The problem with your suggested approach is the resulting lack of holistic context. The problem of OP's approach (direct parsing) is cost and context-window-limits. There has to be a better way.
I don't think it will forget anything as long as everything fits in the context window, but I could totally be wrong. That's the big problem with the "give it everything" approach: if your codebase doesn't fit then it's game over. I've had success limiting what I give it to the relevant files.
Right- "forgetting" assuming a rolling context window maxed at the models max token count. "If it doesn't fit then it's game over" - assuming 8k tokens with each token being ~4 characters, that's a pretty small repo. And that's a motivator behind the similarity search approach.
Using the "give it everything" method will cause it to forget most of what you're feeding it if you have a large repo anyway, right?