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Merge pull request #386 from HeidiSteen/heidist-fresh
misc edits, Mike's corrections on regions, python sample page edits
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articles/search/samples-python.md

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## SDK samples
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Code samples from the Azure SDK development team demonstrate API usage.
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You can find these samples in [**azure-sdk-for-python/tree/main/sdk/search/azure-search-documents/samples**](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/search/azure-search-documents/samples) on GitHub.
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[**azure-sdk-for-python/tree/main/sdk/search/azure-search-documents/samples**](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/search/azure-search-documents/samples) on GitHub provides code samples from the Azure SDK development team, demonstrating API usage.
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## Doc samples
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## Demos
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A demo repo provides proof-of-concept source code for examples or scenarios shown in demonstrations.
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[**azure-search-vector-samples**](https://github.com/Azure/azure-search-vector-samples/blob/main/README.md) on GitHub provides a comprehensive collection of samples organized by scenario or technology.
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[**azure-search-vector-samples**](https://github.com/Azure/azure-search-vector-samples/blob/main/README.md) on GitHub provides a comprehensive collection of samples for vector search scenarios, organized by scenario or technology.
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We also recommend [**azure-search-openai-demo**](https://github.com/Azure-Samples/azure-search-openai-demo/blob/main/README.md). This is a ChatGPT-like experience over enterprise data with Azure OpenAI Python code showing how to use Azure AI Search with the large language models in Azure OpenAI. For background, see this Tech Community blog post: [Revolutionize your Enterprise Data with ChatGPT](https://techcommunity.microsoft.com/t5/ai-applied-ai-blog/revolutionize-your-enterprise-data-with-chatgpt-next-gen-apps-w/ba-p/3762087). |
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articles/search/search-limits-quotas-capacity.md

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## Vector index size limits
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When you index documents with vector fields, Azure AI Search constructs internal vector indexes using the algorithm parameters you provide. The size of these vector indexes is restricted by the memory reserved for vector search for your service's tier (or `SKU`).
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When you index documents with vector fields, Azure AI Search constructs internal vector indexes using the algorithm parameters you provide. The size of these vector indexes is restricted by the memory reserved for vector search for your service's tier (or `SKU`). For guidance on managing and maximizing vector storage, see [Vector index size and staying under limits](vector-search-index-size.md).
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Vector limits vary by [service creation date](vector-search-index-size.md#how-to-check-service-creation-date) and [tier](search-sku-tier.md). For guidance on managing and maximizing vector storage, see [Vector index size and staying under limits](vector-search-index-size.md).
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Vector limits vary by:
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+ [service creation date](vector-search-index-size.md#how-to-check-service-creation-date)
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+ [region](search-region-support.md)
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Higher vector limits from April 2024 onwards exist on *new search services* in regions providing the extra capacity, which is most of them.
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This table shows the progression of vector quota increases in GB over time. The quota is per partition, so if you scale a new Standard (S1) service to 6 partitions, total vector quota is 35 multiplied by 6.
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<sup>4</sup> Higher vector quota for more tiers and regions based on partition size updates.
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The service enforces a vector index size quota **for every partition** in your search service. Each extra partition increases the available vector index size quota. This quota is a hard limit to ensure your service remains healthy, which means that further indexing attempts once the limit is exceeded results in failure. You can resume indexing once you free up available quota by either deleting some vector documents or by scaling up in partitions.
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The service enforces a vector index size quota *for every partition* in your search service. Each extra partition increases the available vector index size quota. This quota is a hard limit to ensure your service remains healthy, which means that further indexing attempts once the limit is exceeded results in failure. You can resume indexing once you free up available quota by either deleting some vector documents or by scaling up in partitions.
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> [!IMPORTANT]
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> Higher vector limits are tied to larger partition sizes. Regions that run on older infrastructure are subject to the July-April limits. Review the [regions list](search-region-support.md) for status on partition storage limits.

articles/search/search-region-support.md

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| North Europe​​ ||||
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| West Europe​​ <sup>1, 2</sup>||||
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| France Central​​ ||||
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| Germany West Central​ <sup>2</sup> || ||
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| Germany West Central​ ​|| ||
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| Italy North​​ | | ||
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| Norway East​​ || ||
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| Poland Central​​ | | | |
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| Spain Central | | ||
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| Spain Central <sup>2</sup> | | ||
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| Sweden Central​​ || ||
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| Switzerland North​ ||||
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| Switzerland West​ ||||
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| Region | AI integration | Semantic ranking | Availability zones |
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|--|--|--|--|
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| South Africa North​ <sup>1</sup> || ||
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<sup>1</sup> This region runs on older infrastructure that has lower storage limits per partition at every tier. Choose a different region if you want [higher limits](search-limits-quotas-capacity.md#service-limits).
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| South Africa North​ || ||
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### Asia Pacific
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