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Hello ,

Sudip

The Titan models are trained using cosine similarity, so the cosine distance metric would be the most appropriate choice.

Thanks,

Abhinav

answered 2 months ago

0

Hi,

Your choice of distance metric is fairly open: the engines will return you embedding vectors and you choose which one you apply.

You have a wide choice well detailled at https://weaviate.io/blog/distance-metrics-in-vector-search

Cosine similarity is a frequent choice: it is normalized so always between 0 and 1. If you choose a non-normlized distance, you become dependent on embeddings vector length, which may become an issue if you want to compare distance across embeddings engines.

Best,

Didier

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Hi, thanks for having accepted my answer!