依旧修pyright喵喵喵~

src/chat/knowledge/embedding_store.py

@@ -2,6 +2,7 @@ import asyncio
 import math
 import os
 from dataclasses import dataclass
+from typing import Any
 
 # import tqdm
 import aiofiles
@@ -121,7 +122,7 @@ class EmbeddingStore:
 
         self.store = {}
 
-        self.faiss_index = None
+        self.faiss_index: Any = None
         self.idx2hash = None
 
     @staticmethod
@@ -158,6 +159,8 @@ class EmbeddingStore:
         from src.config.config import model_config
         from src.llm_models.utils_model import LLMRequest
 
+        assert model_config is not None
+
         # 限制 chunk_size 和 max_workers 在合理范围内
         chunk_size = max(MIN_CHUNK_SIZE, min(chunk_size, MAX_CHUNK_SIZE))
         max_workers = max(MIN_WORKERS, min(max_workers, MAX_WORKERS))
@@ -402,6 +405,7 @@ class EmbeddingStore:
 
     def build_faiss_index(self) -> None:
         """重新构建Faiss索引，以余弦相似度为度量"""
+        assert global_config is not None
         # 获取所有的embedding
         array = []
         self.idx2hash = {}

src/chat/utils/utils_image.py

@@ -344,14 +344,15 @@ class ImageManager:
             # --- 新的帧选择逻辑：均匀抽取4帧 ---
             num_frames = len(all_frames)
             if num_frames <= 4:
-                # 如果总帧数小于等于4，则全部选中
+                # 如果总宽度小于等于4，则全部选中
                 selected_frames = all_frames
+                indices = list(range(num_frames))
             else:
                 # 使用linspace计算4个均匀分布的索引
                 indices = np.linspace(0, num_frames - 1, 4, dtype=int)
                 selected_frames = [all_frames[i] for i in indices]
 
-            logger.debug(f"GIF Frame Analysis: Total frames={num_frames}, Selected indices={indices if num_frames > 4 else list(range(num_frames))}")
+            logger.debug(f"GIF Frame Analysis: Total frames={num_frames}, Selected indices={indices}")
             # --- 帧选择逻辑结束 ---
 
             # 如果选择后连一帧都没有（比如GIF只有一帧且后续处理失败？）或者原始GIF就没帧，也返回None

src/chat/utils/utils_video.py

@@ -37,7 +37,7 @@ _locks_guard = asyncio.Lock()
 
 logger = get_logger("utils_video")
 
-from inkfox import video
+from inkfox import video  # type: ignore
 
 
 class VideoAnalyzer:
@@ -123,7 +123,6 @@ class VideoAnalyzer:
     # ---- 批量分析 ----
     async def _analyze_batch(self, frames: list[tuple[str, float]], question: str | None) -> str:
         from src.llm_models.payload_content.message import MessageBuilder, RoleType
-        from src.llm_models.utils_model import RequestType
 
         prompt = self.batch_analysis_prompt.format(
             personality_core=self.personality_core, personality_side=self.personality_side
@@ -139,12 +138,7 @@ class VideoAnalyzer:
         for b64, _ in frames:
             mb.add_image_content("jpeg", b64)
         message = mb.build()
-        model_info, api_provider, client = self.video_llm._select_model()
+        resp = await self.video_llm.execute_with_messages(
-        resp = await self.video_llm._execute_request(
-            api_provider=api_provider,
-            client=client,
-            request_type=RequestType.RESPONSE,
-            model_info=model_info,
             message_list=[message],
             temperature=None,
             max_tokens=None,

src/chat/utils/utils_video_legacy.py

@@ -31,9 +31,9 @@ def _extract_frames_worker(
     max_image_size: int,
     frame_extraction_mode: str,
     frame_interval_seconds: float | None,
-) -> list[Any] | list[tuple[str, str]]:
+) -> list[tuple[str, float]] | list[tuple[str, str]]:
     """线程池中提取视频帧的工作函数"""
-    frames = []
+    frames: list[tuple[str, float]] = []
     try:
         cap = cv2.VideoCapture(video_path)
         fps = cap.get(cv2.CAP_PROP_FPS)
@@ -42,7 +42,7 @@ def _extract_frames_worker(
 
         if frame_extraction_mode == "time_interval":
             # 新模式：按时间间隔抽帧
-            time_interval = frame_interval_seconds
+            time_interval = frame_interval_seconds or 2.0
             next_frame_time = 0.0
             extracted_count = 0  # 初始化提取帧计数器
 
