446 lines
18 KiB
Python
446 lines
18 KiB
Python
"""
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应用配置模块
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使用 Pydantic BaseModel 实现配置管理，支持：
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- 从 TOML 文件加载用户配置
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- 仅保存用户修改过的配置项
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- 默认配置定义在代码中
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"""
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from __future__ import annotations
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import os
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from pathlib import Path
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from typing import Any
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import tomli
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import tomli_w
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from pydantic import BaseModel, Field, PrivateAttr
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from yuxi.config.static.models import (
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DEFAULT_CHAT_MODEL_PROVIDERS,
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DEFAULT_EMBED_MODELS,
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DEFAULT_RERANKERS,
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ChatModelProvider,
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EmbedModelInfo,
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RerankerInfo,
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)
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from yuxi.utils.logging_config import logger
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class Config(BaseModel):
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"""应用配置类"""
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# ============================================================
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# 基础配置
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# ============================================================
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save_dir: str = Field(default="saves", description="保存目录")
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model_dir: str = Field(default="", description="本地模型目录")
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# ============================================================
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# 功能开关
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# ============================================================
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enable_reranker: bool = Field(default=False, description="是否开启重排序")
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enable_content_guard: bool = Field(default=False, description="是否启用内容审查")
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enable_content_guard_llm: bool = Field(default=False, description="是否启用LLM内容审查")
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enable_web_search: bool = Field(default=False, description="是否启用网络搜索")
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# ============================================================
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# 模型配置
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# ============================================================
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default_model: str = Field(
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default="siliconflow-cn:Pro/MiniMaxAI/MiniMax-M2.5",
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description="默认对话模型",
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)
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fast_model: str = Field(
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default="siliconflow-cn:Pro/MiniMaxAI/MiniMax-M2.5",
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description="快速响应模型",
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)
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embed_model: str = Field(
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default="siliconflow-cn:Pro/BAAI/bge-m3",
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description="默认 Embedding 模型",
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)
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reranker: str = Field(
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default="siliconflow-cn:Pro/BAAI/bge-reranker-v2-m3",
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description="默认 Re-Ranker 模型",
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)
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content_guard_llm_model: str = Field(
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default="siliconflow-cn:Pro/MiniMaxAI/MiniMax-M2.5",
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description="内容审查LLM模型",
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)
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# ============================================================
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# 智能体配置
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# ============================================================
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default_agent_id: str = Field(default="ChatbotAgent", description="默认智能体ID")
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# ============================================================
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# Sandbox 配置
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# ============================================================
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sandbox_provider: str = Field(default="provisioner", description="沙箱提供者")
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sandbox_provisioner_url: str = Field(default="http://sandbox-provisioner:8002", description="沙箱服务地址")
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sandbox_virtual_path_prefix: str = Field(default="/home/gem/user-data", description="沙箱用户目录前缀")
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sandbox_exec_timeout_seconds: int = Field(default=180, description="沙箱执行超时时间（秒）")
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sandbox_max_output_bytes: int = Field(default=262144, description="沙箱最大输出字节数")
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sandbox_keepalive_interval_seconds: int = Field(default=30, description="沙箱保活间隔（秒）")
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# ============================================================
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# 模型信息（只读，不持久化）
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# ============================================================
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model_names: dict[str, ChatModelProvider] = Field(
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default_factory=lambda: DEFAULT_CHAT_MODEL_PROVIDERS.copy(),
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description="聊天模型提供商配置",
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exclude=True,
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)
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embed_model_names: dict[str, EmbedModelInfo] = Field(
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default_factory=lambda: DEFAULT_EMBED_MODELS.copy(),
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description="嵌入模型配置",
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exclude=True,
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)
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reranker_names: dict[str, RerankerInfo] = Field(
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default_factory=lambda: DEFAULT_RERANKERS.copy(),
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description="重排序模型配置",
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exclude=True,
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)
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# ============================================================
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# 运行时状态（不持久化）
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# ============================================================
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model_provider_status: dict[str, bool] = Field(
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default_factory=dict,
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description="模型提供商可用状态",
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exclude=True,
