diff --git a/bot.py b/bot.py
index acc7990ed..7a97f485e 100644
--- a/bot.py
+++ b/bot.py
@@ -10,9 +10,8 @@ import uvicorn
from dotenv import load_dotenv
from nonebot.adapters.onebot.v11 import Adapter
import platform
-from src.plugins.utils.logger_config import setup_logger
+from src.plugins.utils.logger_config import LogModule, LogClassification
-from loguru import logger
# 配置日志格式
@@ -102,7 +101,9 @@ def load_env():
def load_logger():
- setup_logger()
+ global logger # 使得bot.py中其他函数也能调用
+ log_module = LogModule()
+ logger = log_module.setup_logger(LogClassification.BASE)
def scan_provider(env_config: dict):
@@ -174,8 +175,6 @@ def raw_main():
if platform.system().lower() != "windows":
time.tzset()
- # 配置日志
- load_logger()
easter_egg()
init_config()
init_env()
@@ -207,6 +206,8 @@ def raw_main():
if __name__ == "__main__":
try:
+ # 配置日志，使得主程序直接退出时候也能访问logger
+ load_logger()
raw_main()
app = nonebot.get_asgi()
diff --git a/src/plugins/chat/bot.py b/src/plugins/chat/bot.py
index e46391e0f..d22c3aaf7 100644
--- a/src/plugins/chat/bot.py
+++ b/src/plugins/chat/bot.py
@@ -10,7 +10,6 @@ from nonebot.adapters.onebot.v11 import (
PokeNotifyEvent,
GroupRecallNoticeEvent,
FriendRecallNoticeEvent,
-
)
from ..memory_system.memory import hippocampus
@@ -32,10 +31,11 @@ from .utils_image import image_path_to_base64
from .utils_user import get_user_nickname, get_user_cardname, get_groupname
from .willing_manager import willing_manager # 导入意愿管理器
from .message_base import UserInfo, GroupInfo, Seg
-from ..utils.logger_config import setup_logger, LogModule
+from ..utils.logger_config import LogClassification, LogModule
# 配置日志
-logger = setup_logger(LogModule.CHAT)
+log_module = LogModule()
+logger = log_module.setup_logger(LogClassification.CHAT)
class ChatBot:
@@ -92,10 +92,8 @@ class ChatBot:
chat = await chat_manager.get_or_create_stream(
platform=user_info.platform, user_info=user_info, group_info=group_info
)
-
- await self.storage.store_recalled_message(event.message_id, time.time(), chat)
-
+ await self.storage.store_recalled_message(event.message_id, time.time(), chat)
async def handle_message(self, event: MessageEvent, bot: Bot) -> None:
"""处理收到的消息"""
@@ -161,6 +159,7 @@ class ChatBot:
reply_message=event.reply,
platform="qq",
)
+ await message_cq.initialize()
message_json = message_cq.to_dict()
# 进入maimbot
@@ -387,6 +386,7 @@ class ChatBot:
reply_message=None,
platform="qq",
)
+ await message_cq.initialize()
message_json = message_cq.to_dict()
message = MessageRecv(message_json)
diff --git a/src/plugins/chat/cq_code.py b/src/plugins/chat/cq_code.py
index 049419f1c..2edc011b2 100644
--- a/src/plugins/chat/cq_code.py
+++ b/src/plugins/chat/cq_code.py
@@ -1,48 +1,28 @@
import base64
import html
import time
+import asyncio
from dataclasses import dataclass
from typing import Dict, List, Optional, Union
-
+import ssl
import os
-
-import requests
-
-# 解析各种CQ码
-# 包含CQ码类
-import urllib3
+import aiohttp
from loguru import logger
from nonebot import get_driver
-from urllib3.util import create_urllib3_context
from ..models.utils_model import LLM_request
from .config import global_config
from .mapper import emojimapper
from .message_base import Seg
-from .utils_user import get_user_nickname,get_groupname
+from .utils_user import get_user_nickname, get_groupname
from .message_base import GroupInfo, UserInfo
driver = get_driver()
config = driver.config
-# TLS1.3特殊处理 https://github.com/psf/requests/issues/6616
-ctx = create_urllib3_context()
-ctx.load_default_certs()
-ctx.set_ciphers("AES128-GCM-SHA256")
-
-
-class TencentSSLAdapter(requests.adapters.HTTPAdapter):
- def __init__(self, ssl_context=None, **kwargs):
- self.ssl_context = ssl_context
- super().__init__(**kwargs)
-
- def init_poolmanager(self, connections, maxsize, block=False):
- self.poolmanager = urllib3.poolmanager.PoolManager(
- num_pools=connections,
- maxsize=maxsize,
- block=block,
- ssl_context=self.ssl_context,
- )
+# 创建SSL上下文
+ssl_context = ssl.create_default_context()
+ssl_context.set_ciphers("AES128-GCM-SHA256")
@dataclass
@@ -70,14 +50,12 @@ class CQCode:
"""初始化LLM实例"""
pass
- def translate(self):
+ async def translate(self):
"""根据CQ码类型进行相应的翻译处理，转换为Seg对象"""
if self.type == "text":
- self.translated_segments = Seg(
- type="text", data=self.params.get("text", "")
- )
+ self.translated_segments = Seg(type="text", data=self.params.get("text", ""))
elif self.type == "image":
- base64_data = self.translate_image()
+ base64_data = await self.translate_image()
if base64_data:
if self.params.get("sub_type") == "0":
self.translated_segments = Seg(type="image", data=base64_data)
@@ -88,24 +66,20 @@ class CQCode:
elif self.type == "at":
if self.params.get("qq") == "all":
self.translated_segments = Seg(type="text", data="@[全体成员]")
- else:
+ else:
user_nickname = get_user_nickname(self.params.get("qq", ""))
- self.translated_segments = Seg(
- type="text", data=f"[@{user_nickname or '某人'}]"
- )
+ self.translated_segments = Seg(type="text", data=f"[@{user_nickname or '某人'}]")
elif self.type == "reply":
- reply_segments = self.translate_reply()
+ reply_segments = await self.translate_reply()
if reply_segments:
self.translated_segments = Seg(type="seglist", data=reply_segments)
else:
self.translated_segments = Seg(type="text", data="[回复某人消息]")
elif self.type == "face":
face_id = self.params.get("id", "")
- self.translated_segments = Seg(
- type="text", data=f"[{emojimapper.get(int(face_id), '表情')}]"
- )
+ self.translated_segments = Seg(type="text", data=f"[{emojimapper.get(int(face_id), '表情')}]")
elif self.type == "forward":
- forward_segments = self.translate_forward()
+ forward_segments = await self.translate_forward()
if forward_segments:
self.translated_segments = Seg(type="seglist", data=forward_segments)
else:
@@ -113,18 +87,8 @@ class CQCode:
else:
self.translated_segments = Seg(type="text", data=f"[{self.type}]")
- def get_img(self):
- """
- headers = {
- 'User-Agent': 'QQ/8.9.68.11565 CFNetwork/1220.1 Darwin/20.3.0',
- 'Accept': 'image/*;q=0.8',
- 'Accept-Encoding': 'gzip, deflate, br',
- 'Connection': 'keep-alive',
- 'Cache-Control': 'no-cache',
- 'Pragma': 'no-cache'
- }
- """
- # 腾讯专用请求头配置
+ async def get_img(self) -> Optional[str]:
+ """异步获取图片并转换为base64"""
headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.87 Safari/537.36",
"Accept": "text/html, application/xhtml xml, */*",
@@ -133,61 +97,63 @@ class CQCode:
"Content-Type": "application/x-www-form-urlencoded",
"Cache-Control": "no-cache",
}
+
url = html.unescape(self.params["url"])
if not url.startswith(("http://", "https://")):
return None
- # 创建专用会话
- session = requests.session()
- session.adapters.pop("https://", None)
- session.mount("https://", TencentSSLAdapter(ctx))
-
max_retries = 3
for retry in range(max_retries):
try:
- response = session.get(
- url,
- headers=headers,
- timeout=15,
- allow_redirects=True,
- stream=True, # 流式传输避免大内存问题
- )
+ logger.debug(f"获取图片中: {url}")
+ # 设置SSL上下文和创建连接器
+ conn = aiohttp.TCPConnector(ssl=ssl_context)
+ async with aiohttp.ClientSession(connector=conn) as session:
+ async with session.get(
+ url,
+ headers=headers,
+ timeout=aiohttp.ClientTimeout(total=15),
+ allow_redirects=True,
+ ) as response:
+ # 腾讯服务器特殊状态码处理
+ if response.status == 400 and "multimedia.nt.qq.com.cn" in url:
+ return None
- # 腾讯服务器特殊状态码处理
- if response.status_code == 400 and "multimedia.nt.qq.com.cn" in url:
- return None
+ if response.status != 200:
+ raise aiohttp.ClientError(f"HTTP {response.status}")
- if response.status_code != 200:
- raise requests.exceptions.HTTPError(f"HTTP {response.status_code}")
+ # 验证内容类型
+ content_type = response.headers.get("Content-Type", "")
+ if not content_type.startswith("image/"):
+ raise ValueError(f"非图片内容类型: {content_type}")
- # 验证内容类型
- content_type = response.headers.get("Content-Type", "")
- if not content_type.startswith("image/"):
- raise ValueError(f"非图片内容类型: {content_type}")
+ # 读取响应内容
+ content = await response.read()
+ logger.debug(f"获取图片成功: {url}")
- # 转换为Base64
- image_base64 = base64.b64encode(response.content).decode("utf-8")
- self.image_base64 = image_base64
- return image_base64
+ # 转换为Base64
+ image_base64 = base64.b64encode(content).decode("utf-8")
+ self.image_base64 = image_base64
+ return image_base64
- except (requests.exceptions.SSLError, requests.exceptions.HTTPError) as e:
+ except (aiohttp.ClientError, ValueError) as e:
if retry == max_retries - 1:
logger.error(f"最终请求失败: {str(e)}")
- time.sleep(1.5**retry) # 指数退避
+ await asyncio.sleep(1.5**retry) # 指数退避
- except Exception:
- logger.exception("[未知错误]")
+ except Exception as e:
+ logger.exception(f"获取图片时发生未知错误: {str(e)}")
return None
return None
- def translate_image(self) -> Optional[str]:
+ async def translate_image(self) -> Optional[str]:
"""处理图片类型的CQ码，返回base64字符串"""
if "url" not in self.params:
return None
- return self.get_img()
+ return await self.get_img()
- def translate_forward(self) -> Optional[List[Seg]]:
+ async def translate_forward(self) -> Optional[List[Seg]]:
"""处理转发消息，返回Seg列表"""
try:
if "content" not in self.params:
@@ -217,15 +183,16 @@ class CQCode:
else:
if raw_message:
from .message_cq import MessageRecvCQ
- user_info=UserInfo(
- platform='qq',
+
+ user_info = UserInfo(
+ platform="qq",
user_id=msg.get("user_id", 0),
user_nickname=nickname,
)
- group_info=GroupInfo(
- platform='qq',
+ group_info = GroupInfo(
+ platform="qq",
group_id=msg.get("group_id", 0),
- group_name=get_groupname(msg.get("group_id", 0))
+ group_name=get_groupname(msg.get("group_id", 0)),
)
message_obj = MessageRecvCQ(
@@ -235,24 +202,23 @@ class CQCode:
plain_text=raw_message,
group_info=group_info,
)
- content_seg = Seg(
- type="seglist", data=[message_obj.message_segment]
- )
+ await message_obj.initialize()
+ content_seg = Seg(type="seglist", data=[message_obj.message_segment])
else:
content_seg = Seg(type="text", data="[空消息]")
else:
if raw_message:
from .message_cq import MessageRecvCQ
- user_info=UserInfo(
- platform='qq',
+ user_info = UserInfo(
+ platform="qq",
user_id=msg.get("user_id", 0),
user_nickname=nickname,
)
- group_info=GroupInfo(
- platform='qq',
+ group_info = GroupInfo(
+ platform="qq",
group_id=msg.get("group_id", 0),
- group_name=get_groupname(msg.get("group_id", 0))
+ group_name=get_groupname(msg.get("group_id", 0)),
)
message_obj = MessageRecvCQ(
message_id=msg.get("message_id", 0),
@@ -261,9 +227,8 @@ class CQCode:
plain_text=raw_message,
group_info=group_info,
)
- content_seg = Seg(
- type="seglist", data=[message_obj.message_segment]
- )
+ await message_obj.initialize()
+ content_seg = Seg(type="seglist", data=[message_obj.message_segment])
else:
content_seg = Seg(type="text", data="[空消息]")
@@ -277,7 +242,7 @@ class CQCode:
logger.error(f"处理转发消息失败: {str(e)}")
return None
- def translate_reply(self) -> Optional[List[Seg]]:
+ async def translate_reply(self) -> Optional[List[Seg]]:
"""处理回复类型的CQ码，返回Seg列表"""
from .message_cq import MessageRecvCQ
@@ -285,22 +250,19 @@ class CQCode:
return None
if self.reply_message.sender.user_id:
-
message_obj = MessageRecvCQ(
- user_info=UserInfo(user_id=self.reply_message.sender.user_id,user_nickname=self.reply_message.sender.nickname),
+ user_info=UserInfo(
+ user_id=self.reply_message.sender.user_id, user_nickname=self.reply_message.sender.nickname
+ ),
message_id=self.reply_message.message_id,
raw_message=str(self.reply_message.message),
group_info=GroupInfo(group_id=self.reply_message.group_id),
)
-
+ await message_obj.initialize()
segments = []
if message_obj.message_info.user_info.user_id == global_config.BOT_QQ:
- segments.append(
- Seg(
- type="text", data=f"[回复 {global_config.BOT_NICKNAME} 的消息: "
- )
- )
+ segments.append(Seg(type="text", data=f"[回复 {global_config.BOT_NICKNAME} 的消息: "))
else:
segments.append(
Seg(
@@ -318,16 +280,12 @@ class CQCode:
@staticmethod
def unescape(text: str) -> str:
"""反转义CQ码中的特殊字符"""
- return (
- text.replace(",", ",")
- .replace("[", "[")
- .replace("]", "]")
- .replace("&", "&")
- )
+ return text.replace(",", ",").replace("[", "[").replace("]", "]").replace("&", "&")
+
class CQCode_tool:
@staticmethod
- def cq_from_dict_to_class(cq_code: Dict,msg ,reply: Optional[Dict] = None) -> CQCode:
+ def cq_from_dict_to_class(cq_code: Dict, msg, reply: Optional[Dict] = None) -> CQCode:
"""
将CQ码字典转换为CQCode对象
@@ -353,11 +311,9 @@ class CQCode_tool:
params=params,
group_info=msg.message_info.group_info,
user_info=msg.message_info.user_info,
- reply_message=reply
+ reply_message=reply,
)
- # 进行翻译处理
- instance.translate()
return instance
@staticmethod
@@ -383,12 +339,7 @@ class CQCode_tool:
# 确保使用绝对路径
abs_path = os.path.abspath(file_path)
# 转义特殊字符
- escaped_path = (
- abs_path.replace("&", "&")
- .replace("[", "[")
- .replace("]", "]")
- .replace(",", ",")
- )
+ escaped_path = abs_path.replace("&", "&").replace("[", "[").replace("]", "]").replace(",", ",")
# 生成CQ码，设置sub_type=1表示这是表情包
return f"[CQ:image,file=file:///{escaped_path},sub_type=1]"
@@ -403,14 +354,11 @@ class CQCode_tool:
"""
# 转义base64数据
escaped_base64 = (
- base64_data.replace("&", "&")
- .replace("[", "[")
- .replace("]", "]")
- .replace(",", ",")
+ base64_data.replace("&", "&").replace("[", "[").replace("]", "]").replace(",", ",")
)
# 生成CQ码，设置sub_type=1表示这是表情包
return f"[CQ:image,file=base64://{escaped_base64},sub_type=1]"
-
+
@staticmethod
def create_image_cq_base64(base64_data: str) -> str:
"""
@@ -422,10 +370,7 @@ class CQCode_tool:
"""
# 转义base64数据
escaped_base64 = (
- base64_data.replace("&", "&")
- .replace("[", "[")
- .replace("]", "]")
- .replace(",", ",")
