diff --git a/.env b/.env deleted file mode 100644 index 382b70fa0..000000000 --- a/.env +++ /dev/null @@ -1,26 +0,0 @@ -# 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 - -# 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 diff --git a/.github/workflows/docker-image.yml b/.github/workflows/docker-image.yml index 74e6a8cb4..2a5f497fd 100644 --- a/.github/workflows/docker-image.yml +++ b/.github/workflows/docker-image.yml @@ -3,10 +3,11 @@ name: Docker Build and Push on: push: branches: - - main # 推送到main分支时触发 + - main + - debug # 新增 debug 分支触发 tags: - - 'v*' # 推送v开头的tag时触发（例如v1.0.0） - workflow_dispatch: # 允许手动触发 + - 'v*' + workflow_dispatch: jobs: build-and-push: @@ -24,13 +25,24 @@ jobs: username: ${{ secrets.DOCKERHUB_USERNAME }} password: ${{ secrets.DOCKERHUB_TOKEN }} + - name: Determine Image Tags + id: tags + run: | + if [[ "${{ github.ref }}" == refs/tags/* ]]; then + echo "tags=${{ secrets.DOCKERHUB_USERNAME }}/maimbot:${{ github.ref_name }},${{ secrets.DOCKERHUB_USERNAME }}/maimbot:latest" >> $GITHUB_OUTPUT + elif [ "${{ github.ref }}" == "refs/heads/main" ]; then + echo "tags=${{ secrets.DOCKERHUB_USERNAME }}/maimbot:main,${{ secrets.DOCKERHUB_USERNAME }}/maimbot:latest" >> $GITHUB_OUTPUT + elif [ "${{ github.ref }}" == "refs/heads/debug" ]; then + echo "tags=${{ secrets.DOCKERHUB_USERNAME }}/maimbot:debug" >> $GITHUB_OUTPUT + fi + - name: Build and Push Docker Image uses: 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: | - ${{ secrets.DOCKERHUB_USERNAME }}/maimbot:${{ github.ref_name }} - ${{ secrets.DOCKERHUB_USERNAME }}/maimbot:latest - push: true + context: . + file: ./Dockerfile + platforms: linux/amd64,linux/arm64 + tags: ${{ steps.tags.outputs.tags }} + push: true + cache-from: type=registry,ref=${{ secrets.DOCKERHUB_USERNAME }}/maimbot:buildcache + cache-to: type=registry,ref=${{ secrets.DOCKERHUB_USERNAME }}/maimbot:buildcache,mode=max \ No newline at end of file diff --git a/.gitignore b/.gitignore index 5ce300145..51a11d8c2 100644 --- a/.gitignore +++ b/.gitignore @@ -3,15 +3,17 @@ mongodb/ NapCat.Framework.Windows.Once/ log/ /test +/src/test message_queue_content.txt message_queue_content.bat message_queue_window.bat message_queue_window.txt queue_update.txt memory_graph.gml +.env .env.* config/bot_config_dev.toml - +config/bot_config.toml # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] @@ -183,3 +185,6 @@ cython_debug/ # PyPI configuration file .pypirc .env + +# jieba +jieba.cache diff --git a/README.md b/README.md index b0e989c82..7bfa465ae 100644 --- a/README.md +++ b/README.md @@ -32,7 +32,7 @@ > - 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 -**交流群**: 766798517（仅用于开发和建议相关讨论） +**交流群**: 766798517（仅用于开发和建议相关讨论）不建议在群内询问部署问题，我不一定有空回复，会优先写文档和代码 ## 📚 文档 @@ -42,22 +42,22 @@ ## 🎯 功能介绍 ### 💬 聊天功能 -- 支持关键词检索主动发言：对消息的话题topic进行识别，如果检测到麦麦存储过的话题就会主动进行发言，目前有bug,所以现在只会检测主题，不会进行存储 +- 支持关键词检索主动发言：对消息的话题topic进行识别，如果检测到麦麦存储过的话题就会主动进行发言 - 支持bot名字呼唤发言：检测到"麦麦"会主动发言，可配置 -- 使用硅基流动的api进行回复生成，可随机使用R1，V3，R1-distill等模型，未来将加入官网api支持 +- 支持多模型，多厂商自定义配置 - 动态的prompt构建器，更拟人 - 支持图片，转发消息，回复消息的识别 - 错别字和多条回复功能：麦麦可以随机生成错别字，会多条发送回复以及对消息进行reply ### 😊 表情包功能 -- 支持根据发言内容发送对应情绪的表情包：未完善，可以用 -- 会自动偷群友的表情包（未完善，暂时禁用）目前有bug +- 支持根据发言内容发送对应情绪的表情包 +- 会自动偷群友的表情包 ### 📅 日程功能 - 麦麦会自动生成一天的日程，实现更拟人的回复 ### 🧠 记忆功能 -- 对聊天记录进行概括存储，在需要时调用，没写完 +- 对聊天记录进行概括存储，在需要时调用，待完善 ### 📚 知识库功能 - 基于embedding模型的知识库，手动放入txt会自动识别，写完了，暂时禁用 @@ -66,25 +66,27 @@ - 针对每个用户创建"关系"，可以对不同用户进行个性化回复，目前只有极其简单的好感度（WIP） - 针对每个群创建"群印象"，可以对不同群进行个性化回复（WIP） -## 🚧 开发中功能 + + +## 开发计划TODO：LIST - 人格功能：WIP - 群氛围功能：WIP - 图片发送，转发功能：WIP - 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 +- ~~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 +- ~~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 +- 自动生成的回复逻辑，例如自生成的回复方向，回复风格 +- 采用截断生成加快麦麦的反应速度 +- 改进发送消息的触发： ## 📌 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 @@ -99,4 +101,10 @@ 感谢各位大佬！ -[![Contributors](https://contributors-img.web.app/image?repo=SengokuCola/MaiMBot)](https://github.com/SengokuCola/MaiMBot/graphs/contributors) + + + + + +## Stargazers over time +[![Stargazers over time](https://starchart.cc/SengokuCola/MaiMBot.svg?variant=adaptive)](https://starchart.cc/SengokuCola/MaiMBot) \ No newline at end of file diff --git a/bot.py b/bot.py index 906ffc37d..50c8cfaa4 100644 --- a/bot.py +++ b/bot.py @@ -6,6 +6,7 @@ from loguru import logger '''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] @@ -15,25 +16,47 @@ for i, char in enumerate(text): print(rainbow_text) '''彩蛋''' -# 首先加载基础环境变量 +# 初次启动检测 +if not os.path.exists("config/bot_config.toml") or not os.path.exists(".env"): + logger.info("检测到bot_config.toml不存在，正在从模板复制") + import shutil + + shutil.copy("config/bot_config_template.toml", "config/bot_config.toml") + logger.info("复制完成，请修改config/bot_config.toml和.env.prod中的配置后重新启动") + +# 初始化.env 默认ENVIRONMENT=prod +if not os.path.exists(".env"): + with open(".env", "w") as f: + f.write("ENVIRONMENT=prod") + + # 检测.env.prod文件是否存在 + if not os.path.exists(".env.prod"): + logger.error("检测到.env.prod文件不存在") + shutil.copy("template.env", "./.env.prod") + +# 首先加载基础环境变量.env 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("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 os.path.exists(".env.prod"): - logger.success("加载环境变量配置") +# 根据 ENVIRONMENT 加载对应的环境配置 +if os.getenv("ENVIRONMENT") == "prod": + logger.success("加载生产环境变量配置") load_dotenv(".env.prod", override=True) # override=True 允许覆盖已存在的环境变量 +elif os.getenv("ENVIRONMENT") == "dev": + logger.success("加载开发环境变量配置") + load_dotenv(".env.dev", override=True) # override=True 允许覆盖已存在的环境变量 +elif os.path.exists(f".env.{os.getenv('ENVIRONMENT')}"): + logger.success(f"加载{os.getenv('ENVIRONMENT')}环境变量配置") + load_dotenv(f".env.{os.getenv('ENVIRONMENT')}", 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.") + logger.error(f"ENVIRONMENT配置错误，请检查.env文件中的ENVIRONMENT变量对应的.env.{os.getenv('ENVIRONMENT')}是否存在") + exit(1) + +# 检测Key是否存在 +if not os.getenv("SILICONFLOW_KEY"): + logger.error("缺失必要的API KEY") + logger.error(f"请至少在.env.{os.getenv('ENVIRONMENT')}文件中填写SILICONFLOW_KEY后重新启动") exit(1) # 获取所有环境变量 @@ -57,5 +80,4 @@ driver.register_adapter(Adapter) nonebot.load_plugins("src/plugins") if __name__ == "__main__": - - nonebot.run() \ No newline at end of file + nonebot.run() diff --git a/config/bot_config.toml b/config/bot_config.toml deleted file mode 100644 index 83526945c..000000000 --- a/config/bot_config.toml +++ /dev/null @@ -1,61 +0,0 @@ -[bot] -qq = 123 -nickname = "麦麦" - -[message] -min_text_length = 2 -max_context_size = 15 -emoji_chance = 0.2 - -[emoji] -check_interval = 120 -register_interval = 10 - -[cq_code] -enable_pic_translate = false - -[response] -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 - -[others] -enable_advance_output = true - -[groups] -talk_allowed = [ - 123, - 123, -] -talk_frequency_down = [] -ban_user_id = [] - -[model.llm_reasoning] -name = "Pro/deepseek-ai/DeepSeek-R1" -base_url = "SILICONFLOW_BASE_URL" -key = "SILICONFLOW_KEY" - -[model.llm_reasoning_minor] -name = "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B" -base_url = "SILICONFLOW_BASE_URL" -key = "SILICONFLOW_KEY" - -[model.llm_normal] -name = "Pro/deepseek-ai/DeepSeek-V3" -base_url = "SILICONFLOW_BASE_URL" -key = "SILICONFLOW_KEY" - -[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/config/bot_config_template.toml b/config/bot_config_template.toml new file mode 100644 index 000000000..28ffb0ce3 --- /dev/null +++ b/config/bot_config_template.toml @@ -0,0 +1,98 @@ +[bot] +qq = 123 +nickname = "麦麦" + +[personality] +prompt_personality = [ + "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", # 贴吧人格 + "是一个女大学生，你有黑色头发，你会刷小红书" # 小红书人格 + ] +prompt_schedule = "一个曾经学习地质,现在学习心理学和脑科学的女大学生，喜欢刷qq，贴吧，知乎和小红书" + +[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 # 麦麦获得的上文数量 +emoji_chance = 0.2 # 麦麦使用表情包的概率 +ban_words = [ + # "403","张三" + ] + +[emoji] +check_interval = 120 # 检查表情包的时间间隔 +register_interval = 10 # 注册表情包的时间间隔 + +[cq_code] +enable_pic_translate = false + +[response] +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 单位秒 +forget_memory_interval = 300 # 记忆遗忘间隔 单位秒 + +[others] +enable_advance_output = true # 是否启用高级输出 +enable_kuuki_read = true # 是否启用读空气功能 + +[groups] +talk_allowed = [ + 123, + 123, +] #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 + + +#V3 +#name = "deepseek-chat" +#base_url = "DEEP_SEEK_BASE_URL" +#key = "DEEP_SEEK_KEY" + +#R1 +#name = "deepseek-reasoner" +#base_url = "DEEP_SEEK_BASE_URL" +#key = "DEEP_SEEK_KEY" + +#下面的模型若使用硅基流动则不需要更改，使用ds官方则改成.env.prod自定义的宏，使用自定义模型则选择定位相似的模型自己填写 + +[model.llm_reasoning] #R1 +name = "Pro/deepseek-ai/DeepSeek-R1" +base_url = "SILICONFLOW_BASE_URL" +key = "SILICONFLOW_KEY" + +[model.llm_reasoning_minor] #R1蒸馏 +name = "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B" +base_url = "SILICONFLOW_BASE_URL" +key = "SILICONFLOW_KEY" + +[model.llm_normal] #V3 +name = "Pro/deepseek-ai/DeepSeek-V3" +base_url = "SILICONFLOW_BASE_URL" +key = "SILICONFLOW_KEY" + +[model.llm_normal_minor] #V2.5 +name = "deepseek-ai/DeepSeek-V2.5" +base_url = "SILICONFLOW_BASE_URL" +key = "SILICONFLOW_KEY" + +[model.vlm] #图像识别 +name = "deepseek-ai/deepseek-vl2" +base_url = "SILICONFLOW_BASE_URL" +key = "SILICONFLOW_KEY" + +[model.embedding] #嵌入 +name = "BAAI/bge-m3" +base_url = "SILICONFLOW_BASE_URL" +key = "SILICONFLOW_KEY" + +# 主题提取，jieba和snownlp不用api，llm需要api +[topic] +topic_extract='snownlp' # 只支持jieba,snownlp,llm三种选项 + +[topic.llm_topic] +name = "Pro/deepseek-ai/DeepSeek-V3" +base_url = "SILICONFLOW_BASE_URL" +key = "SILICONFLOW_KEY" diff --git a/docs/installation.md b/docs/installation.md index 9fba9ecd2..c988eb7c9 100644 --- a/docs/installation.md +++ b/docs/installation.md @@ -2,7 +2,9 @@ ## 部署方式 -### 🐳 Docker部署（推荐） +如果你不知道Docker是什么，建议寻找相关教程或使用手动部署 + +### 🐳 Docker部署（推荐，但不一定是最新） 1. 获取配置文件： ```bash @@ -25,9 +27,7 @@ NAPCAT_UID=$(id -u) NAPCAT_GID=$(id -g) docker compose restart ```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 ``` @@ -41,33 +41,37 @@ pip install -r requirements.txt - 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` +- 修改环境配置文件：`.env.prod` +- 修改机器人配置文件：`bot_config.toml` -5. **启动服务** +5. **启动麦麦机器人** +- 打开命令行，cd到对应路径 ```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 +- `run_thingking.bat`: 启动可视化推理界面（未完善） + +- ~~`knowledge.bat`: 将`/data/raw_info`下的文本文档载入数据库~~ +- 直接运行 knowledge.py生成知识库 ## ⚙️ 配置说明 ### 环境配置 (.env.prod) ```ini -# API配置（必填） +# API配置,你可以在这里定义你的密钥和base_url +# 你可以选择定义其他服务商提供的KEY，完全可以自定义 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 +# 服务配置,如果你不知道这是什么，保持默认 HOST=127.0.0.1 PORT=8080 -# 数据库配置 +# 数据库配置,如果你不知道这是什么，保持默认 MONGODB_HOST=127.0.0.1 MONGODB_PORT=27017 DATABASE_NAME=MegBot @@ -80,19 +84,58 @@ qq = "你的机器人QQ号" nickname = "麦麦" [message] +min_text_length = 2 max_context_size = 15 emoji_chance = 0.2 +[emoji] +check_interval = 120 +register_interval = 10 + +[cq_code] +enable_pic_translate = false + [response] -api_using = "siliconflow" # 或 "deepseek" +#现已移除deepseek或硅基流动选项，可以直接切换分别配置任意模型 +model_r1_probability = 0.8 #推理模型权重 +model_v3_probability = 0.1 #非推理模型权重 +model_r1_distill_probability = 0.1 + +[memory] +build_memory_interval = 300 [others] -enable_advance_output = false # 是否启用详细日志输出 +enable_advance_output = true # 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 + +[model.llm_reasoning] +name = "Pro/deepseek-ai/DeepSeek-R1" +base_url = "SILICONFLOW_BASE_URL" +key = "SILICONFLOW_KEY" + +[model.llm_reasoning_minor] +name = "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B" +base_url = "SILICONFLOW_BASE_URL" +key = "SILICONFLOW_KEY" + +[model.llm_normal] +name = "Pro/deepseek-ai/DeepSeek-V3" +base_url = "SILICONFLOW_BASE_URL" +key = "SILICONFLOW_KEY" + +[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" ``` ## ⚠️ 注意事项 diff --git a/kill_mongodb.bat b/kill_mongodb.bat deleted file mode 100644 index 366f05d32..000000000 --- a/kill_mongodb.bat +++ /dev/null @@ -1,6 +0,0 @@ -@echo off -echo 正在查找并结束所有 MongoDB 进程... -taskkill /F /IM mongod.exe -taskkill /F /IM mongo.exe -echo MongoDB 进程已结束 -pause \ No newline at end of file diff --git a/run_maimai.bat b/run_maimai.bat index 702d39edc..ff00cc5c1 100644 --- a/run_maimai.bat +++ b/run_maimai.bat @@ -1,3 +1,4 @@ +chcp 65001 call conda activate niuniu cd . diff --git a/setup.py b/setup.py new file mode 100644 index 000000000..a6152a972 --- /dev/null +++ b/setup.py @@ -0,0 +1,11 @@ +from setuptools import setup, find_packages + +setup( + name="maimai-bot", + version="0.1", + packages=find_packages(), + install_requires=[ + 'python-dotenv', + 'pymongo', + ], +) \ No newline at end of file diff --git a/src/common/database.py b/src/common/database.py index 6a997c12a..5928abc42 100644 --- a/src/common/database.py +++ b/src/common/database.py @@ -24,4 +24,25 @@ class Database: def get_instance(cls) -> "Database": if cls._instance is None: raise RuntimeError("Database not initialized") - return cls._instance \ No newline at end of file + return cls._instance + + + #测试用 + + def get_random_group_messages(self, group_id: str, limit: int = 5): + # 先随机获取一条消息 + random_message = list(self.db.messages.aggregate([ + {"$match": {"group_id": group_id}}, + {"$sample": {"size": 1}} + ]))[0] + + # 获取该消息之后的消息 + subsequent_messages = list(self.db.messages.find({ + "group_id": group_id, + "time": {"$gt": random_message["time"]} + }).sort("time", 1).limit(limit)) + + # 将随机消息和后续消息合并 + messages = [random_message] + subsequent_messages + + return messages \ No newline at end of file diff --git a/src/plugins/chat/Segment_builder.py b/src/plugins/chat/Segment_builder.py new file mode 100644 index 000000000..09673a044 --- /dev/null +++ b/src/plugins/chat/Segment_builder.py @@ -0,0 +1,165 @@ +from typing import Dict, List, Union, Optional, Any +import base64 +import os + +""" +OneBot v11 Message Segment Builder + +This module provides classes for building message segments that conform to the +OneBot v11 standard. These segments can be used to construct complex messages +for sending through bots that implement the OneBot interface. +""" + + + +class Segment: + """Base class for all message segments.""" + + def __init__(self, type_: str, data: Dict[str, Any]): + self.type = type_ + self.data = data + + def to_dict(self) -> Dict[str, Any]: + """Convert the segment to a dictionary format.""" + return { + "type": self.type, + "data": self.data + } + + +class Text(Segment): + """Text message segment.""" + + def __init__(self, text: str): + super().__init__("text", {"text": text}) + + +class Face(Segment): + """Face/emoji message segment.""" + + def __init__(self, face_id: int): + super().__init__("face", {"id": str(face_id)}) + + +class Image(Segment): + """Image message segment.""" + + @classmethod + def from_url(cls, url: str) -> 'Image': + """Create an Image segment from a URL.""" + return cls(url=url) + + @classmethod + def from_path(cls, path: str) -> 'Image': + """Create an Image segment from a file path.""" + with open(path, 'rb') as f: + file_b64 = base64.b64encode(f.read()).decode('utf-8') + return cls(file=f"base64://{file_b64}") + + def __init__(self, file: str = None, url: str = None, cache: bool = True): + data = {} + if file: + data["file"] = file + if url: + data["url"] = url + if not cache: + data["cache"] = "0" + super().__init__("image", data) + + +class At(Segment): + """@Someone message segment.""" + + def __init__(self, user_id: Union[int, str]): + data = {"qq": str(user_id)} + super().__init__("at", data) + + +class Record(Segment): + """Voice message segment.""" + + def __init__(self, file: str, magic: bool = False, cache: bool = True): + data = {"file": file} + if magic: + data["magic"] = "1" + if not cache: + data["cache"] = "0" + super().__init__("record", data) + + +class Video(Segment): + """Video message segment.""" + + def __init__(self, file: str): + super().__init__("video", {"file": file}) + + +class Reply(Segment): + """Reply message segment.""" + + def __init__(self, message_id: int): + super().__init__("reply", {"id": str(message_id)}) + + +class MessageBuilder: + """Helper class for building complex messages.""" + + def __init__(self): + self.segments: List[Segment] = [] + + def text(self, text: str) -> 'MessageBuilder': + """Add a text segment.""" + self.segments.append(Text(text)) + return self + + def face(self, face_id: int) -> 'MessageBuilder': + """Add a face/emoji segment.""" + self.segments.append(Face(face_id)) + return self + + def image(self, file: str = None) -> 'MessageBuilder': + """Add an image segment.""" + self.segments.append(Image(file=file)) + return self + + def at(self, user_id: Union[int, str]) -> 'MessageBuilder': + """Add an @someone segment.""" + self.segments.append(At(user_id)) + return self + + def record(self, file: str, magic: bool = False) -> 'MessageBuilder': + """Add a voice record segment.""" + self.segments.append(Record(file, magic)) + return self + + def video(self, file: str) -> 'MessageBuilder': + """Add a video segment.""" + self.segments.append(Video(file)) + return self + + def reply(self, message_id: int) -> 'MessageBuilder': + """Add a reply segment.""" + self.segments.append(Reply(message_id)) + return self + + def build(self) -> List[Dict[str, Any]]: + """Build the message into a list of segment dictionaries.""" + return [segment.to_dict() for segment in self.segments] + + +'''Convenience functions +def text(content: str) -> Dict[str, Any]: + """Create a text message segment.""" + return Text(content).to_dict() + +def image_url(url: str) -> Dict[str, Any]: + """Create an image message segment from URL.""" + return Image.from_url(url).to_dict() + +def image_path(path: str) -> Dict[str, Any]: + """Create an image message segment from file path.""" + return Image.from_path(path).to_dict() + +def at(user_id: Union[int, str]) -> Dict[str, Any]: + """Create an @someone message segment.""" + return At(user_id).to_dict()''' \ No newline at end of file diff --git a/src/plugins/chat/__init__.py b/src/plugins/chat/__init__.py index 4b1f8d77f..ab99f6477 100644 --- a/src/plugins/chat/__init__.py +++ b/src/plugins/chat/__init__.py @@ -10,6 +10,10 @@ import random from .relationship_manager import relationship_manager from ..schedule.schedule_generator import bot_schedule from .willing_manager import willing_manager +from nonebot.rule import to_me +from .bot import chat_bot +from .emoji_manager import emoji_manager +import time # 获取驱动器 @@ -17,12 +21,12 @@ driver = get_driver() config = driver.config Database.initialize( - 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 + 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") @@ -30,8 +34,9 @@ print("\033[1;32m[初始化数据库完成]\033[0m") # 导入其他模块 from .bot import ChatBot from .emoji_manager import emoji_manager -from .message_send_control import message_sender +# from .message_send_control import message_sender from .relationship_manager import relationship_manager +from .message_sender import message_manager,message_sender from ..memory_system.memory import memory_graph,hippocampus # 初始化表情管理器 @@ -40,8 +45,8 @@ 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() -# 注册消息处理器 -group_msg = on_message() +# 注册群消息处理器 +group_msg = on_message(priority=5) # 创建定时任务 scheduler = require("nonebot_plugin_apscheduler").scheduler @@ -66,10 +71,13 @@ async def init_relationships(): async def _(bot: Bot): """Bot连接成功时的处理""" print(f"\033[1;38;5;208m-----------{global_config.BOT_NICKNAME}成功连接！-----------\033[0m") - message_sender.set_bot(bot) - asyncio.create_task(message_sender.start_processor(bot)) await willing_manager.ensure_started() + + + message_sender.set_bot(bot) print("\033[1;38;5;208m-----------消息发送器已启动！-----------\033[0m") + asyncio.create_task(message_manager.start_processor()) + print("\033[1;38;5;208m-----------消息处理器已启动！-----------\033[0m") asyncio.create_task(emoji_manager._periodic_scan(interval_MINS=global_config.EMOJI_REGISTER_INTERVAL)) print("\033[1;38;5;208m-----------开始偷表情包！