This is happening already. The trick is to run a search against an existing search engine, then copy and paste the search results into the language model and ask it to answer questions based on what you provide it.
A small difference between the pattern you describe and the one of the inquiry is where responsibility lies for retrieving and incorporating the augmentation. You describe the pattern where an orchestration layer sits in front of the model, performs the retrieval, and then determines how to serve that information down to the model. The inquiry asks about whether the AI/model itself can perform the retrieval and incorporation function.
It’s a small difference, perhaps, but with some significance since the retrieval and incorporation occurring outside the model has a different set of trade offs. I’m not specifically aware of any work where model architectures are being extended to perform this function directly, but I am keen to learn of such efforts.
This is how the new Bing Assistant works. It's also how search engines like https://you.com/ and https://www.perplexity.ai/ work - as exposed by a prompt leak attack against Perplexity a few weeks ago: https://simonwillison.net/2023/Jan/22/perplexityai/
I wrote a tutorial about one way of implementing this pattern yourself here: https://simonwillison.net/2023/Jan/13/semantic-search-answer...