"""推理裁定与对账的行为测试(issue #16/#17 设计 §4-§5)。 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 推理正文却不上报 `completion_tokens_details`,而 qwen/deepseek 两者都报。库因此 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"。 """ import pytest from loguru import logger from polygateway.providers import ProviderProfile, ThinkingWire, get_provider from polygateway.thinking import ( DEFAULT_CAPABILITIES, ThinkingCapability, ThinkingUnsupportedError, effective_effort, get_capability, observe_thinking, reconcile_thinking, register_capability, resolve_thinking, ) from polygateway.types import Effort, ThinkingObservation _MYSTERY = ProviderProfile( name="mystery", thinking=ThinkingWire(off=None, on_base=None, effort_key=None), strip_think_tags=False, ) """形态完全未知的 provider(issue #5 的守卫对象)。 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** (不知道怎么表达就报错并指路),不是某个段当时的配置。""" def _warnings(): """捕获库发出的 WARNING;loguru 不经标准 logging,pytest 的 caplog 抓不到。""" messages: list[str] = [] sink_id = logger.add(messages.append, level="WARNING") return messages, sink_id class TestObserveThinking: """三态裁定: 证据硬度决定优先级,无信号一律 UNKNOWN。""" def test_reasoning_text_alone_proves_it_happened(self): """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)。""" assert ( observe_thinking(thinking="先解方程 x+y=35", reasoning_tokens=None) is ThinkingObservation.OBSERVED ) def test_blank_text_is_not_evidence(self): """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)。""" assert ( observe_thinking(thinking=" \n\t ", reasoning_tokens=None) is ThinkingObservation.UNKNOWN ) def test_positive_token_count_proves_it_happened(self): """无正文但上游报了推理用量(qwen 非流式形态)。""" assert observe_thinking(thinking="", reasoning_tokens=205) is ThinkingObservation.OBSERVED def test_zero_token_count_is_positive_evidence_of_absence(self): """`0` 是"上报了且为零",与"没上报"语义不同,故是 ABSENT 而非 UNKNOWN。""" assert observe_thinking(thinking="", reasoning_tokens=0) is ThinkingObservation.ABSENT def test_no_signal_at_all_stays_unknown(self): """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。 判成 ABSENT 就是伪装成"没推理"——正是 issue #16/#17 的病根。 """ assert observe_thinking(thinking="", reasoning_tokens=None) is ThinkingObservation.UNKNOWN def test_text_outranks_a_zero_count(self): """转述与事实冲突时事实赢: 正文在,`reasoning_tokens=0` 不能翻案。""" assert ( observe_thinking(thinking="想了想", reasoning_tokens=0) is ThinkingObservation.OBSERVED ) @pytest.mark.parametrize("negative", [-1, -205]) def test_negative_token_count_is_not_evidence_of_absence(self, negative): """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。 当前 transport 已在边界把负数归 `None`,所以这条走不通;但本函数的 docstring 自称"外部输入校验后使用",第二个 transport 直接填该值时, `> 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)。 """ assert observe_thinking(thinking="", reasoning_tokens=negative) is ( ThinkingObservation.UNKNOWN ) class TestThinkingObservationEnum: def test_values_are_stable_strings(self): """取值进遥测落库,改名即历史数据断层。""" assert ThinkingObservation.OBSERVED == "observed" assert ThinkingObservation.ABSENT == "absent" assert ThinkingObservation.UNKNOWN == "unknown" def test_enum_lives_in_the_innermost_layer(self): """枚举必须定义在 `types.py`(最内层)。 它是 `LLMResponse` 的字段类型;定义在决策层 `thinking.py` 会让 `types.py` 反向 import 决策模块,违反 P7 依赖铁律(import-linter 契约执法)。 """ assert ThinkingObservation.__module__ == "polygateway.types" @pytest.mark.parametrize("bogus", ["", "OBSERVED", "yes", "none"]) def test_unknown_strings_are_rejected(bogus): """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)。 两处接住这个 ValueError 后**降级而非作废**(缓存复活内容 + 记 UNKNOWN、遥测 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 进到 `LLMResponse` 与遥测列里。 """ with pytest.raises(ValueError): ThinkingObservation(bogus) class TestThinkingCapability: """issue #5: 能力按 model 登记——同一 provider 内部代际差异是决定性的。""" def test_registered_models_carry_evidence(self): """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。""" for model in ("MiniMax-M3", "MiniMax-M2.7", "MiniMax-M2.5"): cap = get_capability(model) assert cap is not None and cap.evidence.strip() def test_m3_can_disable_but_m2x_cannot(self): assert get_capability("MiniMax-M3").can_disable is True assert get_capability("MiniMax-M2.7").can_disable is False assert get_capability("MiniMax-M2.5").can_disable is False def test_unregistered_model_is_unknown(self): assert get_capability("some-brand-new-model") is None def test_register_capability_is_pure(self): table = register_capability("x-1", ThinkingCapability((Effort.NONE, Effort.AUTO), "实测")) assert get_capability("x-1", table=table) is not None assert get_capability("x-1") is None # 默认表未被污染 def test_default_capabilities_mapping_is_read_only(self): with pytest.raises(TypeError): DEFAULT_CAPABILITIES["hack"] = None # type: ignore[index] class TestResolveThinking: """五道关卡(顺序即语义)与 nearest 映射;设计 §4.1。 