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Merge branch 'main-fix' into main-fix-poke

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Oct_Autumn
2025-03-14 17:54:47 +08:00
committed by GitHub
parent 5be793a054 f4d535c4c6
commit 5078245f1d
10 changed files with 254 additions and 354 deletions
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2
README.md
View File

@@ -17,7 +17,7 @@
- MongoDB 提供数据持久化支持
- NapCat 作为QQ协议端支持
**最新版本: v0.5.13**
**最新版本: v0.5.14** ([查看更新日志](changelog.md))
> [!WARNING]
> 注意，3月12日的v0.5.13, 该版本更新较大，建议单独开文件夹部署，然后转移/data文件 和数据库，数据库可能需要删除messages下的内容（不需要删除记忆）

53
changelog.md
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@@ -1,7 +1,56 @@
# Changelog
AI总结
## [0.5.14] - 2025-3-14
### 🌟 核心功能增强
#### 记忆系统优化
- 修复了构建记忆时重复读取同一段消息导致token消耗暴增的问题
- 优化了记忆相关的工具模型代码
#### 消息处理升级
- 新增了不回答已撤回消息的功能
- 新增每小时自动删除存留超过1小时的撤回消息
- 优化了戳一戳功能的响应机制
- 修复了回复消息未正常发送的问题
- 改进了图片发送错误时的处理机制
#### 日程系统改进
- 修复了长时间运行的bot在跨天后无法生成新日程的问题
- 优化了日程文本解析功能
- 修复了解析日程时遇到markdown代码块等额外内容的处理问题
### 💻 系统架构优化
#### 日志系统升级
- 建立了新的日志系统
- 改进了错误处理机制
- 优化了代码格式化规范
#### 部署支持扩展
- 改进了NAS部署指南，增加HOST设置说明
- 优化了部署文档的完整性
### 🐛 问题修复
#### 功能稳定性
- 修复了utils_model.py中的潜在问题
- 修复了set_reply相关bug
- 修复了回应所有戳一戳的问题
- 优化了bot被戳时的判断逻辑
### 📚 文档更新
- 更新了README.md的内容
- 完善了NAS部署指南
- 优化了部署相关文档
### 主要改进方向
1. 提升记忆系统的效率和稳定性
2. 完善消息处理机制
3. 优化日程系统功能
4. 改进日志和错误处理
5. 加强部署文档的完整性
## [0.5.13] - 2025-3-12
AI总结
### 🌟 核心功能增强
#### 记忆系统升级
- 新增了记忆系统的时间戳功能，包括创建时间和最后修改时间
@@ -82,3 +131,5 @@ AI总结
4. 提升开发体验和代码质量
5. 加强系统安全性和稳定性

71
src/plugins/chat/bot.py
View File

@@ -63,6 +63,7 @@ class ChatBot:
5. 更新关系
6. 更新情绪
"""
await message_cq.initialize()
message_json = message_cq.to_dict()
# 进入maimbot
@@ -331,6 +332,7 @@ class ChatBot:
)
await self.message_process(message_cq)
elif isinstance(event, GroupRecallNoticeEvent) or isinstance(
event, FriendRecallNoticeEvent
):
@@ -428,74 +430,5 @@ class ChatBot:
await self.message_process(message_cq)
async def directly_reply(self, raw_message: str, user_id: int, group_id: int):
"""
直接回复发来的消息，不经过意愿管理器
"""
# 构造用户信息和群组信息
user_info = UserInfo(
user_id=user_id,
user_nickname=get_user_nickname(user_id) or None,
user_cardname=get_user_cardname(user_id) or None,
platform="qq",
)
group_info = GroupInfo(group_id=group_id, group_name=None, platform="qq")
message_cq = MessageRecvCQ(
message_id=None,
user_info=user_info,
raw_message=raw_message,
group_info=group_info,
reply_message=None,
platform="qq",
)
message_json = message_cq.to_dict()
message = MessageRecv(message_json)
groupinfo = message.message_info.group_info
userinfo = message.message_info.user_info
messageinfo = message.message_info
chat = await chat_manager.get_or_create_stream(
platform=messageinfo.platform, user_info=userinfo, group_info=groupinfo
)
message.update_chat_stream(chat)
await message.process()
bot_user_info = UserInfo(
user_id=global_config.BOT_QQ,
user_nickname=global_config.BOT_NICKNAME,
platform=messageinfo.platform,
)
current_time = time.strftime(
"%Y-%m-%d %H:%M:%S", time.localtime(messageinfo.time)
)
logger.info(
f"[{current_time}][{chat.group_info.group_name if chat.group_info else '私聊'}]{chat.user_info.user_nickname}:"
f"{message.processed_plain_text}"
)
# 使用大模型生成回复
response, raw_content = await self.gpt.generate_response(message)
if response:
for msg in response:
message_segment = Seg(type="text", data=msg)
bot_message = MessageSending(
message_id=None,
chat_stream=chat,
bot_user_info=bot_user_info,
sender_info=userinfo,
message_segment=message_segment,
reply=None,
is_head=False,
is_emoji=False,
)
message_manager.add_message(bot_message)
# 创建全局ChatBot实例
chat_bot = ChatBot()