@@ -61,7 +61,7 @@ def _extract_frames_worker(
                     # 调整图像大小
                     if max(pil_image.size) > max_image_size:
                         ratio = max_image_size / max(pil_image.size)
-                        new_size = tuple(int(dim * ratio) for dim in pil_image.size)
+                        new_size = (int(pil_image.size[0] * ratio), int(pil_image.size[1] * ratio))
                         pil_image = pil_image.resize(new_size, Image.Resampling.LANCZOS)
 
                     # 转换为base64
@@ -240,6 +240,7 @@ class LegacyVideoAnalyzer:
             estimated_frames = min(self.max_frames, total_frames // frame_interval + 1)
         else:
             estimated_frames = self.max_frames
+            frame_interval = 1
 
         logger.info(f"计算得出帧间隔: {frame_interval} (将提取约{estimated_frames}帧)")
 
@@ -276,7 +277,7 @@ class LegacyVideoAnalyzer:
                 return await self._extract_frames_fallback(video_path)
 
             logger.info(f"✅ 成功提取{len(frames)}帧 (线程池模式)")
-            return frames
+            return frames  # type: ignore
 
         except Exception as e:
             logger.error(f"线程池帧提取失败: {e}")
@@ -315,7 +316,7 @@ class LegacyVideoAnalyzer:
                     # 调整图像大小
                     if max(pil_image.size) > self.max_image_size:
                         ratio = self.max_image_size / max(pil_image.size)
-                        new_size = tuple(int(dim * ratio) for dim in pil_image.size)
+                        new_size = (int(pil_image.size[0] * ratio), int(pil_image.size[1] * ratio))
                         pil_image = pil_image.resize(new_size, Image.Resampling.LANCZOS)
 
                     # 转换为base64
@@ -463,11 +464,11 @@ class LegacyVideoAnalyzer:
         # logger.info(f"✅ 多帧消息构建完成，包含{len(frames)}张图片")
 
         # 获取模型信息和客户端
-        model_info, api_provider, client = self.video_llm._select_model()
+        model_info, api_provider, client = self.video_llm._select_model()  # type: ignore
         # logger.info(f"使用模型: {model_info.name} 进行多帧分析")
 
         # 直接执行多图片请求
-        api_response = await self.video_llm._execute_request(
+        api_response = await self.video_llm._execute_request(  # type: ignore
             api_provider=api_provider,
             client=client,
             request_type=RequestType.RESPONSE,

src/common/cache_manager.py

@@ -38,6 +38,9 @@ class CacheManager:
         初始化缓存管理器。
         """
         if not hasattr(self, "_initialized"):
+            assert global_config is not None
+            assert model_config is not None
+
             self.default_ttl = default_ttl or 3600
             self.semantic_cache_collection_name = "semantic_cache"
 
@@ -87,6 +90,7 @@ class CacheManager:
                     embedding_array = embedding_array.flatten()
 
                 # 检查维度是否符合预期
+                assert global_config is not None
                 expected_dim = (
                     getattr(CacheManager, "embedding_dimension", None)
                     or global_config.lpmm_knowledge.embedding_dimension

src/common/config_helpers.py

@@ -14,23 +14,29 @@ def resolve_embedding_dimension(fallback: int | None = None, *, sync_global: boo
 
     candidates: list[int | None] = []
 
-    try:
+    if model_config is not None:
-        embedding_task = getattr(model_config.model_task_config, "embedding", None)
+        try:
-        if embedding_task is not None:
+            embedding_task = getattr(model_config.model_task_config, "embedding", None)
-            candidates.append(getattr(embedding_task, "embedding_dimension", None))
+            if embedding_task is not None:
-    except Exception:
+                candidates.append(getattr(embedding_task, "embedding_dimension", None))
+        except Exception:
+            candidates.append(None)
+    else:
         candidates.append(None)
 
