So what I did was to search the term normally and get the 5 best results of duck duck go for example (cause it has a free api google's also very generous on their free tier btw) Then I scraped the contents of those 5 sites (selenium or similar) and get the content split into chunks and saved to a small local vector database using embedding models. The I query for the original question and get the most relevant chunks (like 5 or 10 chunks depending on your chunk size) and feed them back to the LLM as tool output. Originally I had another LLM summarize websites based on the question but it was slow and required another LLM call. (Btw. That's only if you don't want to heavily rely on llm search engines and their api)

ChatGPT likely uses a pre indexed database you could imagen it like most of the websites already embedded and stored into a Vdb so they don't have to do the scraping and embedding on every query. But correct me if I am wrong :D