iomgaa
a1c4273a8b
resolve_thinking now takes an Effort instead of a tri-state bool, and the four gates become five. The new one sits ahead of the generic tier check on purpose: asking for `none` on GLM-5.3 used to fall through to "none is not supported, pick low/high/max", which loses both the fact that the model cannot stop reasoning and the one tier the caller could switch to right now. Without that alternative, downstream goes looking for extra_body — which is how issue #20 happened in the first place. The return type is a ThinkingResolution rather than the payload alone. Under fallback="nearest" the tier that goes out is not the tier that was asked for, and telemetry has to record the one that ran, or task 10 files a call under a tier it never used. Ties in that mapping go to the weaker side: a silent medium -> max is a multiple of the bill, and the library does not raise a caller's price on its own. Two readings the design left implicit, both settled the way its own compatibility promise requires: - `auto` is exempt from the tier list. It means "on, no tier named", which in the body is the absence of the effort key, not a value of it. Checking it against the list would break every existing source that sets ENABLE_THINKING=true against deepseek-v4 or glm-5.3. - `none` is never a mapping target. Turning "think less" into "do not think" reverses the decision instead of cheapening it; a switch-only model maps to `auto` and a model that only has `none` still errors. Both call sites convert enable_thinking in place for now; task 5 folds that into effective_effort along with the source- and call-level tiers.
619 lines
27 KiB
Python
619 lines
27 KiB
Python
"""推理裁定与对账的行为测试(issue #16/#17 设计 §4-§5)。
|
||
|
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判据来自 2026-08-25 实测(findings): MiniMax-M3 在开启档流式路径下返回 185 字符
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推理正文却不上报 `completion_tokens_details`,而 qwen/deepseek 两者都报。库因此
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不能把任何单一信号当权威——本组用例逐条钉死"哪个信号该赢"。
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"""
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||
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import pytest
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from loguru import logger
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||
|
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from polygateway.providers import ProviderProfile, ThinkingWire, get_provider
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from polygateway.thinking import (
|
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DEFAULT_CAPABILITIES,
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ThinkingCapability,
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ThinkingUnsupportedError,
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get_capability,
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observe_thinking,
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reconcile_thinking,
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register_capability,
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resolve_thinking,
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)
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from polygateway.types import Effort, ThinkingObservation
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_MYSTERY = ProviderProfile(
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name="mystery",
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thinking=ThinkingWire(off=None, on_base=None, effort_key=None),
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strip_think_tags=False,
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)
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"""形态完全未知的 provider(issue #5 的守卫对象)。
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||
