What is required to store enrichments in a cognitive search index derived from a Language Studio project?

To store enrichments in a cognitive search index that comes from a Language Studio project, it is essential to update your Azure Cognitive Search solution. This process involves ensuring that the index can handle the enriched data effectively, which may include changes to the schema or configuration that allows it to incorporate additional fields or types of information generated from the language enrichment process.

When you integrate or update the Azure Cognitive Search service, you might need to redefine your index, datasource, and skillset to accommodate the new enrichments coming from the Language Studio. This aligns the backend system with the new capabilities offered by the language models, ensuring that the data retrieval and indexing functions properly reflect the enriched content.

Other options like integrating with Power BI may provide analysis and visualization capabilities but do not directly pertain to the storage of enrichments in the search index. Likewise, configuring settings in the Azure portal is more about management rather than the technical requirements for storing enrichments. Training a new machine learning model is related to building AI capabilities, but it is not necessary for simply storing existing enrichments derived from a pre-trained model in a cognitive search index. Thus, updating the cognitive search solution is the key step required in this context.