Thank you Quentin.
I checked DPR in the Facebook Research repo some days ago and according to their DenseHSWFlatIndexer class, I came up with the following configuration that is the same of the official paper:

import faiss

store_n = 512 # neighbors to stode per node
ef_search = 128 # search depth
ef_construction = 200 # construction time search depth
vector_sz = 768 # vectors dimension
hnsw_index = faiss.IndexHNSWFlat(vector_sz, store_n)
hnsw_index.hnsw.efSearch = ef_search
hnsw_index.hnsw.efConstruction = ef_construction

docs.add_faiss_index(column='embeddings', custom_index=hnsw_index)

They wrote in the comments of the code the following:
# IndexHNSWFlat supports L2 similarity only
# so we have to apply DOT -> L2 similairy space conversion with the help of an extra dimension

Which at the time I did not understand very well. Thus, I just defined the dimension of the vectors as 768 but did not tell the index to use the maximum inner product metric instead of L2. Why do we have to tell the index to use the inner product?