Merge branch 'dev' of https://github.com/MoFox-Studio/MoFox-Core into dev

src/chat/emoji_system/emoji_manager.py

@@ -4,7 +4,6 @@ import binascii
 import hashlib
 import io
 import json
-import json_repair
 import os
 import random
 import re
@@ -12,6 +11,7 @@ import time
 import traceback
 from typing import Any, Optional, cast
 
+import json_repair
 from PIL import Image
 from rich.traceback import install
 from sqlalchemy import select

src/chat/message_receive/storage.py

@@ -3,7 +3,7 @@ import re
 import time
 import traceback
 from collections import deque
-from typing import TYPE_CHECKING, Optional, Any, cast
+from typing import TYPE_CHECKING, Any, Optional, cast
 
 import orjson
 from sqlalchemy import desc, insert, select, update

src/chat/replyer/default_generator.py

@@ -1799,7 +1799,7 @@ class DefaultReplyer:
             )
 
             if content:
-                if not global_config.response_splitter.enable or global_config.response_splitter.split_mode != 'llm':
+                if not global_config.response_splitter.enable or global_config.response_splitter.split_mode != "llm":
                     # 移除 [SPLIT] 标记，防止消息被分割
                     content = content.replace("[SPLIT]", "")
 

src/chat/semantic_interest/auto_trainer.py

@@ -10,9 +10,8 @@ from datetime import datetime, timedelta
 from pathlib import Path
 from typing import Any
 
-from src.common.logger import get_logger
-from src.config.config import global_config
 from src.chat.semantic_interest.trainer import SemanticInterestTrainer
+from src.common.logger import get_logger
 
 logger = get_logger("semantic_interest.auto_trainer")
 
@@ -78,7 +77,7 @@ class AutoTrainer:
         """加载缓存的人设状态"""
         if self.persona_cache_file.exists():
             try:
-                with open(self.persona_cache_file, "r", encoding="utf-8") as f:
+                with open(self.persona_cache_file, encoding="utf-8") as f:
                     cache = json.load(f)
                     self.last_persona_hash = cache.get("persona_hash")
                     last_train_str = cache.get("last_train_time")
@@ -142,7 +141,7 @@ class AutoTrainer:
             return True
 
         if current_hash != self.last_persona_hash:
-            logger.info(f"[自动训练器] 检测到人设变化")
+            logger.info("[自动训练器] 检测到人设变化")
             logger.info(f"  - 旧哈希: {self.last_persona_hash[:8]}")
             logger.info(f"  - 新哈希: {current_hash[:8]}")
             return True
@@ -236,7 +235,7 @@ class AutoTrainer:
             # 创建"latest"符号链接
             self._create_latest_link(model_path)
 
-            logger.info(f"[自动训练器] 训练完成!")
+            logger.info("[自动训练器] 训练完成!")
             logger.info(f"  - 模型: {model_path.name}")
             logger.info(f"  - 准确率: {metrics.get('test_accuracy', 0):.4f}")
 
@@ -265,7 +264,7 @@ class AutoTrainer:
             import shutil
             shutil.copy2(model_path, latest_path)
 
-            logger.info(f"[自动训练器] 已更新 latest 模型")
+            logger.info("[自动训练器] 已更新 latest 模型")
 
         except Exception as e:
             logger.warning(f"[自动训练器] 创建 latest 链接失败: {e}")
@@ -283,7 +282,7 @@ class AutoTrainer:
         """
         # 检查是否已经有任务在运行
         if self._scheduled_task_running:
-            logger.info(f"[自动训练器] 定时任务已在运行，跳过重复启动")
+            logger.info("[自动训练器] 定时任务已在运行，跳过重复启动")
             return
 
         self._scheduled_task_running = True
@@ -330,10 +329,10 @@ class AutoTrainer:
         # 没有找到，返回 latest
         latest_path = self.model_dir / "semantic_interest_latest.pkl"
         if latest_path.exists():
-            logger.debug(f"[自动训练器] 使用 latest 模型")
+            logger.debug("[自动训练器] 使用 latest 模型")
             return latest_path
 
-        logger.warning(f"[自动训练器] 未找到可用模型")
+        logger.warning("[自动训练器] 未找到可用模型")
         return None
 
     def cleanup_old_models(self, keep_count: int = 5):

src/chat/semantic_interest/dataset.py

@@ -3,7 +3,6 @@
 从数据库采样消息并使用 LLM 进行兴趣度标注
 """
 
