RAG does not just mean similarity search. It means retrieving all relevant content, including the AST dependencies. Whatever you would want to know if you were to answer the query yourself.
Than it must be able to search every book and paper ever written because when it comes to deciding if an algorithm is correct I need to read the original paper that defined it and any updates in the literature since then.
Since that rag system doesn't, and probably will never, exist we are stuck with vector embeddings as the common definition everyone working in the field uses and understands.
If you were to do this by hand, would you search every book and paper ever written? That is not feasible so you have to make a trade-off.
For alternatives to vector search, see GraphRAG and AST parsing; e.g., https://vxrl.medium.com/enhancing-llm-code-generation-with-r... or https://github.com/sankalp1999/code_qa
That's what google scholar is for. Use it to find the meta analysis papers and go from there.
Which incidentally shows why RAG just means vector store + embedding model, since your definition means different things to different people and an implementation can't exist until we figure out AGI.