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2026-09-04 起默认表 8 段全部有形态,故未知样本改为显式构造——测的是**机制**
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(不知道怎么表达就报错并指路),不是某个段当时的配置。"""
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||
|
||
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def _warnings():
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"""捕获库发出的 WARNING;loguru 不经标准 logging,pytest 的 caplog 抓不到。"""
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messages: list[str] = []
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sink_id = logger.add(messages.append, level="WARNING")
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return messages, sink_id
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||
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class TestObserveThinking:
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"""三态裁定: 证据硬度决定优先级,无信号一律 UNKNOWN。"""
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||
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def test_reasoning_text_alone_proves_it_happened(self):
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"""推理正文是事实本身: 上游不报 token 数也照样成立(M3 流式实测形态)。"""
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assert (
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observe_thinking(thinking="先解方程 x+y=35", reasoning_tokens=None)
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is ThinkingObservation.OBSERVED
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)
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def test_blank_text_is_not_evidence(self):
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"""纯空白正文不算证据: 网关响应是外部输入,truthy 判据会把空格计成推理(P5)。"""
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assert (
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observe_thinking(thinking=" \n\t ", reasoning_tokens=None)
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is ThinkingObservation.UNKNOWN
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)
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def test_positive_token_count_proves_it_happened(self):
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"""无正文但上游报了推理用量(qwen 非流式形态)。"""
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assert observe_thinking(thinking="", reasoning_tokens=205) is ThinkingObservation.OBSERVED
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def test_zero_token_count_is_positive_evidence_of_absence(self):
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"""`0` 是"上报了且为零",与"没上报"语义不同,故是 ABSENT 而非 UNKNOWN。"""
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assert observe_thinking(thinking="", reasoning_tokens=0) is ThinkingObservation.ABSENT
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def test_no_signal_at_all_stays_unknown(self):
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"""M3 非流式开启档的真实形态: 推理已计费却既无正文也无 token 数。
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判成 ABSENT 就是伪装成"没推理"——正是 issue #16/#17 的病根。
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"""
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assert observe_thinking(thinking="", reasoning_tokens=None) is ThinkingObservation.UNKNOWN
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def test_text_outranks_a_zero_count(self):
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"""转述与事实冲突时事实赢: 正文在,`reasoning_tokens=0` 不能翻案。"""
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assert (
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observe_thinking(thinking="想了想", reasoning_tokens=0) is ThinkingObservation.OBSERVED
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)
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@pytest.mark.parametrize("negative", [-1, -205])
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def test_negative_token_count_is_not_evidence_of_absence(self, negative):
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"""负数是坏数据,不是"上游明确上报未推理"这个最强的正面结论。
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当前 transport 已在边界把负数归 `None`,所以这条走不通;但本函数的
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docstring 自称"外部输入校验后使用",第二个 transport 直接填该值时,
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`> 0 else ABSENT` 会给出一个方向相反的强结论。函数自身必须闭合(P5)。
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"""
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assert observe_thinking(thinking="", reasoning_tokens=negative) is (
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ThinkingObservation.UNKNOWN
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)
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class TestThinkingObservationEnum:
|
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def test_values_are_stable_strings(self):
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"""取值进遥测落库,改名即历史数据断层。"""
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assert ThinkingObservation.OBSERVED == "observed"
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assert ThinkingObservation.ABSENT == "absent"
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assert ThinkingObservation.UNKNOWN == "unknown"
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def test_enum_lives_in_the_innermost_layer(self):
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"""枚举必须定义在 `types.py`(最内层)。
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||
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它是 `LLMResponse` 的字段类型;定义在决策层 `thinking.py` 会让 `types.py`
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反向 import 决策模块,违反 P7 依赖铁律(import-linter 契约执法)。
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"""
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assert ThinkingObservation.__module__ == "polygateway.types"
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||
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||
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@pytest.mark.parametrize("bogus", ["", "OBSERVED", "yes", "none"])
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def test_unknown_strings_are_rejected(bogus):
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"""非法值必须抛 ValueError: 缓存回放与遥测归一化都靠它识别域外取值(设计 §6)。
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两处接住这个 ValueError 后**降级而非作废**(缓存复活内容 + 记 UNKNOWN、遥测
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照常落行),但降级的前提是构造器真的会拒绝——它一旦放行,域外取值就会一路
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进到 `LLMResponse` 与遥测列里。
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"""
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with pytest.raises(ValueError):
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ThinkingObservation(bogus)
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class TestThinkingCapability:
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||
"""issue #5: 能力按 model 登记——同一 provider 内部代际差异是决定性的。"""
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||
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def test_registered_models_carry_evidence(self):
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"""登记必须附实测证据: 表会过期,没有出处就无从判断该不该信。"""
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for model in ("MiniMax-M3", "MiniMax-M2.7", "MiniMax-M2.5"):
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cap = get_capability(model)
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assert cap is not None and cap.evidence.strip()
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||
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def test_m3_can_disable_but_m2x_cannot(self):
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assert get_capability("MiniMax-M3").can_disable is True
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assert get_capability("MiniMax-M2.7").can_disable is False
