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ruff: 清理代码并规范导入顺序

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对整个代码库进行了大规模的清理和重构，主要包括：
- 统一并修复了多个文件中的 `import` 语句顺序，使其符合 PEP 8 规范。
- 移除了大量未使用的导入和变量，减少了代码冗余。
- 修复了多处代码风格问题，例如多余的空行、不一致的引号使用等。
- 简化了异常处理逻辑，移除了不必要的 `noqa` 注释。
- 在多个文件中使用了更现代的类型注解语法（例如 `list[str]` 替代 `List[str]`）。
This commit is contained in:
minecraft1024a
2025-10-05 20:38:56 +08:00
parent 2908cfead1
commit 7a7f737f71
20 changed files with 163 additions and 171 deletions
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2
src/chat/memory_system/__init__.py
View File

@@ -21,6 +21,7 @@ from .memory_chunk import MemoryChunk as Memory
# 遗忘引擎
from .memory_forgetting_engine import ForgettingConfig, MemoryForgettingEngine, get_memory_forgetting_engine
from .memory_formatter import format_memories_bracket_style
# 记忆管理器
from .memory_manager import MemoryManager, MemoryResult, memory_manager
@@ -30,7 +31,6 @@ from .memory_system import MemorySystem, MemorySystemConfig, get_memory_system,
# Vector DB存储系统
from .vector_memory_storage_v2 import VectorMemoryStorage, VectorStorageConfig, get_vector_memory_storage
from .memory_formatter import format_memories_bracket_style
__all__ = [
# 核心数据结构

3
src/chat/memory_system/memory_formatter.py
View File

@@ -17,8 +17,9 @@
"""
from __future__ import annotations
from typing import Any, Iterable
import time
from collections.abc import Iterable
from typing import Any
def _format_timestamp(ts: Any) -> str:

14
src/chat/memory_system/memory_metadata_index.py
View File

@@ -2,9 +2,8 @@
记忆元数据索引。
"""
from dataclasses import dataclass, asdict
from dataclasses import asdict, dataclass
from typing import Any
from time import time
from src.common.logger import get_logger
@@ -12,6 +11,7 @@ logger = get_logger(__name__)
from inkfox.memory import PyMetadataIndex as _RustIndex # type: ignore
@dataclass
class MemoryMetadataIndexEntry:
memory_id: str
@@ -51,7 +51,7 @@ class MemoryMetadataIndex:
if payload:
try:
self._rust.batch_add(payload)
except Exception as ex: # noqa: BLE001
except Exception as ex:
logger.error(f"Rust 元数据批量添加失败: {ex}")
def add_or_update(self, entry: MemoryMetadataIndexEntry):
@@ -88,7 +88,7 @@ class MemoryMetadataIndex:
if flexible_mode:
return list(self._rust.search_flexible(params))
return list(self._rust.search_strict(params))
except Exception as ex: # noqa: BLE001
except Exception as ex:
logger.error(f"Rust 搜索失败返回空: {ex}")
return []
@@ -105,18 +105,18 @@ class MemoryMetadataIndex:
"keywords_count": raw.get("keywords_indexed", 0),
"tags_count": raw.get("tags_indexed", 0),
}
except Exception as ex: # noqa: BLE001
except Exception as ex:
logger.warning(f"读取 Rust stats 失败: {ex}")
return {"total_memories": 0}
def save(self): # 仅调用 rust save
try:
self._rust.save()
except Exception as ex: # noqa: BLE001
except Exception as ex:
logger.warning(f"Rust save 失败: {ex}")
__all__ = [
"MemoryMetadataIndexEntry",
"MemoryMetadataIndex",
"MemoryMetadataIndexEntry",
]

4
src/chat/message_receive/message.py
View File

@@ -263,7 +263,7 @@ class MessageRecv(Message):
logger.warning("视频消息中没有base64数据")
return "[收到视频消息，但数据异常]"
except Exception as e:
logger.error(f"视频处理失败: {str(e)}")
logger.error(f"视频处理失败: {e!s}")
import traceback
logger.error(f"错误详情: {traceback.format_exc()}")
@@ -277,7 +277,7 @@ class MessageRecv(Message):
logger.info("未启用视频识别")
return "[视频]"
except Exception as e:
logger.error(f"处理消息段失败: {str(e)}, 类型: {segment.type}, 数据: {segment.data}")
logger.error(f"处理消息段失败: {e!s}, 类型: {segment.type}, 数据: {segment.data}")
return f"[处理失败的{segment.type}消息]"