+ base64_data.replace("&", "&").replace("[", "[").replace("]", "]").replace(",", ",")
)
# 生成CQ码，设置sub_type=1表示这是表情包
return f"[CQ:image,file=base64://{escaped_base64},sub_type=0]"
diff --git a/src/plugins/chat/emoji_manager.py b/src/plugins/chat/emoji_manager.py
index ab5dad5e9..183568f2f 100644
--- a/src/plugins/chat/emoji_manager.py
+++ b/src/plugins/chat/emoji_manager.py
@@ -18,17 +18,17 @@ from ..chat.utils import get_embedding
from ..chat.utils_image import ImageManager, image_path_to_base64
from ..models.utils_model import LLM_request
-from ..utils.logger_config import setup_logger, LogModule
+from ..utils.logger_config import LogClassification, LogModule
# 配置日志
-logger = setup_logger(LogModule.EMOJI)
+log_module = LogModule()
+logger = log_module.setup_logger(LogClassification.EMOJI)
driver = get_driver()
config = driver.config
image_manager = ImageManager()
-
class EmojiManager:
_instance = None
EMOJI_DIR = os.path.join("data", "emoji") # 表情包存储目录
@@ -43,7 +43,7 @@ class EmojiManager:
self._scan_task = None
self.vlm = LLM_request(model=global_config.vlm, temperature=0.3, max_tokens=1000)
self.llm_emotion_judge = LLM_request(
- model=global_config.llm_emotion_judge, max_tokens=60, temperature=0.8
+ model=global_config.llm_emotion_judge, max_tokens=600, temperature=0.8
) # 更高的温度，更少的token（后续可以根据情绪来调整温度）
def _ensure_emoji_dir(self):
@@ -281,7 +281,6 @@ class EmojiManager:
if description is not None:
embedding = await get_embedding(description)
-
# 准备数据库记录
emoji_record = {
"filename": filename,
diff --git a/src/plugins/chat/llm_generator.py b/src/plugins/chat/llm_generator.py
index b8ae66b84..991d2bf4a 100644
--- a/src/plugins/chat/llm_generator.py
+++ b/src/plugins/chat/llm_generator.py
@@ -25,30 +25,19 @@ class ResponseGenerator:
max_tokens=1000,
stream=True,
)
- self.model_v3 = LLM_request(
- model=global_config.llm_normal, temperature=0.7, max_tokens=1000
- )
- self.model_r1_distill = LLM_request(
- model=global_config.llm_reasoning_minor, temperature=0.7, max_tokens=1000
- )
- self.model_v25 = LLM_request(
- model=global_config.llm_normal_minor, temperature=0.7, max_tokens=1000
- )
+ self.model_v3 = LLM_request(model=global_config.llm_normal, temperature=0.7, max_tokens=3000)
+ self.model_r1_distill = LLM_request(model=global_config.llm_reasoning_minor, temperature=0.7, max_tokens=3000)
+ self.model_v25 = LLM_request(model=global_config.llm_normal_minor, temperature=0.7, max_tokens=3000)
self.current_model_type = "r1" # 默认使用 R1
- async def generate_response(
- self, message: MessageThinking
- ) -> Optional[Union[str, List[str]]]:
+ async def generate_response(self, message: MessageThinking) -> Optional[Union[str, List[str]]]:
"""根据当前模型类型选择对应的生成函数"""
# 从global_config中获取模型概率值并选择模型
rand = random.random()
if rand < global_config.MODEL_R1_PROBABILITY:
self.current_model_type = "r1"
current_model = self.model_r1
- elif (
- rand
- < global_config.MODEL_R1_PROBABILITY + global_config.MODEL_V3_PROBABILITY
- ):
+ elif rand < global_config.MODEL_R1_PROBABILITY + global_config.MODEL_V3_PROBABILITY:
self.current_model_type = "v3"
current_model = self.model_v3
else:
@@ -57,24 +46,20 @@ class ResponseGenerator:
logger.info(f"{global_config.BOT_NICKNAME}{self.current_model_type}思考中")
- model_response = await self._generate_response_with_model(
- message, current_model
- )
+ model_response = await self._generate_response_with_model(message, current_model)
raw_content = model_response
# print(f"raw_content: {raw_content}")
# print(f"model_response: {model_response}")
-
+
if model_response:
- logger.info(f'{global_config.BOT_NICKNAME}的回复是：{model_response}')
+ logger.info(f"{global_config.BOT_NICKNAME}的回复是：{model_response}")
model_response = await self._process_response(model_response)
if model_response:
return model_response, raw_content
return None, raw_content
- async def _generate_response_with_model(
- self, message: MessageThinking, model: LLM_request
- ) -> Optional[str]:
+ async def _generate_response_with_model(self, message: MessageThinking, model: LLM_request) -> Optional[str]:
"""使用指定的模型生成回复"""
sender_name = ""
if message.chat_stream.user_info.user_cardname and message.chat_stream.user_info.user_nickname:
@@ -219,7 +204,7 @@ class ResponseGenerator:
return None, []
processed_response = process_llm_response(content)
-
+
# print(f"得到了处理后的llm返回{processed_response}")
return processed_response
@@ -229,13 +214,11 @@ class InitiativeMessageGenerate:
def __init__(self):
self.model_r1 = LLM_request(model=global_config.llm_reasoning, temperature=0.7)
