refactor: remove legacy LLM_DISABLE_THINKING and LLM_REASONING_EFFORT config, unify thinking_level handling

- Eliminate support for LLM_DISABLE_THINKING and LLM_REASONING_EFFORT in config, code, and tests
- Simplify LLM thinking level logic to rely solely on LLM_THINKING_LEVEL
- Refactor LLMHelper and related endpoints to remove legacy parameter handling
- Update system API and test utilities to match new configuration structure
- Minor code cleanup and formatting improvements

app/api/endpoints/system.py

@@ -12,6 +12,7 @@ from anyio import Path as AsyncPath
 from app.helper.sites import SitesHelper  # noqa  # noqa
 from fastapi import APIRouter, Body, Depends, HTTPException, Header, Request, Response
 from fastapi.responses import StreamingResponse
+from pydantic import BaseModel
 
 from app import schemas
 from app.chain.mediaserver import MediaServerChain
@@ -29,14 +30,14 @@ from app.db.user_oper import (
     get_current_active_superuser_async,
     get_current_active_user_async,
 )
-from app.helper.llm import LLMHelper, LLMTestError, LLMTestTimeout
+from app.helper.image import ImageHelper
+from app.helper.llm import LLMHelper, LLMTestTimeout
 from app.helper.mediaserver import MediaServerHelper
 from app.helper.message import MessageHelper
 from app.helper.progress import ProgressHelper
 from app.helper.rule import RuleHelper
 from app.helper.subscribe import SubscribeHelper
 from app.helper.system import SystemHelper
-from app.helper.image import ImageHelper
 from app.log import logger
 from app.scheduler import Scheduler
 from app.schemas import ConfigChangeEventData
@@ -45,7 +46,6 @@ from app.utils.crypto import HashUtils
 from app.utils.http import RequestUtils, AsyncRequestUtils
 from app.utils.security import SecurityUtils
 from app.utils.url import UrlUtils
-from pydantic import BaseModel
 from version import APP_VERSION
 
 router = APIRouter()
@@ -58,8 +58,6 @@ class LlmTestRequest(BaseModel):
     provider: Optional[str] = None
     model: Optional[str] = None
     thinking_level: Optional[str] = None
-    disable_thinking: Optional[bool] = None
-    reasoning_effort: Optional[str] = None
     api_key: Optional[str] = None
     base_url: Optional[str] = None
 
@@ -271,94 +269,6 @@ def _build_nettest_rules() -> list[dict[str, Any]]:
     return rules
 
 
-def _build_llm_test_data(
-    duration_ms: Optional[int] = None,
-    provider: Optional[str] = None,
-    model: Optional[str] = None,
-) -> dict[str, Any]:
-    """
-    构造 LLM 测试接口的基础返回数据。
-    """
-    data = {
-        "provider": provider if provider is not None else settings.LLM_PROVIDER,
-        "model": model if model is not None else settings.LLM_MODEL,
-    }
-    if duration_ms is not None:
-        data["duration_ms"] = duration_ms
-    return data
-
-
-def _normalize_llm_test_value(
-    value: Optional[str], *, empty_as_none: bool = False
-) -> Optional[str]:
-    """
-    清理来自前端的 LLM 测试字段。
-    """
-    if value is None:
-        return None
-    stripped = value.strip()
-    if empty_as_none and not stripped:
-        return None
-    return stripped
-
-
-def _build_llm_test_snapshot(payload: Optional[LlmTestRequest] = None) -> dict[str, Any]:
-    """
-    冻结当前 LLM 测试所需配置。
-
-    优先使用前端传入的临时参数；未传入时回退到已保存配置，兼容旧调用。
-    """
-    provider = settings.LLM_PROVIDER
-    model = settings.LLM_MODEL
-    thinking_level = _normalize_llm_test_value(
-        getattr(settings, "LLM_THINKING_LEVEL", None), empty_as_none=True
-    )
-    disable_thinking = bool(getattr(settings, "LLM_DISABLE_THINKING", False))
-    reasoning_effort = _normalize_llm_test_value(
-        getattr(settings, "LLM_REASONING_EFFORT", None), empty_as_none=True
-    )
-    api_key = settings.LLM_API_KEY
-    base_url = settings.LLM_BASE_URL
-    enabled = bool(settings.AI_AGENT_ENABLE)
-
-    if payload:
-        if payload.enabled is not None:
-            enabled = bool(payload.enabled)
-        if payload.provider is not None:
-            provider = _normalize_llm_test_value(payload.provider) or ""
-        if payload.model is not None:
-            model = _normalize_llm_test_value(payload.model) or ""
-        if payload.thinking_level is not None:
-            thinking_level = _normalize_llm_test_value(
-                payload.thinking_level, empty_as_none=True
-            )
-        if payload.disable_thinking is not None:
-            disable_thinking = bool(payload.disable_thinking)
-        if payload.reasoning_effort is not None:
-            reasoning_effort = _normalize_llm_test_value(
-                payload.reasoning_effort, empty_as_none=True
-            )
-        if payload.api_key is not None:
-            api_key = _normalize_llm_test_value(payload.api_key, empty_as_none=True)
-        if payload.base_url is not None:
-            base_url = _normalize_llm_test_value(payload.base_url, empty_as_none=True)
-
-    if thinking_level is not None:
-        disable_thinking = None
-        reasoning_effort = None
-
-    return {
-        "enabled": enabled,
-        "provider": provider,
-        "model": model,
-        "thinking_level": thinking_level,
-        "disable_thinking": disable_thinking,
-        "reasoning_effort": reasoning_effort,
-        "api_key": api_key,
-        "base_url": base_url,
-    }
-
-
 def _sanitize_llm_test_error(message: str, api_key: Optional[str] = None) -> str:
     """
     清理错误信息中的敏感字段，避免回显密钥。
@@ -477,6 +387,7 @@ async def fetch_image(
         use_cache=use_cache,
         cookies=cookies,
     )
+
     if content:
         # 检查 If-None-Match
         etag = HashUtils.md5(content)
@@ -489,6 +400,7 @@ async def fetch_image(
             media_type=UrlUtils.get_mime_type(url, "image/jpeg"),
             headers=headers,
         )
+    return None
 
