How do you do that on 5 million documents?
People are usually not querying across 5 million documents in a single scope.
If you want something as simple as "suggest similar tweets" or something across millions of things then embeddings still work.
But if you want something like "compare the documents across these three projects" then you would use full text metadata extraction. Keywords, summaries, table of contents, etc to determine data about each document and each chunk.
To give a real world example, the way Claude Code works versus how Cursor's embedded database works.