Kumar M.

Really sorry Mikyo. I might be doing something wrong here but no luck for me:

One last question and I am done. Can you point me to any articles on “Alternatively you can export and write your traces to remote cloud storage and view them via Phoenix when you need to.”

my email is kmantripragada@raptorsdigital.com

An intro would be great as it would significantly reduce my time to research and try out things that might not work

def launch_app( primary: Optional[Dataset] = None, reference: Optional[Dataset] = None, corpus: Optional[Dataset] = None, trace: Optional[TraceDataset] = None, host: Optional[str] = None, port: Optional[int] = None, run_in_thread: Optional[bool] = True, ) -> Session

Looks like we can run on a central host as per the api documentation at https://docs.arize.com/phoenix/api/session

Any help here from your product/support team would be great. I can get on a call as well as this is time sensitive for me. Thanks a bunch

Best case scenarios for multiple AI engineers working on multiple LLM apps: Install phoenix on a central EC2 or Container for tracking all LLM Applications. Write to this central phoenix server from each notebook using a unique application id.