Search by meaning, not just keyword the backbone of RAG
A vector database stores data as high-dimensional numerical embeddings and searches by semantic similarity rather than exact keyword match the infrastructure that lets a system find "documents about this topic" rather than only "documents containing this exact word."
Vector search is the retrieval half of RAG without it, grounding an LLM's answer in your own data means either feeding it everything (expensive and slow) or matching on keywords (missing anything phrased differently). Semantic search is what makes retrieval actually find the right context.
Vector databases are core infrastructure behind the RAG pipelines we build powering retrieval for AI features that need to ground their answers in a specific, searchable body of knowledge rather than general training data.