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assert get_capability("MiniMax-M2.5").can_disable is False
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def test_unregistered_model_is_unknown(self):
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assert get_capability("some-brand-new-model") is None
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def test_register_capability_is_pure(self):
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table = register_capability("x-1", ThinkingCapability((Effort.NONE, Effort.AUTO), "实测"))
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assert get_capability("x-1", table=table) is not None
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assert get_capability("x-1") is None # 默认表未被污染
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def test_default_capabilities_mapping_is_read_only(self):
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with pytest.raises(TypeError):
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||
DEFAULT_CAPABILITIES["hack"] = None # type: ignore[index]
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||
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||
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class TestResolveThinking:
|
||
"""五道关卡(顺序即语义)与 nearest 映射;设计 §4.1。
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每一关都有独立的失败模式,漏测哪一关,判定顺序被调换都不会被抓住——而顺序
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在本函数里**就是**语义(Phase 4 落进 Phase 5 就丢掉"这个模型根本关不掉")。
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||
"""
|
||
|
||
# —— Phase 1: 不表态 ——
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||
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def test_phase1_absent_effort_injects_nothing(self):
|
||
"""没表态就什么都不注入,用模型自己的默认档(与 `none` 严格区分)。"""
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||
got = resolve_thinking(get_provider("minimax"), None, None, model="MiniMax-M3")
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assert got.payload == {}
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assert got.applied_effort is None
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||
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# —— Phase 2: 形态未知 ——
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@pytest.mark.parametrize("effort", [Effort.NONE, Effort.AUTO, Effort.HIGH])
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def test_phase2_unknown_wire_points_to_register(self, effort):
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"""不知道怎么发就报错并指路;静默放行是 issue #5 修掉的那种欺骗。
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||
|
||
文案必须报出**请求的档位**而非"开/关"方向: `Effort` 是非空字符串,拿它
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的真值判方向会把 `none` 说成"开启形态未知",指错了排查方向。
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||
"""
|
||
with pytest.raises(ThinkingUnsupportedError, match="register_provider") as exc:
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resolve_thinking(_MYSTERY, None, effort, model="kimi-k3")
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||
msg = str(exc.value)
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||
assert "extra_body" in msg
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||
assert "kimi-k3" in msg
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||
assert effort.value in msg
|
||
|
||
def test_phase2_beats_the_capability_checks(self):
|
||
"""形态未知时无从注入,能力如何无关紧要——Phase 2 必须先于 4/5。"""
|
||
cap = ThinkingCapability((Effort.AUTO,), "构造")
|
||
with pytest.raises(ThinkingUnsupportedError, match="register_provider"):
|
||
resolve_thinking(_MYSTERY, cap, Effort.NONE, model="whatever")
|
||
|
||
# —— Phase 3: 能力未登记 ——
|
||
|
||
def test_phase3_unregistered_warns_then_injects(self):
|
||
"""新模型上线不该被库挡住,但也不该假装成功: 喊一声再尽力注入。"""
|
||
messages, sink_id = _warnings()
|
||
try:
|
||
got = resolve_thinking(get_provider("minimax"), None, Effort.NONE, model="MiniMax-M9")