-import asyncio
 import json
 import random
 from datetime import datetime, timedelta
@@ -11,7 +10,6 @@ from pathlib import Path
 from typing import Any
 
 from src.common.logger import get_logger
-from src.config.config import global_config
 
 logger = get_logger("semantic_interest.dataset")
 
@@ -111,16 +109,16 @@ class DatasetGenerator:
     async def initialize(self):
         """初始化 LLM 客户端"""
         try:
-            from src.llm_models.utils_model import LLMRequest
             from src.config.config import model_config
+            from src.llm_models.utils_model import LLMRequest
 
             # 使用 utilities 模型配置（标注更偏工具型）
-            if hasattr(model_config.model_task_config, 'utils'):
+            if hasattr(model_config.model_task_config, "utils"):
                 self.model_client = LLMRequest(
                     model_set=model_config.model_task_config.utils,
                     request_type="semantic_annotation"
                 )
-                logger.info(f"数据集生成器初始化完成，使用 utils 模型")
+                logger.info("数据集生成器初始化完成，使用 utils 模型")
             else:
                 logger.error("未找到 utils 模型配置")
                 self.model_client = None
@@ -149,9 +147,9 @@ class DatasetGenerator:
         Returns:
             消息样本列表
         """
+
         from src.common.database.api.query import QueryBuilder
         from src.common.database.core.models import Messages
-        from sqlalchemy import func, or_
 
         logger.info(f"开始采样消息，时间范围: 最近 {days} 天，目标数量: {max_samples}")
 
@@ -632,7 +630,7 @@ class DatasetGenerator:
 
             # 提取JSON内容
             import re
-            json_match = re.search(r'```json\s*({.*?})\s*```', response, re.DOTALL)
+            json_match = re.search(r"```json\s*({.*?})\s*```", response, re.DOTALL)
             if json_match:
                 json_str = json_match.group(1)
             else:
@@ -703,7 +701,7 @@ class DatasetGenerator:
         Returns:
             (文本列表, 标签列表)
         """
-        with open(path, "r", encoding="utf-8") as f:
+        with open(path, encoding="utf-8") as f:
             data = json.load(f)
 
         texts = [item["message_text"] for item in data]

src/chat/semantic_interest/features_tfidf.py

@@ -3,7 +3,6 @@
 使用字符级 n-gram 提取中文消息的 TF-IDF 特征
 """
 
-from pathlib import Path
 
 from sklearn.feature_extraction.text import TfidfVectorizer
 

src/chat/semantic_interest/model_lr.py

@@ -4,17 +4,15 @@
 """
 
 import time
-from pathlib import Path
 from typing import Any
 
-import joblib
 import numpy as np
 from sklearn.linear_model import LogisticRegression
 from sklearn.metrics import classification_report, confusion_matrix
 from sklearn.model_selection import train_test_split
 
-from src.common.logger import get_logger
 from src.chat.semantic_interest.features_tfidf import TfidfFeatureExtractor
+from src.common.logger import get_logger
 
 logger = get_logger("semantic_interest.model")
 

src/chat/semantic_interest/optimized_scorer.py

@@ -16,7 +16,7 @@ from collections import Counter
 from concurrent.futures import ThreadPoolExecutor
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import Any, Callable
+from typing import Any
 
 import numpy as np
 
@@ -110,7 +110,7 @@ class FastScorer:
         self.total_time = 0.0
 
         # n-gram 正则（预编译）
-        self._tokenize_pattern = re.compile(r'\s+')
+        self._tokenize_pattern = re.compile(r"\s+")
 
     @classmethod
     def from_sklearn_model(
@@ -139,13 +139,13 @@ class FastScorer:
         将 TF-IDF 的 idf 和 LR 的权重合并为单一的 token→weight 字典
         """
         # 获取底层 sklearn 对象
-        if hasattr(vectorizer, 'vectorizer'):
+        if hasattr(vectorizer, "vectorizer"):
             # TfidfFeatureExtractor 包装类
             tfidf = vectorizer.vectorizer
         else:
             tfidf = vectorizer
 
-        if hasattr(model, 'clf'):
+        if hasattr(model, "clf"):
             # SemanticInterestModel 包装类
             clf = model.clf
         else:
@@ -611,7 +611,7 @@ def convert_sklearn_to_fast(
 
