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Azure Logic App created-in template. This can be this means you don’t have to create your personal workflow. Note which you can create your own personal too. However, this isn't lined as part of this web site post. This tutorial exhibits the best way to index data files you add following the workflow generation in an Azure Data files share. 

Seek out azure ai lookup inside the research box and choose the template that aligns with all your information resource. Note that you should be capable to use any Azure Logic Application supported connector of unstructured info so You can utilize it to import info to AI Research using this similar chunking and embedding sample, but you must modify the workflow In keeping with your preferences. 

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 The process of parsing, chunking, vectorizing details and storing it in an index is taken care of by an Azure AI Lookup attribute known as integrated vectorization. For supported data sources, this features also allows automated data ingestion, enrichment and processing.

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