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| """Determinism guards for the memory-augmented prompt path (INV-2). | |
| The few-shot memory layer (trajectory store -> example_extractor -> prompt_enhancer) | |
| must be a deterministic, read-only function of its inputs during grading, so that an | |
| LLM agent's prompt — and therefore any graded run — is reproducible. These tests | |
| lock that the retrieval/enhancement path returns identical output across calls and | |
| does not perform writes. | |
| """ | |
| from __future__ import annotations | |
| from env.learning.example_extractor import example_extractor | |
| from env.learning.prompt_enhancer import prompt_enhancer | |
| def test_enhance_system_prompt_is_deterministic() -> None: | |
| base = "You are an assistant." | |
| a = prompt_enhancer.enhance_system_prompt(base, "hard_full_management", "balanced") | |
| b = prompt_enhancer.enhance_system_prompt(base, "hard_full_management", "balanced") | |
| assert a == b | |
| # With no successful trajectories stored, enhancement is a no-op (base unchanged). | |
| assert a.startswith(base) | |
| def test_example_extraction_is_deterministic() -> None: | |
| first = example_extractor.extract_all_examples("hard_full_management", "balanced") | |
| second = example_extractor.extract_all_examples("hard_full_management", "balanced") | |
| assert first == second | |
| def test_has_examples_is_stable() -> None: | |
| a = prompt_enhancer.has_examples("easy_classification", "balanced") | |
| b = prompt_enhancer.has_examples("easy_classification", "balanced") | |
| assert a == b | |
| assert isinstance(a, bool) | |