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63 lines
2.0 KiB
Python
63 lines
2.0 KiB
Python
from __future__ import annotations
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import pytest
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from deeptutor.book.blocks import _llm_writer
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@pytest.mark.asyncio
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async def test_llm_json_normalizes_array_to_expected_key(monkeypatch: pytest.MonkeyPatch) -> None:
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async def fake_llm_text(**_: object) -> str:
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return '[{"front": "A", "back": "B"}]'
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monkeypatch.setattr(_llm_writer, "llm_text", fake_llm_text)
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data = await _llm_writer.llm_json(
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user_prompt="cards",
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system_prompt="system",
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expected_key="cards",
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)
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assert data == {"cards": [{"front": "A", "back": "B"}]}
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@pytest.mark.asyncio
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async def test_llm_json_uses_single_object_from_array(monkeypatch: pytest.MonkeyPatch) -> None:
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async def fake_llm_text(**_: object) -> str:
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return '[{"code": "print(1)", "language": "python"}]'
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monkeypatch.setattr(_llm_writer, "llm_text", fake_llm_text)
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data = await _llm_writer.llm_json(user_prompt="code", system_prompt="system")
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assert data["code"] == "print(1)"
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assert data["language"] == "python"
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@pytest.mark.asyncio
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async def test_llm_json_retries_structured_calls_with_low_reasoning(
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monkeypatch: pytest.MonkeyPatch,
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) -> None:
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# The retry uses "low" reasoning effort rather than "minimal": vLLM-served
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# local models (e.g. Qwen) reject "minimal" and only accept low/medium/high.
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calls: list[str | None] = []
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async def fake_llm_text(**kwargs: object) -> str:
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effort = kwargs.get("reasoning_effort")
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calls.append(effort if isinstance(effort, str) else None)
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if effort == "low":
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return '{"events": [{"date": "2026", "title": "Ready"}]}'
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return ""
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monkeypatch.setattr(_llm_writer, "llm_text", fake_llm_text)
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data = await _llm_writer.llm_json(
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user_prompt="timeline",
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system_prompt="system",
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expected_key="events",
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)
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assert calls == [None, "low"]
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assert data["events"][0]["title"] == "Ready"
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assert data["_metadata"]["reasoning_retry"] == "low"
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