the `field_extraction.ipynb` notebook for examples of using prebuilt analyzers.\n",
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"\n",
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"First, we create an analyzer from a template to extract invoice fields."
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"After successfully creating an analyzer, you can use it to analyze our input files. You can also list all analyzers available in your resource."
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"After successfully creating an analyzer, you can use it to analyze your input files. You can also list all analyzers available in your resource."
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]
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},
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{
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"source": [
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"## Get Analyzer Details by ID\n",
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"\n",
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"Keep track of the analyzer ID when you create it. Use the ID to retrieve detailed analyzer definitions later."
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"Keep track of the analyzer ID when you create it. You can use the ID to retrieve detailed analyzer definitions later."
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]
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},
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{
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"metadata": {},
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"source": [
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"## Delete an Analyzer\n",
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"If you no longer need an analyzer, delete it using its ID."
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"If you no longer need an analyzer, you can delete it using its ID."
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