A framework combining retrieval and generation models to produce accurate, context-rich responses.
More about Retriever-Generator Framework
Retriever-Generator Framework integrates a retrieval model and a generation model to create responses grounded in retrieved knowledge. The retriever fetches relevant information using methods like dense retrieval or hybrid search, while the generator uses this information to produce coherent, contextually accurate outputs.
This framework underpins systems like retrieval-augmented generation (RAG), enabling applications in question answering and knowledge-grounded generation.
