Instruction 02

Comparing Traditional Search with Vector Search in Azure AI Search

Traditional search relies on exact matches, typically requiring a structured architecture for storing and retrieving data — making it excellent for keyword searches, or any situations where you need to find specific information.

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Understanding the Nearest Neighbor Technique

Vector search uses a technique called Nearest Neighbor to perform data retrieval. This means it matches prompts or queries with embedded documents based on the distance between the two. The closest matches are returned in descending order — this is the same method by which ranking is done (ranking assigns scores to documents returned in relation to the given query).

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Filtering Vector Search

Azure AI Search supports filtering in vector search. A field needs to be filterable — and either vectorized or non-vectorized — to allow filtering.

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Assessing Resource Usage on Azure AI Search

Azure AI Search measures vector indexes in bytes, and they share the same space as your storage provisions. Your service’s memory also affects your vector index quota.

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Tips on Resource Usage and Behavior in Azure AI Search

Don’t forget to delete a deployment when you’re not using it — make it a habit to check your quota in the Azure AI portal regularly to monitor resource usage and save costs.

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