feat:模型支持高级配置

src/config/config.py

@@ -59,7 +59,7 @@ LEGACY_ENV_PATH: Path = (PROJECT_ROOT / ".env").resolve().absolute()
 A_MEMORIX_LEGACY_CONFIG_PATH: Path = (CONFIG_DIR / "a_memorix.toml").resolve().absolute()
 MMC_VERSION: str = "1.0.0"
 CONFIG_VERSION: str = "8.10.1"
-MODEL_CONFIG_VERSION: str = "1.14.6"
+MODEL_CONFIG_VERSION: str = "1.14.8"
 
 logger = get_logger("config")
 

src/config/default_model_config.py

@@ -32,6 +32,13 @@ DEFAULT_TASK_CONFIG_TEMPLATES: dict[str, dict[str, Any]] = {
         "slow_threshold": 120.0,
         "selection_strategy": "random",
     },
+    "learner": {
+        "model_list": [],
+        "max_tokens": 4096,
+        "temperature": 0.5,
+        "slow_threshold": 15.0,
+        "selection_strategy": "random",
+    },
     "planner": {
         "model_list": ["deepseek-v4-flash"],
         "max_tokens": 8000,
@@ -39,13 +46,6 @@ DEFAULT_TASK_CONFIG_TEMPLATES: dict[str, dict[str, Any]] = {
         "slow_threshold": 12.0,
         "selection_strategy": "random",
     },
-    "vlm": {
-        "model_list": ["qwen3.5-flash"],
-        "max_tokens": 512,
-        "temperature": 0.3,
-        "slow_threshold": 15.0,
-        "selection_strategy": "random",
-    },
     "voice": {
         "model_list": [""],
         "max_tokens": 1024,
@@ -53,13 +53,6 @@ DEFAULT_TASK_CONFIG_TEMPLATES: dict[str, dict[str, Any]] = {
         "slow_threshold": 12.0,
         "selection_strategy": "random",
     },
-    "embedding": {
-        "model_list": ["qwen3-embedding"],
-        "max_tokens": 1024,
-        "temperature": 0.3,
-        "slow_threshold": 5.0,
-        "selection_strategy": "random",
-    },
 }
 
 DEFAULT_MODEL_TEMPLATES: list[dict[str, Any]] = [
@@ -89,24 +82,6 @@ DEFAULT_MODEL_TEMPLATES: list[dict[str, Any]] = [
         "price_out": 2.0,
         "visual": False,
         "extra_params": {"enable_thinking": "false"},
-    },
-    {
-        "model_identifier": "qwen3.5-flash",
-        "name": "qwen3.5-flash",
-        "api_provider": "BaiLian",
-        "price_in": 0.2,
-        "price_out": 2.0,
-        "visual": True,
-        "extra_params": {"enable_thinking": "false"},
-    },
-    {
-        "model_identifier": "text-embedding-v4",
-        "name": "qwen3-embedding",
-        "api_provider": "BaiLian",
-        "price_in": 0.5,
-        "price_out": 0.5,
-        "visual": False,
-        "extra_params": {},
     }
 ]
 

src/config/model_configs.py

@@ -436,8 +436,18 @@ class ModelTaskConfig(ConfigBase):
             "x-icon": "message-square",
         },
     )
-    """首要回复模型配置, 还用于表达器和表达方式学习"""
+    """首要回复模型配置"""
     
+    learner: TaskConfig = Field(
+        default_factory=TaskConfig,
+        json_schema_extra={
+            "x-widget": "custom",
+            "x-icon": "graduation-cap",
+            "advanced": True,
+        },
+    )
+    """学习模型配置，用于表达方式学习和黑话学习；留空时自动继用 utils 模型"""
+
     planner: TaskConfig = Field(
         default_factory=TaskConfig,
         json_schema_extra={

src/learners/expression_learner.py

@@ -31,7 +31,7 @@ if TYPE_CHECKING:
 logger = get_logger("expressor")
 
 express_learn_model = LLMServiceClient(
-    task_name="replyer", request_type="expression.learner"
+    task_name="learner", request_type="expression.learner"
 )
 summary_model = LLMServiceClient(task_name="utils", request_type="expression.summary")
 

src/learners/jargon_miner.py

@@ -23,7 +23,7 @@ from .expression_utils import is_single_char_jargon
 
 logger = get_logger("jargon")
 
-llm_inference = LLMServiceClient(task_name="utils", request_type="jargon.inference")
+llm_inference = LLMServiceClient(task_name="learner", request_type="jargon.inference")
 
 
 class JargonEntry(TypedDict):

src/llm_models/utils_model.py

@@ -111,6 +111,10 @@ class LLMOrchestrator:
         task_config = getattr(model_task_config, self.task_name, None)
         if not isinstance(task_config, TaskConfig):
             raise ValueError(f"未找到名为 '{self.task_name}' 的任务配置")
+        if self.task_name == "learner" and not any(str(model_name).strip() for model_name in task_config.model_list):
+            fallback_task_config = getattr(model_task_config, "utils", None)
+            if isinstance(fallback_task_config, TaskConfig):
+                return fallback_task_config
         return task_config
 
     def _refresh_task_config(self) -> TaskConfig: