chenmingxuan
2ddec53954
Localize the web console and persist custom model and capability settings. Use OpenAI Responses exclusively and move runtime limits into environment configuration. Bootstrap uv and the virtual environment on Windows, support optional China mirrors, and package source distributions without environments, build output, or credentials. Update regression tests and repository quality checks for the new behavior.
109 lines
3.1 KiB
Python
109 lines
3.1 KiB
Python
import asyncio
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import logging
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import pytest
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from artemis.services import llm
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@pytest.mark.asyncio
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async def test_timeout_wrapper_cancels_llm_call_when_caller_is_cancelled():
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started = asyncio.Event()
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child_cancelled = asyncio.Event()
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async def pending_llm_call():
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started.set()
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try:
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await asyncio.Event().wait()
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except asyncio.CancelledError:
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child_cancelled.set()
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raise
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invocation = asyncio.create_task(
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llm.invoke_llm_with_timeout_message(
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pending_llm_call(),
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timeout_seconds=60,
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)
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)
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await started.wait()
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invocation.cancel()
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with pytest.raises(asyncio.CancelledError):
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await invocation
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assert child_cancelled.is_set()
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class _RecordingEngine:
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current_step_id = "step-7"
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current_session_id = "session-1"
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def __init__(self):
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self.trace_kwargs = None
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self.published = None
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def record_trace(self, **kwargs):
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self.trace_kwargs = kwargs
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return "trace-1"
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def _publish(self, event_type, payload):
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self.published = (event_type, payload)
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def test_pause_error_is_persisted_and_published(monkeypatch, tmp_path):
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engine = _RecordingEngine()
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monkeypatch.setattr(llm, "_CURRENT_DATA_ENGINE", engine)
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monkeypatch.setattr(llm.settings, "TRACES_PATH", str(tmp_path / "traces"))
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pause_file = llm._handle_llm_pause_and_resume(RuntimeError("503 high demand"))
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assert pause_file.read_text() == "LLM Error: 503 high demand"
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assert engine.trace_kwargs == {
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"type": "llm_call",
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"name": "llm_pause",
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"payload": {"error": "503 high demand", "pause": True},
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"step_id": "step-7",
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"status": "failed",
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}
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event_type, payload = engine.published
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assert event_type == "task_paused"
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assert payload["error"] == "503 high demand"
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assert payload["step_id"] == "step-7"
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assert isinstance(payload["timestamp"], float)
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def test_terminal_failure_aggregates_observed_retries_and_total_wait(monkeypatch, tmp_path):
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engine = _RecordingEngine()
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monkeypatch.setattr(llm, "_CURRENT_DATA_ENGINE", engine)
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monkeypatch.setattr(llm.settings, "TRACES_PATH", str(tmp_path / "traces"))
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monkeypatch.setattr(llm.time, "time", lambda: 120.0)
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retries = [
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{
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"error": "503 high demand",
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"delay": 10.0,
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"provider": "openai",
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"source": "provider_sdk",
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"request_id": "request-2",
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"scheduled_at": 101.0,
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"timestamp": 101.0,
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}
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]
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request_token = llm._ACTIVE_LLM_REQUEST.set(
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{
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"request_id": "request-2",
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"provider": "openai",
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"started_at": 100.0,
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"retries": retries,
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}
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)
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try:
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llm._handle_llm_pause_and_resume(RuntimeError("503 high demand"))
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finally:
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llm._ACTIVE_LLM_REQUEST.reset(request_token)
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payload = engine.trace_kwargs["payload"]
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assert payload["request_id"] == "request-2"
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assert payload["provider"] == "openai"
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assert payload["waited_seconds"] == 20.0
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assert payload["retries"] == retries
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