 
 @router.get("/img/{proxy}", summary="图片代理")
@@ -756,22 +668,27 @@ async def llm_test(
     """
     使用传入配置或当前已保存配置执行一次最小 LLM 调用。
     """
-    snapshot = _build_llm_test_snapshot(payload)
-    data = _build_llm_test_data(
-        provider=snapshot["provider"],
-        model=snapshot["model"],
-    )
-    if not snapshot["enabled"]:
+    if not payload:
+        return schemas.Response(success=False, message="请配置智能助手LLM相关参数后再进行测试")
+
+    if not payload.provider or not payload.model:
+        return schemas.Response(success=False, message="请配置LLM提供商和模型")
+
+    data = {
+        "provider": payload.provider,
+        "model": payload.model,
+    }
+    if not payload.enabled:
         return schemas.Response(success=False, message="请先启用智能助手", data=data)
 
-    if not snapshot["api_key"]:
+    if not payload.api_key or not payload.api_key.strip():
         return schemas.Response(
             success=False,
             message="请先配置 LLM API Key",
             data=data,
         )
 
-    if not (snapshot["model"] or "").strip():
+    if not payload.model or not payload.model.strip():
         return schemas.Response(
             success=False,
             message="请先配置 LLM 模型",
@@ -780,44 +697,28 @@ async def llm_test(
 
     try:
         result = await LLMHelper.test_current_settings(
-            provider=snapshot["provider"],
-            model=snapshot["model"],
-            thinking_level=snapshot["thinking_level"],
-            disable_thinking=snapshot["disable_thinking"],
-            reasoning_effort=snapshot["reasoning_effort"],
-            api_key=snapshot["api_key"],
-            base_url=snapshot["base_url"],
+            provider=payload.provider,
+            model=payload.model,
+            thinking_level=payload.thinking_level,
+            api_key=payload.api_key,
+            base_url=payload.base_url,
         )
         if not result.get("reply_preview"):
             return schemas.Response(
                 success=False,
-                message="模型响应为空",
-                data=_build_llm_test_data(
-                    result.get("duration_ms"),
-                    provider=snapshot["provider"],
-                    model=snapshot["model"],
-                ),
+                message="模型响应为空"
             )
         return schemas.Response(success=True, data=result)
     except (LLMTestTimeout, TimeoutError) as err:
+        logger.warning(err)
         return schemas.Response(
             success=False,
-            message="LLM 调用超时",
-            data=_build_llm_test_data(
-                getattr(err, "duration_ms", None),
-                provider=snapshot["provider"],
-                model=snapshot["model"],
-            ),
+            message="LLM 调用超时"
         )
     except Exception as err:
         return schemas.Response(
             success=False,
-            message=_sanitize_llm_test_error(str(err), snapshot["api_key"]),
-            data=_build_llm_test_data(
-                getattr(err, "duration_ms", None),
-                provider=snapshot["provider"],
-                model=snapshot["model"],
-            ),
+            message=_sanitize_llm_test_error(str(err), payload.api_key)
         )
 
