From 136a8078964e28e33d11e560b8a0b0cf9ff86ec3 Mon Sep 17 00:00:00 2001 From: tcmofashi Date: Sun, 2 Mar 2025 03:45:23 +0800 Subject: [PATCH 01/26] =?UTF-8?q?feat:=20=E6=B7=BB=E5=8A=A0=E4=BA=86?= =?UTF-8?q?=E8=AF=BB=E7=A9=BA=E6=B0=94=E5=8A=9F=E8=83=BD?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/plugins/chat/config.py | 1 + src/plugins/chat/llm_generator.py | 49 ++++++++++++++++++++++++++++- src/plugins/chat/prompt_builder.py | 12 ++++++- src/plugins/chat/willing_manager.py | 2 +- 4 files changed, 61 insertions(+), 3 deletions(-) diff --git a/src/plugins/chat/config.py b/src/plugins/chat/config.py index 05d492789..efaa96523 100644 --- a/src/plugins/chat/config.py +++ b/src/plugins/chat/config.py @@ -47,6 +47,7 @@ class BotConfig: MODEL_R1_DISTILL_PROBABILITY: float = 0.1 # R1蒸馏模型概率 enable_advance_output: bool = False # 是否启用高级输出 + enable_kuuki_read: bool = True # 是否启用读空气功能 @staticmethod def get_default_config_path() -> str: diff --git a/src/plugins/chat/llm_generator.py b/src/plugins/chat/llm_generator.py index 2ea4d7f24..137730ad5 100644 --- a/src/plugins/chat/llm_generator.py +++ b/src/plugins/chat/llm_generator.py @@ -91,7 +91,7 @@ class LLMResponseGenerator: relationship_value = 0.0 # 构建prompt - prompt = prompt_builder._build_prompt( + prompt,prompt_check = prompt_builder._build_prompt( message_txt=message.processed_plain_text, sender_name=sender_name, relationship_value=relationship_value, @@ -106,6 +106,14 @@ class LLMResponseGenerator: "max_tokens": 1024, "temperature": 0.7 } + + default_params_check = { + "model": "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", + "messages": [{"role": "user", "content": prompt_check}], + "stream": False, + "max_tokens": 1024, + "temperature": 0.7 + } # 更新参数 if model_params: @@ -114,7 +122,43 @@ class LLMResponseGenerator: def create_completion(): return self.client.chat.completions.create(**default_params) + def create_completion_check(): + return self.client.chat.completions.create(**default_params_check) + loop = asyncio.get_event_loop() + + # 读空气模块 + reasoning_content_check='' + content_check='' + if global_config.enable_kuuki_read: + response_check = await loop.run_in_executor(None, create_completion_check) + if response_check: + reasoning_content_check = "" + if hasattr(response_check.choices[0].message, "reasoning"): + reasoning_content_check = response_check.choices[0].message.reasoning or reasoning_content_check + elif hasattr(response_check.choices[0].message, "reasoning_content"): + reasoning_content_check = response_check.choices[0].message.reasoning_content or reasoning_content_check + content_check = response_check.choices[0].message.content + print(f"\033[1;32m[读空气]\033[0m 读空气结果为{content_check}") + if 'yes' not in content_check.lower(): + self.db.db.reasoning_logs.insert_one({ + 'time': time.time(), + 'group_id': message.group_id, + 'user': sender_name, + 'message': message.processed_plain_text, + 'model': model_name, + 'reasoning_check': reasoning_content_check, + 'response_check': content_check, + 'reasoning': "", + 'response': "", + 'prompt': prompt, + 'prompt_check': prompt_check, + 'model_params': default_params + }) + return None + + + response = await loop.run_in_executor(None, create_completion) # 检查响应内容 @@ -142,9 +186,12 @@ class LLMResponseGenerator: 'user': sender_name, 'message': message.processed_plain_text, 'model': model_name, + 'reasoning_check': reasoning_content_check, + 'response_check': content_check, 'reasoning': reasoning_content, 'response': content, 'prompt': prompt, + 'prompt_check': prompt_check, 'model_params': default_params }) diff --git a/src/plugins/chat/prompt_builder.py b/src/plugins/chat/prompt_builder.py index da9037cfa..215d926a9 100644 --- a/src/plugins/chat/prompt_builder.py +++ b/src/plugins/chat/prompt_builder.py @@ -186,8 +186,18 @@ class PromptBuilder: prompt += f"{prompt_personality}\n" prompt += f"{prompt_ger}\n" prompt += f"{extra_info}\n" + + 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 = '' + 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 < 4/6: # 第一种人格 + prompt_personality_check = f'''你的网名叫{global_config.BOT_NICKNAME}，曾经是一个学习地质的女大学生，现在学习心理学和脑科学，你会刷贴吧，你正在浏览qq群,{promt_info_prompt} {activate_prompt_check} {extra_check_info}''' + elif personality_choice < 1: # 第二种人格 + prompt_personality_check = f'''你的网名叫{global_config.BOT_NICKNAME}，是一个女大学生，你有黑色头发，你会刷小红书, 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}" - return prompt + return prompt,prompt_check_if_response def get_prompt_info(self,message:str,threshold:float): related_info = '' diff --git a/src/plugins/chat/willing_manager.py b/src/plugins/chat/willing_manager.py index 037c2d517..8f3734a4f 100644 --- a/src/plugins/chat/willing_manager.py +++ b/src/plugins/chat/willing_manager.py @@ -58,7 +58,7 @@ class WillingManager: if group_id in config.talk_frequency_down_groups: reply_probability = reply_probability / 3.5 - if is_mentioned_bot and user_id == int(1026294844): + if is_mentioned_bot and user_id == int(964959351): reply_probability = 1 return reply_probability From 1cd7f80937293c2c3ee0eefb0625a0fe6e815666 Mon Sep 17 00:00:00 2001 From: SengokuCola <1026294844@qq.com> Date: Sun, 2 Mar 2025 15:00:12 +0800 Subject: [PATCH 02/26] =?UTF-8?q?v0.3.2=20=E6=9B=B4=E6=94=B9=E4=BA=86.env?= =?UTF-8?q?=20config=E7=9A=84=E9=80=BB=E8=BE=91=E5=92=8Cmemory=E4=BC=98?= =?UTF-8?q?=E5=8C=96?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit v0.3.2 更改了.env config的逻辑 memory优化 读空气优化 --- config/env.example => .env.prod | 11 +- .gitignore | 1 + README.md | 9 +- bot.py | 64 +++- run_maimai.bat | 3 +- src/plugins/chat/__init__.py | 16 +- src/plugins/chat/bot.py | 25 +- src/plugins/chat/config.py | 11 +- src/plugins/chat/cq_code.py | 14 +- src/plugins/chat/emoji_manager.py | 14 +- src/plugins/chat/llm_generator.py | 76 ++-- src/plugins/chat/prompt_builder.py | 19 +- src/plugins/chat/topic_identifier.py | 9 +- src/plugins/chat/utils.py | 8 +- src/plugins/chat/utils_image.py | 16 +- src/plugins/chat/willing_manager.py | 4 +- src/plugins/knowledege/knowledge_library.py | 16 +- src/plugins/memory_system/draw_memory.py | 92 ++++- src/plugins/memory_system/llm_module.py | 8 +- .../memory_system/llm_module_memory_make.py | 8 +- src/plugins/memory_system/memory.py | 56 +-- src/plugins/memory_system/memory_make.py | 346 ++++++++++-------- src/plugins/schedule/schedule_generator.py | 17 +- src/plugins/schedule/schedule_llm_module.py | 12 +- 24 files changed, 538 insertions(+), 317 deletions(-) rename config/env.example => .env.prod (73%) diff --git a/config/env.example b/.env.prod similarity index 73% rename from config/env.example rename to .env.prod index 9988d58f3..3d795978c 100644 --- a/config/env.example +++ b/.env.prod @@ -1,4 +1,3 @@ -ENVIRONMENT=dev HOST=127.0.0.1 PORT=8080 @@ -11,15 +10,15 @@ PLUGINS=["src2.plugins.chat"] MONGODB_HOST=127.0.0.1 MONGODB_PORT=27017 DATABASE_NAME=MegBot + MONGODB_USERNAME = "" # 默认空值 MONGODB_PASSWORD = "" # 默认空值 MONGODB_AUTH_SOURCE = "" # 默认空值 -#api配置项 +#key and url +CHAT_ANY_WHERE_KEY= SILICONFLOW_KEY= +CHAT_ANY_WHERE_BASE_URL=https://api.chatanywhere.tech/v1 SILICONFLOW_BASE_URL=https://api.siliconflow.cn/v1/ DEEP_SEEK_KEY= -DEEP_SEEK_BASE_URL=https://api.deepseek.com/v1 - - - +DEEP_SEEK_BASE_URL=https://api.deepseek.com/v1 \ No newline at end of file diff --git a/.gitignore b/.gitignore index 265108181..c19b9ce33 100644 --- a/.gitignore +++ b/.gitignore @@ -14,6 +14,7 @@ reasoning_content.bat reasoning_window.bat queue_update.txt memory_graph.gml +.env.dev # Byte-compiled / optimized / DLL files diff --git a/README.md b/README.md index a85fcc4e8..1310d4879 100644 --- a/README.md +++ b/README.md @@ -28,7 +28,7 @@ > ⚠️ **警告**：请自行了解qqbot的风险，麦麦有时候一天被腾讯肘七八次 > ⚠️ **警告**：由于麦麦一直在迭代，所以可能存在一些bug，请自行测试，包括胡言乱语（ -关于麦麦的开发和建议相关的讨论群（不建议发布无关消息）这里不会有麦麦发言！ +关于麦麦的开发和建议相关的讨论群:766798517（不建议发布无关消息）这里不会有麦麦发言！ ## 开发计划TODO：LIST @@ -41,16 +41,13 @@ - config自动生成和检测 - log别用print - 给发送消息写专门的类 +- 改进表情包发送逻辑l -
- -
- ## 📚 详细文档 - [项目详细介绍和架构说明](docs/doc1.md) - 包含完整的项目结构、文件说明和核心功能实现细节(由claude-3.5-sonnet生成) -### 安装方法（还没测试好，现在部署可能遇到未知问题！！！！） +### 安装方法（还没测试好，随时outdated ,现在部署可能遇到未知问题！！！！） #### Linux 使用 Docker Compose 部署 获取项目根目录中的```docker-compose.yml```文件，运行以下命令 diff --git a/bot.py b/bot.py index 8abdbbbe9..8741eca7f 100644 --- a/bot.py +++ b/bot.py @@ -4,27 +4,59 @@ from nonebot.adapters.onebot.v11 import Adapter from dotenv import load_dotenv from loguru import logger - # 加载全局环境变量 -root_dir = os.path.dirname(os.path.abspath(__file__)) -env_path=os.path.join(root_dir, "config",'.env') +'''彩蛋''' +from colorama import init, Fore +init() +text = "多年以后，面对行刑队，张三将会回想起他2023年在会议上讨论人工智能的那个下午" +rainbow_colors = [Fore.RED, Fore.YELLOW, Fore.GREEN, Fore.CYAN, Fore.BLUE, Fore.MAGENTA] +rainbow_text = "" +for i, char in enumerate(text): + rainbow_text += rainbow_colors[i % len(rainbow_colors)] + char +print(rainbow_text) +'''彩蛋''' -logger.info(f"尝试从 {env_path} 加载环境变量配置") -if os.path.exists(env_path): - load_dotenv(env_path) - logger.success("成功加载环境变量配置") +# 首先加载基础环境变量 +if os.path.exists(".env"): + load_dotenv(".env") + logger.success("成功加载基础环境变量配置") else: - logger.error(f"环境变量配置文件不存在: {env_path}") + logger.error("基础环境变量配置文件 .env 不存在") + exit(1) +# 根据 ENVIRONMENT 加载对应的环境配置 +env = os.getenv("ENVIRONMENT") +env_file = f".env.{env}" + +if env_file == ".env.dev" and os.path.exists(env_file): + logger.success("加载开发环境变量配置") + load_dotenv(env_file, override=True) # override=True 允许覆盖已存在的环境变量 +elif env_file == ".env.prod" and os.path.exists(env_file): + logger.success("加载环境变量配置") + load_dotenv(env_file, override=True) # override=True 允许覆盖已存在的环境变量 +else: + logger.error(f"{env}对应的环境配置文件{env_file}不存在,请修改.env文件中的ENVIRONMENT变量为 prod.") + exit(1) -# 初始化 NoneBot nonebot.init( - # napcat 默认使用 8080 端口 - websocket_port=8080, - # 设置日志级别 + # 从环境变量中读取配置 + websocket_port=os.getenv("PORT", 8080), + host=os.getenv("HOST", "127.0.0.1"), log_level="INFO", - # 设置超级用户 - superusers={"你的QQ号"}, - # TODO: 这样写会忽略环境变量需要优化 https://nonebot.dev/docs/appendices/config - _env_file=env_path + # 添加自定义配置 + mongodb_host=os.getenv("MONGODB_HOST", "127.0.0.1"), + mongodb_port=os.getenv("MONGODB_PORT", 27017), + database_name=os.getenv("DATABASE_NAME", "MegBot"), + mongodb_username=os.getenv("MONGODB_USERNAME", ""), + mongodb_password=os.getenv("MONGODB_PASSWORD", ""), + mongodb_auth_source=os.getenv("MONGODB_AUTH_SOURCE", ""), + # API相关配置 + chat_any_where_key=os.getenv("CHAT_ANY_WHERE_KEY", ""), + siliconflow_key=os.getenv("SILICONFLOW_KEY", ""), + chat_any_where_base_url=os.getenv("CHAT_ANY_WHERE_BASE_URL", "https://api.chatanywhere.tech/v1"), + siliconflow_base_url=os.getenv("SILICONFLOW_BASE_URL", "https://api.siliconflow.cn/v1/"), + deep_seek_key=os.getenv("DEEP_SEEK_KEY", ""), + deep_seek_base_url=os.getenv("DEEP_SEEK_BASE_URL", "https://api.deepseek.com/v1"), + # 插件配置 + plugins=os.getenv("PLUGINS", ["src2.plugins.chat"]) ) # 注册适配器 diff --git a/run_maimai.bat b/run_maimai.bat index 0e1bd7eb6..702d39edc 100644 --- a/run_maimai.bat +++ b/run_maimai.bat @@ -2,4 +2,5 @@ call conda activate niuniu cd . REM 执行nb run命令 -nb run \ No newline at end of file +nb run +pause \ No newline at end of file diff --git a/src/plugins/chat/__init__.py b/src/plugins/chat/__init__.py index 6fc896ac8..0f81d30d6 100644 --- a/src/plugins/chat/__init__.py +++ b/src/plugins/chat/__init__.py @@ -11,16 +11,18 @@ from .relationship_manager import relationship_manager from ..schedule.schedule_generator import bot_schedule from .willing_manager import willing_manager + # 获取驱动器 driver = get_driver() +config = driver.config Database.initialize( - host= os.getenv("MONGODB_HOST"), - port= int(os.getenv("MONGODB_PORT")), - db_name= os.getenv("DATABASE_NAME"), - username= os.getenv("MONGODB_USERNAME"), - password= os.getenv("MONGODB_PASSWORD"), - auth_source=os.getenv("MONGODB_AUTH_SOURCE") + host= config.mongodb_host, + port= int(config.mongodb_port), + db_name= config.database_name, + username= config.mongodb_username, + password= config.mongodb_password, + auth_source= config.mongodb_auth_source ) print("\033[1;32m[初始化数据库完成]\033[0m") @@ -37,7 +39,7 @@ emoji_manager.initialize() print(f"\033[1;32m正在唤醒{global_config.BOT_NICKNAME}......\033[0m") # 创建机器人实例 -chat_bot = ChatBot(global_config) +chat_bot = ChatBot() # 注册消息处理器 group_msg = on_message() # 创建定时任务 diff --git a/src/plugins/chat/bot.py b/src/plugins/chat/bot.py index 1b5201645..e68ae93f3 100644 --- a/src/plugins/chat/bot.py +++ b/src/plugins/chat/bot.py @@ -18,10 +18,9 @@ from .utils import is_mentioned_bot_in_txt, calculate_typing_time from ..memory_system.memory import memory_graph class ChatBot: - def __init__(self, config: BotConfig): - self.config = config + def __init__(self): self.storage = MessageStorage() - self.gpt = LLMResponseGenerator(config) + self.gpt = LLMResponseGenerator() self.bot = None # bot 实例引用 self._started = False @@ -39,11 +38,11 @@ class ChatBot: async def handle_message(self, event: GroupMessageEvent, bot: Bot) -> None: """处理收到的群消息""" - if event.group_id not in self.config.talk_allowed_groups: + if event.group_id not in global_config.talk_allowed_groups: return self.bot = bot # 更新 bot 实例 - if event.user_id in self.config.ban_user_id: + if event.user_id in global_config.ban_user_id: return # 打印原始消息内容 @@ -120,7 +119,7 @@ class ChatBot: event.group_id, topic[0] if topic else None, is_mentioned, - self.config, + global_config, event.user_id, message.is_emoji, interested_rate @@ -143,10 +142,14 @@ class ChatBot: response, emotion = await self.gpt.generate_response(message) # 如果生成了回复，发送并记录 - + + ''' + 生成回复后的内容 + + ''' if response: - message_set = MessageSet(event.group_id, self.config.BOT_QQ, think_id) + message_set = MessageSet(event.group_id, global_config.BOT_QQ, think_id) accu_typing_time = 0 for msg in response: print(f"当前消息: {msg}") @@ -157,7 +160,7 @@ class ChatBot: bot_message = Message( group_id=event.group_id, - user_id=self.config.BOT_QQ, + user_id=global_config.BOT_QQ, message_id=think_id, message_based_id=event.message_id, raw_message=msg, @@ -174,7 +177,7 @@ class ChatBot: bot_response_time = tinking_time_point - if random() < self.config.emoji_chance: + if random() < global_config.emoji_chance: emoji_path = await emoji_manager.get_emoji_for_emotion(emotion) if emoji_path: emoji_cq = CQCode.create_emoji_cq(emoji_path) @@ -186,7 +189,7 @@ class ChatBot: bot_message = Message( group_id=event.group_id, - user_id=self.config.BOT_QQ, + user_id=global_config.BOT_QQ, message_id=0, raw_message=emoji_cq, plain_text=emoji_cq, diff --git a/src/plugins/chat/config.py b/src/plugins/chat/config.py index 8c04e1126..0232219f8 100644 --- a/src/plugins/chat/config.py +++ b/src/plugins/chat/config.py @@ -7,6 +7,7 @@ import configparser import tomli import sys from loguru import logger +from nonebot import get_driver @@ -111,7 +112,6 @@ class BotConfig: # 获取配置文件路径 bot_config_path = BotConfig.get_default_config_path() config_dir = os.path.dirname(bot_config_path) -env_path = os.path.join(config_dir, '.env') logger.info(f"尝试从 {bot_config_path} 加载机器人配置") global_config = BotConfig.load_config(config_path=bot_config_path) @@ -126,10 +126,11 @@ class LLMConfig: DEEP_SEEK_BASE_URL: str = None llm_config = LLMConfig() -llm_config.SILICONFLOW_API_KEY = os.getenv('SILICONFLOW_KEY') -llm_config.SILICONFLOW_BASE_URL = os.getenv('SILICONFLOW_BASE_URL') -llm_config.DEEP_SEEK_API_KEY = os.getenv('DEEP_SEEK_KEY') -llm_config.DEEP_SEEK_BASE_URL = os.getenv('DEEP_SEEK_BASE_URL') +config = get_driver().config +llm_config.SILICONFLOW_API_KEY = config.siliconflow_key +llm_config.SILICONFLOW_BASE_URL = config.siliconflow_base_url +llm_config.DEEP_SEEK_API_KEY = config.deep_seek_key +llm_config.DEEP_SEEK_BASE_URL = config.deep_seek_base_url if not global_config.enable_advance_output: diff --git a/src/plugins/chat/cq_code.py b/src/plugins/chat/cq_code.py index 92ca20bd8..ae5d8a257 100644 --- a/src/plugins/chat/cq_code.py +++ b/src/plugins/chat/cq_code.py @@ -7,7 +7,7 @@ from PIL import Image import os from random import random from nonebot.adapters.onebot.v11 import Bot -from .config import global_config, llm_config +from .config import global_config import time import asyncio from .utils_image import storage_image,storage_emoji @@ -16,6 +16,10 @@ from .utils_user import get_user_nickname #包含CQ码类 import urllib3 from urllib3.util import create_urllib3_context +from nonebot import get_driver + +driver = get_driver() +config = driver.config # TLS1.3特殊处理 https://github.com/psf/requests/issues/6616 ctx = create_urllib3_context() @@ -179,7 +183,7 @@ class CQCode: """调用AI接口获取表情包描述""" headers = { "Content-Type": "application/json", - "Authorization": f"Bearer {llm_config.SILICONFLOW_API_KEY}" + "Authorization": f"Bearer {config.siliconflow_key}" } payload = { @@ -206,7 +210,7 @@ class CQCode: } response = requests.post( - f"{llm_config.SILICONFLOW_BASE_URL}chat/completions", + f"{config.siliconflow_base_url}chat/completions", headers=headers, json=payload, timeout=30 @@ -224,7 +228,7 @@ class CQCode: """调用AI接口获取普通图片描述""" headers = { "Content-Type": "application/json", - "Authorization": f"Bearer {llm_config.SILICONFLOW_API_KEY}" + "Authorization": f"Bearer {config.siliconflow_key}" } payload = { @@ -251,7 +255,7 @@ class CQCode: } response = requests.post( - f"{llm_config.SILICONFLOW_BASE_URL}chat/completions", + f"{config.siliconflow_base_url}chat/completions", headers=headers, json=payload, timeout=30 diff --git a/src/plugins/chat/emoji_manager.py b/src/plugins/chat/emoji_manager.py index a4352758d..c8c9dc814 100644 --- a/src/plugins/chat/emoji_manager.py +++ b/src/plugins/chat/emoji_manager.py @@ -10,10 +10,14 @@ import hashlib from datetime import datetime import base64 import shutil -from .config import global_config, llm_config import asyncio import time +from nonebot import get_driver + +driver = get_driver() +config = driver.config + class EmojiManager: _instance = None @@ -93,7 +97,7 @@ class EmojiManager: # 准备请求数据 headers = { "Content-Type": "application/json", - "Authorization": f"Bearer {llm_config.SILICONFLOW_API_KEY}" + "Authorization": f"Bearer {config.siliconflow_key}" } payload = { @@ -115,7 +119,7 @@ class EmojiManager: async with aiohttp.ClientSession() as session: async with session.post( - f"{llm_config.SILICONFLOW_BASE_URL}chat/completions", + f"{config.siliconflow_base_url}chat/completions", headers=headers, json=payload ) as response: @@ -249,7 +253,7 @@ class EmojiManager: async with aiohttp.ClientSession() as session: headers = { "Content-Type": "application/json", - "Authorization": f"Bearer {llm_config.SILICONFLOW_API_KEY}" + "Authorization": f"Bearer {config.siliconflow_key}" } payload = { @@ -276,7 +280,7 @@ class EmojiManager: } async with session.post( - f"{llm_config.SILICONFLOW_BASE_URL}chat/completions", + f"{config.siliconflow_base_url}chat/completions", headers=headers, json=payload ) as response: diff --git a/src/plugins/chat/llm_generator.py b/src/plugins/chat/llm_generator.py index 5ed12c9b6..bfff1d474 100644 --- a/src/plugins/chat/llm_generator.py +++ b/src/plugins/chat/llm_generator.py @@ -1,34 +1,34 @@ from typing import Dict, Any, List, Optional, Union, Tuple from openai import OpenAI import asyncio -import requests from functools import partial from .message import Message -from .config import BotConfig, global_config +from .config import global_config from ...common.database import Database import random import time -import os import numpy as np from .relationship_manager import relationship_manager -from ..schedule.schedule_generator import bot_schedule from .prompt_builder import prompt_builder -from .config import llm_config, global_config +from .config import global_config from .utils import process_llm_response +from nonebot import get_driver + +driver = get_driver() +config = driver.config class LLMResponseGenerator: - def __init__(self, config: BotConfig): - self.config = config - if self.config.API_USING == "siliconflow": + def __init__(self): + if global_config.API_USING == "siliconflow": self.client = OpenAI( - api_key=llm_config.SILICONFLOW_API_KEY, - base_url=llm_config.SILICONFLOW_BASE_URL + api_key=config.siliconflow_key, + base_url=config.siliconflow_base_url ) - elif self.config.API_USING == "deepseek": + elif global_config.API_USING == "deepseek": self.client = OpenAI( - api_key=llm_config.DEEP_SEEK_API_KEY, - base_url=llm_config.DEEP_SEEK_BASE_URL + api_key=config.deep_seek_key, + base_url=config.deep_seek_base_url ) self.db = Database.get_instance() @@ -52,6 +52,7 @@ class LLMResponseGenerator: else: self.current_model_type = 'r1_distill' # 默认使用 R1-Distill + print(f"+++++++++++++++++{global_config.BOT_NICKNAME}{self.current_model_type}思考中+++++++++++++++++") if self.current_model_type == 'r1': model_response = await self._generate_r1_response(message) @@ -83,8 +84,9 @@ class LLMResponseGenerator: print(f"\033[1;32m[关系管理]\033[0m 回复中_当前关系值: {relationship_value}") else: relationship_value = 0.0 + - # 构建prompt + ''' 构建prompt ''' prompt,prompt_check = prompt_builder._build_prompt( message_txt=message.processed_plain_text, sender_name=sender_name, @@ -92,6 +94,7 @@ class LLMResponseGenerator: group_id=message.group_id ) + # 设置默认参数 default_params = { "model": model_name, @@ -113,6 +116,7 @@ class LLMResponseGenerator: if model_params: default_params.update(model_params) + def create_completion(): return self.client.chat.completions.create(**default_params) @@ -122,6 +126,7 @@ class LLMResponseGenerator: loop = asyncio.get_event_loop() # 读空气模块 + air = 0 reasoning_content_check='' content_check='' if global_config.enable_kuuki_read: @@ -135,21 +140,26 @@ class LLMResponseGenerator: content_check = response_check.choices[0].message.content print(f"\033[1;32m[读空气]\033[0m 读空气结果为{content_check}") if 'yes' not in content_check.lower(): - self.db.db.reasoning_logs.insert_one({ - 'time': time.time(), - 'group_id': message.group_id, - 'user': sender_name, - 'message': message.processed_plain_text, - 'model': model_name, - 'reasoning_check': reasoning_content_check, - 'response_check': content_check, - 'reasoning': "", - 'response': "", - 'prompt': prompt, - 'prompt_check': prompt_check, - 'model_params': default_params - }) - return None + air = 1 + #稀释读空气的判定 + if air == 1 and random.random() < 0.3: + self.db.db.reasoning_logs.insert_one({ + 'time': time.time(), + 'group_id': message.group_id, + 'user': sender_name, + 'message': message.processed_plain_text, + 'model': model_name, + 'reasoning_check': reasoning_content_check, + 'response_check': content_check, + 'reasoning': "", + 'response': "", + 'prompt': prompt, + 'prompt_check': prompt_check, + 'model_params': default_params + }) + return None + + @@ -193,7 +203,7 @@ class LLMResponseGenerator: async def _generate_r1_response(self, message: Message) -> Optional[str]: """使用 DeepSeek-R1 模型生成回复""" - if self.config.API_USING == "deepseek": + if global_config.API_USING == "deepseek": return await self._generate_base_response( message, "deepseek-reasoner", @@ -208,7 +218,7 @@ class LLMResponseGenerator: async def _generate_v3_response(self, message: Message) -> Optional[str]: """使用 DeepSeek-V3 模型生成回复""" - if self.config.API_USING == "deepseek": + if global_config.API_USING == "deepseek": return await self._generate_base_response( message, "deepseek-chat", @@ -259,7 +269,7 @@ class LLMResponseGenerator: messages = [{"role": "user", "content": prompt}] loop = asyncio.get_event_loop() - if self.config.API_USING == "deepseek": + if global_config.API_USING == "deepseek": model = "deepseek-chat" else: model = "Pro/deepseek-ai/DeepSeek-V3" @@ -296,4 +306,4 @@ class LLMResponseGenerator: return processed_response, emotion_tags # 创建全局实例 -llm_response = LLMResponseGenerator(global_config) \ No newline at end of file +llm_response = LLMResponseGenerator() \ No newline at end of file diff --git a/src/plugins/chat/prompt_builder.py b/src/plugins/chat/prompt_builder.py index 49402dbfc..ba0e9b4cc 100644 --- a/src/plugins/chat/prompt_builder.py +++ b/src/plugins/chat/prompt_builder.py @@ -66,12 +66,15 @@ class PromptBuilder: overlapping_second_layer.update(overlap) # 合并所有需要的记忆 - if all_first_layer_items: - print(f"\033[1;32m[前额叶]\033[0m 合并所有需要的记忆1: {all_first_layer_items}") - if overlapping_second_layer: - print(f"\033[1;32m[前额叶]\033[0m 合并所有需要的记忆2: {list(overlapping_second_layer)}") + # if all_first_layer_items: + # print(f"\033[1;32m[前额叶]\033[0m 合并所有需要的记忆1: {all_first_layer_items}") + # if overlapping_second_layer: + # print(f"\033[1;32m[前额叶]\033[0m 合并所有需要的记忆2: {list(overlapping_second_layer)}") - all_memories = all_first_layer_items + list(overlapping_second_layer) + # 使用集合去重 + all_memories = list(set(all_first_layer_items) | set(overlapping_second_layer)) + if all_memories: + print(f"\033[1;32m[前额叶]\033[0m 合并所有需要的记忆: {all_memories}") if all_memories: # 只在列表非空时选择随机项 random_item = choice(all_memories) @@ -179,7 +182,11 @@ class PromptBuilder: # prompt += f"{activate_prompt}\n" prompt += f"{prompt_personality}\n" prompt += f"{prompt_ger}\n" - prompt += f"{extra_info}\n" + prompt += f"{extra_info}\n" + + + + '''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 = '' diff --git a/src/plugins/chat/topic_identifier.py b/src/plugins/chat/topic_identifier.py index 75593667b..81956ddc1 100644 --- a/src/plugins/chat/topic_identifier.py +++ b/src/plugins/chat/topic_identifier.py @@ -1,14 +1,17 @@ from typing import Optional, Dict, List from openai import OpenAI from .message import Message -from .config import global_config, llm_config import jieba +from nonebot import get_driver + +driver = get_driver() +config = driver.config class TopicIdentifier: def __init__(self): self.client = OpenAI( - api_key=llm_config.SILICONFLOW_API_KEY, - base_url=llm_config.SILICONFLOW_BASE_URL + api_key=config.siliconflow_key, + base_url=config.siliconflow_base_url ) def identify_topic_llm(self, text: str) -> Optional[str]: diff --git a/src/plugins/chat/utils.py b/src/plugins/chat/utils.py index 4e2235805..91018ee08 100644 --- a/src/plugins/chat/utils.py +++ b/src/plugins/chat/utils.py @@ -4,11 +4,15 @@ from typing import List from .message import Message import requests import numpy as np -from .config import llm_config, global_config +from .config import global_config import re from typing import Dict from collections import Counter import math +from nonebot import get_driver + +driver = get_driver() +config = driver.config def combine_messages(messages: List[Message]) -> str: @@ -64,7 +68,7 @@ def get_embedding(text): "encoding_format": "float" } headers = { - "Authorization": f"Bearer {llm_config.SILICONFLOW_API_KEY}", + "Authorization": f"Bearer {config.siliconflow_key}", "Content-Type": "application/json" } diff --git a/src/plugins/chat/utils_image.py b/src/plugins/chat/utils_image.py index 097294010..68b2fa7f0 100644 --- a/src/plugins/chat/utils_image.py +++ b/src/plugins/chat/utils_image.py @@ -7,6 +7,10 @@ from ...common.database import Database import zlib # 用于 CRC32 import base64 from .config import global_config +from nonebot import get_driver + +driver = get_driver() +config = driver.config def storage_image(image_data: bytes,type: str, max_size: int = 200) -> bytes: @@ -37,12 +41,12 @@ def storage_compress_image(image_data: bytes, max_size: int = 200) -> bytes: # 连接数据库 db = Database( - host= os.getenv("MONGODB_HOST"), - port= int(os.getenv("MONGODB_PORT")), - db_name= os.getenv("DATABASE_NAME"), - username= os.getenv("MONGODB_USERNAME"), - password= os.getenv("MONGODB_PASSWORD"), - auth_source=os.getenv("MONGODB_AUTH_SOURCE") + host= config.mongodb_host, + port= int(config.mongodb_port), + db_name= config.database_name, + username= config.mongodb_username, + password= config.mongodb_password, + auth_source=config.mongodb_auth_source ) # 检查是否已存在相同哈希值的图片 diff --git a/src/plugins/chat/willing_manager.py b/src/plugins/chat/willing_manager.py index 8f3734a4f..e35743577 100644 --- a/src/plugins/chat/willing_manager.py +++ b/src/plugins/chat/willing_manager.py @@ -58,8 +58,8 @@ class WillingManager: if group_id in config.talk_frequency_down_groups: reply_probability = reply_probability / 3.5 - if is_mentioned_bot and user_id == int(964959351): - reply_probability = 1 + # if is_mentioned_bot and user_id == int(1026294844): + # reply_probability = 1 return reply_probability diff --git a/src/plugins/knowledege/knowledge_library.py b/src/plugins/knowledege/knowledge_library.py index cdb591dee..d8c2e1482 100644 --- a/src/plugins/knowledege/knowledge_library.py +++ b/src/plugins/knowledege/knowledge_library.py @@ -3,6 +3,10 @@ import sys import numpy as np import requests import time +from nonebot import get_driver + +driver = get_driver() +config = driver.config # 添加项目根目录到 Python 路径 root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../..")) @@ -13,12 +17,12 @@ from src.plugins.chat.config import llm_config # 直接配置数据库连接信息 Database.initialize( - host= os.getenv("MONGODB_HOST"), - port= int(os.getenv("MONGODB_PORT")), - db_name= os.getenv("DATABASE_NAME"), - username= os.getenv("MONGODB_USERNAME"), - password= os.getenv("MONGODB_PASSWORD"), - auth_source=os.getenv("MONGODB_AUTH_SOURCE") + host= config.mongodb_host, + port= int(config.mongodb_port), + db_name= config.database_name, + username= config.mongodb_username, + password= config.mongodb_password, + auth_source=config.mongodb_auth_source ) class KnowledgeLibrary: diff --git a/src/plugins/memory_system/draw_memory.py b/src/plugins/memory_system/draw_memory.py index a24f95a76..6b5dcd716 100644 --- a/src/plugins/memory_system/draw_memory.py +++ b/src/plugins/memory_system/draw_memory.py @@ -168,10 +168,12 @@ def main(): memory_graph.load_graph_from_db() # 展示两种不同的可视化方式 print("\n按连接数量着色的图谱：") - visualize_graph(memory_graph, color_by_memory=False) + # visualize_graph(memory_graph, color_by_memory=False) + visualize_graph_lite(memory_graph, color_by_memory=False) print("\n按记忆数量着色的图谱：") - visualize_graph(memory_graph, color_by_memory=True) + # visualize_graph(memory_graph, color_by_memory=True) + visualize_graph_lite(memory_graph, color_by_memory=True) # memory_graph.save_graph_to_db() @@ -262,7 +264,89 @@ def visualize_graph(memory_graph: Memory_graph, color_by_memory: bool = False): plt.title(title, fontsize=16, fontfamily='SimHei') plt.show() -if __name__ == "__main__": - main() +def visualize_graph_lite(memory_graph: Memory_graph, color_by_memory: bool = False): + # 设置中文字体 + plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签 + plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号 + G = memory_graph.G + + # 创建一个新图用于可视化 + H = G.copy() + + # 移除只有一条记忆的节点和连接数少于3的节点 + nodes_to_remove = [] + for node in H.nodes(): + memory_items = H.nodes[node].get('memory_items', []) + memory_count = len(memory_items) if isinstance(memory_items, list) else (1 if memory_items else 0) + degree = H.degree(node) + if memory_count <= 2 or degree <= 2: + nodes_to_remove.append(node) + + H.remove_nodes_from(nodes_to_remove) + + # 如果过滤后没有节点，则返回 + if len(H.nodes()) == 0: + print("过滤后没有符合条件的节点可显示") + return + + # 保存图到本地 + nx.write_gml(H, "memory_graph.gml") # 保存为 GML 格式 + + # 根据连接条数或记忆数量设置节点颜色 + node_colors = [] + nodes = list(H.nodes()) # 获取图中实际的节点列表 + + if color_by_memory: + # 计算每个节点的记忆数量 + memory_counts = [] + for node in nodes: + memory_items = H.nodes[node].get('memory_items', []) + if isinstance(memory_items, list): + count = len(memory_items) + else: + count = 1 if memory_items else 0 + memory_counts.append(count) + max_memories = max(memory_counts) if memory_counts else 1 + + for count in memory_counts: + # 使用不同的颜色方案：红色表示记忆多，蓝色表示记忆少 + if max_memories > 0: + intensity = min(1.0, count / max_memories) + color = (intensity, 0, 1.0 - intensity) # 从蓝色渐变到红色 + else: + color = (0, 0, 1) # 如果没有记忆，则为蓝色 + node_colors.append(color) + else: + # 使用原来的连接数量着色方案 + max_degree = max(H.degree(), key=lambda x: x[1])[1] if H.degree() else 1 + for node in nodes: + degree = H.degree(node) + if max_degree > 0: + red = min(1.0, degree / max_degree) + blue = 1.0 - red + color = (red, 0, blue) + else: + color = (0, 0, 1) + node_colors.append(color) + + # 绘制图形 + plt.figure(figsize=(12, 8)) + pos = nx.spring_layout(H, k=1, iterations=50) + nx.draw(H, pos, + with_labels=True, + node_color=node_colors, + node_size=2000, + font_size=10, + font_family='SimHei', + font_weight='bold') + + title = '记忆图谱可视化 - ' + ('按记忆数量着色' if color_by_memory else '按连接数量着色') + plt.title(title, fontsize=16, fontfamily='SimHei') + plt.show() + + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/src/plugins/memory_system/llm_module.py b/src/plugins/memory_system/llm_module.py index 4c9174dac..bd7f60dc3 100644 --- a/src/plugins/memory_system/llm_module.py +++ b/src/plugins/memory_system/llm_module.py @@ -2,14 +2,18 @@ import os import requests from typing import Tuple, Union import time +from nonebot import get_driver + +driver = get_driver() +config = driver.config class LLMModel: # def __init__(self, model_name="deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", **kwargs): def __init__(self, model_name="Pro/deepseek-ai/DeepSeek-V3", **kwargs): self.model_name = model_name self.params = kwargs - self.api_key = os.getenv("SILICONFLOW_KEY") - self.base_url = os.getenv("SILICONFLOW_BASE_URL") + self.api_key = config.siliconflow_key + self.base_url = config.siliconflow_base_url def generate_response(self, prompt: str) -> Tuple[str, str]: """根据输入的提示生成模型的响应""" diff --git a/src/plugins/memory_system/llm_module_memory_make.py b/src/plugins/memory_system/llm_module_memory_make.py index 07cecae9d..04ab6dbc6 100644 --- a/src/plugins/memory_system/llm_module_memory_make.py +++ b/src/plugins/memory_system/llm_module_memory_make.py @@ -3,14 +3,18 @@ import requests from typing import Tuple, Union import time from ..chat.config import BotConfig +from nonebot import get_driver + +driver = get_driver() +config = driver.config class LLMModel: # def __init__(self, model_name="deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", **kwargs): def __init__(self, model_name="Pro/deepseek-ai/DeepSeek-V3", **kwargs): self.model_name = model_name self.params = kwargs - self.api_key = os.getenv("SILICONFLOW_KEY") - self.base_url = os.getenv("SILICONFLOW_BASE_URL") + self.api_key = config.siliconflow_key + self.base_url = config.siliconflow_base_url if not self.api_key or not self.base_url: raise ValueError("环境变量未正确加载：SILICONFLOW_KEY 或 SILICONFLOW_BASE_URL 未设置") diff --git a/src/plugins/memory_system/memory.py b/src/plugins/memory_system/memory.py index 4deb28d63..f2b162afb 100644 --- a/src/plugins/memory_system/memory.py +++ b/src/plugins/memory_system/memory.py @@ -198,8 +198,6 @@ class Hippocampus: time_frequency = {'near':1,'mid':2,'far':2} memory_sample = self.get_memory_sample(chat_size,time_frequency) # print(f"\033[1;32m[记忆构建]\033[0m 获取记忆样本: {memory_sample}") - - for i, input_text in enumerate(memory_sample, 1): #加载进度可视化 progress = (i / len(memory_sample)) * 100 @@ -207,24 +205,25 @@ class Hippocampus: filled_length = int(bar_length * i // len(memory_sample)) bar = '█' * filled_length + '-' * (bar_length - filled_length) print(f"\n进度: [{bar}] {progress:.1f}% ({i}/{len(memory_sample)})") - - # 生成压缩后记忆 - first_memory = set() - first_memory = self.memory_compress(input_text, 2.5) - # 延时防止访问超频 - # time.sleep(5) - #将记忆加入到图谱中 - for topic, memory in first_memory: - topics = segment_text(topic) - print(f"\033[1;34m话题\033[0m: {topic},节点: {topics}, 记忆: {memory}") - for split_topic in topics: - self.memory_graph.add_dot(split_topic,memory) - for split_topic in topics: - for other_split_topic in topics: - if split_topic != other_split_topic: - self.memory_graph.connect_dot(split_topic, other_split_topic) - - self.memory_graph.save_graph_to_db() + if input_text: + # 生成压缩后记忆 + first_memory = set() + first_memory = self.memory_compress(input_text, 2.5) + # 延时防止访问超频 + # time.sleep(5) + #将记忆加入到图谱中 + for topic, memory in first_memory: + topics = segment_text(topic) + print(f"\033[1;34m话题\033[0m: {topic},节点: {topics}, 记忆: {memory}") + for split_topic in topics: + self.memory_graph.add_dot(split_topic,memory) + for split_topic in topics: + for other_split_topic in topics: + if split_topic != other_split_topic: + self.memory_graph.connect_dot(split_topic, other_split_topic) + else: + print(f"空消息 跳过") + self.memory_graph.save_graph_to_db() def memory_compress(self, input_text, rate=1): information_content = calculate_information_content(input_text) @@ -260,16 +259,19 @@ def topic_what(text, topic): return prompt - +from nonebot import get_driver +driver = get_driver() +config = driver.config + start_time = time.time() Database.initialize( - host= os.getenv("MONGODB_HOST"), - port= int(os.getenv("MONGODB_PORT")), - db_name= os.getenv("DATABASE_NAME"), - username= os.getenv("MONGODB_USERNAME"), - password= os.getenv("MONGODB_PASSWORD"), - auth_source=os.getenv("MONGODB_AUTH_SOURCE") + host= config.mongodb_host, + port= int(config.mongodb_port), + db_name= config.database_name, + username= config.mongodb_username, + password= config.mongodb_password, + auth_source=config.mongodb_auth_source ) #创建记忆图 memory_graph = Memory_graph() diff --git a/src/plugins/memory_system/memory_make.py b/src/plugins/memory_system/memory_make.py index 0c1c0d219..7fb8af15a 100644 --- a/src/plugins/memory_system/memory_make.py +++ b/src/plugins/memory_system/memory_make.py @@ -13,7 +13,38 @@ import os sys.path.append("C:/GitHub/MaiMBot") # 添加项目根目录到 Python 路径 from src.common.database import Database # 使用正确的导入语法 from src.plugins.memory_system.llm_module import LLMModel - + +def calculate_information_content(text): + """计算文本的信息量（熵）""" + # 统计字符频率 + char_count = Counter(text) + total_chars = len(text) + + # 计算熵 + entropy = 0 + for count in char_count.values(): + probability = count / total_chars + entropy -= probability * math.log2(probability) + + return entropy + +def get_cloest_chat_from_db(db, length: int, timestamp: str): + """从数据库中获取最接近指定时间戳的聊天记录""" + chat_text = '' + closest_record = db.db.messages.find_one({"time": {"$lte": timestamp}}, sort=[('time', -1)]) + + if closest_record: + closest_time = closest_record['time'] + group_id = closest_record['group_id'] # 获取groupid + # 获取该时间戳之后的length条消息，且groupid相同 + chat_record = list(db.db.messages.find({"time": {"$gt": closest_time}, "group_id": group_id}).sort('time', 1).limit(length)) + for record in chat_record: + time_str = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(int(record['time']))) + chat_text += f'[{time_str}] {record["user_nickname"] or "用户" + str(record["user_id"])}: {record["processed_plain_text"]}\n' + return chat_text + + return '' + class Memory_graph: def __init__(self): self.G = nx.Graph() # 使用 networkx 的图结构 @@ -102,7 +133,8 @@ class Memory_graph: # 从数据库中根据时间戳获取离其最近的聊天记录 chat_text = '' closest_record = self.db.db.messages.find_one({"time": {"$lte": timestamp}}, sort=[('time', -1)]) # 调试输出 - print(f"距离time最近的消息时间: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(int(closest_record['time'])))}") + + # print(f"距离time最近的消息时间: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(int(closest_record['time'])))}") if closest_record: closest_time = closest_record['time'] @@ -110,8 +142,9 @@ class Memory_graph: # 获取该时间戳之后的length条消息，且groupid相同 chat_record = list(self.db.db.messages.find({"time": {"$gt": closest_time}, "group_id": group_id}).sort('time', 1).limit(length)) for record in chat_record: - time_str = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(int(record['time']))) - chat_text += f'[{time_str}] {record["user_nickname"] or "用户" + str(record["user_id"])}: {record["processed_plain_text"]}\n' # 添加发送者和时间信息 + if record: + time_str = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(int(record['time']))) + chat_text += f'[{time_str}] {record["user_nickname"] or "用户" + str(record["user_id"])}: {record["processed_plain_text"]}\n' # 添加发送者和时间信息 return chat_text return [] # 如果没有找到记录，返回空列表 @@ -187,155 +220,80 @@ class Memory_graph: for edge in edges: self.G.add_edge(edge['source'], edge['target'], num=edge.get('num', 1)) -def calculate_information_content(text): - - """计算文本的信息量（熵）""" - # 统计字符频率 - char_count = Counter(text) - total_chars = len(text) - - # 计算熵 - entropy = 0 - for count in char_count.values(): - probability = count / total_chars - entropy -= probability * math.log2(probability) - - return entropy - - -# Database.initialize( -# global_config.MONGODB_HOST, -# global_config.MONGODB_PORT, -# global_config.DATABASE_NAME -# ) -# memory_graph = Memory_graph() - -# llm_model = LLMModel() -# llm_model_small = LLMModel(model_name="deepseek-ai/DeepSeek-V2.5") - -# memory_graph.load_graph_from_db() - - - -def main(): - # 初始化数据库 - Database.initialize( - host= os.getenv("MONGODB_HOST"), - port= int(os.getenv("MONGODB_PORT")), - db_name= os.getenv("DATABASE_NAME"), - username= os.getenv("MONGODB_USERNAME"), - password= os.getenv("MONGODB_PASSWORD"), - auth_source=os.getenv("MONGODB_AUTH_SOURCE") - ) - - memory_graph = Memory_graph() - # 创建LLM模型实例 - llm_model = LLMModel() - llm_model_small = LLMModel(model_name="deepseek-ai/DeepSeek-V2.5") - - # 使用当前时间戳进行测试 - current_timestamp = datetime.datetime.now().timestamp() - chat_text = [] - - chat_size =25 - - for _ in range(30): # 循环10次 - random_time = current_timestamp - random.randint(1, 3600*10) # 随机时间 - print(f"随机时间戳对应的时间: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(random_time))}") - chat_ = memory_graph.get_random_chat_from_db(chat_size, random_time) - chat_text.append(chat_) # 拼接所有text - # time.sleep(1) - - - - for i, input_text in enumerate(chat_text, 1): +# 海马体 +class Hippocampus: + def __init__(self,memory_graph:Memory_graph): + self.memory_graph = memory_graph + self.llm_model = LLMModel() + self.llm_model_small = LLMModel(model_name="deepseek-ai/DeepSeek-V2.5") - progress = (i / len(chat_text)) * 100 - bar_length = 30 - filled_length = int(bar_length * i // len(chat_text)) - bar = '█' * filled_length + '-' * (bar_length - filled_length) - print(f"\n进度: [{bar}] {progress:.1f}% ({i}/{len(chat_text)})") + def get_memory_sample(self,chat_size=20,time_frequency:dict={'near':2,'mid':4,'far':3}): + current_timestamp = datetime.datetime.now().timestamp() + chat_text = [] + #短期：1h 中期：4h 长期：24h + for _ in range(time_frequency.get('near')): # 循环10次 + random_time = current_timestamp - random.randint(1, 3600) # 随机时间 + chat_ = get_cloest_chat_from_db(db=self.memory_graph.db, length=chat_size, timestamp=random_time) + chat_text.append(chat_) + for _ in range(time_frequency.get('mid')): # 循环10次 + random_time = current_timestamp - random.randint(3600, 3600*4) # 随机时间 + chat_ = get_cloest_chat_from_db(db=self.memory_graph.db, length=chat_size, timestamp=random_time) + chat_text.append(chat_) + for _ in range(time_frequency.get('far')): # 循环10次 + random_time = current_timestamp - random.randint(3600*4, 3600*24) # 随机时间 + chat_ = get_cloest_chat_from_db(db=self.memory_graph.db, length=chat_size, timestamp=random_time) + chat_text.append(chat_) + return chat_text + + def build_memory(self,chat_size=12): + #最近消息获取频率 + time_frequency = {'near':1,'mid':2,'far':2} + memory_sample = self.get_memory_sample(chat_size,time_frequency) - # print(input_text) - first_memory = set() - first_memory = memory_compress(input_text, llm_model_small, llm_model_small, rate=2.5) - # time.sleep(5) - - #将记忆加入到图谱中 - for topic, memory in first_memory: - topics = segment_text(topic) - print(f"\033[1;34m话题\033[0m: {topic},节点: {topics}, 记忆: {memory}") - for split_topic in topics: - memory_graph.add_dot(split_topic,memory) - for split_topic in topics: - for other_split_topic in topics: - if split_topic != other_split_topic: - memory_graph.connect_dot(split_topic, other_split_topic) - - # memory_graph.store_memory() - - # 展示两种不同的可视化方式 - print("\n按连接数量着色的图谱：") - visualize_graph(memory_graph, color_by_memory=False) - - print("\n按记忆数量着色的图谱：") - visualize_graph(memory_graph, color_by_memory=True) - - memory_graph.save_graph_to_db() - # memory_graph.load_graph_from_db() - - while True: - query = input("请输入新的查询概念（输入'退出'以结束）：") - if query.lower() == '退出': - break - items_list = memory_graph.get_related_item(query) - if items_list: - # print(items_list) - for memory_item in items_list: - print(memory_item) - else: - print("未找到相关记忆。") + #加载进度可视化 + for i, input_text in enumerate(memory_sample, 1): + progress = (i / len(memory_sample)) * 100 + bar_length = 30 + filled_length = int(bar_length * i // len(memory_sample)) + bar = '█' * filled_length + '-' * (bar_length - filled_length) + print(f"\n进度: [{bar}] {progress:.1f}% ({i}/{len(memory_sample)})") + # print(f"第{i}条消息: {input_text}") + if input_text: + # 生成压缩后记忆 + first_memory = set() + first_memory = self.memory_compress(input_text, 2.5) + #将记忆加入到图谱中 + for topic, memory in first_memory: + topics = segment_text(topic) + print(f"\033[1;34m话题\033[0m: {topic},节点: {topics}, 记忆: {memory}") + for split_topic in topics: + self.memory_graph.add_dot(split_topic,memory) + for split_topic in topics: + for other_split_topic in topics: + if split_topic != other_split_topic: + self.memory_graph.connect_dot(split_topic, other_split_topic) + else: + print(f"空消息 跳过") - while True: - query = input("请输入问题：") - - if query.lower() == '退出': - break - - topic_prompt = find_topic(query, 3) - topic_response = llm_model.generate_response(topic_prompt) + self.memory_graph.save_graph_to_db() + + def memory_compress(self, input_text, rate=1): + information_content = calculate_information_content(input_text) + print(f"文本的信息量（熵）: {information_content:.4f} bits") + topic_num = max(1, min(5, int(information_content * rate / 4))) + topic_prompt = find_topic(input_text, topic_num) + topic_response = self.llm_model.generate_response(topic_prompt) # 检查 topic_response 