An embedding is a numeric vector that represents the meaning or features of an item. Texts with similar meanings tend to receive nearby vectors, even when they use different words.
For search, the system embeds documents once and stores their vectors. It embeds each query with the same model, compares the query vector with stored vectors, and returns the nearest results. This supports matches such as “close my account” with “cancel a subscription.”
Embeddings are useful for semantic search, recommendations, clustering, and retrieval for language models. They are not perfect relevance labels. Quality also depends on chunking, metadata, index settings, and evaluation on real queries.
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