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 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")。 """ # —— Phase 1: 不表态 —— 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)。""" got = resolve_thinking(get_provider("minimax"), None, None, model="MiniMax-M3") assert got.payload == {} assert got.applied_effort is None # —— Phase 2: 形态未知 —— @pytest.mark.parametrize("effort", [Effort.NONE, Effort.AUTO, Effort.HIGH]) def test_phase2_unknown_wire_points_to_register(self, effort): """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。 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 的真值判方向会把 `none` 说成"开启形态未知",指错了排查方向。 """ with pytest.raises(ThinkingUnsupportedError, match="register_provider") as exc: resolve_thinking(_MYSTERY, None, effort, model="kimi-k3") msg = str(exc.value) assert "extra_body" in msg assert "kimi-k3" in msg assert effort.value in msg def test_phase2_reads_the_form_the_asked_for_tier_needs(self): """请求 `none` 只需要**关闭**形态: 开启形态未知与这次请求无关。 旧版 `slot = thinking_on if enable_thinking else thinking_off` 即按请求方向 取字段;档位化后一度写成"只看 `on_base`",于是一个已注册了关闭形态的自定义 provider 在请求 `none` 时被误拒,还被指向它已经做过的 `register_provider` ——指错方向比不指更糟(设计 §2 处置表第 2 条,2026-09-05 独立验证查出)。 """ profile = ProviderProfile( name="off_only", thinking=ThinkingWire( off={"thinking": {"type": "disabled"}}, on_base=None, effort_key=None ), strip_think_tags=False, ) cap = ThinkingCapability((Effort.NONE, Effort.AUTO), "构造: 关得掉,开启形态却未登记") got = resolve_thinking(profile, cap, Effort.NONE, model="x-1") assert got.payload == {"thinking": {"type": "disabled"}} assert got.applied_effort is Effort.NONE @pytest.mark.parametrize("effort", [Effort.AUTO, Effort.HIGH]) def test_phase2_still_fires_when_the_on_form_is_the_missing_half(self, effort): """反方向不得被一并放过: 要开推理而开启形态未知,仍须报错并指路注册。""" profile = ProviderProfile( name="off_only", thinking=ThinkingWire( off={"thinking": {"type": "disabled"}}, on_base=None, effort_key=None ), strip_think_tags=False, ) cap = ThinkingCapability((Effort.NONE, Effort.AUTO, Effort.HIGH), "构造") with pytest.raises(ThinkingUnsupportedError, match="register_provider") as exc: resolve_thinking(profile, cap, effort, model="x-1") assert effort.value in str(exc.value) 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) 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 class TestEffectiveEffort: """三层优先级的**唯一**判定处(设计 §4.2): 请求级 > 源级 > 语法糖 > 不表态。 收口成一个纯函数,是因为它此前在装配守卫与 transport 里各写了一份就地转换: 两份各自演化的判定,迟早会在"装配期放行、运行期报错"这种最难查的形态上分叉。 """ def test_request_beats_source(self): assert ( effective_effort( request_effort=Effort.MAX, source_effort=Effort.LOW, enable_thinking=None ) is Effort.MAX ) def test_source_beats_sugar(self): assert ( effective_effort(request_effort=None, source_effort=Effort.HIGH, enable_thinking=None) is Effort.HIGH ) def test_none_request_does_not_clear_source(self): """请求级"没表态"绝不能被读成"要求关闭"——那会静默改掉源级的默认档。""" assert ( effective_effort(request_effort=None, source_effort=Effort.LOW, enable_thinking=None) is Effort.LOW ) def test_request_none_tier_is_an_opinion(self): """`Effort.NONE` 是一次明确的表态,必须压过源级档位而不是被当成缺省。""" assert ( effective_effort( request_effort=Effort.NONE, source_effort=Effort.MAX, enable_thinking=None ) is Effort.NONE ) def test_enable_thinking_true_is_auto(self): """`True` → `auto`(开启但不指定强度),而**不是**旧版硬编码的 medium。""" assert ( effective_effort(request_effort=None, source_effort=None, enable_thinking=True) is Effort.AUTO ) def test_enable_thinking_false_is_the_none_tier(self): assert ( effective_effort(request_effort=None, source_effort=None, enable_thinking=False) is Effort.NONE ) def test_sugar_is_the_last_word_only(self): """语法糖排在最末: 显式配了档位就以档位为准(矛盾组合已被构造期挡下)。""" assert ( effective_effort(request_effort=None, source_effort=Effort.LOW, enable_thinking=True) is Effort.LOW ) def test_all_absent_is_no_opinion(self): """三层都不表态 → None(随模型默认),与 `Effort.NONE` 严格区分。""" assert ( effective_effort(request_effort=None, source_effort=None, enable_thinking=None) is None )