是否为元组 if isinstance(topic_response, tuple): topics = topic_response[0].split(",") # 假设第一个元素是我们需要的字符串 else: topics = topic_response.split(",") - print(topics) - - for keyword in topics: - items_list = memory_graph.get_related_item(keyword) - if items_list: - print(items_list) - -def memory_compress(input_text, llm_model, llm_model_small, rate=1): - information_content = calculate_information_content(input_text) - print(f"文本的信息量（熵）: {information_content:.4f} bits") - topic_num = max(1, min(5, int(information_content * rate / 4))) - print(topic_num) - topic_prompt = find_topic(input_text, topic_num) - topic_response = llm_model.generate_response(topic_prompt) - # 检查 topic_response 是否为元组 - if isinstance(topic_response, tuple): - topics = topic_response[0].split(",") # 假设第一个元素是我们需要的字符串 - else: - topics = topic_response.split(",") - print(topics) - compressed_memory = set() - for topic in topics: - topic_what_prompt = topic_what(input_text,topic) - topic_what_response = llm_model_small.generate_response(topic_what_prompt) - compressed_memory.add((topic.strip(), topic_what_response[0])) # 将话题和记忆作为元组存储 - return compressed_memory - + compressed_memory = set() + for topic in topics: + topic_what_prompt = topic_what(input_text,topic) + topic_what_response = self.llm_model_small.generate_response(topic_what_prompt) + compressed_memory.add((topic.strip(), topic_what_response[0])) # 将话题和记忆作为元组存储 + return compressed_memory def segment_text(text): seg_text = list(jieba.cut(text)) @@ -356,18 +314,37 @@ def visualize_graph(memory_graph: Memory_graph, color_by_memory: bool = False): G = memory_graph.G + # 创建一个新图用于可视化 + H = G.copy() + + # 移除只有一条记忆的节点和连接数少于3的节点 + nodes_to_remove = [] + for node in H.nodes(): + memory_items = H.nodes[node].get('memory_items', []) + memory_count = len(memory_items) if isinstance(memory_items, list) else (1 if memory_items else 0) + degree = H.degree(node) + if memory_count <= 1 or degree <= 2: + nodes_to_remove.append(node) + + H.remove_nodes_from(nodes_to_remove) + + # 如果过滤后没有节点，则返回 + if len(H.nodes()) == 0: + print("过滤后没有符合条件的节点可显示") + return + # 保存图到本地 - nx.write_gml(G, "memory_graph.gml") # 保存为 GML 格式 + nx.write_gml(H, "memory_graph.gml") # 保存为 GML 格式 # 根据连接条数或记忆数量设置节点颜色 node_colors = [] - nodes = list(G.nodes()) # 获取图中实际的节点列表 + nodes = list(H.nodes()) # 获取图中实际的节点列表 if color_by_memory: # 计算每个节点的记忆数量 memory_counts = [] for node in nodes: - memory_items = G.nodes[node].get('memory_items', []) + memory_items = H.nodes[node].get('memory_items', []) if isinstance(memory_items, list): count = len(memory_items) else: @@ -385,9 +362,9 @@ def visualize_graph(memory_graph: Memory_graph, color_by_memory: bool = False): node_colors.append(color) else: # 使用原来的连接数量着色方案 - max_degree = max(G.degree(), key=lambda x: x[1])[1] if G.degree() else 1 + max_degree = max(H.degree(), key=lambda x: x[1])[1] if H.degree() else 1 for node in nodes: - degree = G.degree(node) + degree = H.degree(node) if max_degree > 0: red = min(1.0, degree / max_degree) blue = 1.0 - red @@ -398,8 +375,8 @@ def visualize_graph(memory_graph: Memory_graph, color_by_memory: bool = False): # 绘制图形 plt.figure(figsize=(12, 8)) - pos = nx.spring_layout(G, k=1, iterations=50) - nx.draw(G, pos, + pos = nx.spring_layout(H, k=1, iterations=50) + nx.draw(H, pos, with_labels=True, node_color=node_colors, node_size=2000, @@ -411,6 +388,71 @@ def visualize_graph(memory_graph: Memory_graph, color_by_memory: bool = False): plt.title(title, fontsize=16, fontfamily='SimHei') plt.show() +def main(): + # 初始化数据库 + Database.initialize( + host= os.getenv("MONGODB_HOST"), + port= int(os.getenv("MONGODB_PORT")), + db_name= os.getenv("DATABASE_NAME"), + username= os.getenv("MONGODB_USERNAME"), + password= os.getenv("MONGODB_PASSWORD"), + auth_source=os.getenv("MONGODB_AUTH_SOURCE") + ) + + start_time = time.time() + + # 创建记忆图 + memory_graph = Memory_graph() + # 加载数据库中存储的记忆图 + memory_graph.load_graph_from_db() + # 创建海马体 + hippocampus = Hippocampus(memory_graph) + + end_time = time.time() + print(f"\033[32m[加载海马体耗时: {end_time - start_time:.2f} 秒]\033[0m") + + # 构建记忆 + hippocampus.build_memory(chat_size=25) + + # 展示两种不同的可视化方式 + print("\n按连接数量着色的图谱：") + visualize_graph(memory_graph, color_by_memory=False) + + print("\n按记忆数量着色的图谱：") + visualize_graph(memory_graph, color_by_memory=True) + + # 交互式查询 + while True: + query = input("请输入新的查询概念（输入'退出'以结束）：") + if query.lower() == '退出': + break + items_list = memory_graph.get_related_item(query) + if items_list: + for memory_item in items_list: + print(memory_item) + else: + print("未找到相关记忆。") + + while True: + query = input("请输入问题：") + + if query.lower() == '退出': + break + + topic_prompt = find_topic(query, 3) + topic_response = hippocampus.llm_model.generate_response(topic_prompt) + # 检查 topic_response 是否为元组 + if isinstance(topic_response, tuple): + topics = topic_response[0].split(",") # 假设第一个元素是我们需要的字符串 + else: + topics = topic_response.split(",") + print(topics) + + for keyword in topics: + items_list = memory_graph.get_related_item(keyword) + if items_list: + print(items_list) + if __name__ == "__main__": main() diff --git a/src/plugins/schedule/schedule_generator.py b/src/plugins/schedule/schedule_generator.py index 097a89de8..a33c4b279 100644 --- a/src/plugins/schedule/schedule_generator.py +++ b/src/plugins/schedule/schedule_generator.py @@ -4,14 +4,19 @@ from typing import List, Dict from .schedule_llm_module import LLMModel from ...common.database import Database # 使用正确的导入语法 from ..chat.config import global_config +from nonebot import get_driver + +driver = get_driver() +config = driver.config + Database.initialize( - host= os.getenv("MONGODB_HOST"), - port= int(os.getenv("MONGODB_PORT")), - db_name= os.getenv("DATABASE_NAME"), - username= os.getenv("MONGODB_USERNAME"), - password= os.getenv("MONGODB_PASSWORD"), - auth_source=os.getenv("MONGODB_AUTH_SOURCE") + host= config.mongodb_host, + port= int(config.mongodb_port), + db_name= config.database_name, + username= config.mongodb_username, + password= config.mongodb_password, + auth_source=config.mongodb_auth_source ) class ScheduleGenerator: diff --git a/src/plugins/schedule/schedule_llm_module.py b/src/plugins/schedule/schedule_llm_module.py index ebf039c7e..408e7d546 100644 --- a/src/plugins/schedule/schedule_llm_module.py +++ b/src/plugins/schedule/schedule_llm_module.py @@ -1,20 +1,24 @@ import os import requests from typing import Tuple, Union +from nonebot import get_driver + +driver = get_driver() +config = driver.config class LLMModel: # def __init__(self, model_name="deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", **kwargs): def __init__(self, model_name="Pro/deepseek-ai/DeepSeek-R1",api_using=None, **kwargs): if api_using == "deepseek": - self.api_key = os.getenv("DEEP_SEEK_KEY") - self.base_url = os.getenv("DEEP_SEEK_BASE_URL") + self.api_key = config.deep_seek_key + self.base_url = config.deep_seek_base_url if model_name != "Pro/deepseek-ai/DeepSeek-R1": self.model_name = model_name else: self.model_name = "deepseek-reasoner" else: - self.api_key = os.getenv("SILICONFLOW_KEY") - self.base_url = os.getenv("SILICONFLOW_BASE_URL") + self.api_key = config.siliconflow_key + self.base_url = config.siliconflow_base_url self.model_name = model_name self.params = kwargs From 10c3f907209c5c0ff6d7faf18d2434f16f6064d5 Mon Sep 17 00:00:00 2001 From: SengokuCola <1026294844@qq.com> Date: Sun, 2 Mar 2025 15:04:23 +0800 Subject: [PATCH 03/26] Update README.md --- README.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 1310d4879..00afd83e8 100644 --- a/README.md +++ b/README.md @@ -75,8 +75,9 @@ NAPCAT_UID=$(id -u) NAPCAT_GID=$(id -g) docker compose restart - 在Napcat的网络设置中添加ws反向代理:ws://localhost:8080/onebot/v11/ws 4. **配置文件设置** - - 将.env文件打开，填上你的apikey（硅基流动或deepseekapi） - - 将bot_config.toml文件打开，并填写相关内容，不然无法正常运行 + - 修改.env的 变量值为 prod + - 将.env.prod文件打开，填上你的apikey（硅基流动或deepseekapi） + - 将bot_config_toml改名为bot_config.toml，打开并填写相关内容，不然无法正常运行 #### .env 文件配置说明 ```ini From b98314da4fca0181a53049c406fb409a72a3d893 Mon Sep 17 00:00:00 2001 From: SengokuCola <1026294844@qq.com> Date: Sun, 2 Mar 2025 18:36:36 +0800 Subject: [PATCH 04/26] =?UTF-8?q?v0.3.3=20=E5=BC=82=E6=AD=A5=E5=A4=84?= =?UTF-8?q?=E7=90=86=E8=AE=B0=E5=BF=86=EF=BC=8C=E4=BF=AE=E5=A4=8D=E4=BA=86?= =?UTF-8?q?GUI?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/gui/reasoning_gui.py | 34 ++++++++++++-- src/plugins/chat/__init__.py | 2 +- src/plugins/chat/config.py | 1 + src/plugins/chat/prompt_builder.py | 11 +++-- src/plugins/memory_system/llm_module.py | 42 +++++++++--------- .../memory_system/llm_module_memory_make.py | 40 +++++++++-------- src/plugins/memory_system/memory.py | 44 +++++++++---------- src/plugins/schedule/schedule_llm_module.py | 26 ++++++----- 8 files changed, 116 insertions(+), 84 deletions(-) diff --git a/src/gui/reasoning_gui.py b/src/gui/reasoning_gui.py index f12b59798..61fb34560 100644 --- a/src/gui/reasoning_gui.py +++ b/src/gui/reasoning_gui.py @@ -7,6 +7,23 @@ import threading import queue import sys import os +from dotenv import load_dotenv + +# 获取当前文件的目录 +current_dir = os.path.dirname(os.path.abspath(__file__)) +# 获取项目根目录 +root_dir = os.path.abspath(os.path.join(current_dir, '..', '..')) + +# 加载环境变量 +if os.path.exists(os.path.join(root_dir, '.env.dev')): + load_dotenv(os.path.join(root_dir, '.env.dev')) + print("成功加载开发环境配置") +elif os.path.exists(os.path.join(root_dir, '.env.prod')): + load_dotenv(os.path.join(root_dir, '.env.prod')) + print("成功加载生产环境配置") +else: + print("未找到环境配置文件") + sys.exit(1) from pymongo import MongoClient from typing import Optional @@ -14,14 +31,23 @@ from typing import Optional class Database: _instance: Optional["Database"] = None - def __init__(self, host: str, port: int, db_name: str): - self.client = MongoClient(host, port) + def __init__(self, host: str, port: int, db_name: str, username: str = None, password: str = None, auth_source: str = None): + if username and password: + self.client = MongoClient( + host=host, + port=port, + username=username, + password=password, + authSource=auth_source or 'admin' + ) + else: + self.client = MongoClient(host, port) self.db = self.client[db_name] @classmethod - def initialize(cls, host: str, port: int, db_name: str) -> "Database": + def initialize(cls, host: str, port: int, db_name: str, username: str = None, password: str = None, auth_source: str = None) -> "Database": if cls._instance is None: - cls._instance = cls(host, port, db_name) + cls._instance = cls(host, port, db_name, username, password, auth_source) return cls._instance @classmethod diff --git a/src/plugins/chat/__init__.py b/src/plugins/chat/__init__.py index 0f81d30d6..3fdcec922 100644 --- a/src/plugins/chat/__init__.py +++ b/src/plugins/chat/__init__.py @@ -90,7 +90,7 @@ async def monitor_relationships(): async def build_memory_task(): """每30秒执行一次记忆构建""" print("\033[1;32m[记忆构建]\033[0m 开始构建记忆...") - hippocampus.build_memory(chat_size=12) + await hippocampus.build_memory(chat_size=30) print("\033[1;32m[记忆构建]\033[0m 记忆构建完成") diff --git a/src/plugins/chat/config.py b/src/plugins/chat/config.py index 0232219f8..dc7c14203 100644 --- a/src/plugins/chat/config.py +++ b/src/plugins/chat/config.py @@ -136,3 +136,4 @@ llm_config.DEEP_SEEK_BASE_URL = config.deep_seek_base_url if not global_config.enable_advance_output: # logger.remove() pass + diff --git a/src/plugins/chat/prompt_builder.py b/src/plugins/chat/prompt_builder.py index ba0e9b4cc..01fe3e82d 100644 --- a/src/plugins/chat/prompt_builder.py +++ b/src/plugins/chat/prompt_builder.py @@ -72,12 +72,15 @@ class PromptBuilder: # print(f"\033[1;32m[前额叶]\033[0m 合并所有需要的记忆2: {list(overlapping_second_layer)}") # 使用集合去重 - all_memories = list(set(all_first_layer_items) | set(overlapping_second_layer)) + # 从每个来源随机选择2条记忆（如果有的话） + selected_first_layer = random.sample(all_first_layer_items, min(2, len(all_first_layer_items))) if all_first_layer_items else [] + selected_second_layer = random.sample(list(overlapping_second_layer), min(2, len(overlapping_second_layer))) if overlapping_second_layer else [] + + # 合并并去重 + all_memories = list(set(selected_first_layer + selected_second_layer)) if all_memories: print(f"\033[1;32m[前额叶]\033[0m 合并所有需要的记忆: {all_memories}") - - if all_memories: # 只在列表非空时选择随机项 - random_item = choice(all_memories) + random_item = " ".join(all_memories) memory_prompt = f"看到这些聊天，你想起来{random_item}\n" else: memory_prompt = "" # 如果没有记忆，则返回空字符串 diff --git a/src/plugins/memory_system/llm_module.py b/src/plugins/memory_system/llm_module.py index bd7f60dc3..34e4e4af1 100644 --- a/src/plugins/memory_system/llm_module.py +++ b/src/plugins/memory_system/llm_module.py @@ -3,6 +3,8 @@ import requests from typing import Tuple, Union import time from nonebot import get_driver +import aiohttp +import asyncio driver = get_driver() config = driver.config @@ -15,7 +17,7 @@ class LLMModel: self.api_key = config.siliconflow_key self.base_url = config.siliconflow_base_url - def generate_response(self, prompt: str) -> Tuple[str, str]: + async def generate_response(self, prompt: str) -> Tuple[str, str]: """根据输入的提示生成模型的响应""" headers = { "Authorization": f"Bearer {self.api_key}", @@ -34,32 +36,32 @@ class LLMModel: api_url = f"{self.base_url.rstrip('/')}/chat/completions" max_retries = 3 - base_wait_time = 15 # 基础等待时间（秒） + base_wait_time = 15 for retry in range(max_retries): try: - response = requests.post(api_url, headers=headers, json=data) + async with aiohttp.ClientSession() as session: + async with session.post(api_url, headers=headers, json=data) as response: + if response.status == 429: + wait_time = base_wait_time * (2 ** retry) # 指数退避 + print(f"遇到请求限制(429)，等待{wait_time}秒后重试...") + await asyncio.sleep(wait_time) + continue + + response.raise_for_status() # 检查其他响应状态 + + result = await response.json() + if "choices" in result and len(result["choices"]) > 0: + content = result["choices"][0]["message"]["content"] + reasoning_content = result["choices"][0]["message"].get("reasoning_content", "") + return content, reasoning_content + return "没有返回结果", "" - if response.status_code == 429: - wait_time = base_wait_time * (2 ** retry) # 指数退避 - print(f"遇到请求限制(429)，等待{wait_time}秒后重试...") - time.sleep(wait_time) - continue - - response.raise_for_status() # 检查其他响应状态 - - result = response.json() - if "choices" in result and len(result["choices"]) > 0: - content = result["choices"][0]["message"]["content"] - reasoning_content = result["choices"][0]["message"].get("reasoning_content", "") - return content, reasoning_content - return "没有返回结果", "" - - except requests.exceptions.RequestException as e: + except Exception as e: if retry < max_retries - 1: # 如果还有重试机会 wait_time = base_wait_time * (2 ** retry) print(f"请求失败，等待{wait_time}秒后重试... 错误: {str(e)}") - time.sleep(wait_time) + await asyncio.sleep(wait_time) else: return f"请求失败: {str(e)}", "" diff --git a/src/plugins/memory_system/llm_module_memory_make.py b/src/plugins/memory_system/llm_module_memory_make.py index 04ab6dbc6..5b01a2917 100644 --- a/src/plugins/memory_system/llm_module_memory_make.py +++ b/src/plugins/memory_system/llm_module_memory_make.py @@ -4,6 +4,8 @@ from typing import Tuple, Union import time from ..chat.config import BotConfig from nonebot import get_driver +import aiohttp +import asyncio driver = get_driver() config = driver.config @@ -21,7 +23,7 @@ class LLMModel: print(f"API URL: {self.base_url}") # 打印 base_url 用于调试 - def generate_response(self, prompt: str) -> Tuple[str, str]: + async def generate_response(self, prompt: str) -> Tuple[str, str]: """根据输入的提示生成模型的响应""" headers = { "Authorization": f"Bearer {self.api_key}", @@ -44,28 +46,28 @@ class LLMModel: for retry in range(max_retries): try: - response = requests.post(api_url, headers=headers, json=data) + async with aiohttp.ClientSession() as session: + async with session.post(api_url, headers=headers, json=data) as response: + if response.status == 429: + wait_time = base_wait_time * (2 ** retry) # 指数退避 + print(f"遇到请求限制(429)，等待{wait_time}秒后重试...") + await asyncio.sleep(wait_time) + continue + + response.raise_for_status() # 检查其他响应状态 + + result = await response.json() + if "choices" in result and len(result["choices"]) > 0: + content = result["choices"][0]["message"]["content"] + reasoning_content = result["choices"][0]["message"].get("reasoning_content", "") + return content, reasoning_content + return "没有返回结果", "" - if response.status_code == 429: - wait_time = base_wait_time * (2 ** retry) # 指数退避 - print(f"遇到请求限制(429)，等待{wait_time}秒后重试...") - time.sleep(wait_time) - continue - - response.raise_for_status() # 检查其他响应状态 - - result = response.json() - if "choices" in result and len(result["choices"]) > 0: - content = result["choices"][0]["message"]["content"] - reasoning_content = result["choices"][0]["message"].get("reasoning_content", "") - return content, reasoning_content - return "没有返回结果", "" - - except requests.exceptions.RequestException as e: + except Exception as e: if retry < max_retries - 1: # 如果还有重试机会 wait_time = base_wait_time * (2 ** retry) print(f"请求失败，等待{wait_time}秒后重试... 错误: {str(e)}") - time.sleep(wait_time) + await asyncio.sleep(wait_time) else: return f"请求失败: {str(e)}", "" diff --git a/src/plugins/memory_system/memory.py b/src/plugins/memory_system/memory.py index f2b162afb..cab41f903 100644 --- a/src/plugins/memory_system/memory.py +++ b/src/plugins/memory_system/memory.py @@ -193,7 +193,25 @@ class Hippocampus: chat_text.append(chat_) return chat_text - def build_memory(self,chat_size=12): + async def memory_compress(self, input_text, rate=1): + information_content = calculate_information_content(input_text) + print(f"文本的信息量（熵）: {information_content:.4f} bits") + topic_num = max(1, min(5, int(information_content * rate / 4))) + topic_prompt = find_topic(input_text, topic_num) + topic_response = await self.llm_model.generate_response(topic_prompt) + # 检查 topic_response 是否为元组 + if isinstance(topic_response, tuple): + topics = topic_response[0].split(",") # 假设第一个元素是我们需要的字符串 + else: + topics = topic_response.split(",") + compressed_memory = set() + for topic in topics: + topic_what_prompt = topic_what(input_text,topic) + topic_what_response = await self.llm_model_small.generate_response(topic_what_prompt) + compressed_memory.add((topic.strip(), topic_what_response[0])) # 将话题和记忆作为元组存储 + return compressed_memory + + async def build_memory(self,chat_size=12): #最近消息获取频率 time_frequency = {'near':1,'mid':2,'far':2} memory_sample = self.get_memory_sample(chat_size,time_frequency) @@ -208,9 +226,7 @@ class Hippocampus: if input_text: # 生成压缩后记忆 first_memory = set() - first_memory = self.memory_compress(input_text, 2.5) - # 延时防止访问超频 - # time.sleep(5) + first_memory = await self.memory_compress(input_text, 2.5) #将记忆加入到图谱中 for topic, memory in first_memory: topics = segment_text(topic) @@ -224,26 +240,6 @@ class Hippocampus: else: print(f"空消息 跳过") self.memory_graph.save_graph_to_db() - - def memory_compress(self, input_text, rate=1): - information_content = calculate_information_content(input_text) - print(f"文本的信息量（熵）: {information_content:.4f} bits") - topic_num = max(1, min(5, int(information_content * rate / 4))) - # print(topic_num) - topic_prompt = find_topic(input_text, topic_num) - topic_response = self.llm_model.generate_response(topic_prompt) - # 检查 topic_response 是否为元组 - if isinstance(topic_response, tuple): - topics = topic_response[0].split(",") # 假设第一个元素是我们需要的字符串 - else: - topics = topic_response.split(",") - # print(topics) - compressed_memory = set() - for topic in topics: - topic_what_prompt = topic_what(input_text,topic) - topic_what_response = self.llm_model_small.generate_response(topic_what_prompt) - compressed_memory.add((topic.strip(), topic_what_response[0])) # 将话题和记忆作为元组存储 - return compressed_memory def segment_text(text): diff --git a/src/plugins/schedule/schedule_llm_module.py b/src/plugins/schedule/schedule_llm_module.py index 408e7d546..cf88a8653 100644 --- a/src/plugins/schedule/schedule_llm_module.py +++ b/src/plugins/schedule/schedule_llm_module.py @@ -1,5 +1,6 @@ import os import requests +import aiohttp from typing import Tuple, Union from nonebot import get_driver @@ -22,7 +23,7 @@ class LLMModel: self.model_name = model_name self.params = kwargs - def generate_response(self, prompt: str) -> Tuple[str, str]: + async def generate_response(self, prompt: str) -> Tuple[str, str]: """根据输入的提示生成模型的响应""" headers = { "Authorization": f"Bearer {self.api_key}", @@ -41,17 +42,18 @@ class LLMModel: api_url = f"{self.base_url.rstrip('/')}/chat/completions" try: - response = requests.post(api_url, headers=headers, json=data) - response.raise_for_status() # 检查响应状态 - - result = response.json() - if "choices" in result and len(result["choices"]) > 0: - content = result["choices"][0]["message"]["content"] - reasoning_content = result["choices"][0]["message"].get("reasoning_content", "") - return content, reasoning_content # 返回内容和推理内容 - return "没有返回结果", "" # 返回两个值 - - except requests.exceptions.RequestException as e: + async with aiohttp.ClientSession() as session: + async with session.post(api_url, headers=headers, json=data) as response: + response.raise_for_status() # 检查响应状态 + + result = await response.json() + if "choices" in result and len(result["choices"]) > 0: + content = result["choices"][0]["message"]["content"] + reasoning_content = result["choices"][0]["message"].get("reasoning_content", "") + return content, reasoning_content # 返回内容和推理内容 + return "没有返回结果", "" # 返回两个值 + + except Exception as e: return f"请求失败: {str(e)}", "" # 返回错误信息和空字符串 # 示例用法 From a7e6bc14dc9a71ad7eaea0eaae9b950f5d2919cd Mon Sep 17 00:00:00 2001 From: NepPure Date: Sun, 2 Mar 2025 19:17:05 +0800 Subject: [PATCH 05/26] =?UTF-8?q?=E4=BF=AE=E5=A4=8D=E7=8E=AF=E5=A2=83?= =?UTF-8?q?=E5=8F=98=E9=87=8F=E4=BD=8D=E7=BD=AE=E5=B9=B6=E6=8C=82=E8=BD=BD?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Dockerfile | 3 +-- docker-compose.yml | 7 ++++--- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/Dockerfile b/Dockerfile index 111466ff3..6a7aa09c0 100644 --- a/Dockerfile +++ b/Dockerfile @@ -2,8 +2,7 @@ FROM nonebot/nb-cli:latest WORKDIR / COPY . /MaiMBot/ WORKDIR /MaiMBot -RUN mv config/env.example config/.env \ -&& mv config/bot_config_toml config/bot_config.toml +RUN mv config/bot_config_toml config/bot_config.toml RUN pip install --upgrade -r requirements.txt VOLUME [ "/MaiMBot/config" ] EXPOSE 8080 diff --git a/docker-compose.yml b/docker-compose.yml index 4c136b284..cfe787c04 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -27,7 +27,7 @@ services: - mongodb:/data/db - mongodbCONFIG:/data/configdb image: mongo:latest - + maimbot: container_name: maimbot environment: @@ -41,8 +41,8 @@ services: volumes: - maimbotCONFIG:/MaiMBot/config - maimbotDATA:/MaiMBot/data + - ./.env.prod:/MaiMBot/.env.prod image: sengokucola/maimbot:latest - volumes: maimbotCONFIG: @@ -51,4 +51,5 @@ volumes: napcatCONFIG: mongodb: mongodbCONFIG: - + + From 352a10d4ee53663b339cfdd607cb60b80f36e0d0 Mon Sep 17 00:00:00 2001 From: NepPure Date: Sun, 2 Mar 2025 19:27:53 +0800 Subject: [PATCH 06/26] add colorama --- requirements.txt | Bin 520 -> 540 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/requirements.txt b/requirements.txt index 3f0fe05ddd8064040ea79e51e0a7998bbc477374..11536d12ac13cd06045bcb620cb223d5e19ba7ec 100644 GIT binary patch delta 28 gcmeBRnZvSygGq#!fr}xTA)g@!h>I8!8FGOb08&^5U;qFB delta 7 OcmbQk(!sKUg9!i$Jptzc From c1f2b6a0eb942b0ae1f659dd4fb352e8fbae04f6 Mon Sep 17 00:00:00 2001 From: NepPure Date: Sun, 2 Mar 2025 19:36:04 +0800 Subject: [PATCH 07/26] =?UTF-8?q?