How to assign new observations to cluster using distance matrix and kmedoids?

I have a dataframe that holds the Word Mover's Distance between each document in my dataframe. I am running kmediods on this to generate clusters.

       1      2     3      4      5   
  1  0.00   0.05  0.07   0.04   0.05
  2  0.05   0.00  0.06   0.04   0.05
  3. 0.07   0.06  0.00   0.06   0.06
  4  0.04   0.04. 0.06   0.00   0.04
  5  0.05   0.05  0.06   0.04   0.00

  kmed = KMedoids(n_clusters= 3, random_state=123, method  ='pam').fit(distance)

After running on this initial matrix and generating clusters, I want to add new points to be clustered. After adding a new document to the distance matrix I end up with:

       1      2     3      4      5      6
  1  0.00   0.05  0.07   0.04   0.05   0.12
  2  0.05   0.00  0.06   0.04   0.05   0.21 
  3. 0.07   0.06  0.00   0.06   0.06   0.01
  4  0.04   0.04. 0.06   0.00   0.04   0.05
  5  0.05   0.05  0.06   0.04   0.00   0.12
  6. 0.12   0.21  0.01   0.05   0.12   0.00

I have tried using kmed.predict on the new row.

kmed.predict(new_distance.loc[-1: ])

However, this gives me an error of incompatible dimensions X.shape[1] == 6 while Y.shape[1] == 5.

How can I use this distance of the new document to determine which cluster it should be a part of? Is this even possible, or do I have to recompute clusters every time? Thanks!

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