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Technical implementation · AI Search Infrastructure

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Definition

A vector database is a specialized database that stores content as high-dimensional numerical vectors — mathematical representations of meaning — rather than as text. It enables AI systems to find semantically similar content at speed, even when the exact words don’t match. Vector databases are the infrastructure layer that makes RAG systems work. When a user submits a query to an AI search tool, the system converts the query to a vector, searches the vector database for content with similar meaning, and retrieves the most relevant passages to inform its answer. Understanding vector databases helps explain why semantic relevance matters more than keyword matching — the retrieval system is operating on meaning, not text.

RAG

Embedding

Semantic search

Dense retrieval

Retrieval pipeline

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