The RAG workflow comprises three key steps. First, the
corpus is partitioned into discrete chunks, upon which vec-
tor indices are constructed utilizing an encoder model. Sec-
ond, RAG identifies and retrieves chunks based on their vec-
tor similarity to the query and indexed chunks. Finally, the
model synthesizes a response conditioned on the contextual
information gleaned from the retrieved chunks. These steps
form the fundamental framework of the RAG process, under-
pinning its information retrieval and context-aware genera-
tion capabilities. Next, we will provide an introduction to the
RAG research framework.
3 RAG Framework
The RAG research paradigm is continuously evolving, and
this section primarily delineates its progression. We cate-
gorize it into three types: Naive RAG, Advanced RAG, and
Modular RAG. While RAG were cost-effective and surpassed
the performance of the native LLM, they also exhibited sev-
eral limitations. The development of Advanced RAG and
RAG forLargeLanguageModels-ASurvey