How does Retrieval-Augmented Generation leverage Vector Databases?

23 Eki 07:45

I'm trying to understand the interplay between Retrieval‑Augmented Generation (RAG) and vector databases. Specifically, how does the vector store index and retrieve dense embeddings to provide relevant context for the generator? What are the typical steps for building the embedding store, handling updates, and ensuring low‑latency retrieval at inference time? Any insights or recommended approaches would be appreciated.