  Generative retrieval, which is a new advanced paradigm for document
retrieval, has recently attracted research interests, since it encodes all
documents into the model and directly generates the retrieved documents.
However, its power is still underutilized since it heavily relies on the
"preprocessed" document identifiers (docids), thus limiting its retrieval
performance and ability to retrieve new documents. In this paper, we propose a
novel fully end-to-end retrieval paradigm. It can not only end-to-end learn the
best docids for existing and new documents automatically via a semantic
indexing module, but also perform end-to-end document retrieval via an
encoder-decoder-based generative model, namely Auto Search Indexer (ASI).
Besides, we design a reparameterization mechanism to combine the above two
modules into a joint optimization framework. Extensive experimental results
demonstrate the superiority of our model over advanced baselines on both public
and industrial datasets and also verify the ability to deal with new documents.
