Advanced RAG architectures
RAG for Optimizing Generative AI - Response generation from LLMs enhanced by information retrieval

The basic RAG architecture can be made more complex, to better handle sophisticated questions that require several sources of information, that rely on expert knowledge, or that need to follow an incremental exploratory approach. The three variants presented here propose a dynamic adaptation, depending on the nature and content of the questions, which exploits orchestration and memory mechanisms applicable to query sequences and which are integrated into frameworks such as QueryPipeline and QueryEngineTool from LlamaIndex (§