#=20=E6=82=A8=E4=B8=8D=E5=BA=94=E8=AF=A5?= =?UTF-8?q?=E4=BF=AE=E6=94=B9=E9=BB=98=E8=AE=A4=E5=80=BC=EF=BC=8C=E8=BF=99?= =?UTF-8?q?=E4=B8=AA=E6=96=87=E4=BB=B6=E8=A2=AB=E4=BB=93=E5=BA=93=E7=B4=A2?= =?UTF-8?q?=E5=BC=95=EF=BC=8C=E8=AF=B7=E4=BF=AE=E6=94=B9.env.prod?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .env | 25 +++++++++++++++++++++++++ .gitignore | 3 +-- 2 files changed, 26 insertions(+), 2 deletions(-) create mode 100644 .env diff --git a/.env b/.env new file mode 100644 index 000000000..2c8e76c18 --- /dev/null +++ b/.env @@ -0,0 +1,25 @@ +# 您不应该修改默认值，这个文件被仓库索引，请修改.env.prod +HOST=127.0.0.1 +PORT=8080 + +COMMAND_START=["/"] + +# 插件配置 +PLUGINS=["src2.plugins.chat"] + +# 默认配置 +MONGODB_HOST=127.0.0.1 +MONGODB_PORT=27017 +DATABASE_NAME=MegBot + +MONGODB_USERNAME = "" # 默认空值 +MONGODB_PASSWORD = "" # 默认空值 +MONGODB_AUTH_SOURCE = "" # 默认空值 + +#key and url +CHAT_ANY_WHERE_KEY= +SILICONFLOW_KEY= +CHAT_ANY_WHERE_BASE_URL=https://api.chatanywhere.tech/v1 +SILICONFLOW_BASE_URL=https://api.siliconflow.cn/v1/ +DEEP_SEEK_KEY= +DEEP_SEEK_BASE_URL=https://api.deepseek.com/v1 \ No newline at end of file diff --git a/.gitignore b/.gitignore index c19b9ce33..097126da9 100644 --- a/.gitignore +++ b/.gitignore @@ -14,7 +14,7 @@ reasoning_content.bat reasoning_window.bat queue_update.txt memory_graph.gml -.env.dev +.env.* # Byte-compiled / optimized / DLL files @@ -147,7 +147,6 @@ celerybeat.pid *.sage.py # Environments -.env .venv env/ venv/ From 3db73fe03c6ea4874503defdcd7b7a4ed7e398ab Mon Sep 17 00:00:00 2001 From: NepPure Date: Sun, 2 Mar 2025 19:37:32 +0800 Subject: [PATCH 08/26] ENVIRONMENT=prod --- .env | 1 + 1 file changed, 1 insertion(+) diff --git a/.env b/.env index 2c8e76c18..6e242e1f5 100644 --- a/.env +++ b/.env @@ -1,4 +1,5 @@ # 您不应该修改默认值，这个文件被仓库索引，请修改.env.prod +ENVIRONMENT=prod HOST=127.0.0.1 PORT=8080 From 304562d93a7831bf1f63e4e6d096d011136db714 Mon Sep 17 00:00:00 2001 From: SengokuCola <1026294844@qq.com> Date: Sun, 2 Mar 2025 19:43:14 +0800 Subject: [PATCH 09/26] =?UTF-8?q?=E4=BF=AE=E5=A4=8D=E4=BA=86=E6=97=A5?= =?UTF-8?q?=E7=A8=8B=E5=BC=82=E6=AD=A5bug?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/plugins/schedule/schedule_llm_module.py | 23 ++++++++++----------- 1 file changed, 11 insertions(+), 12 deletions(-) diff --git a/src/plugins/schedule/schedule_llm_module.py b/src/plugins/schedule/schedule_llm_module.py index cf88a8653..1fcad52d2 100644 --- a/src/plugins/schedule/schedule_llm_module.py +++ b/src/plugins/schedule/schedule_llm_module.py @@ -23,7 +23,7 @@ class LLMModel: self.model_name = model_name self.params = kwargs - async def generate_response(self, prompt: str) -> Tuple[str, str]: + def generate_response(self, prompt: str) -> Tuple[str, str]: """根据输入的提示生成模型的响应""" headers = { "Authorization": f"Bearer {self.api_key}", @@ -42,17 +42,16 @@ class LLMModel: api_url = f"{self.base_url.rstrip('/')}/chat/completions" try: - async with aiohttp.ClientSession() as session: - async with session.post(api_url, headers=headers, json=data) as response: - response.raise_for_status() # 检查响应状态 - - result = await response.json() - if "choices" in result and len(result["choices"]) > 0: - content = result["choices"][0]["message"]["content"] - reasoning_content = result["choices"][0]["message"].get("reasoning_content", "") - return content, reasoning_content # 返回内容和推理内容 - return "没有返回结果", "" # 返回两个值 - + response = requests.post(api_url, headers=headers, json=data) + response.raise_for_status() # 检查响应状态 + + result = response.json() + if "choices" in result and len(result["choices"]) > 0: + content = result["choices"][0]["message"]["content"] + reasoning_content = result["choices"][0]["message"].get("reasoning_content", "") + return content, reasoning_content # 返回内容和推理内容 + return "没有返回结果", "" # 返回两个值 + except Exception as e: return f"请求失败: {str(e)}", "" # 返回错误信息和空字符串 From f48f33a999904c5fe2ce6ec0aec07ee5af4b6821 Mon Sep 17 00:00:00 2001 From: HYY1116 Date: Sun, 2 Mar 2025 19:48:25 +0800 Subject: [PATCH 10/26] =?UTF-8?q?fix:=20=E6=A8=A1=E5=9E=8B=E4=BC=98?= =?UTF-8?q?=E5=85=88=E4=BD=BF=E7=94=A8=E6=97=A0=E9=9C=80=E4=BB=98=E8=B4=B9?= =?UTF-8?q?=E6=A8=A1=E5=9E=8B=EF=BC=8C=E5=B9=B6=E6=A8=A1=E5=9E=8B=E5=90=8D?= =?UTF-8?q?=E7=A7=B0=E5=85=A8=E5=B1=80=E5=8C=96?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- config/bot_config_toml | 45 ------------------- src/plugins/chat/config.py | 8 ++++ src/plugins/chat/llm_generator.py | 14 +++--- src/plugins/chat/topic_identifier.py | 3 +- src/plugins/memory_system/llm_module.py | 4 +- .../memory_system/llm_module_memory_make.py | 4 +- src/plugins/schedule/schedule_generator.py | 6 +-- src/plugins/schedule/schedule_llm_module.py | 9 ++-- 8 files changed, 27 insertions(+), 66 deletions(-) delete mode 100644 config/bot_config_toml diff --git a/config/bot_config_toml b/config/bot_config_toml deleted file mode 100644 index 83a3c4971..000000000 --- a/config/bot_config_toml +++ /dev/null @@ -1,45 +0,0 @@ -[bot] -qq = 123456 #填入你的机器人QQ -nickname = "麦麦" #你希望bot被称呼的名字 - -[message] -min_text_length = 2 # 与麦麦聊天时麦麦只会回答文本大于等于此数的消息 -max_context_size = 15 # 麦麦获得的上下文数量，超出数量后自动丢弃 -emoji_chance = 0.2 # 麦麦使用表情包的概率 - -[emoji] -check_interval = 120 -register_interval = 10 - -[cq_code] -enable_pic_translate = false - - -[response] -api_using = "siliconflow" # 选择大模型API，可选值为siliconflow,deepseek，建议使用siliconflow，因为识图api目前只支持siliconflow的deepseek-vl2模型 -model_r1_probability = 0.8 # 麦麦回答时选择R1模型的概率 -model_v3_probability = 0.1 # 麦麦回答时选择V3模型的概率 -model_r1_distill_probability = 0.1 # 麦麦回答时选择R1蒸馏模型的概率 - -[memory] -build_memory_interval = 300 # 记忆构建间隔 - - - -[others] -enable_advance_output = true # 开启后输出更多日志,false关闭true开启 - - -[groups] - -talk_allowed = [ - 123456,12345678 -] #可以回复消息的群 - -talk_frequency_down = [ - 123456,12345678 -] #降低回复频率的群 - -ban_user_id = [ - 123456,12345678 -] #禁止回复消息的QQ号 diff --git a/src/plugins/chat/config.py b/src/plugins/chat/config.py index dc7c14203..68ec6784b 100644 --- a/src/plugins/chat/config.py +++ b/src/plugins/chat/config.py @@ -33,6 +33,11 @@ class BotConfig: EMOJI_REGISTER_INTERVAL: int = 10 # 表情包注册间隔（分钟） API_USING: str = "siliconflow" # 使用的API + DEEPSEEK_MODEL_R1: str = "deepseek-reasoner" # deepseek-R1模型 + DEEPSEEK_MODEL_V3: str = "deepseek-chat" # deepseek-V3模型 + SILICONFLOW_MODEL_R1: str = "deepseek-ai/DeepSeek-R1" # siliconflow-R1模型 + SILICONFLOW_MODEL_R1_DISTILL: str = "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B" # siliconflow-R1蒸馏模型 + SILICONFLOW_MODEL_V3: str = "deepseek-ai/DeepSeek-V3" # siliconflow-V3模型 MODEL_R1_PROBABILITY: float = 0.8 # R1模型概率 MODEL_V3_PROBABILITY: float = 0.1 # V3模型概率 MODEL_R1_DISTILL_PROBABILITY: float = 0.1 # R1蒸馏模型概率 @@ -82,6 +87,9 @@ class BotConfig: config.MODEL_V3_PROBABILITY = response_config.get("model_v3_probability", config.MODEL_V3_PROBABILITY) config.MODEL_R1_DISTILL_PROBABILITY = response_config.get("model_r1_distill_probability", config.MODEL_R1_DISTILL_PROBABILITY) config.API_USING = response_config.get("api_using", config.API_USING) + if response_config.get("api_using", config.API_PAID): + config.SILICONFLOW_MODEL_R1 = "Pro/deepseek-ai/DeepSeek-R1" + config.SILICONFLOW_MODEL_V3 = "Pro/deepseek-ai/DeepSeek-V3" # 消息配置 if "message" in toml_dict: diff --git a/src/plugins/chat/llm_generator.py b/src/plugins/chat/llm_generator.py index bfff1d474..682be8a87 100644 --- a/src/plugins/chat/llm_generator.py +++ b/src/plugins/chat/llm_generator.py @@ -206,13 +206,13 @@ class LLMResponseGenerator: if global_config.API_USING == "deepseek": return await self._generate_base_response( message, - "deepseek-reasoner", + global_config.DEEPSEEK_MODEL_R1, {"temperature": 0.7, "max_tokens": 1024} ) else: return await self._generate_base_response( message, - "Pro/deepseek-ai/DeepSeek-R1", + global_config.SILICONFLOW_MODEL_R1, {"temperature": 0.7, "max_tokens": 1024} ) @@ -221,13 +221,13 @@ class LLMResponseGenerator: if global_config.API_USING == "deepseek": return await self._generate_base_response( message, - "deepseek-chat", + global_config.DEEPSEEK_MODEL_V3, {"temperature": 0.8, "max_tokens": 1024} ) else: return await self._generate_base_response( message, - "Pro/deepseek-ai/DeepSeek-V3", + global_config.SILICONFLOW_MODEL_V3, {"temperature": 0.8, "max_tokens": 1024} ) @@ -235,7 +235,7 @@ class LLMResponseGenerator: """使用 DeepSeek-R1-Distill-Qwen-32B 模型生成回复""" return await self._generate_base_response( message, - "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", + global_config.SILICONFLOW_MODEL_R1_DISTILL, {"temperature": 0.7, "max_tokens": 1024} ) @@ -270,9 +270,9 @@ class LLMResponseGenerator: loop = asyncio.get_event_loop() if global_config.API_USING == "deepseek": - model = "deepseek-chat" + model = global_config.DEEPSEEK_MODEL_V3 else: - model = "Pro/deepseek-ai/DeepSeek-V3" + model = global_config.SILICONFLOW_MODEL_V3 create_completion = partial( self.client.chat.completions.create, model=model, diff --git a/src/plugins/chat/topic_identifier.py b/src/plugins/chat/topic_identifier.py index 81956ddc1..34ac4e714 100644 --- a/src/plugins/chat/topic_identifier.py +++ b/src/plugins/chat/topic_identifier.py @@ -3,6 +3,7 @@ from openai import OpenAI from .message import Message import jieba from nonebot import get_driver +from .config import global_config driver = get_driver() config = driver.config @@ -24,7 +25,7 @@ class TopicIdentifier: 消息内容：{text}""" response = self.client.chat.completions.create( - model="Pro/deepseek-ai/DeepSeek-V3", + model=global_config.SILICONFLOW_MODEL_V3, messages=[{"role": "user", "content": prompt}], temperature=0.8, max_tokens=10 diff --git a/src/plugins/memory_system/llm_module.py b/src/plugins/memory_system/llm_module.py index 34e4e4af1..c2b6cf042 100644 --- a/src/plugins/memory_system/llm_module.py +++ b/src/plugins/memory_system/llm_module.py @@ -5,13 +5,13 @@ import time from nonebot import get_driver import aiohttp import asyncio - +from src.plugins.chat.config import global_config driver = get_driver() config = driver.config class LLMModel: # def __init__(self, model_name="deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", **kwargs): - def __init__(self, model_name="Pro/deepseek-ai/DeepSeek-V3", **kwargs): + def __init__(self, model_name=global_config.SILICONFLOW_MODEL_V3, **kwargs): self.model_name = model_name self.params = kwargs self.api_key = config.siliconflow_key diff --git a/src/plugins/memory_system/llm_module_memory_make.py b/src/plugins/memory_system/llm_module_memory_make.py index 5b01a2917..f59354570 100644 --- a/src/plugins/memory_system/llm_module_memory_make.py +++ b/src/plugins/memory_system/llm_module_memory_make.py @@ -2,17 +2,17 @@ import os import requests from typing import Tuple, Union import time -from ..chat.config import BotConfig from nonebot import get_driver import aiohttp import asyncio +from src.plugins.chat.config import BotConfig, global_config driver = get_driver() config = driver.config class LLMModel: # def __init__(self, model_name="deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", **kwargs): - def __init__(self, model_name="Pro/deepseek-ai/DeepSeek-V3", **kwargs): + def __init__(self, model_name=global_config.SILICONFLOW_MODEL_V3, **kwargs): self.model_name = model_name self.params = kwargs self.api_key = config.siliconflow_key diff --git a/src/plugins/schedule/schedule_generator.py b/src/plugins/schedule/schedule_generator.py index a33c4b279..63e3235a3 100644 --- a/src/plugins/schedule/schedule_generator.py +++ b/src/plugins/schedule/schedule_generator.py @@ -3,7 +3,7 @@ import os from typing import List, Dict from .schedule_llm_module import LLMModel from ...common.database import Database # 使用正确的导入语法 -from ..chat.config import global_config +from src.plugins.chat.config import global_config from nonebot import get_driver driver = get_driver() @@ -22,9 +22,9 @@ Database.initialize( class ScheduleGenerator: def __init__(self): if global_config.API_USING == "siliconflow": - self.llm_scheduler = LLMModel(model_name="Pro/deepseek-ai/DeepSeek-V3") + self.llm_scheduler = LLMModel(model_name=global_config.SILICONFLOW_MODEL_V3) elif global_config.API_USING == "deepseek": - self.llm_scheduler = LLMModel(model_name="deepseek-chat",api_using="deepseek") + self.llm_scheduler = LLMModel(model_name=global_config.DEEPSEEK_MODEL_V3) self.db = Database.get_instance() today = datetime.datetime.now() diff --git a/src/plugins/schedule/schedule_llm_module.py b/src/plugins/schedule/schedule_llm_module.py index cf88a8653..34dad6fec 100644 --- a/src/plugins/schedule/schedule_llm_module.py +++ b/src/plugins/schedule/schedule_llm_module.py @@ -3,20 +3,17 @@ import requests import aiohttp from typing import Tuple, Union from nonebot import get_driver - +from src.plugins.chat.config import global_config driver = get_driver() config = driver.config class LLMModel: # def __init__(self, model_name="deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", **kwargs): - def __init__(self, model_name="Pro/deepseek-ai/DeepSeek-R1",api_using=None, **kwargs): + def __init__(self, model_name=global_config.SILICONFLOW_MODEL_R1,api_using=None, **kwargs): if api_using == "deepseek": self.api_key = config.deep_seek_key self.base_url = config.deep_seek_base_url - if model_name != "Pro/deepseek-ai/DeepSeek-R1": - self.model_name = model_name - else: - self.model_name = "deepseek-reasoner" + self.model_name = global_config.DEEPSEEK_MODEL_R1 else: self.api_key = config.siliconflow_key self.base_url = config.siliconflow_base_url From 0bcd18c821e5fc351d73b39c17fbab92d2123774 Mon Sep 17 00:00:00 2001 From: HYY1116 Date: Sun, 2 Mar 2025 19:51:14 +0800 Subject: [PATCH 11/26] =?UTF-8?q?=E6=BC=8F=E6=94=B9=E4=B8=80=E5=B0=8F?= =?UTF-8?q?=E5=A4=84?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/plugins/chat/config.py | 1 + 1 file changed, 1 insertion(+) diff --git a/src/plugins/chat/config.py b/src/plugins/chat/config.py index 68ec6784b..54d125408 100644 --- a/src/plugins/chat/config.py +++ b/src/plugins/chat/config.py @@ -33,6 +33,7 @@ class BotConfig: EMOJI_REGISTER_INTERVAL: int = 10 # 表情包注册间隔（分钟） API_USING: str = "siliconflow" # 使用的API + API_PAID: bool = False # 是否使用付费API DEEPSEEK_MODEL_R1: str = "deepseek-reasoner" # deepseek-R1模型 DEEPSEEK_MODEL_V3: str = "deepseek-chat" # deepseek-V3模型 SILICONFLOW_MODEL_R1: str = "deepseek-ai/DeepSeek-R1" # siliconflow-R1模型 From 6e2d229cb74d98f21d3f3a91356173776c2353e8 Mon Sep 17 00:00:00 2001 From: HYY1116 Date: Sun, 2 Mar 2025 19:59:00 +0800 Subject: [PATCH 12/26] =?UTF-8?q?=E5=9B=9E=E6=9D=A5=E5=90=A7=EF=BC=8C?= =?UTF-8?q?=E6=88=91=E7=9A=84config?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | 1 - config/bot_config.toml | 46 ++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 46 insertions(+), 1 deletion(-) create mode 100644 config/bot_config.toml diff --git a/.gitignore b/.gitignore index 097126da9..94b4bb855 100644 --- a/.gitignore +++ b/.gitignore @@ -3,7 +3,6 @@ mongodb/ NapCat.Framework.Windows.Once/ log/ src/plugins/memory -config/bot_config.toml /test message_queue_content.txt message_queue_content.bat diff --git a/config/bot_config.toml b/config/bot_config.toml new file mode 100644 index 000000000..d668fcf59 --- /dev/null +++ b/config/bot_config.toml @@ -0,0 +1,46 @@ +[bot] +qq = 123456 #填入你的机器人QQ +nickname = "麦麦" #你希望bot被称呼的名字 + +[message] +min_text_length = 2 # 与麦麦聊天时麦麦只会回答文本大于等于此数的消息 +max_context_size = 15 # 麦麦获得的上下文数量，超出数量后自动丢弃 +emoji_chance = 0.2 # 麦麦使用表情包的概率 + +[emoji] +check_interval = 120 +register_interval = 10 + +[cq_code] +enable_pic_translate = false + + +[response] +api_using = "siliconflow" # 选择大模型API，可选值为siliconflow,deepseek，建议使用siliconflow，因为识图api目前只支持siliconflow的deepseek-vl2模型 +api_paid = false #是否使用付费api，目前此选项只影响siliconflow，其deepseek模型的api分为可用赠送余额和不可以用的，此选项为false时使用赠送余额 +model_r1_probability = 0.8 # 麦麦回答时选择R1模型的概率 +model_v3_probability = 0.1 # 麦麦回答时选择V3模型的概率 +model_r1_distill_probability = 0.1 # 麦麦回答时选择R1蒸馏模型的概率 + +[memory] +build_memory_interval = 300 # 记忆构建间隔 + + + +[others] +enable_advance_output = true # 开启后输出更多日志,false关闭true开启 + + +[groups] + +talk_allowed = [ + 123456,12345678 +] #可以回复消息的群 + +talk_frequency_down = [ + 123456,12345678 +] #降低回复频率的群 + +ban_user_id = [ + 123456,12345678 +] #禁止回复消息的QQ号 From 5e9401c27aa1a026fbe5041c1bb7d74d4d88e0b3 Mon Sep 17 00:00:00 2001 From: HYY <95246895+HYY1116@users.noreply.github.com> Date: Sun, 2 Mar 2025 20:00:21 +0800 Subject: [PATCH 13/26] Update .gitignore --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 94b4bb855..8df5d72a8 100644 --- a/.gitignore +++ b/.gitignore @@ -4,6 +4,7 @@ NapCat.Framework.Windows.Once/ log/ src/plugins/memory /test +config/bot_config.toml message_queue_content.txt message_queue_content.bat message_queue_window.bat From f04ae091e285d28e23e926fb5aaffecd79835e84 Mon Sep 17 00:00:00 2001 From: NepPure Date: Sun, 2 Mar 2025 20:34:40 +0800 Subject: [PATCH 14/26] =?UTF-8?q?=E4=B8=8D=E5=86=8D=E9=9C=80=E8=A6=81?= =?UTF-8?q?=E9=87=8D=E5=91=BD=E5=90=8Dconfig?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Dockerfile | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/Dockerfile b/Dockerfile index 6a7aa09c0..225a90a1b 100644 --- a/Dockerfile +++ b/Dockerfile @@ -2,8 +2,7 @@ FROM nonebot/nb-cli:latest WORKDIR / COPY . /MaiMBot/ WORKDIR /MaiMBot -RUN mv config/bot_config_toml config/bot_config.toml RUN pip install --upgrade -r requirements.txt VOLUME [ "/MaiMBot/config" ] EXPOSE 8080 -ENTRYPOINT [ "nb","run" ] \ No newline at end of file +ENTRYPOINT [ "nb","run" ] From 12e759f3ad5a3c4d9b782aa01b55ec73aa88ad80 Mon Sep 17 00:00:00 2001 From: SengokuCola <1026294844@qq.com> Date: Sun, 2 Mar 2025 20:56:38 +0800 Subject: [PATCH 15/26] =?UTF-8?q?=E5=B1=8E=E5=B1=B1=E4=BF=AE=E8=A1=A5?= =?UTF-8?q?=E6=9C=AF?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 修改了bot_toml加载逻辑 --- .gitignore | 2 +- config/bot_config.toml | 11 ++--- src/plugins/chat/config.py | 72 +++++++++++++++++++++++++----- src/plugins/chat/cq_code.py | 2 +- src/plugins/chat/prompt_builder.py | 4 +- 5 files changed, 72 insertions(+), 19 deletions(-) diff --git a/.gitignore b/.gitignore index 8df5d72a8..a9cdc192f 100644 --- a/.gitignore +++ b/.gitignore @@ -15,7 +15,7 @@ reasoning_window.bat queue_update.txt memory_graph.gml .env.* - +config/bot_config_dev.toml # Byte-compiled / optimized / DLL files __pycache__/ diff --git a/config/bot_config.toml b/config/bot_config.toml index d668fcf59..4863da245 100644 --- a/config/bot_config.toml +++ b/config/bot_config.toml @@ -1,5 +1,5 @@ [bot] -qq = 123456 #填入你的机器人QQ +qq = 123 #填入你的机器人QQ nickname = "麦麦" #你希望bot被称呼的名字 [message] @@ -17,7 +17,7 @@ enable_pic_translate = false [response] api_using = "siliconflow" # 选择大模型API，可选值为siliconflow,deepseek，建议使用siliconflow，因为识图api目前只支持siliconflow的deepseek-vl2模型 -api_paid = false #是否使用付费api，目前此选项只影响siliconflow，其deepseek模型的api分为可用赠送余额和不可以用的，此选项为false时使用赠送余额 +api_paid = true #是否使用付费api，目前此选项只影响siliconflow，其deepseek模型的api分为可用赠送余额和不可以用的，此选项为false时使用赠送余额 model_r1_probability = 0.8 # 麦麦回答时选择R1模型的概率 model_v3_probability = 0.1 # 麦麦回答时选择V3模型的概率 model_r1_distill_probability = 0.1 # 麦麦回答时选择R1蒸馏模型的概率 @@ -34,13 +34,14 @@ enable_advance_output = true # 开启后输出更多日志,false关闭true开启 [groups] talk_allowed = [ - 123456,12345678 + 123, + 123, ] #可以回复消息的群 talk_frequency_down = [ - 123456,12345678 + ] #降低回复频率的群 ban_user_id = [ - 123456,12345678 + ] #禁止回复消息的QQ号 diff --git a/src/plugins/chat/config.py b/src/plugins/chat/config.py index 54d125408..b85cbc2e6 100644 --- a/src/plugins/chat/config.py +++ b/src/plugins/chat/config.py @@ -47,20 +47,59 @@ class BotConfig: enable_kuuki_read: bool = True # 是否启用读空气功能 @staticmethod - def get_default_config_path() -> str: - """获取默认配置文件路径""" + def get_config_dir() -> str: + """获取配置文件目录""" current_dir = os.path.dirname(os.path.abspath(__file__)) root_dir = os.path.abspath(os.path.join(current_dir, '..', '..', '..')) config_dir = os.path.join(root_dir, 'config') - return os.path.join(config_dir, 'bot_config.toml') + if not os.path.exists(config_dir): + os.makedirs(config_dir) + return config_dir + + @staticmethod + def create_default_config(config_path: str) -> None: + """创建默认配置文件""" + default_config = """[bot] +qq = 1 # 填入你的机器人QQ +nickname = "麦麦" # 你希望bot被称呼的名字 + +[message] +min_text_length = 2 # 与麦麦聊天时麦麦只会回答文本大于等于此数的消息 +max_context_size = 15 # 麦麦获得的上下文数量，超出数量后自动丢弃 +emoji_chance = 0.2 # 麦麦使用表情包的概率 + +[emoji] +check_interval = 120 +register_interval = 10 + +[cq_code] +enable_pic_translate = false + +[response] +api_using = "siliconflow" # 选择大模型API，可选值为siliconflow,deepseek +api_paid = false # 是否使用付费api +model_r1_probability = 0.8 # 麦麦回答时选择R1模型的概率 +model_v3_probability = 0.1 # 麦麦回答时选择V3模型的概率 +model_r1_distill_probability = 0.1 # 麦麦回答时选择R1蒸馏模型的概率 + +[memory] +build_memory_interval = 300 # 记忆构建间隔 + +[others] +enable_advance_output = false # 开启后输出更多日志 + +[groups] +talk_allowed = [] # 可以回复消息的群 +talk_frequency_down = [] # 降低回复频率的群 +ban_user_id = [] # 禁止回复消息的QQ号 +""" + with open(config_path, "w", encoding="utf-8") as f: + f.write(default_config) + logger.success(f"已创建默认配置文件: {config_path}") @classmethod def load_config(cls, config_path: str = None) -> "BotConfig": """从TOML配置文件加载配置""" - if config_path is None: - config_path = cls.get_default_config_path() - logger.info(f"使用默认配置文件路径: {config_path}") - config = cls() if os.path.exists(config_path): with open(config_path, "rb") as f: @@ -119,12 +158,25 @@ class BotConfig: return config # 获取配置文件路径 -bot_config_path = BotConfig.get_default_config_path() -config_dir = os.path.dirname(bot_config_path) -logger.info(f"尝试从 {bot_config_path} 加载机器人配置") +bot_config_floder_path = BotConfig.get_config_dir() +print(f"正在品鉴配置文件目录: {bot_config_floder_path}") +bot_config_path = os.path.join(bot_config_floder_path, "bot_config_dev.toml") +if not os.path.exists(bot_config_path): + # 如果开发环境配置文件不存在，则使用默认配置文件 + bot_config_path = os.path.join(bot_config_floder_path, "bot_config.toml") + logger.info("使用默认配置文件") +else: + logger.info("已找到开发环境配置文件") + global_config = BotConfig.load_config(config_path=bot_config_path) + +# config_dir = os.path.dirname(bot_config_path) + +# logger.info(f"尝试从 {bot_config_path} 加载机器人配置") +# global_config = BotConfig.load_config(config_path=bot_config_path) + @dataclass class LLMConfig: """机器人配置类""" diff --git a/src/plugins/chat/cq_code.py b/src/plugins/chat/cq_code.py index ae5d8a257..386124c92 100644 --- a/src/plugins/chat/cq_code.py +++ b/src/plugins/chat/cq_code.py @@ -349,7 +349,7 @@ class CQCode: # 创建Message对象 from .message import Message if self.reply_message == None: - print(f"\033[1;31m[错误]\033[0m 回复消息为空") + # print(f"\033[1;31m[错误]\033[0m 回复消息为空") return '[回复某人消息]' if self.reply_message.sender.user_id: diff --git a/src/plugins/chat/prompt_builder.py b/src/plugins/chat/prompt_builder.py index 01fe3e82d..272229416 100644 --- a/src/plugins/chat/prompt_builder.py +++ b/src/plugins/chat/prompt_builder.py @@ -150,9 +150,9 @@ class PromptBuilder: prompt_personality = '' personality_choice = random.random() if personality_choice < 4/6: # 第一种人格 - prompt_personality = f'''{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，曾经是一个学习地质的女大学生，现在学习心理学和脑科学，你会刷贴吧，你正在浏览qq群,{promt_info_prompt}, + prompt_personality = f'''{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，现在学习心理学和脑科学，你会刷贴吧，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。