177
src/plugins/chat/cq_code.py
View File

@@ -1,19 +1,14 @@
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
@@ -25,24 +20,9 @@ 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)
@@ -90,22 +68,18 @@ class CQCode:
self.translated_segments = Seg(type="text", data="@[全体成员]")
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(
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=15,
timeout=aiohttp.ClientTimeout(total=15),
allow_redirects=True,
stream=True, # 流式传输避免大内存问题
)
) as response:
# 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:
if response.status == 400 and "multimedia.nt.qq.com.cn" in url:
return None
if response.status_code != 200:
raise requests.exceptions.HTTPError(f"HTTP {response.status_code}")
if response.status != 200:
raise aiohttp.ClientError(f"HTTP {response.status}")
# 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")
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',
platform="qq",
user_id=msg.get("user_id", 0),
user_nickname=nickname,
)
group_info = GroupInfo(
platform='qq',
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(
@@ -235,9 +202,8 @@ 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]")
else:
@@ -245,14 +211,14 @@ class CQCode:
from .message_cq import MessageRecvCQ
user_info = UserInfo(
platform='qq',
platform="qq",
user_id=msg.get("user_id", 0),
user_nickname=nickname,
)
group_info = GroupInfo(
platform='qq',
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(
@@ -318,12 +280,8 @@ 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
@@ -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]"
@@ -403,10 +354,7 @@ 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=1]"
@@ -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]"

4
src/plugins/chat/emoji_manager.py
View File

@@ -28,7 +28,6 @@ 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 +42,7 @@ 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 +280,6 @@ class EmojiManager:
if description is not None:
embedding = await get_embedding(description)
# 准备数据库记录
emoji_record = {
"filename": filename,

56
src/plugins/chat/llm_generator.py
View File

@@ -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:
@@ -57,37 +46,28 @@ 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 = (
message.chat_stream.user_info.user_nickname
or f"用户{message.chat_stream.user_info.user_id}"
)
sender_name = message.chat_stream.user_info.user_nickname or f"用户{message.chat_stream.user_info.user_id}"
if message.chat_stream.user_info.user_cardname:
sender_name = f"[({message.chat_stream.user_info.user_id}){message.chat_stream.user_info.user_nickname}]{message.chat_stream.user_info.user_cardname}"
# 获取关系值
relationship_value = (
relationship_manager.get_relationship(
message.chat_stream
).relationship_value
relationship_manager.get_relationship(message.chat_stream).relationship_value
if relationship_manager.get_relationship(message.chat_stream)
else 0.0
)
@@ -212,13 +192,11 @@ 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}")
@@ -230,16 +208,12 @@ 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

1
src/plugins/chat/message.py
View File

@@ -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"""

23
src/plugins/chat/message_cq.py
View File

@@ -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 = []

