research.
DPR consists in three models:
Question encoder: encode questions as vectors
Context encoder: encode contexts as vectors
Reader: extract the answer of the questions inside retrieved contexts, along with a relevance score (high if the
  inferred span actually answers the question).
DPR's pipeline (not implemented yet) uses a retrieval step to find the top k contexts given a certain question, and
then it calls the reader with the question and the retrieved documents to get the answer.
RAG
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, Patrick Lewis,
Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau
Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela
Retrieval-augmented generation ("RAG") models combine the powers of pretrained dense retrieval (DPR) and Seq2Seq
