fix(aibox): make --model effective in WebSocket server mode (H3)
--model was accepted by parse_args but only consumed inside handle_once_mode, so `python aibox.py --model kimi` (server mode) silently ignored the override. The model now flows as a process-wide override: main() -> EnhancedAIManager(model=...) -> DeepSeekChat(model=...) in all three AI creation paths: add_ai (main dialogue), WebSocketHandler._create_session_ai (per-connection sessions), and TaskScheduler._create_task_ai (scheduled tasks). Manager logs "CLI 模型覆盖已启用" at startup for observability. --once mode plumbing is unchanged; --model=None keeps config-driven behavior. Tests: new TestH3ModelPlumbing (6 cases) mocking DeepSeekChat to assert the constructor kwarg across manager/session/task paths plus parse_args; full suite 288 passed.
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@@ -552,7 +552,7 @@ AI 可以通过 `update_soul` 工具动态更新 `99_SOUL.md`，通过 `update_i
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| `reasoning_enabled` | 档案级思考模式开关（缺省回退 `api.reasoning_enabled`） |
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| `timeout` | 请求超时秒数 |
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**选择优先级**：`--model <档案名>`（命中 `models` 节）→ `api.default_model` 指向档案 → 回退旧 `api` 扁平节。
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**选择优先级**：`--model <档案名>`（命中 `models` 节）→ `api.default_model` 指向档案 → 回退旧 `api` 扁平节。`--model` 为**进程级覆盖**：WebSocket 服务模式（主对话、每个会话实例、定时任务实例）与 `--once` 一次性模式均生效。
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`--model` 传**裸模型名**（未命中档案）时，沿用默认端点与 Key，仅覆盖模型名——即 "同端点换模型" 语义。
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@@ -657,7 +657,7 @@ python aibox.py [选项]
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--show-reasoning 一次性模式输出思考流（默认静默，走 stderr）
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--no-save-memory 一次性模式禁用记忆工具（干净跳过，不进请求 schema）
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--worker-only 一次性模式纯执行面（worker profile，无人格/记忆工具）
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--model NAME 模型档案名（models 节）或裸模型名（同端点换模型）
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--model NAME 模型档案名（models 节）或裸模型名（同端点换模型）；服务模式与 --once 均生效（进程级覆盖）
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--temperature T 覆盖配置的对话温度
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--once-timeout SEC 一次性模式全局超时（默认 300 秒）
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```
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+21
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@@ -116,9 +116,11 @@ class TaskScheduler:
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# 每个任务使用独立的命令执行器，互不共享，避免 cancel() 误杀其他任务的子进程
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command_executor=SubprocessCommandExecutor(self.logger),
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prompt_manager=self.ai_manager.prompt_manager if self.ai_manager else None,
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skill_manager=self._get_shared_skill_manager()
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skill_manager=self._get_shared_skill_manager(),
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# H3：CLI --model 为进程级覆盖，定时任务实例同样生效
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model=getattr(self.ai_manager, "model", None) if self.ai_manager else None
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)
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def set_websocket_handler(self, handler):
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"""设置WebSocket处理器，用于广播任务执行状态"""
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self.ws_handler = handler
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@@ -1938,7 +1940,9 @@ class WebSocketHandler:
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# 每个连接使用独立的命令执行器，互不共享，避免 cancel() 误杀其他用户/任务正在运行的子进程
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command_executor=SubprocessCommandExecutor(self.logger),
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prompt_manager=self.ai_manager.prompt_manager,
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skill_manager=self._get_shared_skill_manager()
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skill_manager=self._get_shared_skill_manager(),
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# H3：CLI --model 进程级覆盖传导到每个会话实例
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model=getattr(self.ai_manager, "model", None)
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)
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def _get_session(self, websocket) -> dict:
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@@ -2569,18 +2573,24 @@ class WebSocketHandler:
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class EnhancedAIManager:
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"""增强的AI对话管理器 - 集成任务调度器"""
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def __init__(self, debug_level: int = None, load_history: bool = None, command_executor: CommandExecutor = None):
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def __init__(self, debug_level: int = None, load_history: bool = None, command_executor: CommandExecutor = None,
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model: str = None):
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self.ai: Optional[DeepSeekChat] = None
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self.dialogue_history: List[Dict] = []
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self.config = ConfigManager()
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self.command_executor = command_executor or SubprocessCommandExecutor()
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self.prompt_manager = PromptManager() # 创建提示词管理器
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self.scheduler: Optional[TaskScheduler] = None # 任务调度器
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# H3：CLI --model 进程级覆盖（档案名或裸模型名），传导到本进程创建的所有 AI 实例
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# （主对话 add_ai / WebSocket 会话 / 定时任务），此前服务模式该参数被静默忽略
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self.model = model
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if debug_level is None:
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debug_level = self.config.get("system.debug_level", 1)
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self.logger = Logger("Manager", debug_level)
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if model:
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self.logger.info(f"CLI 模型覆盖已启用: {model}")
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self.running = True
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# 确保 ~/.aibox 目录存在
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@@ -2628,7 +2638,8 @@ class EnhancedAIManager:
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memory_db=self.memory_db,
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memo_db=self.memo_db,
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command_executor=self.command_executor,
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prompt_manager=self.prompt_manager
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prompt_manager=self.prompt_manager,
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model=self.model # H3：--model 进程级覆盖
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)
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self.ai = ai
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@@ -2852,7 +2863,7 @@ def parse_args():
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parser.add_argument('--no-save-memory', action='store_true', help='一次性对话不注册记忆工具，禁止写入记忆数据库')
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parser.add_argument('--show-reasoning', action='store_true', help='一次性对话在 stderr 显示思考过程流（默认不显示）')
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parser.add_argument('--worker-only', action='store_true', help='一次性对话以 worker 纯执行面模式运行（7 种执行工具，无记忆/人格/派生）')
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parser.add_argument('--model', type=str, metavar='NAME', help='覆盖配置中的模型名（如 deepseek-chat / deepseek-reasoner）')
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parser.add_argument('--model', type=str, metavar='NAME', help='覆盖配置中的模型：档案名（models 节，如 kimi）或裸模型名（同端点换模型）。服务模式与 --once 均生效（进程级覆盖）')
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parser.add_argument('--temperature', type=float, metavar='T', help='覆盖默认采样温度（0.0~2.0，越低越确定）')
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return parser.parse_args()
