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Expand Up @@ -13,10 +13,6 @@ Without a vector database, you would need to train your model (or models) or re-

A vector database determines what other data (represented as vectors) is near your input query. This allows you to build different use-cases on top of a vector database, including:

- Semantic search, used to return results similar to the input of the query.
- Classification, used to return the grouping (or groupings) closest to the input query.
- Recommendation engines, used to return content similar to the input based on different criteria (for example previous product sales, or user history).
- Anomaly detection, used to identify whether specific data points are similar to existing data, or different.
- Semantic search, used to return results similar to the input of the query.
- Classification, used to return the grouping (or groupings) closest to the input query.
- Recommendation engines, used to return content similar to the input based on different criteria (for example previous product sales, or user history).
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