{is_bot_prompt} - 请注意把握群里的聊天内容，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 < 1: # 第二种人格 prompt_personality = f'''{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，是一个女大学生，你有黑色头发，你会刷小红书, 你正在浏览qq群,{promt_info_prompt}, From 927ae4484f86cdda55f23e2ef08c94df03cab463 Mon Sep 17 00:00:00 2001 From: SengokuCola <1026294844@qq.com> Date: Sun, 2 Mar 2025 20:59:50 +0800 Subject: [PATCH 16/26] Update .gitignore --- .gitignore | 5 ----- 1 file changed, 5 deletions(-) diff --git a/.gitignore b/.gitignore index a9cdc192f..48c0c13c3 100644 --- a/.gitignore +++ b/.gitignore @@ -2,16 +2,11 @@ data/ mongodb/ NapCat.Framework.Windows.Once/ log/ -src/plugins/memory /test -config/bot_config.toml message_queue_content.txt message_queue_content.bat message_queue_window.bat message_queue_window.txt -reasoning_content.txt -reasoning_content.bat -reasoning_window.bat queue_update.txt memory_graph.gml .env.* From 65d983f37f9c1f6eec120ee7b1ca93edb2330b24 Mon Sep 17 00:00:00 2001 From: SengokuCola <1026294844@qq.com> Date: Sun, 2 Mar 2025 21:03:56 +0800 Subject: [PATCH 17/26] =?UTF-8?q?=E8=B0=A2=E8=B0=A2=E5=A4=A7=E5=AE=B6?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 1 --- .env | 2 +- bot.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.env b/.env index 6e242e1f5..709d22d34 100644 --- a/.env +++ b/.env @@ -1,5 +1,5 @@ # 您不应该修改默认值，这个文件被仓库索引，请修改.env.prod -ENVIRONMENT=prod +ENVIRONMENT=dev HOST=127.0.0.1 PORT=8080 diff --git a/bot.py b/bot.py index 8741eca7f..489ab1c36 100644 --- a/bot.py +++ b/bot.py @@ -29,7 +29,7 @@ env_file = f".env.{env}" if env_file == ".env.dev" and os.path.exists(env_file): logger.success("加载开发环境变量配置") load_dotenv(env_file, override=True) # override=True 允许覆盖已存在的环境变量 -elif env_file == ".env.prod" and os.path.exists(env_file): +elif os.path.exists(".env.prod"): logger.success("加载环境变量配置") load_dotenv(env_file, override=True) # override=True 允许覆盖已存在的环境变量 else: From 6462c3b3606d1bedc62fc7818a7d32c3d1e182a6 Mon Sep 17 00:00:00 2001 From: SengokuCola <1026294844@qq.com> Date: Sun, 2 Mar 2025 23:51:47 +0800 Subject: [PATCH 18/26] =?UTF-8?q?v0.4.0=20=E6=94=AF=E6=8C=81=E4=BB=BB?= =?UTF-8?q?=E6=84=8F=E6=9B=BF=E6=8D=A2=E7=9A=84=E6=A8=A1=E5=9E=8B=EF=BC=8C?= =?UTF-8?q?=E6=94=B9=E8=BF=9B=E9=85=8D=E7=BD=AE=E6=96=87=E4=BB=B6?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 好好好好好好好好好 --- .env.prod | 8 +- bot.py | 35 +-- config/auto_format.py | 46 ++++ config/bot_config.toml | 62 +++-- docs/qq.png | Bin 195176 -> 0 bytes src/plugins/chat/__init__.py | 1 + src/plugins/chat/bot.py | 4 +- src/plugins/chat/config.py | 81 ++---- src/plugins/chat/cq_code.py | 106 ++------ src/plugins/chat/emoji_manager.py | 107 ++------ src/plugins/chat/llm_generator.py | 287 +++++--------------- src/plugins/memory_system/llm_module.py | 68 ----- src/plugins/memory_system/memory.py | 9 +- src/plugins/models/utils_model.py | 199 ++++++++++++++ src/plugins/schedule/schedule_generator.py | 29 +- src/plugins/schedule/schedule_llm_module.py | 61 ----- 16 files changed, 459 insertions(+), 644 deletions(-) create mode 100644 config/auto_format.py delete mode 100644 docs/qq.png delete mode 100644 src/plugins/memory_system/llm_module.py create mode 100644 src/plugins/models/utils_model.py delete mode 100644 src/plugins/schedule/schedule_llm_module.py diff --git a/.env.prod b/.env.prod index 3d795978c..f00cd5169 100644 --- a/.env.prod +++ b/.env.prod @@ -16,9 +16,11 @@ MONGODB_PASSWORD = "" # 默认空值 MONGODB_AUTH_SOURCE = "" # 默认空值 #key and url -CHAT_ANY_WHERE_KEY= -SILICONFLOW_KEY= + CHAT_ANY_WHERE_BASE_URL=https://api.chatanywhere.tech/v1 SILICONFLOW_BASE_URL=https://api.siliconflow.cn/v1/ +DEEP_SEEK_BASE_URL=https://api.deepseek.com/v1 + DEEP_SEEK_KEY= -DEEP_SEEK_BASE_URL=https://api.deepseek.com/v1 \ No newline at end of file +CHAT_ANY_WHERE_KEY= +SILICONFLOW_KEY= \ No newline at end of file diff --git a/bot.py b/bot.py index 489ab1c36..cf1b82ff6 100644 --- a/bot.py +++ b/bot.py @@ -36,28 +36,18 @@ else: logger.error(f"{env}对应的环境配置文件{env_file}不存在,请修改.env文件中的ENVIRONMENT变量为 prod.") exit(1) -nonebot.init( - # 从环境变量中读取配置 - websocket_port=os.getenv("PORT", 8080), - host=os.getenv("HOST", "127.0.0.1"), - log_level="INFO", - # 添加自定义配置 - mongodb_host=os.getenv("MONGODB_HOST", "127.0.0.1"), - mongodb_port=os.getenv("MONGODB_PORT", 27017), - database_name=os.getenv("DATABASE_NAME", "MegBot"), - mongodb_username=os.getenv("MONGODB_USERNAME", ""), - mongodb_password=os.getenv("MONGODB_PASSWORD", ""), - mongodb_auth_source=os.getenv("MONGODB_AUTH_SOURCE", ""), - # API相关配置 - chat_any_where_key=os.getenv("CHAT_ANY_WHERE_KEY", ""), - siliconflow_key=os.getenv("SILICONFLOW_KEY", ""), - chat_any_where_base_url=os.getenv("CHAT_ANY_WHERE_BASE_URL", "https://api.chatanywhere.tech/v1"), - siliconflow_base_url=os.getenv("SILICONFLOW_BASE_URL", "https://api.siliconflow.cn/v1/"), - deep_seek_key=os.getenv("DEEP_SEEK_KEY", ""), - deep_seek_base_url=os.getenv("DEEP_SEEK_BASE_URL", "https://api.deepseek.com/v1"), - # 插件配置 - plugins=os.getenv("PLUGINS", ["src2.plugins.chat"]) -) +# 获取所有环境变量 +env_config = {key: os.getenv(key) for key in os.environ} + +# 设置基础配置 +base_config = { + "websocket_port": int(env_config.get("PORT", 8080)), + "host": env_config.get("HOST", "127.0.0.1"), + "log_level": "INFO", +} + +# 合并配置 +nonebot.init(**base_config, **env_config) # 注册适配器 driver = nonebot.get_driver() @@ -67,4 +57,5 @@ driver.register_adapter(Adapter) nonebot.load_plugins("src/plugins") if __name__ == "__main__": + nonebot.run() \ No newline at end of file diff --git a/config/auto_format.py b/config/auto_format.py new file mode 100644 index 000000000..9bc27da29 --- /dev/null +++ b/config/auto_format.py @@ -0,0 +1,46 @@ +import tomli +import tomli_w +import sys +from pathlib import Path +import os + +def sync_configs(): + # 读取两个配置文件 + try: + with open('bot_config_dev.toml', 'rb') as f: # tomli需要使用二进制模式读取 + dev_config = tomli.load(f) + + with open('bot_config.toml', 'rb') as f: + prod_config = tomli.load(f) + except FileNotFoundError as e: + print(f"错误：找不到配置文件 - {e}") + sys.exit(1) + except tomli.TOMLDecodeError as e: + print(f"错误：TOML格式解析失败 - {e}") + sys.exit(1) + + # 递归合并配置 + def merge_configs(source, target): + for key, value in source.items(): + if key not in target: + target[key] = value + elif isinstance(value, dict) and isinstance(target[key], dict): + merge_configs(value, target[key]) + + # 将dev配置的新属性合并到prod配置中 + merge_configs(dev_config, prod_config) + + # 保存更新后的配置 + try: + with open('bot_config.toml', 'wb') as f: # tomli_w需要使用二进制模式写入 + tomli_w.dump(prod_config, f) + print("配置文件同步完成！") + except Exception as e: + print(f"错误：保存配置文件失败 - {e}") + sys.exit(1) + +if __name__ == '__main__': + # 确保在正确的目录下运行 + script_dir = Path(__file__).parent + os.chdir(script_dir) + sync_configs() diff --git a/config/bot_config.toml b/config/bot_config.toml index 4863da245..83526945c 100644 --- a/config/bot_config.toml +++ b/config/bot_config.toml @@ -1,11 +1,11 @@ [bot] -qq = 123 #填入你的机器人QQ -nickname = "麦麦" #你希望bot被称呼的名字 +qq = 123 +nickname = "麦麦" [message] -min_text_length = 2 # 与麦麦聊天时麦麦只会回答文本大于等于此数的消息 -max_context_size = 15 # 麦麦获得的上下文数量，超出数量后自动丢弃 -emoji_chance = 0.2 # 麦麦使用表情包的概率 +min_text_length = 2 +max_context_size = 15 +emoji_chance = 0.2 [emoji] check_interval = 120 @@ -14,34 +14,48 @@ register_interval = 10 [cq_code] enable_pic_translate = false - [response] -api_using = "siliconflow" # 选择大模型API，可选值为siliconflow,deepseek，建议使用siliconflow，因为识图api目前只支持siliconflow的deepseek-vl2模型 -api_paid = true #是否使用付费api，目前此选项只影响siliconflow，其deepseek模型的api分为可用赠送余额和不可以用的，此选项为false时使用赠送余额 -model_r1_probability = 0.8 # 麦麦回答时选择R1模型的概率 -model_v3_probability = 0.1 # 麦麦回答时选择V3模型的概率 -model_r1_distill_probability = 0.1 # 麦麦回答时选择R1蒸馏模型的概率 +api_using = "siliconflow" +api_paid = true +model_r1_probability = 0.8 +model_v3_probability = 0.1 +model_r1_distill_probability = 0.1 [memory] -build_memory_interval = 300 # 记忆构建间隔 - - +build_memory_interval = 300 [others] -enable_advance_output = true # 开启后输出更多日志,false关闭true开启 - +enable_advance_output = true [groups] - talk_allowed = [ - 123, - 123, -] #可以回复消息的群 + 123, + 123, +] +talk_frequency_down = [] +ban_user_id = [] -talk_frequency_down = [ +[model.llm_reasoning] +name = "Pro/deepseek-ai/DeepSeek-R1" +base_url = "SILICONFLOW_BASE_URL" +key = "SILICONFLOW_KEY" -] #降低回复频率的群 +[model.llm_reasoning_minor] +name = "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B" +base_url = "SILICONFLOW_BASE_URL" +key = "SILICONFLOW_KEY" -ban_user_id = [ +[model.llm_normal] +name = "Pro/deepseek-ai/DeepSeek-V3" +base_url = "SILICONFLOW_BASE_URL" +key = "SILICONFLOW_KEY" -] #禁止回复消息的QQ号 +[model.llm_normal_minor] +name = "deepseek-ai/DeepSeek-V2.5" +base_url = "SILICONFLOW_BASE_URL" +key = "SILICONFLOW_KEY" + +[model.vlm] +name = "deepseek-ai/deepseek-vl2" +base_url = "SILICONFLOW_BASE_URL" +key = "SILICONFLOW_KEY" diff --git a/docs/qq.png b/docs/qq.png deleted file mode 100644 index 3c31347b658e6977f3cda32125b1b4d55f907c9f..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 195176 zcmd3NV|!*z({=2L?M!T(v2EM7t%*6YZQHi(iEZ0X-dy)_JpbYS+WW)quCD4@wN_P_ zysX$S7;G3IAfR6o;=+nRK)_BwKp=!r5I-w!;$f;lK!iXN!UD?fnHODVwYbYJbZ;)t z2OO`sxCfai5{wDR5@Ctr)KaRoBonuqGt1p7K8N4cj!D<=_g4tpYx9hFV&nml`#w^b+ 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zunBy`R2!m57&6crivT1;sYE!jUp=>p!wash)Ah8Nv7GZs_Z)D}za%ZQFXCt@1e^jS zlc3y2Z#Zrv-l0wRB@gn1N-}-coQVhhaX^pv4oe81-H8!#Mk?~VjNF8CuR z9-Qb0*yEf3F&y;Egt!e?=z+YS0;0ZNP- z`kpR(IVEkun610@`=#lv2_KlTnsbU*+5)Z z6UlCeXos@?n&Kqm^gUYq?+s$ZxcJ_KC|KBT*xhzm+ z#^3Mf39c#ozy1Oa9RedzVnI|C=VR+Q=62UuoN6EzYL@E6YNpYuxp}SN^@kkE!I``0t(` tP=-L?9sc*R+ None: - """创建默认配置文件""" - default_config = """[bot] -qq = 1 # 填入你的机器人QQ -nickname = "麦麦" # 你希望bot被称呼的名字 - -[message] -min_text_length = 2 # 与麦麦聊天时麦麦只会回答文本大于等于此数的消息 -max_context_size = 15 # 麦麦获得的上下文数量，超出数量后自动丢弃 -emoji_chance = 0.2 # 麦麦使用表情包的概率 - -[emoji] -check_interval = 120 -register_interval = 10 - -[cq_code] -enable_pic_translate = false - -[response] -api_using = "siliconflow" # 选择大模型API，可选值为siliconflow,deepseek -api_paid = false # 是否使用付费api -model_r1_probability = 0.8 # 麦麦回答时选择R1模型的概率 -model_v3_probability = 0.1 # 麦麦回答时选择V3模型的概率 -model_r1_distill_probability = 0.1 # 麦麦回答时选择R1蒸馏模型的概率 - -[memory] -build_memory_interval = 300 # 记忆构建间隔 - -[others] -enable_advance_output = false # 开启后输出更多日志 - -[groups] -talk_allowed = [] # 可以回复消息的群 -talk_frequency_down = [] # 降低回复频率的群 -ban_user_id = [] # 禁止回复消息的QQ号 -""" - with open(config_path, "w", encoding="utf-8") as f: - f.write(default_config) - logger.success(f"已创建默认配置文件: {config_path}") @classmethod def load_config(cls, config_path: str = None) -> "BotConfig": @@ -127,9 +89,26 @@ ban_user_id = [] # 禁止回复消息的QQ号 config.MODEL_V3_PROBABILITY = response_config.get("model_v3_probability", config.MODEL_V3_PROBABILITY) config.MODEL_R1_DISTILL_PROBABILITY = response_config.get("model_r1_distill_probability", config.MODEL_R1_DISTILL_PROBABILITY) config.API_USING = response_config.get("api_using", config.API_USING) - if response_config.get("api_using", config.API_PAID): - config.SILICONFLOW_MODEL_R1 = "Pro/deepseek-ai/DeepSeek-R1" - config.SILICONFLOW_MODEL_V3 = "Pro/deepseek-ai/DeepSeek-V3" + config.API_PAID = response_config.get("api_paid", config.API_PAID) + + # 加载模型配置 + if "model" in toml_dict: + model_config = toml_dict["model"] + + if "llm_reasoning" in model_config: + config.llm_reasoning = model_config["llm_reasoning"] + + if "llm_reasoning_minor" in model_config: + config.llm_reasoning_minor = model_config["llm_reasoning_minor"] + + if "llm_normal" in model_config: + config.llm_normal = model_config["llm_normal"] + + if "llm_normal_minor" in model_config: + config.llm_normal_minor = model_config["llm_normal_minor"] + + if "vlm" in model_config: + config.vlm = model_config["vlm"] # 消息配置 if "message" in toml_dict: @@ -172,10 +151,6 @@ else: global_config = BotConfig.load_config(config_path=bot_config_path) -# config_dir = os.path.dirname(bot_config_path) - -# logger.info(f"尝试从 {bot_config_path} 加载机器人配置") -# global_config = BotConfig.load_config(config_path=bot_config_path) @dataclass class LLMConfig: diff --git a/src/plugins/chat/cq_code.py b/src/plugins/chat/cq_code.py index 386124c92..4d70736cd 100644 --- a/src/plugins/chat/cq_code.py +++ b/src/plugins/chat/cq_code.py @@ -12,6 +12,7 @@ import time import asyncio from .utils_image import storage_image,storage_emoji from .utils_user import get_user_nickname +from ..models.utils_model import LLM_request #解析各种CQ码 #包含CQ码类 import urllib3 @@ -57,6 +58,11 @@ class CQCode: translated_plain_text: Optional[str] = None reply_message: Dict = None # 存储回复消息 image_base64: Optional[str] = None + _llm: Optional[LLM_request] = None + + def __post_init__(self): + """初始化LLM实例""" + self._llm = LLM_request(model=global_config.vlm, temperature=0.4, max_tokens=300) def translate(self): """根据CQ码类型进行相应的翻译处理""" @@ -161,7 +167,7 @@ class CQCode: # 将 base64 字符串转换为字节类型 image_bytes = base64.b64decode(base64_str) storage_emoji(image_bytes) - return self.get_image_description(base64_str) + return self.get_emoji_description(base64_str) else: return '[表情包]' @@ -181,93 +187,23 @@ class CQCode: def get_emoji_description(self, image_base64: str) -> str: """调用AI接口获取表情包描述""" - headers = { - "Content-Type": "application/json", - "Authorization": f"Bearer {config.siliconflow_key}" - } - - payload = { - "model": "deepseek-ai/deepseek-vl2", - "messages": [ - { - "role": "user", - "content": [ - { - "type": "text", - "text": "这是一个表情包，请用简短的中文描述这个表情包传达的情感和含义。最多20个字。" - }, - { - "type": "image_url", - "image_url": { - "url": f"data:image/jpeg;base64,{image_base64}" - } - } - ] - } - ], - "max_tokens": 50, - "temperature": 0.4 - } - - response = requests.post( - f"{config.siliconflow_base_url}chat/completions", - headers=headers, - json=payload, - timeout=30 - ) - - if response.status_code == 200: - result_json = response.json() - if "choices" in result_json and len(result_json["choices"]) > 0: - description = result_json["choices"][0]["message"]["content"] - return f"[表情包：{description}]" - - raise ValueError(f"AI接口调用失败: {response.text}") + try: + prompt = "这是一个表情包，请用简短的中文描述这个表情包传达的情感和含义。最多20个字。" + description, _ = self._llm.generate_response_for_image_sync(prompt, image_base64) + return f"[表情包：{description}]" + except Exception as e: + print(f"\033[1;31m[错误]\033[0m AI接口调用失败: {str(e)}") + return "[表情包]" def get_image_description(self, image_base64: str) -> str: """调用AI接口获取普通图片描述""" - headers = { - "Content-Type": "application/json", - "Authorization": f"Bearer {config.siliconflow_key}" - } - - payload = { - "model": "deepseek-ai/deepseek-vl2", - "messages": [ - { - "role": "user", - "content": [ - { - "type": "text", - "text": "请用中文描述这张图片的内容。如果有文字，请把文字都描述出来。并尝试猜测这个图片的含义。最多200个字。" - }, - { - "type": "image_url", - "image_url": { - "url": f"data:image/jpeg;base64,{image_base64}" - } - } - ] - } - ], - "max_tokens": 300, - "temperature": 0.6 - } - - response = requests.post( - f"{config.siliconflow_base_url}chat/completions", - headers=headers, - json=payload, - timeout=30 - ) - - if response.status_code == 200: - result_json = response.json() - if "choices" in result_json and len(result_json["choices"]) > 0: - description = result_json["choices"][0]["message"]["content"] - return f"[图片：{description}]" - - raise ValueError(f"AI接口调用失败: {response.text}") + try: + prompt = "请用中文描述这张图片的内容。如果有文字，请把文字都描述出来。并尝试猜测这个图片的含义。最多200个字。" + description, _ = self._llm.generate_response_for_image_sync(prompt, image_base64) + return f"[图片：{description}]" + except Exception as e: + print(f"\033[1;31m[错误]\033[0m AI接口调用失败: {str(e)}") + return "[图片]" def translate_forward(self) -> str: """处理转发消息""" diff --git a/src/plugins/chat/emoji_manager.py b/src/plugins/chat/emoji_manager.py index c8c9dc814..58713e296 100644 --- a/src/plugins/chat/emoji_manager.py +++ b/src/plugins/chat/emoji_manager.py @@ -14,6 +14,8 @@ import asyncio import time from nonebot import get_driver +from ..chat.config import global_config +from ..models.utils_model import LLM_request driver = get_driver() config = driver.config @@ -43,6 +45,7 @@ class EmojiManager: def __init__(self): self.db = Database.get_instance() self._scan_task = None + self.llm = LLM_request(model=global_config.vlm, temperature=0.3, max_tokens=50) def _ensure_emoji_dir(self): """确保表情存储目录存在""" @@ -87,55 +90,23 @@ class EmojiManager: print(f"\033[1;31m[错误]\033[0m 记录表情使用失败: {str(e)}") async def _get_emotion_from_text(self, text: str) -> List[str]: - """从文本中识别情感关键词，使用DeepSeek API进行分析 + """从文本中识别情感关键词 Args: text: 输入文本 Returns: List[str]: 匹配到的情感标签列表 """ try: - # 准备请求数据 - headers = { - "Content-Type": "application/json", - "Authorization": f"Bearer {config.siliconflow_key}" - } + prompt = f'分析这段文本："{text}"，从"happy,angry,sad,surprised,disgusted,fearful,neutral"中选出最匹配的1个情感标签。只需要返回标签，不要输出其他任何内容。' - payload = { - "model": "deepseek-ai/DeepSeek-V3", - "messages": [ - { - "role": "user", - "content": [ - { - "type": "text", - "text": f'分析这段文本："{text}"，从"happy,angry,sad,surprised,disgusted,fearful,neutral"中选出最匹配的1个情感标签。只需要返回标签，不要输出其他任何内容。' - } - ] - } - ], - "max_tokens": 50, - "temperature": 0.3 - } + content, _ = await self.llm.generate_response(prompt) + emotion = content.strip().lower() - async with aiohttp.ClientSession() as session: - async with session.post( - f"{config.siliconflow_base_url}chat/completions", - headers=headers, - json=payload - ) as response: - if response.status != 200: - print(f"\033[1;31m[错误]\033[0m API请求失败: {await response.text()}") - return ['neutral'] - - result = json.loads(await response.text()) - if "choices" in result and len(result["choices"]) > 0: - emotion = result["choices"][0]["message"]["content"].strip().lower() - # 确保返回的标签是有效的 - if emotion in self.EMOTION_KEYWORDS: - print(f"\033[1;32m[成功]\033[0m 识别到的情感: {emotion}") - return [emotion] # 返回单个情感标签的列表 + if emotion in self.EMOTION_KEYWORDS: + print(f"\033[1;32m[成功]\033[0m 识别到的情感: {emotion}") + return [emotion] - return ['neutral'] # 如果无法识别情感，返回neutral + return ['neutral'] except Exception as e: print(f"\033[1;31m[错误]\033[0m 情感分析失败: {str(e)}") @@ -250,52 +221,20 @@ class EmojiManager: async def _get_emoji_tag(self, image_base64: str) -> str: """获取表情包的标签""" - async with aiohttp.ClientSession() as session: - headers = { - "Content-Type": "application/json", - "Authorization": f"Bearer {config.siliconflow_key}" - } + try: + prompt = '这是一个表情包，请从"happy", "angry", "sad", "surprised", "disgusted", "fearful", "neutral"中选出1个情感标签。只输出标签，不要输出其他任何内容，只输出情感标签就好' - payload = { - "model": "deepseek-ai/deepseek-vl2", - "messages": [ - { - "role": "user", - "content": [ - { - "type": "text", - "text": '这是一个表情包，请从"happy", "angry", "sad", "surprised", "disgusted", "fearful", "neutral"中选出1个情感标签。