|
||
finally:
|
||
logger.remove(sink_id)
|
||
assert got.payload == {"reasoning_effort": "none"}
|
||
assert got.applied_effort is Effort.NONE
|
||
assert any("MiniMax-M9" in m for m in messages)
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||
|
||
def test_phase3_can_be_silenced_on_the_hot_path(self):
|
||
"""装配期已经喊过一次,逐次调用再喊只会刷屏;判定结果不受影响。"""
|
||
messages, sink_id = _warnings()
|
||
try:
|
||
got = resolve_thinking(
|
||
get_provider("minimax"),
|
||
None,
|
||
Effort.NONE,
|
||
model="MiniMax-M9",
|
||
warn_unregistered=False,
|
||
)
|
||
finally:
|
||
logger.remove(sink_id)
|
||
assert got.payload == {"reasoning_effort": "none"}
|
||
assert not [m for m in messages if "MiniMax-M9" in m]
|
||
|
||
def test_phase3_does_not_validate_tiers(self):
|
||
"""能力未知就没有清单可比对,拿空清单去拒绝档位等于凭空报错。"""
|
||
got = resolve_thinking(
|
||
get_provider("zhipu"),
|
||
None,
|
||
Effort.XHIGH,
|
||
model="glm-9-not-registered",
|
||
warn_unregistered=False,
|
||
)
|
||
assert got.payload == {"thinking": {"type": "enabled"}, "reasoning_effort": "xhigh"}
|
||
assert got.applied_effort is Effort.XHIGH
|
||
|
||
# —— Phase 4: 关不掉 ——
|
||
|
||
def test_phase4_before_phase5(self):
|
||
"""请求 `none` 而模型关不掉: 文案必须给出可执行替代与 env 键名。
|
||
|
||
若落进 Phase 5 的通用分支,报错会退化成"不支持 none,可选 low/high/max",
|
||
丢掉"这个模型根本关不掉"这个关键信息——下游随后就会去找 extra_body 那条
|
||
绕过的路,而那正是 issue #20 的成因。
|
||
"""
|
||
cap = get_capability("glm-5.3")
|
||
with pytest.raises(ThinkingUnsupportedError) as exc:
|
||
resolve_thinking(get_provider("zhipu"), cap, Effort.NONE, model="glm-5.3")
|
||
msg = str(exc.value)
|
||
assert "glm-5.3" in msg
|
||
assert "'low'" in msg, "必须给出 cheapest_effort 的值"
|
||
assert "REASONING_EFFORT" in msg, "必须给出 env 键名"
|
||
assert "可选档位" not in msg, "退化成 Phase 5 的通用文案即失去可执行替代"
|
||
|
||
def test_phase4_never_maps_even_with_nearest(self):
|
||
"""`none` 不走映射: 把"关不掉"映射成"开着最低档"就是又一次静默降级。"""
|
||
cap = get_capability("glm-5.3")
|
||
with pytest.raises(ThinkingUnsupportedError, match="REASONING_EFFORT"):
|
||
resolve_thinking(
|
||
get_provider("zhipu"), cap, Effort.NONE, model="glm-5.3", fallback="nearest"
|
||
)
|
||
|
||
def test_phase4_only_blocks_the_off_direction(self):
|
||
"""关不掉 ≠ 开不了: M2.x 默认就在推理,开的方向不该被拦。"""
|
||
cap = get_capability("MiniMax-M2.7")
|
||
got = resolve_thinking(get_provider("minimax"), cap, Effort.AUTO, model="MiniMax-M2.7")
|
||
assert got.payload == {}
|
||
assert got.applied_effort is Effort.AUTO
|
||
|
||
def test_phase4_passes_when_none_is_registered(self):
|
||
cap = get_capability("MiniMax-M3")
|
||
got = resolve_thinking(get_provider("minimax"), cap, Effort.NONE, model="MiniMax-M3")
|
||
assert got.payload == {"reasoning_effort": "none"}
|
||
assert got.applied_effort is Effort.NONE
|
||
|
||
# —— Phase 5: 档位打空 ——
|
||
|
||
def test_phase5_lists_tiers_for_tiered_model(self):
|
||
"""档位型模型: 文案必须列出它真有的档,否则下游只能猜。"""
|
||
cap = get_capability("glm-5.3")
|
||
with pytest.raises(ThinkingUnsupportedError) as exc:
|
||
resolve_thinking(get_provider("zhipu"), cap, Effort.MEDIUM, model="glm-5.3")
|
||
msg = str(exc.value)
|
||
assert "medium" in msg and "glm-5.3" in msg
|
||
assert "可选档位" in msg
|
||
assert "low" in msg and "high" in msg and "max" in msg
|
||
|
||
def test_phase5_says_toggle_only_for_switch_model(self):
|
||
"""纯开关型模型没有档位,对它说"可选档位"是错的(设计 §3.2 第三个派生量)。"""
|
||
cap = get_capability("MiniMax-M3") # (none, auto): 能开能关,但没有强度档
|
||
with pytest.raises(ThinkingUnsupportedError) as exc:
|
||
resolve_thinking(get_provider("minimax"), cap, Effort.HIGH, model="MiniMax-M3")
|
||
msg = str(exc.value)
|
||
assert "可选档位" not in msg
|
||
assert "该模型只有开关" in msg
|
||
assert "auto" in msg and "none" in msg
|
||
|
||
def test_phase5_wording_forks_on_is_tiered(self):
|
||
"""两条分叉必须真的不同——同一句话套两种模型等于没分叉。"""
|
||
with pytest.raises(ThinkingUnsupportedError) as tiered:
|
||
resolve_thinking(
|
||