 
@@ -891,7 +792,7 @@ async def get_logging(
                     # 找到第一个完整的行
                     first_newline = content.find("\n")
                     if first_newline != -1:
-                        content = content[first_newline + 1 :]
+                        content = content[first_newline + 1:]
                 else:
                     # 小文件直接读取全部内容
                     content = await f.read()
@@ -899,7 +800,7 @@ async def get_logging(
                 # 按行分割并添加到队列，只保留非空行
                 lines = [line.strip() for line in content.splitlines() if line.strip()]
                 # 只取最后N行
-                for line in lines[-max(length, 50) :]:
+                for line in lines[-max(length, 50):]:
                     lines_queue.append(line)
 
             # 输出历史日志
@@ -1039,7 +940,6 @@ async def nettest_targets(_: schemas.TokenPayload = Depends(verify_token)):
 async def nettest(
         target_id: Optional[str] = None,
         url: Optional[str] = None,
-    proxy: Optional[bool] = None,
         include: Optional[str] = None,
         _: schemas.TokenPayload = Depends(verify_token),
 ):

app/core/config.py

@@ -505,12 +505,8 @@ class ConfigModel(BaseModel):
     LLM_PROVIDER: str = "deepseek"
     # LLM模型名称
     LLM_MODEL: str = "deepseek-chat"
-    # 统一思考模式/深度配置：off/auto/minimal/low/medium/high/max/xhigh
-    LLM_THINKING_LEVEL: Optional[str] = None
-    # 兼容旧配置：是否尽量关闭模型的思考/推理能力（新配置优先）
-    LLM_DISABLE_THINKING: bool = True
-    # 兼容旧配置：思考强度（新配置优先）
-    LLM_REASONING_EFFORT: Optional[str] = None
+    # 思考模式/深度配置：off/auto/minimal/low/medium/high/max/xhigh
+    LLM_THINKING_LEVEL: Optional[str] = 'off'
     # LLM是否支持图片输入，开启后消息图片会按多模态输入发送给模型
     LLM_SUPPORT_IMAGE_INPUT: bool = True
     # LLM API密钥

app/helper/llm.py

@@ -6,6 +6,8 @@ import time
 from functools import wraps
 from typing import Any, List
 
+from langchain_core.messages import convert_to_messages
+
 from app.core.config import settings
 from app.log import logger
 
@@ -71,7 +73,8 @@ def _get_httpx_proxy_key() -> str:
         if "proxy" in params:
             return "proxy"
         return "proxies"
-    except Exception:
+    except Exception as e:
+        logger.warning(f"检测 httpx 代理参数失败，默认使用 'proxies'：{e}")
         return "proxies"
 
 
@@ -111,20 +114,6 @@ def _is_deepseek_thinking_enabled(model_name: str | None, extra_body: Any) -> bo
     return False
 
 
-def _extract_input_messages(input_: Any) -> list[Any]:
-    """
-    将 chat model 输入还原为原始 BaseMessage 序列。
-    """
-    try:
-        from langchain_core.messages import convert_to_messages
-
-        return list(convert_to_messages(input_))
-    except Exception:
-        if isinstance(input_, list):
-            return list(input_)
-        return []
-
-
 def _patch_deepseek_reasoning_content_support():
     """
     修补 langchain-deepseek 在 tool-call 场景下遗漏 reasoning_content 回传的问题。
@@ -153,7 +142,7 @@ def _patch_deepseek_reasoning_content_support():
         payload = original_get_request_payload(self, input_, stop=stop, **kwargs)
 
         try:
-            original_messages = _extract_input_messages(input_)
+            original_messages = convert_to_messages(input_)
             payload_messages = payload.get("messages") or []
             model_name = getattr(self, "model_name", None) or getattr(
                 self, "model", None
@@ -189,9 +178,9 @@ def _patch_deepseek_reasoning_content_support():
                             reasoning_content = captured_reasoning
 
                 message["reasoning_content"] = reasoning_content
-        except Exception as err:
+        except Exception as e:
             logger.warning(
-                f"修补 langchain-deepseek reasoning_content 请求载荷时失败，将继续使用原始载荷: {err}"
+                f"修补 langchain-deepseek reasoning_content 请求载荷时失败，将继续使用原始载荷: {e}"
             )
 
         return payload
@@ -208,15 +197,6 @@ class LLMHelper:
         {"off", "auto", "minimal", "low", "medium", "high", "max", "xhigh"}
     )
 
