Knowledge graph vs. vector database for RAG: which is best?

This post provides a technical comparison of knowledge graphs and vector databases for RAG systems, detailing their respective strengths, weaknesses, and use cases. It explains how RAG works and discusses the advantages and limitations of each approach, including scalability, retrieval performance, and data modeling. The post highlights Meilisearch's hybrid search capabilities as a solution that combines keyword matching, vector embeddings, and reranking in a single engine, simplifying RAG pipeline development and reducing infrastructure overhead. It also touches on the concept of semantic search within these different paradigms.