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)
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valuable_model_provider: list[str] = Field(
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default_factory=list,
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description="可用的模型提供商列表",
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exclude=True,
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)
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# 内部状态
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_config_file: Path | None = PrivateAttr(default=None)
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_user_modified_fields: set[str] = PrivateAttr(default_factory=set)
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_modified_providers: set[str] = PrivateAttr(default_factory=set) # 记录具体修改的模型提供商
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model_config = {"arbitrary_types_allowed": True, "extra": "allow"}
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def __init__(self, **data):
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super().__init__(**data)
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self._setup_paths()
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self._load_user_config()
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self._load_custom_providers()
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self._handle_environment()
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def _setup_paths(self) -> None:
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"""设置配置文件路径"""
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self.save_dir = os.getenv("SAVE_DIR") or self.save_dir
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self._config_file = Path(self.save_dir) / "config" / "base.toml"
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self._config_file.parent.mkdir(parents=True, exist_ok=True)
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def _load_user_config(self) -> None:
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"""从 TOML 文件加载用户配置"""
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if not self._config_file or not self._config_file.exists():
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logger.info(f"Config file not found, using defaults: {self._config_file}")
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return
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logger.info(f"Loading config from {self._config_file}")
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try:
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with open(self._config_file, "rb") as f:
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user_config = tomli.load(f)
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# 记录用户修改的字段
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self._user_modified_fields = set(user_config.keys())
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# 更新配置
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for key, value in user_config.items():
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if key == "model_names":
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# 特殊处理模型配置
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self._load_model_names(value)
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elif hasattr(self, key):
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setattr(self, key, value)
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else:
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logger.warning(f"Unknown config key: {key}")
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# 确保默认智能体为 ChatbotAgent（兼容旧配置）
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if not self.default_agent_id:
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self.default_agent_id = "ChatbotAgent"
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logger.info("default_agent_id not set, using default: ChatbotAgent")
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except Exception as e:
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logger.error(f"Failed to load config from {self._config_file}: {e}")
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def _load_model_names(self, model_names_data: dict[str, Any]) -> None:
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"""加载用户自定义的模型配置"""
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for provider, provider_data in (model_names_data or {}).items():
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try:
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if provider in self.model_names:
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# 合并现有提供商的配置
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merged = self.model_names[provider].model_dump() | dict(provider_data or {})
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self.model_names[provider] = ChatModelProvider(**merged)
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else:
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# 添加新的提供商
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self.model_names[provider] = ChatModelProvider(**provider_data)
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except Exception as e: # noqa: BLE001
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logger.warning(f"Skip invalid model provider config {provider}: {e}")
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def _load_custom_providers(self) -> None:
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"""从独立的TOML文件加载自定义供应商配置
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[弃用] 这个方法已弃用，建议直接在网页中添加，custom_providers.toml 将在 0.7.x 版本中移除
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"""
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custom_config_file = self._config_file.parent / "custom_providers.toml"
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if not custom_config_file.exists():
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logger.info(f"Custom providers config file not found: {custom_config_file}")
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return
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logger.info(f"Loading custom providers from {custom_config_file}")
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try:
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with open(custom_config_file, "rb") as f:
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custom_config = tomli.load(f)
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# 加载自定义供应商
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if "model_names" in custom_config:
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self._load_custom_model_providers(custom_config["model_names"])
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except Exception as e:
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logger.error(f"Failed to load custom providers from {custom_config_file}: {e}")
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def _load_custom_model_providers(self, providers_data: dict[str, Any]) -> None:
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"""加载自定义模型供应商"""
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for provider, provider_data in (providers_data or {}).items():
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try:
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payload = dict(provider_data or {})
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payload["custom"] = True
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self.model_names[provider] = ChatModelProvider(**payload)
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except Exception as e: # noqa: BLE001
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logger.warning(f"Skip invalid custom provider {provider}: {e}")
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def _handle_environment(self) -> None:
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"""处理环境变量和运行时状态"""
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# 处理模型目录
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self.model_dir = os.environ.get("MODEL_DIR") or self.model_dir
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if self.model_dir:
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if os.path.exists(self.model_dir):
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logger.debug(f"Model directory ({self.model_dir}) contains: {os.listdir(self.model_dir)}")
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else:
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logger.debug(f"Model directory ({self.model_dir}) does not exist. If not configured, please ignore it.")