     # 从 vectorizer 配置推断 n-gram range
     if config is None:
-        vconfig = vectorizer.get_config() if hasattr(vectorizer, 'get_config') else {}
+        vconfig = vectorizer.get_config() if hasattr(vectorizer, "get_config") else {}
         config = FastScorerConfig(
             ngram_range=vconfig.get("ngram_range", (2, 4)),
             weight_prune_threshold=1e-4,

src/chat/semantic_interest/runtime_scorer.py

@@ -16,11 +16,10 @@ from pathlib import Path
 from typing import Any
 
 import joblib
-import numpy as np
 
-from src.common.logger import get_logger
 from src.chat.semantic_interest.features_tfidf import TfidfFeatureExtractor
 from src.chat.semantic_interest.model_lr import SemanticInterestModel
+from src.common.logger import get_logger
 
 logger = get_logger("semantic_interest.scorer")
 
@@ -611,7 +610,7 @@ class ModelManager:
         async with self._lock:
             # 检查是否已经启动
             if self._auto_training_started:
-                logger.debug(f"[模型管理器] 自动训练任务已启动，跳过")
+                logger.debug("[模型管理器] 自动训练任务已启动，跳过")
                 return
 
             try:

src/chat/semantic_interest/trainer.py

@@ -3,16 +3,15 @@
 统一的训练流程入口，包含数据采样、标注、训练、评估
 """
 
-import asyncio
 from datetime import datetime
 from pathlib import Path
 from typing import Any
 
 import joblib
 
-from src.common.logger import get_logger
 from src.chat.semantic_interest.dataset import DatasetGenerator, generate_training_dataset
 from src.chat.semantic_interest.model_lr import train_semantic_model
+from src.common.logger import get_logger
 
 logger = get_logger("semantic_interest.trainer")
 
@@ -110,7 +109,6 @@ class SemanticInterestTrainer:
         logger.info(f"开始训练模型，数据集: {dataset_path}")
 
         # 加载数据集
-        from src.chat.semantic_interest.dataset import DatasetGenerator
         texts, labels = DatasetGenerator.load_dataset(dataset_path)
 
         # 训练模型

src/chat/utils/utils.py

@@ -13,7 +13,7 @@ from src.common.data_models.database_data_model import DatabaseUserInfo
 
 # MessageRecv 已被移除，现在使用 DatabaseMessages
 from src.common.logger import get_logger
-from src.common.message_repository import count_and_length_messages, count_messages, find_messages
+from src.common.message_repository import count_and_length_messages, find_messages
 from src.config.config import global_config, model_config
 from src.llm_models.utils_model import LLMRequest
 from src.person_info.person_info import PersonInfoManager, get_person_info_manager

src/common/data_models/bot_interest_data_model.py

@@ -10,6 +10,7 @@ from typing import Any
 import numpy as np
 
 from src.config.config import model_config
+
 from . import BaseDataModel
 
 

src/common/database/optimization/preloader.py

@@ -9,11 +9,10 @@
 
 import asyncio
 import time
-from collections import defaultdict
+from collections import OrderedDict, defaultdict
 from collections.abc import Awaitable, Callable
 from dataclasses import dataclass, field
 from typing import Any
-from collections import OrderedDict
 
 from sqlalchemy import select
 from sqlalchemy.ext.asyncio import AsyncSession

src/llm_models/utils_model.py

@@ -29,7 +29,6 @@ from enum import Enum
 from typing import Any, ClassVar, Literal
 
 import numpy as np
-
 from rich.traceback import install
 
 from src.common.logger import get_logger

src/main.py

@@ -7,7 +7,7 @@ import time
 import traceback
 from collections.abc import Callable, Coroutine
 from random import choices
-from typing import Any, cast
+from typing import Any
 
 from rich.traceback import install
 

src/memory_graph/short_term_manager.py

@@ -11,11 +11,10 @@ import asyncio
 import json
 import re
 import uuid
-import json_repair
 from pathlib import Path
 from typing import Any
-from collections import defaultdict
 
