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fix: Update type hints to use newer Python syntax

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- Replace Dict, List, Optional with dict, list,  < /dev/null |  None syntax
- Fix abstract method implementation in message.py
- Improve type annotations and function return types
- Remove unreachable code in get_current_task_tool.py
- Refactor HTML elements to use style attributes

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
晴猫
2025-05-01 06:55:05 +09:00
parent 3d001da30e
commit 263e8d196a
29 changed files with 125 additions and 143 deletions
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16
README.md
View File

@@ -1,6 +1,6 @@
# 麦麦！MaiCore-MaiMBot (编辑中)
<br />
<div align="center">
<div style="text-align: center">
![Python Version](https://img.shields.io/badge/Python-3.10+-blue)
![License](https://img.shields.io/github/license/SengokuCola/MaiMBot?label=协议)
@@ -12,7 +12,7 @@
</div>
<p align="center">
<p style="text-align: center">
<a href="https://github.com/MaiM-with-u/MaiBot/">
<img src="depends-data/maimai.png" alt="Logo" style="width: 200px">
</a>
@@ -21,8 +21,8 @@
画师：略nd
</a>
<h3 align="center">MaiBot(麦麦)</h3>
<p align="center">
<h3 style="text-align: center">MaiBot(麦麦)</h3>
<p style="text-align: center">
一款专注于<strong> 群组聊天 </strong>的赛博网友
<br />
<a href="https://docs.mai-mai.org"><strong>探索本项目的文档 »</strong></a>
@@ -50,7 +50,7 @@
- 🧠 **持久记忆系统**：基于MongoDB的长期记忆存储
- 🔄 **动态人格系统**：自适应的性格特征
<div align="center">
<div style="text-align: center">
<a href="https://www.bilibili.com/video/BV1amAneGE3P" target="_blank">
<img src="depends-data/video.png" style="max-width: 200px" alt="麦麦演示视频">
<br>
@@ -97,9 +97,9 @@
- [四群](https://qm.qq.com/q/wlH5eT8OmQ) 729957033【已满】
<div align="left">
<h2>📚 文档 </h2>
</div>
## 📚 文档
### (部分内容可能过时，请注意版本对应)

7
scripts/import_openie.py
View File

@@ -8,7 +8,6 @@ import sys
import os
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
from typing import Dict, List
from src.plugins.knowledge.src.lpmmconfig import PG_NAMESPACE, global_config
from src.plugins.knowledge.src.embedding_store import EmbeddingManager
@@ -26,8 +25,8 @@ logger = get_module_logger("LPMM知识库-OpenIE导入")
def hash_deduplicate(
raw_paragraphs: Dict[str, str],
triple_list_data: Dict[str, List[List[str]]],
raw_paragraphs: dict[str, str],
triple_list_data: dict[str, list[list[str]]],
stored_pg_hashes: set,
stored_paragraph_hashes: set,
):
@@ -126,7 +125,7 @@ def main():
)
# 初始化Embedding库
embed_manager = embed_manager = EmbeddingManager(llm_client_list[global_config["embedding"]["provider"]])
embed_manager = EmbeddingManager(llm_client_list[global_config["embedding"]["provider"]])
logger.info("正在从文件加载Embedding库")
try:
embed_manager.load_from_file()

