These vectors are lower-dimensional than traditional vectors though, aren't they? Vector embeddings are in the hundreds to low thousands range of dimensions (roughly between 128-1024), whereas TF-IDF has the same dimension as your vocabulary. It's also not just about being flat-out better, but about increasing the recall of queries, as you're grabbing content that doesn't contain the keywords directly, but is still relevant. You are also free to mix the two approaches together in one result set, which gives the best of both.