Update existing traces

How updates work

Every trace streamed to Openlayer has an inference_id, which is a unique identifier.

If you provide your own inference IDs, you can easily reference and update those traces later.

If you don’t, Openlayer auto-generates them for you.

For maximum flexibility, set custom inference IDs when tracing. This makes it simple to tie traces to feedback, business outcomes, or other systems.

Example: Add a ground truth

Let’s say you want to add a ground_truth column for a previously logged trace.

from openlayer import Openlayer
from openlayer.types.inference_pipelines import row_update_params

row_updates = {
    "ground_truth": "The sun is 94.471 million miles from the earth."
}

config = row_update_params.Config(
    ground_truth_column_name="ground_truth"
)

client = Openlayer()
client.inference_pipelines.rows.update(
    inference_pipeline_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    inference_id="832y98d3",
    row=row_updates,
    config=config,
)

This updates the trace with inference_id="832y98d3" by attaching a ground truth.

Using custom inference IDs

When tracing with the @trace decorator, you can set your own inference IDs. This makes it easy to correlate requests with later feedback or business signals.

from openlayer.lib import trace, update_current_trace

@trace()
def process_chat_message(user_id: str, message: str, conversation_id: str):
    custom_id = f"chat_{conversation_id}_{user_id}"
    update_current_trace(inference_id=custom_id)

    response = generate_ai_response(message)

    # Store custom_id in your DB for later updates
    store_for_feedback(custom_id, user_id, message, response)

    return response

Later, use that custom ID (chat_{conversation_id}_{user_id}) in your update calls.

​How updates work

​Example: Add a ground truth

​Using custom inference IDs

How updates work

Example: Add a ground truth

Using custom inference IDs