-    try:
+    if global_config is not None:
-        candidates.append(getattr(global_config.lpmm_knowledge, "embedding_dimension", None))
+        try:
-    except Exception:
+            candidates.append(getattr(global_config.lpmm_knowledge, "embedding_dimension", None))
+        except Exception:
+            candidates.append(None)
+    else:
         candidates.append(None)
 
     candidates.append(fallback)
 
     resolved: int | None = next((int(dim) for dim in candidates if dim and int(dim) > 0), None)
 
-    if resolved and sync_global:
+    if resolved and sync_global and global_config is not None:
         try:
             if getattr(global_config.lpmm_knowledge, "embedding_dimension", None) != resolved:
                 global_config.lpmm_knowledge.embedding_dimension = resolved  # type: ignore[attr-defined]

src/common/database/utils/decorators.py

@@ -10,7 +10,7 @@ import asyncio
 import functools
 import hashlib
 import time
-from collections.abc import Awaitable, Callable
+from collections.abc import Awaitable, Callable, Coroutine
 from typing import Any, ParamSpec, TypeVar
 
 from sqlalchemy.exc import DBAPIError, OperationalError
@@ -82,7 +82,7 @@ def retry(
             return await session.execute(stmt)
     """
 
-    def decorator(func: Callable[P, Awaitable[R]]) -> Callable[P, Awaitable[R]]:
+    def decorator(func: Callable[P, Coroutine[Any, Any, R]]) -> Callable[P, Coroutine[Any, Any, R]]:
         @functools.wraps(func)
         async def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
             last_exception = None
@@ -130,7 +130,7 @@ def timeout(seconds: float):
             return await session.execute(complex_stmt)
     """
 
-    def decorator(func: Callable[P, Awaitable[R]]) -> Callable[P, Awaitable[R]]:
+    def decorator(func: Callable[P, Coroutine[Any, Any, R]]) -> Callable[P, Coroutine[Any, Any, R]]:
         @functools.wraps(func)
         async def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
             try:
@@ -166,7 +166,7 @@ def cached(
             return await query_user(user_id)
     """
 
-    def decorator(func: Callable[P, Awaitable[R]]) -> Callable[P, Awaitable[R]]:
+    def decorator(func: Callable[P, Coroutine[Any, Any, R]]) -> Callable[P, Coroutine[Any, Any, R]]:
         @functools.wraps(func)
         async def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
             # 延迟导入避免循环依赖
@@ -228,7 +228,7 @@ def measure_time(log_slow: float | None = None):
             return await session.execute(stmt)
     """
 
-    def decorator(func: Callable[P, Awaitable[R]]) -> Callable[P, Awaitable[R]]:
+    def decorator(func: Callable[P, Coroutine[Any, Any, R]]) -> Callable[P, Coroutine[Any, Any, R]]:
         @functools.wraps(func)
         async def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
             start_time = time.perf_counter()
@@ -270,7 +270,7 @@ def transactional(auto_commit: bool = True, auto_rollback: bool = True):
         函数需要接受session参数
     """
 
-    def decorator(func: Callable[P, Awaitable[R]]) -> Callable[P, Awaitable[R]]:
+    def decorator(func: Callable[P, Coroutine[Any, Any, R]]) -> Callable[P, Coroutine[Any, Any, R]]:
         @functools.wraps(func)
         async def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
             # 查找session参数
@@ -335,7 +335,7 @@ def db_operation(
             return await complex_operation()
     """
 
-    def decorator(func: Callable[P, Awaitable[R]]) -> Callable[P, Awaitable[R]]:
+    def decorator(func: Callable[P, Coroutine[Any, Any, R]]) -> Callable[P, Coroutine[Any, Any, R]]:
         # 从内到外应用装饰器
         wrapped = func
 

src/common/logger.py

@@ -21,10 +21,15 @@ from structlog.typing import EventDict, WrappedLogger
 class DaemonQueueListener(QueueListener):
     """QueueListener 的工作线程作为守护进程运行，以避免阻塞关闭。"""
 
