test(otel): drop the wall-clock bound from the long-prompt attribute fit test

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
This commit is contained in:
yucheng 2026-10-04 10:19:32 +00:00 • committed by Yucheng He
parent b21e8ea009
commit e7aea5d0b8

View file

@ -6,7 +6,6 @@ backends, so one trace lights up every configured destination.
"""
import json
import time
from collections.abc import Mapping
from itertools import chain
from typing import Final
@ -434,8 +433,8 @@ def test_openinference_raw_tool_arguments_fall_back_to_repr_instead_of_raising()
)
)
attrs: Final = OpenInferenceMapper().map(data)
assert (
attrs["llm.output_messages.0.message.tool_calls.0.tool_call.function.arguments"] == repr(raw_arguments)
assert attrs["llm.output_messages.0.message.tool_calls.0.tool_call.function.arguments"] == repr(
raw_arguments
), label
assert attrs["llm.output_messages.0.message.role"] == "assistant"
@ -482,24 +481,13 @@ def test_openinference_payload_tool_arguments_with_raw_objects_map_without_raisi
assert json.loads(attrs["metadata"]) == {"user_api_key_alias": "edge-key"}
def test_openinference_attribute_fit_stays_linear_on_long_prompts():
"""Attribute fitting is linear in the message count, not quadratic.
Rescanning the full group map once per message made the fit quadratic in
the prompt length (~1.9s of callback time at 8000 messages); indexing the
groups once keeps it in milliseconds. The bound sits far above the linear
runtime so it cannot flake on slow runners, while the quadratic path
exceeds it several times over.
"""
def test_openinference_attribute_fit_keeps_pinned_messages_on_long_prompts():
messages: Final = _long_prompt_messages(4000)
mapped: Final = OpenInferenceMapper().map(
_llm_call(messages_in=messages, promoted_metadata={"user_api_key_alias": "k"})
)
started: Final = time.perf_counter()
fitted: Final = fit_indexed_messages(mapped, 128)
elapsed: Final = time.perf_counter() - started
assert elapsed < 0.25
assert len(fitted) <= 128
assert fitted["llm.input_messages.0.message.role"] == "user"
assert fitted["llm.input_messages.3999.message.role"] == "assistant"