| """
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| Phase 2 smoke test: in-process exercise of reset() / step() / state.
|
|
|
| Uses the stub LLM backend (set via env var) so this runs in <1s and proves
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| the env plumbing works without downloading any model.
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| """
|
|
|
| from __future__ import annotations
|
|
|
| import os
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| import sys
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| from pathlib import Path
|
|
|
|
|
| sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
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|
|
| os.environ.setdefault("PROMPTOPS_LLM_BACKEND", "stub")
|
|
|
| from src.envs.promptops_arena.server.environment import PromptOpsArenaEnvironment
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| from src.envs.promptops_arena.models import PromptOpsAction
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| from src.envs.promptops_arena import llm_under_test
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|
|
|
|
| GOOD_PROMPTS = {
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| "math": (
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| "You are a careful math solver. Read the problem, think step by step, "
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| "then put ONLY the final numeric answer inside <answer>...</answer> tags. "
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| "Do not include units."
|
| ),
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| "code": (
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| "You are a Python coder. Output ONLY a single ```python code block``` "
|
| "containing the requested function. No prose, no examples, no print statements."
|
| ),
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| "json": (
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| "You are a JSON extractor. Output ONLY a single ```json code block``` "
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| "containing a valid JSON object that matches the requested schema. "
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| "No prose."
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| ),
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| }
|
|
|
|
|
| def run(task_type: str) -> dict:
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| env = PromptOpsArenaEnvironment(max_turns=3, split="train", seed=42, task_types=[task_type])
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| obs = env.reset()
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| print(f"\n=== {task_type.upper()} | task_id={env.state.task_id} ===")
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| print(f"task: {obs.task_text}")
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|
|
| action = PromptOpsAction(new_system_prompt=GOOD_PROMPTS[task_type])
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| obs2 = env.step(action)
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| print(f"completion: {obs2.last_completion[:120]!r}")
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| print(f"reward components: {obs2.reward_components}")
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| print(f"done: {obs2.done}, edit_turn: {obs2.edit_turn}, solved: {env.state.solved}")
|
| return {
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| "task_type": task_type,
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| "reward": obs2.last_reward,
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| "components": obs2.reward_components,
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| "solved": env.state.solved,
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| "step_count": env.state.step_count,
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| }
|
|
|
|
|
| def main() -> int:
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| print(f"LLM backend: {llm_under_test.backend_name()}")
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| results = []
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| for tt in ("math", "code", "json"):
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| results.append(run(tt))
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|
|
| print("\n=== Summary ===")
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| for r in results:
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| print(f" {r['task_type']:5s}: reward={r['reward']:+.3f} solved={r['solved']} "
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| f"step_count={r['step_count']} components={r['components']}")
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|
|
|
|
| ok = all(r["components"].get("total") is not None for r in results)
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| return 0 if ok else 1
|
|
|
|
|
| if __name__ == "__main__":
|
| raise SystemExit(main())
|
|
|
