Yeah, they do store n-dimensional vectors, but they're part of the LLM zeitgest. The basic idea is that given a particular corpus of text, an LLM can produce a

Yeah, they do store n-dimensional vectors, but they're part of the LLM zeitgest. The basic idea is that given a particular corpus of text, an LLM can produce a very high-dimensional vector that encodes the features of that text, according to its training parameters. Vectors that are near each other in the high-dimensional space represent pieces of text which the LLM "thinks" are similar to each other. So, by using databases which can efficiently answer geometric nearest-neighbor questions, you can quickly search for chunks of text that are similar to each other.