-----------\033[0m") @@ -79,19 +87,27 @@ async def _(bot: Bot): async def _(bot: Bot, event: GroupMessageEvent, state: T_State): await chat_bot.handle_message(event, bot) -''' -@scheduler.scheduled_job("interval", seconds=300000, id="monitor_relationships") -async def monitor_relationships(): - """每15秒打印一次关系数据""" - relationship_manager.print_all_relationships() -''' - # 添加build_memory定时任务 @scheduler.scheduled_job("interval", seconds=global_config.build_memory_interval, id="build_memory") 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...") - await hippocampus.build_memory(chat_size=30) - print("\033[1;32m[记忆构建]\033[0m 记忆构建完成") + print("\033[1;32m[记忆构建]\033[0m -------------------------------------------开始构建记忆-------------------------------------------") + start_time = time.time() + await hippocampus.operation_build_memory(chat_size=20) + end_time = time.time() + print(f"\033[1;32m[记忆构建]\033[0m -------------------------------------------记忆构建完成：耗时: {end_time - start_time:.2f} 秒-------------------------------------------") + +@scheduler.scheduled_job("interval", seconds=global_config.forget_memory_interval, id="forget_memory") +async def forget_memory_task(): + """每30秒执行一次记忆构建""" + # print("\033[1;32m[记忆遗忘]\033[0m 开始遗忘记忆...") + # await hippocampus.operation_forget_topic(percentage=0.1) + # print("\033[1;32m[记忆遗忘]\033[0m 记忆遗忘完成") +@scheduler.scheduled_job("interval", seconds=global_config.build_memory_interval + 10, id="merge_memory") +async def merge_memory_task(): + """每30秒执行一次记忆构建""" + # print("\033[1;32m[记忆整合]\033[0m 开始整合") + # await hippocampus.operation_merge_memory(percentage=0.1) + # print("\033[1;32m[记忆整合]\033[0m 记忆整合完成") diff --git a/src/plugins/chat/bot.py b/src/plugins/chat/bot.py index 838232309..e3525b3bb 100644 --- a/src/plugins/chat/bot.py +++ b/src/plugins/chat/bot.py @@ -1,21 +1,22 @@ from nonebot.adapters.onebot.v11 import GroupMessageEvent, Message as EventMessage, Bot -from .message import Message,MessageSet +from .message import Message, MessageSet, Message_Sending from .config import BotConfig, global_config from .storage import MessageStorage from .llm_generator import ResponseGenerator -from .message_stream import MessageStream, MessageStreamContainer +# from .message_stream import MessageStream, MessageStreamContainer from .topic_identifier import topic_identifier from random import random, choice from .emoji_manager import emoji_manager # 导入表情包管理器 import time import os from .cq_code import CQCode # 导入CQCode模块 -from .message_send_control import message_sender # 导入消息发送控制器 +from .message_sender import message_manager # 导入新的消息管理器 from .message import Message_Thinking # 导入 Message_Thinking 类 from .relationship_manager import relationship_manager from .willing_manager import willing_manager # 导入意愿管理器 from .utils import is_mentioned_bot_in_txt, calculate_typing_time from ..memory_system.memory import memory_graph +from loguru import logger class ChatBot: def __init__(self): @@ -25,15 +26,12 @@ class ChatBot: self._started = False self.emoji_chance = 0.2 # 发送表情包的基础概率 - self.message_streams = MessageStreamContainer() - self.message_sender = message_sender + # self.message_streams = MessageStreamContainer() async def _ensure_started(self): """确保所有任务已启动""" if not self._started: - # 只保留必要的任务 self._started = True - async def handle_message(self, event: GroupMessageEvent, bot: Bot) -> None: """处理收到的群消息""" @@ -44,60 +42,41 @@ class ChatBot: if event.user_id in global_config.ban_user_id: return - - # 打印原始消息内容 - ''' - print(f"\n\033[1;33m[消息详情]\033[0m") - # print(f"- 原始消息: {str(event.raw_message)}") - print(f"- post_type: {event.post_type}") - print(f"- sub_type: {event.sub_type}") - print(f"- user_id: {event.user_id}") - print(f"- message_type: {event.message_type}") - # print(f"- message_id: {event.message_id}") - # print(f"- message: {event.message}") - print(f"- original_message: {event.original_message}") - print(f"- raw_message: {event.raw_message}") - # print(f"- font: {event.font}") - print(f"- sender: {event.sender}") - # print(f"- to_me: {event.to_me}") - - if event.reply: - print(f"\n\033[1;33m[回复消息详情]\033[0m") - # print(f"- message_id: {event.reply.message_id}") - print(f"- message_type: {event.reply.message_type}") - print(f"- sender: {event.reply.sender}") - # print(f"- time: {event.reply.time}") - print(f"- message: {event.reply.message}") - print(f"- raw_message: {event.reply.raw_message}") - # print(f"- original_message: {event.reply.original_message}") - ''' - group_info = await bot.get_group_info(group_id=event.group_id) - - - sender_info = await bot.get_group_member_info(group_id=event.group_id, user_id=event.user_id, no_cache=True) - await relationship_manager.update_relationship(user_id = event.user_id, data = sender_info) await relationship_manager.update_relationship_value(user_id = event.user_id, relationship_value = 0.5) - # print(f"\033[1;32m[关系管理]\033[0m 更新关系值: {relationship_manager.get_relationship(event.user_id).relationship_value}") - message = Message( group_id=event.group_id, user_id=event.user_id, message_id=event.message_id, + user_cardname=sender_info['card'], raw_message=str(event.original_message), plain_text=event.get_plaintext(), reply_message=event.reply, ) + + # 过滤词 + for word in global_config.ban_words: + if word in message.detailed_plain_text: + logger.info(f"\033[1;32m[{message.group_name}]{message.user_nickname}:\033[0m {message.processed_plain_text}") + logger.info(f"\033[1;32m[过滤词识别]\033[0m 消息中含有{word}，filtered") + return current_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(message.time)) - topic = topic_identifier.identify_topic_jieba(message.processed_plain_text) - print(f"\033[1;32m[主题识别]\033[0m 主题: {topic}") + + + topic=await topic_identifier.identify_topic_llm(message.processed_plain_text) + + + # topic1 = topic_identifier.identify_topic_jieba(message.processed_plain_text) + # topic2 = await topic_identifier.identify_topic_llm(message.processed_plain_text) + # topic3 = topic_identifier.identify_topic_snownlp(message.processed_plain_text) + logger.info(f"\033[1;32m[主题识别]\033[0m 使用{global_config.topic_extract}主题: {topic}") all_num = 0 interested_num = 0 @@ -110,9 +89,7 @@ class ChatBot: print(f"\033[1;32m[前额叶]\033[0m 对|{current_topic}|有印象") interested_rate = interested_num / all_num if all_num > 0 else 0 - await self.storage.store_message(message, topic[0] if topic else None) - is_mentioned = is_mentioned_bot_in_txt(message.processed_plain_text) reply_probability = willing_manager.change_reply_willing_received( @@ -127,54 +104,71 @@ class ChatBot: current_willing = willing_manager.get_willing(event.group_id) - print(f"\033[1;32m[{current_time}][{message.group_name}]{message.user_nickname}:\033[0m {message.processed_plain_text}\033[1;36m[回复意愿:{current_willing:.2f}][概率:{reply_probability:.1f}]\033[0m") + print(f"\033[1;32m[{current_time}][{message.group_name}]{message.user_nickname}:\033[0m {message.processed_plain_text}\033[1;36m[回复意愿:{current_willing:.2f}][概率:{reply_probability * 100:.1f}%]\033[0m") + response = "" - # 创建思考消息 + if random() < reply_probability: + tinking_time_point = round(time.time(), 2) think_id = 'mt' + str(tinking_time_point) thinking_message = Message_Thinking(message=message,message_id=think_id) - message_sender.send_temp_container.add_message(thinking_message) + + message_manager.add_message(thinking_message) willing_manager.change_reply_willing_sent(thinking_message.group_id) 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 is None: + # thinking_message.interupt=True if response: - message_set = MessageSet(event.group_id, global_config.BOT_QQ, think_id) + # print(f"\033[1;32m[思考结束]\033[0m 思考结束，已得到回复，开始回复") + # 找到并删除对应的thinking消息 + container = message_manager.get_container(event.group_id) + thinking_message = None + # 找到message,删除 + for msg in container.messages: + if isinstance(msg, Message_Thinking) and msg.message_id == think_id: + thinking_message = msg + container.messages.remove(msg) + print(f"\033[1;32m[思考消息删除]\033[0m 已找到思考消息对象，开始删除") + break + + #记录开始思考的时间，避免从思考到回复的时间太久 + thinking_start_time = thinking_message.thinking_start_time + message_set = MessageSet(event.group_id, global_config.BOT_QQ, think_id) # 发送消息的id和产生发送消息的message_thinking是一致的 + #计算打字时间，1是为了模拟打字，2是避免多条回复乱序 accu_typing_time = 0 + + # print(f"\033[1;32m[开始回复]\033[0m 开始将回复1载入发送容器") for msg in response: - print(f"当前消息: {msg}") + # print(f"\033[1;32m[回复内容]\033[0m {msg}") + #通过时间改变时间戳 typing_time = calculate_typing_time(msg) accu_typing_time += typing_time - timepoint = tinking_time_point+accu_typing_time - # print(f"\033[1;32m[调试]\033[0m 消息: {msg}，添加！, 累计打字时间: {accu_typing_time:.2f}秒") + timepoint = tinking_time_point + accu_typing_time - bot_message = Message( + bot_message = Message_Sending( group_id=event.group_id, user_id=global_config.BOT_QQ, message_id=think_id, - message_based_id=event.message_id, raw_message=msg, plain_text=msg, processed_plain_text=msg, user_nickname=global_config.BOT_NICKNAME, group_name=message.group_name, - time=timepoint + time=timepoint, #记录了回复生成的时间 + thinking_start_time=thinking_start_time, #记录了思考开始的时间 + reply_message_id=message.message_id ) message_set.add_message(bot_message) - message_sender.send_temp_container.update_thinking_message(message_set) - - + #message_set 可以直接加入 message_manager + print(f"\033[1;32m[回复]\033[0m 将回复载入发送容器") + message_manager.add_message(message_set) bot_response_time = tinking_time_point if random() < global_config.emoji_chance: @@ -187,20 +181,24 @@ class ChatBot: else: bot_response_time = bot_response_time + 1 - bot_message = Message( - group_id=event.group_id, - user_id=global_config.BOT_QQ, - message_id=0, - raw_message=emoji_cq, - plain_text=emoji_cq, - processed_plain_text=emoji_cq, - user_nickname=global_config.BOT_NICKNAME, - group_name=message.group_name, - time=bot_response_time, - is_emoji=True, - translate_cq=False - ) - message_sender.send_temp_container.add_message(bot_message) + bot_message = Message_Sending( + group_id=event.group_id, + user_id=global_config.BOT_QQ, + message_id=0, + raw_message=emoji_cq, + plain_text=emoji_cq, + processed_plain_text=emoji_cq, + user_nickname=global_config.BOT_NICKNAME, + group_name=message.group_name, + time=bot_response_time, + is_emoji=True, + translate_cq=False, + thinking_start_time=thinking_start_time, + # reply_message_id=message.message_id + ) + message_manager.add_message(bot_message) - # 如果收到新消息，提高回复意愿 - willing_manager.change_reply_willing_after_sent(event.group_id) \ No newline at end of file + willing_manager.change_reply_willing_after_sent(event.group_id) + +# 创建全局ChatBot实例 +chat_bot = ChatBot() \ No newline at end of file diff --git a/src/plugins/chat/config.py b/src/plugins/chat/config.py index 55ceb07b0..be599f48a 100644 --- a/src/plugins/chat/config.py +++ b/src/plugins/chat/config.py @@ -1,8 +1,6 @@ from dataclasses import dataclass, field from typing import Dict, Any, Optional, Set import os -from nonebot.log import logger, default_format -import logging import configparser import tomli import sys @@ -28,16 +26,24 @@ class BotConfig: talk_frequency_down_groups = set() ban_user_id = set() - build_memory_interval: int = 60 # 记忆构建间隔（秒） + build_memory_interval: int = 30 # 记忆构建间隔（秒） + forget_memory_interval: int = 300 # 记忆遗忘间隔（秒） EMOJI_CHECK_INTERVAL: int = 120 # 表情包检查间隔（分钟） EMOJI_REGISTER_INTERVAL: int = 10 # 表情包注册间隔（分钟） + + ban_words = set() # 模型配置 llm_reasoning: Dict[str, str] = field(default_factory=lambda: {}) llm_reasoning_minor: Dict[str, str] = field(default_factory=lambda: {}) llm_normal: Dict[str, str] = field(default_factory=lambda: {}) llm_normal_minor: Dict[str, str] = field(default_factory=lambda: {}) + embedding: Dict[str, str] = field(default_factory=lambda: {}) vlm: Dict[str, str] = field(default_factory=lambda: {}) + + # 主题提取配置 + topic_extract: str = 'snownlp' # 只支持jieba,snownlp,llm + llm_topic_extract: Dict[str, str] = field(default_factory=lambda: {}) API_USING: str = "siliconflow" # 使用的API API_PAID: bool = False # 是否使用付费API @@ -47,6 +53,13 @@ class BotConfig: 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 + + # 默认人设 + PROMPT_PERSONALITY=[ + "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", + "是一个女大学生，你有黑色头发，你会刷小红书" + ] + PROMPT_SCHEDULE_GEN="一个曾经学习地质,现在学习心理学和脑科学的女大学生，喜欢刷qq，贴吧，知乎和小红书" @staticmethod def get_config_dir() -> str: @@ -66,6 +79,15 @@ class BotConfig: if os.path.exists(config_path): with open(config_path, "rb") as f: toml_dict = tomli.load(f) + + if 'personality' in toml_dict: + personality_config=toml_dict['personality'] + personality=personality_config.get('prompt_personality') + if len(personality) >= 2: + logger.info(f"载入自定义人格:{personality}") + config.PROMPT_PERSONALITY=personality_config.get('prompt_personality',config.PROMPT_PERSONALITY) + logger.info(f"载入自定义日程prompt:{personality_config.get('prompt_schedule',config.PROMPT_SCHEDULE_GEN)}") + config.PROMPT_SCHEDULE_GEN=personality_config.get('prompt_schedule',config.PROMPT_SCHEDULE_GEN) if "emoji" in toml_dict: emoji_config = toml_dict["emoji"] @@ -103,12 +125,25 @@ class BotConfig: if "llm_normal" in model_config: config.llm_normal = model_config["llm_normal"] + config.llm_topic_extract = 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"] + + if "embedding" in model_config: + config.embedding = model_config["embedding"] + + if 'topic' in toml_dict: + topic_config=toml_dict['topic'] + if 'topic_extract' in topic_config: + config.topic_extract=topic_config.get('topic_extract',config.topic_extract) + logger.info(f"载入自定义主题提取为{config.topic_extract}") + if config.topic_extract=='llm' and 'llm_topic' in topic_config: + config.llm_topic_extract=topic_config['llm_topic'] + logger.info(f"载入自定义主题提取模型为{config.llm_topic_extract['name']}") # 消息配置 if "message" in toml_dict: @@ -116,10 +151,12 @@ class BotConfig: config.MIN_TEXT_LENGTH = msg_config.get("min_text_length", config.MIN_TEXT_LENGTH) config.MAX_CONTEXT_SIZE = msg_config.get("max_context_size", config.MAX_CONTEXT_SIZE) config.emoji_chance = msg_config.get("emoji_chance", config.emoji_chance) - + config.ban_words=msg_config.get("ban_words",config.ban_words) + if "memory" in toml_dict: memory_config = toml_dict["memory"] config.build_memory_interval = memory_config.get("build_memory_interval", config.build_memory_interval) + config.forget_memory_interval = memory_config.get("forget_memory_interval", config.forget_memory_interval) # 群组配置 if "groups" in toml_dict: @@ -131,6 +168,7 @@ class BotConfig: if "others" in toml_dict: others_config = toml_dict["others"] config.enable_advance_output = others_config.get("enable_advance_output", config.enable_advance_output) + config.enable_kuuki_read = others_config.get("enable_kuuki_read", config.enable_kuuki_read) logger.success(f"成功加载配置文件: {config_path}") @@ -144,32 +182,14 @@ 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") + logger.info("使用bot配置文件") else: - logger.info("已找到开发环境配置文件") + logger.info("已找到开发bot配置文件") global_config = BotConfig.load_config(config_path=bot_config_path) - -@dataclass -class LLMConfig: - """机器人配置类""" - # 基础配置 - SILICONFLOW_API_KEY: str = None - SILICONFLOW_BASE_URL: str = None - DEEP_SEEK_API_KEY: str = None - DEEP_SEEK_BASE_URL: str = None - -llm_config = LLMConfig() -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: - # logger.remove() + logger.remove() pass diff --git a/src/plugins/chat/emoji_manager.py b/src/plugins/chat/emoji_manager.py index 58713e296..2311b2459 100644 --- a/src/plugins/chat/emoji_manager.py +++ b/src/plugins/chat/emoji_manager.py @@ -12,6 +12,8 @@ import base64 import shutil import asyncio import time +from PIL import Image +import io from nonebot import get_driver from ..chat.config import global_config @@ -235,37 +237,107 @@ class EmojiManager: except Exception as e: print(f"\033[1;31m[错误]\033[0m 获取标签失败: {str(e)}") + return "skip" print(f"\033[1;32m[调试信息]\033[0m 使用默认标签: neutral") return "skip" # 默认标签 + async def _compress_image(self, image_path: str, target_size: int = 0.8 * 1024 * 1024) -> Optional[str]: + """压缩图片并返回base64编码 + Args: + image_path: 图片文件路径 + target_size: 目标文件大小（字节），默认0.8MB + Returns: + Optional[str]: 成功返回base64编码的图片数据，失败返回None + """ + try: + file_size = os.path.getsize(image_path) + if file_size <= target_size: + # 如果文件已经小于目标大小，直接读取并返回base64 + with open(image_path, 'rb') as f: + return base64.b64encode(f.read()).decode('utf-8') + + # 打开图片 + with Image.open(image_path) as img: + # 获取原始尺寸 + original_width, original_height = img.size + + # 计算缩放比例 + scale = min(1.0, (target_size / file_size) ** 0.5) + + # 计算新的尺寸 + new_width = int(original_width * scale) + new_height = int(original_height * scale) + + # 创建内存缓冲区 + output_buffer = io.BytesIO() + + # 如果是GIF，处理所有帧 + if getattr(img, "is_animated", False): + frames = [] + for frame_idx in range(img.n_frames): + img.seek(frame_idx) + new_frame = img.copy() + new_frame = new_frame.resize((new_width, new_height), Image.Resampling.LANCZOS) + frames.append(new_frame) + + # 保存到缓冲区 + frames[0].save( + output_buffer, + format='GIF', + save_all=True, + append_images=frames[1:], + optimize=True, + duration=img.info.get('duration', 100), + loop=img.info.get('loop', 0) + ) + else: + # 处理静态图片 + resized_img = img.resize((new_width, new_height), Image.Resampling.LANCZOS) + + # 保存到缓冲区，保持原始格式 + if img.format == 'PNG' and img.mode in ('RGBA', 'LA'): + resized_img.save(output_buffer, format='PNG', optimize=True) + else: + resized_img.save(output_buffer, format='JPEG', quality=95, optimize=True) + + # 获取压缩后的数据并转换为base64 + compressed_data = output_buffer.getvalue() + print(f"\033[1;32m[成功]\033[0m 压缩图片: {os.path.basename(image_path)} ({original_width}x{original_height} -> {new_width}x{new_height})") + + return base64.b64encode(compressed_data).decode('utf-8') + + except Exception as e: + print(f"\033[1;31m[错误]\033[0m 压缩图片失败: {os.path.basename(image_path)}, 错误: {str(e)}") + return None + async def scan_new_emojis(self): """扫描新的表情包""" try: emoji_dir = "data/emoji" os.makedirs(emoji_dir, exist_ok=True) - # 获取所有jpg文件 - files_to_process = [f for f in os.listdir(emoji_dir) if f.endswith('.jpg')] + # 获取所有支持的图片文件 + files_to_process = [f for f in os.listdir(emoji_dir) if f.lower().endswith(('.jpg', '.jpeg', '.png', '.gif'))] for filename in files_to_process: + image_path = os.path.join(emoji_dir, filename) + # 检查是否已经注册过 existing_emoji = self.db.db['emoji'].find_one({'filename': filename}) if existing_emoji: continue - - image_path = os.path.join(emoji_dir, filename) - # 读取图片数据 - with open(image_path, 'rb') as f: - image_data = f.read() - # 将图片转换为base64 - image_base64 = base64.b64encode(image_data).decode('utf-8') + # 压缩图片并获取base64编码 + image_base64 = await self._compress_image(image_path) + if image_base64 is None: + os.remove(image_path) + continue # 获取表情包的情感标签 tag = await self._get_emoji_tag(image_base64) if not tag == "skip": - # 准备数据库记录 + # 准备数据库记录 emoji_record = { 'filename': filename, 'path': image_path, @@ -279,7 +351,6 @@ class EmojiManager: print(f"标签: {tag}") else: print(f"\033[1;33m[警告]\033[0m 跳过表情包: {filename}") - except Exception as e: print(f"\033[1;31m[错误]\033[0m 扫描表情包失败: {str(e)}") diff --git a/src/plugins/chat/llm_generator.py b/src/plugins/chat/llm_generator.py index a9c028827..04f2e73ad 100644 --- a/src/plugins/chat/llm_generator.py +++ b/src/plugins/chat/llm_generator.py @@ -21,9 +21,9 @@ config = driver.config class ResponseGenerator: def __init__(self): - self.model_r1 = LLM_request(model=global_config.llm_reasoning, temperature=0.7) - self.model_v3 = LLM_request(model=global_config.llm_normal, temperature=0.7) - self.model_r1_distill = LLM_request(model=global_config.llm_reasoning_minor, temperature=0.7) + self.model_r1 = LLM_request(model=global_config.llm_reasoning, temperature=0.7,max_tokens=1000) + self.model_v3 = LLM_request(model=global_config.llm_normal, temperature=0.7,max_tokens=1000) + self.model_r1_distill = LLM_request(model=global_config.llm_reasoning_minor, temperature=0.7,max_tokens=1000) self.db = Database.get_instance() self.current_model_type = 'r1' # 默认使用 R1 @@ -50,12 +50,19 @@ class ResponseGenerator: 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}") + valuedict={ + 'happy':0.5,'angry':-1,'sad':-0.5,'surprised':0.5,'disgusted':-1.5,'fearful':-0.25,'neutral':0.25 + } + await relationship_manager.update_relationship_value(message.user_id, relationship_value=valuedict[emotion[0]]) + return model_response, emotion return None, [] 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}" + if message.user_cardname: + sender_name=f"[({message.user_id}){message.user_nickname}]{message.user_cardname}" # 获取关系值 relationship_value = relationship_manager.get_relationship(message.user_id).relationship_value if relationship_manager.get_relationship(message.user_id) else 0.0 @@ -70,25 +77,29 @@ class ResponseGenerator: group_id=message.group_id ) - # 读空气模块 - if global_config.enable_kuuki_read: - 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 + # 读空气模块 简化逻辑，先停用 + # if global_config.enable_kuuki_read: + # 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 # 生成回复 - content, reasoning_content = await model.generate_response(prompt) + try: + content, reasoning_content = await model.generate_response(prompt) + except Exception as e: + print(f"生成回复时出错: {e}") + return None # 保存到数据库 self._save_to_db( @@ -97,15 +108,17 @@ class ResponseGenerator: prompt=prompt, prompt_check=prompt_check, content=content, - content_check=content_check if global_config.enable_kuuki_read else "", + # 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 "" + # 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): 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): + content: str, reasoning_content: str,): """保存对话记录到数据库""" self.db.db.reasoning_logs.insert_one({ 'time': time.time(), @@ -113,8 +126,8 @@ class ResponseGenerator: 'user': sender_name, 'message': message.processed_plain_text, 'model': self.current_model_type, - 'reasoning_check': reasoning_content_check, - 'response_check': content_check, + # 'reasoning_check': reasoning_content_check, + # 'response_check': content_check, 'reasoning': reasoning_content, 'response': content, 'prompt': prompt, @@ -129,9 +142,12 @@ class ResponseGenerator: 内容：{content} 输出： ''' - content, _ = await self.model_v3.generate_response(prompt) - return [content.strip()] if content else ["neutral"] + content=content.strip() + if content in ['happy','angry','sad','surprised','disgusted','fearful','neutral']: + return [content] + else: + return ["neutral"] except Exception as e: print(f"获取情感标签时出错: {e}") @@ -145,4 +161,42 @@ class ResponseGenerator: emotion_tags = await self._get_emotion_tags(content) processed_response = process_llm_response(content) - return processed_response, emotion_tags \ No newline at end of file + return processed_response, emotion_tags + + +class InitiativeMessageGenerate: + def __init__(self): + self.db = Database.get_instance() + 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 + ) + + def gen_response(self, message: Message): + topic_select_prompt, dots_for_select, prompt_template = ( + prompt_builder._build_initiative_prompt_select(message.group_id) + ) + content_select, reasoning = self.model_v3.generate_response(topic_select_prompt) + print(f"[DEBUG] {content_select} {reasoning}") + topics_list = [dot[0] for dot in dots_for_select] + if content_select: + if content_select in topics_list: + select_dot = dots_for_select[topics_list.index(content_select)] + else: + return None + else: + return None + prompt_check, memory = prompt_builder._build_initiative_prompt_check( + select_dot[1], prompt_template + ) + content_check, reasoning_check = self.model_v3.generate_response(prompt_check) + print(f"[DEBUG] {content_check} {reasoning_check}") + if "yes" not in content_check.lower(): + return None + prompt = prompt_builder._build_initiative_prompt( + select_dot, prompt_template, memory + ) + content, reasoning = self.model_r1.generate_response(prompt) + print(f"[DEBUG] {content} {reasoning}") + return content diff --git a/src/plugins/chat/message.py b/src/plugins/chat/message.py index f5ea0db0d..d6e400e15 100644 --- a/src/plugins/chat/message.py +++ b/src/plugins/chat/message.py @@ -8,7 +8,7 @@ from ...common.database import Database from PIL import Image from .config import global_config import urllib3 -from .utils_user import get_user_nickname +from .utils_user import get_user_nickname,get_user_cardname,get_groupname from .utils_cq import parse_cq_code from .cq_code import cq_code_tool,CQCode @@ -21,46 +21,46 @@ urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning) #它还定义了两个辅助属性：keywords用于提取消息的关键词，is_plain_text用于判断消息是否为纯文本。 + @dataclass class Message: """消息数据类""" + message_id: int = None + time: float = None + group_id: int = None + group_name: str = None # 群名称 + user_id: int = None user_nickname: str = None # 用户昵称 - group_name: str = None # 群名称 + user_cardname: str=None # 用户群昵称 - message_id: int = None - raw_message: str = None - plain_text: str = None - - message_based_id: int = None - reply_message: Dict = None # 存储回复消息 + raw_message: str = None # 原始消息，包含未解析的cq码 + plain_text: str = None # 纯文本 message_segments: List[Dict] = None # 存储解析后的消息片段 processed_plain_text: str = None # 用于存储处理后的plain_text detailed_plain_text: str = None # 用于存储详细可读文本 - time: float = None + reply_message: Dict = None # 存储 回复的 源消息 is_emoji: bool = False # 是否是表情包 has_emoji: bool = False # 是否包含表情包 translate_cq: bool = True # 是否翻译cq码 - - - reply_benefits: float = 0.0 - - type: str = 'received' # 消息类型，可以是received或者send def __post_init__(self): if self.time is None: self.time = int(time.time()) + + if not self.group_name: + self.group_name = get_groupname(self.group_id) if not self.user_nickname: self.user_nickname = get_user_nickname(self.user_id) - - if not self.group_name: - self.group_name = self.get_groupname(self.group_id) + + if not self.user_cardname: + self.user_cardname=get_user_cardname(self.user_id) if not self.processed_plain_text: if self.raw_message: @@ -71,24 +71,12 @@ class Message: ) #将详细翻译为详细可读文本 time_str = time.strftime("%m-%d %H:%M:%S", time.localtime(self.time)) - name = self.user_nickname or f"用户{self.user_id}" + try: + name = f"{self.user_nickname}(ta的昵称:{self.user_cardname},ta的id:{self.user_id})" + except: + name = self.user_nickname or f"用户{self.user_id}" content = self.processed_plain_text self.detailed_plain_text = f"[{time_str}] {name}: {content}\n" - - - def get_groupname(self, group_id: int) -> str: - if not group_id: - return "未知群" - group_id = int(group_id) - # 使用数据库单例 - db = Database.get_instance() - # 查找用户，打印查询条件和结果 - query = {'group_id': group_id} - group = db.db.group_info.find_one(query) - if group: - return group.get('group_name') - else: - return f"群{group_id}" def parse_message_segments(self, message: str) -> List[CQCode]: """ @@ -159,49 +147,58 @@ class Message_Thinking: self.group_id = message.group_id self.user_id = message.user_id self.user_nickname = message.user_nickname + self.user_cardname = message.user_cardname self.group_name = message.group_name self.message_id = message_id # 思考状态相关属性 - self.thinking_text = "正在思考..." - self.time = int(time.time()) + self.thinking_start_time = int(time.time()) self.thinking_time = 0 + self.interupt=False def update_thinking_time(self): - self.thinking_time = round(time.time(), 2) - self.time + self.thinking_time = round(time.time(), 2) - self.thinking_start_time - @property - def processed_plain_text(self) -> str: - """获取处理后的文本""" - return self.thinking_text + +@dataclass +class Message_Sending(Message): + """发送中的消息类""" + thinking_start_time: float = None # 思考开始时间 + thinking_time: float = None # 思考时间 - def __str__(self) -> str: - return f"[思考中] 群:{self.group_id} 用户:{self.user_nickname} 时间:{self.time} 消息ID:{self.message_id}" - - + reply_message_id: int = None # 存储 回复的 源消息ID + + def update_thinking_time(self): + self.thinking_time = round(time.time(), 2) - self.thinking_start_time + return self.thinking_time + + + class MessageSet: - """消息集合类，可以存储多个相关的消息""" + """消息集合类，可以存储多个发送消息""" def __init__(self, group_id: int, user_id: int, message_id: str): self.group_id = group_id self.user_id = user_id self.message_id = message_id - self.messages: List[Message] = [] + self.messages: List[Message_Sending] = [] # 修改类型标注 self.time = round(time.time(), 2) - def add_message(self, message: Message) -> None: - """添加消息到集合""" + def add_message(self, message: Message_Sending) -> None: + """添加消息到集合，只接受Message_Sending类型""" + if not isinstance(message, Message_Sending): + raise TypeError("MessageSet只能添加Message_Sending类型的消息") self.messages.append(message) # 按时间排序 self.messages.sort(key=lambda x: x.time) - def get_message_by_index(self, index: int) -> Optional[Message]: + def get_message_by_index(self, index: int) -> Optional[Message_Sending]: """通过索引获取消息""" if 0 <= index < len(self.messages): return self.messages[index] return None - def get_message_by_time(self, target_time: float) -> Optional[Message]: + def get_message_by_time(self, target_time: float) -> Optional[Message_Sending]: """获取最接近指定时间的消息""" if not self.messages: return None @@ -222,7 +219,7 @@ class MessageSet: """清空所有消息""" self.messages.clear() - def remove_message(self, message: Message) -> bool: + def remove_message(self, message: Message_Sending) -> bool: """移除指定消息""" if message in self.messages: self.messages.remove(message) @@ -236,8 +233,4 @@ class MessageSet: return len(self.messages) - - - - diff --git a/src/plugins/chat/message_send_control.py b/src/plugins/chat/message_send_control.py deleted file mode 100644 index 0ddb79c5f..000000000 --- a/src/plugins/chat/message_send_control.py +++ /dev/null @@ -1,251 +0,0 @@ -from typing import Union, List, Optional, Deque, Dict -from nonebot.adapters.onebot.v11 import Bot, MessageSegment -import asyncio -import random -import os -from .message import Message, Message_Thinking, MessageSet -from .cq_code import CQCode -from collections import deque -import time -from .storage import MessageStorage -from .config import global_config -from .cq_code import cq_code_tool - -if os.name == "nt": - from .message_visualizer import message_visualizer - - - -class SendTemp: - """单个群组的临时消息队列管理器""" - def __init__(self, group_id: int, max_size: int = 100): - self.group_id = group_id - self.max_size = max_size - self.messages: Deque[Union[Message, Message_Thinking]] = deque(maxlen=max_size) - self.last_send_time = 0 - - def add(self, message: Message) -> None: - """按时间顺序添加消息到队列""" - if not self.messages: - self.messages.append(message) - return - - # 按时间顺序插入 - if message.time >= self.messages[-1].time: - self.messages.append(message) - return - - # 使用二分查找找到合适的插入位置 - messages_list = list(self.messages) - left, right = 0, len(messages_list) - - while left < right: - mid = (left + right) // 2 - if messages_list[mid].time < message.time: - left = mid + 1 - else: - right = mid - - # 重建消息队列，保持时间顺序 - new_messages = deque(maxlen=self.max_size) - new_messages.extend(messages_list[:left]) - new_messages.append(message) - new_messages.extend(messages_list[left:]) - self.messages = new_messages - def get_earliest_message(self) -> Optional[Message]: - """获取时间最早的消息""" - message = self.messages.popleft() if self.messages else None - return message - - def clear(self) -> None: - """清空队列""" - self.messages.clear() - - def get_all(self, group_id: Optional[int] = None) -> List[Union[Message, Message_Thinking]]: - """获取所有待发送的消息""" - if group_id is None: - return list(self.messages) - return [msg for msg in self.messages if msg.group_id == group_id] - - def peek_next(self) -> Optional[Union[Message, Message_Thinking]]: - """查看下一条要发送的消息（不移除）""" - return self.messages[0] if self.messages else None - - def has_messages(self) -> bool: - """检查是否有待发送的消息""" - return bool(self.messages) - - def count(self, group_id: Optional[int] = None) -> int: - """获取待发送消息数量""" - if group_id is None: - return len(self.messages) - return len([msg for msg in self.messages if msg.group_id == group_id]) - - def get_last_send_time(self) -> float: - """获取最后一次发送时间""" - return self.last_send_time - - def update_send_time(self): - """更新最后发送时间""" - self.last_send_time = time.time() - -class SendTempContainer: - """管理所有群组的消息缓存容器""" - def __init__(self): - self.temp_queues: Dict[int, SendTemp] = {} - - def get_queue(self, group_id: int) -> SendTemp: - """获取或创建群组的消息队列""" - if group_id not in self.temp_queues: - self.temp_queues[group_id] = SendTemp(group_id) - return self.temp_queues[group_id] - - def add_message(self, message: Message) -> None: - """添加消息到对应群组的队列""" - queue = self.get_queue(message.group_id) - queue.add(message) - - def get_group_messages(self, group_id: int) -> List[Union[Message, Message_Thinking]]: - """获取指定群组的所有待发送消息""" - queue = self.get_queue(group_id) - return queue.get_all() - - def has_messages(self, group_id: int) -> bool: - """检查指定群组是否有待发送消息""" - queue = self.get_queue(group_id) - return queue.has_messages() - - def get_all_groups(self) -> List[int]: - """获取所有有待发送消息的群组ID""" - return list(self.temp_queues.keys()) - - def update_thinking_message(self, message_obj: Union[Message, MessageSet]) -> bool: - queue = self.get_queue(message_obj.group_id) - # 使用列表解析找到匹配的消息索引 - matching_indices = [ - i for i, msg in enumerate(queue.messages) - if msg.message_id == message_obj.message_id - ] - - if not matching_indices: - return False - - index = matching_indices[0] # 获取第一个匹配的索引 - - # 将消息转换为列表以便修改 - messages = list(queue.messages) - - # 根据消息类型处理 - if isinstance(message_obj, MessageSet): - messages.pop(index) - # 在原位置插入新消息组 - for i, single_message in enumerate(message_obj.messages): - messages.insert(index + i, single_message) - # print(f"\033[1;34m[调试]\033[0m 添加消息组中的第{i+1}条消息: {single_message}") - else: - # 直接替换原消息 - messages[index] = message_obj - # print(f"\033[1;34m[调试]\033[0m 已更新消息: {message_obj}") - - # 重建队列 - queue.messages.clear() - for msg in messages: - queue.messages.append(msg) - - return True - - -class MessageSendControl: - """消息发送控制器""" - def __init__(self): - self.typing_speed = (0.1, 0.3) # 每个字符的打字时间范围(秒) - self.message_interval = (0.5, 1) # 多条消息间的间隔时间范围(秒) - self.max_retry = 3 # 最大重试次数 - self.send_temp_container = SendTempContainer() - self._running = True - self._paused = False - self._current_bot = None - self.storage = MessageStorage() # 添加存储实例 - try: - message_visualizer.start() - except(NameError): - pass - - def set_bot(self, bot: Bot): - """设置当前bot实例""" - self._current_bot = bot - - async def process_group_messages(self, group_id: int): - queue = self.send_temp_container.get_queue(group_id) - if queue.has_messages(): - message = queue.peek_next() - # 处理消息的逻辑 - if isinstance(message, Message_Thinking): - message.update_thinking_time() - thinking_time = message.thinking_time - if thinking_time < 90: # 最少思考2秒 - if int(thinking_time) % 15 == 0: - print(f"\033[1;34m[调试]\033[0m 消息正在思考中，已思考{thinking_time:.1f}秒") - return - else: - print(f"\033[1;34m[调试]\033[0m 思考消息超时，移除") - queue.get_earliest_message() # 移除超时的思考消息 - return - elif isinstance(message, Message): - message = queue.get_earliest_message() - if message and message.processed_plain_text: - print(f"- 群组: {group_id} - 内容: {message.processed_plain_text}") - cost_time = round(time.time(), 2) - message.time - if cost_time > 40: - message.processed_plain_text = cq_code_tool.create_reply_cq(message.message_based_id) + message.processed_plain_text - cur_time = time.time() - await self._current_bot.send_group_msg( - group_id=group_id, - message=str(message.processed_plain_text), - auto_escape=False - ) - cost_time = round(time.time(), 2) - cur_time - print(f"\033[1;34m[调试]\033[0m 消息发送时间: {cost_time}秒") - current_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(message.time)) - print(f"\033[1;32m群 {group_id} 消息, 用户 {global_config.BOT_NICKNAME}, 时间: {current_time}:\033[0m {str(message.processed_plain_text)}") - - if message.is_emoji: - message.processed_plain_text = "[表情包]" - await self.storage.store_message(message, None) - else: - await self.storage.store_message(message, None) - - - - queue.update_send_time() - if queue.has_messages(): - await asyncio.sleep( - random.uniform( - self.message_interval[0], - self.message_interval[1] - ) - ) - - async def start_processor(self, bot: Bot): - """启动消息处理器""" - self._current_bot = bot - - while self._running: - await asyncio.sleep(1.5) - tasks = [] - for group_id in self.send_temp_container.get_all_groups(): - tasks.append(self.process_group_messages(group_id)) - - # 并行处理所有群组的消息 - await asyncio.gather(*tasks) - try: - message_visualizer.update_content(self.send_temp_container) - except(NameError): - pass - - def set_typing_speed(self, min_speed: float, max_speed: float): - """设置打字速度范围""" - self.typing_speed = (min_speed, max_speed) - -# 创建全局实例 -message_sender = MessageSendControl() diff --git a/src/plugins/chat/message_sender.py b/src/plugins/chat/message_sender.py new file mode 100644 index 000000000..970fd3682 --- /dev/null +++ b/src/plugins/chat/message_sender.py @@ -0,0 +1,225 @@ +from typing import Union, List, Optional, Dict +from collections import deque +from .message import Message, Message_Thinking, MessageSet, Message_Sending +import time +import asyncio +from nonebot.adapters.onebot.v11 import Bot +from .config import global_config +from .storage import MessageStorage +from .cq_code import cq_code_tool +import random +from .utils import calculate_typing_time + +class Message_Sender: + """发送器""" + def __init__(self): + self.message_interval = (0.5, 1) # 消息间隔时间范围(秒) + self.last_send_time = 0 + self._current_bot = None + + def set_bot(self, bot: Bot): + """设置当前bot实例""" + self._current_bot = bot + + async def send_group_message( + self, + group_id: int, + send_text: str, + auto_escape: bool = False, + reply_message_id: int = None, + at_user_id: int = None + ) -> None: + + if not self._current_bot: + raise RuntimeError("Bot未设置，请先调用set_bot方法设置bot实例") + + message = send_text + + # 如果需要回复 + if reply_message_id: + reply_cq = cq_code_tool.create_reply_cq(reply_message_id) + message = reply_cq + message + + # 如果需要at + # if at_user_id: + # at_cq = cq_code_tool.create_at_cq(at_user_id) + # message = at_cq + " " + message + + + typing_time = calculate_typing_time(message) + if typing_time > 10: + typing_time = 10 + await asyncio.sleep(typing_time) + + # 发送消息 + try: + await self._current_bot.send_group_msg( + group_id=group_id, + message=message, + auto_escape=auto_escape + ) + print(f"\033[1;34m[调试]\033[0m 发送消息{message}成功") + except Exception as e: + print(f"发生错误 {e}") + print(f"\033[1;34m[调试]\033[0m 发送消息{message}失败") + + +class MessageContainer: + """单个群的发送/思考消息容器""" + def __init__(self, group_id: int, max_size: int = 100): + self.group_id = group_id + self.max_size = max_size + self.messages = [] + self.last_send_time = 0 + self.thinking_timeout = 20 # 思考超时时间（秒） + + def get_timeout_messages(self) -> List[Message_Sending]: + """获取所有超时的Message_Sending对象（思考时间超过30秒），按thinking_start_time排序""" + current_time = time.time() + timeout_messages = [] + + for msg in self.messages: + if isinstance(msg, Message_Sending): + if current_time - msg.thinking_start_time > self.thinking_timeout: + timeout_messages.append(msg) + + # 按thinking_start_time排序，时间早的在前面 + timeout_messages.sort(key=lambda x: x.thinking_start_time) + + return timeout_messages + + def get_earliest_message(self) -> Optional[Union[Message_Thinking, Message_Sending]]: + """获取thinking_start_time最早的消息对象""" + if not self.messages: + return None + earliest_time = float('inf') + earliest_message = None + for msg in self.messages: + msg_time = msg.thinking_start_time + if msg_time < earliest_time: + earliest_time = msg_time + earliest_message = msg + return earliest_message + + def add_message(self, message: Union[Message_Thinking, Message_Sending]) -> None: + """添加消息到队列""" + print(f"\033[1;32m[添加消息]\033[0m 添加消息到对应群") + if isinstance(message, MessageSet): + for single_message in message.messages: + self.messages.append(single_message) + else: + self.messages.append(message) + + def remove_message(self, message: Union[Message_Thinking, Message_Sending]) -> bool: + """移除消息，如果消息存在则返回True，否则返回False""" + try: + if message in self.messages: + self.messages.remove(message) + return True + return False + except Exception as e: + print(f"\033[1;31m[错误]\033[0m 移除消息时发生错误: {e}") + return False + + def has_messages(self) -> bool: + """检查是否有待发送的消息""" + return bool(self.messages) + + def get_all_messages(self) -> List[Union[Message, Message_Thinking]]: + """获取所有消息""" + return list(self.messages) + + +class MessageManager: + """管理所有群的消息容器""" + def __init__(self): + self.containers: Dict[int, MessageContainer] = {} + self.storage = MessageStorage() + self._running = True + + def get_container(self, group_id: int) -> MessageContainer: + """获取或创建群的消息容器""" + if group_id not in self.containers: + self.containers[group_id] = MessageContainer(group_id) + return