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@@ -3142,7 +3153,8 @@ def main():
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manager = EnhancedAIManager(
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debug_level=args.debug,
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command_executor=command_executor
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command_executor=command_executor,
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model=args.model # H3：--model 服务模式同样生效（进程级覆盖）
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)
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if handle_check_commands(manager):
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@@ -297,6 +297,97 @@ class TestH2SchemaValidation:
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create_llm_client(_cfg(models=models, default_model=None), model="kimi")
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# ==================== H3：--model 服务模式传导 ====================
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class TestH3ModelPlumbing:
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"""H3：--model 此前仅在 --once 模式生效，WebSocket 服务模式被静默忽略。
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验证进程级覆盖传导链：main() → EnhancedAIManager(model) →
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add_ai / WebSocketHandler._create_session_ai / TaskScheduler._create_task_ai
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三类 AI 实例的 DeepSeekChat(model=...) 构造参数。
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"""
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def _stub_logger(self):
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return types.SimpleNamespace(level=0, info=lambda *a, **k: None,
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warning=lambda *a, **k: None,
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error=lambda *a, **k: None)
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def test_manager_stores_and_passes_model_to_add_ai(self):
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"""EnhancedAIManager(model) → add_ai() → DeepSeekChat(model=...)"""
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from aibox import EnhancedAIManager
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mgr = EnhancedAIManager.__new__(EnhancedAIManager) # 跳过重初始化，只测传导
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mgr.model = "kimi"
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mgr.logger = self._stub_logger()
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mgr.history_manager = None
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mgr.memory_db = None
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mgr.memo_db = None
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mgr.command_executor = None
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mgr.prompt_manager = None
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with mock.patch("aibox.DeepSeekChat") as mc:
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mgr.add_ai()
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assert mc.call_args.kwargs.get("model") == "kimi"
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def test_manager_default_model_is_none(self):
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"""未指定 --model → model=None，DeepSeekChat 走配置默认"""
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from aibox import EnhancedAIManager
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mgr = EnhancedAIManager.__new__(EnhancedAIManager)
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mgr.model = None
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mgr.logger = self._stub_logger()
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mgr.history_manager = None
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mgr.memory_db = None
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mgr.memo_db = None
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mgr.command_executor = None
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mgr.prompt_manager = None
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with mock.patch("aibox.DeepSeekChat") as mc:
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mgr.add_ai()
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assert mc.call_args.kwargs.get("model") is None
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def test_session_ai_receives_model(self):
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"""WebSocketHandler._create_session_ai → DeepSeekChat(model=...)"""
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from aibox import WebSocketHandler
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handler = WebSocketHandler.__new__(WebSocketHandler)
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handler.ai_manager = types.SimpleNamespace(
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model="kimi", memory_db=None, memo_db=None, prompt_manager=None)
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handler.logger = self._stub_logger()
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handler._shared_skill_manager = object() # 非 None 即复用，不触发扫描
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with mock.patch("aibox.DeepSeekChat") as mc, \
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mock.patch("aibox.SubprocessCommandExecutor"):
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handler._create_session_ai()
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assert mc.call_args.kwargs.get("model") == "kimi"
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def test_task_ai_receives_model(self):
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"""TaskScheduler._create_task_ai → DeepSeekChat(model=...)"""
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from aibox import TaskScheduler
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sched = TaskScheduler.__new__(TaskScheduler)
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sched.ai_manager = types.SimpleNamespace(
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model="kimi", memory_db=None, memo_db=None, prompt_manager=None)
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sched.logger = self._stub_logger()
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sched._shared_skill_manager = object()
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with mock.patch("aibox.DeepSeekChat") as mc, \
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mock.patch("aibox.SubprocessCommandExecutor"):
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sched._create_task_ai()
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assert mc.call_args.kwargs.get("model") == "kimi"
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def test_task_ai_without_manager_passes_none(self):
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"""调度器未挂 manager（ai_manager=None）→ model=None 不报错"""
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from aibox import TaskScheduler
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sched = TaskScheduler.__new__(TaskScheduler)
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sched.ai_manager = None
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sched.logger = self._stub_logger()
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sched._shared_skill_manager = object()
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with mock.patch("aibox.DeepSeekChat") as mc, \
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mock.patch("aibox.SubprocessCommandExecutor"):
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sched._create_task_ai()
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assert mc.call_args.kwargs.get("model") is None
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def test_parse_args_model(self):
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"""CLI 解析：--model kimi → args.model"""
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from aibox import parse_args
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with mock.patch("sys.argv", ["aibox.py", "--model", "kimi"]):
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args = parse_args()
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assert args.model == "kimi"
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# ==================== DeepSeekChat 档案级 reasoning 读取 ====================
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class TestChatReasoningFlag:
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