get_provider("zhipu"), get_capability("glm-5.3"), Effort.MEDIUM, model="glm-5.3"
|
||
)
|
||
with pytest.raises(ThinkingUnsupportedError) as switch:
|
||
resolve_thinking(
|
||
get_provider("minimax"),
|
||
get_capability("MiniMax-M3"),
|
||
Effort.MEDIUM,
|
||
model="MiniMax-M3",
|
||
)
|
||
assert str(tiered.value) != str(switch.value)
|
||
|
||
def test_phase5_passes_a_supported_tier(self):
|
||
cap = get_capability("glm-5.3")
|
||
got = resolve_thinking(get_provider("zhipu"), cap, Effort.MAX, model="glm-5.3")
|
||
assert got.payload == {"thinking": {"type": "enabled"}, "reasoning_effort": "max"}
|
||
assert got.applied_effort is Effort.MAX
|
||
|
||
def test_auto_never_trips_phase5(self):
|
||
"""`auto` = 不指定档位,可满足性只取决于 wire 有没有 on_base。
|
||
|
||
它不是写进 `effort_key` 的取值,故不受档位清单约束。反过来判会让存量的
|
||
`ENABLE_THINKING=true`(T5 起等价于 auto)在 deepseek/glm-5.3 这类清单里
|
||
没有 auto 的模型上当场报错——设计 §12 明确承诺存量配置继续可跑。
|
||
"""
|
||
cap = get_capability("deepseek-v4-pro") # (none, high, max),清单里没有 auto
|
||
got = resolve_thinking(get_provider("deepseek"), cap, Effort.AUTO, model="deepseek-v4-pro")
|
||
assert got.payload == {"thinking": {"type": "enabled"}}
|
||
assert got.applied_effort is Effort.AUTO
|
||
|
||
# —— nearest 映射(fallback 的逃生口)——
|
||
|
||
def test_nearest_ties_go_cheaper(self):
|
||
"""等距取弱: 省钱优先,库不替下游涨价(一次 medium→max 是数倍账单)。"""
|
||
cap = get_capability("glm-5.3") # (low, high, max)
|
||
messages, sink_id = _warnings()
|
||
try:
|
||
got = resolve_thinking(
|
||
get_provider("zhipu"), cap, Effort.MEDIUM, model="glm-5.3", fallback="nearest"
|
||
)
|
||
finally:
|
||
logger.remove(sink_id)
|
||
assert got.payload == {"thinking": {"type": "enabled"}, "reasoning_effort": "low"}
|
||
assert any("glm-5.3" in m and "medium" in m and "low" in m for m in messages)
|
||
|
||
def test_nearest_ties_go_cheaper_on_the_strong_side_too(self):
|
||
"""xhigh 与 high/max 位序各差 1,同样取弱侧——规则不因方向而变。"""
|
||
cap = get_capability("glm-5.3")
|
||
got = resolve_thinking(
|
||
get_provider("zhipu"), cap, Effort.XHIGH, model="glm-5.3", fallback="nearest"
|
||
)
|
||
assert got.applied_effort is Effort.HIGH
|
||
|
||
def test_nearest_goes_up_when_the_only_neighbour_is_stronger(self):
|
||
"""minimal 之下无档可选,映射必须上行到 low,而不是无解报错。"""
|
||
cap = get_capability("glm-5.3")
|
||
got = resolve_thinking(
|
||
get_provider("zhipu"), cap, Effort.MINIMAL, model="glm-5.3", fallback="nearest"
|
||
)
|
||
assert got.applied_effort is Effort.LOW
|
||
|
||
def test_nearest_never_turns_reasoning_off(self):
|
||
"""请求"想得浅一点"绝不能被映射成"别想了": 那是方向反转,不是省钱。"""
|
||
cap = get_capability("MiniMax-M3") # (none, auto)
|
||
got = resolve_thinking(
|
||
get_provider("minimax"), cap, Effort.HIGH, model="MiniMax-M3", fallback="nearest"
|
||
)
|
||
assert got.applied_effort is Effort.AUTO
|
||
assert got.payload == {}
|
||
|
||
def test_nearest_still_errors_when_no_on_tier_exists(self):
|
||
"""只能关不能开的模型,映射无解——报错而非挑一个反向的档。"""
|
||
cap = ThinkingCapability((Effort.NONE,), "构造: 只登记了关闭档")
|
||
with pytest.raises(ThinkingUnsupportedError, match="only-off"):
|
||
resolve_thinking(
|
||
get_provider("minimax"), cap, Effort.HIGH, model="only-off", fallback="nearest"
|
||
)
|
||
|
||
def test_error_fallback_is_the_default(self):
|
||
"""默认关闭映射的理由是钱: 静默的 medium→max 在 GLM-5.3 上是数倍账单。"""
|
||
cap = get_capability("glm-5.3")
|
||
with pytest.raises(ThinkingUnsupportedError):
|
||
resolve_thinking(get_provider("zhipu"), cap, Effort.MEDIUM, model="glm-5.3")
|
||
|
||
def test_resolution_reports_applied_effort_after_mapping(self):
|
||
"""遥测记的必须是**实际**发出去的档,否则压测按档分组时挂在从未发出的档下。"""
|
||
cap = get_capability("glm-5.3")
|
||
got = resolve_thinking(
|
||
get_provider("zhipu"), cap, Effort.MEDIUM, model="glm-5.3", fallback="nearest"
|
||
)
|
||
assert got.applied_effort is Effort.LOW
|
||
assert got.applied_effort is not Effort.MEDIUM
|
||
|
||
# —— 注入形态 ——
|
||
|
||
def test_auto_injects_on_base_only(self):
|
||
"""`auto` 逐字节等于旧的 `thinking_on`: 开启,但不附任何档位。"""