self.model_v3 = LLM_request(model=global_config.llm_normal, temperature=0.7)
- self.model_r1_distill = LLM_request(
- model=global_config.llm_reasoning_minor, temperature=0.7
- )
+ self.model_r1_distill = LLM_request(model=global_config.llm_reasoning_minor, temperature=0.7)
def gen_response(self, message: Message):
- topic_select_prompt, dots_for_select, prompt_template = (
- prompt_builder._build_initiative_prompt_select(message.group_id)
+ topic_select_prompt, dots_for_select, prompt_template = prompt_builder._build_initiative_prompt_select(
+ message.group_id
)
content_select, reasoning = self.model_v3.generate_response(topic_select_prompt)
logger.debug(f"{content_select} {reasoning}")
@@ -247,16 +230,12 @@ class InitiativeMessageGenerate:
return None
else:
return None
- prompt_check, memory = prompt_builder._build_initiative_prompt_check(
- select_dot[1], prompt_template
- )
+ prompt_check, memory = prompt_builder._build_initiative_prompt_check(select_dot[1], prompt_template)
content_check, reasoning_check = self.model_v3.generate_response(prompt_check)
logger.info(f"{content_check} {reasoning_check}")
if "yes" not in content_check.lower():
return None
- prompt = prompt_builder._build_initiative_prompt(
- select_dot, prompt_template, memory
- )
+ prompt = prompt_builder._build_initiative_prompt(select_dot, prompt_template, memory)
content, reasoning = self.model_r1.generate_response_async(prompt)
logger.debug(f"[DEBUG] {content} {reasoning}")
return content
diff --git a/src/plugins/chat/message.py b/src/plugins/chat/message.py
index b93b3fb28..125e8dd6f 100644
--- a/src/plugins/chat/message.py
+++ b/src/plugins/chat/message.py
@@ -329,6 +329,7 @@ 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"""
diff --git a/src/plugins/chat/message_cq.py b/src/plugins/chat/message_cq.py
index 4c46d3bf2..435bdf19e 100644
--- a/src/plugins/chat/message_cq.py
+++ b/src/plugins/chat/message_cq.py
@@ -57,16 +57,20 @@ class MessageRecvCQ(MessageCQ):
# 私聊消息不携带group_info
if group_info is None:
pass
-
elif group_info.group_name is None:
group_info.group_name = get_groupname(group_info.group_id)
# 解析消息段
- self.message_segment = self._parse_message(raw_message, reply_message)
+ self.message_segment = None # 初始化为None
self.raw_message = raw_message
+ # 异步初始化在外部完成
- def _parse_message(self, message: str, reply_message: Optional[Dict] = None) -> Seg:
- """解析消息内容为Seg对象"""
+ async def initialize(self):
+ """异步初始化方法"""
+ self.message_segment = await self._parse_message(self.raw_message)
+
+ async def _parse_message(self, message: str, reply_message: Optional[Dict] = None) -> Seg:
+ """异步解析消息内容为Seg对象"""
cq_code_dict_list = []
segments = []
@@ -98,9 +102,10 @@ class MessageRecvCQ(MessageCQ):
# 转换CQ码为Seg对象
for code_item in cq_code_dict_list:
- message_obj = cq_code_tool.cq_from_dict_to_class(code_item, msg=self, reply=reply_message)
- if message_obj.translated_segments:
- segments.append(message_obj.translated_segments)
+ cq_code_obj = cq_code_tool.cq_from_dict_to_class(code_item, msg=self, reply=reply_message)
+ await cq_code_obj.translate() # 异步调用translate
+ if cq_code_obj.translated_segments:
+ segments.append(cq_code_obj.translated_segments)
# 如果只有一个segment，直接返回
if len(segments) == 1:
@@ -133,9 +138,7 @@ class MessageSendCQ(MessageCQ):
self.message_segment = message_segment
self.raw_message = self._generate_raw_message()
- def _generate_raw_message(
- self,
- ) -> str:
+ def _generate_raw_message(self) -> str:
"""将Seg对象转换为raw_message"""
segments = []
diff --git a/src/plugins/chat/prompt_builder.py b/src/plugins/chat/prompt_builder.py
index a4b0b1686..ae94db825 100644
--- a/src/plugins/chat/prompt_builder.py
+++ b/src/plugins/chat/prompt_builder.py
@@ -15,8 +15,8 @@ from .relationship_manager import relationship_manager
class PromptBuilder:
def __init__(self):
- self.prompt_built = ''
- self.activate_messages = ''
+ self.prompt_built = ""
+ self.activate_messages = ""
async def _build_prompt(self,
chat_stream,
@@ -24,13 +24,13 @@ class PromptBuilder:
sender_name: str = "某人",
stream_id: Optional[int] = None) -> tuple[str, str]:
"""构建prompt
-
+
Args:
message_txt: 消息文本
sender_name: 发送者昵称
# relationship_value: 关系值
group_id: 群组ID
-
+
Returns:
str: 构建好的prompt
"""
@@ -88,46 +88,43 @@ class PromptBuilder:
current_date = time.strftime("%Y-%m-%d", time.localtime())
current_time = time.strftime("%H:%M:%S", time.localtime())
bot_schedule_now_time, bot_schedule_now_activity = bot_schedule.get_current_task()
