No, this is the current state of the art: https://supabase.com/blog/chatgpt-supabase-docs

  It's built with Supabase/Postgres, and consists of several key parts:
  
  Parsing the Supabase docs into sections.
  Creating embeddings for each section using OpenAI's embeddings API.
  Storing the embeddings in Postgres using the pgvector extension.
  Getting a user's question.
  Query the Postgres database for the most relevant documents related to the question.
  Inject these documents as context for GPT-3 to reference in its answer.
  Streaming the results back to the user in realtime.

The same thing could be done with search engine results and from recent demos it looks like this is the kind of analytic augmentation that MS and OpenAI have added to Bing.