text: Title: [FEATURE]: euclidean_l2 and cosine distance have identical ROC curves, so you could drop one of them in benchmarks.
  Body: ### Description
  
  Let u and v be unit vectors (i.e. you've already divided by the euclidean norm).  Let n = len(u)  Then the cosine distance is 1 - sum(u[i]*v[i] for i in range(n)).  On the other hand, the square of the euclidean distance is sum((u[i] - v[i])**2 for i in range(n)) = sum(u[i]*u[i] + v[i]*v[i] - 2*u[i]*v[i] for i in range(n)) = sum(u[i]*u[i]) + sum(v[i]*v[i]) - 2*sum(u[i]*v[i]) = 2 - 2*sum(u[i]*v[i]), which is twice the cosine distance.  So the metric provide the same information.
  
  ### Additional Info
  
  _No response_