- prompt_date = f'''今天是{current_date}，现在是{current_time}，你今天的日程是：\n{bot_schedule.today_schedule}\n你现在正在{bot_schedule_now_activity}\n'''
+ prompt_date = f"""今天是{current_date}，现在是{current_time}，你今天的日程是：\n{bot_schedule.today_schedule}\n你现在正在{bot_schedule_now_activity}\n"""
# 知识构建
start_time = time.time()
- prompt_info = ''
- promt_info_prompt = ''
+ prompt_info = ""
+ promt_info_prompt = ""
prompt_info = await self.get_prompt_info(message_txt, threshold=0.5)
if prompt_info:
- prompt_info = f'''你有以下这些[知识]：{prompt_info}请你记住上面的[
- 知识]，之后可能会用到-'''
+ prompt_info = f"""你有以下这些[知识]：{prompt_info}请你记住上面的[
+ 知识]，之后可能会用到-"""
end_time = time.time()
logger.debug(f"知识检索耗时: {(end_time - start_time):.3f}秒")
# 获取聊天上下文
- chat_in_group=True
- chat_talking_prompt = ''
+ chat_in_group = True
+ chat_talking_prompt = ""
if stream_id:
- chat_talking_prompt = get_recent_group_detailed_plain_text(stream_id, limit=global_config.MAX_CONTEXT_SIZE,combine = True)
- chat_stream=chat_manager.get_stream(stream_id)
+ chat_talking_prompt = get_recent_group_detailed_plain_text(
+ stream_id, limit=global_config.MAX_CONTEXT_SIZE, combine=True
+ )
+ chat_stream = chat_manager.get_stream(stream_id)
if chat_stream.group_info:
chat_talking_prompt = f"以下是群里正在聊天的内容：\n{chat_talking_prompt}"
else:
- chat_in_group=False
+ chat_in_group = False
chat_talking_prompt = f"以下是你正在和{sender_name}私聊的内容：\n{chat_talking_prompt}"
# print(f"\033[1;34m[调试]\033[0m 已从数据库获取群 {group_id} 的消息记录:{chat_talking_prompt}")
-
-
-
+
# 使用新的记忆获取方法
- memory_prompt = ''
+ memory_prompt = ""
start_time = time.time()
# 调用 hippocampus 的 get_relevant_memories 方法
relevant_memories = await hippocampus.get_relevant_memories(
- text=message_txt,
- max_topics=5,
- similarity_threshold=0.4,
- max_memory_num=5
+ text=message_txt, max_topics=5, similarity_threshold=0.4, max_memory_num=5
)
if relevant_memories:
@@ -147,7 +144,7 @@ class PromptBuilder:
logger.info(f"回忆耗时: {(end_time - start_time):.3f}秒")
# 激活prompt构建
- activate_prompt = ''
+ activate_prompt = ""
if chat_in_group:
activate_prompt = f"以上是群里正在进行的聊天，{memory_prompt}，\
{relation_prompt}{relation_prompt2}现在昵称为 '{sender_name}' 的用户说的:{message_txt}。引起了你的注意。请分析聊天记录，根据你和他的关系和态度进行回复，明确你的立场和情感。"
@@ -155,20 +152,22 @@ class PromptBuilder:
activate_prompt = f"以上是你正在和{sender_name}私聊的内容，{memory_prompt} 现在昵称为 '{sender_name}' 的用户说的:{message_txt}。引起了你的注意，{relation_prompt}{mood_prompt}，你的回复态度是{relation_prompt2}"
# 关键词检测与反应
- keywords_reaction_prompt = ''
+ keywords_reaction_prompt = ""
for rule in global_config.keywords_reaction_rules:
if rule.get("enable", False):
if any(keyword in message_txt.lower() for keyword in rule.get("keywords", [])):
- logger.info(f"检测到以下关键词之一：{rule.get('keywords', [])}，触发反应：{rule.get('reaction', '')}")
- keywords_reaction_prompt += rule.get("reaction", "") + '，'
-
- #人格选择
- personality=global_config.PROMPT_PERSONALITY
+ logger.info(
+ f"检测到以下关键词之一：{rule.get('keywords', [])}，触发反应：{rule.get('reaction', '')}"
+ )
+ keywords_reaction_prompt += rule.get("reaction", "") + "，"
+
+ # 人格选择
+ personality = global_config.PROMPT_PERSONALITY
probability_1 = global_config.PERSONALITY_1
probability_2 = global_config.PERSONALITY_2
probability_3 = global_config.PERSONALITY_3
-
- prompt_personality = f'{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，你还有很多别名:{"/".join(global_config.BOT_ALIAS_NAMES)}，'
+
+ prompt_personality = f"{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，你还有很多别名:{'/'.join(global_config.BOT_ALIAS_NAMES)}，"
personality_choice = random.random()
if personality_choice < probability_1: # 第一种人格
@@ -185,13 +184,13 @@ class PromptBuilder:
请你表达自己的见解和观点。可以有个性。'''
# 中文高手(新加的好玩功能)
- prompt_ger = ''
+ prompt_ger = ""
if random.random() < 0.04:
- prompt_ger += '你喜欢用倒装句'
+ prompt_ger += "你喜欢用倒装句"
if random.random() < 0.02:
- prompt_ger += '你喜欢用反问句'
+ prompt_ger += "你喜欢用反问句"
if random.random() < 0.01:
- prompt_ger += '你喜欢用文言文'
+ prompt_ger += "你喜欢用文言文"
# 额外信息要求
extra_info = f'''但是记得你的回复态度和你的立场，切记你回复的人是{sender_name}，不要输出你的思考过程，只需要输出最终的回复，务必简短一些，尤其注意在没明确提到时不要过多提及自身的背景, 不要直接回复别人发的表情包，记住不要输出多余内容(包括前后缀，冒号和引号，括号，表情等)，只需要输出回复内容就好，不要输出其他任何内容'''
@@ -225,38 +224,38 @@ class PromptBuilder:
current_date = time.strftime("%Y-%m-%d", time.localtime())
current_time = time.strftime("%H:%M:%S", time.localtime())
bot_schedule_now_time, bot_schedule_now_activity = bot_schedule.get_current_task()
- prompt_date = f'''今天是{current_date}，现在是{current_time}，你今天的日程是：\n{bot_schedule.today_schedule}\n你现在正在{bot_schedule_now_activity}\n'''
+ prompt_date = f"""今天是{current_date}，现在是{current_time}，你今天的日程是：\n{bot_schedule.today_schedule}\n你现在正在{bot_schedule_now_activity}\n"""
