Numerical vector representations of text, images, or other data that capture semantic meaning. Embeddings allow AI systems to measure similarity between concepts by comparing positions in high-dimensional vector space.
An embedding converts text into a list of numbers (typically 768–3,072 dimensions) that represent its meaning. Texts with similar meanings have embeddings that are close together in vector space.
Embeddings power semantic search, recommendation systems, and RAG pipelines. Instead of keyword matching, you convert a query to an embedding and find database entries with the most similar embeddings — retrieving conceptually related content even without exact keyword overlap.
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