Integrate sparse and dense vectors to enhance knowledge retrieval in RAG using Amazon OpenSearch Service
In the context of Retrieval-Augmented Generation (RAG), knowledge retrieval plays a crucial role, because the effectiveness of retrieval directly impacts the maximum potential of large language model (LLM) generation. Currently, in RAG retrieval, the most common approach is to use semantic search based on dense vectors. However, dense embeddings do not perform well in understanding…