Looks similar to what we're doing in Pythagora with CodeMonkey agent (prompt: https://github.com/Pythagora-io/gpt-pilot/blob/main/core/prompts/code-monkey/descr

Looks similar to what we're doing in Pythagora with CodeMonkey agent (prompt: https://github.com/Pythagora-io/gpt-pilot/blob/main/core/prompts/code-monkey/describe_file.prompt https://github.com/Pythagora-io/gpt-pilot/blob/main/core/pro..., code: https://github.com/Pythagora-io/gpt-pilot/blob/main/core/agents/code_monkey.py#L91 https://github.com/Pythagora-io/gpt-pilot/blob/main/core/age...) I think everyone who's seriously tackled the "code RAG" problem is aware a naive vector approach doesn't work, and some hybrid approach is needed (see also Paul's comments on Aider). Intuitively, I expect a combo of lsp/treesitter directed by LLM + vector-RAG over "wiki" / metadata would be a viable approach. Very exciting to see all the research into this!