Graphs RAG, vectors, and connected context

Graph databases offer a significant advantage over traditional vector stores for RAG applications. While vector stores excel at naive/semantic similarity search using cosine similarity or Euclidean distance to retrieve similar chunks, graph databases add a layer of connected context. By combining vector embeddings with graph relationships, you can retrieve not just semantically similar chunks but also the related entities connected to those chunks, providing richer, more contextually complete results for AI applications.