RAG
----------------------------------------------------

Overview
~~~~~~~~~~~~~~~~~~~~~

Retrieval-augmented generation ("RAG") models combine the powers of pretrained dense retrieval (DPR) and
sequence-to-sequence models. RAG models retrieve documents, pass them to a seq2seq model, then marginalize to generate
outputs. The retriever and seq2seq modules are initialized from pretrained models, and fine-tuned jointly, allowing
both retrieval and generation to adapt to downstream tasks.

It is based on the paper `Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
<https://arxiv.org/abs/2005.11401>`__ by 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.

The abstract from the paper is the following:

*Large pre-trained language models have been shown to store factual knowledge
in their parameters, and achieve state-of-the-art results when fine-tuned on
downstream NLP tasks. However, their ability to access and precisely manipulate
knowledge is still limited, and hence on knowledge-intensive tasks, their
performance lags behind task-specific architectures. Additionally, providing
provenance for their decisions and updating their world knowledge remain open
research problems. Pre-trained models with a differentiable access mechanism to
explicit nonparametric memory can overcome this issue, but have so far been only
investigated for extractive downstream tasks. We explore a general-purpose
fine-tuning recipe for retrieval-augmented generation (RAG) — models which combine
pre-trained parametric and non-parametric memory for language generation. We
introduce RAG models where the parametric memory is a pre-trained seq2seq model and
the non-parametric memory is a dense vector index of Wikipedia, accessed with
a pre-trained neural retriever. We compare two RAG formulations, one which
conditions on the same retrieved passages across the whole generated sequence, the
other can use different passages per token. We fine-tune and evaluate our models
on a wide range of knowledge-intensive NLP tasks and set the state-of-the-art
on three open domain QA tasks, outperforming parametric seq2seq models and
task-specific retrieve-and-extract architectures. For language generation tasks, we
find that RAG models generate more specific, diverse and factual language than a
state-of-the-art parametric-only seq2seq baseline.*



RagConfig
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.RagConfig
    :members:


RagTokenizer
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.RagTokenizer
    :members: prepare_seq2seq_batch


Rag specific outputs
~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.modeling_rag.RetrievAugLMMarginOutput
    :members:

.. autoclass:: transformers.modeling_rag.RetrievAugLMOutput
    :members:

RagRetriever
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.RagRetriever
    :members:


RagModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.RagModel
    :members: forward


RagSequenceForGeneration
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.RagSequenceForGeneration
    :members: forward, generate


RagTokenForGeneration
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.RagTokenForGeneration
    :members: forward, generate
