A dense numeric representation of text that captures meaning and intent, so machines can compare content by similarity rather than keywords.
A vector embedding is a dense list of numbers that represents a piece of text as a point in "meaning space." Embedding models learn to place text so that similar meanings land near each other, letting machines compare content by intent rather than by matching keywords.
This is how modern search actually reads your content. When Google processes a query or crawls a page, it produces embeddings to judge query intent, content relevance, and the relationships between concepts and entities — not just literal word overlap. Embeddings are the input to vector search and the "retrieve" half of retrieval-augmented generation.
Google's open EmbeddingGemma model gives a rare window into how its own embeddings behave, and choices like dimension size and quantisation (see vector embedding optimization) trade accuracy against speed and storage.