41
src/chat/utils/utils_video.py
View File

@@ -1,5 +1,4 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""纯 inkfox 视频关键帧分析工具
仅依赖 `inkfox.video` 提供的 Rust 扩展能力：
@@ -14,25 +13,25 @@
from __future__ import annotations
import os
import io
import asyncio
import base64
import tempfile
from pathlib import Path
from typing import List, Tuple, Optional, Dict, Any
import hashlib
import io
import os
import tempfile
import time
from pathlib import Path
from typing import Any
from PIL import Image
from src.common.database.sqlalchemy_models import Videos, get_db_session # type: ignore
from src.common.logger import get_logger
from src.config.config import global_config, model_config
from src.llm_models.utils_model import LLMRequest
from src.common.database.sqlalchemy_models import Videos, get_db_session # type: ignore
# 简易并发控制：同一 hash 只处理一次
_video_locks: Dict[str, asyncio.Lock] = {}
_video_locks: dict[str, asyncio.Lock] = {}
_locks_guard = asyncio.Lock()
logger = get_logger("utils_video")
@@ -90,7 +89,7 @@ class VideoAnalyzer:
logger.debug(f"获取系统信息失败: {e}")
# ---- 关键帧提取 ----
async def extract_keyframes(self, video_path: str) -> List[Tuple[str, float]]:
async def extract_keyframes(self, video_path: str) -> list[tuple[str, float]]:
"""提取关键帧并返回 (base64, timestamp_seconds) 列表"""
with tempfile.TemporaryDirectory() as tmp:
result = video.extract_keyframes_from_video( # type: ignore[attr-defined]
@@ -105,7 +104,7 @@ class VideoAnalyzer:
)
files = sorted(Path(tmp).glob("keyframe_*.jpg"))[: self.max_frames]
total_ms = getattr(result, "total_time_ms", 0)
frames: List[Tuple[str, float]] = []
frames: list[tuple[str, float]] = []
for i, f in enumerate(files):
img = Image.open(f).convert("RGB")
if max(img.size) > self.max_image_size:
@@ -119,7 +118,7 @@ class VideoAnalyzer:
return frames
# ---- 批量分析 ----
async def _analyze_batch(self, frames: List[Tuple[str, float]], question: Optional[str]) -> str:
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(
@@ -149,8 +148,8 @@ class VideoAnalyzer:
return resp.content or "❌ 未获得响应"
# ---- 逐帧分析 ----
async def _analyze_sequential(self, frames: List[Tuple[str, float]], question: Optional[str]) -> str:
results: List[str] = []
async def _analyze_sequential(self, frames: list[tuple[str, float]], question: str | None) -> str:
results: list[str] = []
for i, (b64, ts) in enumerate(frames):
prompt = f"分析第{i+1}帧" + (f" (时间: {ts:.2f}s)" if self.enable_frame_timing else "")
if question:
@@ -174,7 +173,7 @@ class VideoAnalyzer:
return "\n".join(results)
# ---- 主入口 ----
async def analyze_video(self, video_path: str, question: Optional[str] = None) -> Tuple[bool, str]:
async def analyze_video(self, video_path: str, question: str | None = None) -> tuple[bool, str]:
if not os.path.exists(video_path):
return False, "❌ 文件不存在"
frames = await self.extract_keyframes(video_path)
@@ -189,10 +188,10 @@ class VideoAnalyzer:
async def analyze_video_from_bytes(
self,
video_bytes: bytes,
filename: Optional[str] = None,
prompt: Optional[str] = None,
question: Optional[str] = None,
) -> Dict[str, str]:
filename: str | None = None,
prompt: str | None = None,
question: str | None = None,
) -> dict[str, str]:
"""从内存字节分析视频，兼容旧调用 (prompt / question 二选一) 返回 {"summary": str}."""
if not video_bytes:
return {"summary": "❌ 空视频数据"}
@@ -271,7 +270,7 @@ class VideoAnalyzer:
# ---- 外部接口 ----
_INSTANCE: Optional[VideoAnalyzer] = None
_INSTANCE: VideoAnalyzer | None = None
def get_video_analyzer() -> VideoAnalyzer:
@@ -285,7 +284,7 @@ def is_video_analysis_available() -> bool:
return True
def get_video_analysis_status() -> Dict[str, Any]:
def get_video_analysis_status() -> dict[str, Any]:
try:
info = video.get_system_info() # type: ignore[attr-defined]
except Exception as e: # pragma: no cover
@@ -297,4 +296,4 @@ def get_video_analysis_status() -> Dict[str, Any]:
"modes": ["auto", "batch", "sequential"],
"max_frames_default": inst.max_frames,
"implementation": "inkfox",
}
}