The Mechanics of Vector-Based Document Search
A technical overview of the underlying processes that power semantic discovery through embeddings and vector logic.
Questions answered
how does semantic search work
It works by transforming text into numerical vectors (embeddings) that represent meaning. When a query is made, the system calculates the mathematical proximity between the query vector and the data vectors in your enterprise graph to find the closest conceptual matches.
how does ai document search work semantic search vector embeddings
AI document search uses neural networks to generate vector embeddings for every paragraph in your documents. These embeddings are stored in a vector database, allowing for rapid similarity searches that identify relevant content even if the exact keywords are missing.
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