只输出标签，不要输出其他任何内容，只输出情感标签就好' - }, - { - "type": "image_url", - "image_url": { - "url": f"data:image/jpeg;base64,{image_base64}" - } - } - ] - } - ], - "max_tokens": 60, - "temperature": 0.3 - } + content, _ = await self.llm.generate_response_for_image(prompt, image_base64) + tag_result = content.strip().lower() - async with session.post( - f"{config.siliconflow_base_url}chat/completions", - headers=headers, - json=payload - ) as response: - if response.status == 200: - result = await response.json() - if "choices" in result and len(result["choices"]) > 0: - tag_result = result["choices"][0]["message"]["content"].strip().lower() - - valid_tags = ["happy", "angry", "sad", "surprised", "disgusted", "fearful", "neutral"] - for tag_match in valid_tags: - if tag_match in tag_result or tag_match == tag_result: - return tag_match - print(f"\033[1;33m[警告]\033[0m 无效的标签: {tag_match}, 跳过") - else: - print(f"\033[1;31m[错误]\033[0m 获取标签失败, 状态码: {response.status}") + valid_tags = ["happy", "angry", "sad", "surprised", "disgusted", "fearful", "neutral"] + for tag_match in valid_tags: + if tag_match in tag_result or tag_match == tag_result: + return tag_match + print(f"\033[1;33m[警告]\033[0m 无效的标签: {tag_result}, 跳过") + + except Exception as e: + print(f"\033[1;31m[错误]\033[0m 获取标签失败: {str(e)}") print(f"\033[1;32m[调试信息]\033[0m 使用默认标签: neutral") return "skip" # 默认标签 diff --git a/src/plugins/chat/llm_generator.py b/src/plugins/chat/llm_generator.py index 682be8a87..a9c028827 100644 --- a/src/plugins/chat/llm_generator.py +++ b/src/plugins/chat/llm_generator.py @@ -13,249 +13,113 @@ from .prompt_builder import prompt_builder from .config import global_config from .utils import process_llm_response from nonebot import get_driver +from ..models.utils_model import LLM_request driver = get_driver() config = driver.config -class LLMResponseGenerator: +class ResponseGenerator: def __init__(self): - if global_config.API_USING == "siliconflow": - self.client = OpenAI( - api_key=config.siliconflow_key, - base_url=config.siliconflow_base_url - ) - elif global_config.API_USING == "deepseek": - self.client = OpenAI( - api_key=config.deep_seek_key, - base_url=config.deep_seek_base_url - ) - + 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.db = Database.get_instance() - - # 当前使用的模型类型 self.current_model_type = 'r1' # 默认使用 R1 async def generate_response(self, message: Message) -> 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 - model_r1_probability = global_config.MODEL_R1_PROBABILITY - model_v3_probability = global_config.MODEL_V3_PROBABILITY - model_r1_distill_probability = global_config.MODEL_R1_DISTILL_PROBABILITY - - # 生成随机数并根据概率选择模型 + # 从global_config中获取模型概率值并选择模型 rand = random.random() - if rand < model_r1_probability: + if rand < global_config.MODEL_R1_PROBABILITY: self.current_model_type = 'r1' - elif rand < model_r1_probability + model_v3_probability: + current_model = self.model_r1 + elif rand < global_config.MODEL_R1_PROBABILITY + global_config.MODEL_V3_PROBABILITY: self.current_model_type = 'v3' + current_model = self.model_v3 else: - self.current_model_type = 'r1_distill' # 默认使用 R1-Distill - + self.current_model_type = 'r1_distill' + current_model = self.model_r1_distill print(f"+++++++++++++++++{global_config.BOT_NICKNAME}{self.current_model_type}思考中+++++++++++++++++") - if self.current_model_type == 'r1': - model_response = await self._generate_r1_response(message) - elif self.current_model_type == 'v3': - model_response = await self._generate_v3_response(message) - else: - model_response = await self._generate_r1_distill_response(message) - # 打印情感标签 - print(f'{global_config.BOT_NICKNAME}的回复是：{model_response}') - model_response, emotion = await self._process_response(model_response) + model_response = await self._generate_response_with_model(message, current_model) if model_response: - print(f"为 '{model_response}' 获取到的情感标签为：{emotion}") - - return model_response, emotion + print(f'{global_config.BOT_NICKNAME}的回复是：{model_response}') + model_response, emotion = await self._process_response(model_response) + if model_response: + print(f"为 '{model_response}' 获取到的情感标签为：{emotion}") + return model_response, emotion + return None, [] - async def _generate_base_response( - self, - message: Message, - model_name: str, - model_params: Optional[Dict[str, Any]] = None - ) -> Optional[str]: + async def _generate_response_with_model(self, message: Message, model: LLM_request) -> Optional[str]: + """使用指定的模型生成回复""" sender_name = message.user_nickname or f"用户{message.user_id}" # 获取关系值 - if relationship_manager.get_relationship(message.user_id): - relationship_value = relationship_manager.get_relationship(message.user_id).relationship_value + relationship_value = relationship_manager.get_relationship(message.user_id).relationship_value if relationship_manager.get_relationship(message.user_id) else 0.0 + if relationship_value != 0.0: print(f"\033[1;32m[关系管理]\033[0m 回复中_当前关系值: {relationship_value}") - else: - relationship_value = 0.0 - - ''' 构建prompt ''' - prompt,prompt_check = prompt_builder._build_prompt( + # 构建prompt + prompt, prompt_check = prompt_builder._build_prompt( message_txt=message.processed_plain_text, sender_name=sender_name, relationship_value=relationship_value, group_id=message.group_id ) - - - # 设置默认参数 - default_params = { - "model": model_name, - "messages": [{"role": "user", "content": prompt}], - "stream": False, - "max_tokens": 1024, - "temperature": 0.7 - } - - default_params_check = { - "model": "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", - "messages": [{"role": "user", "content": prompt_check}], - "stream": False, - "max_tokens": 1024, - "temperature": 0.7 - } - - # 更新参数 - if model_params: - default_params.update(model_params) - - - def create_completion(): - return self.client.chat.completions.create(**default_params) - - def create_completion_check(): - return self.client.chat.completions.create(**default_params_check) - - loop = asyncio.get_event_loop() # 读空气模块 - air = 0 - reasoning_content_check='' - content_check='' if global_config.enable_kuuki_read: - response_check = await loop.run_in_executor(None, create_completion_check) - if response_check: - reasoning_content_check = "" - if hasattr(response_check.choices[0].message, "reasoning"): - reasoning_content_check = response_check.choices[0].message.reasoning or reasoning_content_check - elif hasattr(response_check.choices[0].message, "reasoning_content"): - reasoning_content_check = response_check.choices[0].message.reasoning_content or reasoning_content_check - content_check = response_check.choices[0].message.content - print(f"\033[1;32m[读空气]\033[0m 读空气结果为{content_check}") - if 'yes' not in content_check.lower(): - air = 1 - #稀释读空气的判定 - if air == 1 and random.random() < 0.3: - self.db.db.reasoning_logs.insert_one({ - 'time': time.time(), - 'group_id': message.group_id, - 'user': sender_name, - 'message': message.processed_plain_text, - 'model': model_name, - 'reasoning_check': reasoning_content_check, - 'response_check': content_check, - 'reasoning': "", - 'response': "", - 'prompt': prompt, - 'prompt_check': prompt_check, - 'model_params': default_params - }) - return None - - - - + content_check, reasoning_content_check = await self.model_v3.generate_response(prompt_check) + print(f"\033[1;32m[读空气]\033[0m 读空气结果为{content_check}") + if 'yes' not in content_check.lower() and random.random() < 0.3: + self._save_to_db( + message=message, + sender_name=sender_name, + prompt=prompt, + prompt_check=prompt_check, + content="", + content_check=content_check, + reasoning_content="", + reasoning_content_check=reasoning_content_check + ) + return None - response = await loop.run_in_executor(None, create_completion) + # 生成回复 + content, reasoning_content = await model.generate_response(prompt) - # 检查响应内容 - if not response: - print("请求未返回任何内容") - return None - - if not response.choices or not response.choices[0].message.content: - print("请求返回的内容无效:", response) - return None - - content = response.choices[0].message.content - - # 获取推理内容 - reasoning_content = "" - if hasattr(response.choices[0].message, "reasoning"): - reasoning_content = response.choices[0].message.reasoning or reasoning_content - elif hasattr(response.choices[0].message, "reasoning_content"): - reasoning_content = response.choices[0].message.reasoning_content or reasoning_content - # 保存到数据库 + self._save_to_db( + message=message, + sender_name=sender_name, + prompt=prompt, + prompt_check=prompt_check, + content=content, + content_check=content_check if global_config.enable_kuuki_read else "", + reasoning_content=reasoning_content, + reasoning_content_check=reasoning_content_check if global_config.enable_kuuki_read else "" + ) + + return content + + def _save_to_db(self, message: Message, sender_name: str, prompt: str, prompt_check: str, + content: str, content_check: str, reasoning_content: str, reasoning_content_check: str): + """保存对话记录到数据库""" self.db.db.reasoning_logs.insert_one({ 'time': time.time(), 'group_id': message.group_id, 'user': sender_name, 'message': message.processed_plain_text, - 'model': model_name, + 'model': self.current_model_type, 'reasoning_check': reasoning_content_check, 'response_check': content_check, 'reasoning': reasoning_content, 'response': content, 'prompt': prompt, - 'prompt_check': prompt_check, - 'model_params': default_params + 'prompt_check': prompt_check }) - - return content - - async def _generate_r1_response(self, message: Message) -> Optional[str]: - """使用 DeepSeek-R1 模型生成回复""" - if global_config.API_USING == "deepseek": - return await self._generate_base_response( - message, - global_config.DEEPSEEK_MODEL_R1, - {"temperature": 0.7, "max_tokens": 1024} - ) - else: - return await self._generate_base_response( - message, - global_config.SILICONFLOW_MODEL_R1, - {"temperature": 0.7, "max_tokens": 1024} - ) - - async def _generate_v3_response(self, message: Message) -> Optional[str]: - """使用 DeepSeek-V3 模型生成回复""" - if global_config.API_USING == "deepseek": - return await self._generate_base_response( - message, - global_config.DEEPSEEK_MODEL_V3, - {"temperature": 0.8, "max_tokens": 1024} - ) - else: - return await self._generate_base_response( - message, - global_config.SILICONFLOW_MODEL_V3, - {"temperature": 0.8, "max_tokens": 1024} - ) - - async def _generate_r1_distill_response(self, message: Message) -> Optional[str]: - """使用 DeepSeek-R1-Distill-Qwen-32B 模型生成回复""" - return await self._generate_base_response( - message, - global_config.SILICONFLOW_MODEL_R1_DISTILL, - {"temperature": 0.7, "max_tokens": 1024} - ) - - async def _get_group_chat_context(self, message: Message) -> str: - """获取群聊上下文""" - recent_messages = self.db.db.messages.find( - {"group_id": message.group_id} - ).sort("time", -1).limit(15) - - messages_list = list(recent_messages)[::-1] - group_chat = "" - - for msg_dict in messages_list: - time_str = time.strftime("%m-%d %H:%M:%S", time.localtime(msg_dict['time'])) - display_name = msg_dict.get('user_nickname', f"用户{msg_dict['user_id']}") - content = msg_dict.get('processed_plain_text', msg_dict['plain_text']) - - group_chat += f"[{time_str}] {display_name}: {content}\n" - - return group_chat async def _get_emotion_tags(self, content: str) -> List[str]: """提取情感标签""" @@ -266,33 +130,12 @@ class LLMResponseGenerator: 输出： ''' - messages = [{"role": "user", "content": prompt}] - - loop = asyncio.get_event_loop() - if global_config.API_USING == "deepseek": - model = global_config.DEEPSEEK_MODEL_V3 - else: - model = global_config.SILICONFLOW_MODEL_V3 - create_completion = partial( - self.client.chat.completions.create, - model=model, - messages=messages, - stream=False, - max_tokens=30, - temperature=0.6 - ) - response = await loop.run_in_executor(None, create_completion) - - if response.choices[0].message.content: - # 确保返回的是列表格式 - emotion_tag = response.choices[0].message.content.strip() - return [emotion_tag] # 将单个标签包装成列表返回 - - return ["neutral"] # 如果无法获取情感标签，返回默认值 + content, _ = await self.model_v3.generate_response(prompt) + return [content.strip()] if content else ["neutral"] except Exception as e: print(f"获取情感标签时出错: {e}") - return ["neutral"] # 发生错误时返回默认值 + return ["neutral"] async def _process_response(self, content: str) -> Tuple[List[str], List[str]]: """处理响应内容，返回处理后的内容和情感标签""" @@ -300,10 +143,6 @@ class LLMResponseGenerator: return None, [] emotion_tags = await self._get_emotion_tags(content) - processed_response = process_llm_response(content) - return processed_response, emotion_tags - -# 创建全局实例 -llm_response = LLMResponseGenerator() \ No newline at end of file + return processed_response, emotion_tags \ No newline at end of file diff --git a/src/plugins/memory_system/llm_module.py b/src/plugins/memory_system/llm_module.py deleted file mode 100644 index c2b6cf042..000000000 --- a/src/plugins/memory_system/llm_module.py +++ /dev/null @@ -1,68 +0,0 @@ -import os -import requests -from typing import Tuple, Union -import time -from nonebot import get_driver -import aiohttp -import asyncio -from src.plugins.chat.config import global_config -driver = get_driver() -config = driver.config - -class LLMModel: - # def __init__(self, model_name="deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", **kwargs): - def __init__(self, model_name=global_config.SILICONFLOW_MODEL_V3, **kwargs): - self.model_name = model_name - self.params = kwargs - self.api_key = config.siliconflow_key - self.base_url = config.siliconflow_base_url - - async def generate_response(self, prompt: str) -> Tuple[str, str]: - """根据输入的提示生成模型的响应""" - headers = { - "Authorization": f"Bearer {self.api_key}", - "Content-Type": "application/json" - } - - # 构建请求体 - data = { - "model": self.model_name, - "messages": [{"role": "user", "content": prompt}], - "temperature": 0.5, - **self.params - } - - # 发送请求到完整的chat/completions端点 - api_url = f"{self.base_url.rstrip('/')}/chat/completions" - - max_retries = 3 - base_wait_time = 15 - - for retry in range(max_retries): - try: - async with aiohttp.ClientSession() as session: - async with session.post(api_url, headers=headers, json=data) as response: - if response.status == 429: - wait_time = base_wait_time * (2 ** retry) # 指数退避 - print(f"遇到请求限制(429)，等待{wait_time}秒后重试...") - await asyncio.sleep(wait_time) - continue - - response.raise_for_status() # 检查其他响应状态 - - result = await response.json() - if "choices" in result and len(result["choices"]) > 0: - content = result["choices"][0]["message"]["content"] - reasoning_content = result["choices"][0]["message"].get("reasoning_content", "") - return content, reasoning_content - return "没有返回结果", "" - - except Exception as e: - if retry < max_retries - 1: # 如果还有重试机会 - wait_time = base_wait_time * (2 ** retry) - print(f"请求失败，等待{wait_time}秒后重试... 错误: {str(e)}") - await asyncio.sleep(wait_time) - else: - return f"请求失败: {str(e)}", "" - - return "达到最大重试次数，请求仍然失败", "" \ No newline at end of file diff --git a/src/plugins/memory_system/memory.py b/src/plugins/memory_system/memory.py index cab41f903..a051192a5 100644 --- a/src/plugins/memory_system/memory.py +++ b/src/plugins/memory_system/memory.py @@ -1,19 +1,16 @@ # -*- coding: utf-8 -*- import os import jieba -from .llm_module import LLMModel import networkx as nx import matplotlib.pyplot as plt -import math from collections import Counter import datetime import random import time from ..chat.config import global_config -import sys from ...common.database import Database # 使用正确的导入语法 from ..chat.utils import calculate_information_content, get_cloest_chat_from_db - +from ..models.utils_model import LLM_request class Memory_graph: def __init__(self): self.G = nx.Graph() # 使用 networkx 的图结构 @@ -169,8 +166,8 @@ class Memory_graph: class Hippocampus: def __init__(self,memory_graph:Memory_graph): self.memory_graph = memory_graph - self.llm_model = LLMModel() - self.llm_model_small = LLMModel(model_name="deepseek-ai/DeepSeek-V2.5") + self.llm_model = LLM_request(model = global_config.llm_normal,temperature=0.5) + self.llm_model_small = LLM_request(model = global_config.llm_normal_minor,temperature=0.5) def get_memory_sample(self,chat_size=20,time_frequency:dict={'near':2,'mid':4,'far':3}): current_timestamp = datetime.datetime.now().timestamp() diff --git a/src/plugins/models/utils_model.py b/src/plugins/models/utils_model.py new file mode 100644 index 000000000..5f4d02df5 --- /dev/null +++ b/src/plugins/models/utils_model.py @@ -0,0 +1,199 @@ +import aiohttp +import asyncio +import requests +import time +from typing import Tuple, Union +from nonebot import get_driver +from ..chat.config import global_config +driver = get_driver() +config = driver.config + +class LLM_request: + def __init__(self, model = global_config.llm_normal,**kwargs): + # 将大写的配置键转换为小写并从config中获取实际值 + try: + self.api_key = getattr(config, model["key"]) + self.base_url = getattr(config, model["base_url"]) + except AttributeError as e: + raise ValueError(f"配置错误：找不到对应的配置项 - {str(e)}") + self.model_name = model["name"] + self.params = kwargs + + async def generate_response(self, prompt: str) -> Tuple[str, str]: + """根据输入的提示生成模型的异步响应""" + headers = { + "Authorization": f"Bearer {self.api_key}", + "Content-Type": "application/json" + } + + # 构建请求体 + data = { + "model": self.model_name, + "messages": [{"role": "user", "content": prompt}], + **self.params + } + + # 发送请求到完整的chat/completions端点 + api_url = f"{self.base_url.rstrip('/')}/chat/completions" + + max_retries = 3 + base_wait_time = 15 + + for retry in range(max_retries): + try: + async with aiohttp.ClientSession() as session: + async with session.post(api_url, headers=headers, json=data) as response: + if response.status == 429: + wait_time = base_wait_time * (2 ** retry) # 指数退避 + print(f"遇到请求限制(429)，等待{wait_time}秒后重试...") + await asyncio.sleep(wait_time) + continue + + response.raise_for_status() # 检查其他响应状态 + + result = await response.json() + if "choices" in result and len(result["choices"]) > 0: + content = result["choices"][0]["message"]["content"] + reasoning_content = result["choices"][0]["message"].get("reasoning_content", "") + return content, reasoning_content + return "没有返回结果", "" + + except Exception as e: + if retry < max_retries - 1: # 如果还有重试机会 + wait_time = base_wait_time * (2 ** retry) + print(f"请求失败，等待{wait_time}秒后重试... 错误: {str(e)}") + await asyncio.sleep(wait_time) + else: + return f"请求失败: {str(e)}", "" + + return "达到最大重试次数，请求仍然失败", "" + + async def generate_response_for_image(self, prompt: str, image_base64: str) -> Tuple[str, str]: + """根据输入的提示和图片生成模型的异步响应""" + headers = { + "Authorization": f"Bearer {self.api_key}", + "Content-Type": "application/json" + } + + # 构建请求体 + data = { + "model": self.model_name, + "messages": [ + { + "role": "user", + "content": [ + { + "type": "text", + "text": prompt + }, + { + "type": "image_url", + "image_url": { + "url": f"data:image/jpeg;base64,{image_base64}" + } + } + ] + } + ], + **self.params + } + + # 发送请求到完整的chat/completions端点 + api_url = f"{self.base_url.rstrip('/')}/chat/completions" + + max_retries = 3 + base_wait_time = 15 + + for retry in range(max_retries): + try: + async with aiohttp.ClientSession() as session: + async with session.post(api_url, headers=headers, json=data) as response: + if response.status == 429: + wait_time = base_wait_time * (2 ** retry) # 指数退避 + print(f"遇到请求限制(429)，等待{wait_time}秒后重试...") + await asyncio.sleep(wait_time) + continue + + response.raise_for_status() # 检查其他响应状态 + + result = await response.json() + if "choices" in result and len(result["choices"]) > 0: + content = result["choices"][0]["message"]["content"] + reasoning_content = result["choices"][0]["message"].get("reasoning_content", "") + return content, reasoning_content + return "没有返回结果", "" + + except Exception as e: + if retry < max_retries - 1: # 如果还有重试机会 + wait_time = base_wait_time * (2 ** retry) + print(f"请求失败，等待{wait_time}秒后重试... 错误: {str(e)}") + await asyncio.sleep(wait_time) + else: + return f"请求失败: {str(e)}", "" + + return "达到最大重试次数，请求仍然失败", "" + + def generate_response_for_image_sync(self, prompt: str, image_base64: str) -> Tuple[str, str]: + """同步方法：根据输入的提示和图片生成模型的响应""" + headers = { + "Authorization": f"Bearer {self.api_key}", + "Content-Type": "application/json" + } + + # 构建请求体 + data = { + "model": self.model_name, + "messages": [ + { + "role": "user", + "content": [ + { + "type": "text", + "text": prompt + }, + { + "type": "image_url", + "image_url": { + "url": f"data:image/jpeg;base64,{image_base64}" + } + } + ] + } + ], + **self.params + } + + # 发送请求到完整的chat/completions端点 + api_url = f"{self.base_url.rstrip('/')}/chat/completions" + + max_retries = 3 + base_wait_time = 15 + + for retry in range(max_retries): + try: + response = requests.post(api_url, headers=headers, json=data, timeout=30) + + if response.status_code == 429: + wait_time = base_wait_time * (2 ** retry) # 指数退避 + print(f"遇到请求限制(429)，等待{wait_time}秒后重试...") + time.sleep(wait_time) + continue + + response.raise_for_status() # 检查其他响应状态 + + result = response.json() + if "choices" in result and len(result["choices"]) > 0: + content = result["choices"][0]["message"]["content"] + reasoning_content = result["choices"][0]["message"].get("reasoning_content", "") + return content, reasoning_content + return "没有返回结果", "" + + except Exception as e: + if retry < max_retries - 1: # 如果还有重试机会 + wait_time = base_wait_time * (2 ** retry) + print(f"请求失败，等待{wait_time}秒后重试... 错误: {str(e)}") + time.sleep(wait_time) + else: + return f"请求失败: {str(e)}", "" + + return "达到最大重试次数，请求仍然失败", "" diff --git a/src/plugins/schedule/schedule_generator.py b/src/plugins/schedule/schedule_generator.py index 63e3235a3..24b2a32dd 100644 --- a/src/plugins/schedule/schedule_generator.py +++ b/src/plugins/schedule/schedule_generator.py @@ -1,10 +1,10 @@ import datetime import os from typing import List, Dict -from .schedule_llm_module import LLMModel from ...common.database import Database # 使用正确的导入语法 from src.plugins.chat.config import global_config from nonebot import get_driver +from ..models.utils_model import LLM_request driver = get_driver() config = driver.config @@ -21,22 +21,27 @@ Database.initialize( class ScheduleGenerator: def __init__(self): - if global_config.API_USING == "siliconflow": - self.llm_scheduler = LLMModel(model_name=global_config.SILICONFLOW_MODEL_V3) - elif global_config.API_USING == "deepseek": - self.llm_scheduler = LLMModel(model_name=global_config.DEEPSEEK_MODEL_V3) + #根据global_config.llm_normal这一字典配置指定模型 + # self.llm_scheduler = LLMModel(model = global_config.llm_normal,temperature=0.9) + self.llm_scheduler = LLM_request(model = global_config.llm_normal,temperature=0.9) self.db = Database.get_instance() - + self.today_schedule_text = "" + self.today_schedule = {} + self.tomorrow_schedule_text = "" + self.tomorrow_schedule = {} + self.yesterday_schedule_text = "" + self.yesterday_schedule = {} + + async def initialize(self): today = datetime.datetime.now() tomorrow = datetime.datetime.now() + datetime.timedelta(days=1) yesterday = datetime.datetime.now() - datetime.timedelta(days=1) - self.today_schedule_text, self.today_schedule = self.generate_daily_schedule(target_date=today) - - self.tomorrow_schedule_text, self.tomorrow_schedule = self.generate_daily_schedule(target_date=tomorrow,read_only=True) - self.yesterday_schedule_text, self.yesterday_schedule = self.generate_daily_schedule(target_date=yesterday,read_only=True) + self.today_schedule_text, self.today_schedule = await self.generate_daily_schedule(target_date=today) + self.tomorrow_schedule_text, self.tomorrow_schedule = await self.generate_daily_schedule(target_date=tomorrow,read_only=True) + self.yesterday_schedule_text, self.yesterday_schedule = await self.generate_daily_schedule(target_date=yesterday,read_only=True) - def generate_daily_schedule(self, target_date: datetime.datetime = None,read_only:bool = False) -> Dict[str, str]: + async def generate_daily_schedule(self, target_date: datetime.datetime = None,read_only:bool = False) -> Dict[str, str]: if target_date is None: target_date = datetime.datetime.now() @@ -60,7 +65,7 @@ class ScheduleGenerator: 3. 