the augmentation techniques. The paper highlights
the state-of-the-art
technologies embedded in
each of these critical components, providing a
profound understanding of the advancements in
RAG systems. Furthermore, this paper introduces
the metrics and benchmarks for assessing RAG
models, along with the most up-to-date evaluation
In conclusion, the paper delineates
framework.
prospective avenues for research,
including the
identification of challenges,
the expansion of
multi-modalities, and the progression of the RAG
infrastructure and its ecosystem. 1.
RAG forLargeLanguageModels-ASurvey