Advanced RAG: Multi-Query Retriever Approach

A Simple Retrieval-Augmented Generation (RAG) generates final results through a two-step process. First, the query is transformed into an embedding vector, which is then used to perform a similarity search against a pre-computed database of document vectors to retrieve the most relevant documents. After retrieving relevant documents, the RAG system merges their content with the original query to form a…