28
src/api/config_api.py
View File

@@ -1,4 +1,4 @@
from typing import Dict, List, Optional
from typing import List, Optional
import strawberry
# from packaging.version import Version, InvalidVersion
@@ -128,22 +128,22 @@ class BotConfig:
enable_pfc_chatting: bool # 是否启用PFC聊天
# 模型配置
llm_reasoning: Dict[str, str] # LLM推理
# llm_reasoning_minor: Dict[str, str]
llm_normal: Dict[str, str] # LLM普通
llm_topic_judge: Dict[str, str] # LLM话题判断
llm_summary: Dict[str, str] # LLM话题总结
llm_emotion_judge: Dict[str, str] # LLM情感判断
embedding: Dict[str, str] # 嵌入
vlm: Dict[str, str] # VLM
moderation: Dict[str, str] # 审核
llm_reasoning: dict[str, str] # LLM推理
# llm_reasoning_minor: dict[str, str]
llm_normal: dict[str, str] # LLM普通
llm_topic_judge: dict[str, str] # LLM话题判断
llm_summary: dict[str, str] # LLM话题总结
llm_emotion_judge: dict[str, str] # LLM情感判断
embedding: dict[str, str] # 嵌入
vlm: dict[str, str] # VLM
moderation: dict[str, str] # 审核
# 实验性
llm_observation: Dict[str, str] # LLM观察
llm_sub_heartflow: Dict[str, str] # LLM子心流
llm_heartflow: Dict[str, str] # LLM心流
llm_observation: dict[str, str] # LLM观察
llm_sub_heartflow: dict[str, str] # LLM子心流
llm_heartflow: dict[str, str] # LLM心流
api_urls: Dict[str, str] # API URLs
api_urls: dict[str, str] # API URLs
@strawberry.type

6
src/common/logger.py
View File

@@ -1,5 +1,5 @@
from loguru import logger
from typing import Dict, Optional, Union, List, Tuple
from typing import Optional, Union, List, Tuple
import sys
import os
from types import ModuleType
@@ -75,8 +75,8 @@ if default_handler_id is not None:
LoguruLogger = logger.__class__
# 全局注册表：记录模块与处理器ID的映射
_handler_registry: Dict[str, List[int]] = {}
_custom_style_handlers: Dict[Tuple[str, str], List[int]] = {} # 记录自定义样式处理器ID
_handler_registry: dict[str, List[int]] = {}
_custom_style_handlers: dict[Tuple[str, str], List[int]] = {} # 记录自定义样式处理器ID
# 获取日志存储根地址
current_file_path = Path(__file__).resolve()

8
src/common/message_repository.py
View File

@@ -7,11 +7,11 @@ logger = get_module_logger(__name__)
def find_messages(
message_filter: Dict[str, Any],
message_filter: dict[str, Any],
sort: Optional[List[tuple[str, int]]] = None,
limit: int = 0,
limit_mode: str = "latest",
) -> List[Dict[str, Any]]:
) -> List[dict[str, Any]]:
"""
根据提供的过滤器、排序和限制条件查找消息。
@@ -26,7 +26,7 @@ def find_messages(
"""
try:
query = db.messages.find(message_filter)
results: List[Dict[str, Any]] = []
results: List[dict[str, Any]] = []
if limit > 0:
if limit_mode == "earliest":
@@ -56,7 +56,7 @@ def find_messages(
return []
def count_messages(message_filter: Dict[str, Any]) -> int:
def count_messages(message_filter: dict[str, Any]) -> int:
"""
根据提供的过滤器计算消息数量。

4
src/config/config.py
View File

@@ -271,8 +271,8 @@ class BotConfig:
enable_pfc_chatting: bool = False # 是否启用PFC聊天
# 模型配置
llm_reasoning: Dict[str, str] = field(default_factory=lambda: {})
# llm_reasoning_minor: Dict[str, str] = field(default_factory=lambda: {})
llm_reasoning: dict[str, str] = field(default_factory=lambda: {})
# llm_reasoning_minor: dict[str, str] = field(default_factory=lambda: {})
llm_normal: Dict[str, str] = field(default_factory=lambda: {})
llm_topic_judge: Dict[str, str] = field(default_factory=lambda: {})
llm_summary: Dict[str, str] = field(default_factory=lambda: {})

6
src/do_tool/not_used/change_mood.py
View File

@@ -3,7 +3,7 @@ from src.config.config import global_config
from src.common.logger_manager import get_logger
from src.plugins.moods.moods import MoodManager
from typing import Dict, Any
from typing import Any
logger = get_logger("change_mood_tool")
@@ -22,7 +22,7 @@ class ChangeMoodTool(BaseTool):
"required": ["text", "response_set"],
}
async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> Dict[str, Any]:
async def execute(self, function_args: dict[str, Any], message_txt: str = "") -> dict[str, Any]:
"""执行心情改变
Args:
@@ -30,7 +30,7 @@ class ChangeMoodTool(BaseTool):
message_txt: 原始消息文本
Returns:
Dict: 工具执行结果
dict: 工具执行结果
"""
try:
response_set = function_args.get("response_set")

