feat:模型支持高级配置
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@@ -53,6 +53,7 @@ export interface PackTaskConfigs {
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utils_small?: PackTaskConfig
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tool_use?: PackTaskConfig
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replyer?: PackTaskConfig
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learner?: PackTaskConfig
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planner?: PackTaskConfig
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vlm?: PackTaskConfig
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voice?: PackTaskConfig
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@@ -106,6 +106,7 @@ function ModelConfigPageContent() {
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const [jumpToPage, setJumpToPage] = useState('')
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const [advancedTemperatureMode, setAdvancedTemperatureMode] = useState(false)
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const [advancedTaskSettingsVisible, setAdvancedTaskSettingsVisible] = useState(false)
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// 模型 Combobox 状态
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const [modelComboboxOpen, setModelComboboxOpen] = useState(false)
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@@ -155,7 +156,9 @@ function ModelConfigPageContent() {
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// 检查是否有模型
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if (!task.model_list || task.model_list.length === 0) {
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if (key !== 'learner') {
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emptyTaskList.push(key)
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}
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continue
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}
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@@ -939,14 +942,26 @@ function ModelConfigPageContent() {
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{/* 模型任务配置标签页 */}
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<TabsContent value="tasks" className="space-y-6 mt-0">
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<div className="flex flex-col gap-3 sm:flex-row sm:items-center sm:justify-between">
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<p className="text-sm text-muted-foreground">
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为不同的任务配置使用的模型和参数
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</p>
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{taskConfigSchema?.fields.some((field) => field.advanced) && (
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<Button
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type="button"
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variant={advancedTaskSettingsVisible ? 'secondary' : 'outline'}
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size="sm"
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onClick={() => setAdvancedTaskSettingsVisible((current) => !current)}
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>
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高级设置
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</Button>
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)}
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</div>
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{taskConfig && taskConfigSchema && (
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<div className="grid gap-4 sm:gap-6">
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{taskConfigSchema.fields
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.filter(f => f.type === 'object')
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.filter(f => f.type === 'object' && (advancedTaskSettingsVisible || !f.advanced))
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.map((field, index) => {
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const desc = field.description || field.name
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const commaIdx = desc.search(/[,,]/)
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@@ -59,7 +59,7 @@ LEGACY_ENV_PATH: Path = (PROJECT_ROOT / ".env").resolve().absolute()
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A_MEMORIX_LEGACY_CONFIG_PATH: Path = (CONFIG_DIR / "a_memorix.toml").resolve().absolute()
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MMC_VERSION: str = "1.0.0"
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CONFIG_VERSION: str = "8.10.1"
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MODEL_CONFIG_VERSION: str = "1.14.6"
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MODEL_CONFIG_VERSION: str = "1.14.8"
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logger = get_logger("config")
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@@ -32,6 +32,13 @@ DEFAULT_TASK_CONFIG_TEMPLATES: dict[str, dict[str, Any]] = {
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"slow_threshold": 120.0,
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"selection_strategy": "random",
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},
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"learner": {
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"model_list": [],
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"max_tokens": 4096,
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"temperature": 0.5,
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"slow_threshold": 15.0,
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"selection_strategy": "random",
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},
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"planner": {
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"model_list": ["deepseek-v4-flash"],
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"max_tokens": 8000,
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@@ -39,13 +46,6 @@ DEFAULT_TASK_CONFIG_TEMPLATES: dict[str, dict[str, Any]] = {
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"slow_threshold": 12.0,
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"selection_strategy": "random",
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},
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"vlm": {
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"model_list": ["qwen3.5-flash"],
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"max_tokens": 512,
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"temperature": 0.3,
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"slow_threshold": 15.0,
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"selection_strategy": "random",
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},
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"voice": {
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"model_list": [""],
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"max_tokens": 1024,
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@@ -53,13 +53,6 @@ DEFAULT_TASK_CONFIG_TEMPLATES: dict[str, dict[str, Any]] = {
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"slow_threshold": 12.0,
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"selection_strategy": "random",
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},
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"embedding": {
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"model_list": ["qwen3-embedding"],
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"max_tokens": 1024,
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"temperature": 0.3,
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"slow_threshold": 5.0,
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"selection_strategy": "random",
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},
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}
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DEFAULT_MODEL_TEMPLATES: list[dict[str, Any]] = [
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@@ -89,24 +82,6 @@ DEFAULT_MODEL_TEMPLATES: list[dict[str, Any]] = [
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"price_out": 2.0,
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"visual": False,
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"extra_params": {"enable_thinking": "false"},
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},
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{
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"model_identifier": "qwen3.5-flash",
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"name": "qwen3.5-flash",
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"api_provider": "BaiLian",
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"price_in": 0.2,
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"price_out": 2.0,
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"visual": True,
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"extra_params": {"enable_thinking": "false"},
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},
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{
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"model_identifier": "text-embedding-v4",
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"name": "qwen3-embedding",
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"api_provider": "BaiLian",
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"price_in": 0.5,
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"price_out": 0.5,
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"visual": False,
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"extra_params": {},
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}
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]
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@@ -436,7 +436,17 @@ class ModelTaskConfig(ConfigBase):
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"x-icon": "message-square",
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},
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)
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"""首要回复模型配置, 还用于表达器和表达方式学习"""
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"""首要回复模型配置"""
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learner: TaskConfig = Field(
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default_factory=TaskConfig,
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json_schema_extra={
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"x-widget": "custom",
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"x-icon": "graduation-cap",
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"advanced": True,
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},
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)
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"""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"""
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planner: TaskConfig = Field(
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default_factory=TaskConfig,
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@@ -31,7 +31,7 @@ if TYPE_CHECKING:
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logger = get_logger("expressor")
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express_learn_model = LLMServiceClient(
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task_name="replyer", request_type="expression.learner"
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task_name="learner", request_type="expression.learner"
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)
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summary_model = LLMServiceClient(task_name="utils", request_type="expression.summary")
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@@ -23,7 +23,7 @@ from .expression_utils import is_single_char_jargon
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logger = get_logger("jargon")
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llm_inference = LLMServiceClient(task_name="utils", request_type="jargon.inference")
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llm_inference = LLMServiceClient(task_name="learner", request_type="jargon.inference")
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class JargonEntry(TypedDict):
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@@ -111,6 +111,10 @@ class LLMOrchestrator:
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task_config = getattr(model_task_config, self.task_name, None)
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if not isinstance(task_config, TaskConfig):
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raise ValueError(f"未找到名为 '{self.task_name}' 的任务配置")
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if self.task_name == "learner" and not any(str(model_name).strip() for model_name in task_config.model_list):
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fallback_task_config = getattr(model_task_config, "utils", None)
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if isinstance(fallback_task_config, TaskConfig):
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return fallback_task_config
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return task_config
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def _refresh_task_config(self) -> TaskConfig:
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