Embeddings from things like one-hot, count vectorization, tf-idf, etc into dimensionality reduction techniques like SVD and PCA have been around for a long time and also provided the ability to compare any two pieces of text to each other. Yes, neural networks and LLMs have provided the ability for the context of each word to affect the whole document's embedding and capture more meaning, potentially that pesky "semantic" sort even; but they still are fundamentally a dimensionality reduction technique.