Retrieval-augmented generation (RAG) techniques have proven to be effective
in integrating up-to-date information, mitigating hallucinations, and enhancing
response quality, particularly in specialized domains. While many RAG
approaches have been proposed to enhance large language models through
query-dependent retrievals, these approaches still suffer from their complex
implementation and prolonged response times. Typically, a RAG workflow involves
multiple processing steps, each of which can be executed in various ways. Here,
we investigate existing RAG approaches and their potential combinations to
identify optimal RAG practices. Through extensive experiments, we suggest
several strategies for deploying RAG that balance both performance and
efficiency. Moreover, we demonstrate that multimodal retrieval techniques can
significantly enhance question-answering capabilities about visual inputs and
accelerate the generation of multimodal content using a "retrieval as
generation" strategy.

通过研究现有的 RAG 方法及其潜在组合，我们提出了几种既能兼顾性能又能提高效率的 RAG 策略，并证明多模态检索技术能显著增强对视觉输入的问答能力，并使用 “检索即生成” 策略加速多模态内容的生成。