Codebase-scale retrieval using AST-derived graphs + BM25 — reducing LLM context from 100K to 5K tokens [D]

Wanted to share an approach I've been using for retrieval-augmented generation over large codebases and get feedback from people thinking about similar problems. The problem Naive codebase RAG typically works by chunking files into text segments and embedding them for similarity search. This breaks down on code because semantic similarity at the chunk level doesn't capture structural relationships - a function in file A calling a type defined in file C won't surface that dependency through embedding proximity alone.