+import json_repair
 import numpy as np
 
 from src.common.logger import get_logger

src/person_info/relationship_fetcher.py

@@ -182,7 +182,7 @@ class RelationshipFetcher:
                         kw_lower = kw.lower()
                         # 排除聊天互动、情感需求等不是真实兴趣的词汇
                         if not any(excluded in kw_lower for excluded in [
-                            '亲亲', '撒娇', '被宠', '被夸', '聊天', '互动', '关心', '专注', '需要'
+                            "亲亲", "撒娇", "被宠", "被夸", "聊天", "互动", "关心", "专注", "需要"
                         ]):
                             filtered_keywords.append(kw)
 

src/plugin_system/utils/permission_decorators.py

@@ -11,7 +11,6 @@ from inspect import iscoroutinefunction
 from src.chat.message_receive.chat_stream import ChatStream
 from src.plugin_system.apis.logging_api import get_logger
 from src.plugin_system.apis.permission_api import permission_api
-from src.plugin_system.apis.send_api import text_to_stream
 
 logger = get_logger(__name__)
 

src/plugins/built_in/affinity_flow_chatter/core/affinity_interest_calculator.py

@@ -292,7 +292,7 @@ class AffinityInterestCalculator(BaseInterestCalculator):
             return
 
         # 防止并发初始化（使用锁）
-        if not hasattr(self, '_init_lock'):
+        if not hasattr(self, "_init_lock"):
             self._init_lock = asyncio.Lock()
 
         async with self._init_lock:
@@ -354,7 +354,7 @@ class AffinityInterestCalculator(BaseInterestCalculator):
                         logger.debug("[语义评分] 已有模型，跳过自动训练启动")
 
                 except FileNotFoundError:
-                    logger.warning(f"[语义评分] 未找到训练模型，将自动训练...")
+                    logger.warning("[语义评分] 未找到训练模型，将自动训练...")
                     # 触发首次训练
                     trained, model_path = await auto_trainer.auto_train_if_needed(
                         persona_info=persona_info,
@@ -464,7 +464,7 @@ class AffinityInterestCalculator(BaseInterestCalculator):
         logger.info("[语义评分] 开始重新加载模型...")
 
         # 检查人设是否变化
-        if hasattr(self, 'model_manager') and self.model_manager:
+        if hasattr(self, "model_manager") and self.model_manager:
             persona_info = self._get_current_persona_info()
             reloaded = await self.model_manager.check_and_reload_for_persona(persona_info)
             if reloaded:

src/plugins/built_in/kokoro_flow_chatter/prompt_modules_unified.py

@@ -117,7 +117,7 @@ def build_custom_decision_module() -> str:
     custom_prompt = getattr(kfc_config, "custom_decision_prompt", "")
 
     # 调试输出
-    logger.debug(f"[自定义决策提示词] 原始值: {repr(custom_prompt)}, 类型: {type(custom_prompt)}")
+    logger.debug(f"[自定义决策提示词] 原始值: {custom_prompt!r}, 类型: {type(custom_prompt)}")
 
     if not custom_prompt or not custom_prompt.strip():
         logger.debug("[自定义决策提示词] 为空或仅含空白字符，跳过")

src/plugins/built_in/napcat_adapter/src/handlers/to_core/message_handler.py

@@ -2,21 +2,28 @@
 
 from __future__ import annotations
 
+import asyncio
 import base64
 import time
 from pathlib import Path
 from typing import TYPE_CHECKING, Any
 
-from mofox_wire import (
+import orjson
-    MessageBuilder,
+from mofox_wire import MessageBuilder, SegPayload
-    SegPayload,
-)
 
 from src.common.logger import get_logger
 from src.plugin_system.apis import config_api
 
 from ...event_models import ACCEPT_FORMAT, QQ_FACE, RealMessageType
-from ..utils import *
+from ..utils import (
+    get_forward_message,
+    get_group_info,
+    get_image_base64,
+    get_member_info,
+    get_message_detail,
+    get_record_detail,
+    get_self_info,
+)
 
 if TYPE_CHECKING:
     from ....plugin import NapcatAdapter
@@ -300,8 +307,7 @@ class MessageHandler:
         try:
             if file_path and Path(file_path).exists():
                 # 本地文件处理
-                with open(file_path, "rb") as f:
+                video_data = await asyncio.to_thread(Path(file_path).read_bytes)
-                    video_data = f.read()
                 video_base64 = base64.b64encode(video_data).decode("utf-8")
                 logger.debug(f"视频文件大小: {len(video_data) / (1024 * 1024):.2f} MB")
 

src/plugins/built_in/napcat_adapter/src/handlers/to_core/meta_event_handler.py

@@ -22,6 +22,7 @@ class MetaEventHandler:
         self.adapter = adapter
         self.plugin_config: dict[str, Any] | None = None
         self._interval_checking = False
+        self._heartbeat_task: asyncio.Task | None = None
 