self.containers[group_id] + + def add_message(self, message: Union[Message_Thinking, Message_Sending, MessageSet]) -> None: + container = self.get_container(message.group_id) + container.add_message(message) + + async def process_group_messages(self, group_id: int): + """处理群消息""" + # if int(time.time() / 3) == time.time() / 3: + # print(f"\033[1;34m[调试]\033[0m 开始处理群{group_id}的消息") + container = self.get_container(group_id) + if container.has_messages(): + #最早的对象，可能是思考消息，也可能是发送消息 + message_earliest = container.get_earliest_message() #一个message_thinking or message_sending + + #一个月后删了 + if not message_earliest: + print(f"\033[1;34m[BUG，如果出现这个，说明有BUG，3月4日留]\033[0m ") + return + + #如果是思考消息 + if isinstance(message_earliest, Message_Thinking): + #优先等待这条消息 + message_earliest.update_thinking_time() + thinking_time = message_earliest.thinking_time + print(f"\033[1;34m[调试]\033[0m 消息正在思考中，已思考{int(thinking_time)}秒") + else:# 如果不是message_thinking就只能是message_sending + print(f"\033[1;34m[调试]\033[0m 消息'{message_earliest.processed_plain_text}'正在发送中") + #直接发，等什么呢 + if message_earliest.update_thinking_time() < 30: + await message_sender.send_group_message(group_id, message_earliest.processed_plain_text, auto_escape=False) + else: + await message_sender.send_group_message(group_id, message_earliest.processed_plain_text, auto_escape=False, reply_message_id=message_earliest.reply_message_id) + + #移除消息 + if message_earliest.is_emoji: + message_earliest.processed_plain_text = "[表情包]" + await self.storage.store_message(message_earliest, None) + + container.remove_message(message_earliest) + + #获取并处理超时消息 + message_timeout = container.get_timeout_messages() #也许是一堆message_sending + if message_timeout: + print(f"\033[1;34m[调试]\033[0m 发现{len(message_timeout)}条超时消息") + for msg in message_timeout: + if msg == message_earliest: + continue # 跳过已经处理过的消息 + + try: + #发送 + if msg.update_thinking_time() < 30: + await message_sender.send_group_message(group_id, msg.processed_plain_text, auto_escape=False) + else: + await message_sender.send_group_message(group_id, msg.processed_plain_text, auto_escape=False, reply_message_id=msg.reply_message_id) + + #如果是表情包，则替换为"[表情包]" + if msg.is_emoji: + msg.processed_plain_text = "[表情包]" + await self.storage.store_message(msg, None) + + # 安全地移除消息 + if not container.remove_message(msg): + print(f"\033[1;33m[警告]\033[0m 尝试删除不存在的消息") + except Exception as e: + print(f"\033[1;31m[错误]\033[0m 处理超时消息时发生错误: {e}") + continue + + async def start_processor(self): + """启动消息处理器""" + while self._running: + await asyncio.sleep(1) + tasks = [] + for group_id in self.containers.keys(): + tasks.append(self.process_group_messages(group_id)) + + await asyncio.gather(*tasks) + +# 创建全局消息管理器实例 +message_manager = MessageManager() +# 创建全局发送器实例 +message_sender = Message_Sender() diff --git a/src/plugins/chat/message_stream.py b/src/plugins/chat/message_stream.py deleted file mode 100644 index 23a8b7b9d..000000000 --- a/src/plugins/chat/message_stream.py +++ /dev/null @@ -1,264 +0,0 @@ -from typing import List, Optional, Dict -from .message import Message -import time -from collections import deque -from datetime import datetime, timedelta -import os -import json -import asyncio - -class MessageStream: - """单个群组的消息流容器""" - def __init__(self, group_id: int, max_size: int = 1000): - self.group_id = group_id - self.messages = deque(maxlen=max_size) - self.max_size = max_size - self.last_save_time = time.time() - - # 确保日志目录存在 - self.log_dir = os.path.join("log", str(self.group_id)) - os.makedirs(self.log_dir, exist_ok=True) - - # 启动自动保存任务 - asyncio.create_task(self._auto_save()) - - async def _auto_save(self): - """每30秒自动保存一次消息记录""" - while True: - await asyncio.sleep(30) # 等待30秒 - await self.save_to_log() - - async def save_to_log(self): - """将消息保存到日志文件""" - try: - current_time = time.time() - # 只有有新消息时才保存 - if not self.messages or self.last_save_time == current_time: - return - - # 生成日志文件名 (使用当前日期) - date_str = time.strftime("%Y-%m-%d", time.localtime(current_time)) - log_file = os.path.join(self.log_dir, f"chat_{date_str}.log") - - # 获取需要保存的新消息 - new_messages = [ - msg for msg in self.messages - if msg.time > self.last_save_time - ] - - if not new_messages: - return - - # 将消息转换为可序列化的格式 - message_logs = [] - for msg in new_messages: - message_logs.append({ - "time": time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(msg.time)), - "user_id": msg.user_id, - "user_nickname": msg.user_nickname, - "message_id": msg.message_id, - "raw_message": msg.raw_message, - "processed_text": msg.processed_plain_text - }) - - # 追加写入日志文件 - with open(log_file, "a", encoding="utf-8") as f: - for log in message_logs: - f.write(json.dumps(log, ensure_ascii=False) + "\n") - - self.last_save_time = current_time - - except Exception as e: - print(f"\033[1;31m[错误]\033[0m 保存群 {self.group_id} 的消息日志失败: {str(e)}") - - def add_message(self, message: Message) -> None: - """按时间顺序添加新消息到队列 - - 使用改进的二分查找算法来保持消息的时间顺序，同时优化内存使用。 - - Args: - message: Message对象，要添加的新消息 - """ - - # 空队列或消息应该添加到末尾的情况 - if (not self.messages or - message.time >= self.messages[-1].time): - self.messages.append(message) - return - - # 消息应该添加到开头的情况 - if message.time <= self.messages[0].time: - self.messages.appendleft(message) - return - - # 使用二分查找在现有队列中找到合适的插入位置 - left, right = 0, len(self.messages) - 1 - while left <= right: - mid = (left + right) // 2 - if self.messages[mid].time < message.time: - left = mid + 1 - else: - right = mid - 1 - - temp = list(self.messages) - temp.insert(left, message) - - # 如果超出最大长度，移除多余的消息 - if len(temp) > self.max_size: - temp = temp[-self.max_size:] - - # 重建队列 - self.messages = deque(temp, maxlen=self.max_size) - - async def get_recent_messages_from_db(self, count: int = 10) -> List[Message]: - """从数据库中获取最近的消息记录 - - Args: - count: 需要获取的消息数量 - - Returns: - List[Message]: 最近的消息列表 - """ - try: - from ...common.database import Database - db = Database.get_instance() - - # 从数据库中查询最近的消息 - recent_messages = list(db.db.messages.find( - {"group_id": self.group_id}, - { - "time": 1, - "user_id": 1, - "user_nickname": 1, - "message_id": 1, - "raw_message": 1, - "processed_text": 1 - } - ).sort("time", -1).limit(count)) - - if not recent_messages: - return [] - - # 转换为 Message 对象 - from .message import Message - messages = [] - for msg_data in recent_messages: - msg = Message( - time=msg_data["time"], - user_id=msg_data["user_id"], - user_nickname=msg_data.get("user_nickname", ""), - message_id=msg_data["message_id"], - raw_message=msg_data["raw_message"], - processed_plain_text=msg_data.get("processed_text", ""), - group_id=self.group_id - ) - messages.append(msg) - - return list(reversed(messages)) # 返回按时间正序的消息 - - except Exception as e: - print(f"\033[1;31m[错误]\033[0m 从数据库获取群 {self.group_id} 的最近消息记录失败: {str(e)}") - return [] - - def get_recent_messages(self, count: int = 10) -> List[Message]: - """获取最近的n条消息（从内存队列）""" - print(f"\033[1;34m[调试]\033[0m 从内存获取群 {self.group_id} 的最近{count}条消息记录") - return list(self.messages)[-count:] - - def get_messages_in_timerange(self, - start_time: Optional[float] = None, - end_time: Optional[float] = None) -> List[Message]: - """获取时间范围内的消息""" - if start_time is None: - start_time = time.time() - 3600 - if end_time is None: - end_time = time.time() - - return [ - msg for msg in self.messages - if start_time <= msg.time <= end_time - ] - - def get_user_messages(self, user_id: int, count: int = 10) -> List[Message]: - """获取特定用户的最近消息""" - user_messages = [msg for msg in self.messages if msg.user_id == user_id] - return user_messages[-count:] - - def clear_old_messages(self, hours: int = 24) -> None: - """清理旧消息""" - cutoff_time = time.time() - (hours * 3600) - self.messages = deque( - [msg for msg in self.messages if msg.time > cutoff_time], - maxlen=self.max_size - ) - -class MessageStreamContainer: - """管理所有群组的消息流容器""" - def __init__(self, max_size: int = 1000): - self.streams: Dict[int, MessageStream] = {} - self.max_size = max_size - - async def save_all_logs(self): - """保存所有群组的消息日志""" - for stream in self.streams.values(): - await stream.save_to_log() - - def add_message(self, message: Message) -> None: - """添加消息到对应群组的消息流""" - if not message.group_id: - return - - if message.group_id not in self.streams: - self.streams[message.group_id] = MessageStream(message.group_id, self.max_size) - - self.streams[message.group_id].add_message(message) - - def get_stream(self, group_id: int) -> Optional[MessageStream]: - """获取特定群组的消息流""" - return self.streams.get(group_id) - - def get_all_streams(self) -> Dict[int, MessageStream]: - """获取所有群组的消息流""" - return self.streams - - def clear_old_messages(self, hours: int = 24) -> None: - """清理所有群组的旧消息""" - for stream in self.streams.values(): - stream.clear_old_messages(hours) - - def get_group_stats(self, group_id: int) -> Dict: - """获取群组的消息统计信息""" - stream = self.streams.get(group_id) - if not stream: - return { - "total_messages": 0, - "unique_users": 0, - "active_hours": [], - "most_active_user": None - } - - messages = stream.messages - user_counts = {} - hour_counts = {} - - for msg in messages: - user_counts[msg.user_id] = user_counts.get(msg.user_id, 0) + 1 - hour = datetime.fromtimestamp(msg.time).hour - hour_counts[hour] = hour_counts.get(hour, 0) + 1 - - most_active_user = max(user_counts.items(), key=lambda x: x[1])[0] if user_counts else None - active_hours = sorted( - hour_counts.items(), - key=lambda x: x[1], - reverse=True - )[:5] - - return { - "total_messages": len(messages), - "unique_users": len(user_counts), - "active_hours": active_hours, - "most_active_user": most_active_user - } - -# 创建全局实例 -message_stream_container = MessageStreamContainer() diff --git a/src/plugins/chat/message_visualizer.py b/src/plugins/chat/message_visualizer.py deleted file mode 100644 index 0469af8f6..000000000 --- a/src/plugins/chat/message_visualizer.py +++ /dev/null @@ -1,138 +0,0 @@ -import subprocess -import threading -import queue -import os -import time -from typing import Dict -from .message import Message_Thinking - -class MessageVisualizer: - def __init__(self): - self.process = None - self.message_queue = queue.Queue() - self.is_running = False - self.content_file = "message_queue_content.txt" - - def start(self): - if self.process is None: - # 创建用于显示的批处理文件 - with open("message_queue_window.bat", "w", encoding="utf-8") as f: - f.write('@echo off\n') - f.write('chcp 65001\n') # 设置UTF-8编码 - f.write('title Message Queue Visualizer\n') - f.write('echo Waiting for message queue updates...\n') - f.write(':loop\n') - f.write('if exist "queue_update.txt" (\n') - f.write(' type "queue_update.txt" > "message_queue_content.txt"\n') - f.write(' del "queue_update.txt"\n') - f.write(' cls\n') - f.write(' type "message_queue_content.txt"\n') - f.write(')\n') - f.write('timeout /t 1 /nobreak >nul\n') - f.write('goto loop\n') - - # 清空内容文件 - with open(self.content_file, "w", encoding="utf-8") as f: - f.write("") - - # 启动新窗口 - startupinfo = subprocess.STARTUPINFO() - startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW - self.process = subprocess.Popen( - ['cmd', '/c', 'start', 'message_queue_window.bat'], - shell=True, - startupinfo=startupinfo - ) - self.is_running = True - - # 启动处理线程 - threading.Thread(target=self._process_messages, daemon=True).start() - - def _process_messages(self): - while self.is_running: - try: - # 获取新消息 - text = self.message_queue.get(timeout=1) - # 写入更新文件 - with open("queue_update.txt", "w", encoding="utf-8") as f: - f.write(text) - except queue.Empty: - continue - except Exception as e: - print(f"处理队列可视化内容时出错: {e}") - - def update_content(self, send_temp_container): - """更新显示内容""" - if not self.is_running: - return - - current_time = time.strftime("%Y-%m-%d %H:%M:%S") - display_text = f"Message Queue Status - {current_time}\n" - display_text += "=" * 50 + "\n\n" - - # 遍历所有群组的队列 - for group_id, queue in send_temp_container.temp_queues.items(): - display_text += f"\n{'='*20} 群组: {queue.group_id} {'='*20}\n" - display_text += f"消息队列长度: {len(queue.messages)}\n" - display_text += f"最后发送时间: {time.strftime('%H:%M:%S', time.localtime(queue.last_send_time))}\n" - display_text += "\n消息队列内容:\n" - - # 显示队列中的消息 - if not queue.messages: - display_text += " [空队列]\n" - else: - for i, msg in enumerate(queue.messages): - msg_time = time.strftime("%H:%M:%S", time.localtime(msg.time)) - display_text += f"\n--- 消息 {i+1} ---\n" - - if isinstance(msg, Message_Thinking): - display_text += f"类型: \033[1;33m思考中消息\033[0m\n" - display_text += f"时间: {msg_time}\n" - display_text += f"消息ID: {msg.message_id}\n" - display_text += f"群组: {msg.group_id}\n" - display_text += f"用户: {msg.user_nickname}({msg.user_id})\n" - display_text += f"内容: {msg.thinking_text}\n" - display_text += f"思考时间: {int(msg.thinking_time)}秒\n" - else: - display_text += f"类型: 普通消息\n" - display_text += f"时间: {msg_time}\n" - display_text += f"消息ID: {msg.message_id}\n" - display_text += f"群组: {msg.group_id}\n" - display_text += f"用户: {msg.user_nickname}({msg.user_id})\n" - if hasattr(msg, 'is_emoji') and msg.is_emoji: - display_text += f"内容: [表情包消息]\n" - else: - # 显示原始消息和处理后的消息 - display_text += f"原始内容: {msg.raw_message[:50]}...\n" - display_text += f"处理后内容: {msg.processed_plain_text[:50]}...\n" - - if msg.reply_message: - display_text += f"回复消息: {str(msg.reply_message)[:50]}...\n" - - display_text += f"\n{'-' * 50}\n" - - # 添加统计信息 - display_text += "\n总体统计:\n" - display_text += f"活跃群组数: {len(send_temp_container.temp_queues)}\n" - total_messages = sum(len(q.messages) for q in send_temp_container.temp_queues.values()) - display_text += f"总消息数: {total_messages}\n" - thinking_messages = sum( - sum(1 for msg in q.messages if isinstance(msg, Message_Thinking)) - for q in send_temp_container.temp_queues.values() - ) - display_text += f"思考中消息数: {thinking_messages}\n" - - self.message_queue.put(display_text) - - def stop(self): - self.is_running = False - if self.process: - self.process.terminate() - self.process = None - # 清理文件 - for file in ["message_queue_window.bat", "message_queue_content.txt", "queue_update.txt"]: - if os.path.exists(file): - os.remove(file) - -# 创建全局单例 -message_visualizer = MessageVisualizer() diff --git a/src/plugins/chat/prompt_builder.py b/src/plugins/chat/prompt_builder.py index 25e06a764..ba22a403d 100644 --- a/src/plugins/chat/prompt_builder.py +++ b/src/plugins/chat/prompt_builder.py @@ -36,7 +36,9 @@ class PromptBuilder: memory_prompt = '' start_time = time.time() # 记录开始时间 - topic = topic_identifier.identify_topic_jieba(message_txt) + # topic = await topic_identifier.identify_topic_llm(message_txt) + topic = topic_identifier.identify_topic_snownlp(message_txt) + # print(f"\033[1;32m[pb主题识别]\033[0m 主题: {topic}") all_first_layer_items = [] # 存储所有第一层记忆 @@ -64,15 +66,7 @@ class PromptBuilder: if overlap: # print(f"\033[1;32m[前额叶]\033[0m 发现主题 '{current_topic}' 和 '{other_topic}' 有共同的第二层记忆: {overlap}") 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)}") - - # 使用集合去重 - # 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 [] @@ -147,15 +141,15 @@ class PromptBuilder: is_bot_prompt = '' #人格选择 + personality=global_config.PROMPT_PERSONALITY 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}，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}，{personality[0]}, 你正在浏览qq群,{promt_info_prompt}, 现在请你给出日常且口语化的回复，平淡一些，尽量简短一些。{is_bot_prompt} 请注意把握群里的聊天内容，不要刻意突出自身学科背景，不要回复的太有条理，可以有个性。''' 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}, - + prompt_personality = f'''{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[1]}, 你正在浏览qq群，{promt_info_prompt}, 现在请你给出日常且口语化的回复，请表现你自己的见解，不要一昧迎合，尽量简短一些。{is_bot_prompt} 请你表达自己的见解和观点。可以有个性。''' @@ -170,7 +164,7 @@ class PromptBuilder: #额外信息要求 - extra_info = '''但是记得回复平淡一些，简短一些,记住不要输出多余内容(包括前后缀，冒号和引号，括号，表情等),只需要输出回复内容就好，不要输出其他任何内容''' + extra_info = '''但是记得回复平淡一些，简短一些，尤其注意在没明确提到时不要过多提及自身的背景, 记住不要输出多余内容(包括前后缀，冒号和引号，括号，表情等)，只需要输出回复内容就好，不要输出其他任何内容''' @@ -195,25 +189,69 @@ class PromptBuilder: 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}''' + prompt_personality_check = f'''你的网名叫{global_config.BOT_NICKNAME}，{personality[0]}, 你正在浏览qq群，{promt_info_prompt} {activate_prompt_check} {extra_check_info}''' elif personality_choice < 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_personality_check = f'''你的网名叫{global_config.BOT_NICKNAME}，{personality[1]}, 你正在浏览qq群，{promt_info_prompt} {activate_prompt_check} {extra_check_info}''' prompt_check_if_response=f"{prompt_info}\n{prompt_date}\n{chat_talking_prompt}\n{prompt_personality_check}" return prompt,prompt_check_if_response + + def _build_initiative_prompt_select(self,group_id): + current_date = time.strftime("%Y-%m-%d", time.localtime()) + current_time = time.strftime("%H:%M:%S", time.localtime()) + bot_schedule_now_time,bot_schedule_now_activity = bot_schedule.get_current_task() + prompt_date = f'''今天是{current_date}，现在是{current_time}，你今天的日程是：\n{bot_schedule.today_schedule}\n你现在正在{bot_schedule_now_activity}\n''' + + chat_talking_prompt = '' + if group_id: + chat_talking_prompt = get_recent_group_detailed_plain_text(self.db, group_id, limit=global_config.MAX_CONTEXT_SIZE,combine = True) + + chat_talking_prompt = f"以下是群里正在聊天的内容：\n{chat_talking_prompt}" + # print(f"\033[1;34m[调试]\033[0m 已从数据库获取群 {group_id} 的消息记录:{chat_talking_prompt}") + + # 获取主动发言的话题 + all_nodes=memory_graph.dots + all_nodes=filter(lambda dot:len(dot[1]['memory_items'])>3,all_nodes) + nodes_for_select=random.sample(all_nodes,5) + topics=[info[0] for info in nodes_for_select] + infos=[info[1] for info in nodes_for_select] + + #激活prompt构建 + activate_prompt = '' + activate_prompt = f"以上是群里正在进行的聊天。" + personality=global_config.PROMPT_PERSONALITY + 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}，{personality[0]}''' + elif personality_choice < 1: # 第二种人格 + prompt_personality = f'''{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[1]}''' + + topics_str=','.join(f"\"{topics}\"") + prompt_for_select=f"你现在想在群里发言，回忆了一下，想到几个话题，分别是{topics_str}，综合当前状态以及群内气氛，请你在其中选择一个合适的话题，注意只需要输出话题，除了话题什么也不要输出(双引号也不要输出)" + + prompt_initiative_select=f"{prompt_date}\n{prompt_personality}\n{prompt_for_select}" + prompt_regular=f"{prompt_date}\n{prompt_personality}" + + return prompt_initiative_select,nodes_for_select,prompt_regular + + def _build_initiative_prompt_check(self,selected_node,prompt_regular): + memory=random.sample(selected_node['memory_items'],3) + memory='\n'.join(memory) + prompt_for_check=f"{prompt_regular}你现在想在群里发言，回忆了一下，想到一个话题,是{selected_node['concept']}，关于这个话题的记忆有\n{memory}\n，以这个作为主题发言合适吗？请在把握群里的聊天内容的基础上，综合群内的氛围，如果认为应该发言请输出yes，否则输出no，请注意是决定是否需要发言，而不是编写回复内容，除了yes和no不要输出任何回复内容。" + return prompt_for_check,memory + + def _build_initiative_prompt(self,selected_node,prompt_regular,memory): + prompt_for_initiative=f"{prompt_regular}你现在想在群里发言，回忆了一下，想到一个话题,是{selected_node['concept']}，关于这个话题的记忆有\n{memory}\n，请在把握群里的聊天内容的基础上，综合群内的氛围，以日常且口语化的口吻，简短且随意一点进行发言，不要说的太有条理，可以有个性。记住不要输出多余内容(包括前后缀，冒号和引号，括号，表情等)" + return prompt_for_initiative + def get_prompt_info(self,message:str,threshold:float): related_info = '' - if len(message) > 10: - message_segments = [message[i:i+10] for i in range(0, len(message), 10)] - for segment in message_segments: - embedding = get_embedding(segment) - related_info += self.get_info_from_db(embedding,threshold=threshold) - - else: - embedding = get_embedding(message) - related_info += self.get_info_from_db(embedding,threshold=threshold) + print(f"\033[1;34m[调试]\033[0m 获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}") + embedding = get_embedding(message) + related_info += self.get_info_from_db(embedding,threshold=threshold) return related_info diff --git a/src/plugins/chat/storage.py b/src/plugins/chat/storage.py index 4e2455aa6..08b52b7ca 100644 --- a/src/plugins/chat/storage.py +++ b/src/plugins/chat/storage.py @@ -23,6 +23,7 @@ class MessageStorage: "processed_plain_text": message.processed_plain_text, "time": message.time, "user_nickname": message.user_nickname, + "user_cardname": message.user_cardname, "group_name": message.group_name, "topic": topic, "detailed_plain_text": message.detailed_plain_text, @@ -37,6 +38,7 @@ class MessageStorage: "processed_plain_text": '[表情包]', "time": message.time, "user_nickname": message.user_nickname, + "user_cardname": message.user_cardname, "group_name": message.group_name, "topic": topic, "detailed_plain_text": message.detailed_plain_text, diff --git a/src/plugins/chat/thinking_idea.py b/src/plugins/chat/thinking_idea.py new file mode 100644 index 000000000..0cc300219 --- /dev/null +++ b/src/plugins/chat/thinking_idea.py @@ -0,0 +1,14 @@ +#Broca's Area +# 功能：语言产生、语法处理和言语运动控制。 +# 损伤后果：布洛卡失语症（表达困难，但理解保留）。 + +import time + + +class Thinking_Idea: + def __init__(self, message_id: str): + self.messages = [] # 消息列表集合 + self.current_thoughts = [] # 当前思考内容列表 + self.time = time.time() # 创建时间 + self.id = str(int(time.time() * 1000)) # 使用时间戳生成唯一标识ID + \ No newline at end of file diff --git a/src/plugins/chat/topic_identifier.py b/src/plugins/chat/topic_identifier.py index 34ac4e714..812d4e321 100644 --- a/src/plugins/chat/topic_identifier.py +++ b/src/plugins/chat/topic_identifier.py @@ -4,19 +4,20 @@ from .message import Message import jieba from nonebot import get_driver from .config import global_config +from snownlp import SnowNLP +from ..models.utils_model import LLM_request driver = get_driver() config = driver.config class TopicIdentifier: def __init__(self): - self.client = OpenAI( - api_key=config.siliconflow_key, - base_url=config.siliconflow_base_url - ) + self.llm_client = LLM_request(model=global_config.llm_topic_extract) + self.select=global_config.topic_extract + - def identify_topic_llm(self, text: str) -> Optional[str]: - """识别消息主题""" + async def identify_topic_llm(self, text: str) -> Optional[List[str]]: + """识别消息主题，返回主题列表""" prompt = f"""判断这条消息的主题，如果没有明显主题请回复"无主题"，要求： 1. 主题通常2-4个字，必须简短，要求精准概括，不要太具体。 @@ -24,77 +25,42 @@ class TopicIdentifier: 消息内容：{text}""" - response = self.client.chat.completions.create( - model=global_config.SILICONFLOW_MODEL_V3, - messages=[{"role": "user", "content": prompt}], - temperature=0.8, - max_tokens=10 - ) + # 使用 LLM_request 类进行请求 + topic, _ = await self.llm_client.generate_response(prompt) - if not response or not response.choices: - print(f"\033[1;31m[错误]\033[0m OpenAI API 返回为空") + if not topic: + print(f"\033[1;31m[错误]\033[0m LLM API 返回为空") return None - # 从 OpenAI API 响应中获取第一个选项的消息内容,并去除首尾空白字符 - topic = response.choices[0].message.content.strip() if response.choices[0].message.content else None - - if topic == "无主题": - return None - else: - # print(f"[主题分析结果]{text[:20]}... : {topic}") - split_topic = self.parse_topic(topic) - return split_topic - - - def parse_topic(self, topic: str) -> List[str]: - """解析主题，返回主题列表""" + # 直接在这里处理主题解析 if not topic or topic == "无主题": - return [] - return [t.strip() for t in topic.split(",") if t.strip()] + return None + + # 解析主题字符串为列表 + topic_list = [t.strip() for t in topic.split(",") if t.strip()] + + print(f"\033[1;32m[主题识别]\033[0m 主题: {topic_list}") + return topic_list if topic_list else None - def identify_topic_jieba(self, text: str) -> Optional[str]: - """使用jieba识别主题""" - words = jieba.lcut(text) - # 去除停用词和标点符号 - stop_words = { - '的', '了', '和', '是', '就', '都', '而', '及', '与', '这', '那', '但', '然', '却', - '因为', '所以', '如果', '虽然', '一个', '我', '你', '他', '她', '它', '我们', '你们', - '他们', '在', '有', '个', '把', '被', '让', '给', '从', '向', '到', '又', '也', '很', - '啊', '吧', '呢', '吗', '呀', '哦', '哈', '么', '嘛', '啦', '哎', '唉', '哇', '嗯', - '哼', '哪', '什么', '怎么', '为什么', '怎样', '如何', '什么样', '这样', '那样', '这么', - '那么', '多少', '几', '谁', '哪里', '哪儿', '什么时候', '何时', '为何', '怎么办', - '怎么样', '这些', '那些', '一些', '一点', '一下', '一直', '一定', '一般', '一样', - '一会儿', '一边', '一起', - # 添加更多量词 - '个', '只', '条', '张', '片', '块', '本', '册', '页', '幅', '面', '篇', '份', - '朵', '颗', '粒', '座', '幢', '栋', '间', '层', '家', '户', '位', '名', '群', - '双', '对', '打', '副', '套', '批', '组', '串', '包', '箱', '袋', '瓶', '罐', - # 添加更多介词 - '按', '按照', '把', '被', '比', '比如', '除', '除了', '当', '对', '对于', - '根据', '关于', '跟', '和', '将', '经', '经过', '靠', '连', '论', '通过', - '同', '往', '为', '为了', '围绕', '于', '由', '由于', '与', '在', '沿', '沿着', - '依', '依照', '以', '因', '因为', '用', '由', '与', '自', '自从' - } + def identify_topic_snownlp(self, text: str) -> Optional[List[str]]: + """使用 SnowNLP 进行主题识别 - # 过滤掉停用词和标点符号,只保留名词和动词 - filtered_words = [] - for word in words: - if word not in stop_words and not word.strip() in { - '。', '，', '、', '：', '；', '！', '？', '"', '"', ''', ''', - '（', '）', '【', '】', '《', '》', '…', '—', '·', '、', '~', - '～', '+', '=', '-' - }: - filtered_words.append(word) - - # 统计词频 - word_freq = {} - for word in filtered_words: - word_freq[word] = word_freq.get(word, 0) + 1 - - # 按词频排序，取前3个 - sorted_words = sorted(word_freq.items(), key=lambda x: x[1], reverse=True) - top_words = [word for word, freq in sorted_words[:3]] - - return top_words if top_words else None + Args: + text (str): 需要识别主题的文本 + + Returns: + Optional[List[str]]: 返回识别出的主题关键词列表，如果无法识别则返回 None + """ + if not text or len(text.strip()) == 0: + return None + + try: + s = SnowNLP(text) + # 提取前3个关键词作为主题 + keywords = s.keywords(5) + return keywords if keywords else None + except Exception as e: + print(f"\033[1;31m[错误]\033[0m SnowNLP 处理失败: {str(e)}") + return None topic_identifier = TopicIdentifier() \ No newline at end of file diff --git a/src/plugins/chat/utils.py b/src/plugins/chat/utils.py index a2a7a6f50..aa16268ef 100644 --- a/src/plugins/chat/utils.py +++ b/src/plugins/chat/utils.py @@ -10,6 +10,7 @@ from typing import Dict from collections import Counter import math from nonebot import get_driver +from ..models.utils_model import LLM_request driver = get_driver() config = driver.config @@ -37,7 +38,10 @@ def combine_messages(messages: List[Message]) -> str: def db_message_to_str (message_dict: Dict) -> str: print(f"message_dict: {message_dict}") time_str = time.strftime("%m-%d %H:%M:%S", time.localtime(message_dict["time"])) - name = message_dict.get("user_nickname", "") or f"用户{message_dict['user_id']}" + try: + name="[(%s)%s]%s" % (message_dict['user_id'],message_dict.get("user_nickname", ""),message_dict.get("user_cardname", "")) + except: + name = message_dict.get("user_nickname", "") or f"用户{message_dict['user_id']}" content = message_dict.get("processed_plain_text", "") result = f"[{time_str}] {name}: {content}\n" print(f"result: {result}") @@ -61,25 +65,9 @@ def is_mentioned_bot_in_txt(message: str) -> bool: return False def get_embedding(text): - url = "https://api.siliconflow.cn/v1/embeddings" - payload = { - "model": "BAAI/bge-m3", - "input": text, - "encoding_format": "float" - } - headers = { - "Authorization": f"Bearer {config.siliconflow_key}", - "Content-Type": "application/json" - } - - response = requests.request("POST", url, json=payload, headers=headers) - - if response.status_code != 200: - print(f"API请求失败: {response.status_code}") - print(f"错误信息: {response.text}") - return None - - return response.json()['data'][0]['embedding'] + """获取文本的embedding向量""" + llm = LLM_request(model=global_config.embedding) + return llm.get_embedding_sync(text) def cosine_similarity(v1, v2): dot_product = np.dot(v1, v2) @@ -87,13 +75,11 @@ def cosine_similarity(v1, v2): norm2 = np.linalg.norm(v2) return dot_product / (norm1 * norm2) -def calculate_information_content(text): +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 @@ -102,23 +88,37 @@ def calculate_information_content(text): return entropy def get_cloest_chat_from_db(db, length: int, timestamp: str): - # 从数据库中根据时间戳获取离其最近的聊天记录 + """从数据库中获取最接近指定时间戳的聊天记录，并记录读取次数""" chat_text = '' - closest_record = db.db.messages.find_one({"time": {"$lte": timestamp}}, sort=[('time', -1)]) # 调试输出 - # print(f"距离time最近的消息时间: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(int(closest_record['time'])))}") + closest_record = db.db.messages.find_one({"time": {"$lte": timestamp}}, sort=[('time', -1)]) - if closest_record: + if closest_record and closest_record.get('memorized', 0) < 4: 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": {"$gt": closest_time}, "group_id": group_id}).sort('time', 1).limit(length)) - for record in chat_record: - time_str = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(int(record['time']))) - chat_text += f'[{time_str}] {record["user_nickname"] or "用户" + str(record["user_id"])}: {record["processed_plain_text"]}\n' # 添加发送者和时间信息 + chat_records = list(db.db.messages.find( + {"time": {"$gt": closest_time}, "group_id": group_id} + ).sort('time', 1).limit(length)) + + # 更新每条消息的memorized属性 + for record in chat_records: + # 检查当前记录的memorized值 + current_memorized = record.get('memorized', 0) + if current_memorized > 3: + # print(f"消息已读取3次，跳过") + return '' + + # 更新memorized值 + db.db.messages.update_one( + {"_id": record["_id"]}, + {"$set": {"memorized": current_memorized + 1}} + ) + + chat_text += record["detailed_plain_text"] + return chat_text - - return [] # 如果没有找到记录，返回空列表 - + print(f"消息已读取3次，跳过") + return '' def get_recent_group_messages(db, group_id: int, limit: int = 12) -> list: """从数据库获取群组最近的消息记录 @@ -135,14 +135,14 @@ def get_recent_group_messages(db, group_id: int, limit: int = 12) -> list: # 从数据库获取最近消息 recent_messages = list(db.db.messages.find( {"group_id": group_id}, - { - "time": 1, - "user_id": 1, - "user_nickname": 1, - "message_id": 1, - "raw_message": 1, - "processed_text": 1 - } + # { + # "time": 1, + # "user_id": 1, + # "user_nickname": 1, + # "message_id": 1, + # "raw_message": 1, + # "processed_text": 1 + # } ).sort("time", -1).limit(limit)) if not recent_messages: @@ -152,16 +152,20 @@ def get_recent_group_messages(db, group_id: int, limit: int = 12) -> list: from .message import Message message_objects = [] for msg_data in recent_messages: - msg = Message( - time=msg_data["time"], - user_id=msg_data["user_id"], - user_nickname=msg_data.get("user_nickname", ""), - message_id=msg_data["message_id"], - raw_message=msg_data["raw_message"], - processed_plain_text=msg_data.get("processed_text", ""), - group_id=group_id - ) - message_objects.append(msg) + try: + msg = Message( + time=msg_data["time"], + user_id=msg_data["user_id"], + user_nickname=msg_data.get("user_nickname", ""), + message_id=msg_data["message_id"], + raw_message=msg_data["raw_message"], + processed_plain_text=msg_data.get("processed_text", ""), + group_id=group_id + ) + message_objects.append(msg) + except KeyError: + print("[WARNING] 数据库中存在无效的消息") + continue # 按时间正序排列 message_objects.reverse() diff --git a/src/plugins/chat/utils_image.py b/src/plugins/chat/utils_image.py index 68b2fa7f0..922ab5228 100644 --- a/src/plugins/chat/utils_image.py +++ b/src/plugins/chat/utils_image.py @@ -6,32 +6,27 @@ import os from ...common.database import Database import zlib # 用于 CRC32 import base64 -from .config import global_config from nonebot import get_driver +from loguru import logger driver = get_driver() config = driver.config -def storage_image(image_data: bytes,type: str, max_size: int = 200) -> bytes: - if type == 'image': - return storage_compress_image(image_data, max_size) - elif type == 'emoji': - return storage_emoji(image_data) - else: - raise ValueError(f"不支持的图片类型: {type}") - -def storage_compress_image(image_data: bytes, max_size: int = 200) -> bytes: +def storage_compress_image(base64_data: str, max_size: int = 200) -> str: """ - 压缩图片到指定大小（单位：KB）并在数据库中记录图片信息 + 压缩base64格式的图片到指定大小（单位：KB）并在数据库中记录图片信息 Args: - image_data: 图片字节数据 - group_id: 群组ID - user_id: 用户ID + base64_data: base64编码的图片数据 max_size: 最大文件大小（KB） + Returns: + str: 压缩后的base64图片数据 """ try: + # 将base64转换为字节数据 + image_data = base64.b64decode(base64_data) + # 使用 CRC32 计算哈希值 hash_value = format(zlib.crc32(image_data) & 0xFFFFFFFF, 'x') @@ -41,11 +36,11 @@ def storage_compress_image(image_data: bytes, max_size: int = 200) -> bytes: # 连接数据库 db = Database( - host= config.mongodb_host, - port= int(config.mongodb_port), - db_name= config.database_name, - username= config.mongodb_username, - password= config.mongodb_password, + 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 ) @@ -55,14 +50,14 @@ def storage_compress_image(image_data: bytes, max_size: int = 200) -> bytes: if existing_image: print(f"\033[1;33m[提示]\033[0m 发现重复图片，使用已存在的文件: {existing_image['path']}") - return image_data + return base64_data # 将字节数据转换为图片对象 img = Image.open(io.BytesIO(image_data)) # 如果是动图，直接返回原图 if getattr(img, 'is_animated', False): - return image_data + return base64_data # 计算当前大小（KB） current_size = len(image_data) / 1024 @@ -127,14 +122,16 @@ def storage_compress_image(image_data: bytes, max_size: int = 200) -> bytes: except Exception as db_error: print(f"\033[1;31m[错误]\033[0m 数据库操作失败: {str(db_error)}") - - return compressed_data + + # 将压缩后的数据转换为base64 + compressed_base64 = base64.b64encode(compressed_data).decode('utf-8') + return compressed_base64 except Exception as e: print(f"\033[1;31m[错误]\033[0m 压缩图片失败: {str(e)}") import traceback print(traceback.format_exc()) - return image_data + return base64_data def storage_emoji(image_data: bytes) -> bytes: """ @@ -215,4 +212,78 @@ def storage_image(image_data: bytes) -> bytes: except Exception as e: print(f"\033[1;31m[错误]\033[0m 保存图片失败: {str(e)}") - return image_data \ No newline at end of file + return image_data + +def compress_base64_image_by_scale(base64_data: str, target_size: int = 0.8 * 1024 * 1024) -> str: + """压缩base64格式的图片到指定大小 + Args: + base64_data: base64编码的图片数据 + target_size: 目标文件大小（字节），默认0.8MB + Returns: + str: 压缩后的base64图片数据 + """ + try: + # 将base64转换为字节数据 + image_data = base64.b64decode(base64_data) + + # 如果已经小于目标大小，直接返回原图 + if len(image_data) <= target_size: + return base64_data + + # 将字节数据转换为图片对象 + img = Image.open(io.BytesIO(image_data)) + + # 获取原始尺寸 + original_width, original_height = img.size + + # 计算缩放比例 + scale = min(1.0, (target_size / len(image_data)) ** 0.5) + + # 计算新的尺寸 + new_width = int(original_width * scale) + new_height = int(original_height * scale) + + # 创建内存缓冲区 + output_buffer = io.BytesIO() + + # 如果是GIF，处理所有帧 + if getattr(img, "is_animated", False): + frames = [] + for frame_idx in range(img.n_frames): + img.seek(frame_idx) + new_frame = img.copy() + new_frame = new_frame.resize((new_width, new_height), Image.Resampling.LANCZOS) + frames.append(new_frame) + + # 保存到缓冲区 + frames[0].save( + output_buffer, + format='GIF', + save_all=True, + append_images=frames[1:], + optimize=True, + duration=img.info.get('duration', 100), + loop=img.info.get('loop', 0) + ) + else: + # 处理静态图片 + resized_img = img.resize((new_width, new_height), Image.Resampling.LANCZOS) + + # 保存到缓冲区，保持原始格式 + if img.format == 'PNG' and img.mode in ('RGBA', 'LA'): + resized_img.save(output_buffer, format='PNG', optimize=True) + else: + resized_img.save(output_buffer, format='JPEG', quality=95, optimize=True) + + # 获取压缩后的数据并转换为base64 + compressed_data = output_buffer.getvalue() + logger.success(f"压缩图片: {original_width}x{original_height} -> {new_width}x{new_height}") + logger.info(f"压缩前大小: {len(image_data)/1024:.1f}KB, 压缩后大小: {len(compressed_data)/1024:.1f}KB") + + return base64.b64encode(compressed_data).decode('utf-8') + + except Exception as e: + logger.error(f"压缩图片失败: {str(e)}") + import traceback + logger.error(traceback.format_exc()) + return base64_data \ No newline at end of file diff --git a/src/plugins/chat/utils_user.py b/src/plugins/chat/utils_user.py index 7963b888a..bb8c30948 100644 --- a/src/plugins/chat/utils_user.py +++ b/src/plugins/chat/utils_user.py @@ -5,4 +5,13 @@ def get_user_nickname(user_id: int) -> str: if int(user_id) == int(global_config.BOT_QQ): return global_config.BOT_NICKNAME # print(user_id) - return relationship_manager.get_name(user_id) \ No newline at end of file + return relationship_manager.get_name(user_id) + +def get_user_cardname(user_id: int) -> str: + if int(user_id) == int(global_config.BOT_QQ): + return global_config.BOT_NICKNAME +# print(user_id) + return '' + +def get_groupname(group_id: int) -> str: + return f"群{group_id}" \ No newline at end of file diff --git a/src/plugins/chat/willing_manager.py b/src/plugins/chat/willing_manager.py index e35743577..7559406f9 100644 --- a/src/plugins/chat/willing_manager.py +++ b/src/plugins/chat/willing_manager.py @@ -9,9 +9,8 @@ class WillingManager: async def _decay_reply_willing(self): """定期衰减回复意愿""" while True: - await asyncio.sleep(3) + await asyncio.sleep(5) for group_id in self.group_reply_willing: - # 每分钟衰减10%的回复意愿 self.group_reply_willing[group_id] = max(0, self.group_reply_willing[group_id] * 0.6) def get_willing(self, group_id: int) -> float: @@ -26,13 +25,7 @@ class WillingManager: """改变指定群组的回复意愿并返回回复概率""" current_willing = self.group_reply_willing.get(group_id, 0) - print(f"初始意愿: {current_willing}") - - # if topic and current_willing < 1: - # current_willing += 0.2 - # elif topic: - # current_willing += 0.05 - + # print(f"初始意愿: {current_willing}") if is_mentioned_bot and current_willing < 1.0: current_willing += 0.9 print(f"被提及, 当前意愿: {current_willing}") @@ -44,23 +37,23 @@ class WillingManager: current_willing *= 0.15 print(f"表情包, 当前意愿: {current_willing}") - if interested_rate > 0.6: + if interested_rate > 0.65: print(f"兴趣度: {interested_rate}, 当前意愿: {current_willing}") - current_willing += interested_rate-0.45 + current_willing += interested_rate-0.6 self.group_reply_willing[group_id] = min(current_willing, 3.0) - reply_probability = (current_willing - 0.5) * 2 + reply_probability = max((current_willing - 0.55) * 1.9, 0) if group_id not in config.talk_allowed_groups: current_willing = 0 reply_probability = 0 if group_id in config.talk_frequency_down_groups: reply_probability = reply_probability / 3.5 - - # if is_mentioned_bot and user_id == int(1026294844): - # reply_probability = 1 - + + reply_probability = min(reply_probability, 1) + if reply_probability < 0: + reply_probability = 0 return reply_probability def change_reply_willing_sent(self, group_id: int): @@ -72,7 +65,7 @@ class WillingManager: """发送消息后提高群组的回复意愿""" current_willing = self.group_reply_willing.get(group_id, 0) if current_willing < 1: - self.group_reply_willing[group_id] = min(1, current_willing + 0.3) + self.group_reply_willing[group_id] = min(1, current_willing + 0.2) async def ensure_started(self): """确保衰减任务已启动""" @@ -82,4 +75,4 @@ class WillingManager: self._started = True # 创建全局实例 -willing_manager = WillingManager() \ No newline at end of file +willing_manager = WillingManager() diff --git a/src/plugins/knowledege/knowledge_library.py b/src/plugins/knowledege/knowledge_library.py index d8c2e1482..d7071985e 100644 --- a/src/plugins/knowledege/knowledge_library.py +++ b/src/plugins/knowledege/knowledge_library.py @@ -3,26 +3,28 @@ import sys import numpy as np import requests import time -from nonebot import get_driver - -driver = get_driver() -config = driver.config +from dotenv import load_dotenv # 添加项目根目录到 Python 路径 root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../..")) sys.path.append(root_path) -from src.common.database import Database -from src.plugins.chat.config import llm_config +# 加载根目录下的env.edv文件 +env_path = os.path.join(root_path, ".env.dev") +if not os.path.exists(env_path): + raise FileNotFoundError(f"配置文件不存在: {env_path}") +load_dotenv(env_path) -# 直接配置数据库连接信息 +from src.common.database import Database + +# 从环境变量获取配置 Database.initialize( - 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 + host=os.getenv("MONGODB_HOST", "localhost"), + port=int(os.getenv("MONGODB_PORT", "27017")), + db_name=os.getenv("DATABASE_NAME", "maimai"), + username=os.getenv("MONGODB_USERNAME"), + password=os.getenv("MONGODB_PASSWORD"), + auth_source=os.getenv("MONGODB_AUTH_SOURCE", "admin") ) class KnowledgeLibrary: @@ -30,6 +32,9 @@ class KnowledgeLibrary: self.db = Database.get_instance() self.raw_info_dir = "data/raw_info" self._ensure_dirs() + self.api_key = os.getenv("SILICONFLOW_KEY") + if not self.api_key: + raise ValueError("SILICONFLOW_API_KEY 环境变量未设置") def _ensure_dirs(self): """确保必要的目录存在""" @@ -44,7 +49,7 @@ class KnowledgeLibrary: "encoding_format": "float" } headers = { - "Authorization": f"Bearer {llm_config.SILICONFLOW_API_KEY}", + "Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json" } @@ -74,7 +79,7 @@ class KnowledgeLibrary: content = f.read() # 按1024字符分段 - segments = [content[i:i+300] for i in range(0, len(content), 300)] + segments = [content[i:i+600] for i in range(0, len(content), 600)] # 处理每个分段 for segment in segments: diff --git a/src/plugins/memory_system/draw_memory.py b/src/plugins/memory_system/draw_memory.py index 6b5dcd716..fad3f5f30 100644 --- a/src/plugins/memory_system/draw_memory.py +++ b/src/plugins/memory_system/draw_memory.py @@ -2,7 +2,6 @@ import os import sys import jieba -from llm_module import LLMModel import networkx as nx import matplotlib.pyplot as plt import math @@ -10,10 +9,19 @@ from collections import Counter import datetime import random import time -# from chat.config import global_config +from dotenv import load_dotenv import sys +import asyncio +import aiohttp +from typing import Tuple + 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 + +# 加载.env.dev文件 +env_path = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))), '.env.dev') +load_dotenv(env_path) + class Memory_graph: def __init__(self): @@ -112,7 +120,11 @@ class Memory_graph: 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 + try: + displayname="[(%s)%s]%s" % (record["user_id"],record["user_nickname"],record["user_cardname"]) + except: + displayname=record["user_nickname"] or "用户" + str(record["user_id"]) + chat_text += f'[{time_str}] {displayname}: {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 @@ -154,38 +166,32 @@ class Memory_graph: 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") + host=os.getenv("MONGODB_HOST", "127.0.0.1"), + port=int(os.getenv("MONGODB_PORT", "27017")), + db_name=os.getenv("DATABASE_NAME", "MegBot"), + 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 - 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_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_lite(memory_graph, color_by_memory=True) - - # memory_graph.save_graph_to_db() + # 只显示一次优化后的图形 + visualize_graph_lite(memory_graph) 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) + first_layer_items, second_layer_items = memory_graph.get_related_item(query) + if first_layer_items or second_layer_items: + print("\n第一层记忆：") + for item in first_layer_items: + print(item) + print("\n第二层记忆：") + for item in second_layer_items: + print(item) else: print("未找到相关记忆。") @@ -255,7 +261,7 @@ def visualize_graph(memory_graph: Memory_graph, color_by_memory: bool = False): nx.draw(G, pos, with_labels=True, node_color=node_colors, - node_size=2000, + node_size=200, font_size=10, font_family='SimHei', font_weight='bold') @@ -281,7 +287,7 @@ def visualize_graph_lite(memory_graph: Memory_graph, color_by_memory: bool = Fal 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: + if memory_count < 5 or degree < 2: # 改为小于2而不是小于等于2 nodes_to_remove.append(node) H.remove_nodes_from(nodes_to_remove) @@ -294,55 +300,55 @@ def visualize_graph_lite(memory_graph: Memory_graph, color_by_memory: bool = Fal # 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 + # 计算节点大小和颜色 node_colors = [] - nodes = list(H.nodes()) # 获取图中实际的节点列表 + node_sizes = [] + nodes = list(H.nodes()) - 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 + # 获取最大记忆数和最大度数用于归一化 + max_memories = 1 + max_degree = 1 + for node in 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) + max_memories = max(max_memories, memory_count) + max_degree = max(max_degree, degree) + + # 计算每个节点的大小和颜色 + for node in nodes: + # 计算节点大小（基于记忆数量） + 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) + # 使用指数函数使变化更明显 + ratio = memory_count / max_memories + size = 500 + 5000 * (ratio ** 2) # 使用平方函数使差异更明显 + node_sizes.append(size) - 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) + # 计算节点颜色（基于连接数） + degree = H.degree(node) + # 红色分量随着度数增加而增加 + red = min(1.0, degree / max_degree) + # 蓝色分量随着度数减少而增加 + blue = 1.0 - red + color = (red, 0, blue) + node_colors.append(color) # 绘制图形 plt.figure(figsize=(12, 8)) - pos = nx.spring_layout(H, k=1, iterations=50) + pos = nx.spring_layout(H, k=1.5, iterations=50) # 增加k值使节点分布更开 nx.draw(H, pos, with_labels=True, node_color=node_colors, - node_size=2000, + node_size=node_sizes, font_size=10, font_family='SimHei', - font_weight='bold') + font_weight='bold', + edge_color='gray', + width=0.5, + alpha=0.7) - 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') + title = '记忆图谱可视化 - 节点大小表示记忆数量，颜色表示连接数' plt.title(title, fontsize=16, fontfamily='SimHei') plt.show() diff --git a/src/plugins/memory_system/llm_module_memory_make.py b/src/plugins/memory_system/llm_module_memory_make.py deleted file mode 100644 index f59354570..000000000 --- a/src/plugins/memory_system/llm_module_memory_make.py +++ /dev/null @@ -1,74 +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 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=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 - - 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") - - print(f"API URL: {self.base_url}") # 打印 base_url 用于调试 - - 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 # 基础等待时间（秒） - - 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 a051192a5..4d20d05a9 100644 --- a/src/plugins/memory_system/memory.py +++ b/src/plugins/memory_system/memory.py @@ -9,15 +9,26 @@ import random import time from ..chat.config import global_config 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 +import math +from ..chat.utils import calculate_information_content, get_cloest_chat_from_db + + + + + class Memory_graph: def __init__(self): self.G = nx.Graph() # 使用 networkx 的图结构 self.db = Database.get_instance() def connect_dot(self, concept1, concept2): - self.G.add_edge(concept1, concept2) + # 如果边已存在，增加 strength + if self.G.has_edge(concept1, concept2): + self.G[concept1][concept2]['strength'] = self.G[concept1][concept2].get('strength', 1) + 1 + else: + # 如果是新边，初始化 strength 为 1 + self.G.add_edge(concept1, concept2, strength=1) def add_dot(self, concept, memory): if concept in self.G: @@ -38,9 +49,7 @@ class Memory_graph: if concept in self.G: # 从图中获取节点数据 node_data = self.G.nodes[concept] - # print(node_data) - # 创建新的Memory_dot对象 - return concept,node_data + return concept, node_data return None def get_related_item(self, topic, depth=1): @@ -52,7 +61,6 @@ class Memory_graph: # 获取相邻节点 neighbors = list(self.G.neighbors(topic)) - # print(f"第一层: {topic}") # 获取当前节点的记忆项 node_data = self.get_dot(topic) @@ -69,7 +77,6 @@ class Memory_graph: if depth >= 2: # 获取相邻节点的记忆项 for neighbor in neighbors: - # print(f"第二层: {neighbor}") node_data = self.get_dot(neighbor) if node_data: concept, data = node_data @@ -87,87 +94,59 @@ class Memory_graph: # 返回所有节点对应的 Memory_dot 对象 return [self.get_dot(node) for node in self.G.nodes()] - def save_graph_to_db(self): - # 保存节点 - for node in self.G.nodes(data=True): - concept = node[0] - memory_items = node[1].get('memory_items', []) + def forget_topic(self, topic): + """随机删除指定话题中的一条记忆，如果话题没有记忆则移除该话题节点""" + if topic not in self.G: + return None - # 查找是否存在同名节点 - existing_node = self.db.db.graph_data.nodes.find_one({'concept': concept}) - if existing_node: - # 如果存在,合并memory_items并去重 - existing_items = existing_node.get('memory_items', []) - if not isinstance(existing_items, list): - existing_items = [existing_items] if existing_items else [] - - # 合并并去重 - all_items = list(set(existing_items + memory_items)) - - # 更新节点 - self.db.db.graph_data.nodes.update_one( - {'concept': concept}, - {'$set': {'memory_items': all_items}} - ) - else: - # 如果不存在,创建新节点 - node_data = { - 'concept': concept, - 'memory_items': memory_items - } - self.db.db.graph_data.nodes.insert_one(node_data) + # 获取话题节点数据 + node_data = self.G.nodes[topic] - # 保存边 - for edge in self.G.edges(): - source, target = edge + # 如果节点存在memory_items + if 'memory_items' in node_data: + memory_items = node_data['memory_items'] - # 查找是否存在同样的边 - existing_edge = self.db.db.graph_data.edges.find_one({ - 'source': source, - 'target': target - }) - - if existing_edge: - # 如果存在,增加num属性 - num = existing_edge.get('num', 1) + 1 - self.db.db.graph_data.edges.update_one( - {'source': source, 'target': target}, - {'$set': {'num': num}} - ) - else: - # 如果不存在,创建新边 - edge_data = { - 'source': source, - 'target': target, - 'num': 1 - } - self.db.db.graph_data.edges.insert_one(edge_data) - - def load_graph_from_db(self): - # 清空当前图 - self.G.clear() - # 加载节点 - nodes = self.db.db.graph_data.nodes.find() - for node in nodes: - memory_items = node.get('memory_items', []) + # 确保memory_items是列表 if not isinstance(memory_items, list): memory_items = [memory_items] if memory_items else [] - self.G.add_node(node['concept'], memory_items=memory_items) - # 加载边 - edges = self.db.db.graph_data.edges.find() - for edge in edges: - self.G.add_edge(edge['source'], edge['target'], num=edge.get('num', 1)) - - - + + # 如果有记忆项可以删除 + if memory_items: + # 随机选择一个记忆项删除 + removed_item = random.choice(memory_items) + memory_items.remove(removed_item) + + # 更新节点的记忆项 + if memory_items: + self.G.nodes[topic]['memory_items'] = memory_items + else: + # 如果没有记忆项了，删除整个节点 + self.G.remove_node(topic) + + return removed_item + + return None # 海马体 class Hippocampus: def __init__(self,memory_graph:Memory_graph): self.memory_graph = memory_graph - 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) + self.llm_model_get_topic = LLM_request(model = global_config.llm_normal_minor,temperature=0.5) + self.llm_model_summary = LLM_request(model = global_config.llm_normal,temperature=0.5) + + def calculate_node_hash(self, concept, memory_items): + """计算节点的特征值""" + if not isinstance(memory_items, list): + memory_items = [memory_items] if memory_items else [] + sorted_items = sorted(memory_items) + content = f"{concept}:{'|'.join(sorted_items)}" + return hash(content) + + def calculate_edge_hash(self, source, target): + """计算边的特征值""" + nodes = sorted([source, target]) + return hash(f"{nodes[0]}:{nodes[1]}") def get_memory_sample(self,chat_size=20,time_frequency:dict={'near':2,'mid':4,'far':3}): current_timestamp = datetime.datetime.now().timestamp() @@ -175,82 +154,340 @@ class Hippocampus: #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 - # print(f"获得 最近 随机时间戳对应的时间: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(random_time))}") 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 - # print(f"获得 最近 随机时间戳对应的时间: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(random_time))}") 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 - # print(f"获得 最近 随机时间戳对应的时间: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(random_time))}") chat_ = get_cloest_chat_from_db(db=self.memory_graph.db, length=chat_size, timestamp=random_time) chat_text.append(chat_) - return chat_text + return [text for text in chat_text if text] - 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() + async def memory_compress(self, input_text, compress_rate=0.1): + print(input_text) + + #获取topics + topic_num = self.calculate_topic_num(input_text, compress_rate) + topics_response = await self.llm_model_get_topic.generate_response(self.find_topic_llm(input_text, topic_num)) + # 修改话题处理逻辑 + print(f"话题: {topics_response[0]}") + topics = [topic.strip() for topic in topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",") if topic.strip()] + print(f"话题: {topics}") + + # 创建所有话题的请求任务 + tasks = [] 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 + topic_what_prompt = self.topic_what(input_text, topic) + # 创建异步任务 + task = self.llm_model_summary.generate_response_async(topic_what_prompt) + tasks.append((topic.strip(), task)) + + # 等待所有任务完成 + compressed_memory = set() + for topic, task in tasks: + response = await task + if response: + compressed_memory.add((topic, response[0])) + 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} + + def calculate_topic_num(self,text, compress_rate): + """计算文本的话题数量""" + information_content = calculate_information_content(text) + topic_by_length = text.count('\n')*compress_rate + topic_by_information_content = max(1, min(5, int((information_content-3) * 2))) + topic_num = int((topic_by_length + topic_by_information_content)/2) + print(f"topic_by_length: {topic_by_length}, topic_by_information_content: {topic_by_information_content}, topic_num: {topic_num}") + return topic_num + + async def operation_build_memory(self,chat_size=20): + # 最近消息获取频率 + time_frequency = {'near':2,'mid':4,'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 + # 加载进度可视化 + all_topics = [] 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)})") - if input_text: - # 生成压缩后记忆 - first_memory = set() - 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) - 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) + + # 生成压缩后记忆 ,表现为 (话题,记忆) 的元组 + compressed_memory = set() + compress_rate = 0.1 + compressed_memory = await self.memory_compress(input_text, compress_rate) + print(f"\033[1;33m压缩后记忆数量\033[0m: {len(compressed_memory)}") + + # 将记忆加入到图谱中 + for topic, memory in compressed_memory: + print(f"\033[1;32m添加节点\033[0m: {topic}") + self.memory_graph.add_dot(topic, memory) + all_topics.append(topic) # 收集所有话题 + for i in range(len(all_topics)): + for j in range(i + 1, len(all_topics)): + print(f"\033[1;32m连接节点\033[0m: {all_topics[i]} 和 {all_topics[j]}") + self.memory_graph.connect_dot(all_topics[i], all_topics[j]) + + self.sync_memory_to_db() + + def sync_memory_to_db(self): + """检查并同步内存中的图结构与数据库""" + # 获取数据库中所有节点和内存中所有节点 + db_nodes = list(self.memory_graph.db.db.graph_data.nodes.find()) + memory_nodes = list(self.memory_graph.G.nodes(data=True)) + + # 转换数据库节点为字典格式，方便查找 + db_nodes_dict = {node['concept']: node for node in db_nodes} + + # 检查并更新节点 + for concept, data in memory_nodes: + memory_items = data.get('memory_items', []) + if not isinstance(memory_items, list): + memory_items = [memory_items] if memory_items else [] + + # 计算内存中节点的特征值 + memory_hash = self.calculate_node_hash(concept, memory_items) + + if concept not in db_nodes_dict: + # 数据库中缺少的节点，添加 + node_data = { + 'concept': concept, + 'memory_items': memory_items, + 'hash': memory_hash + } + self.memory_graph.db.db.graph_data.nodes.insert_one(node_data) else: - print(f"空消息 跳过") - self.memory_graph.save_graph_to_db() + # 获取数据库中节点的特征值 + db_node = db_nodes_dict[concept] + db_hash = db_node.get('hash', None) + + # 如果特征值不同，则更新节点 + if db_hash != memory_hash: + self.memory_graph.db.db.graph_data.nodes.update_one( + {'concept': concept}, + {'$set': { + 'memory_items': memory_items, + 'hash': memory_hash + }} + ) + + # 检查并删除数据库中多余的节点 + memory_concepts = set(node[0] for node in memory_nodes) + for db_node in db_nodes: + if db_node['concept'] not in memory_concepts: + self.memory_graph.db.db.graph_data.nodes.delete_one({'concept': db_node['concept']}) + + # 处理边的信息 + db_edges = list(self.memory_graph.db.db.graph_data.edges.find()) + memory_edges = list(self.memory_graph.G.edges()) + + # 创建边的哈希值字典 + db_edge_dict = {} + for edge in db_edges: + edge_hash = self.calculate_edge_hash(edge['source'], edge['target']) + db_edge_dict[(edge['source'], edge['target'])] = { + 'hash': edge_hash, + 'strength': edge.get('strength', 1) + } + + # 检查并更新边 + for source, target in memory_edges: + edge_hash = self.calculate_edge_hash(source, target) + edge_key = (source, target) + strength = self.memory_graph.G[source][target].get('strength', 1) + + if edge_key not in db_edge_dict: + # 添加新边 + edge_data = { + 'source': source, + 'target': target, + 'strength': strength, + 'hash': edge_hash + } + self.memory_graph.db.db.graph_data.edges.insert_one(edge_data) + else: + # 检查边的特征值是否变化 + if db_edge_dict[edge_key]['hash'] != edge_hash: + self.memory_graph.db.db.graph_data.edges.update_one( + {'source': source, 'target': target}, + {'$set': { + 'hash': edge_hash, + 'strength': strength + }} + ) + + # 删除多余的边 + memory_edge_set = set(memory_edges) + for edge_key in db_edge_dict: + if edge_key not in memory_edge_set: + source, target = edge_key + self.memory_graph.db.db.graph_data.edges.delete_one({ + 'source': source, + 'target': target + }) + + def sync_memory_from_db(self): + """从数据库同步数据到内存中的图结构""" + # 清空当前图 + self.memory_graph.G.clear() + + # 从数据库加载所有节点 + nodes = self.memory_graph.db.db.graph_data.nodes.find() + for node in nodes: + concept = node['concept'] + memory_items = node.get('memory_items', []) + # 确保memory_items是列表 + if not isinstance(memory_items, list): + memory_items = [memory_items] if memory_items else [] + # 添加节点到图中 + self.memory_graph.G.add_node(concept, memory_items=memory_items) + + # 从数据库加载所有边 + edges = self.memory_graph.db.db.graph_data.edges.find() + for edge in edges: + source = edge['source'] + target = edge['target'] + strength = edge.get('strength', 1) # 获取 strength，默认为 1 + # 只有当源节点和目标节点都存在时才添加边 + if source in self.memory_graph.G and target in self.memory_graph.G: + self.memory_graph.G.add_edge(source, target, strength=strength) + + async def operation_forget_topic(self, percentage=0.1): + """随机选择图中一定比例的节点进行检查，根据条件决定是否遗忘""" + # 获取所有节点 + all_nodes = list(self.memory_graph.G.nodes()) + # 计算要检查的节点数量 + check_count = max(1, int(len(all_nodes) * percentage)) + # 随机选择节点 + nodes_to_check = random.sample(all_nodes, check_count) + + forgotten_nodes = [] + for node in nodes_to_check: + # 获取节点的连接数 + connections = self.memory_graph.G.degree(node) + + # 获取节点的内容条数 + memory_items = self.memory_graph.G.nodes[node].get('memory_items', []) + if not isinstance(memory_items, list): + memory_items = [memory_items] if memory_items else [] + content_count = len(memory_items) + + # 检查连接强度 + weak_connections = True + if connections > 1: # 只有当连接数大于1时才检查强度 + for neighbor in self.memory_graph.G.neighbors(node): + strength = self.memory_graph.G[node][neighbor].get('strength', 1) + if strength > 2: + weak_connections = False + break + + # 如果满足遗忘条件 + if (connections <= 1 and weak_connections) or content_count <= 2: + removed_item = self.memory_graph.forget_topic(node) + if removed_item: + forgotten_nodes.append((node, removed_item)) + print(f"遗忘节点 {node} 的记忆: {removed_item}") + + # 同步到数据库 + if forgotten_nodes: + self.sync_memory_to_db() + print(f"完成遗忘操作，共遗忘 {len(forgotten_nodes)} 个节点的记忆") + else: + print("本次检查没有节点满足遗忘条件") + + async def merge_memory(self, topic): + """ + 对指定话题的记忆进行合并压缩 + + Args: + topic: 要合并的话题节点 + """ + # 获取节点的记忆项 + memory_items = self.memory_graph.G.nodes[topic].get('memory_items', []) + if not isinstance(memory_items, list): + memory_items = [memory_items] if memory_items else [] + + # 如果记忆项不足，直接返回 + if len(memory_items) < 10: + return + + # 随机选择10条记忆 + selected_memories = random.sample(memory_items, 10) + + # 拼接成文本 + merged_text = "\n".join(selected_memories) + print(f"\n[合并记忆] 话题: {topic}") + print(f"选择的记忆:\n{merged_text}") + + # 使用memory_compress生成新的压缩记忆 + compressed_memories = await self.memory_compress(merged_text, 0.1) + + # 从原记忆列表中移除被选中的记忆 + for memory in selected_memories: + memory_items.remove(memory) + + # 添加新的压缩记忆 + for _, compressed_memory in compressed_memories: + memory_items.append(compressed_memory) + print(f"添加压缩记忆: {compressed_memory}") + + # 更新节点的记忆项 + self.memory_graph.G.nodes[topic]['memory_items'] = memory_items + print(f"完成记忆合并，当前记忆数量: {len(memory_items)}") + + async def operation_merge_memory(self, percentage=0.1): + """ + 随机检查一定比例的节点，对内容数量超过100的节点进行记忆合并 + + Args: + percentage: 要检查的节点比例，默认为0.1（10%） + """ + # 获取所有节点 + all_nodes = list(self.memory_graph.G.nodes()) + # 计算要检查的节点数量 + check_count = max(1, int(len(all_nodes) * percentage)) + # 随机选择节点 + nodes_to_check = random.sample(all_nodes, check_count) + + merged_nodes = [] + for node in nodes_to_check: + # 获取节点的内容条数 + memory_items = self.memory_graph.G.nodes[node].get('memory_items', []) + if not isinstance(memory_items, list): + memory_items = [memory_items] if memory_items else [] + content_count = len(memory_items) + + # 如果内容数量超过100，进行合并 + if content_count > 100: + print(f"\n检查节点: {node}, 当前记忆数量: {content_count}") + await self.merge_memory(node) + merged_nodes.append(node) + + # 同步到数据库 + if merged_nodes: + self.sync_memory_to_db() + print(f"\n完成记忆合并操作，共处理 {len(merged_nodes)} 个节点") + else: + print("\n本次检查没有需要合并的节点") + + def find_topic_llm(self,text, topic_num): + prompt = f'这是一段文字：{text}。请你从这段话中总结出{topic_num}个关键的概念，可以是名词，动词，或者特定人物，帮我列出来，用逗号,隔开，尽可能精简。只需要列举{topic_num}个话题就好，不要有序号，不要告诉我其他内容。' + return prompt + + def topic_what(self,text, topic): + prompt = f'这是一段文字：{text}。我想让你基于这段文字来概括"{topic}"这个概念，帮我总结成一句自然的话，可以包含时间和人物，以及具体的观点。只输出这句话就好' + return prompt def segment_text(text): seg_text = list(jieba.cut(text)) return seg_text -def find_topic(text, topic_num): - prompt = f'这是一段文字：{text}。请你从这段话中总结出{topic_num}个话题，帮我列出来，用逗号隔开，尽可能精简。只需要列举{topic_num}个话题就好，不要告诉我其他内容。' - return prompt - -def topic_what(text, topic): - prompt = f'这是一段文字：{text}。我想知道这记忆里有什么关于{topic}的话题，帮我总结成一句自然的话，可以包含时间和人物。