-    def _configure_listener(self):
+    def start(self):
-        super()._configure_listener()
+        """Start the listener.
-        if hasattr(self, "_thread") and self._thread is not None:  # type: ignore[attr-defined]
+        This starts up a background thread to monitor the queue for
-            self._thread.daemon = True  # type: ignore[attr-defined]
+        LogRecords to process.
+        """
+        # 覆盖 start 方法以设置 daemon=True
+        # 注意：_monitor 是 QueueListener 的内部方法
+        self._thread = threading.Thread(target=self._monitor, daemon=True)  # type: ignore
+        self._thread.start()
 
     def stop(self):
         """停止监听器，避免在退出时无限期阻塞。"""

src/common/message/api.py

@@ -1,4 +1,5 @@
 import os
+from typing import Any
 
 from mofox_wire import MessageServer
 
@@ -18,6 +19,8 @@ def get_global_api() -> MessageServer:
     if global_api is not None:
         return global_api
 
+    assert global_config is not None
+
     bus_config = global_config.message_bus
     host = os.getenv("HOST", "127.0.0.1")
     port_str = os.getenv("PORT", "8000")
@@ -27,7 +30,7 @@ def get_global_api() -> MessageServer:
     except ValueError:
         port = 8000
 
-    kwargs: dict[str, object] = {
+    kwargs: dict[str, Any] = {
         "host": host,
         "port": port,
         "app": get_global_server().get_app(),

src/common/message_repository.py

@@ -52,6 +52,7 @@ async def find_messages(
         消息字典列表，如果出错则返回空列表。
     """
     try:
+        assert global_config is not None
         async with get_db_session() as session:
             query = select(Messages)
 

src/common/remote.py

@@ -42,6 +42,7 @@ class TelemetryHeartBeatTask(AsyncTask):
     @staticmethod
     def _get_sys_info() -> dict[str, str]:
         """获取系统信息"""
+        assert global_config is not None
         info_dict = {
             "os_type": "Unknown",
             "py_version": platform.python_version(),

src/common/security.py

@@ -16,6 +16,7 @@ async def get_api_key(api_key: str = Security(api_key_header_auth)) -> str:
     FastAPI 依赖项，用于验证API密钥。
     从请求头中提取 X-API-Key 并验证它是否存在于配置的有效密钥列表中。
     """
+    assert bot_config is not None
     valid_keys = bot_config.plugin_http_system.plugin_api_valid_keys
     if not valid_keys:
         logger.warning("API密钥认证已启用，但未配置任何有效的API密钥。所有请求都将被拒绝。")

src/common/server.py

@@ -30,6 +30,7 @@ def rate_limit_exceeded_handler(request: Request, exc: Exception) -> Response:
 
 class Server:
     def __init__(self, host: str | None = None, port: int | None = None, app_name: str = "MoFox-Bot"):
+        assert bot_config is not None
         # 根据配置初始化速率限制器
         limiter = Limiter(
             key_func=get_remote_address,

src/config/config.py

@@ -176,7 +176,7 @@ def _remove_obsolete_keys(target: TOMLDocument | dict | Table, reference: TOMLDo
         if key not in reference:
             del target[key]
         elif isinstance(target.get(key), dict | Table) and isinstance(reference.get(key), dict | Table):
-            _remove_obsolete_keys(target[key], reference[key])
+            _remove_obsolete_keys(target[key], reference[key])  # type: ignore
 
 
 def _update_dict(target: TOMLDocument | dict | Table, source: TOMLDocument | dict):
@@ -433,9 +433,9 @@ class Config(ValidatedConfigBase):
 class APIAdapterConfig(ValidatedConfigBase):
     """API Adapter配置类"""
 
-    models: list[ModelInfo] = Field(..., min_items=1, description="模型列表")
+    models: list[ModelInfo] = Field(..., min_length=1, description="模型列表")
     model_task_config: ModelTaskConfig = Field(..., description="模型任务配置")
-    api_providers: list[APIProvider] = Field(..., min_items=1, description="API提供商列表")
+    api_providers: list[APIProvider] = Field(..., min_length=1, description="API提供商列表")
 
     def __init__(self, **data):
         super().__init__(**data)

src/config/config_base.py

@@ -177,7 +177,9 @@ class ValidatedConfigBase(BaseModel):
                 element_index = field_path[1]
 
