Struggling with poor performance on fine-tuned davinci model

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I was struggling with a bug (manipulating arrays) in the code available here em013 Doing and Embedded Semantic Search - YouTube

It works better using

df['similarities'] = df.ada_embedding.apply(lambda x: np.dot(x, searchvector) / (np.linalg.norm(x) * np.linalg.norm(searchvector)))

Than

df['similarities'] = df.ada_embedding.apply(lambda x: cosine_similarity(x, searchvector))

Cheers

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