     def set_plugin_config(self, config: dict[str, Any]) -> None:
         """设置插件配置"""
@@ -41,7 +42,7 @@ class MetaEventHandler:
                 self_id = raw.get("self_id")
                 if not self._interval_checking and self_id:
                     # 第一次收到心跳包时才启动心跳检查
-                    asyncio.create_task(self.check_heartbeat(self_id))
+                    self._heartbeat_task = asyncio.create_task(self.check_heartbeat(self_id))
                 self.last_heart_beat = time.time()
                 interval = raw.get("interval")
                 if interval:

src/plugins/built_in/siliconflow_api_index_tts/plugin.py

@@ -7,6 +7,7 @@ import asyncio
 import base64
 import hashlib
 from pathlib import Path
+from typing import ClassVar
 
 import aiohttp
 import toml
@@ -139,25 +140,34 @@ class SiliconFlowIndexTTSAction(BaseAction):
     action_description = "使用SiliconFlow API进行高质量的IndexTTS语音合成，支持零样本语音克隆"
 
     # 关键词配置
-    activation_keywords = ["克隆语音", "模仿声音", "语音合成", "indextts", "声音克隆", "语音生成", "仿声", "变声"]
+    activation_keywords: ClassVar[list[str]] = [
+        "克隆语音",
+        "模仿声音",
+        "语音合成",
+        "indextts",
+        "声音克隆",
+        "语音生成",
+        "仿声",
+        "变声",
+    ]
     keyword_case_sensitive = False
 
     # 动作参数定义
-    action_parameters = {
+    action_parameters: ClassVar[dict[str, str]] = {
         "text": "需要合成语音的文本内容，必填，应当清晰流畅",
-        "speed": "语速（可选），范围0.1-3.0，默认1.0"
+        "speed": "语速（可选），范围0.1-3.0，默认1.0",
     }
 
     # 动作使用场景
-    action_require = [
+    action_require: ClassVar[list[str]] = [
         "当用户要求语音克隆或模仿某个声音时使用",
         "当用户明确要求进行语音合成时使用",
         "当需要高质量语音输出时使用",
-        "当用户要求变声或仿声时使用"
+        "当用户要求变声或仿声时使用",
     ]
 
     # 关联类型 - 支持语音消息
-    associated_types = ["voice"]
+    associated_types: ClassVar[list[str]] = ["voice"]
 
     async def execute(self) -> tuple[bool, str]:
         """执行SiliconFlow IndexTTS语音合成"""
@@ -258,11 +268,11 @@ class SiliconFlowTTSCommand(BaseCommand):
 
     command_name = "sf_tts"
     command_description = "使用SiliconFlow IndexTTS进行语音合成"
-    command_aliases = ["sftts", "sf语音", "硅基语音"]
+    command_aliases: ClassVar[list[str]] = ["sftts", "sf语音", "硅基语音"]
 
-    command_parameters = {
+    command_parameters: ClassVar[dict[str, dict[str, object]]] = {
         "text": {"type": str, "required": True, "description": "要合成的文本"},
-        "speed": {"type": float, "required": False, "description": "语速 (0.1-3.0)"}
+        "speed": {"type": float, "required": False, "description": "语速 (0.1-3.0)"},
     }
 
     async def execute(self, text: str, speed: float = 1.0) -> tuple[bool, str]:
@@ -341,14 +351,14 @@ class SiliconFlowIndexTTSPlugin(BasePlugin):
 
     # 必需的抽象属性
     enable_plugin: bool = True
-    dependencies: list[str] = []
+    dependencies: ClassVar[list[str]] = []
     config_file_name: str = "config.toml"
 
     # Python依赖
-    python_dependencies = ["aiohttp>=3.8.0"]
+    python_dependencies: ClassVar[list[str]] = ["aiohttp>=3.8.0"]
 
     # 配置描述
-    config_section_descriptions = {
+    config_section_descriptions: ClassVar[dict[str, str]] = {
         "plugin": "插件基本配置",
         "components": "组件启用配置",
         "api": "SiliconFlow API配置",
@@ -356,7 +366,7 @@ class SiliconFlowIndexTTSPlugin(BasePlugin):
     }
 
