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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?

6 comments

WWhiteFang_Jr

Hi, Yes you can change the default mode of embedding by changing the mode.
https://github.com/run-llama/llama_index/blob/79518c4cc39981140b2e87c9a701b09d74d47e9a/llama-index-core/llama_index/core/base/embeddings/base.py#L37

WWhiteFang_Jr

You'll have to subclass your embedding model and change the similarity mode as per your fit.

AAzathoth

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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WWhiteFang_Jr

Maybe @Logan M can help with this query

LLogan M

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

AAzathoth

OK, Thanks

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