Embeddings
An embedding is a list of numbers that represents the meaning of a piece of text, so that texts with similar meanings end up with similar numbers — letting software compare ideas rather than just matching keywords.
How it works
A model reads a sentence and outputs a long list of numbers (a vector). Two sentences that mean similar things — “what are your opening hours?” and “when are you open?” — produce vectors that sit close together, even though they share almost no words.
This is what makes meaning-based search possible: instead of matching exact words, the system measures how close two embeddings are to judge whether they’re about the same topic.
How SiteMind uses it
When SiteMind reads your website, it creates embeddings for each piece of content. At question time it embeds the visitor’s question and finds the closest matching passages — so it understands what someone means, not just the words they typed.
Related terms
Vector database
A vector database stores content as embeddings and can instantly find the pieces whose meaning is closest to a query.
Semantic search
Semantic search finds content by meaning rather than exact keywords, so it can match a question to the right answer even when the words differ.
Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) is a method where an AI looks up relevant information from a trusted source before answering, so its replies are grounded in facts instead of guesses.
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