- chat_talking_prompt = ''
+ chat_talking_prompt = ""
if group_id:
- chat_talking_prompt = get_recent_group_detailed_plain_text(group_id,
- limit=global_config.MAX_CONTEXT_SIZE,
- combine=True)
+ chat_talking_prompt = get_recent_group_detailed_plain_text(
+ group_id, limit=global_config.MAX_CONTEXT_SIZE, combine=True
+ )
chat_talking_prompt = f"以下是群里正在聊天的内容：\n{chat_talking_prompt}"
# print(f"\033[1;34m[调试]\033[0m 已从数据库获取群 {group_id} 的消息记录:{chat_talking_prompt}")
# 获取主动发言的话题
all_nodes = memory_graph.dots
- all_nodes = filter(lambda dot: len(dot[1]['memory_items']) > 3, all_nodes)
+ all_nodes = filter(lambda dot: len(dot[1]["memory_items"]) > 3, all_nodes)
nodes_for_select = random.sample(all_nodes, 5)
topics = [info[0] for info in nodes_for_select]
infos = [info[1] for info in nodes_for_select]
# 激活prompt构建
- activate_prompt = ''
+ activate_prompt = ""
activate_prompt = "以上是群里正在进行的聊天。"
personality = global_config.PROMPT_PERSONALITY
- prompt_personality = ''
+ prompt_personality = ""
personality_choice = random.random()
if personality_choice < probability_1: # 第一种人格
- prompt_personality = f'''{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[0]}'''
+ prompt_personality = f"""{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[0]}"""
elif personality_choice < probability_1 + probability_2: # 第二种人格
- prompt_personality = f'''{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[1]}'''
+ prompt_personality = f"""{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[1]}"""
else: # 第三种人格
- prompt_personality = f'''{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[2]}'''
+ prompt_personality = f"""{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[2]}"""
- topics_str = ','.join(f"\"{topics}\"")
+ topics_str = ",".join(f'"{topics}"')
prompt_for_select = f"你现在想在群里发言，回忆了一下，想到几个话题，分别是{topics_str}，综合当前状态以及群内气氛，请你在其中选择一个合适的话题，注意只需要输出话题，除了话题什么也不要输出(双引号也不要输出)"
prompt_initiative_select = f"{prompt_date}\n{prompt_personality}\n{prompt_for_select}"
@@ -265,8 +264,8 @@ class PromptBuilder:
return prompt_initiative_select, nodes_for_select, prompt_regular
def _build_initiative_prompt_check(self, selected_node, prompt_regular):
- memory = random.sample(selected_node['memory_items'], 3)
- memory = '\n'.join(memory)
+ memory = random.sample(selected_node["memory_items"], 3)
+ memory = "\n".join(memory)
prompt_for_check = f"{prompt_regular}你现在想在群里发言，回忆了一下，想到一个话题,是{selected_node['concept']}，关于这个话题的记忆有\n{memory}\n，以这个作为主题发言合适吗？请在把握群里的聊天内容的基础上，综合群内的氛围，如果认为应该发言请输出yes，否则输出no，请注意是决定是否需要发言，而不是编写回复内容，除了yes和no不要输出任何回复内容。"
return prompt_for_check, memory
@@ -275,7 +274,7 @@ class PromptBuilder:
return prompt_for_initiative
async def get_prompt_info(self, message: str, threshold: float):
- related_info = ''
+ related_info = ""
logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}")
embedding = await get_embedding(message)
related_info += self.get_info_from_db(embedding, threshold=threshold)
@@ -284,7 +283,7 @@ class PromptBuilder:
def get_info_from_db(self, query_embedding: list, limit: int = 1, threshold: float = 0.5) -> str:
if not query_embedding:
- return ''
+ return ""
# 使用余弦相似度计算
pipeline = [
{
@@ -296,12 +295,14 @@ class PromptBuilder:
"in": {
"$add": [
"$$value",
- {"$multiply": [
- {"$arrayElemAt": ["$embedding", "$$this"]},
- {"$arrayElemAt": [query_embedding, "$$this"]}
- ]}
+ {
+ "$multiply": [
+ {"$arrayElemAt": ["$embedding", "$$this"]},
+ {"$arrayElemAt": [query_embedding, "$$this"]},
+ ]
+ },
]
- }
+ },
}
},
"magnitude1": {
@@ -309,7 +310,7 @@ class PromptBuilder:
"$reduce": {
"input": "$embedding",
"initialValue": 0,
- "in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]}
+ "in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
}
}
},
@@ -318,19 +319,13 @@ class PromptBuilder:
"$reduce": {
"input": query_embedding,
"initialValue": 0,
- "in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]}
+ "in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
}
}
- }
- }
- },
- {
- "$addFields": {
- "similarity": {
- "$divide": ["$dotProduct", {"$multiply": ["$magnitude1", "$magnitude2"]}]
- }
+ },
}
},
+ {"$addFields": {"similarity": {"$divide": ["$dotProduct", {"$multiply": ["$magnitude1", "$magnitude2"]}]}}},
{
"$match": {
"similarity": {"$gte": threshold} # 只保留相似度大于等于阈值的结果
@@ -338,17 +333,17 @@ class PromptBuilder:
},
{"$sort": {"similarity": -1}},
{"$limit": limit},
- {"$project": {"content": 1, "similarity": 1}}
+ {"$project": {"content": 1, "similarity": 1}},