|
||
got = resolve_thinking(
|
||
get_provider("qwen"), get_capability("qwen3.7-plus"), Effort.AUTO, model="qwen3.7-plus"
|
||
)
|
||
assert got.payload == {"enable_thinking": True}
|
||
|
||
def test_effort_key_none_rejects_a_tier(self):
|
||
"""qwen 系只有开关没有档位键: 硬塞一个档位只会发出一个厂商不认的字段。"""
|
||
cap = ThinkingCapability((Effort.NONE, Effort.LOW), "构造: 假设它有档位")
|
||
with pytest.raises(ThinkingUnsupportedError, match="没有档位键"):
|
||
resolve_thinking(get_provider("qwen"), cap, Effort.LOW, model="qwen-hypothetical")
|
||
|
||
def test_provider_without_an_off_form_says_which_half_is_missing(self):
|
||
"""`off is None` ≠ `on_base is None`: 前者是"关不了",后者是"不知道怎么发"。"""
|
||
profile = ProviderProfile(
|
||
name="no_off",
|
||
thinking=ThinkingWire(off=None, on_base={}, effort_key="reasoning_effort"),
|
||
strip_think_tags=False,
|
||
)
|
||
cap = ThinkingCapability((Effort.NONE, Effort.LOW), "构造: 能力表说能关,形态却没有")
|
||
with pytest.raises(ThinkingUnsupportedError, match="没有关闭形态") as exc:
|
||
resolve_thinking(profile, cap, Effort.NONE, model="x-1")
|
||
assert "register_provider" not in str(exc.value), "形态已知,不该指向注册"
|
||
|
||
|
||
class TestReconcileThinking:
|
||
"""声明 × 观测对账(设计 §5): 矛盾出文案,不表态出 None。
|
||
|
||
文案本身是被断言对象——判定与日志分离正是为此: 告警内容可直接比对,不必
|
||
去解析日志格式。
|
||
"""
|
||
|
||
_CAP = ThinkingCapability(
|
||
(Effort.NONE, Effort.AUTO), "2026-08-02 实测 reasoning_effort=none 可关闭"
|
||
)
|
||
|
||
def test_off_but_observed_with_a_registered_capability_blames_the_table(self):
|
||
"""已登记却实测推理了 = 能力表漂移: 必须附 evidence 与更新指路。"""
|
||
msg = reconcile_thinking(
|
||
enable_thinking=False,
|
||
observation=ThinkingObservation.OBSERVED,
|
||
capability=self._CAP,
|
||
model="MiniMax-M3",
|
||
)
|
||
assert msg is not None
|
||
assert "MiniMax-M3" in msg
|
||
assert "2026-08-02 实测 reasoning_effort=none 可关闭" in msg
|
||
assert "register_capability" in msg
|
||
|
||
def test_off_but_observed_unregistered_never_claims_a_table_entry(self):
|
||
"""未登记模型没有"能力表声称"这回事——说它就是撒谎。"""
|
||
msg = reconcile_thinking(
|
||
enable_thinking=False,
|
||
observation=ThinkingObservation.OBSERVED,
|
||
capability=None,
|
||
model="MiniMax-M9",
|
||
)
|
||
assert msg is not None
|
||
assert "MiniMax-M9" in msg
|
||
assert "能力表" not in msg
|
||
assert "register_capability" in msg
|
||
|
||
def test_registered_and_unregistered_wordings_differ(self):
|
||
registered = reconcile_thinking(
|
||
enable_thinking=False,
|
||
observation=ThinkingObservation.OBSERVED,
|
||
capability=self._CAP,
|
||
model="MiniMax-M3",
|
||
)
|
||
unregistered = reconcile_thinking(
|
||
enable_thinking=False,
|
||
observation=ThinkingObservation.OBSERVED,
|
||
capability=None,
|
||
model="MiniMax-M3",
|
||
)
|
||
assert registered != unregistered
|
||
|
||
@pytest.mark.parametrize("capability", [None, _CAP])
|
||
def test_on_but_absent_is_a_contradiction(self, capability):
|
||
"""上游明确上报未推理: 这是唯一的正面证伪,与能力表登记与否无关。"""
|
||
msg = reconcile_thinking(
|
||
enable_thinking=True,
|
||
observation=ThinkingObservation.ABSENT,
|
||
capability=capability,
|
||
model="qwen3.7-plus",
|
||
)
|
||
assert msg is not None
|
||
assert "qwen3.7-plus" in msg
|
||
|
||
@pytest.mark.parametrize("capability", [None, _CAP])
|
||
def test_on_but_unknown_admits_it_cannot_confirm(self, capability):
|
||
"""issue #17 的诚实版本: 明说"我注入了,但我看不见结果"。"""
|
||
msg = reconcile_thinking(
|
||
enable_thinking=True,
|
||
observation=ThinkingObservation.UNKNOWN,
|
||
capability=capability,
|
||
model="MiniMax-M3",
|
||
)
|
||
assert msg is not None
|
||
assert "MiniMax-M3" in msg
|
||
|
||
def test_off_and_absent_stays_silent(self):
|
||
"""要求关闭 + 上游明确上报未推理 = 要求被满足,没有可报的矛盾。
|
||
|
||
这一格与 `test_off_and_unknown_stays_silent` 的沉默理由**不同**: 那里是
|
||
"没有证伪力",这里是"正面证实要求已满足"。两者都必须沉默,漏测哪一格,
|
||
把 Phase 2 的判据写成 `is ABSENT` 之类的反向条件都不会被抓住。
|
||
"""
|
||
assert (
|
||
reconcile_thinking(
|
||
enable_thinking=False,
|
||
observation=ThinkingObservation.ABSENT,
