A vector database stores data as high-dimensional numerical vectors (embeddings) and enables fast similarity searches — finding the most semantically similar items to a query. Unlike traditional relational databases that match exact values, vector databases return results based on conceptual closeness. They are a core component of RAG systems, AI-powered search, and recommendation engines. Popular vector databases include Pinecone, Weaviate, Chroma, and pgvector. They enable AI applications to access and retrieve relevant information efficiently at scale.
Vector databases are the retrieval layer behind most production chatbots, powering the similarity search that lets a bot answer from your own content. We build on this architecture in our AI chatbot development services.