晚上的计划和休息时间 请按照时间顺序列出具体时间点和对应的活动，用一个时间点而不是时间段来表示时间，用逗号,隔开时间与活动，格式为"时间,活动"，例如"08:00,起床"。""" - schedule_text, _ = self.llm_scheduler.generate_response(prompt) + schedule_text, _ = await self.llm_scheduler.generate_response(prompt) # print(self.schedule_text) self.db.db.schedule.insert_one({"date": date_str, "schedule": schedule_text}) else: diff --git a/src/plugins/schedule/schedule_llm_module.py b/src/plugins/schedule/schedule_llm_module.py deleted file mode 100644 index 21e3f7681..000000000 --- a/src/plugins/schedule/schedule_llm_module.py +++ /dev/null @@ -1,61 +0,0 @@ -import os -import requests -import aiohttp -from typing import Tuple, Union -from nonebot import get_driver -from src.plugins.chat.config import global_config -driver = get_driver() -config = driver.config - -class LLMModel: - # def __init__(self, model_name="deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", **kwargs): - def __init__(self, model_name=global_config.SILICONFLOW_MODEL_R1,api_using=None, **kwargs): - if api_using == "deepseek": - self.api_key = config.deep_seek_key - self.base_url = config.deep_seek_base_url - self.model_name = global_config.DEEPSEEK_MODEL_R1 - else: - self.api_key = config.siliconflow_key - self.base_url = config.siliconflow_base_url - self.model_name = model_name - self.params = kwargs - - def generate_response(self, prompt: str) -> Tuple[str, str]: - """根据输入的提示生成模型的响应""" - headers = { - "Authorization": f"Bearer {self.api_key}", - "Content-Type": "application/json" - } - - # 构建请求体 - data = { - "model": self.model_name, - "messages": [{"role": "user", "content": prompt}], - "temperature": 0.9, - **self.params - } - - # 发送请求到完整的chat/completions端点 - api_url = f"{self.base_url.rstrip('/')}/chat/completions" - - try: - response = requests.post(api_url, headers=headers, json=data) - response.raise_for_status() # 检查响应状态 - - result = response.json() - if "choices" in result and len(result["choices"]) > 0: - content = result["choices"][0]["message"]["content"] - reasoning_content = result["choices"][0]["message"].get("reasoning_content", "") - return content, reasoning_content # 返回内容和推理内容 - return "没有返回结果", "" # 返回两个值 - - except Exception as e: - return f"请求失败: {str(e)}", "" # 返回错误信息和空字符串 - -# 示例用法 -if __name__ == "__main__": - model = LLMModel() # 默认使用 DeepSeek-V3 模型 - prompt = "你好，你喜欢我吗？" - result, reasoning = model.generate_response(prompt) - print("回复内容:", result) - print("推理内容:", reasoning) \ No newline at end of file From 7ccd27520298be5f0d9983c1b01d312037fa059f Mon Sep 17 00:00:00 2001 From: SengokuCola <1026294844@qq.com> Date: Mon, 3 Mar 2025 01:19:22 +0800 Subject: [PATCH 19/26] Update .env --- .env | 40 ++++++++++++++++++++-------------------- 1 file changed, 20 insertions(+), 20 deletions(-) diff --git a/.env b/.env index 709d22d34..06c26cdf3 100644 --- a/.env +++ b/.env @@ -1,26 +1,26 @@ # 您不应该修改默认值，这个文件被仓库索引，请修改.env.prod -ENVIRONMENT=dev -HOST=127.0.0.1 -PORT=8080 +ENVIRONMENT=prod +# HOST=127.0.0.1 +# PORT=8080 -COMMAND_START=["/"] +# COMMAND_START=["/"] -# 插件配置 -PLUGINS=["src2.plugins.chat"] +# # 插件配置 +# PLUGINS=["src2.plugins.chat"] -# 默认配置 -MONGODB_HOST=127.0.0.1 -MONGODB_PORT=27017 -DATABASE_NAME=MegBot +# # 默认配置 +# MONGODB_HOST=127.0.0.1 +# MONGODB_PORT=27017 +# DATABASE_NAME=MegBot -MONGODB_USERNAME = "" # 默认空值 -MONGODB_PASSWORD = "" # 默认空值 -MONGODB_AUTH_SOURCE = "" # 默认空值 +# MONGODB_USERNAME = "" # 默认空值 +# MONGODB_PASSWORD = "" # 默认空值 +# MONGODB_AUTH_SOURCE = "" # 默认空值 -#key and url -CHAT_ANY_WHERE_KEY= -SILICONFLOW_KEY= -CHAT_ANY_WHERE_BASE_URL=https://api.chatanywhere.tech/v1 -SILICONFLOW_BASE_URL=https://api.siliconflow.cn/v1/ -DEEP_SEEK_KEY= -DEEP_SEEK_BASE_URL=https://api.deepseek.com/v1 \ No newline at end of file +# #key and url +# CHAT_ANY_WHERE_KEY= +# SILICONFLOW_KEY= +# CHAT_ANY_WHERE_BASE_URL=https://api.chatanywhere.tech/v1 +# SILICONFLOW_BASE_URL=https://api.siliconflow.cn/v1/ +# DEEP_SEEK_KEY= +# DEEP_SEEK_BASE_URL=https://api.deepseek.com/v1 \ No newline at end of file From d805e6ea2ccfdcc64f99b7e76a80597c9d45e47a Mon Sep 17 00:00:00 2001 From: KawaiiYusora Date: Mon, 3 Mar 2025 01:25:32 +0800 Subject: [PATCH 20/26] =?UTF-8?q?env.prod=E6=97=A0=E6=B3=95=E5=8A=A0?= =?UTF-8?q?=E8=BD=BD?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- bot.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bot.py b/bot.py index cf1b82ff6..78bf34b32 100644 --- a/bot.py +++ b/bot.py @@ -31,7 +31,7 @@ if env_file == ".env.dev" and os.path.exists(env_file): load_dotenv(env_file, override=True) # override=True 允许覆盖已存在的环境变量 elif os.path.exists(".env.prod"): logger.success("加载环境变量配置") - load_dotenv(env_file, override=True) # override=True 允许覆盖已存在的环境变量 + load_dotenv(".env.prod", override=True) # override=True 允许覆盖已存在的环境变量 else: logger.error(f"{env}对应的环境配置文件{env_file}不存在,请修改.env文件中的ENVIRONMENT变量为 prod.") exit(1) From 568ceea3a072f36cd8216bc6476d8ce27034aaa5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=87=8E=E5=8E=9F=E5=B0=8F=E7=89=9B?= Date: Mon, 3 Mar 2025 11:11:39 +0800 Subject: [PATCH 21/26] =?UTF-8?q?docker=E7=BC=96=E8=AF=91=E6=80=A7?= =?UTF-8?q?=E8=83=BD=E4=BC=98=E5=8C=96?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .dockerignore | 6 ++++++ .gitea/workflows/docker-image.yml | 36 +++++++++++++++++++++++++++++++ Dockerfile | 15 ++++++++++--- 3 files changed, 54 insertions(+), 3 deletions(-) create mode 100644 .dockerignore create mode 100644 .gitea/workflows/docker-image.yml diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 000000000..0ed9090fd --- /dev/null +++ b/.dockerignore @@ -0,0 +1,6 @@ +.git +__pycache__ +*.pyc +*.pyo +*.pyd +.DS_Store \ No newline at end of file diff --git a/.gitea/workflows/docker-image.yml b/.gitea/workflows/docker-image.yml new file mode 100644 index 000000000..a5ac92778 --- /dev/null +++ b/.gitea/workflows/docker-image.yml @@ -0,0 +1,36 @@ +name: Docker Build and Push + +on: + push: + branches: + - aijiang # 推送到main分支时触发 + tags: + - "*" # 推送v开头的tag时触发（例如v1.0.0） + workflow_dispatch: # 允许手动触发 + +jobs: + build-and-push: + runs-on: ubuntu-latest + steps: + - name: Checkout code + uses: https://gitea.neppure.vip/actions/actions/checkout@v4 + + - name: Set up Docker Buildx + uses: https://gitea.neppure.vip/actions/docker/setup-buildx-action@v3 + + - name: Login to Docker Hub + uses: https://gitea.neppure.vip/actions/docker/login-action@v3 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_TOKEN }} + + - name: Build and Push Docker Image + uses: https://gitea.neppure.vip/actions/docker/build-push-action@v5 + with: + context: . # Docker构建上下文路径 + file: ./Dockerfile # Dockerfile路径 + platforms: linux/amd64,linux/arm64 # 支持arm架构 + tags: | + gitea.neppure.vip/NepPure/maimbot:${{ github.ref_name }} + gitea.neppure.vip/NepPure/maimbot:latest + push: true diff --git a/Dockerfile b/Dockerfile index 225a90a1b..8f2b88f64 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,8 +1,17 @@ FROM nonebot/nb-cli:latest -WORKDIR / -COPY . /MaiMBot/ + +# 设置工作目录 WORKDIR /MaiMBot + +# 先复制依赖列表 +COPY requirements.txt . + +# 安装依赖（这层会被缓存直到requirements.txt改变） RUN pip install --upgrade -r requirements.txt + +# 然后复制项目代码 +COPY . . + VOLUME [ "/MaiMBot/config" ] EXPOSE 8080 -ENTRYPOINT [ "nb","run" ] +ENTRYPOINT [ "nb","run" ] \ No newline at end of file From ed74a6ec0f890297d04cfe44f38446b3e0a42975 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=87=8E=E5=8E=9F=E5=B0=8F=E7=89=9B?= Date: Mon, 3 Mar 2025 11:15:16 +0800 Subject: [PATCH 22/26] del more --- .gitea/workflows/docker-image.yml | 36 ------------------------------- 1 file changed, 36 deletions(-) delete mode 100644 .gitea/workflows/docker-image.yml diff --git a/.gitea/workflows/docker-image.yml b/.gitea/workflows/docker-image.yml deleted file mode 100644 index a5ac92778..000000000 --- a/.gitea/workflows/docker-image.yml +++ /dev/null @@ -1,36 +0,0 @@ -name: Docker Build and Push - -on: - push: - branches: - - aijiang # 推送到main分支时触发 - tags: - - "*" # 推送v开头的tag时触发（例如v1.0.0） - workflow_dispatch: # 允许手动触发 - -jobs: - build-and-push: - runs-on: ubuntu-latest - steps: - - name: Checkout code - uses: https://gitea.neppure.vip/actions/actions/checkout@v4 - - - name: Set up Docker Buildx - uses: https://gitea.neppure.vip/actions/docker/setup-buildx-action@v3 - - - name: Login to Docker Hub - uses: https://gitea.neppure.vip/actions/docker/login-action@v3 - with: - username: ${{ secrets.DOCKERHUB_USERNAME }} - password: ${{ secrets.DOCKERHUB_TOKEN }} - - - name: Build and Push Docker Image - uses: https://gitea.neppure.vip/actions/docker/build-push-action@v5 - with: - context: . # Docker构建上下文路径 - file: ./Dockerfile # Dockerfile路径 - platforms: linux/amd64,linux/arm64 # 支持arm架构 - tags: | - gitea.neppure.vip/NepPure/maimbot:${{ github.ref_name }} - gitea.neppure.vip/NepPure/maimbot:latest - push: true From e991a27ed49525f0b38d72fe66812fbca44cd314 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=87=8E=E5=8E=9F=E5=B0=8F=E7=89=9B?= Date: Mon, 3 Mar 2025 11:17:05 +0800 Subject: [PATCH 23/26] =?UTF-8?q?=E5=A2=9E=E5=8A=A0=E6=8C=82=E8=BD=BD?= =?UTF-8?q?=E7=82=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Dockerfile | 1 + 1 file changed, 1 insertion(+) diff --git a/Dockerfile b/Dockerfile index 8f2b88f64..c4aedc94a 100644 --- a/Dockerfile +++ b/Dockerfile @@ -13,5 +13,6 @@ RUN pip install --upgrade -r requirements.txt COPY . . VOLUME [ "/MaiMBot/config" ] +VOLUME [ "/MaiMBot/data" ] EXPOSE 8080 ENTRYPOINT [ "nb","run" ] \ No newline at end of file From 1c27eb29b50cbf3577f352f4cb61bd90d1a6a503 Mon Sep 17 00:00:00 2001 From: SengokuCola <1026294844@qq.com> Date: Mon, 3 Mar 2025 12:41:13 +0800 Subject: [PATCH 24/26] =?UTF-8?q?readme=E9=94=90=E6=84=8F=E7=BC=96?= =?UTF-8?q?=E8=BE=91=E4=B8=AD?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 174 +++++++------------------------------------ docs/installation.md | 102 +++++++++++++++++++++++++ docs/video.png | Bin 0 -> 28002 bytes 3 files changed, 130 insertions(+), 146 deletions(-) create mode 100644 docs/installation.md create mode 100644 docs/video.png diff --git a/README.md b/README.md index 00afd83e8..c611d85ef 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,6 @@

- ![Python Version](https://img.shields.io/badge/Python-3.x-blue) ![License](https://img.shields.io/github/license/SengokuCola/MaiMBot) ![Status](https://img.shields.io/badge/状态-开发中-yellow) @@ -12,163 +11,33 @@ ## 📝 项目简介 -**麦麦qq机器人的源代码仓库** +**麦麦是一个基于大语言模型的智能QQ群聊机器人** -基于llm、napcat、nonebot和mongodb的专注于群聊天的qqbot +- 🤖 基于 nonebot2 框架开发 +- 🧠 LLM 提供对话能力 +- 💾 MongoDB 提供数据持久化支持 +- 🌐 NapCat 作为QQ协议端支持 -> ⚠️ **警告**：代码可能随时更改，目前版本不一定是稳定版本 -> ⚠️ **警告**：请自行了解qqbot的风险，麦麦有时候一天被腾讯肘七八次 -> ⚠️ **警告**：由于麦麦一直在迭代，所以可能存在一些bug，请自行测试，包括胡言乱语（ +> ⚠️ **注意事项** +> - 项目处于活跃开发阶段，代码可能随时更改 +> - 文档未完善，有问题可以提交 Issue 或者 Discussion +> - QQ机器人存在被限制风险，请自行了解，谨慎使用 +> - 由于持续迭代，可能存在一些已知或未知的bug -关于麦麦的开发和建议相关的讨论群:766798517（不建议发布无关消息）这里不会有麦麦发言！ +**交流群**: 766798517（仅用于开发和建议相关讨论） -## 开发计划TODO：LIST +## 📚 文档 -- 兼容gif的解析和保存 -- 小程序转发链接解析 -- 对思考链长度限制 -- 修复已知bug -- 完善文档 -- 修复转发 -- config自动生成和检测 -- log别用print -- 给发送消息写专门的类 -- 改进表情包发送逻辑l - - -## 📚 详细文档 -- [项目详细介绍和架构说明](docs/doc1.md) - 包含完整的项目结构、文件说明和核心功能实现细节(由claude-3.5-sonnet生成) - -### 安装方法（还没测试好，随时outdated ,现在部署可能遇到未知问题！！！！） - -#### Linux 使用 Docker Compose 部署 -获取项目根目录中的```docker-compose.yml```文件，运行以下命令 -```bash -NAPCAT_UID=$(id -u) NAPCAT_GID=$(id -g) docker compose up -d -``` -配置文件修改完成后，运行以下命令 -```bash -NAPCAT_UID=$(id -u) NAPCAT_GID=$(id -g) docker compose restart -``` - -#### 手动运行 -1. **创建Python环境** - 推荐使用conda或其他虚拟环境进行依赖安装，防止出现依赖版本冲突问题 - ```bash - # 安装requirements - pip install -r requirements.txt - ``` -2. **MongoDB设置** - - 安装并运行mongodb - - 麦麦bot会自动连接默认的mongodb，端口和数据库名可配置 - -3. **Napcat配置** - - 安装并运行Napcat，登录 - - 在Napcat的网络设置中添加ws反向代理:ws://localhost:8080/onebot/v11/ws - -4. **配置文件设置** - - 修改.env的 变量值为 prod - - 将.env.prod文件打开，填上你的apikey（硅基流动或deepseekapi） - - 将bot_config_toml改名为bot_config.toml，打开并填写相关内容，不然无法正常运行 - - #### .env 文件配置说明 - ```ini - # 环境配置 - ENVIRONMENT=dev # 开发环境设置 - HOST=127.0.0.1 # 主机地址 - PORT=8080 # 端口号 - - # 命令前缀设置 - COMMAND_START=["/"] # 命令起始符 - - # 插件配置 - PLUGINS=["src2.plugins.chat"] # 启用的插件列表 - - # MongoDB配置 - MONGODB_HOST=127.0.0.1 # MongoDB主机地址 - MONGODB_PORT=27017 # MongoDB端口 - DATABASE_NAME=MegBot # 数据库名称 - MONGODB_USERNAME="" # MongoDB用户名（可选） - MONGODB_PASSWORD="" # MongoDB密码（可选） - MONGODB_AUTH_SOURCE="" # MongoDB认证源（可选） - - #api配置项，建议siliconflow必填，识图需要这个 - SILICONFLOW_KEY= - SILICONFLOW_BASE_URL=https://api.siliconflow.cn/v1/ - DEEP_SEEK_KEY= - DEEP_SEEK_BASE_URL=https://api.deepseek.com/v1 - ``` - - #### bot_config.toml 文件配置说明 - ```toml - # 数据库设置 - [database] - host = "127.0.0.1" # MongoDB主机地址 - port = 27017 # MongoDB端口 - name = "MegBot" # 数据库名称 - - # 机器人基本设置 - [bot] - qq = # 你的机器人QQ号（必填） - nickname = "麦麦" # 机器人昵称 - - # 消息处理设置 - [message] - min_text_length = 2 # 最小响应文本长度 - max_context_size = 15 # 上下文最大保存数量 - emoji_chance = 0.2 # 表情包使用概率 - - # 表情包功能设置 - [emoji] - check_interval = 120 # 表情检查间隔（秒） - register_interval = 10 # 表情注册间隔（秒） - - # CQ码设置 - [cq_code] - enable_pic_translate = false # 是否启用图片转换（无效） - - # 响应设置 - [response] - api_using = "siliconflow" # 回复使用的API（siliconflow/deepseek） - model_r1_probability = 0.8 # R1模型使用概率 - model_v3_probability = 0.1 # V3模型使用概率 - model_r1_distill_probability = 0.1 # R1蒸馏模型使用概率（对deepseek api 无效） - - # 其他设置 - [others] - enable_advance_output = false # 是否启用详细日志输出 - - # 群组设置 - [groups] - talk_allowed = [ # 允许回复的群号列表 - # 在这里添加群号,逗号隔开 - ] - - talk_frequency_down = [ # 降低回复频率的群号列表 - # 在这里添加群号,逗号隔开 - ] - - ban_user_id = [ # 禁止回复的用户QQ号列表 - # 在这里添加QQ号,逗号隔开 - ] - ``` - -5. **运行麦麦** - 在含有bot.py程序的目录下运行（如果使用了虚拟环境需要先进入虚拟环境） - ```bash - nb run - ``` -6. **运行其他组件** - run_thingking.bat 可以启动可视化的推理界面（未完善）和消息队列及其他信息预览（WIP） - knowledge.bat可以将/data/raw_info下的文本文档载入到数据库（未启动） +- [安装与配置指南](docs/installation.md) - 详细的部署和配置说明 +- [项目架构说明](docs/doc1.md) - 项目结构和核心功能实现细节 ## 🎯 功能介绍 @@ -204,6 +73,19 @@ NAPCAT_UID=$(id -u) NAPCAT_GID=$(id -g) docker compose restart - 幽默和meme功能：WIP的WIP - 让麦麦玩mc：WIP的WIP的WIP +## 开发计划TODO：LIST + +- 兼容gif的解析和保存 +- 小程序转发链接解析 +- 对思考链长度限制 +- 修复已知bug +- 完善文档 +- 修复转发 +- config自动生成和检测 +- log别用print +- 给发送消息写专门的类 +- 改进表情包发送逻辑 + ## 📌 注意事项 纯编程外行，面向cursor编程，很多代码史一样多多包涵 diff --git a/docs/installation.md b/docs/installation.md new file mode 100644 index 000000000..9fba9ecd2 --- /dev/null +++ b/docs/installation.md @@ -0,0 +1,102 @@ +# 🔧 安装与配置指南 + +## 部署方式 + +### 🐳 Docker部署（推荐） + +1. 获取配置文件： +```bash +wget https://raw.githubusercontent.com/SengokuCola/MaiMBot/main/docker-compose.yml +``` + +2. 启动服务： +```bash +NAPCAT_UID=$(id -u) NAPCAT_GID=$(id -g) docker compose up -d +``` + +3. 修改配置后重启： +```bash +NAPCAT_UID=$(id -u) NAPCAT_GID=$(id -g) docker compose restart +``` + +### 📦 手动部署 + +1. **环境准备** +```bash +# 创建虚拟环境（推荐） +python -m venv venv +source venv/bin/activate # Linux +venv\\Scripts\\activate # Windows + +# 安装依赖 +pip install -r requirements.txt +``` + +2. **配置MongoDB** +- 安装并启动MongoDB服务 +- 默认连接本地27017端口 + +3. **配置NapCat** +- 安装并登录NapCat +- 添加反向WS：`ws://localhost:8080/onebot/v11/ws` + +4. **配置文件设置** +- 复制并修改环境配置：`.env.prod` +- 复制并修改机器人配置：`bot_config.toml` + +5. **启动服务** +```bash +nb run +``` + +6. **其他组件** +- `run_thingking.bat`: 启动可视化推理界面（未完善）和消息队列预览 +- `knowledge.bat`: 将`/data/raw_info`下的文本文档载入数据库 + +## ⚙️ 配置说明 + +### 环境配置 (.env.prod) +```ini +# API配置（必填） +SILICONFLOW_KEY=your_key +SILICONFLOW_BASE_URL=https://api.siliconflow.cn/v1/ +DEEP_SEEK_KEY=your_key +DEEP_SEEK_BASE_URL=https://api.deepseek.com/v1 + +# 服务配置 +HOST=127.0.0.1 +PORT=8080 + +# 数据库配置 +MONGODB_HOST=127.0.0.1 +MONGODB_PORT=27017 +DATABASE_NAME=MegBot +``` + +### 机器人配置 (bot_config.toml) +```toml +[bot] +qq = "你的机器人QQ号" +nickname = "麦麦" + +[message] +max_context_size = 15 +emoji_chance = 0.2 + +[response] +api_using = "siliconflow" # 或 "deepseek" + +[others] +enable_advance_output = false # 是否启用详细日志输出 + +[groups] +talk_allowed = [] # 允许回复的群号列表 +talk_frequency_down = [] # 降低回复频率的群号列表 +ban_user_id = [] # 禁止回复的用户QQ号列表 +``` + +## ⚠️ 注意事项 + +- 目前部署方案仍在测试中，可能存在未知问题 +- 配置文件中的API密钥请妥善保管，不要泄露 +- 建议先在测试环境中运行，确认无误后再部署到生产环境 \ No newline at end of file diff --git a/docs/video.png b/docs/video.png new file mode 100644 index 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zuiL3DvtK(enbmeXXTIcju?qw;C1QQNJi1kmS@@o=6~D{7KE_yb-*?aBBi>G_*v@d< zts{K<_NpbuO6pXcTI5Nz@qIXQB#b-zUWfd$dS(J?6HTpMLq0jI$UuHB{UA%j@u|)| zQU%#@ez@IYzZVPnd-HQx5O@Avd|WJOYFPLBz-_LlE}p?Y>s1c0iCyMbQrUC``cve7 z)n`qGzbXVSex8pYy2)O}5-_$jz3JgMSB*5oEt^Omre3bWPD_c{T%4-Ha@6ppuEM{Y4QH@Qi~_c{6t5 zb^V44jl@BCLi>*~m?stb zh$aHU&4}g(6Cnfo6)BkVEu@f6nk=Ro?03ahx9M_dkZlWsDWA|}mE(lS49LkwC=HyO zEh>b1e87}fq)B;HenMR$V2`cqpWUpMpYJD_az~I+TpG&HGsu@L+H-LuuNDgOll Date: Mon, 3 Mar 2025 15:16:19 +0800 Subject: [PATCH 25/26] =?UTF-8?q?=E7=8E=A9=E5=90=A7?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 1 --- .gitignore | 1 + README.md | 4 +- char_frequency.json | 12012 ++++++++++++++++++++++++++++++ requirements.txt | Bin 540 -> 582 bytes src/test/emotion_cal.py | 70 + src/test/emotion_cal_bert.py | 74 + src/test/emotion_cal_hanlp.py | 62 + src/test/emotion_cal_snownlp.py | 53 + src/test/snownlp_demo.py | 54 + src/test/typo.py | 488 ++ src/test/typo_word.py | 301 + 11 files changed, 13117 insertions(+), 2 deletions(-) create mode 100644 char_frequency.json create mode 100644 src/test/emotion_cal.py create mode 100644 src/test/emotion_cal_bert.py create mode 100644 src/test/emotion_cal_hanlp.py create mode 100644 src/test/emotion_cal_snownlp.py create mode 100644 src/test/snownlp_demo.py create mode 100644 src/test/typo.py create mode 100644 src/test/typo_word.py diff --git a/.gitignore b/.gitignore index 48c0c13c3..5ce300145 100644 --- a/.gitignore +++ b/.gitignore @@ -182,3 +182,4 @@ cython_debug/ # PyPI configuration file .pypirc +.env diff --git a/README.md b/README.md index c611d85ef..b0e989c82 100644 --- a/README.md +++ b/README.md @@ -11,12 +11,12 @@ ## 📝 项目简介 -**麦麦是一个基于大语言模型的智能QQ群聊机器人** +**🍔麦麦是一个基于大语言模型的智能QQ群聊机器人** - 🤖 基于 nonebot2 框架开发 - 🧠 LLM 提供对话能力 - 💾 MongoDB 提供数据持久化支持 -- 🌐 NapCat 作为QQ协议端支持 +- 🐧 NapCat 作为QQ协议端支持
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rcmbQka*Snz43icw0~bR9LnRPrGUNei5D5|k;bI^=pP?K`<^bgZ{lE!1 delta 7 OcmX@cGKXb@3=;qf?*cgh diff --git a/src/test/emotion_cal.py b/src/test/emotion_cal.py new file mode 100644 index 000000000..eaf0cbcf0 --- /dev/null +++ b/src/test/emotion_cal.py @@ -0,0 +1,70 @@ +from textblob import TextBlob +import jieba +from translate import Translator + +def analyze_emotion(text): + """ + 分析文本的情感,返回情感极性和主观性得分 + :param text: 输入文本 + :return: (情感极性, 主观性) 元组 + 情感极性: -1(非常消极) 到 1(非常积极) + 主观性: 0(客观) 到 1(主观) + """ + try: + # 创建翻译器 + translator = Translator(to_lang="en", from_lang="zh") + + # 如果是中文文本,先翻译成英文 + # 因为TextBlob的情感分析主要基于英文 + translated_text = translator.translate(text) + + # 创建TextBlob对象 + blob = TextBlob(translated_text) + + # 获取情感极性和主观性 + polarity = blob.sentiment.polarity + subjectivity = blob.sentiment.subjectivity + + return polarity, subjectivity + + except Exception as e: + print(f"分析过程中出现错误: {str(e)}") + return None, None + +def get_emotion_description(polarity, subjectivity): + """ + 根据情感极性和主观性生成描述性文字 + """ + if polarity is None or subjectivity is None: + return "无法分析情感" + + # 情感极性描述 + if polarity > 0.5: + emotion = "非常积极" + elif polarity > 0: + emotion = "较为积极" + elif polarity == 0: + emotion = "中性" + elif polarity > -0.5: + emotion = "较为消极" + else: + emotion = "非常消极" + + # 主观性描述 + if subjectivity > 0.7: + subj = "非常主观" + elif subjectivity > 0.3: + subj = "较为主观" + else: + subj = "较为客观" + + return f"情感倾向: {emotion}, 表达方式: {subj}" + +if __name__ == "__main__": + # 测试样例 + test_text = "今天天气真好,我感到非常开心！" + polarity, subjectivity = analyze_emotion(test_text) + print(f"测试文本: {test_text}") + print(f"情感极性: {polarity:.2f}") + print(f"主观性得分: {subjectivity:.2f}") + print(get_emotion_description(polarity, subjectivity)) \ No newline at end of file diff --git a/src/test/emotion_cal_bert.py b/src/test/emotion_cal_bert.py new file mode 100644 index 000000000..7469e64d4 --- /dev/null +++ b/src/test/emotion_cal_bert.py @@ -0,0 +1,74 @@ +from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer + +def setup_bert_analyzer(): + """ + 设置中文BERT情感分析器 + """ + # 使用专门针对中文情感分析的模型 + model_name = "uer/roberta-base-finetuned-jd-binary-chinese" + + try: + # 加载模型和分词器 + tokenizer = AutoTokenizer.from_pretrained(model_name) + model = AutoModelForSequenceClassification.from_pretrained(model_name) + + # 创建情感分析pipeline + analyzer = pipeline("sentiment-analysis", + model=model, + tokenizer=tokenizer) + + return analyzer + except Exception as e: + print(f"模型加载错误: {str(e)}") + return None + +def analyze_emotion_bert(text, analyzer): + """ + 使用BERT模型进行中文情感分析 + """ + try: + if not analyzer: + return None + + # 进行情感分析 + result = analyzer(text)[0] + + return { + 'label': result['label'], + 'score': result['score'] + } + except Exception as e: + print(f"分析过程中出现错误: {str(e)}") + return None + +def get_emotion_description_bert(result): + """ + 将BERT的情感分析结果转换为描述性文字 + """ + if not result: + return "无法分析情感" + + label = "积极" if result['label'] == 'positive' else "消极" + confidence = result['score'] + + if confidence > 0.9: + strength = "强烈" + elif confidence > 0.7: + strength = "明显" + else: + strength = "轻微" + + return f"{strength}{label}" + +if __name__ == "__main__": + # 初始化分析器 + analyzer = setup_bert_analyzer() + + # 测试样例 + test_text = "这个产品质量很好，使用起来非常方便，推荐购买！" + result = analyze_emotion_bert(test_text, analyzer) + + print(f"测试文本: {test_text}") + if result: + print(f"情感倾向: {get_emotion_description_bert(result)}") + print(f"置信度: {result['score']:.2f}") \ No newline at end of file diff --git a/src/test/emotion_cal_hanlp.py b/src/test/emotion_cal_hanlp.py new file mode 100644 index 000000000..072dc7126 --- /dev/null +++ b/src/test/emotion_cal_hanlp.py @@ -0,0 +1,62 @@ +import hanlp + +def analyze_emotion_hanlp(text): + """ + 使用HanLP进行中文情感分析 + """ + try: + # 使用更基础的模型 + tokenizer = hanlp.load('PKU_NAME_MERGED_SIX_MONTHS_CONVSEG') + + # 分词 + words = tokenizer(text) + + # 简单的情感词典方法 + positive_words = {'好', '棒', '优秀', '喜欢', '开心', '快乐', '美味', '推荐', '优质', '满意'} + negative_words = {'差', '糟', '烂', '讨厌', '失望', '难受', '恶心', '不满', '差劲', '垃圾'} + + # 计算情感得分 + score = 0 + for word in words: + if word in positive_words: + score += 1 + elif word in negative_words: + score -= 1 + + # 归一化得分 + if score > 0: + return 1 + elif score < 0: + return 0 + else: + return 0.5 + + except Exception as e: + print(f"分析过程中出现错误: {str(e)}") + return None + +def get_emotion_description_hanlp(score): + """ + 将HanLP的情感分析结果转换为描述性文字 + """ + if score is None: + return "无法分析情感" + elif score == 1: + return "积极" + elif score == 0: + return "消极" + else: + return "中性" + +if __name__ == "__main__": + # 测试样例 + test_texts = [ + "这家餐厅的服务态度很好，菜品也很美味！", + "这个产品质量太差了，一点都不值这个价", + "今天天气不错，但是工作很累" + ] + + for test_text in test_texts: + result = analyze_emotion_hanlp(test_text) + print(f"\n测试文本: {test_text}") + print(f"情感倾向: {get_emotion_description_hanlp(result)}") \ No newline at end of file diff --git a/src/test/emotion_cal_snownlp.py b/src/test/emotion_cal_snownlp.py new file mode 100644 index 000000000..272a91df0 --- /dev/null +++ b/src/test/emotion_cal_snownlp.py @@ -0,0 +1,53 @@ +from snownlp import SnowNLP + +def analyze_emotion_snownlp(text): + """ + 使用SnowNLP进行中文情感分析 + :param text: 输入文本 + :return: 情感得分(0-1之间，越接近1越积极) + """ + try: + s = SnowNLP(text) + sentiment_score = s.sentiments + + # 获取文本的关键词 + keywords = s.keywords(3) + + return { + 'sentiment_score': sentiment_score, + 'keywords': keywords, + 'summary': s.summary(1) # 生成文本摘要 + } + except Exception as e: + print(f"分析过程中出现错误: {str(e)}") + return None + +def get_emotion_description_snownlp(score): + """ + 将情感得分转换为描述性文字 + """ + if score is None: + return "无法分析情感" + + if score > 0.8: + return "非常积极" + elif score > 0.6: + return "较为积极" + elif score > 0.4: + return "中性偏积极" + elif score > 0.2: + return "中性偏消极" + else: + return "消极" + +if __name__ == "__main__": + # 测试样例 + test_text = "我们学校有免费的gpt4用" + result = analyze_emotion_snownlp(test_text) + + if result: + print(f"测试文本: {test_text}") + print(f"情感得分: {result['sentiment_score']:.2f}") + print(f"情感倾向: {get_emotion_description_snownlp(result['sentiment_score'])}") + print(f"关键词: {', '.join(result['keywords'])}") + print(f"文本摘要: {result['summary'][0]}") \ No newline at end of file diff --git a/src/test/snownlp_demo.py b/src/test/snownlp_demo.py new file mode 100644 index 000000000..29cb7ef98 --- /dev/null +++ b/src/test/snownlp_demo.py @@ -0,0 +1,54 @@ +from snownlp import SnowNLP + +def demo_snownlp_features(text): + """ + 展示SnowNLP的主要功能 + :param text: 输入文本 + """ + print(f"\n=== SnowNLP功能演示 ===") + print(f"输入文本: {text}") + + # 创建SnowNLP对象 + s = SnowNLP(text) + + # 1. 