141
src/plugins/chat/prompt_builder.py
View File

@@ -14,16 +14,16 @@ from .chat_stream import chat_manager
class PromptBuilder:
def __init__(self):
self.prompt_built = ''
self.activate_messages = ''
self.prompt_built = ""
self.activate_messages = ""
async def _build_prompt(self,
async def _build_prompt(
self,
message_txt: str,
sender_name: str = "某人",
relationship_value: float = 0.0,
stream_id: Optional[int] = None) -> tuple[str, str]:
stream_id: Optional[int] = None,
) -> tuple[str, str]:
"""构建prompt
Args:
@@ -56,26 +56,28 @@ 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_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_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}"
@@ -84,18 +86,13 @@ class PromptBuilder:
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:
@@ -115,19 +112,21 @@ 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} 现在昵称为 '{sender_name}' 的用户说的:{message_txt}。引起了你的注意,你和ta{relation_prompt},{mood_prompt},你想要{relation_prompt_2}。"
else:
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,你和ta{relation_prompt},{mood_prompt},你想要{relation_prompt_2}。"
# 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", "") + '，'
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
@@ -135,36 +134,36 @@ class PromptBuilder:
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 chat_in_group:
prompt_in_group = f"你正在浏览{chat_stream.platform}群"
else:
prompt_in_group = f"你正在{chat_stream.platform}上和{sender_name}私聊"
if personality_choice < probability_1: # 第一种人格
prompt_personality += f'''{personality[0]}, 你正在浏览qq群,{promt_info_prompt},
prompt_personality += f"""{personality[0]}, 你正在浏览qq群,{promt_info_prompt},
现在请你给出日常且口语化的回复，平淡一些，尽量简短一些。{keywords_reaction_prompt}
请注意把握群里的聊天内容，不要刻意突出自身学科背景，不要回复的太有条理，可以有个性。'''
请注意把握群里的聊天内容，不要刻意突出自身学科背景，不要回复的太有条理，可以有个性。"""
elif personality_choice < probability_1 + probability_2: # 第二种人格
prompt_personality += f'''{personality[1]}, 你正在浏览qq群，{promt_info_prompt},
prompt_personality += f"""{personality[1]}, 你正在浏览qq群，{promt_info_prompt},
现在请你给出日常且口语化的回复，请表现你自己的见解，不要一昧迎合，尽量简短一些。{keywords_reaction_prompt}
请你表达自己的见解和观点。可以有个性。'''
请你表达自己的见解和观点。可以有个性。"""
else: # 第三种人格
prompt_personality += f'''{personality[2]}, 你正在浏览qq群，{promt_info_prompt},
prompt_personality += f"""{personality[2]}, 你正在浏览qq群，{promt_info_prompt},
现在请你给出日常且口语化的回复，请表现你自己的见解，不要一昧迎合，尽量简短一些。{keywords_reaction_prompt}
请你表达自己的见解和观点。可以有个性。'''
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 = '''但是记得回复平淡一些，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'''
extra_info = """但是记得回复平淡一些，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"""
# 合并prompt
prompt = ""
@@ -175,16 +174,16 @@ class PromptBuilder:
prompt += f"{prompt_ger}\n"
prompt += f"{extra_info}\n"
'''读空气prompt处理'''
"""读空气prompt处理"""
activate_prompt_check = f"以上是群里正在进行的聊天，昵称为 '{sender_name}' 的用户说的:{message_txt}。引起了你的注意,你和他{relation_prompt}，你想要{relation_prompt_2}，但是这不一定是合适的时机，请你决定是否要回应这条消息。"
prompt_personality_check = ''
prompt_personality_check = ""
extra_check_info = f"请注意把握群里的聊天内容的基础上，综合群内的氛围，例如，和{global_config.BOT_NICKNAME}相关的话题要积极回复,如果是at自己的消息一定要回复，如果自己正在和别人聊天一定要回复，其他话题如果合适搭话也可以回复，如果认为应该回复请输出yes，否则输出no，请注意是决定是否需要回复，而不是编写回复内容，除了yes和no不要输出任何回复内容。"
if personality_choice < probability_1: # 第一种人格
prompt_personality_check = f'''你的网名叫{global_config.BOT_NICKNAME}，{personality[0]}, 你正在浏览qq群，{promt_info_prompt} {activate_prompt_check} {extra_check_info}'''
prompt_personality_check = f"""你的网名叫{global_config.BOT_NICKNAME}，{personality[0]}, 你正在浏览qq群，{promt_info_prompt} {activate_prompt_check} {extra_check_info}"""
elif personality_choice < probability_1 + probability_2: # 第二种人格
prompt_personality_check = f'''你的网名叫{global_config.BOT_NICKNAME}，{personality[1]}, 你正在浏览qq群，{promt_info_prompt} {activate_prompt_check} {extra_check_info}'''
prompt_personality_check = f"""你的网名叫{global_config.BOT_NICKNAME}，{personality[1]}, 你正在浏览qq群，{promt_info_prompt} {activate_prompt_check} {extra_check_info}"""
else: # 第三种人格
prompt_personality_check = f'''你的网名叫{global_config.BOT_NICKNAME}，{personality[2]}, 你正在浏览qq群，{promt_info_prompt} {activate_prompt_check} {extra_check_info}'''
prompt_personality_check = f"""你的网名叫{global_config.BOT_NICKNAME}，{personality[2]}, 你正在浏览qq群，{promt_info_prompt} {activate_prompt_check} {extra_check_info}"""
prompt_check_if_response = f"{prompt_info}\n{prompt_date}\n{chat_talking_prompt}\n{prompt_personality_check}"
@@ -194,38 +193,38 @@ 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}"
@@ -234,8 +233,8 @@ 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
@@ -244,7 +243,7 @@ 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)
@@ -253,7 +252,7 @@ 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 = [
{
@@ -265,12 +264,14 @@ class PromptBuilder:
"in": {
"$add": [
"$$value",
{"$multiply": [
{
"$multiply": [
{"$arrayElemAt": ["$embedding", "$$this"]},
{"$arrayElemAt": [query_embedding, "$$this"]}
]}
{"$arrayElemAt": [query_embedding, "$$this"]},
]
}
},
]
},
}
},
"magnitude1": {
@@ -278,7 +279,7 @@ class PromptBuilder:
"$reduce": {
"input": "$embedding",
"initialValue": 0,
"in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]}
"in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
}
}
},
@@ -287,19 +288,13 @@ 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
@@ -307,17 +302,17 @@ 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()

2
src/plugins/chat/utils_image.py
View File

@@ -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"""