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articles/ai-services/content-understanding/language-region-support.md

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## Region support
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To use Azure AI Content Understanding, create your Azure AI Service resource in a supported region. All data at rest is stored in the selected region. For lower latency or increased capacity, you can specify the [processing location](./concepts/analyzers-overview.md#data-processing-location) where analysis occurs. Content Understanding is available in the following regions. When the processing location is set to `geography` or `data zone`, the corresponding locations are shown.
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To use Azure AI Content Understanding, create your Azure AI Service resource in a supported region. All data at rest is stored in the selected region. For lower latency or increased capacity, you can specify the processing location where analysis occurs. Content Understanding is available in the following regions. When the processing location is set to `geography` or `data zone`, the corresponding locations are shown.
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| Identifier | Region | Geography | Data Zone |
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|-----------------|----------------|-----------------|------------------|

articles/ai-services/content-understanding/quickstart/use-rest-api.md

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## Next steps
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Learn to [create a custom analyzer](../concepts/analyzers-overview.md) to better fit your use case.
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Learn more about [analyzers](../concepts/analyzer-templates.md) for your use case.
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articles/ai-services/content-understanding/toc.yml

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- name: Content Understanding Azure AI Foundry portal
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displayName: quickstart, extract, text, images, OCR, optical character recognition
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href: quickstart/use-ai-foundry.md
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- name: Capabilities
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- name: Analyzers and features
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items:
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- name: Overview
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displayName: content understanding capabilities, document, text, images, video, audio, visual, structured, content, field, extraction
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href: concepts/analyzers-overview.md
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- name: Analyzer templates
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displayName: analyzer, templates, document, text, images, video, audio, multimodal, visual, structured, content, field, extraction
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href: concepts/analyzer-templates.md

articles/ai-services/content-understanding/tutorial/build-rag-solution.md

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* **API Version:** This tutorial uses the latest preview [API version](/rest/api/contentunderstanding/analyzers?view=rest-contentunderstanding-2024-12-01-preview&preview&preserve-view=true): `2024-12-01-preview`.
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* **Python Environment:** Install [Python 3.11](https://www.python.org/downloads/) to execute the provided code samples and scripts.
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* This tutorial follows this sample code can be found in our [Python notebook](https://github.com/Azure-Samples/azure-ai-search-with-content-understanding-python#samples). Follow the [README](https://github.com/Azure-Samples/azure-ai-search-with-content-understanding-python/blob/main/README.md) to create essential resources, grant resources the right Access control(IAM) roles and install all packages needed for this tutorial.
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* The [multimodal data](../concepts/analyzers-overview.md) used in this tutorial consists of documents, images, audio, and video. They're designed to guide you through the process of building a robust RAG solution with Azure AI Content Understanding.
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* The multimodal data used in this tutorial consists of documents, images, audio, and video. They're designed to guide you through the process of building a robust RAG solution with Azure AI Content Understanding.
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## Extract data
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* [Try a multimodal content solution accelerator](https://github.com/microsoft/content-processing-solution-accelerator)
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* [Learn more Content Understanding capabilities](../concepts/analyzers-overview.md)
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* [Learn more Content Understanding analyzers](../concepts/analyzer-templates.md)

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