As far as my understanding goes, there are multiple parts to what you are bringing up. In your simplified example of a 2D plane, yeah, you are right, I think.

As far as my understanding goes, there are multiple parts to what you are bringing up. In your simplified example of a 2D plane, yeah, you are right, I think. However that's why we have more than two dimensions. E.g. If we just have a few more dimensions, we could have one dimension per attribute "lion-ness", "bear-ness", "tiger-ness" of the document, so going higher on the "lion-ness" dimension doesn't have any impact on the other two dimensions, and the conflict is resolved. Of course in practice, we can't spare one dimension per animal, and that's where embedding models come in, which take care of mapping a string of text into a high-dimensional vector space, and in the process compresses topics, context, etc.. If that model were to be constructed badly, it is possible that we may actually end up with such a bug as you described, where mention of multiple topics produces a not-so-optimal embedding. Another part that's also relevant in practice is that embedding vectors can be very sensitive (depending on model used) to how you "chunk" (= divide the input string). E.g. if you create chunks where each sentence that is about a bear, a tiger and a lion are separated you will of course get clearer embeddings regarding the individual animals. If you chunk all of them together you will probably receive an "apex-predator"-like embedding, that performs better if you are doing vector search on a more generalized level. That can indeed cause a challenge if you'd like to implementa generalized search that can handle both individual word retrieval as well as concept based retrieval.