You can think of a full-text index as being like a vector database that's highly specialized and optimized for the use-case where your documents and queries are both represented as "bags of words", i.e. very high-dimensional and very sparse.

Which works great when you want to retrieve documents that actually contain the specific keywords in your search query, as opposed to using embeddings to find something roughly in the same semantic ballpark.