Srilakshmi C.

Hi Damith S., thanks for your question! You can upload your experiment runs when you create the experiment, via createExperiment. There isn't necessarily a separate "write experiment results" endpoint; you will include everything in the experimentRuns array when calling that function. a possible workflow you could follow is:

Hassan K., what endpoint are you using for your project? you may have to update your client code:

from phoenix.client import Client
px_client = Client(endpoint="http://your-phoenix-server:6006")

also, the '503 Bad Gateway' message typically means that it might just be a network issue, meaning that the Phoenix server was probably unavailable or not responding to the client request. This is a server-side issue, and may not be a problem with your code syntax. could you try respinning phoenix instance and run these again?

hey Hassan K., was looking into that this morning. I’ll do some checking and will respond here when I have an update!

okay got it. I'll go ahead and try to reproduce this error. In the meantime could you try: (1) hard refresh / incognito (cache mismatch can cause JSON.parse errors), (2) restart the Phoenix container, (3) upgrade to latest Phoenix, and (4) filter to a smaller time window before loading spans (default pulls all spans) & let me know if the issue is still persistent & we'll try to see what else we can do!

Thanks for your question Tomer R.! Let us know if you have any other questions!

the labs are fairly walkthroughs so not too much extra time spent there. I would say maybe max an extra hour for all labs combined

Hi Nas, I think the bot here is mostly correct! To answer your questions, 1) message.reasoning_ is unfortunately not supported today in open inference but you can make a ticket here! 2) From my understanding, I think you can do the 2 message workaround. I think another potential could be making a child span with the reasoning content? meaning, a child span named reasoning with its own llm.output_messages[0].message.content 3) I would suggest opening an issue abt this into the openinference repo as an enhancement! Hope this helps & let me know if there's anything else we can help with!

Hi Adam H. , Prompt name will map to one prompt ID. within a prompt, if you have multiple prompt versions, each of those can be differentiated with the version_id. for how to get these prompts into your code, you can run this code block, but replace with your prompt name:

from arize.experimental.prompt_hub import ArizePromptClient, Prompt, LLMProvider

client = ArizePromptClient(api_key=os.environ["ARIZE_API_KEY"], space_id=os.environ["ARIZE_SPACE_ID"])

//replace the line below with your prompt name
prompt = client.pull_prompt("test-basic")

//or if you want to pull all your prompts, you can run the following line
prompts = client.pull_prompts()

more info on the code functions here: https://arize.com/docs/ax/reference/reference/prompt-hub-api#core-components In the UI, it might look something like this. The prompt ID & prompt name, found in the top left, will stay the same regardless of which version you are looking at. The version ID will change from version to version.

Hi Shubham A., you can get your annotations by pulling down your trace dataset in AX & your annotations should be in that dataset by default. Here's a code snippet that might help:

from arize.exporter import ArizeExportClient
from arize.utils.types import Environments
from datetime import datetime, timedelta, timezone

client = ArizeExportClient(api_key=os.environ["ARIZE_API_KEY"])
model_id = "[insert project name]"

primary_df = client.export_model_to_df(
    space_id=os.environ["ARIZE_SPACE_ID"],
    model_id=model_id,
    environment=Environments.TRACING,
    start_time=datetime.now(timezone.utc) - timedelta(days=365),
    end_time=datetime.now(timezone.utc)
)

primary_df['annotation.test.label']

Hope this helps for now! Please feel free to ask any more questions you have