What you’re asking is precisely the nature of GPT Plugins. But it is possible to do this today if you shape the solution in a real-time fashion.
- Imagine an embeddings-based chatbot that uses vectors that point to answers (in a database).
- Imagine those answers also contain “pointers” to dynamic values related to that vector.
- Such “pointers” are actually API references to the latest values for these dynamic values.
- The ChatBot app uses embeddings to locate the best answer; it executes the API references, bundles all the data into a sensible prompt, and sends it to completion.
It’s not rocket science. It’s a simple matter of architecture and programming.
I’ve done it here using Coda and CustomGPT, and here using Mem, Airtable, and Pinecone.
