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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
已回答 4 個月前
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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- AWS 官方已更新 3 年前
Hi, thanks for having accepted my answer!