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Merge pull request #1415 from HeidiSteen/heidist-ignite
Clean up and consistency pass
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articles/search/cognitive-search-attach-cognitive-services.md

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# Attach an Azure AI multi-service resource to a skillset in Azure AI Search
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When configuring an optional [AI enrichment pipeline](cognitive-search-concept-intro.md) in Azure AI Search, you can enrich a small number of documents free of charge, limited to 20 transactions daily per index. For larger and more frequent workloads, you should attach a billable [**Azure AI multi-service resource**](/azure/ai-services/multi-service-resource?pivots=azportal).
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When configuring an optional [AI enrichment pipeline](cognitive-search-concept-intro.md) in Azure AI Search, you can enrich a small number of documents free of charge, limited to 20 transactions daily per index. For larger and more frequent workloads, you should attach a billable [Azure AI multi-service resource](/azure/ai-services/multi-service-resource?pivots=azportal).
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A multi-service account provides a collection of Azure AI services, rather than individual services. Providing an account in an Azure AI Search [**skillset**](/rest/api/searchservice/skillsets/create) allows Microsoft to charge you for using these services:
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A multi-service account provides a collection of Azure AI services, rather than individual services. Providing an account in an Azure AI Search [skillset](/rest/api/searchservice/skillsets/create) allows Microsoft to charge you for using these services:
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+ [Azure AI Vision](/azure/ai-services/computer-vision/overview) for image analysis, optical character recognition (OCR), and multimodal text and image embedding.
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+ [Azure AI Language](/azure/ai-services/language-service/overview) for language detection, entity recognition, sentiment analysis, and key phrase extraction

articles/search/cognitive-search-skill-document-intelligence-layout.md

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The **Document Layout** skill analyzes a document to extract regions of interest and their inter-relationships to produce a syntactical representation of the document in Markdown format. This skill uses the [Document Intelligence layout model](/azure/ai-services/document-intelligence/concept-layout) provided in [Azure AI Document Intelligence](/azure/ai-services/document-intelligence/overview).
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This article is the reference documentation for the Document Layout skill.
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This article is the reference documentation for the Document Layout skill. For usage information, see [Structure-aware chunking and vectorization](search-how-to-semantic-chunking.md).
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The **Document Layout** skill calls the [Document Intelligence Public preview version 2024-07-31-preview](/rest/api/aiservices/operation-groups?view=rest-aiservices-v4.0%20(2024-07-31-preview)&preserve-view=true). It's currently only available in the following Azure regions:
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articles/search/search-api-migration.md

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[`2024-11-01-preview`](/rest/api/searchservice/search-service-api-versions#2024-11-01-preview) query rewrite, Document Layout skill, keyless billing for skills processing, Markdown parsing mode, and rescoring options for compressed vectors.
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If you're upgrading from `2024-09-01-preview`, you can use the new preview APIs with no change to existing code. However, the new version introduces changes to `vectorSearch.compressions`, replacing `rerankWithOriginalVectors` with `enableRescoring`, and moving `defaultOversampling` to a new `rescoringOptions` property object. For a comparison of the syntax, see [Compress vectors using scalar or binary quantization](vector-search-how-to-quantization.md#add-compressions-to-a-search-index).
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If you're upgrading from `2024-09-01-preview`, you can use the new preview APIs with no change to existing code.
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However, the new version introduces syntax changes to `vectorSearch.compressions`:
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+ Replaces `rerankWithOriginalVectors` with `enableRescoring`
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+ Moves `defaultOversampling` to a new `rescoringOptions` property object
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Backwards compatibility is preserved due to an internal API mapping, but we recommend changing the syntax if you adopt the new preview version. For a comparison of the syntax, see [Compress vectors using scalar or binary quantization](vector-search-how-to-quantization.md#add-compressions-to-a-search-index).
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## Upgrade to 2024-09-01-preview
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