只输出这句话就好' - return prompt - from nonebot import get_driver driver = get_driver() @@ -259,19 +496,19 @@ config = driver.config start_time = time.time() Database.initialize( - 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 + host= config.MONGODB_HOST, + port= 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() -#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) +#从数据库加载记忆图 +hippocampus.sync_memory_from_db() end_time = time.time() print(f"\033[32m[加载海马体耗时: {end_time - start_time:.2f} 秒]\033[0m") \ No newline at end of file diff --git a/src/plugins/memory_system/memory_make.py b/src/plugins/memory_system/memory_make.py deleted file mode 100644 index 7fb8af15a..000000000 --- a/src/plugins/memory_system/memory_make.py +++ /dev/null @@ -1,459 +0,0 @@ -# -*- coding: utf-8 -*- -import sys -import jieba -import networkx as nx -import matplotlib.pyplot as plt -import math -from collections import Counter -import datetime -import random -import time -import os -# from chat.config import global_config -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 - self.db = Database.get_instance() - - def connect_dot(self, concept1, concept2): - self.G.add_edge(concept1, concept2) - - def add_dot(self, concept, memory): - if concept in self.G: - # 如果节点已存在，将新记忆添加到现有列表中 - if 'memory_items' in self.G.nodes[concept]: - if not isinstance(self.G.nodes[concept]['memory_items'], list): - # 如果当前不是列表，将其转换为列表 - self.G.nodes[concept]['memory_items'] = [self.G.nodes[concept]['memory_items']] - self.G.nodes[concept]['memory_items'].append(memory) - else: - self.G.nodes[concept]['memory_items'] = [memory] - else: - # 如果是新节点，创建新的记忆列表 - self.G.add_node(concept, memory_items=[memory]) - - def get_dot(self, concept): - # 检查节点是否存在于图中 - if concept in self.G: - # 从图中获取节点数据 - node_data = self.G.nodes[concept] - # print(node_data) - # 创建新的Memory_dot对象 - return concept,node_data - return None - - def get_related_item(self, topic, depth=1): - if topic not in self.G: - return [], [] - - first_layer_items = [] - second_layer_items = [] - - # 获取相邻节点 - neighbors = list(self.G.neighbors(topic)) - # print(f"第一层: {topic}") - - # 获取当前节点的记忆项 - node_data = self.get_dot(topic) - if node_data: - concept, data = node_data - if 'memory_items' in data: - memory_items = data['memory_items'] - if isinstance(memory_items, list): - first_layer_items.extend(memory_items) - else: - first_layer_items.append(memory_items) - - # 只在depth=2时获取第二层记忆 - if depth >= 2: - # 获取相邻节点的记忆项 - for neighbor in neighbors: - # print(f"第二层: {neighbor}") - node_data = self.get_dot(neighbor) - if node_data: - concept, data = node_data - if 'memory_items' in data: - memory_items = data['memory_items'] - if isinstance(memory_items, list): - second_layer_items.extend(memory_items) - else: - second_layer_items.append(memory_items) - - return first_layer_items, second_layer_items - - def store_memory(self): - for node in self.G.nodes(): - dot_data = { - "concept": node - } - self.db.db.store_memory_dots.insert_one(dot_data) - - @property - def dots(self): - # 返回所有节点对应的 Memory_dot 对象 - return [self.get_dot(node) for node in self.G.nodes()] - - - def get_random_chat_from_db(self, length: int, timestamp: str): - # 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": {"$lte": timestamp}}, sort=[('time', -1)]) # 调试输出 - - # print(f"距离time最近的消息时间: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(int(closest_record['time'])))}") - - if closest_record: - closest_time = closest_record['time'] - group_id = closest_record['group_id'] # 获取groupid - # 获取该时间戳之后的length条消息，且groupid相同 - chat_record = list(self.db.db.messages.find({"time": {"$gt": closest_time}, "group_id": group_id}).sort('time', 1).limit(length)) - for record in chat_record: - 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 - - def save_graph_to_db(self): - # 保存节点 - for node in self.G.nodes(data=True): - concept = node[0] - memory_items = node[1].get('memory_items', []) - - # 查找是否存在同名节点 - existing_node = self.db.db.graph_data.nodes.find_one({'concept': concept}) - if existing_node: - # 如果存在,合并memory_items并去重 - existing_items = existing_node.get('memory_items', []) - if not isinstance(existing_items, list): - existing_items = [existing_items] if existing_items else [] - - # 合并并去重 - all_items = list(set(existing_items + memory_items)) - - # 更新节点 - self.db.db.graph_data.nodes.update_one( - {'concept': concept}, - {'$set': {'memory_items': all_items}} - ) - else: - # 如果不存在,创建新节点 - node_data = { - 'concept': concept, - 'memory_items': memory_items - } - self.db.db.graph_data.nodes.insert_one(node_data) - - # 保存边 - for edge in self.G.edges(): - source, target = edge - - # 查找是否存在同样的边 - existing_edge = self.db.db.graph_data.edges.find_one({ - 'source': source, - 'target': target - }) - - if existing_edge: - # 如果存在,增加num属性 - num = existing_edge.get('num', 1) + 1 - self.db.db.graph_data.edges.update_one( - {'source': source, 'target': target}, - {'$set': {'num': num}} - ) - else: - # 如果不存在,创建新边 - edge_data = { - 'source': source, - 'target': target, - 'num': 1 - } - self.db.db.graph_data.edges.insert_one(edge_data) - - def load_graph_from_db(self): - # 清空当前图 - self.G.clear() - # 加载节点 - nodes = self.db.db.graph_data.nodes.find() - for node in nodes: - memory_items = node.get('memory_items', []) - if not isinstance(memory_items, list): - memory_items = [memory_items] if memory_items else [] - self.G.add_node(node['concept'], memory_items=memory_items) - # 加载边 - edges = self.db.db.graph_data.edges.find() - for edge in edges: - self.G.add_edge(edge['source'], edge['target'], num=edge.get('num', 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") - - 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) - - #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") - - 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(",") - 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)) - return seg_text - -def find_topic(text, topic_num): - prompt = f'这是一段文字：{text}。请你从这段话中总结出{topic_num}个话题，帮我列出来，用逗号隔开，尽可能精简。只需要列举{topic_num}个话题就好，不要告诉我其他内容。' - return prompt - -def topic_what(text, topic): - prompt = f'这是一段文字：{text}。我想知道这记忆里有什么关于{topic}的话题，帮我总结成一句自然的话，可以包含时间和人物。只输出这句话就好' - return prompt - -def visualize_graph(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 <= 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(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() - -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/memory_system/memory_manual_build.py b/src/plugins/memory_system/memory_manual_build.py new file mode 100644 index 000000000..d6aa2f669 --- /dev/null +++ b/src/plugins/memory_system/memory_manual_build.py @@ -0,0 +1,786 @@ +# -*- coding: utf-8 -*- +import sys +import jieba +import networkx as nx +import matplotlib.pyplot as plt +import math +from collections import Counter +import datetime +import random +import time +import os +from dotenv import load_dotenv +import pymongo +from loguru import logger +from pathlib import Path +from snownlp import SnowNLP +# from chat.config import global_config +sys.path.append("C:/GitHub/MaiMBot") # 添加项目根目录到 Python 路径 +from src.common.database import Database +from src.plugins.memory_system.offline_llm import LLMModel + +# 获取当前文件的目录 +current_dir = Path(__file__).resolve().parent +# 获取项目根目录（上三层目录） +project_root = current_dir.parent.parent.parent +# env.dev文件路径 +env_path = project_root / ".env.dev" + +# 加载环境变量 +if env_path.exists(): + logger.info(f"从 {env_path} 加载环境变量") + load_dotenv(env_path) +else: + logger.warning(f"未找到环境变量文件: {env_path}") + logger.info("将使用默认配置") + +class Database: + _instance = None + db = None + + @classmethod + def get_instance(cls): + if cls._instance is None: + cls._instance = cls() + return cls._instance + + def __init__(self): + if not Database.db: + 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") + ) + + @classmethod + def initialize(cls, host, port, db_name, username=None, password=None, auth_source="admin"): + try: + if username and password: + uri = f"mongodb://{username}:{password}@{host}:{port}/{db_name}?authSource={auth_source}" + else: + uri = f"mongodb://{host}:{port}" + + client = pymongo.MongoClient(uri) + cls.db = client[db_name] + # 测试连接 + client.server_info() + logger.success("MongoDB连接成功!") + + except Exception as e: + logger.error(f"初始化MongoDB失败: {str(e)}") + raise + +def calculate_information_content(text): + """计算文本的信息量（熵）""" + char_count = Counter(text) + total_chars = len(text) + + 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，并记录读取次数""" + chat_text = '' + closest_record = db.db.messages.find_one({"time": {"$lte": timestamp}}, sort=[('time', -1)]) + + if closest_record and closest_record.get('memorized', 0) < 4: + closest_time = closest_record['time'] + group_id = closest_record['group_id'] # 获取groupid + # 获取该时间戳之后的length条消息，且groupid相同 + chat_records = list(db.db.messages.find( + {"time": {"$gt": closest_time}, "group_id": group_id} + ).sort('time', 1).limit(length)) + + # 更新每条消息的memorized属性 + for record in chat_records: + # 检查当前记录的memorized值 + current_memorized = record.get('memorized', 0) + if current_memorized > 3: + print(f"消息已读取3次，跳过") + return '' + + # 更新memorized值 + db.db.messages.update_one( + {"_id": record["_id"]}, + {"$set": {"memorized": current_memorized + 1}} + ) + + chat_text += record["detailed_plain_text"] + + return chat_text + print(f"消息已读取3次，跳过") + return '' + +class Memory_graph: + def __init__(self): + self.G = nx.Graph() # 使用 networkx 的图结构 + self.db = Database.get_instance() + + def connect_dot(self, concept1, concept2): + # 如果边已存在，增加 strength + if self.G.has_edge(concept1, concept2): + self.G[concept1][concept2]['strength'] = self.G[concept1][concept2].get('strength', 1) + 1 + else: + # 如果是新边，初始化 strength 为 1 + self.G.add_edge(concept1, concept2, strength=1) + + def add_dot(self, concept, memory): + if concept in self.G: + # 如果节点已存在，将新记忆添加到现有列表中 + if 'memory_items' in self.G.nodes[concept]: + if not isinstance(self.G.nodes[concept]['memory_items'], list): + # 如果当前不是列表，将其转换为列表 + self.G.nodes[concept]['memory_items'] = [self.G.nodes[concept]['memory_items']] + self.G.nodes[concept]['memory_items'].append(memory) + else: + self.G.nodes[concept]['memory_items'] = [memory] + else: + # 如果是新节点，创建新的记忆列表 + self.G.add_node(concept, memory_items=[memory]) + + def get_dot(self, concept): + # 检查节点是否存在于图中 + if concept in self.G: + # 从图中获取节点数据 + node_data = self.G.nodes[concept] + return concept, node_data + return None + + def get_related_item(self, topic, depth=1): + if topic not in self.G: + return [], [] + + first_layer_items = [] + second_layer_items = [] + + # 获取相邻节点 + neighbors = list(self.G.neighbors(topic)) + + # 获取当前节点的记忆项 + node_data = self.get_dot(topic) + if node_data: + concept, data = node_data + if 'memory_items' in data: + memory_items = data['memory_items'] + if isinstance(memory_items, list): + first_layer_items.extend(memory_items) + else: + first_layer_items.append(memory_items) + + # 只在depth=2时获取第二层记忆 + if depth >= 2: + # 获取相邻节点的记忆项 + for neighbor in neighbors: + node_data = self.get_dot(neighbor) + if node_data: + concept, data = node_data + if 'memory_items' in data: + memory_items = data['memory_items'] + if isinstance(memory_items, list): + second_layer_items.extend(memory_items) + else: + second_layer_items.append(memory_items) + + return first_layer_items, second_layer_items + + @property + def dots(self): + # 返回所有节点对应的 Memory_dot 对象 + return [self.get_dot(node) for node in self.G.nodes()] + +# 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") + self.llm_model_get_topic = LLMModel(model_name="Pro/Qwen/Qwen2.5-7B-Instruct") + self.llm_model_summary = LLMModel(model_name="Qwen/Qwen2.5-32B-Instruct") + + 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*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('mid')): # 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_) + for _ in range(time_frequency.get('far')): # 循环10次 + random_time = current_timestamp - random.randint(3600*24, 3600*24*7) # 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 for chat in chat_text if chat] + + def calculate_topic_num(self,text, compress_rate): + """计算文本的话题数量""" + information_content = calculate_information_content(text) + topic_by_length = text.count('\n')*compress_rate + topic_by_information_content = max(1, min(5, int((information_content-3) * 2))) + topic_num = int((topic_by_length + topic_by_information_content)/2) + print(f"topic_by_length: {topic_by_length}, topic_by_information_content: {topic_by_information_content}, topic_num: {topic_num}") + return topic_num + + async def memory_compress(self, input_text, compress_rate=0.1): + print(input_text) + + #获取topics + topic_num = self.calculate_topic_num(input_text, compress_rate) + topics_response = await self.llm_model_get_topic.generate_response_async(self.find_topic_llm(input_text, topic_num)) + # 修改话题处理逻辑 + topics = [topic.strip() for topic in topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",") if topic.strip()] + print(f"话题: {topics}") + + # 创建所有话题的请求任务 + tasks = [] + for topic in topics: + topic_what_prompt = self.topic_what(input_text, topic) + # 创建异步任务 + task = self.llm_model_small.generate_response_async(topic_what_prompt) + tasks.append((topic.strip(), task)) + + # 等待所有任务完成 + compressed_memory = set() + for topic, task in tasks: + response = await task + if response: + compressed_memory.add((topic, response[0])) + + return compressed_memory + + async def operation_build_memory(self, chat_size=12): + # 最近消息获取频率 + time_frequency = {'near': 3, 'mid': 8, 'far': 5} + memory_sample = self.get_memory_sample(chat_size, time_frequency) + + all_topics = [] # 用于存储所有话题 + + for i, input_text in enumerate(memory_sample, 1): + # 加载进度可视化 + all_topics = [] + 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)})") + + # 生成压缩后记忆 ,表现为 (话题,记忆) 的元组 + compressed_memory = set() + compress_rate = 0.1 + compressed_memory = await self.memory_compress(input_text, compress_rate) + print(f"\033[1;33m压缩后记忆数量\033[0m: {len(compressed_memory)}") + + # 将记忆加入到图谱中 + for topic, memory in compressed_memory: + print(f"\033[1;32m添加节点\033[0m: {topic}") + self.memory_graph.add_dot(topic, memory) + all_topics.append(topic) # 收集所有话题 + for i in range(len(all_topics)): + for j in range(i + 1, len(all_topics)): + print(f"\033[1;32m连接节点\033[0m: {all_topics[i]} 和 {all_topics[j]}") + self.memory_graph.connect_dot(all_topics[i], all_topics[j]) + + + + + self.sync_memory_to_db() + + def sync_memory_from_db(self): + """ + 从数据库同步数据到内存中的图结构 + 将清空当前内存中的图，并从数据库重新加载所有节点和边 + """ + # 清空当前图 + self.memory_graph.G.clear() + + # 从数据库加载所有节点 + nodes = self.memory_graph.db.db.graph_data.nodes.find() + for node in nodes: + concept = node['concept'] + memory_items = node.get('memory_items', []) + # 确保memory_items是列表 + if not isinstance(memory_items, list): + memory_items = [memory_items] if memory_items else [] + # 添加节点到图中 + self.memory_graph.G.add_node(concept, memory_items=memory_items) + + # 从数据库加载所有边 + edges = self.memory_graph.db.db.graph_data.edges.find() + for edge in edges: + source = edge['source'] + target = edge['target'] + strength = edge.get('strength', 1) # 获取 strength，默认为 1 + # 只有当源节点和目标节点都存在时才添加边 + if source in self.memory_graph.G and target in self.memory_graph.G: + self.memory_graph.G.add_edge(source, target, strength=strength) + + logger.success("从数据库同步记忆图谱完成") + + def calculate_node_hash(self, concept, memory_items): + """ + 计算节点的特征值 + """ + if not isinstance(memory_items, list): + memory_items = [memory_items] if memory_items else [] + # 将记忆项排序以确保相同内容生成相同的哈希值 + sorted_items = sorted(memory_items) + # 组合概念和记忆项生成特征值 + content = f"{concept}:{'|'.join(sorted_items)}" + return hash(content) + + def calculate_edge_hash(self, source, target): + """ + 计算边的特征值 + """ + # 对源节点和目标节点排序以确保相同的边生成相同的哈希值 + nodes = sorted([source, target]) + return hash(f"{nodes[0]}:{nodes[1]}") + + def sync_memory_to_db(self): + """ + 检查并同步内存中的图结构与数据库 + 使用特征值(哈希值)快速判断是否需要更新 + """ + # 获取数据库中所有节点和内存中所有节点 + db_nodes = list(self.memory_graph.db.db.graph_data.nodes.find()) + memory_nodes = list(self.memory_graph.G.nodes(data=True)) + + # 转换数据库节点为字典格式，方便查找 + db_nodes_dict = {node['concept']: node for node in db_nodes} + + # 检查并更新节点 + for concept, data in memory_nodes: + memory_items = data.get('memory_items', []) + if not isinstance(memory_items, list): + memory_items = [memory_items] if memory_items else [] + + # 计算内存中节点的特征值 + memory_hash = self.calculate_node_hash(concept, memory_items) + + if concept not in db_nodes_dict: + # 数据库中缺少的节点，添加 + logger.info(f"添加新节点: {concept}") + node_data = { + 'concept': concept, + 'memory_items': memory_items, + 'hash': memory_hash + } + self.memory_graph.db.db.graph_data.nodes.insert_one(node_data) + else: + # 获取数据库中节点的特征值 + db_node = db_nodes_dict[concept] + db_hash = db_node.get('hash', None) + + # 如果特征值不同，则更新节点 + if db_hash != memory_hash: + logger.info(f"更新节点内容: {concept}") + self.memory_graph.db.db.graph_data.nodes.update_one( + {'concept': concept}, + {'$set': { + 'memory_items': memory_items, + 'hash': memory_hash + }} + ) + + # 检查并删除数据库中多余的节点 + memory_concepts = set(node[0] for node in memory_nodes) + for db_node in db_nodes: + if db_node['concept'] not in memory_concepts: + logger.info(f"删除多余节点: {db_node['concept']}") + self.memory_graph.db.db.graph_data.nodes.delete_one({'concept': db_node['concept']}) + + # 处理边的信息 + db_edges = list(self.memory_graph.db.db.graph_data.edges.find()) + memory_edges = list(self.memory_graph.G.edges()) + + # 创建边的哈希值字典 + db_edge_dict = {} + for edge in db_edges: + edge_hash = self.calculate_edge_hash(edge['source'], edge['target']) + db_edge_dict[(edge['source'], edge['target'])] = { + 'hash': edge_hash, + 'num': edge.get('num', 1) + } + + # 检查并更新边 + for source, target in memory_edges: + edge_hash = self.calculate_edge_hash(source, target) + edge_key = (source, target) + + if edge_key not in db_edge_dict: + # 添加新边 + logger.info(f"添加新边: {source} - {target}") + edge_data = { + 'source': source, + 'target': target, + 'num': 1, + 'hash': edge_hash + } + self.memory_graph.db.db.graph_data.edges.insert_one(edge_data) + else: + # 检查边的特征值是否变化 + if db_edge_dict[edge_key]['hash'] != edge_hash: + logger.info(f"更新边: {source} - {target}") + self.memory_graph.db.db.graph_data.edges.update_one( + {'source': source, 'target': target}, + {'$set': {'hash': edge_hash}} + ) + + # 删除多余的边 + memory_edge_set = set(memory_edges) + for edge_key in db_edge_dict: + if edge_key not in memory_edge_set: + source, target = edge_key + logger.info(f"删除多余边: {source} - {target}") + self.memory_graph.db.db.graph_data.edges.delete_one({ + 'source': source, + 'target': target + }) + + logger.success("完成记忆图谱与数据库的差异同步") + + def find_topic_llm(self,text, topic_num): + # prompt = f'这是一段文字：{text}。请你从这段话中总结出{topic_num}个话题，帮我列出来，用逗号隔开，尽可能精简。只需要列举{topic_num}个话题就好，不要告诉我其他内容。' + prompt = f'这是一段文字：{text}。请你从这段话中总结出{topic_num}个关键的概念，可以是名词，动词，或者特定人物，帮我列出来，用逗号,隔开，尽可能精简。只需要列举{topic_num}个话题就好，不要有序号，不要告诉我其他内容。' + return prompt + + def topic_what(self,text, topic): + # prompt = f'这是一段文字：{text}。我想知道这段文字里有什么关于{topic}的话题，帮我总结成一句自然的话，可以包含时间和人物，以及具体的观点。只输出这句话就好' + prompt = f'这是一段文字：{text}。我想让你基于这段文字来概括"{topic}"这个概念，帮我总结成一句自然的话，可以包含时间和人物，以及具体的观点。