     # 配置schema
-    config_schema = {
+    config_schema: ClassVar[dict[str, dict[str, ConfigField]]] = {
         "plugin": {
             "enabled": ConfigField(type=bool, default=False, description="是否启用插件"),
             "config_version": ConfigField(type=str, default="2.0.0", description="配置文件版本"),

src/plugins/built_in/siliconflow_api_index_tts/upload_voice.py

@@ -43,8 +43,7 @@ class VoiceUploader:
             raise FileNotFoundError(f"音频文件不存在: {audio_path}")
 
         # 读取音频文件并转换为base64
-        with open(audio_path, "rb") as f:
+        audio_data = await asyncio.to_thread(audio_path.read_bytes)
-            audio_data = f.read()
 
         audio_base64 = base64.b64encode(audio_data).decode("utf-8")
 

src/plugins/built_in/system_management/plugin.py

@@ -347,8 +347,10 @@ class SystemCommand(PlusCommand):
             return
 
         response_parts = [f"🧩 已注册的提示词组件 (共 {len(components)} 个):"]
-        for comp in components:
+
-            response_parts.append(f"• `{comp.name}` (来自: `{comp.plugin_name}`)")
+        response_parts.extend(
+            [f"• `{comp.name}` (来自: `{comp.plugin_name}`)" for comp in components]
+        )
 
         await self._send_long_message("\n".join(response_parts))
 
@@ -586,8 +588,10 @@ class SystemCommand(PlusCommand):
 
         for plugin_name, comps in by_plugin.items():
             response_parts.append(f"🔌 **{plugin_name}**:")
-            for comp in comps:
+
-                response_parts.append(f"  ❌ `{comp.name}` ({comp.component_type.value})")
+            response_parts.extend(
+                [f"  ❌ `{comp.name}` ({comp.component_type.value})" for comp in comps]
+            )
 
         await self._send_long_message("\n".join(response_parts))
 

src/plugins/built_in/web_search_tool/engines/serper_engine.py

@@ -121,13 +121,17 @@ class SerperSearchEngine(BaseSearchEngine):
 
             # 添加有机搜索结果
             if "organic" in data:
-                for result in data["organic"][:num_results]:
+                results.extend(
-                    results.append({
+                    [
+                        {
                             "title": result.get("title", "无标题"),
                             "url": result.get("link", ""),
                             "snippet": result.get("snippet", ""),
                             "provider": "Serper",
-                    })
+                        }
+                        for result in data["organic"][:num_results]
+                    ]
+                )
 
             logger.info(f"Serper搜索成功: 查询='{query}', 结果数={len(results)}")
             return results

src/plugins/built_in/web_search_tool/plugin.py

@@ -4,6 +4,8 @@ Web Search Tool Plugin
 一个功能强大的网络搜索和URL解析插件，支持多种搜索引擎和解析策略。
 """
 
+from typing import ClassVar
+
 from src.common.logger import get_logger
 from src.plugin_system import BasePlugin, ComponentInfo, ConfigField, register_plugin
 from src.plugin_system.apis import config_api
@@ -30,7 +32,7 @@ class WEBSEARCHPLUGIN(BasePlugin):
     # 插件基本信息
     plugin_name: str = "web_search_tool"  # 内部标识符
     enable_plugin: bool = True
-    dependencies: list[str] = []  # 插件依赖列表
+    dependencies: ClassVar[list[str]] = []  # 插件依赖列表
 
     def __init__(self, *args, **kwargs):
         """初始化插件，立即加载所有搜索引擎"""
@@ -80,11 +82,14 @@ class WEBSEARCHPLUGIN(BasePlugin):
     config_file_name: str = "config.toml"  # 配置文件名
 
     # 配置节描述
-    config_section_descriptions = {"plugin": "插件基本信息", "proxy": "链接本地解析代理配置"}
+    config_section_descriptions: ClassVar[dict[str, str]] = {
+        "plugin": "插件基本信息",
+        "proxy": "链接本地解析代理配置",
+    }
 
     # 配置Schema定义
     # 注意：EXA配置和组件设置已迁移到主配置文件(bot_config.toml)的[exa]和[web_search]部分
-    config_schema: dict = {
+    config_schema: ClassVar[dict[str, dict[str, ConfigField]]] = {
         "plugin": {
             "name": ConfigField(type=str, default="WEB_SEARCH_PLUGIN", description="插件名称"),
             "version": ConfigField(type=str, default="1.0.0", description="插件版本"),