mirror of
https://github.com/open-jarvis/OpenJarvis.git
synced 2026-07-28 14:07:55 +00:00
928776a71c
CI's lint job ran ruff check but never ruff format --check, letting format drift land silently (79 files had drifted from the pinned ruff 0.15.1). Add the ruff format --check step to ci.yml, reformat the 79 drifted files with the pinned ruff (mechanical only — verified AST-identical to before across all files, no logic changes), and add a Makefile whose test target mirrors the actual CI lane. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
145 lines
4.6 KiB
Python
145 lines
4.6 KiB
Python
"""Tests for LearningOrchestrator opt-in skill optimization (Plan 2A)."""
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from __future__ import annotations
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from pathlib import Path
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from unittest.mock import MagicMock, patch
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class TestOrchestratorSkillAutoOptimize:
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def test_auto_optimize_disabled_by_default_does_not_call_skill_optimizer(
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self, tmp_path: Path
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) -> None:
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from openjarvis.learning.learning_orchestrator import (
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LearningOrchestrator,
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)
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store = MagicMock()
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store.list_traces.return_value = []
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orchestrator = LearningOrchestrator(
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trace_store=store,
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config_dir=tmp_path,
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)
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with patch(
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"openjarvis.learning.agents.skill_optimizer.SkillOptimizer.optimize"
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) as mock_optimize:
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orchestrator._maybe_optimize_skills(auto_optimize=False)
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mock_optimize.assert_not_called()
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def test_auto_optimize_enabled_calls_skill_optimizer(self, tmp_path: Path) -> None:
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from openjarvis.learning.learning_orchestrator import (
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LearningOrchestrator,
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)
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store = MagicMock()
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store.list_traces.return_value = []
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orchestrator = LearningOrchestrator(
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trace_store=store,
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config_dir=tmp_path,
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)
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with patch(
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"openjarvis.learning.agents.skill_optimizer.SkillOptimizer.optimize",
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return_value={},
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) as mock_optimize:
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orchestrator._maybe_optimize_skills(
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auto_optimize=True,
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optimizer="dspy",
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min_traces_per_skill=5,
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)
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mock_optimize.assert_called_once()
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class TestOrchestratorRunSkillTrigger:
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"""End-to-end: LearningOrchestrator.run() invokes _maybe_optimize_skills
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when learning.skills.auto_optimize is true (Plan 2A C2 fix)."""
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def _make_store(self) -> MagicMock:
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store = MagicMock()
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# Just enough surface area for orchestrator.run() to short-circuit
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store.list_traces.return_value = []
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return store
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def _make_config(self, *, auto_optimize: bool):
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from openjarvis.core.config import (
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JarvisConfig,
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LearningConfig,
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SkillsLearningConfig,
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)
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cfg = JarvisConfig()
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cfg.learning = LearningConfig()
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cfg.learning.skills = SkillsLearningConfig(
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auto_optimize=auto_optimize,
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optimizer="dspy",
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min_traces_per_skill=5,
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)
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return cfg
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def test_run_does_not_call_skill_optimizer_when_disabled(
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self, tmp_path: Path
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) -> None:
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from openjarvis.learning.learning_orchestrator import (
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LearningOrchestrator,
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)
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store = self._make_store()
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orchestrator = LearningOrchestrator(
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trace_store=store,
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config_dir=tmp_path,
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)
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with patch(
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"openjarvis.core.config.load_config",
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return_value=self._make_config(auto_optimize=False),
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):
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with patch(
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"openjarvis.learning.agents.skill_optimizer.SkillOptimizer.optimize"
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) as mock_optimize:
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orchestrator.run()
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mock_optimize.assert_not_called()
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def test_run_calls_skill_optimizer_when_enabled(self, tmp_path: Path) -> None:
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from openjarvis.learning.agents.skill_optimizer import (
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SkillOptimizationResult,
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)
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from openjarvis.learning.learning_orchestrator import (
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LearningOrchestrator,
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)
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store = self._make_store()
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orchestrator = LearningOrchestrator(
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trace_store=store,
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config_dir=tmp_path,
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)
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fake_results = {
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"research-skill": SkillOptimizationResult(
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skill_name="research-skill",
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status="optimized",
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trace_count=10,
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),
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}
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with patch(
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"openjarvis.core.config.load_config",
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return_value=self._make_config(auto_optimize=True),
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):
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with patch(
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"openjarvis.learning.agents.skill_optimizer.SkillOptimizer.optimize",
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return_value=fake_results,
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) as mock_optimize:
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result = orchestrator.run()
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mock_optimize.assert_called_once()
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# The orchestrator should record the skill optimization
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# results in the returned dict
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assert "skill_optimization" in result
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assert "research-skill" in result["skill_optimization"]
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assert (
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result["skill_optimization"]["research-skill"]["status"]
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== "optimized"
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)
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