I see this topic was already answered in Github 10 from Quentin.
So, I’d love to add the answer here for convenience 

The retriever is now part of the nlp library.
You can install it with
pip install datasets

and load the retriever:
from datasets import load_dataset 


wiki = load_dataset("wiki_dpr", with_embeddings=False, with_index=True, split="train")
The retriever is basically a dense index over wikipedia passages.
To query it using the DPR question encoder you can do:

from transformers import DPRQuestionEncoderTokenizer, DPRQuestionEncoder 
question_tokenizer = DPRQuestionEncoderTokenizer.from_pretrained('facebook/dpr-question_encoder-single-nq-base') 
question_encoder = DPRQuestionEncoder.from_pretrained('facebook/dpr-question_encoder-single-nq-base') 
question = "What is love ?" 

question_emb = question_encoder(**question_tokenizer(question, return_tensors="pt"))[0].detach().numpy() 

passages_scores, passages = wiki.get_nearest_examples("embeddings", question_emb, k=20) # get k nearest neighbors