Question

A retail company stores product documents in an Azure Cosmos DB for NoSQL container. The product catalog team wants to add a semantic product search feature to their website that returns products based on meaning rather than exact keyword matches. They plan to use Azure AI Search over the Cosmos DB data. The team already generates text embeddings for each product description. Which Azure AI Search capability should they configure to satisfy this semantic-similarity requirement?

Accepted Answer

Define a vector field in the index and use the Cosmos DB indexer with a field mapping to store the precomputed embeddings, then issue vector queries against that field.

Answer

Enable the built-in Cosmos DB analytical store and query it directly from Azure AI Search using T-SQL.

Answer

Configure a scoring profile that boosts documents based on the freshness of the product's last-modified timestamp.

Answer

Create a synonym map that lists related product terms so the search engine can expand keyword queries automatically.