-    @staticmethod
-    def _should_disable_thinking(disable_thinking: bool | None = None) -> bool:
-        """
-        判断本次调用是否应尝试关闭模型思考能力。
-        """
-        if disable_thinking is not None:
-            return bool(disable_thinking)
-        return bool(getattr(settings, "LLM_DISABLE_THINKING", False))
-
     @staticmethod
     def _normalize_model_name(model_name: str | None) -> str:
         """
@@ -224,109 +204,6 @@ class LLMHelper:
         """
         return (model_name or "").strip().lower()
 
-    @classmethod
-    def _normalize_thinking_level_value(cls, value: str | None) -> str | None:
-        """
-        统一清理思考级别/强度值，并兼容常见别名。
-        """
-        if value is None:
-            return None
-
-        normalized = str(value).strip().lower()
-        if not normalized:
-            return None
-
-        alias_map = {
-            "none": "off",
-            "disabled": "off",
-            "disable": "off",
-            "enabled": "auto",
-            "enable": "auto",
-            "default": "auto",
-            "dynamic": "auto",
-        }
-        return alias_map.get(normalized, normalized)
-
-    @classmethod
-    def _normalize_thinking_level(
-        cls, thinking_level: str | None = None
-    ) -> str | None:
-        """
-        统一清理 thinking_level 配置。
-        """
-        value = (
-            thinking_level
-            if thinking_level is not None
-            else getattr(settings, "LLM_THINKING_LEVEL", None)
-        )
-        normalized = cls._normalize_thinking_level_value(value)
-        if not normalized:
-            return None
-
-        if normalized not in cls._SUPPORTED_THINKING_LEVELS:
-            logger.warning(f"忽略不支持的 thinking_level 配置: {normalized}")
-            return None
-        return normalized
-
-    @classmethod
-    def _normalize_reasoning_effort(
-        cls, reasoning_effort: str | None = None
-    ) -> str | None:
-        """
-        统一清理 legacy reasoning_effort 配置。
-        """
-        value = (
-            reasoning_effort
-            if reasoning_effort is not None
-            else getattr(settings, "LLM_REASONING_EFFORT", None)
-        )
-        return cls._normalize_thinking_level(value)
-
-    @classmethod
-    def _resolve_thinking_level(
-        cls,
-        thinking_level: str | None = None,
-        disable_thinking: bool | None = None,
-        reasoning_effort: str | None = None,
-    ) -> str:
-        """
-        统一解析本次调用的思考配置。
-
-        优先级：
-        1. 新字段 `thinking_level`
-        2. 本次调用传入的 legacy 字段
-        3. 已保存的新字段 `LLM_THINKING_LEVEL`
-        4. 已保存的 legacy 字段
-        """
-        explicit_level = cls._normalize_thinking_level(thinking_level)
-        if explicit_level:
-            return explicit_level
-
-        explicit_effort = (
-            cls._normalize_reasoning_effort(reasoning_effort)
-            if reasoning_effort is not None
-            else None
-        )
-        if disable_thinking is not None or reasoning_effort is not None:
-            if disable_thinking is not None and bool(disable_thinking):
-                return "off"
-            return explicit_effort or "auto"
-
-        configured_level = cls._normalize_thinking_level(
-            getattr(settings, "LLM_THINKING_LEVEL", None)
-        )
-        if configured_level:
-            return configured_level
-
-        legacy_disable = getattr(settings, "LLM_DISABLE_THINKING", None)
-        legacy_effort = cls._normalize_reasoning_effort(
-            getattr(settings, "LLM_REASONING_EFFORT", None)
-        )
-        if legacy_disable is not None:
-            return "off" if bool(legacy_disable) else (legacy_effort or "auto")
-
-        return legacy_effort or "off"
-
     @classmethod
     def _normalize_deepseek_reasoning_effort(
             cls, thinking_level: str | None = None
@@ -334,16 +211,15 @@ class LLMHelper:
         """
         DeepSeek 文档当前建议使用 high/max；兼容常见 effort 别名。
         """
-        normalized = cls._normalize_thinking_level(thinking_level)
-        if not normalized or normalized in {"off", "auto"}:
+        if not thinking_level or thinking_level in {"off", "auto"}:
             return None
 
