Knowledge graphs and vector databases serve different purposes in RAG systems. Knowledge graphs excel at structured data with complex relationships, providing explainability and multi-hop reasoning ideal for domains like healthcare and finance. Vector databases handle unstructured data at scale, offering fast semantic
Table of contents
What is a knowledge graph?What is a vector database?How does RAG work?Do you need a vector database for RAG?When should you use a knowledge graph for RAG?When is a vector database better for RAG?What are the key differences between knowledge graphs and vector databases?What are the advantages of using knowledge graphs in RAG?What are the advantages of using vector databases in RAG?What are the limitations of knowledge graphs in RAG?What are the limitations of vector databases in RAG?How can you combine knowledge graphs and vector databases in RAG?How does semantic search differ in knowledge graphs and vector databases?What are popular vector databases used for RAG?Examples of knowledge graph platforms for RAGHow do you choose between a knowledge graph and a vector database for RAG?Choosing the right path: knowledge graph vs. vector database for RAGSort: