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Updated 4 months ago

Hello, I use VectorStoreIndex to

Hello, I use VectorStoreIndex to evaluate embedding model. I think default similarity function is cos similarity. But as I check node score, they are all between (0, 1) rather than (-1, 1). It seems like maybe normalized L2 function is being used.
Why all similarity score is between 0-1? Can I change default similarity function of VectorStoreIndex to some specific function instead of default cos function?
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You'll have to subclass your embedding model and change the similarity mode as per your fit.
Thanks a lot. With your kind help now I know how to change similarity fn. But still 1 more question. Why all similarity scores are between (0, 1). Since I am using default COSINE fn, it should be in (-1, 1) right?
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Maybe @Logan M can help with this query
This is the default calculation

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product = np.dot(embedding1, embedding2)
norm = np.linalg.norm(embedding1) * np.linalg.norm(embedding2)
return product / norm
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