2
src/do_tool/not_used/change_relationship.py
View File

@@ -19,7 +19,7 @@ class RelationshipTool(BaseTool):
"required": ["text", "changed_value", "reason"],
}
async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> dict:
async def execute(self, function_args: dict[str, Any], message_txt: str = "") -> dict:
"""执行工具功能
Args:

9
src/do_tool/not_used/get_current_task.py
View File

@@ -1,7 +1,7 @@
from src.do_tool.tool_can_use.base_tool import BaseTool
from src.plugins.schedule.schedule_generator import bot_schedule
from src.common.logger import get_module_logger
from typing import Dict, Any
from typing import Any
from datetime import datetime
logger = get_module_logger("get_current_task_tool")
@@ -21,7 +21,7 @@ class GetCurrentTaskTool(BaseTool):
"required": ["start_time", "end_time"],
}
async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> Dict[str, Any]:
async def execute(self, function_args: dict[str, Any], message_txt: str = "") -> dict[str, Any]:
"""执行获取当前任务或指定时间段的日程信息
Args:
@@ -29,7 +29,7 @@ class GetCurrentTaskTool(BaseTool):
message_txt: 原始消息文本，此工具不使用
Returns:
Dict: 工具执行结果
dict: 工具执行结果
"""
start_time = function_args.get("start_time")
end_time = function_args.get("end_time")
@@ -55,5 +55,6 @@ class GetCurrentTaskTool(BaseTool):
task_info = "\n".join(task_list)
else:
task_info = f"在 {start_time} 到 {end_time} 之间没有找到日程信息"
else:
task_info = "请提供有效的开始时间和结束时间"
return {"name": "get_current_task", "content": f"日程信息: {task_info}"}

6
src/do_tool/not_used/mid_chat_mem.py
View File

@@ -1,6 +1,6 @@
from src.do_tool.tool_can_use.base_tool import BaseTool
from src.common.logger import get_module_logger
from typing import Dict, Any
from typing import Any
logger = get_module_logger("get_mid_memory_tool")
@@ -18,7 +18,7 @@ class GetMidMemoryTool(BaseTool):
"required": ["id"],
}
async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> Dict[str, Any]:
async def execute(self, function_args: dict[str, Any], message_txt: str = "") -> dict[str, Any]:
"""执行记忆获取
Args:
@@ -26,7 +26,7 @@ class GetMidMemoryTool(BaseTool):
message_txt: 原始消息文本
Returns:
Dict: 工具执行结果
dict: 工具执行结果
"""
try:
id = function_args.get("id")

2
src/do_tool/not_used/send_emoji.py
View File

@@ -17,7 +17,7 @@ class SendEmojiTool(BaseTool):
"required": ["text"],
}
async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> Dict[str, Any]:
async def execute(self, function_args: dict[str, Any], message_txt: str = "") -> dict[str, Any]:
text = function_args.get("text", message_txt)
return {
"name": "send_emoji",

2
src/do_tool/tool_can_use/README.md
View File

@@ -42,7 +42,7 @@ class MyNewTool(BaseTool):
message_txt: 原始消息文本
Returns:
Dict: 包含执行结果的字典，必须包含name和content字段
dict: 包含执行结果的字典，必须包含name和content字段
"""
# 实现工具逻辑
result = f"工具执行结果: {function_args.get('param1')}"

