A vector database is a system built to store embeddings and quickly find the items most similar in meaning to a given query. Instead of matching keywords, it searches by closeness in numeric space. This makes it a core building block for semantic search and retrieval-augmented generation, where an AI needs to pull the most relevant documents from a large collection.
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