It's doing abstractive summarization over the search results, using GPT-3. The pipeline is:

  - Search using Google
  - Run some filters to exclude SEO spam, etc.
  - Scrape the pages that are returned
  - Find chunks of text likely to align with the answer (comparing embeddings)
  - Feed the most likely chunks into GPT-3 to get a summary

It is leveraging GPT-3 to produce better summaries, and it isn't purely extractive - the LLM uses context and knowledge to generate a better summary.

I want to experiment with a local model next, versus using GPT-3.