Initial commit: PROMETHEUS v0.1.0 - Prompt optimizer

- Clean architecture (domain/application/infrastructure)
- DSPy-based evolution engine with scoring
- CLI via pyproject.toml entry point
- Unit + integration tests (~300 tests)
- Configs for glm-5.1 and glm-4.5-air models
- Z.AI endpoint integration

tests/unit/test_entities.py

@@ -0,0 +1,99 @@
+"""Unit tests for domain entities."""
+from __future__ import annotations
+
+from prometheus.domain.entities import (
+    Candidate,
+    EvalResult,
+    OptimizationState,
+    Prompt,
+    SyntheticExample,
+    Trajectory,
+)
+
+
+class TestPrompt:
+    def test_prompt_text(self) -> None:
+        p = Prompt(text="Hello")
+        assert p.text == "Hello"
+
+    def test_prompt_len(self) -> None:
+        p = Prompt(text="Hello")
+        assert len(p) == 5
+
+    def test_prompt_frozen(self) -> None:
+        p = Prompt(text="Hello")
+        try:
+            p.text = "World"  # type: ignore[misc]
+            raise AssertionError("Should have raised FrozenInstanceError")
+        except AttributeError:
+            pass
+
+    def test_prompt_default_metadata(self) -> None:
+        p = Prompt(text="Hello")
+        assert p.metadata == {}
+
+    def test_prompt_custom_metadata(self) -> None:
+        p = Prompt(text="Hello", metadata={"key": "value"})
+        assert p.metadata["key"] == "value"
+
+
+class TestSyntheticExample:
+    def test_default_category(self) -> None:
+        ex = SyntheticExample(input_text="test")
+        assert ex.category == "default"
+
+    def test_default_id(self) -> None:
+        ex = SyntheticExample(input_text="test")
+        assert ex.id == 0
+
+
+class TestEvalResult:
+    def test_total_score(self) -> None:
+        result = EvalResult(
+            scores=[0.3, 0.5, 0.4],
+            feedbacks=["a", "b", "c"],
+            trajectories=[],
+        )
+        assert result.total_score == 1.2
+
+    def test_mean_score(self) -> None:
+        result = EvalResult(
+            scores=[0.3, 0.5, 0.4],
+            feedbacks=["a", "b", "c"],
+            trajectories=[],
+        )
+        assert abs(result.mean_score - 0.4) < 1e-9
+
+    def test_mean_score_empty(self) -> None:
+        result = EvalResult(scores=[], feedbacks=[], trajectories=[])
+        assert result.mean_score == 0.0
+
+
+class TestTrajectory:
+    def test_trajectory_fields(self) -> None:
+        t = Trajectory(
+            input_text="in",
+            output_text="out",
+            score=0.8,
+            feedback="good",
+            prompt_used="test",
+        )
+        assert t.input_text == "in"
+        assert t.score == 0.8
+
+
+class TestCandidate:
+    def test_candidate_defaults(self) -> None:
+        c = Candidate(prompt=Prompt(text="test"))
+        assert c.best_score == 0.0
+        assert c.generation == 0
+        assert c.parent_id is None
+
+
+class TestOptimizationState:
+    def test_default_state(self) -> None:
+        state = OptimizationState()
+        assert state.iteration == 0
+        assert state.best_candidate is None
+        assert state.candidates == []
+        assert state.total_llm_calls == 0