Enhancing Your RAG with Knowledge Graphs Using KnowHow

Retrieval Augmented Generation (RAG) is a technique that enhances large language models (LLMs) by providing them with additional knowledge and long-term memories through vector databases like Milvus and Zilliz Cloud. While RAG can address many LLM headaches, it may be insufficient for more advanced requirements such as customization or greater control of the retrieved results. Knowledge Graphs (KG) can be incorporated into the RAG pipeline to improve performance and accuracy. By integrating KGs with RAG systems, users can enhance contextual understanding, improve accuracy and factual consistency, enable multi-hop reasoning capabilities, facilitate efficient information retrieval, provide transparent and traceable outputs, synthesize knowledge across domains, and handle ambiguity more effectively.