-        if normalized in {"minimal", "low", "medium", "high"}:
+        if thinking_level in {"minimal", "low", "medium", "high"}:
             return "high"
-        if normalized in {"max", "xhigh"}:
+        if thinking_level in {"max", "xhigh"}:
             return "max"
 
-        logger.warning(f"忽略不支持的 DeepSeek reasoning_effort 配置: {normalized}")
+        logger.warning(f"忽略不支持的 DeepSeek reasoning_effort 配置: {thinking_level}")
         return None
 
     @classmethod
@@ -353,14 +229,13 @@ class LLMHelper:
         """
         OpenAI reasoning_effort 支持更细粒度的 effort，统一做最近似映射。
         """
-        normalized = cls._normalize_thinking_level(thinking_level)
-        if not normalized or normalized == "auto":
+        if not thinking_level or thinking_level == "auto":
             return None
-        if normalized == "off":
+        if thinking_level == "off":
             return "none"
-        if normalized == "max":
+        if thinking_level == "max":
             return "xhigh"
-        return normalized
+        return thinking_level
 
     @classmethod
     def _build_google_thinking_kwargs(
@@ -443,9 +318,7 @@ class LLMHelper:
             cls,
             provider: str,
             model: str | None,
-        thinking_level: str | None = None,
-        disable_thinking: bool | None = None,
-        reasoning_effort: str | None = None,
+            thinking_level: str | None = None
     ) -> dict[str, Any]:
         """
         按 provider/model 生成思考模式相关参数。
@@ -455,28 +328,23 @@ class LLMHelper:
         """
         provider_name = (provider or "").strip().lower()
         model_name = cls._normalize_model_name(model)
-        resolved_thinking_level = cls._resolve_thinking_level(
-            thinking_level=thinking_level,
-            disable_thinking=disable_thinking,
-            reasoning_effort=reasoning_effort,
-        )
 
         if provider_name == "deepseek":
-            if resolved_thinking_level == "off":
+            if thinking_level == "off":
                 return {"extra_body": {"thinking": {"type": "disabled"}}}
-            if resolved_thinking_level == "auto":
+            if thinking_level == "auto":
                 return {}
 
             kwargs: dict[str, Any] = {"extra_body": {"thinking": {"type": "enabled"}}}
             deepseek_effort = cls._normalize_deepseek_reasoning_effort(
-                resolved_thinking_level
+                thinking_level
             )
             if deepseek_effort:
                 kwargs["reasoning_effort"] = deepseek_effort
             return kwargs
 
         if model_name.startswith(("kimi-k2.5", "kimi-k2.6", "kimi-k2-thinking")):
-            return cls._build_kimi_thinking_kwargs(model_name, resolved_thinking_level)
+            return cls._build_kimi_thinking_kwargs(model_name, thinking_level)
 
         if not model_name:
             return {}
@@ -486,14 +354,14 @@ class LLMHelper:
                 ("gpt-5", "o1", "o3", "o4")
         ):
             openai_effort = cls._normalize_openai_reasoning_effort(
-                resolved_thinking_level
+                thinking_level
             )
             return {"reasoning_effort": openai_effort} if openai_effort else {}
 
         # Gemini 使用 google-genai / langchain-google-genai 内置思考控制参数。
         if provider_name == "google":
             return cls._build_google_thinking_kwargs(
-                model_name, resolved_thinking_level
+                model_name, thinking_level
             )
 