12
src/do_tool/tool_can_use/base_tool.py
View File

@@ -22,11 +22,11 @@ class BaseTool:
parameters = None
@classmethod
def get_tool_definition(cls) -> Dict[str, Any]:
def get_tool_definition(cls) -> dict[str, Any]:
"""获取工具定义，用于LLM工具调用
Returns:
Dict: 工具定义字典
dict: 工具定义字典
"""
if not cls.name or not cls.description or not cls.parameters:
raise NotImplementedError(f"工具类 {cls.__name__} 必须定义 name, description 和 parameters 属性")
@@ -36,14 +36,14 @@ class BaseTool:
"function": {"name": cls.name, "description": cls.description, "parameters": cls.parameters},
}
async def execute(self, function_args: Dict[str, Any]) -> Dict[str, Any]:
async def execute(self, function_args: dict[str, Any]) -> dict[str, Any]:
"""执行工具函数
Args:
function_args: 工具调用参数
Returns:
Dict: 工具执行结果
dict: 工具执行结果
"""
raise NotImplementedError("子类必须实现execute方法")
@@ -88,11 +88,11 @@ def discover_tools():
logger.info(f"工具发现完成，共注册 {len(TOOL_REGISTRY)} 个工具")
def get_all_tool_definitions() -> List[Dict[str, Any]]:
def get_all_tool_definitions() -> List[dict[str, Any]]:
"""获取所有已注册工具的定义
Returns:
List[Dict]: 工具定义列表
List[dict]: 工具定义列表
"""
return [tool_class().get_tool_definition() for tool_class in TOOL_REGISTRY.values()]

7
src/do_tool/tool_can_use/compare_numbers_tool.py
View File

@@ -1,6 +1,6 @@
from src.do_tool.tool_can_use.base_tool import BaseTool
from src.common.logger import get_module_logger
from typing import Dict, Any
from typing import Any
logger = get_module_logger("compare_numbers_tool")
@@ -19,15 +19,14 @@ class CompareNumbersTool(BaseTool):
"required": ["num1", "num2"],
}
async def execute(self, function_args: Dict[str, Any]) -> Dict[str, Any]:
async def execute(self, function_args: dict[str, Any]) -> dict[str, Any]:
"""执行比较两个数的大小
Args:
function_args: 工具参数
message_txt: 原始消息文本
Returns:
Dict: 工具执行结果
dict: 工具执行结果
"""
try:
num1 = function_args.get("num1")

7
src/do_tool/tool_can_use/get_knowledge.py
View File

@@ -2,7 +2,7 @@ from src.do_tool.tool_can_use.base_tool import BaseTool
from src.plugins.chat.utils import get_embedding
from src.common.database import db
from src.common.logger_manager import get_logger
from typing import Dict, Any, Union
from typing import Any, Union
logger = get_logger("get_knowledge_tool")
@@ -21,15 +21,14 @@ class SearchKnowledgeTool(BaseTool):
"required": ["query"],
}
async def execute(self, function_args: Dict[str, Any]) -> Dict[str, Any]:
async def execute(self, function_args: dict[str, Any]) -> dict[str, Any]:
"""执行知识库搜索
Args:
function_args: 工具参数
message_txt: 原始消息文本
Returns:
Dict: 工具执行结果
dict: 工具执行结果
"""
try:
query = function_args.get("query")

1
src/do_tool/tool_can_use/get_memory.py
View File

@@ -25,7 +25,6 @@ class GetMemoryTool(BaseTool):
Args:
function_args: 工具参数
message_txt: 原始消息文本
Returns:
Dict: 工具执行结果

1
src/do_tool/tool_can_use/get_time_date.py
View File

@@ -22,7 +22,6 @@ class GetCurrentDateTimeTool(BaseTool):
Args:
function_args: 工具参数（此工具不使用）
message_txt: 原始消息文本（此工具不使用）
Returns:
Dict: 工具执行结果

1
src/do_tool/tool_can_use/lpmm_get_knowledge.py
View File

@@ -29,7 +29,6 @@ class SearchKnowledgeFromLPMMTool(BaseTool):
Args:
function_args: 工具参数
message_txt: 原始消息文本
Returns:
Dict: 工具执行结果

1
src/do_tool/tool_use.py
View File

@@ -106,7 +106,6 @@ class ToolUser:
Args:
message_txt: 用户消息文本
sender_name: 发送者名称
chat_stream: 聊天流对象
observation: 观察对象（可选）

