Retrieval-Augmented Generation (RAG) is a prevalent approach to infuse a
private knowledge base of documents with Large Language Models (LLM) to build
Generative Q\&A (Question-Answering) systems. However, RAG accuracy becomes
increasingly challenging as the corpus of documents scales up, with Retrievers
playing an outsized role in the overall RAG accuracy by extracting the most
relevant document from the corpus to provide context to the LLM. In this paper,
we propose the 'Blended RAG' method of leveraging semantic search techniques,
such as Dense Vector indexes and Sparse Encoder indexes, blended with hybrid
query strategies. Our study achieves better retrieval results and sets new
benchmarks for IR (Information Retrieval) datasets like NQ and TREC-COVID
datasets. We further extend such a 'Blended Retriever' to the RAG system to
demonstrate far superior results on Generative Q\&A datasets like SQUAD, even
surpassing fine-tuning performance.

基于 Retrieval-Augmented Generation (RAG) 方法，结合语义搜索技术，如稠密向量索引和稀疏编码器索引，以及混合查询策略，我们提出了 ' 混合 RAG' 方法。通过在 IR 数据集和 Generative Q&A 数据集上取得更好的检索结果并创造新的基准，我们进一步将这种 ' 混合 Retriever' 扩展到 RAG 系统，甚至超过 fine-tuning 性能。