Not without other tooling. Things like langchain and llama_index would be good starting points. An approach would be to use llama_index to create embedding vectors for each section of the pdf, then you query and it gets a vector for your query -> gets the context -> puts it into gpt + your query -> returns the result.
I've seen people say it's better to ask gpt for a fake answer then use the embedding of that answer to search (so you're looking for context that looks like the answer). I don't know if that's supported in those tools.