For Titan embedding model which distance metric to use

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As per best practice to choose distance metric "Use the distance metric that matches the model that you're using". For Titan embedding model which distance metric to use.

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Sudip
asked 2 months ago235 views
2 Answers
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Accepted Answer

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
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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

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answered 2 months ago
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reviewed 2 months ago
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