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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
回答済み 4ヶ月前
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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
Hi, thanks for having accepted my answer!