                 # 尝试获取父字段的类型信息
-                parent_field_info = cls.model_fields.get(parent_field)
+                parent_field_info = None
+                if isinstance(parent_field, str):
+                    parent_field_info = cls.model_fields.get(parent_field)
 
                 if parent_field_info and hasattr(parent_field_info, "annotation"):
                     expected_type = parent_field_info.annotation
@@ -214,7 +216,9 @@ class ValidatedConfigBase(BaseModel):
             # 处理模型类型错误
             elif error_type in ["model_type", "dict_type", "is_instance_of"]:
                 field_name = field_path[0] if field_path else "unknown"
-                field_info = cls.model_fields.get(field_name)
+                field_info = None
+                if isinstance(field_name, str):
+                    field_info = cls.model_fields.get(field_name)
 
                 if field_info and hasattr(field_info, "annotation"):
                     expected_type = field_info.annotation

src/config/official_configs.py

@@ -638,15 +638,15 @@ class PlanningSystemConfig(ValidatedConfigBase):
     """规划系统配置 (日程与月度计划)"""
 
     # --- 日程生成 (原 ScheduleConfig) ---
-    schedule_enable: bool = Field(True, description="是否启用每日日程生成功能")
+    schedule_enable: bool = Field(default=True, description="是否启用每日日程生成功能")
-    schedule_guidelines: str = Field("", description="日程生成指导原则")
+    schedule_guidelines: str = Field(default="", description="日程生成指导原则")
 
     # --- 月度计划 (原 MonthlyPlanSystemConfig) ---
-    monthly_plan_enable: bool = Field(True, description="是否启用月度计划系统")
+    monthly_plan_enable: bool = Field(default=True, description="是否启用月度计划系统")
-    monthly_plan_guidelines: str = Field("", description="月度计划生成指导原则")
+    monthly_plan_guidelines: str = Field(default="", description="月度计划生成指导原则")
-    max_plans_per_month: int = Field(10, description="每月最多生成的计划数量")
+    max_plans_per_month: int = Field(default=10, description="每月最多生成的计划数量")
-    avoid_repetition_days: int = Field(7, description="避免在多少天内重复使用同一个月度计划")
+    avoid_repetition_days: int = Field(default=7, description="避免在多少天内重复使用同一个月度计划")
-    completion_threshold: int = Field(3, description="一个月度计划被使用多少次后算作完成")
+    completion_threshold: int = Field(default=3, description="一个月度计划被使用多少次后算作完成")
 
 
 class DependencyManagementConfig(ValidatedConfigBase):

src/individuality/individuality.py

@@ -6,10 +6,18 @@ import orjson
 from rich.traceback import install
 
 from src.common.logger import get_logger
-from src.config.config import global_config, model_config
+from src.config.config import global_config as _global_config, model_config as _model_config
 from src.llm_models.utils_model import LLMRequest
 from src.person_info.person_info import get_person_info_manager
 
+if _global_config is None:
+    raise ValueError("global_config is not initialized")
+if _model_config is None:
+    raise ValueError("model_config is not initialized")
+
+global_config = _global_config
+model_config = _model_config
+
 install(extra_lines=3)
 
 logger = get_logger("individuality")

src/llm_models/utils_model.py

@@ -1152,6 +1152,25 @@ class LLMRequest:
 
         return embeddings, model_info.name  # type: ignore[return-value]
 
+    async def execute_with_messages(
+        self,
+        message_list: list[Message],
+        temperature: float | None = None,
+        max_tokens: int | None = None,
+    ) -> APIResponse:
+        """
+        使用自定义消息列表执行请求（支持多模态/多图）。
+        """
+        start_time = time.time()
+        response, model_info = await self._strategy.execute_with_failover(
+            RequestType.RESPONSE,
+            message_list=message_list,
+            temperature=temperature,
+            max_tokens=max_tokens,
+        )
+        await self._record_usage(model_info, response.usage, time.time() - start_time, "/chat/completions")
+        return response
+
     async def _record_usage(self, model_info: ModelInfo, usage: UsageRecord | None, time_cost: float, endpoint: str):
         """
         记录模型使用情况。