How To Improve RAG Quality by Storing Knowledge Graph in Vector Database

Retrieval-Augmented Generation (RAG) techniques, by integrating external knowledge bases, provide additional contextual information for LLMs, effectively alleviating issues such as hallucination and insufficient domain knowledge of LLM. However, relying solely on general knowledge bases has its limitations, especially when dealing with complex entity relationships and multi-hop questions, where the model often struggles to provide accurate answers.  Introducing Knowledge Graphs (KGs) into the RAG system provides a new solution to this problem. KGs present entities and their relationships in a structured manner, offering more refined contextual information during retrieval. By leveraging the abundant relational data of KGs, RAG can not only pinpoint relevant knowledge more accurately but also better handle complex question-answering scenarios, such as comparing entity relationships or answering multi-hop questions.