|
||
capability=self._CAP,
|
||
model="qwen3.7-plus",
|
||
)
|
||
is None
|
||
)
|
||
|
||
def test_off_and_unknown_stays_silent(self):
|
||
"""UNKNOWN 没有证伪力: 拿它报警等于每次关闭调用都喊(M3 关闭档恒落此档)。"""
|
||
assert (
|
||
reconcile_thinking(
|
||
enable_thinking=False,
|
||
observation=ThinkingObservation.UNKNOWN,
|
||
capability=self._CAP,
|
||
model="MiniMax-M3",
|
||
)
|
||
is None
|
||
)
|
||
|
||
@pytest.mark.parametrize(
|
||
"observation",
|
||
[ThinkingObservation.OBSERVED, ThinkingObservation.ABSENT, ThinkingObservation.UNKNOWN],
|
||
)
|
||
def test_no_request_no_grievance(self, observation):
|
||
"""调用方不表态,就无从谈"违背"。"""
|
||
assert (
|
||
reconcile_thinking(
|
||
enable_thinking=None,
|
||
observation=observation,
|
||
capability=self._CAP,
|
||
model="MiniMax-M3",
|
||
)
|
||
is None
|
||
)
|
||
|
||
def test_on_and_observed_is_exactly_what_was_asked_for(self):
|
||
assert (
|
||
reconcile_thinking(
|
||
enable_thinking=True,
|
||
observation=ThinkingObservation.OBSERVED,
|
||
capability=self._CAP,
|
||
model="MiniMax-M3",
|
||
)
|
||
is None
|
||
)
|
||
|
||
|
||
class TestEffortVocabulary:
|
||
"""八档封闭词汇(设计 §3.1);`auto` 不可省——9 个纯开关型模型无强度档可填。"""
|
||
|
||
def test_none_and_auto_are_distinct_members(self):
|
||
assert Effort.NONE != Effort.AUTO
|
||
assert Effort("none") is Effort.NONE
|
||
assert Effort("auto") is Effort.AUTO
|
||
|
||
def test_vocabulary_is_exactly_eight(self):
|
||
assert len(list(Effort)) == 8
|
||
|
||
def test_values_are_wire_literals(self):
|
||
# 档位值直接写进请求体,改名即改变发出去的字节
|
||
assert [e.value for e in Effort] == [
|
||
"none",
|
||
"auto",
|
||
"minimal",
|
||
"low",
|
||
"medium",
|
||
"high",
|
||
"xhigh",
|
||
"max",
|
||
]
|
||
|
||
|
||
class TestCapabilityTierList:
|
||
"""能力表从 bool 变成档位清单(设计 §3.2);三个派生量不存字段,存了必漂移。"""
|
||
|
||
def test_capability_derives_can_disable(self):
|
||
assert ThinkingCapability((Effort.NONE, Effort.AUTO), "实测").can_disable is True
|
||
assert ThinkingCapability((Effort.LOW, Effort.MAX), "实测").can_disable is False
|
||
|
||
def test_cheapest_effort_skips_none(self):
|
||
# 「关不掉时的可执行替代」取的是除 none 外最弱的一档
|
||
assert (
|
||
ThinkingCapability((Effort.LOW, Effort.HIGH, Effort.MAX), "实测").cheapest_effort
|
||
is Effort.LOW
|
||
)
|
||
assert (
|
||
ThinkingCapability((Effort.NONE, Effort.HIGH, Effort.MAX), "实测").cheapest_effort
|
||
is Effort.HIGH
|
||
)
|
||
assert ThinkingCapability((Effort.NONE, Effort.AUTO), "实测").cheapest_effort is Effort.AUTO
|
||
assert ThinkingCapability((Effort.AUTO,), "实测").cheapest_effort is Effort.AUTO
|
||
|
||
def test_cheapest_effort_is_none_when_only_none(self):
|
||
# 只能关不能开: 没有可推荐的「最省的开启档」
|
||
assert ThinkingCapability((Effort.NONE,), "实测").cheapest_effort is None
|
||
|
||
def test_is_tiered_excludes_none_and_auto(self):
|
||
# 纯开关型模型不该被告知「可选档位」——它没有档位
|
||
assert ThinkingCapability((Effort.NONE, Effort.AUTO), "实测").is_tiered is False
|
||
assert ThinkingCapability((Effort.AUTO,), "实测").is_tiered is False
|
||
assert ThinkingCapability((Effort.LOW, Effort.MAX), "实测").is_tiered is True
|
||
|
||
def test_empty_efforts_rejected(self):
|
||
with pytest.raises(ValueError, match="至少"):
|
||
ThinkingCapability((), "实测")
|
||
|
||
def test_duplicate_efforts_rejected(self):
|
||
with pytest.raises(ValueError, match="重复"):
|
||
ThinkingCapability((Effort.LOW, Effort.LOW), "实测")
|
||
|
||
def test_glm53_cannot_be_disabled(self):
|
||
# 三源一致(智谱官方文档/cherry-studio/OpenRouter): thinking.type 只接受 enabled
|
||
cap = get_capability("glm-5.3")
|
||
assert cap is not None
|
||
assert cap.can_disable is False
|
||
assert cap.cheapest_effort is Effort.LOW
|
||
|
||
def test_m2_series_still_cannot_be_disabled(self):
|
||
# 迁移回归: 旧表用 can_disable=False 表达的事实,新表用「none 不在清单里」表达
|
||
assert get_capability("MiniMax-M2.7").can_disable is False
|
||
assert get_capability("MiniMax-M2.5").can_disable is False
|
||
assert get_capability("MiniMax-M3").can_disable is True
|