         return {}
@@ -511,14 +379,22 @@ class LLMHelper:
             provider: str | None = None,
             model: str | None = None,
             thinking_level: str | None = None,
-        disable_thinking: bool | None = None,
-        reasoning_effort: str | None = None,
             api_key: str | None = None,
             base_url: str | None = None,
     ):
         """
         获取LLM实例
         :param streaming: 是否启用流式输出
+        :param provider: LLM提供商，默认为配置项LLM_PROVIDER
+        :param model: 模型名称，默认为配置项LLM_MODEL
+        :param thinking_level: 思考模式级别，默认为 None（即自动判断
+            是否启用思考模式）。支持的级别包括 "off"（关闭）、"auto"（自动）、"minimal"、"low"、"medium"、"high"、"max"/"xhigh"（最大）。
+            不同模型对思考模式的支持和表现不同，具体映射关系请
+            参考代码实现。对于不支持思考模式的模型，该参数将被忽略。
+        :param api_key: API Key，默认为
+            配置项LLM_API_KEY。对于某些提供商（
+            如 DeepSeek），可能需要同时提供 base_url。
+        :param base_url: API Base URL，默认为配置项LLM_BASE_URL。
         :return: LLM实例
         """
         provider_name = str(
@@ -530,9 +406,7 @@ class LLMHelper:
         thinking_kwargs = LLMHelper._build_thinking_kwargs(
             provider=provider_name,
             model=model_name,
-            thinking_level=thinking_level,
-            disable_thinking=disable_thinking,
-            reasoning_effort=reasoning_effort,
+            thinking_level=thinking_level
         )
 
         if not api_key_value:
@@ -648,8 +522,6 @@ class LLMHelper:
             provider: str | None = None,
             model: str | None = None,
             thinking_level: str | None = None,
-        disable_thinking: bool | None = None,
-        reasoning_effort: str | None = None,
             api_key: str | None = None,
             base_url: str | None = None,
     ) -> dict:
@@ -666,8 +538,6 @@ class LLMHelper:
             provider=provider_name,
             model=model_name,
             thinking_level=thinking_level,
-            disable_thinking=disable_thinking,
-            reasoning_effort=reasoning_effort,
             api_key=api_key_value,
             base_url=base_url_value,
         )

scripts/local_setup.py

@@ -1086,14 +1086,6 @@ def _env_llm_thinking_level_default() -> str:
         "xhigh",
     }:
         return normalized
-
-    legacy_disable = _env_bool("LLM_DISABLE_THINKING", True)
-    legacy_effort = _normalize_choice(_env_default("LLM_REASONING_EFFORT", ""))
-    legacy_effort = alias_map.get(legacy_effort, legacy_effort)
-    if legacy_disable:
-        return "off"
-    if legacy_effort in {"minimal", "low", "medium", "high", "max", "xhigh"}:
-        return legacy_effort
     return "auto"
 
 
@@ -1550,7 +1542,7 @@ def _load_auth_site_definitions_inner() -> dict[str, Any]:
     if str(ROOT) not in sys.path:
         sys.path.insert(0, str(ROOT))
 
-    from app.helper.sites import SitesHelper
+    from app.helper.sites import SitesHelper  # noqa
 
     auth_sites = SitesHelper().get_authsites() or {}
     definitions: dict[str, Any] = {}
@@ -1887,7 +1879,7 @@ def _apply_local_system_config_inner(config_payload: dict[str, Any]) -> None:
     ):
         system_config.set(SystemConfigKey.UserSiteAuthParams, site_auth_item)
         try:
-            from app.helper.sites import SitesHelper
+            from app.helper.sites import SitesHelper  # noqa
 
             status, msg = SitesHelper().check_user(
                 site_auth_item.get("site"), site_auth_item.get("params")

tests/test_langchain_deepseek_compat.py

@@ -72,8 +72,6 @@ sys.modules["app.core.config"].settings.LLM_MODEL = "deepseek-v4-pro"
 sys.modules["app.core.config"].settings.LLM_API_KEY = "sk-test"
 sys.modules["app.core.config"].settings.LLM_BASE_URL = "https://api.deepseek.com"
 sys.modules["app.core.config"].settings.LLM_THINKING_LEVEL = None
-sys.modules["app.core.config"].settings.LLM_DISABLE_THINKING = False
-sys.modules["app.core.config"].settings.LLM_REASONING_EFFORT = None
 sys.modules["app.core.config"].settings.LLM_TEMPERATURE = 0.1
 sys.modules["app.core.config"].settings.LLM_MAX_CONTEXT_TOKENS = 64
 sys.modules["app.core.config"].settings.PROXY_HOST = None

tests/test_llm_helper_testcall.py

@@ -39,8 +39,6 @@ _stub_module(
         LLM_API_KEY="global-key",
         LLM_BASE_URL="https://global.example.com",
         LLM_THINKING_LEVEL=None,
-        LLM_DISABLE_THINKING=False,
-        LLM_REASONING_EFFORT=None,
         LLM_TEMPERATURE=0.1,
         LLM_MAX_CONTEXT_TOKENS=64,
         PROXY_HOST=None,