]
results = list(db.knowledges.aggregate(pipeline))
# print(f"\033[1;34m[调试]\033[0m获取知识库内容结果: {results}")
if not results:
- return ''
+ return ""
# 返回所有找到的内容，用换行分隔
- return '\n'.join(str(result['content']) for result in results)
+ return "\n".join(str(result["content"]) for result in results)
prompt_builder = PromptBuilder()
diff --git a/src/plugins/chat/utils_image.py b/src/plugins/chat/utils_image.py
index dd6d7d4d1..6d900ba54 100644
--- a/src/plugins/chat/utils_image.py
+++ b/src/plugins/chat/utils_image.py
@@ -34,7 +34,7 @@ class ImageManager:
self._ensure_description_collection()
self._ensure_image_dir()
self._initialized = True
- self._llm = LLM_request(model=global_config.vlm, temperature=0.4, max_tokens=300)
+ self._llm = LLM_request(model=global_config.vlm, temperature=0.4, max_tokens=1000)
def _ensure_image_dir(self):
"""确保图像存储目录存在"""
diff --git a/src/plugins/memory_system/memory.py b/src/plugins/memory_system/memory.py
index 9113fb3ba..0952e0024 100644
--- a/src/plugins/memory_system/memory.py
+++ b/src/plugins/memory_system/memory.py
@@ -19,10 +19,11 @@ from ..chat.utils import (
)
from ..models.utils_model import LLM_request
-from ..utils.logger_config import setup_logger, LogModule
+from ..utils.logger_config import LogClassification, LogModule
# 配置日志
-logger = setup_logger(LogModule.MEMORY)
+log_module = LogModule()
+logger = log_module.setup_logger(LogClassification.MEMORY)
logger.info("初始化记忆系统")
diff --git a/src/plugins/utils/logger_config.py b/src/plugins/utils/logger_config.py
index cc15d53a4..fff5a50d3 100644
--- a/src/plugins/utils/logger_config.py
+++ b/src/plugins/utils/logger_config.py
@@ -1,71 +1,77 @@
import sys
-from loguru import logger
+import loguru
from enum import Enum
-class LogModule(Enum):
+class LogClassification(Enum):
BASE = "base"
MEMORY = "memory"
EMOJI = "emoji"
CHAT = "chat"
-def setup_logger(log_type: LogModule = LogModule.BASE):
- """配置日志格式
-
- Args:
- log_type: 日志类型，可选值：BASE(基础日志)、MEMORY(记忆系统日志)、EMOJI(表情包系统日志)
- """
- # 移除默认的处理器
- logger.remove()
-
- # 基础日志格式
- base_format = "{time:HH:mm:ss} | {level: <8} | {name}:{function}:{line} - {message}"
-
- chat_format = "{time:HH:mm:ss} | {level: <8} | {name}:{function}:{line} - {message}"
-
- # 记忆系统日志格式
- memory_format = "{time:HH:mm} | {level: <8} | 海马体 | {message}"
-
- # 表情包系统日志格式
- emoji_format = "{time:HH:mm} | {level: <8} | 表情包 | {function}:{line} - {message}"
- # 根据日志类型选择日志格式和输出
- if log_type == LogModule.CHAT:
- logger.add(
- sys.stderr,
- format=chat_format,
- # level="INFO"
- )
- elif log_type == LogModule.MEMORY:
- # 同时输出到控制台和文件
- logger.add(
- sys.stderr,
- format=memory_format,
- # level="INFO"
- )
- logger.add(
- "logs/memory.log",
- format=memory_format,
- level="INFO",
- rotation="1 day",
- retention="7 days"
- )
- elif log_type == LogModule.EMOJI:
- logger.add(
- sys.stderr,
- format=emoji_format,
- # level="INFO"
- )
- logger.add(
- "logs/emoji.log",
- format=emoji_format,
- level="INFO",
- rotation="1 day",
- retention="7 days"
- )
- else: # BASE
- logger.add(
- sys.stderr,
- format=base_format,
- level="INFO"
- )
-
- return logger
+class LogModule:
+ logger = loguru.logger.opt()
+
+ def __init__(self):
+ pass
+ def setup_logger(self, log_type: LogClassification):
+ """配置日志格式
+
+ Args:
+ log_type: 日志类型，可选值：BASE(基础日志)、MEMORY(记忆系统日志)、EMOJI(表情包系统日志)
+ """
+ # 移除默认日志处理器
+ self.logger.remove()
+
+ # 基础日志格式
+ base_format = "{time:HH:mm:ss} | {level: <8} | {name}:{function}:{line} - {message}"
+
+ chat_format = "{time:HH:mm:ss} | {level: <8} | {name}:{function}:{line} - {message}"
+
+ # 记忆系统日志格式
+ memory_format = "{time:HH:mm} | {level: <8} | 海马体 | {message}"
+
+ # 表情包系统日志格式
+ emoji_format = "{time:HH:mm} | {level: <8} | 表情包 | {function}:{line} - {message}"
+ # 根据日志类型选择日志格式和输出
+ if log_type == LogClassification.CHAT:
+ self.logger.add(
+ sys.stderr,
+ format=chat_format,
+ # level="INFO"
+ )
+ elif log_type == LogClassification.MEMORY:
+
+ # 同时输出到控制台和文件
+ self.logger.add(
+ sys.stderr,
+ format=memory_format,
+ # level="INFO"
+ )
+ self.logger.add(
+ "logs/memory.log",
+ format=memory_format,
+ level="INFO",
+ rotation="1 day",
+ retention="7 days"
+ )
+ elif log_type == LogClassification.EMOJI:
+ self.logger.add(
+ sys.stderr,
+ format=emoji_format,
+ # level="INFO"
+ )
+ self.logger.add(
+ "logs/emoji.log",
+ format=emoji_format,
+ level="INFO",
+ rotation="1 day",
+ retention="7 days"
+ )
+ else: # BASE
+ self.logger.add(
+ sys.stderr,
+ format=base_format,
+ level="INFO"
+ )
+
+ return self.logger