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/integration/test_dspy_adapters.py

@@ -0,0 +1,29 @@
+"""Integration tests for DSPy adapters using DSPy mock LM."""
+from __future__ import annotations
+
+import dspy
+import pytest
+
+from prometheus.domain.entities import Prompt
+from prometheus.infrastructure.llm_adapter import DSPyLLMAdapter
+
+
+@pytest.fixture
+def mock_lm() -> dspy.LM:
+    """Create a DSPy mock LM that returns predictable responses."""
+    lm = dspy.utils.DummyLM(
+        [
+            {"output": "Mock output response"},
+        ]
+    )
+    dspy.configure(lm=lm)
+    return lm
+
+
+class TestDSPyLLMAdapter:
+    def test_execute_returns_response(self, mock_lm: dspy.LM) -> None:
+        adapter = DSPyLLMAdapter(model="openai/gpt-4o-mini")
+        prompt = Prompt(text="Answer the question.")
+        result = adapter.execute(prompt, "What is 2+2?")
+        assert isinstance(result, str)
+        assert len(result) > 0

tests/integration/test_full_pipeline.py

@@ -0,0 +1,74 @@
+"""End-to-end pipeline test with mocked LLM calls."""
+from __future__ import annotations
+
+from unittest.mock import MagicMock
+
+from prometheus.application.bootstrap import SyntheticBootstrap
+from prometheus.application.dto import OptimizationConfig
+from prometheus.application.evaluator import PromptEvaluator
+from prometheus.application.use_cases import OptimizePromptUseCase
+from prometheus.domain.entities import EvalResult, Prompt, SyntheticExample, Trajectory
+from prometheus.domain.ports import JudgePort, LLMPort, ProposerPort
+
+
+def _make_eval(scores: list[float]) -> EvalResult:
+    return EvalResult(
+        scores=scores,
+        feedbacks=["feedback"] * len(scores),
+        trajectories=[
+            Trajectory(f"in{i}", f"out{i}", s, "feedback", "prompt")
+            for i, s in enumerate(scores)
+        ],
+    )
+
+
+class TestFullPipeline:
+    def test_pipeline_produces_result(self) -> None:
+        """Full pipeline with mocked ports produces an OptimizationResult."""
+        mock_llm = MagicMock(spec=LLMPort)
+        mock_llm.execute.return_value = "mock response"
+
+        mock_judge = MagicMock(spec=JudgePort)
+        # Initial eval (low), then alternating current/new evals per iteration
+        eval_sequence = [
+            _make_eval([0.3, 0.3, 0.3, 0.3, 0.3]),  # initial seed eval
+        ]
+        for _ in range(5):  # 5 iterations
+            eval_sequence.append(_make_eval([0.4, 0.4, 0.4, 0.4, 0.4]))  # current eval
+            eval_sequence.append(_make_eval([0.6, 0.6, 0.6, 0.6, 0.6]))  # new eval (accepted)
+        mock_judge.judge_batch.return_value = [(0.5, "ok")] * 5
+
+        mock_proposer = MagicMock(spec=ProposerPort)
+        mock_proposer.propose.return_value = Prompt(text="Improved prompt")
+
+        evaluator = PromptEvaluator(mock_llm, mock_judge)
+        evaluator.evaluate = MagicMock(side_effect=eval_sequence)
+
+        mock_gen = MagicMock()
+        mock_gen.generate_inputs.return_value = [
+            SyntheticExample(input_text=f"synth input {i}", id=i) for i in range(20)
+        ]
+        bootstrap = SyntheticBootstrap(generator=mock_gen, seed=42)
+
+        use_case = OptimizePromptUseCase(
+            evaluator=evaluator,
+            proposer=mock_proposer,
+            bootstrap=bootstrap,
+        )
+
+        config = OptimizationConfig(
+            seed_prompt="Answer questions.",
+            task_description="Answer questions accurately.",
+            max_iterations=5,
+            n_synthetic_inputs=20,
+            minibatch_size=5,
+            seed=42,
+        )
+
+        result = use_case.execute(config)
+
+        assert result.initial_prompt == "Answer questions."
+        assert result.optimized_prompt == "Improved prompt"
+        assert result.iterations_used == 5
+        assert result.total_llm_calls > 0
+        assert result.final_score > result.initial_score