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# 检查模型提供商的环境变量
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self.model_provider_status = {}
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for provider, info in self.model_names.items():
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env_var = info.env
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if env_var == "NO_API_KEY":
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self.model_provider_status[provider] = True
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else:
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api_key = os.environ.get(env_var)
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# 如果获取到的值与环境变量名不同，说明环境变量存在或配置了直接值
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self.model_provider_status[provider] = bool(api_key or info.custom)
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# 检查网络搜索
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if os.getenv("TAVILY_API_KEY"):
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self.enable_web_search = True
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# 获取可用的模型提供商
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self.valuable_model_provider = [k for k, v in self.model_provider_status.items() if v]
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# 处理 Sandbox 配置
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self.sandbox_provider = (os.getenv("SANDBOX_PROVIDER") or self.sandbox_provider or "provisioner").strip()
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self.sandbox_provisioner_url = (
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os.getenv("SANDBOX_PROVISIONER_URL") or self.sandbox_provisioner_url or "http://sandbox-provisioner:8002"
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).strip()
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self.sandbox_virtual_path_prefix = (
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os.getenv("SANDBOX_VIRTUAL_PATH_PREFIX") or self.sandbox_virtual_path_prefix or "/home/gem/user-data"
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).strip()
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self.sandbox_exec_timeout_seconds = int(
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os.getenv("SANDBOX_EXEC_TIMEOUT_SECONDS") or self.sandbox_exec_timeout_seconds or 180
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)
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self.sandbox_max_output_bytes = int(
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os.getenv("SANDBOX_MAX_OUTPUT_BYTES") or self.sandbox_max_output_bytes or 262144
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)
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self.sandbox_keepalive_interval_seconds = int(
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os.getenv("SANDBOX_KEEPALIVE_INTERVAL_SECONDS") or self.sandbox_keepalive_interval_seconds or 30
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)
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# 验证 Sandbox 配置
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if self.sandbox_provider.lower() != "provisioner":
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raise ValueError("Only sandbox_provider=provisioner is supported.")
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if not self.sandbox_provisioner_url:
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raise ValueError("SANDBOX_PROVISIONER_URL is required when sandbox provider is provisioner.")
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if not self.sandbox_virtual_path_prefix.startswith("/"):
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self.sandbox_virtual_path_prefix = f"/{self.sandbox_virtual_path_prefix}"
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if not self.valuable_model_provider:
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raise ValueError("No model provider available, please check your `.env` file.")
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def save(self) -> None:
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"""保存配置到 TOML 文件（仅保存用户修改的字段）"""
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if not self._config_file:
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logger.warning("Config file path not set")
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return
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logger.info(f"Saving config to {self._config_file}")
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# 获取默认配置
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default_config = Config.model_construct()
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# 对比当前配置和默认配置，找出用户修改的字段
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user_modified = {}
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for field_name in self.model_fields.keys():
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# 跳过 exclude=True 的字段
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field_info = self.model_fields[field_name]
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if field_info.exclude:
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continue
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current_value = getattr(self, field_name)
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default_value = getattr(default_config, field_name)
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# 如果值不同，说明用户修改了
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if current_value != default_value:
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user_modified[field_name] = current_value
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# 写入 TOML 文件
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try:
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with open(self._config_file, "wb") as f:
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tomli_w.dump(user_modified, f)
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logger.info(f"Config saved to {self._config_file}")
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except Exception as e:
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logger.error(f"Failed to save config to {self._config_file}: {e}")
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def dump_config(self) -> dict[str, Any]:
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"""导出配置为字典（用于 API 返回）"""
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config_dict = self.model_dump(
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exclude={
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"model_names",
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"embed_model_names",
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"reranker_names",
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"model_provider_status",
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"valuable_model_provider",
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}
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)
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# 添加模型信息（转换为字典格式供前端使用）
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config_dict["model_names"] = {provider: info.model_dump() for provider, info in self.model_names.items()}
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config_dict["embed_model_names"] = {
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model_id: info.model_dump() for model_id, info in self.embed_model_names.items()
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}
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config_dict["reranker_names"] = {model_id: info.model_dump() for model_id, info in self.reranker_names.items()}
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# 添加运行时状态信息
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config_dict["model_provider_status"] = self.model_provider_status
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config_dict["valuable_model_provider"] = self.valuable_model_provider