分词 + print(f"\n1. 分词结果:") + print(f" {' | '.join(s.words)}") + + # 2. 情感分析 + print(f"\n2. 情感分析:") + sentiment = s.sentiments + print(f" 情感得分: {sentiment:.2f}") + print(f" 情感倾向: {'积极' if sentiment > 0.5 else '消极' if sentiment < 0.5 else '中性'}") + + # 3. 关键词提取 + print(f"\n3. 关键词提取:") + print(f" {', '.join(s.keywords(3))}") + + # 4. 词性标注 + print(f"\n4. 词性标注:") + print(f" {' '.join([f'{word}/{tag}' for word, tag in s.tags])}") + + # 5. 拼音转换 + print(f"\n5. 拼音:") + print(f" {' '.join(s.pinyin)}") + + # 6. 文本摘要 + if len(text) > 100: # 只对较长文本生成摘要 + print(f"\n6. 文本摘要:") + print(f" {' '.join(s.summary(3))}") + +if __name__ == "__main__": + # 测试用例 + test_texts = [ + "这家新开的餐厅很不错，菜品种类丰富，味道可口，服务态度也很好，价格实惠，强烈推荐大家来尝试！", + "这部电影剧情混乱，演技浮夸，特效粗糙，配乐难听，完全浪费了我的时间和票价。", + """人工智能正在改变我们的生活方式。它能够帮助我们完成复杂的计算任务， + 提供个性化的服务推荐，优化交通路线，辅助医疗诊断。但同时我们也要警惕 + 人工智能带来的问题，比如隐私安全、就业变化等。如何正确认识和利用人工智能， + 是我们每个人都需要思考的问题。""" + ] + + for text in test_texts: + demo_snownlp_features(text) + print("\n" + "="*50) \ No newline at end of file diff --git a/src/test/typo.py b/src/test/typo.py new file mode 100644 index 000000000..c452589ce --- /dev/null +++ b/src/test/typo.py @@ -0,0 +1,488 @@ +""" +错别字生成器 - 流程说明 + +整体替换逻辑： +1. 数据准备 + - 加载字频词典：使用jieba词典计算汉字使用频率 + - 创建拼音映射：建立拼音到汉字的映射关系 + - 加载词频信息：从jieba词典获取词语使用频率 + +2. 分词处理 + - 使用jieba将输入句子分词 + - 区分单字词和多字词 + - 保留标点符号和空格 + +3. 词语级别替换（针对多字词） + - 触发条件：词长>1 且 随机概率<0.3 + - 替换流程： + a. 获取词语拼音 + b. 生成所有可能的同音字组合 + c. 过滤条件： + - 必须是jieba词典中的有效词 + - 词频必须达到原词频的10%以上 + - 综合评分(词频70%+字频30%)必须达到阈值 + d. 按综合评分排序，选择最合适的替换词 + +4. 字级别替换（针对单字词或未进行整词替换的多字词） + - 单字替换概率：0.3 + - 多字词中的单字替换概率：0.3 * (0.7 ^ (词长-1)) + - 替换流程： + a. 获取字的拼音 + b. 声调错误处理（20%概率） + c. 获取同音字列表 + d. 过滤条件： + - 字频必须达到最小阈值 + - 频率差异不能过大（指数衰减计算） + e. 按频率排序选择替换字 + +5. 频率控制机制 + - 字频控制：使用归一化的字频（0-1000范围） + - 词频控制：使用jieba词典中的词频 + - 频率差异计算：使用指数衰减函数 + - 最小频率阈值：确保替换字/词不会太生僻 + +6. 输出信息 + - 原文和错字版本的对照 + - 每个替换的详细信息（原字/词、替换后字/词、拼音、频率） + - 替换类型说明（整词替换/声调错误/同音字替换） + - 词语分析和完整拼音 + +注意事项： +1. 所有替换都必须使用有意义的词语 +2. 替换词的使用频率不能过低 +3. 多字词优先考虑整词替换 +4. 考虑声调变化的情况 +5. 保持标点符号和空格不变 +""" + +from pypinyin import pinyin, Style +from collections import defaultdict +import json +import os +import unicodedata +import jieba +import jieba.posseg as pseg +from pathlib import Path +import random +import math +import time + +def load_or_create_char_frequency(): + """ + 加载或创建汉字频率字典 + """ + cache_file = Path("char_frequency.json") + + # 如果缓存文件存在，直接加载 + if cache_file.exists(): + with open(cache_file, 'r', encoding='utf-8') as f: + return json.load(f) + + # 使用内置的词频文件 + char_freq = defaultdict(int) + dict_path = os.path.join(os.path.dirname(jieba.__file__), 'dict.txt') + + # 读取jieba的词典文件 + with open(dict_path, 'r', encoding='utf-8') as f: + for line in f: + word, freq = line.strip().split()[:2] + # 对词中的每个字进行频率累加 + for char in word: + if is_chinese_char(char): + char_freq[char] += int(freq) + + # 归一化频率值 + max_freq = max(char_freq.values()) + normalized_freq = {char: freq/max_freq * 1000 for char, freq in char_freq.items()} + + # 保存到缓存文件 + with open(cache_file, 'w', encoding='utf-8') as f: + json.dump(normalized_freq, f, ensure_ascii=False, indent=2) + + return normalized_freq + +# 创建拼音到汉字的映射字典 +def create_pinyin_dict(): + """ + 创建拼音到汉字的映射字典 + """ + # 常用汉字范围 + chars = [chr(i) for i in range(0x4e00, 0x9fff)] + pinyin_dict = defaultdict(list) + + # 为每个汉字建立拼音映射 + for char in chars: + try: + py = pinyin(char, style=Style.TONE3)[0][0] + pinyin_dict[py].append(char) + except Exception: + continue + + return pinyin_dict + +def is_chinese_char(char): + """ + 判断是否为汉字 + """ + try: + return '\u4e00' <= char <= '\u9fff' + except: + return False + +def get_pinyin(sentence): + """ + 将中文句子拆分成单个汉字并获取其拼音 + :param sentence: 输入的中文句子 + :return: 每个汉字及其拼音的列表 + """ + # 将句子拆分成单个字符 + characters = list(sentence) + + # 获取每个字符的拼音 + result = [] + for char in characters: + # 跳过空格和非汉字字符 + if char.isspace() or not is_chinese_char(char): + continue + # 获取拼音（数字声调） + py = pinyin(char, style=Style.TONE3)[0][0] + result.append((char, py)) + + return result + +def get_homophone(char, py, pinyin_dict, char_frequency, min_freq=5): + """ + 获取同音字，按照使用频率排序 + """ + homophones = pinyin_dict[py] + # 移除原字并过滤低频字 + if char in homophones: + homophones.remove(char) + + # 过滤掉低频字 + homophones = [h for h in homophones if char_frequency.get(h, 0) >= min_freq] + + # 按照字频排序 + sorted_homophones = sorted(homophones, + key=lambda x: char_frequency.get(x, 0), + reverse=True) + + # 只返回前10个同音字，避免输出过多 + return sorted_homophones[:10] + +def get_similar_tone_pinyin(py): + """ + 获取相似声调的拼音 + 例如：'ni3' 可能返回 'ni2' 或 'ni4' + 处理特殊情况： + 1. 轻声（如 'de5' 或 'le'） + 2. 非数字结尾的拼音 + """ + # 检查拼音是否为空或无效 + if not py or len(py) < 1: + return py + + # 如果最后一个字符不是数字，说明可能是轻声或其他特殊情况 + if not py[-1].isdigit(): + # 为非数字结尾的拼音添加数字声调1 + return py + '1' + + base = py[:-1] # 去掉声调 + tone = int(py[-1]) # 获取声调 + + # 处理轻声（通常用5表示）或无效声调 + if tone not in [1, 2, 3, 4]: + return base + str(random.choice([1, 2, 3, 4])) + + # 正常处理声调 + possible_tones = [1, 2, 3, 4] + possible_tones.remove(tone) # 移除原声调 + new_tone = random.choice(possible_tones) # 随机选择一个新声调 + return base + str(new_tone) + +def calculate_replacement_probability(orig_freq, target_freq, max_freq_diff=200): + """ + 根据频率差计算替换概率 + 频率差越大，概率越低 + :param orig_freq: 原字频率 + :param target_freq: 目标字频率 + :param max_freq_diff: 最大允许的频率差 + :return: 0-1之间的概率值 + """ + if target_freq > orig_freq: + return 1.0 # 如果替换字频率更高，保持原有概率 + + freq_diff = orig_freq - target_freq + if freq_diff > max_freq_diff: + return 0.0 # 频率差太大，不替换 + + # 使用指数衰减函数计算概率 + # 频率差为0时概率为1，频率差为max_freq_diff时概率接近0 + return math.exp(-3 * freq_diff / max_freq_diff) + +def get_similar_frequency_chars(char, py, pinyin_dict, char_frequency, num_candidates=5, min_freq=5, tone_error_rate=0.2): + """ + 获取与给定字频率相近的同音字，可能包含声调错误 + """ + homophones = [] + + # 有20%的概率使用错误声调 + if random.random() < tone_error_rate: + wrong_tone_py = get_similar_tone_pinyin(py) + homophones.extend(pinyin_dict[wrong_tone_py]) + + # 添加正确声调的同音字 + homophones.extend(pinyin_dict[py]) + + if not homophones: + return None + + # 获取原字的频率 + orig_freq = char_frequency.get(char, 0) + + # 计算所有同音字与原字的频率差，并过滤掉低频字 + freq_diff = [(h, char_frequency.get(h, 0)) + for h in homophones + if h != char and char_frequency.get(h, 0) >= min_freq] + + if not freq_diff: + return None + + # 计算每个候选字的替换概率 + candidates_with_prob = [] + for h, freq in freq_diff: + prob = calculate_replacement_probability(orig_freq, freq) + if prob > 0: # 只保留有效概率的候选字 + candidates_with_prob.append((h, prob)) + + if not candidates_with_prob: + return None + + # 根据概率排序 + candidates_with_prob.sort(key=lambda x: x[1], reverse=True) + + # 返回概率最高的几个字 + return [char for char, _ in candidates_with_prob[:num_candidates]] + +def get_word_pinyin(word): + """ + 获取词语的拼音列表 + """ + return [py[0] for py in pinyin(word, style=Style.TONE3)] + +def segment_sentence(sentence): + """ + 使用jieba分词，返回词语列表 + """ + return list(jieba.cut(sentence)) + +def get_word_homophones(word, pinyin_dict, char_frequency, min_freq=5): + """ + 获取整个词的同音词，只返回高频的有意义词语 + :param word: 输入词语 + :param pinyin_dict: 拼音字典 + :param char_frequency: 字频字典 + :param min_freq: 最小频率阈值 + :return: 同音词列表 + """ + if len(word) == 1: + return [] + + # 获取词的拼音 + word_pinyin = get_word_pinyin(word) + word_pinyin_str = ''.join(word_pinyin) + + # 创建词语频率字典 + word_freq = defaultdict(float) + + # 遍历所有可能的同音字组合 + candidates = [] + for py in word_pinyin: + chars = pinyin_dict.get(py, []) + if not chars: + return [] + candidates.append(chars) + + # 生成所有可能的组合 + import itertools + all_combinations = itertools.product(*candidates) + + # 获取jieba词典和词频信息 + dict_path = os.path.join(os.path.dirname(jieba.__file__), 'dict.txt') + valid_words = {} # 改用字典存储词语及其频率 + with open(dict_path, 'r', encoding='utf-8') as f: + for line in f: + parts = line.strip().split() + if len(parts) >= 2: + word_text = parts[0] + word_freq = float(parts[1]) # 获取词频 + valid_words[word_text] = word_freq + + # 获取原词的词频作为参考 + original_word_freq = valid_words.get(word, 0) + min_word_freq = original_word_freq * 0.1 # 设置最小词频为原词频的10% + + # 过滤和计算频率 + homophones = [] + for combo in all_combinations: + new_word = ''.join(combo) + if new_word != word and new_word in valid_words: + new_word_freq = valid_words[new_word] + # 只保留词频达到阈值的词 + if new_word_freq >= min_word_freq: + # 计算词的平均字频（考虑字频和词频） + char_avg_freq = sum(char_frequency.get(c, 0) for c in new_word) / len(new_word) + # 综合评分：结合词频和字频 + combined_score = (new_word_freq * 0.7 + char_avg_freq * 0.3) + if combined_score >= min_freq: + homophones.append((new_word, combined_score)) + + # 按综合分数排序并限制返回数量 + sorted_homophones = sorted(homophones, key=lambda x: x[1], reverse=True) + return [word for word, _ in sorted_homophones[:5]] # 限制返回前5个结果 + +def create_typo_sentence(sentence, pinyin_dict, char_frequency, error_rate=0.5, min_freq=5, tone_error_rate=0.2, word_replace_rate=0.3): + """ + 创建包含同音字错误的句子，支持词语级别和字级别的替换 + 只使用高频的有意义词语进行替换 + """ + result = [] + typo_info = [] + + # 分词 + words = segment_sentence(sentence) + + for word in words: + # 如果是标点符号或空格，直接添加 + if all(not is_chinese_char(c) for c in word): + result.append(word) + continue + + # 获取词语的拼音 + word_pinyin = get_word_pinyin(word) + + # 尝试整词替换 + if len(word) > 1 and random.random() < word_replace_rate: + word_homophones = get_word_homophones(word, pinyin_dict, char_frequency, min_freq) + if word_homophones: + typo_word = random.choice(word_homophones) + # 计算词的平均频率 + orig_freq = sum(char_frequency.get(c, 0) for c in word) / len(word) + typo_freq = sum(char_frequency.get(c, 0) for c in typo_word) / len(typo_word) + + # 添加到结果中 + result.append(typo_word) + typo_info.append((word, typo_word, + ' '.join(word_pinyin), + ' '.join(get_word_pinyin(typo_word)), + orig_freq, typo_freq)) + continue + + # 如果不进行整词替换，则进行单字替换 + if len(word) == 1: + char = word + py = word_pinyin[0] + if random.random() < error_rate: + similar_chars = get_similar_frequency_chars(char, py, pinyin_dict, char_frequency, + min_freq=min_freq, tone_error_rate=tone_error_rate) + if similar_chars: + typo_char = random.choice(similar_chars) + typo_freq = char_frequency.get(typo_char, 0) + orig_freq = char_frequency.get(char, 0) + replace_prob = calculate_replacement_probability(orig_freq, typo_freq) + if random.random() < replace_prob: + result.append(typo_char) + typo_py = pinyin(typo_char, style=Style.TONE3)[0][0] + typo_info.append((char, typo_char, py, typo_py, orig_freq, typo_freq)) + continue + result.append(char) + else: + # 处理多字词的单字替换 + word_result = [] + for i, (char, py) in enumerate(zip(word, word_pinyin)): + # 词中的字替换概率降低 + word_error_rate = error_rate * (0.7 ** (len(word) - 1)) + + if random.random() < word_error_rate: + similar_chars = get_similar_frequency_chars(char, py, pinyin_dict, char_frequency, + min_freq=min_freq, tone_error_rate=tone_error_rate) + if similar_chars: + typo_char = random.choice(similar_chars) + typo_freq = char_frequency.get(typo_char, 0) + orig_freq = char_frequency.get(char, 0) + replace_prob = calculate_replacement_probability(orig_freq, typo_freq) + if random.random() < replace_prob: + word_result.append(typo_char) + typo_py = pinyin(typo_char, style=Style.TONE3)[0][0] + typo_info.append((char, typo_char, py, typo_py, orig_freq, typo_freq)) + continue + word_result.append(char) + result.append(''.join(word_result)) + + return ''.join(result), typo_info + +def format_frequency(freq): + """ + 格式化频率显示 + """ + return f"{freq:.2f}" + +def main(): + # 记录开始时间 + start_time = time.time() + + # 首先创建拼音字典和加载字频统计 + print("正在加载汉字数据库，请稍候...") + pinyin_dict = create_pinyin_dict() + char_frequency = load_or_create_char_frequency() + + # 获取用户输入 + sentence = input("请输入中文句子：") + + # 创建包含错别字的句子 + typo_sentence, typo_info = create_typo_sentence(sentence, pinyin_dict, char_frequency, + error_rate=0.3, min_freq=5, + tone_error_rate=0.2, word_replace_rate=0.3) + + # 打印结果 + print("\n原句：", sentence) + print("错字版：", typo_sentence) + + if typo_info: + print("\n错别字信息：") + for orig, typo, orig_py, typo_py, orig_freq, typo_freq in typo_info: + # 判断是否为词语替换 + is_word = ' ' in orig_py + if is_word: + error_type = "整词替换" + else: + tone_error = orig_py[:-1] == typo_py[:-1] and orig_py[-1] != typo_py[-1] + error_type = "声调错误" if tone_error else "同音字替换" + + print(f"原文：{orig}({orig_py}) [频率：{format_frequency(orig_freq)}] -> " + f"替换：{typo}({typo_py}) [频率：{format_frequency(typo_freq)}] [{error_type}]") + + # 获取拼音结果 + result = get_pinyin(sentence) + + # 打印完整拼音 + print("\n完整拼音：") + print(" ".join(py for _, py in result)) + + # 打印词语分析 + print("\n词语分析：") + words = segment_sentence(sentence) + for word in words: + if any(is_chinese_char(c) for c in word): + word_pinyin = get_word_pinyin(word) + print(f"词语：{word}") + print(f"拼音：{' '.join(word_pinyin)}") + print("---") + + # 计算并打印总耗时 + end_time = time.time() + total_time = end_time - start_time + print(f"\n总耗时：{total_time:.2f}秒") + +if __name__ == "__main__": + main() diff --git a/src/test/typo_word.py b/src/test/typo_word.py new file mode 100644 index 000000000..b6982c0ed --- /dev/null +++ b/src/test/typo_word.py @@ -0,0 +1,301 @@ +from pypinyin import pinyin, Style +from collections import defaultdict +import json +import os +import unicodedata +import jieba +import jieba.posseg as pseg +from pathlib import Path +import random +import math + +def load_or_create_char_frequency(): + """ + 加载或创建汉字频率字典 + """ + cache_file = Path("char_frequency.json") + + # 如果缓存文件存在，直接加载 + if cache_file.exists(): + with open(cache_file, 'r', encoding='utf-8') as f: + return json.load(f) + + # 使用内置的词频文件 + char_freq = defaultdict(int) + dict_path = os.path.join(os.path.dirname(jieba.__file__), 'dict.txt') + + # 读取jieba的词典文件 + with open(dict_path, 'r', encoding='utf-8') as f: + for line in f: + word, freq = line.strip().split()[:2] + # 对词中的每个字进行频率累加 + for char in word: + if is_chinese_char(char): + char_freq[char] += int(freq) + + # 归一化频率值 + max_freq = max(char_freq.values()) + normalized_freq = {char: freq/max_freq * 1000 for char, freq in char_freq.items()} + + # 保存到缓存文件 + with open(cache_file, 'w', encoding='utf-8') as f: + json.dump(normalized_freq, f, ensure_ascii=False, indent=2) + + return normalized_freq + +# 创建拼音到汉字的映射字典 +def create_pinyin_dict(): + """ + 创建拼音到汉字的映射字典 + """ + # 常用汉字范围 + chars = [chr(i) for i in range(0x4e00, 0x9fff)] + pinyin_dict = defaultdict(list) + + # 为每个汉字建立拼音映射 + for char in chars: + try: + py = pinyin(char, style=Style.TONE3)[0][0] + pinyin_dict[py].append(char) + except Exception: + continue + + return pinyin_dict + +def is_chinese_char(char): + """ + 判断是否为汉字 + """ + try: + return '\u4e00' <= char <= '\u9fff' + except: + return False + +def get_pinyin(sentence): + """ + 将中文句子拆分成单个汉字并获取其拼音 + :param sentence: 输入的中文句子 + :return: 每个汉字及其拼音的列表 + """ + # 将句子拆分成单个字符 + characters = list(sentence) + + # 获取每个字符的拼音 + result = [] + for char in characters: + # 跳过空格和非汉字字符 + if char.isspace() or not is_chinese_char(char): + continue + # 获取拼音（数字声调） + py = pinyin(char, style=Style.TONE3)[0][0] + result.append((char, py)) + + return result + +def get_homophone(char, py, pinyin_dict, char_frequency, min_freq=5): + """ + 获取同音字，按照使用频率排序 + """ + homophones = pinyin_dict[py] + # 移除原字并过滤低频字 + if char in homophones: + homophones.remove(char) + + # 过滤掉低频字 + homophones = [h for h in homophones if char_frequency.get(h, 0) >= min_freq] + + # 按照字频排序 + sorted_homophones = sorted(homophones, + key=lambda x: char_frequency.get(x, 0), + reverse=True) + + # 只返回前10个同音字，避免输出过多 + return sorted_homophones[:10] + +def get_similar_tone_pinyin(py): + """ + 获取相似声调的拼音 + 例如：'ni3' 可能返回 'ni2' 或 'ni4' + """ + base = py[:-1] # 去掉声调 + tone = int(py[-1]) # 获取声调 + possible_tones = [1, 2, 3, 4] + possible_tones.remove(tone) # 移除原声调 + new_tone = random.choice(possible_tones) # 随机选择一个新声调 + return base + str(new_tone) + +def calculate_replacement_probability(orig_freq, target_freq, max_freq_diff=200): + """ + 根据频率差计算替换概率 + 频率差越大，概率越低 + :param orig_freq: 原字频率 + :param target_freq: 目标字频率 + :param max_freq_diff: 最大允许的频率差 + :return: 0-1之间的概率值 + """ + if target_freq > orig_freq: + return 1.0 # 如果替换字频率更高，保持原有概率 + + freq_diff = orig_freq - target_freq + if freq_diff > max_freq_diff: + return 0.0 # 频率差太大，不替换 + + # 使用指数衰减函数计算概率 + # 频率差为0时概率为1，频率差为max_freq_diff时概率接近0 + return math.exp(-3 * freq_diff / max_freq_diff) + +def get_similar_frequency_chars(char, py, pinyin_dict, char_frequency, num_candidates=5, min_freq=5, tone_error_rate=0.2): + """ + 获取与给定字频率相近的同音字，可能包含声调错误 + """ + homophones = [] + + # 有20%的概率使用错误声调 + if random.random() < tone_error_rate: + wrong_tone_py = get_similar_tone_pinyin(py) + homophones.extend(pinyin_dict[wrong_tone_py]) + + # 添加正确声调的同音字 + homophones.extend(pinyin_dict[py]) + + if not homophones: + return None + + # 获取原字的频率 + orig_freq = char_frequency.get(char, 0) + + # 计算所有同音字与原字的频率差，并过滤掉低频字 + freq_diff = [(h, char_frequency.get(h, 0)) + for h in homophones + if h != char and char_frequency.get(h, 0) >= min_freq] + + if not freq_diff: + return None + + # 计算每个候选字的替换概率 + candidates_with_prob = [] + for h, freq in freq_diff: + prob = calculate_replacement_probability(orig_freq, freq) + if prob > 0: # 只保留有效概率的候选字 + candidates_with_prob.append((h, prob)) + + if not candidates_with_prob: + return None + + # 根据概率排序 + candidates_with_prob.sort(key=lambda x: x[1], reverse=True) + + # 返回概率最高的几个字 + return [char for char, _ in candidates_with_prob[:num_candidates]] + +def create_typo_sentence(sentence, pinyin_dict, char_frequency, error_rate=0.5, min_freq=5, tone_error_rate=0.2): + """ + 创建包含同音字错误的句子，保留原文标点符号 + """ + result = [] + typo_info = [] + + # 获取每个字的拼音 + chars_with_pinyin = get_pinyin(sentence) + + # 创建原字到拼音的映射，用于跟踪已处理的字符 + processed_chars = {char: py for char, py in chars_with_pinyin} + + # 遍历原句中的每个字符 + char_index = 0 + for i, char in enumerate(sentence): + if char.isspace(): + # 保留空格 + result.append(char) + elif char in processed_chars: + # 处理汉字 + py = processed_chars[char] + # 基础错误率 + if random.random() < error_rate: + # 获取频率相近的同音字（可能包含声调错误） + similar_chars = get_similar_frequency_chars(char, py, pinyin_dict, char_frequency, + min_freq=min_freq, tone_error_rate=tone_error_rate) + if similar_chars: + # 随机选择一个替换字 + typo_char = random.choice(similar_chars) + # 获取替换字的频率 + typo_freq = char_frequency.get(typo_char, 0) + orig_freq = char_frequency.get(char, 0) + + # 计算实际替换概率 + replace_prob = calculate_replacement_probability(orig_freq, typo_freq) + + # 根据频率差进行概率替换 + if random.random() < replace_prob: + result.append(typo_char) + # 获取替换字的实际拼音 + typo_py = pinyin(typo_char, style=Style.TONE3)[0][0] + typo_info.append((char, typo_char, py, typo_py, orig_freq, typo_freq)) + else: + result.append(char) + else: + result.append(char) + else: + result.append(char) + char_index += 1 + else: + # 保留非汉字字符（标点符号等） + result.append(char) + + return ''.join(result), typo_info + +def format_frequency(freq): + """ + 格式化频率显示 + """ + return f"{freq:.2f}" + +def main(): + # 首先创建拼音字典和加载字频统计 + print("正在加载汉字数据库，请稍候...") + pinyin_dict = create_pinyin_dict() + char_frequency = load_or_create_char_frequency() + + # 获取用户输入 + sentence = input("请输入中文句子：") + + # 创建包含错别字的句子 + typo_sentence, typo_info = create_typo_sentence(sentence, pinyin_dict, char_frequency, + min_freq=5, tone_error_rate=0.2) + + # 打印结果 + print("\n原句：", sentence) + print("错字版：", typo_sentence) + + if typo_info: + print("\n错别字信息：") + for orig, typo, orig_py, typo_py, orig_freq, typo_freq in typo_info: + tone_error = orig_py[:-1] == typo_py[:-1] and orig_py[-1] != typo_py[-1] + error_type = "声调错误" if tone_error else "同音字替换" + print(f"原字：{orig}({orig_py}) [频率：{format_frequency(orig_freq)}] -> " + f"错字：{typo}({typo_py}) [频率：{format_frequency(typo_freq)}] [{error_type}]") + + # 获取拼音结果 + result = get_pinyin(sentence) + + # 打印完整拼音 + print("\n完整拼音：") + print(" ".join(py for _, py in result)) + + # 打印所有可能的同音字 + print("\n每个字的所有同音字（按频率排序，仅显示频率>=5的字）：") + for char, py in result: + homophones = get_homophone(char, py, pinyin_dict, char_frequency, min_freq=5) + char_freq = char_frequency.get(char, 0) + print(f"{char}: {py} [频率：{format_frequency(char_freq)}]") + if homophones: + homophone_info = [] + for h in homophones: + h_freq = char_frequency.get(h, 0) + homophone_info.append(f"{h}[{format_frequency(h_freq)}]") + print(f"同音字: {'，'.join(homophone_info)}") + else: + print("没有找到频率>=5的同音字") + +if __name__ == "__main__": + main() From 079bab753aa00826f203f6a17e8a0f1af5249c0d Mon Sep 17 00:00:00 2001 From: SengokuCola <1026294844@qq.com> Date: Mon, 3 Mar 2025 17:30:08 +0800 Subject: [PATCH 26/26] littelchange --- .env | 2 +- bot.py | 2 +- src/plugins/chat/prompt_builder.py | 2 +- src/plugins/chat/utils.py | 2 ++ src/plugins/models/utils_model.py | 4 ++-- 5 files changed, 7 insertions(+), 5 deletions(-) diff --git a/.env b/.env index 06c26cdf3..8e50bc5d9 100644 --- a/.env +++ b/.env @@ -1,5 +1,5 @@ # 您不应该修改默认值，这个文件被仓库索引，请修改.env.prod -ENVIRONMENT=prod +ENVIRONMENT=dev # HOST=127.0.0.1 # PORT=8080 diff --git a/bot.py b/bot.py index 78bf34b32..906ffc37d 100644 --- a/bot.py +++ b/bot.py @@ -25,7 +25,7 @@ else: # 根据 ENVIRONMENT 加载对应的环境配置 env = os.getenv("ENVIRONMENT") env_file = f".env.{env}" - + if env_file == ".env.dev" and os.path.exists(env_file): logger.success("加载开发环境变量配置") load_dotenv(env_file, override=True) # override=True 允许覆盖已存在的环境变量 diff --git a/src/plugins/chat/prompt_builder.py b/src/plugins/chat/prompt_builder.py index 272229416..25e06a764 100644 --- a/src/plugins/chat/prompt_builder.py +++ b/src/plugins/chat/prompt_builder.py @@ -118,7 +118,7 @@ class PromptBuilder: prompt_info = self.get_prompt_info(message_txt,threshold=0.5) if prompt_info: prompt_info = f'''\n----------------------------------------------------\n你有以下这些[知识]：\n{prompt_info}\n请你记住上面的[知识]，之后可能会用到\n----------------------------------------------------\n''' - promt_info_prompt = '你有一些[知识]，在上面可以参考。' + # promt_info_prompt = '你有一些[知识]，在上面可以参考。' end_time = time.time() print(f"\033[1;32m[知识检索]\033[0m 耗时: {(end_time - start_time):.3f}秒") diff --git a/src/plugins/chat/utils.py b/src/plugins/chat/utils.py index 91018ee08..a2a7a6f50 100644 --- a/src/plugins/chat/utils.py +++ b/src/plugins/chat/utils.py @@ -185,6 +185,8 @@ def get_recent_group_detailed_plain_text(db, group_id: int, limit: int = 12,comb message_detailed_plain_text = '' message_detailed_plain_text_list = [] + # 反转消息列表，使最新的消息在最后 + recent_messages.reverse() if combine: for msg_db_data in recent_messages: diff --git a/src/plugins/models/utils_model.py b/src/plugins/models/utils_model.py index 5f4d02df5..f911d7495 100644 --- a/src/plugins/models/utils_model.py +++ b/src/plugins/models/utils_model.py @@ -166,8 +166,8 @@ class LLM_request: # 发送请求到完整的chat/completions端点 api_url = f"{self.base_url.rstrip('/')}/chat/completions" - max_retries = 3 - base_wait_time = 15 + max_retries = 2 + base_wait_time = 6 for retry in range(max_retries): try: