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articles/search/search-get-started-portal-image-search.md

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@@ -127,7 +127,7 @@ On the **Advanced settings** page, you can optionally add new fields. By default
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| text_vector | Text vectors | Collection(Edm.single). Vector representation of the chunk. Searchable and retrievable, but not filterable, facetable, or sortable.|
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| image_vector | Image vectors | Collection(Edm.single). Vector representation of the image. Searchable and retrievable, but not filterable, facetable, or sortable.|
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You can't modify the generated fields or their attributes, but you can add new fields if your data source provides them. For example, Azure blob storage provides a collection of metadata fields.
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You can't modify the generated fields or their attributes, but you can add new fields if your data source provides them. For example, Azure Blob Storage provides a collection of metadata fields.
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1. Select **Add new**.
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+ An indexer that drives the indexing pipeline.
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+ A data source connection to Blob Storage.
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+ A data source connection to Azure Blob Storage.
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+ An index with vector fields, text fields, vectorizers, vector profiles, and vector algorithms. You can't modify the default index during the wizard workflow. Indexes conform to the [2024-05-01-preview REST API](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true) so that you can use preview features.
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articles/search/search-get-started-portal-import-vectors.md

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+ Supported data sources are Azure Blob Storage, Azure Data Lake Storage (ADLS) Gen2, or OneLake files and shortcuts.
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+ Supported embedding models are hosted on Azure OpenAI, Azure AI Studio model catalog, Azure AI Vision multimodal.
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+ Index schema provides vector and nonvector fields for chunked data.
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+ You can add fields, but you can't delete autogenerated fields.
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+ You can add fields, but you can't delete or modify generated fields.
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+ Document parsing mode creates chunks (one search document per chunk).
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+ Chunking is nonconfigurable. The effective settings are:
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```json
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textSplitMode: "pages",
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maximumPageLength: 2000,
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pageOverlapLength: 500
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"textSplitMode": "pages",
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"maximumPageLength": 2000,
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"pageOverlapLength": 500,
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"maximumPagesToTake": 0, #unlimited
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"unit": "characters",
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```
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## Prerequisites
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This section points you to data that works for this quickstart.
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### [Azure Blob storage](#tab/sample-data-storage)
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### [Azure Blob Storage](#tab/sample-data-storage)
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1. Sign in to the [Azure portal](https://portal.azure.com/) with your Azure account, and go to your Azure Storage account.
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The next step is to connect to a data source to use for the search index.
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### [Azure Blob storage](#tab/connect-data-storage)
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### [Azure Blob Storage](#tab/connect-data-storage)
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1. On the **Set up your data connection** page, select **Azure Blob Storage**.
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| text_vector | Text vectors | Collection(Edm.single). Vector representation of the chunk. Searchable and retrievable, but not filterable, facetable, or sortable.|
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| image_vector | Image vectors | Collection(Edm.single). Vector representation of the image. Searchable and retrievable, but not filterable, facetable, or sortable.|
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You can't modify the generated fields or their attributes, but you can add new fields if your data source provides them. For example, Azure blob storage provides a collection of metadata fields.
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You can't modify the generated fields or their attributes, but you can add new fields if your data source provides them. For example, Azure Blob Storage provides a collection of metadata fields.
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1. Select **Add new**.
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