12
src/individuality/scene.py
View File

@@ -1,9 +1,9 @@
import json
from typing import Dict
import os
from typing import Any
def load_scenes() -> Dict:
def load_scenes() -> dict[str, Any]:
"""
从JSON文件加载场景数据
@@ -20,7 +20,7 @@ def load_scenes() -> Dict:
PERSONALITY_SCENES = load_scenes()
def get_scene_by_factor(factor: str) -> Dict:
def get_scene_by_factor(factor: str) -> dict | None:
"""
根据人格因子获取对应的情景测试
@@ -28,12 +28,12 @@ def get_scene_by_factor(factor: str) -> Dict:
factor (str): 人格因子名称
Returns:
Dict: 包含情景描述的字典
dict: 包含情景描述的字典
"""
return PERSONALITY_SCENES.get(factor, None)
return PERSONALITY_SCENES.get(factor,None)
def get_all_scenes() -> Dict:
def get_all_scenes() -> dict:
"""
获取所有情景测试

1
src/plugins/PFC/chat_observer.py
View File

@@ -23,6 +23,7 @@ class ChatObserver:
Args:
stream_id: 聊天流ID
private_name: 私聊名称
Returns:
ChatObserver: 观察器实例

1
src/plugins/PFC/pfc_manager.py
View File

@@ -33,6 +33,7 @@ class PFCManager:
Args:
stream_id: 聊天流ID
private_name: 私聊名称
Returns:
Optional[Conversation]: 对话实例，创建失败则返回None

1
src/plugins/PFC/pfc_utils.py
View File

@@ -18,6 +18,7 @@ def get_items_from_json(
Args:
content: 包含JSON的文本
private_name: 私聊名称
*items: 要提取的字段名
default_values: 字段的默认值，格式为 {字段名: 默认值}
required_types: 字段的必需类型，格式为 {字段名: 类型}

2
src/plugins/PFC/reply_checker.py
View File

@@ -29,6 +29,8 @@ class ReplyChecker:
Args:
reply: 生成的回复
goal: 对话目标
chat_history: 对话历史记录
chat_history_text: 对话历史记录文本
retry_count: 当前重试次数
Returns:

80
src/plugins/chat/message.py
View File

@@ -1,6 +1,7 @@
import time
from abc import abstractmethod
from dataclasses import dataclass
from typing import Dict, List, Optional, Union
from typing import Optional, Any
import urllib3
@@ -58,12 +59,37 @@ class Message(MessageBase):
# 回复消息
self.reply = reply
async def _process_message_segments(self, segment: Seg) -> str:
"""递归处理消息段，转换为文字描述
Args:
segment: 要处理的消息段
Returns:
str: 处理后的文本
"""
if segment.type == "seglist":
# 处理消息段列表
segments_text = []
for seg in segment.data:
processed = await self._process_message_segments(seg)
if processed:
segments_text.append(processed)
return " ".join(segments_text)
else:
# 处理单个消息段
return await self._process_single_segment(segment)
@abstractmethod
async def _process_single_segment(self, segment):
pass
@dataclass
class MessageRecv(Message):
"""接收消息类，用于处理从MessageCQ序列化的消息"""
def __init__(self, message_dict: Dict):
def __init__(self, message_dict: dict[str, Any]):
"""从MessageCQ的字典初始化
Args:
@@ -90,26 +116,7 @@ class MessageRecv(Message):
self.processed_plain_text = await self._process_message_segments(self.message_segment)
self.detailed_plain_text = self._generate_detailed_text()
async def _process_message_segments(self, segment: Seg) -> str:
"""递归处理消息段，转换为文字描述
Args:
segment: 要处理的消息段
Returns:
str: 处理后的文本
"""
if segment.type == "seglist":
# 处理消息段列表
segments_text = []
for seg in segment.data:
processed = await self._process_message_segments(seg)
if processed:
segments_text.append(processed)
return " ".join(segments_text)
else:
# 处理单个消息段
return await self._process_single_segment(segment)
async def _process_single_segment(self, seg: Seg) -> str:
"""处理单个消息段
@@ -179,28 +186,7 @@ class MessageProcessBase(Message):
self.thinking_time = round(time.time() - self.thinking_start_time, 2)
return self.thinking_time
async def _process_message_segments(self, segment: Seg) -> str:
"""递归处理消息段，转换为文字描述
Args:
segment: 要处理的消息段
Returns:
str: 处理后的文本
"""
if segment.type == "seglist":
# 处理消息段列表
segments_text = []
for seg in segment.data:
processed = await self._process_message_segments(seg)
if processed:
segments_text.append(processed)
return " ".join(segments_text)
else:
# 处理单个消息段
return await self._process_single_segment(segment)
async def _process_single_segment(self, seg: Seg) -> Union[str, None]:
async def _process_single_segment(self, seg: Seg) -> str | None:
"""处理单个消息段
Args:
@@ -278,7 +264,7 @@ class MessageSending(MessageProcessBase):
message_id: str,
chat_stream: ChatStream,
bot_user_info: UserInfo,
sender_info: UserInfo, # 用来记录发送者信息,用于私聊回复
sender_info: UserInfo | None, # 用来记录发送者信息,用于私聊回复
message_segment: Seg,
reply: Optional["MessageRecv"] = None,
is_head: bool = False,
@@ -303,7 +289,7 @@ class MessageSending(MessageProcessBase):
self.is_emoji = is_emoji
self.apply_set_reply_logic = apply_set_reply_logic
def set_reply(self, reply: Optional["MessageRecv"] = None) -> None:
def set_reply(self, reply: Optional["MessageRecv"] = None):
"""设置回复消息"""
if self.message_info.format_info is not None and "reply" in self.message_info.format_info.accept_format:
if reply:
@@ -317,7 +303,6 @@ class MessageSending(MessageProcessBase):
self.message_segment,
],
)
return self
async def process(self) -> None:
"""处理消息内容，生成纯文本和详细文本"""
@@ -342,6 +327,7 @@ class MessageSending(MessageProcessBase):
reply=thinking.reply,
is_head=is_head,
is_emoji=is_emoji,
sender_info=None,
)
def to_dict(self):
@@ -361,7 +347,7 @@ class MessageSet:
def __init__(self, chat_stream: ChatStream, message_id: str):
self.chat_stream = chat_stream
self.message_id = message_id
self.messages: List[MessageSending] = []
self.messages: list[MessageSending] = []
self.time = round(time.time(), 3) # 保留3位小数
def add_message(self, message: MessageSending) -> None:

16
src/plugins/chat/message_sender.py
View File

@@ -1,7 +1,7 @@
# src/plugins/chat/message_sender.py
import asyncio
import time
from typing import Dict, List, Optional, Union
from typing import Union
# from ...common.database import db # 数据库依赖似乎不需要了，注释掉
from ..message.api import global_api
@@ -70,7 +70,7 @@ class MessageContainer:
def __init__(self, chat_id: str, max_size: int = 100):
self.chat_id = chat_id
self.max_size = max_size
self.messages: List[Union[MessageThinking, MessageSending]] = [] # 明确类型
self.messages: list[MessageThinking | MessageSending] = [] # 明确类型
self.last_send_time = 0
self.thinking_wait_timeout = 20 # 思考等待超时时间（秒） - 从旧 sender 合并
@@ -78,7 +78,7 @@ class MessageContainer:
"""计算当前容器中思考消息的数量"""
return sum(1 for msg in self.messages if isinstance(msg, MessageThinking))
def get_timeout_sending_messages(self) -> List[MessageSending]:
def get_timeout_sending_messages(self) -> list[MessageSending]:
"""获取所有超时的MessageSending对象（思考时间超过20秒），按thinking_start_time排序 - 从旧 sender 合并"""
current_time = time.time()
timeout_messages = []
@@ -94,7 +94,7 @@ class MessageContainer:
timeout_messages.sort(key=lambda x: x.thinking_start_time)
return timeout_messages
def get_earliest_message(self) -> Optional[Union[MessageThinking, MessageSending]]:
def get_earliest_message(self):
"""获取thinking_start_time最早的消息对象"""
if not self.messages:
return None
@@ -108,7 +108,7 @@ class MessageContainer:
earliest_message = msg
return earliest_message
def add_message(self, message: Union[MessageThinking, MessageSending, MessageSet]) -> None:
def add_message(self, message: Union[MessageThinking, MessageSending, MessageSet]):
"""添加消息到队列"""
if isinstance(message, MessageSet):
for single_message in message.messages:
@@ -116,7 +116,7 @@ class MessageContainer:
else:
self.messages.append(message)
def remove_message(self, message_to_remove: Union[MessageThinking, MessageSending]) -> bool:
def remove_message(self, message_to_remove: Union[MessageThinking, MessageSending]):
"""移除指定的消息对象，如果消息存在则返回True，否则返回False"""
try:
_initial_len = len(self.messages)
@@ -138,7 +138,7 @@ class MessageContainer:
"""检查是否有待发送的消息"""
return bool(self.messages)
def get_all_messages(self) -> List[Union[MessageSending, MessageThinking]]:
def get_all_messages(self) -> list[MessageThinking | MessageSending]:
"""获取所有消息"""
return list(self.messages) # 返回副本
@@ -148,7 +148,7 @@ class MessageManager:
def __init__(self):
self._processor_task = None
self.containers: Dict[str, MessageContainer] = {}
self.containers: dict[str, MessageContainer] = {}
self.storage = MessageStorage() # 添加 storage 实例
self._running = True # 处理器运行状态
self._container_lock = asyncio.Lock() # 保护 containers 字典的锁