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fields_info = {}
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for field_name, field_info in Config.model_fields.items():
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if not field_info.exclude: # 排除内部字段
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fields_info[field_name] = {
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"des": field_info.description,
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"default": field_info.default,
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"type": field_info.annotation.__name__
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if hasattr(field_info.annotation, "__name__")
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else str(field_info.annotation),
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"exclude": field_info.exclude if hasattr(field_info, "exclude") else False,
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}
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config_dict["_config_items"] = fields_info
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return config_dict
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def get_model_choices(self) -> list[str]:
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"""获取所有可用的聊天模型列表"""
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choices = []
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for provider, info in self.model_names.items():
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if self.model_provider_status.get(provider, False):
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for model in info.models:
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choices.append(f"{provider}/{model}")
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return choices
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def get_embed_model_choices(self) -> list[str]:
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"""获取所有可用的嵌入模型列表"""
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return list(self.embed_model_names.keys())
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def get_reranker_choices(self) -> list[str]:
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"""获取所有可用的重排序模型列表"""
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return list(self.reranker_names.keys())
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# ============================================================
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# 兼容旧代码的方法
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# ============================================================
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def __getitem__(self, key: str) -> Any:
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"""支持字典式访问 config[key]"""
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logger.warning("Using deprecated dict-style access for Config. Please use attribute access instead.")
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return getattr(self, key, None)
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def __setitem__(self, key: str, value: Any):
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"""支持字典式赋值 config[key] = value"""
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logger.warning("Using deprecated dict-style assignment for Config. Please use attribute access instead.")
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setattr(self, key, value)
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def update(self, other: dict[str, Any]) -> None:
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"""批量更新配置（兼容旧代码）"""
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for key, value in other.items():
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if hasattr(self, key):
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setattr(self, key, value)
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else:
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logger.warning(f"Unknown config key: {key}")
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def _save_models_to_file(self, provider_name: str | None = None) -> None:
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"""保存模型配置到主配置文件
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Args:
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provider_name: 如果提供，只保存特定provider的修改；否则保存所有model_names
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"""
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if not self._config_file:
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logger.warning("Config file path not set")
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return
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logger.info(f"Saving models config to {self._config_file}")
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try:
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# 读取现有配置
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user_config = {}
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if self._config_file.exists():
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with open(self._config_file, "rb") as f:
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user_config = tomli.load(f)
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# 初始化 model_names 配置（如果不存在）
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if "model_names" not in user_config:
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user_config["model_names"] = {}
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if provider_name:
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# 只保存特定 provider 的修改
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if provider_name in self.model_names:
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user_config["model_names"][provider_name] = self.model_names[provider_name].model_dump()
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# 记录具体修改的 provider
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self._modified_providers.add(provider_name)
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logger.info(f"Saved models config for provider: {provider_name}")
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else:
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# 保存所有 model_names
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user_config["model_names"] = {
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provider: info.model_dump() for provider, info in self.model_names.items()
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}
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# 记录整个 model_names 字段的修改
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self._user_modified_fields.add("model_names")
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logger.info("Saved all models config")
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# 写入配置文件
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with open(self._config_file, "wb") as f:
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tomli_w.dump(user_config, f)
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logger.info(f"Models config saved to {self._config_file}")
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except Exception as e:
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logger.error(f"Failed to save models config to {self._config_file}: {e}")
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def get_custom_providers(self) -> dict[str, ChatModelProvider]:
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"""获取所有自定义供应商
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Returns:
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自定义供应商字典
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"""
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return {k: v for k, v in self.model_names.items() if v.custom}
|
||
|
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||
# 全局配置实例
|
||
config = Config()
|