fix: Replace done_callback with coroutine chain for judge tracking

packages/sdk/server-ai/src/ldai/managed_model.py

@@ -62,19 +62,16 @@ def _track_judge_results(
         input_text: str,
         output_text: str,
     ) -> asyncio.Task[List[JudgeResult]]:
-        eval_task = self._ai_config.evaluator.evaluate(input_text, output_text)
-
-        def _on_done(task: asyncio.Task) -> None:
-            if task.cancelled():
-                return
-            if task.exception() is not None:
-                return
-            for r in task.result():
+        evaluator_task = self._ai_config.evaluator.evaluate(input_text, output_text)
+
+        async def _run_and_track(eval_task: asyncio.Task) -> List[JudgeResult]:
+            results = await eval_task
+            for r in results:
                 if r.success:
                     tracker.track_judge_result(r)
+            return results
 
-        eval_task.add_done_callback(_on_done)
-        return eval_task
+        return asyncio.create_task(_run_and_track(evaluator_task))
 
     def get_messages(self, include_config_messages: bool = False) -> List[LDMessage]:
         """

packages/sdk/server-ai/tests/test_managed_model.py

@@ -0,0 +1,237 @@
+"""Tests for ManagedModel — specifically the evaluations tracking chain."""
+
+import asyncio
+from typing import List
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+from ldai.evaluator import Evaluator
+from ldai.managed_model import ManagedModel
+from ldai.models import AICompletionConfig, LDMessage, ModelConfig, ProviderConfig
+from ldai.providers.types import JudgeResult, LDAIMetrics, ModelResponse
+from ldai.tracker import LDAIConfigTracker
+
+
+def _make_ai_completion_config(evaluator: Evaluator) -> AICompletionConfig:
+    """Build a minimal AICompletionConfig wired to the given evaluator."""
+    return AICompletionConfig(
+        key='test-config',
+        enabled=True,
+        create_tracker=MagicMock(return_value=MagicMock(spec=LDAIConfigTracker)),
+        model=ModelConfig('gpt-4'),
+        provider=ProviderConfig('openai'),
+        messages=[LDMessage(role='system', content='You are helpful.')],
+        evaluator=evaluator,
+    )
+
+
+def _make_model_response(content: str = 'response text') -> ModelResponse:
+    return ModelResponse(
+        message=LDMessage(role='assistant', content=content),
+        metrics=LDAIMetrics(success=True, usage=None),
+    )
+
+
+class TestManagedModelInvokeReturnsImmediately:
+    """invoke() must return before the evaluations task resolves."""
+
+    @pytest.mark.asyncio
+    async def test_invoke_returns_before_evaluations_resolve(self):
+        """invoke() should return a ModelResponse before evaluations complete."""
+        # Set up a barrier so the evaluation coroutine doesn't complete until we release it
+        barrier = asyncio.Event()
+
+        async def _slow_evaluate(input_text: str, output_text: str) -> List[JudgeResult]:
+            await barrier.wait()
+            return []
+
+        evaluator = MagicMock(spec=Evaluator)
+        evaluator.evaluate = MagicMock(
+            side_effect=lambda i, o: asyncio.create_task(_slow_evaluate(i, o))
+        )
+
+        mock_runner = MagicMock()
+        mock_runner.invoke_model = AsyncMock(return_value=_make_model_response())
+
+        config = _make_ai_completion_config(evaluator)
+        mock_tracker = MagicMock(spec=LDAIConfigTracker)
+        mock_tracker.track_metrics_of_async = AsyncMock(return_value=_make_model_response())
+        config = AICompletionConfig(
+            key='test-config',
+            enabled=True,
+            create_tracker=MagicMock(return_value=mock_tracker),
+            model=ModelConfig('gpt-4'),
+            provider=ProviderConfig('openai'),
+            messages=[],
+            evaluator=evaluator,
+        )
+
+        model = ManagedModel(config, mock_runner)
+        response = await model.invoke('Hello')
+
+        # invoke() returned — evaluations task should still be pending
+        assert response is not None
+        assert response.evaluations is not None
+        assert not response.evaluations.done(), "evaluations task should still be pending"
+
+        # Release the barrier and let it finish cleanly
+        barrier.set()
+        await response.evaluations
+
+    @pytest.mark.asyncio
+    async def test_await_evaluations_collects_results(self):
+        """await response.evaluations should return the list of JudgeResult instances."""
+        judge_result = JudgeResult(
+            judge_config_key='judge-key',
+            success=True,
+            sampled=True,
+            metric_key='$ld:ai:judge:relevance',
+            score=0.9,
+            reasoning='Good response',
+        )
+
+        async def _evaluate_coro(input_text: str, output_text: str) -> List[JudgeResult]:
+            return [judge_result]
+
+        evaluator = MagicMock(spec=Evaluator)
+        evaluator.evaluate = MagicMock(
+            side_effect=lambda i, o: asyncio.create_task(_evaluate_coro(i, o))
+        )
+
+        mock_runner = MagicMock()
+        mock_runner.invoke_model = AsyncMock(return_value=_make_model_response())
+
+        mock_tracker = MagicMock(spec=LDAIConfigTracker)
+        mock_tracker.track_metrics_of_async = AsyncMock(return_value=_make_model_response())
+        config = AICompletionConfig(
+            key='test-config',
+            enabled=True,
+            create_tracker=MagicMock(return_value=mock_tracker),
+            model=ModelConfig('gpt-4'),
+            provider=ProviderConfig('openai'),
+            messages=[],
+            evaluator=evaluator,
+        )
+
+        model = ManagedModel(config, mock_runner)
+        response = await model.invoke('Hello')
+
+        results = await response.evaluations  # type: ignore[misc]
+        assert results == [judge_result]
+
+    @pytest.mark.asyncio
+    async def test_tracking_fires_inside_awaited_chain(self):
+        """tracker.track_judge_result() must be called when evaluations are awaited."""
+        judge_result = JudgeResult(
+            judge_config_key='judge-key',
+            success=True,
+            sampled=True,
+            metric_key='$ld:ai:judge:relevance',
+            score=0.85,
+            reasoning='Relevant answer',
+        )
+
+        async def _evaluate_coro(input_text: str, output_text: str) -> List[JudgeResult]:
+            return [judge_result]
+
+        evaluator = MagicMock(spec=Evaluator)
+        evaluator.evaluate = MagicMock(
+            side_effect=lambda i, o: asyncio.create_task(_evaluate_coro(i, o))
+        )
+
+        mock_runner = MagicMock()
+        mock_runner.invoke_model = AsyncMock(return_value=_make_model_response())
+
+        mock_tracker = MagicMock(spec=LDAIConfigTracker)
+        mock_tracker.track_metrics_of_async = AsyncMock(return_value=_make_model_response())
+        mock_tracker.track_judge_result = MagicMock()
+
+        config = AICompletionConfig(
+            key='test-config',
+            enabled=True,
+            create_tracker=MagicMock(return_value=mock_tracker),
+            model=ModelConfig('gpt-4'),
+            provider=ProviderConfig('openai'),
+            messages=[],
+            evaluator=evaluator,
+        )
+
+        model = ManagedModel(config, mock_runner)
+        response = await model.invoke('Hello')
+
+        # Tracking should NOT have fired yet (before we await evaluations)
+        mock_tracker.track_judge_result.assert_not_called()
+
+        # Now await the evaluations task — tracking fires inside the chain
+        await response.evaluations  # type: ignore[misc]
+
+        mock_tracker.track_judge_result.assert_called_once_with(judge_result)
+
+    @pytest.mark.asyncio
+    async def test_tracking_not_called_for_failed_judge_result(self):
+        """tracker.track_judge_result() should NOT be called for unsuccessful judge results."""
+        failed_result = JudgeResult(
+            success=False,
+            sampled=True,
+            metric_key='$ld:ai:judge:relevance',
+            error_message='Judge evaluation failed',
+        )
+
+        async def _evaluate_coro(input_text: str, output_text: str) -> List[JudgeResult]:
+            return [failed_result]
+
+        evaluator = MagicMock(spec=Evaluator)
+        evaluator.evaluate = MagicMock(
+            side_effect=lambda i, o: asyncio.create_task(_evaluate_coro(i, o))
+        )
+
+        mock_runner = MagicMock()
+        mock_runner.invoke_model = AsyncMock(return_value=_make_model_response())
+
+        mock_tracker = MagicMock(spec=LDAIConfigTracker)
+        mock_tracker.track_metrics_of_async = AsyncMock(return_value=_make_model_response())
+        mock_tracker.track_judge_result = MagicMock()
+
+        config = AICompletionConfig(
+            key='test-config',
+            enabled=True,
+            create_tracker=MagicMock(return_value=mock_tracker),
+            model=ModelConfig('gpt-4'),
+            provider=ProviderConfig('openai'),
+            messages=[],
+            evaluator=evaluator,
+        )
+
+        model = ManagedModel(config, mock_runner)
+        response = await model.invoke('Hello')
+        await response.evaluations  # type: ignore[misc]
+
+        mock_tracker.track_judge_result.assert_not_called()
+
+    @pytest.mark.asyncio
+    async def test_noop_evaluator_returns_empty_list(self):
+        """With a noop evaluator, awaiting evaluations should return an empty list."""
+        evaluator = Evaluator.noop()
+
+        mock_runner = MagicMock()
+        mock_runner.invoke_model = AsyncMock(return_value=_make_model_response())
+
+        mock_tracker = MagicMock(spec=LDAIConfigTracker)
+        mock_tracker.track_metrics_of_async = AsyncMock(return_value=_make_model_response())
+
+        config = AICompletionConfig(
+            key='test-config',
+            enabled=True,
+            create_tracker=MagicMock(return_value=mock_tracker),
+            model=ModelConfig('gpt-4'),
+            provider=ProviderConfig('openai'),
+            messages=[],
+            evaluator=evaluator,
+        )
+
+        model = ManagedModel(config, mock_runner)
+        response = await model.invoke('Hello')
+        results = await response.evaluations  # type: ignore[misc]
+
+        assert results == []