22
src/plugins/chat/utils.py
View File

@@ -2,7 +2,6 @@ import random
import time
import re
from collections import Counter
from typing import Dict, List, Optional
import jieba
import numpy as np
@@ -26,7 +25,7 @@ def is_english_letter(char: str) -> bool:
return "a" <= char.lower() <= "z"
def db_message_to_str(message_dict: Dict) -> str:
def db_message_to_str(message_dict: dict) -> str:
logger.debug(f"message_dict: {message_dict}")
time_str = time.strftime("%m-%d %H:%M:%S", time.localtime(message_dict["time"]))
try:
@@ -35,7 +34,7 @@ def db_message_to_str(message_dict: Dict) -> str:
message_dict.get("user_nickname", ""),
message_dict.get("user_cardname", ""),
)
except Exception:
except Exception as e:
name = message_dict.get("user_nickname", "") or f"用户{message_dict['user_id']}"
content = message_dict.get("processed_plain_text", "")
result = f"[{time_str}] {name}: {content}\n"
@@ -77,13 +76,13 @@ def is_mentioned_bot_in_message(message: MessageRecv) -> tuple[bool, float]:
if not is_mentioned:
# 判断是否被回复
if re.match(
f"\[回复 [\s\S]*?\({str(global_config.BOT_QQ)}\)：[\s\S]*?\]，说：", message.processed_plain_text
f"\[回复 [\s\S]*?\({str(global_config.BOT_QQ)}\)：[\s\S]*?]，说：" , message.processed_plain_text
):
is_mentioned = True
else:
# 判断内容中是否被提及
message_content = re.sub(r"@[\s\S]*?（(\d+)）", "", message.processed_plain_text)
message_content = re.sub(r"\[回复 [\s\S]*?\(((\d+)|未知id)\)：[\s\S]*?\]，说：", "", message_content)
message_content = re.sub(r"\[回复 [\s\S]*?\(((\d+)|未知id)\)：[\s\S]*?]，说：", "", message_content)
for keyword in keywords:
if keyword in message_content:
is_mentioned = True
@@ -223,7 +222,7 @@ def get_recent_group_speaker(chat_stream_id: int, sender, limit: int = 12) -> li
return who_chat_in_group
def split_into_sentences_w_remove_punctuation(text: str) -> List[str]:
def split_into_sentences_w_remove_punctuation(text: str) -> list[str]:
"""将文本分割成句子，并根据概率合并
1. 识别分割点（, ， 。 ; 空格），但如果分割点左右都是英文字母则不分割。
2. 将文本分割成 (内容, 分隔符) 的元组。
@@ -370,7 +369,7 @@ def random_remove_punctuation(text: str) -> str:
return result
def process_llm_response(text: str) -> List[str]:
def process_llm_response(text: str) -> list[str]:
# 先保护颜文字
if global_config.enable_kaomoji_protection:
protected_text, kaomoji_mapping = protect_kaomoji(text)
@@ -379,7 +378,7 @@ def process_llm_response(text: str) -> List[str]:
protected_text = text
kaomoji_mapping = {}
# 提取被 () 或 [] 包裹且包含中文的内容
pattern = re.compile(r"[\(\[\（](?=.*[\u4e00-\u9fff]).*?[\)\]\）]")
pattern = re.compile(r"[(\[（](?=.*[一-鿿]).*?[)\]）]")
# _extracted_contents = pattern.findall(text)
_extracted_contents = pattern.findall(protected_text) # 在保护后的文本上查找
# 去除 () 和 [] 及其包裹的内容
@@ -554,7 +553,7 @@ def protect_kaomoji(sentence):
r"[^()\[\]（）【】]*?" # 非括号字符（惰性匹配）
r"[^一-龥a-zA-Z0-9\s]" # 非中文、非英文、非数字、非空格字符（必须包含至少一个）
r"[^()\[\]（）【】]*?" # 非括号字符（惰性匹配）
r"[\)\]）】" # 右括号
r"[)\]）】" # 右括号
r"]"
r")"
r"|"
@@ -704,7 +703,7 @@ def count_messages_between(start_time: float, end_time: float, stream_id: str) -
return 0, 0
def translate_timestamp_to_human_readable(timestamp: float, mode: str = "normal") -> Optional[str]:
def translate_timestamp_to_human_readable(timestamp: float, mode: str = "normal") -> str:
"""将时间戳转换为人类可读的时间格式
Args:
@@ -732,10 +731,9 @@ def translate_timestamp_to_human_readable(timestamp: float, mode: str = "normal"
return f"{int(diff / 86400)}天前:\n"
else:
return time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(timestamp)) + ":\n"
elif mode == "lite":
else: # mode = "lite" or unknown
# 只返回时分秒格式，喵~
return time.strftime("%H:%M:%S", time.localtime(timestamp))
return None
def parse_text_timestamps(text: str, mode: str = "normal") -> str:

4
src/plugins/memory_system/Hippocampus.py
View File

@@ -511,7 +511,7 @@ class Hippocampus:
"""从文本中提取关键词并获取相关记忆。
Args:
topic (str): 记忆主题
keywords (list): 输入文本
max_memory_num (int, optional): 返回的记忆条目数量上限。默认为3，表示最多返回3条与输入文本相关度最高的记忆。
max_memory_length (int, optional): 每个主题最多返回的记忆条目数量。默认为2，表示每个主题最多返回2条相似度最高的记忆。
max_depth (int, optional): 记忆检索深度。默认为3。值越大，检索范围越广，可以获取更多间接相关的记忆，但速度会变慢。
@@ -829,7 +829,7 @@ class EntorhinalCortex:
return chat_samples
@staticmethod
def random_get_msg_snippet(target_timestamp: float, chat_size: int, max_memorized_time_per_msg: int) -> list:
def random_get_msg_snippet(target_timestamp: float, chat_size: int, max_memorized_time_per_msg: int) -> list | None:
"""从数据库中随机获取指定时间戳附近的消息片段 (使用 chat_message_builder)"""
try_count = 0
time_window_seconds = random.randint(300, 1800) # 随机时间窗口，5到30分钟

3
src/plugins/schedule/schedule_generator.py
View File

@@ -1,7 +1,6 @@
import datetime
import os
import sys
from typing import Dict
import asyncio
from dateutil import tz
@@ -162,7 +161,7 @@ class ScheduleGenerator:
async def generate_daily_schedule(
self,
target_date: datetime.datetime = None,
) -> Dict[str, str]:
) -> dict[str, str]:
daytime_prompt = self.construct_daytime_prompt(target_date)
daytime_response, _ = await self.llm_scheduler_all.generate_response_async(daytime_prompt)
return daytime_response