只输出这句话就好' + return prompt + + def remove_node_from_db(self, topic): + """ + 从数据库中删除指定节点及其相关的边 + + Args: + topic: 要删除的节点概念 + """ + # 删除节点 + self.memory_graph.db.db.graph_data.nodes.delete_one({'concept': topic}) + # 删除所有涉及该节点的边 + self.memory_graph.db.db.graph_data.edges.delete_many({ + '$or': [ + {'source': topic}, + {'target': topic} + ] + }) + + def forget_topic(self, topic): + """ + 随机删除指定话题中的一条记忆，如果话题没有记忆则移除该话题节点 + 只在内存中的图上操作，不直接与数据库交互 + + Args: + topic: 要删除记忆的话题 + + Returns: + removed_item: 被删除的记忆项，如果没有删除任何记忆则返回 None + """ + if topic not in self.memory_graph.G: + return None + + # 获取话题节点数据 + node_data = self.memory_graph.G.nodes[topic] + + # 如果节点存在memory_items + if 'memory_items' in node_data: + memory_items = node_data['memory_items'] + + # 确保memory_items是列表 + if not isinstance(memory_items, list): + memory_items = [memory_items] if memory_items else [] + + # 如果有记忆项可以删除 + if memory_items: + # 随机选择一个记忆项删除 + removed_item = random.choice(memory_items) + memory_items.remove(removed_item) + + # 更新节点的记忆项 + if memory_items: + self.memory_graph.G.nodes[topic]['memory_items'] = memory_items + else: + # 如果没有记忆项了，删除整个节点 + self.memory_graph.G.remove_node(topic) + + return removed_item + + return None + + async def operation_forget_topic(self, percentage=0.1): + """ + 随机选择图中一定比例的节点进行检查，根据条件决定是否遗忘 + + Args: + percentage: 要检查的节点比例，默认为0.1（10%） + """ + # 获取所有节点 + all_nodes = list(self.memory_graph.G.nodes()) + # 计算要检查的节点数量 + check_count = max(1, int(len(all_nodes) * percentage)) + # 随机选择节点 + nodes_to_check = random.sample(all_nodes, check_count) + + forgotten_nodes = [] + for node in nodes_to_check: + # 获取节点的连接数 + connections = self.memory_graph.G.degree(node) + + # 获取节点的内容条数 + memory_items = self.memory_graph.G.nodes[node].get('memory_items', []) + if not isinstance(memory_items, list): + memory_items = [memory_items] if memory_items else [] + content_count = len(memory_items) + + # 检查连接强度 + weak_connections = True + if connections > 1: # 只有当连接数大于1时才检查强度 + for neighbor in self.memory_graph.G.neighbors(node): + strength = self.memory_graph.G[node][neighbor].get('strength', 1) + if strength > 2: + weak_connections = False + break + + # 如果满足遗忘条件 + if (connections <= 1 and weak_connections) or content_count <= 2: + removed_item = self.forget_topic(node) + if removed_item: + forgotten_nodes.append((node, removed_item)) + logger.info(f"遗忘节点 {node} 的记忆: {removed_item}") + + # 同步到数据库 + if forgotten_nodes: + self.sync_memory_to_db() + logger.info(f"完成遗忘操作，共遗忘 {len(forgotten_nodes)} 个节点的记忆") + else: + logger.info("本次检查没有节点满足遗忘条件") + + async def merge_memory(self, topic): + """ + 对指定话题的记忆进行合并压缩 + + Args: + topic: 要合并的话题节点 + """ + # 获取节点的记忆项 + memory_items = self.memory_graph.G.nodes[topic].get('memory_items', []) + if not isinstance(memory_items, list): + memory_items = [memory_items] if memory_items else [] + + # 如果记忆项不足，直接返回 + if len(memory_items) < 10: + return + + # 随机选择10条记忆 + selected_memories = random.sample(memory_items, 10) + + # 拼接成文本 + merged_text = "\n".join(selected_memories) + print(f"\n[合并记忆] 话题: {topic}") + print(f"选择的记忆:\n{merged_text}") + + # 使用memory_compress生成新的压缩记忆 + compressed_memories = await self.memory_compress(merged_text, 0.1) + + # 从原记忆列表中移除被选中的记忆 + for memory in selected_memories: + memory_items.remove(memory) + + # 添加新的压缩记忆 + for _, compressed_memory in compressed_memories: + memory_items.append(compressed_memory) + print(f"添加压缩记忆: {compressed_memory}") + + # 更新节点的记忆项 + self.memory_graph.G.nodes[topic]['memory_items'] = memory_items + print(f"完成记忆合并，当前记忆数量: {len(memory_items)}") + + async def operation_merge_memory(self, percentage=0.1): + """ + 随机检查一定比例的节点，对内容数量超过100的节点进行记忆合并 + + Args: + percentage: 要检查的节点比例，默认为0.1（10%） + """ + # 获取所有节点 + all_nodes = list(self.memory_graph.G.nodes()) + # 计算要检查的节点数量 + check_count = max(1, int(len(all_nodes) * percentage)) + # 随机选择节点 + nodes_to_check = random.sample(all_nodes, check_count) + + merged_nodes = [] + for node in nodes_to_check: + # 获取节点的内容条数 + memory_items = self.memory_graph.G.nodes[node].get('memory_items', []) + if not isinstance(memory_items, list): + memory_items = [memory_items] if memory_items else [] + content_count = len(memory_items) + + # 如果内容数量超过100，进行合并 + if content_count > 100: + print(f"\n检查节点: {node}, 当前记忆数量: {content_count}") + await self.merge_memory(node) + merged_nodes.append(node) + + # 同步到数据库 + if merged_nodes: + self.sync_memory_to_db() + print(f"\n完成记忆合并操作，共处理 {len(merged_nodes)} 个节点") + else: + print("\n本次检查没有需要合并的节点") + + +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() + + # 计算节点大小和颜色 + node_colors = [] + node_sizes = [] + nodes = list(H.nodes()) + + # 获取最大记忆数用于归一化节点大小 + max_memories = 1 + for node in 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) + max_memories = max(max_memories, memory_count) + + # 计算每个节点的大小和颜色 + for node in nodes: + # 计算节点大小（基于记忆数量） + 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) + # 使用指数函数使变化更明显 + ratio = memory_count / max_memories + size = 400 + 2000 * (ratio ** 2) # 增大节点大小 + node_sizes.append(size) + + # 计算节点颜色（基于连接数） + degree = H.degree(node) + if degree >= 30: + node_colors.append((1.0, 0, 0)) # 亮红色 (#FF0000) + else: + # 将1-10映射到0-1的范围 + color_ratio = (degree - 1) / 29.0 if degree > 1 else 0 + # 使用蓝到红的渐变 + red = min(0.9, color_ratio) + blue = max(0.0, 1.0 - color_ratio) + node_colors.append((red, 0, blue)) + + # 绘制图形 + plt.figure(figsize=(16, 12)) # 减小图形尺寸 + pos = nx.spring_layout(H, + k=1, # 调整节点间斥力 + iterations=100, # 增加迭代次数 + scale=1.5, # 减小布局尺寸 + weight='strength') # 使用边的strength属性作为权重 + + nx.draw(H, pos, + with_labels=True, + node_color=node_colors, + node_size=node_sizes, + font_size=12, # 保持增大的字体大小 + font_family='SimHei', + font_weight='bold', + edge_color='gray', + width=1.5) # 统一的边宽度 + + title = '记忆图谱可视化 - 节点大小表示记忆数量\n节点颜色：蓝(弱连接)到红(强连接)渐变，边的透明度表示连接强度\n连接强度越大的节点距离越近' + plt.title(title, fontsize=16, fontfamily='SimHei') + plt.show() + +async def main(): + # 初始化数据库 + logger.info("正在初始化数据库连接...") + db = Database.get_instance() + start_time = time.time() + + test_pare = {'do_build_memory':True,'do_forget_topic':False,'do_visualize_graph':True,'do_query':False,'do_merge_memory':False} + + # 创建记忆图 + memory_graph = Memory_graph() + + # 创建海马体 + hippocampus = Hippocampus(memory_graph) + + # 从数据库同步数据 + hippocampus.sync_memory_from_db() + + end_time = time.time() + logger.info(f"\033[32m[加载海马体耗时: {end_time - start_time:.2f} 秒]\033[0m") + + # 构建记忆 + if test_pare['do_build_memory']: + logger.info("开始构建记忆...") + chat_size = 20 + await hippocampus.operation_build_memory(chat_size=chat_size) + + end_time = time.time() + logger.info(f"\033[32m[构建记忆耗时: {end_time - start_time:.2f} 秒,chat_size={chat_size},chat_count = 16]\033[0m") + + if test_pare['do_forget_topic']: + logger.info("开始遗忘记忆...") + await hippocampus.operation_forget_topic(percentage=0.1) + + end_time = time.time() + logger.info(f"\033[32m[遗忘记忆耗时: {end_time - start_time:.2f} 秒]\033[0m") + + if test_pare['do_merge_memory']: + logger.info("开始合并记忆...") + await hippocampus.operation_merge_memory(percentage=0.1) + + end_time = time.time() + logger.info(f"\033[32m[合并记忆耗时: {end_time - start_time:.2f} 秒]\033[0m") + + if test_pare['do_visualize_graph']: + # 展示优化后的图形 + logger.info("生成记忆图谱可视化...") + print("\n生成优化后的记忆图谱：") + visualize_graph_lite(memory_graph) + + if test_pare['do_query']: + # 交互式查询 + while True: + query = input("\n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: + first_layer, second_layer = items_list + if first_layer: + print("\n直接相关的记忆：") + for item in first_layer: + print(f"- {item}") + if second_layer: + print("\n间接相关的记忆：") + for item in second_layer: + print(f"- {item}") + else: + print("未找到相关记忆。") + + +if __name__ == "__main__": + import asyncio + asyncio.run(main()) + + diff --git a/src/plugins/memory_system/offline_llm.py b/src/plugins/memory_system/offline_llm.py new file mode 100644 index 000000000..5e877dceb --- /dev/null +++ b/src/plugins/memory_system/offline_llm.py @@ -0,0 +1,125 @@ +import os +import requests +from typing import Tuple, Union +import time +import aiohttp +import asyncio +from loguru import logger + +class LLMModel: + def __init__(self, model_name="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") + + 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") + + logger.info(f"API URL: {self.base_url}") # 使用 logger 记录 base_url + + def generate_response(self, prompt: str) -> Union[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" + logger.info(f"Request URL: {api_url}") # 记录请求的 URL + + max_retries = 3 + base_wait_time = 15 # 基础等待时间（秒） + + for retry in range(max_retries): + try: + response = requests.post(api_url, headers=headers, json=data) + + if response.status_code == 429: + wait_time = base_wait_time * (2 ** retry) # 指数退避 + logger.warning(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) + logger.error(f"[回复]请求失败，等待{wait_time}秒后重试... 错误: {str(e)}") + time.sleep(wait_time) + else: + logger.error(f"请求失败: {str(e)}") + return f"请求失败: {str(e)}", "" + + logger.error("达到最大重试次数，请求仍然失败") + return "达到最大重试次数，请求仍然失败", "" + + async def generate_response_async(self, prompt: str) -> Union[str, Tuple[str, str]]: + """异步方式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" + logger.info(f"Request URL: {api_url}") # 记录请求的 URL + + max_retries = 3 + base_wait_time = 15 + + async with aiohttp.ClientSession() as session: + for retry in range(max_retries): + try: + 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 + logger.warning(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) + logger.error(f"[回复]请求失败，等待{wait_time}秒后重试... 错误: {str(e)}") + await asyncio.sleep(wait_time) + else: + logger.error(f"请求失败: {str(e)}") + return f"请求失败: {str(e)}", "" + + logger.error("达到最大重试次数，请求仍然失败") + return "达到最大重试次数，请求仍然失败", "" diff --git a/src/plugins/models/utils_model.py b/src/plugins/models/utils_model.py index f911d7495..11d7e2b72 100644 --- a/src/plugins/models/utils_model.py +++ b/src/plugins/models/utils_model.py @@ -2,20 +2,26 @@ import aiohttp import asyncio import requests import time +import re from typing import Tuple, Union from nonebot import get_driver +from loguru import logger from ..chat.config import global_config +from ..chat.utils_image import compress_base64_image_by_scale + driver = get_driver() config = driver.config + class LLM_request: - def __init__(self, model = global_config.llm_normal,**kwargs): + def __init__(self, model, **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)}") + logger.error(f"配置错误：找不到对应的配置项 - {str(e)}") + raise ValueError(f"配置错误：找不到对应的配置项 - {str(e)}") from e self.model_name = model["name"] self.params = kwargs @@ -25,48 +31,62 @@ class LLM_request: "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" - + logger.info(f"发送请求到URL: {api_url}") # 记录请求的URL + 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...") + logger.warning(f"遇到请求限制(429)，等待{wait_time}秒后重试...") await asyncio.sleep(wait_time) continue + if response.status in [500, 503]: + logger.error(f"服务器错误: {response.status}") + raise RuntimeError("服务器负载过高，模型恢复失败QAQ") + 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", "") + message = result["choices"][0]["message"] + content = message.get("content", "") + think_match = None + reasoning_content = message.get("reasoning_content", "") + if not reasoning_content: + think_match = re.search(r'(.*?)', content, re.DOTALL) + if think_match: + reasoning_content = think_match.group(1).strip() + content = re.sub(r'.*?', '', content, flags=re.DOTALL).strip() 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)}") + logger.error(f"[回复]请求失败，等待{wait_time}秒后重试... 错误: {str(e)}", exc_info=True) 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", "" + logger.critical(f"请求失败: {str(e)}", exc_info=True) + raise RuntimeError(f"API请求失败: {str(e)}") + + logger.error("达到最大重试次数，请求仍然失败") + raise RuntimeError("达到最大重试次数，API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""" @@ -74,43 +94,116 @@ class LLM_request: "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}" + def build_request_data(img_base64: str): + return { + "model": self.model_name, + "messages": [ + { + "role": "user", + "content": [ + { + "type": "text", + "text": prompt + }, + { + "type": "image_url", + "image_url": { + "url": f"data:image/jpeg;base64,{img_base64}" + } } - } - ] - } - ], - **self.params - } + ] + } + ], + **self.params + } + # 发送请求到完整的chat/completions端点 api_url = f"{self.base_url.rstrip('/')}/chat/completions" - + logger.info(f"发送请求到URL: {api_url}") # 记录请求的URL + max_retries = 3 base_wait_time = 15 + current_image_base64 = image_base64 + current_image_base64 = compress_base64_image_by_scale(current_image_base64) + + for retry in range(max_retries): try: + data = build_request_data(current_image_base64) 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...") + logger.warning(f"遇到请求限制(429)，等待{wait_time}秒后重试...") + await asyncio.sleep(wait_time) + continue + + elif response.status == 413: + logger.warning("图片太大(413)，尝试压缩...") + current_image_base64 = compress_base64_image_by_scale(current_image_base64) + 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: + message = result["choices"][0]["message"] + content = message.get("content", "") + think_match = None + reasoning_content = message.get("reasoning_content", "") + if not reasoning_content: + think_match = re.search(r'(.*?)', content, re.DOTALL) + if think_match: + reasoning_content = think_match.group(1).strip() + content = re.sub(r'.*?', '', content, flags=re.DOTALL).strip() + 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) + logger.error(f"[image回复]请求失败，等待{wait_time}秒后重试... 错误: {str(e)}", exc_info=True) + await asyncio.sleep(wait_time) + else: + logger.critical(f"请求失败: {str(e)}", exc_info=True) + raise RuntimeError(f"API请求失败: {str(e)}") + + logger.error("达到最大重试次数，请求仍然失败") + raise RuntimeError("达到最大重试次数，API请求仍然失败") + + async def generate_response_async(self, prompt: str) -> Union[str, Tuple[str, str]]: + """异步方式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" + logger.info(f"Request URL: {api_url}") # 记录请求的 URL + + max_retries = 3 + base_wait_time = 15 + + async with aiohttp.ClientSession() as session: + for retry in range(max_retries): + try: + 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 + logger.warning(f"遇到请求限制(429)，等待{wait_time}秒后重试...") await asyncio.sleep(wait_time) continue @@ -122,16 +215,20 @@ class LLM_request: 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", "" + + except Exception as e: + if retry < max_retries - 1: # 如果还有重试机会 + wait_time = base_wait_time * (2 ** retry) + logger.error(f"[回复]请求失败，等待{wait_time}秒后重试... 错误: {str(e)}") + await asyncio.sleep(wait_time) + else: + logger.error(f"请求失败: {str(e)}") + return f"请求失败: {str(e)}", "" + + logger.error("达到最大重试次数，请求仍然失败") + 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""" @@ -139,7 +236,9 @@ class LLM_request: "Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json" } - + + image_base64=compress_base64_image_by_scale(image_base64) + # 构建请求体 data = { "model": self.model_name, @@ -162,38 +261,160 @@ class LLM_request: ], **self.params } - + # 发送请求到完整的chat/completions端点 api_url = f"{self.base_url.rstrip('/')}/chat/completions" - + logger.info(f"发送请求到URL: {api_url}") # 记录请求的URL + max_retries = 2 base_wait_time = 6 - + 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...") + wait_time = base_wait_time * (2 ** retry) + logger.warning(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", "") + message = result["choices"][0]["message"] + content = message.get("content", "") + think_match = None + reasoning_content = message.get("reasoning_content", "") + if not reasoning_content: + think_match = re.search(r'(.*?)', content, re.DOTALL) + if think_match: + reasoning_content = think_match.group(1).strip() + content = re.sub(r'.*?', '', content, flags=re.DOTALL).strip() 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)}") + logger.error(f"[image_sync回复]请求失败，等待{wait_time}秒后重试... 错误: {str(e)}", exc_info=True) time.sleep(wait_time) else: - return f"请求失败: {str(e)}", "" + logger.critical(f"请求失败: {str(e)}", exc_info=True) + raise RuntimeError(f"API请求失败: {str(e)}") + + logger.error("达到最大重试次数，请求仍然失败") + raise RuntimeError("达到最大重试次数，API请求仍然失败") + + def get_embedding_sync(self, text: str, model: str = "BAAI/bge-m3") -> Union[list, None]: + """同步方法：获取文本的embedding向量 - return "达到最大重试次数，请求仍然失败", "" + Args: + text: 需要获取embedding的文本 + model: 使用的模型名称，默认为"BAAI/bge-m3" + + Returns: + list: embedding向量，如果失败则返回None + """ + headers = { + "Authorization": f"Bearer {self.api_key}", + "Content-Type": "application/json" + } + + data = { + "model": model, + "input": text, + "encoding_format": "float" + } + + api_url = f"{self.base_url.rstrip('/')}/embeddings" + logger.info(f"发送请求到URL: {api_url}") # 记录请求的URL + + max_retries = 2 + base_wait_time = 6 + + 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) + logger.warning(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() + + result = response.json() + if 'data' in result and len(result['data']) > 0: + return result['data'][0]['embedding'] + return None + + except Exception as e: + if retry < max_retries - 1: + wait_time = base_wait_time * (2 ** retry) + logger.error(f"[embedding_sync]请求失败，等待{wait_time}秒后重试... 错误: {str(e)}", exc_info=True) + time.sleep(wait_time) + else: + logger.critical(f"embedding请求失败: {str(e)}", exc_info=True) + return None + + logger.error("达到最大重试次数，embedding请求仍然失败") + return None + + async def get_embedding(self, text: str, model: str = "BAAI/bge-m3") -> Union[list, None]: + """异步方法：获取文本的embedding向量 + + Args: + text: 需要获取embedding的文本 + model: 使用的模型名称，默认为"BAAI/bge-m3" + + Returns: + list: embedding向量，如果失败则返回None + """ + headers = { + "Authorization": f"Bearer {self.api_key}", + "Content-Type": "application/json" + } + + data = { + "model": model, + "input": text, + "encoding_format": "float" + } + + api_url = f"{self.base_url.rstrip('/')}/embeddings" + logger.info(f"发送请求到URL: {api_url}") # 记录请求的URL + + 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) + logger.warning(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() + + result = await response.json() + if 'data' in result and len(result['data']) > 0: + return result['data'][0]['embedding'] + return None + + except Exception as e: + if retry < max_retries - 1: + wait_time = base_wait_time * (2 ** retry) + logger.error(f"[embedding]请求失败，等待{wait_time}秒后重试... 错误: {str(e)}", exc_info=True) + await asyncio.sleep(wait_time) + else: + logger.critical(f"embedding请求失败: {str(e)}", exc_info=True) + return None + + logger.error("达到最大重试次数，embedding请求仍然失败") + return None diff --git a/src/plugins/schedule/schedule_generator.py b/src/plugins/schedule/schedule_generator.py index 24b2a32dd..c9a1c8910 100644 --- a/src/plugins/schedule/schedule_generator.py +++ b/src/plugins/schedule/schedule_generator.py @@ -1,22 +1,23 @@ import datetime import os -from typing import List, Dict +from typing import List, Dict, Union 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 - +from loguru import logger +import json driver = get_driver() config = driver.config Database.initialize( - 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 + 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: @@ -42,8 +43,6 @@ class ScheduleGenerator: self.yesterday_schedule_text, self.yesterday_schedule = await self.generate_daily_schedule(target_date=yesterday,read_only=True) 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() date_str = target_date.strftime("%Y-%m-%d") weekday = target_date.strftime("%A") @@ -59,15 +58,20 @@ class ScheduleGenerator: elif read_only == False: print(f"{date_str}的日程不存在，准备生成新的日程。") - prompt = f"""我是{global_config.BOT_NICKNAME}，一个曾经学习地质,现在学习心理学和脑科学的女大学生，喜欢刷qq，贴吧，知乎和小红书，请为我生成{date_str}（{weekday}）的日程安排，包括： + prompt = f"""我是{global_config.BOT_NICKNAME}，{global_config.PROMPT_SCHEDULE_GEN}，请为我生成{date_str}（{weekday}）的日程安排，包括："""+\ + """ 1. 早上的学习和工作安排 2. 下午的活动和任务 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"。""" + 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JSON格式返回日程表，仅返回内容，不要返回注释，时间采用24小时制，格式为{"时间": "活动","时间": "活动",...}。""" - schedule_text, _ = await self.llm_scheduler.generate_response(prompt) + try: + schedule_text, _ = await self.llm_scheduler.generate_response(prompt) + self.db.db.schedule.insert_one({"date": date_str, "schedule": schedule_text}) + except Exception as e: + logger.error(f"生成日程失败: {str(e)}") + schedule_text = "生成日程时出错了" # print(self.schedule_text) - self.db.db.schedule.insert_one({"date": date_str, "schedule": schedule_text}) else: print(f"{date_str}的日程不存在。") schedule_text = "忘了" @@ -77,20 +81,15 @@ class ScheduleGenerator: schedule_form = self._parse_schedule(schedule_text) return schedule_text,schedule_form - def _parse_schedule(self, schedule_text: str) -> Dict[str, str]: + def _parse_schedule(self, schedule_text: str) -> Union[bool, Dict[str, str]]: """解析日程文本，转换为时间和活动的字典""" - schedule_dict = {} - # 按行分割日程文本 - lines = schedule_text.strip().split('\n') - for line in lines: - # print(line) - if ',' in line: - # 假设格式为 "时间: 活动" - time_str, activity = line.split(',', 1) - # print(time_str) - # print(activity) - schedule_dict[time_str.strip()] = activity.strip() - return schedule_dict + try: + schedule_dict = json.loads(schedule_text) + return schedule_dict + except json.JSONDecodeError as e: + print(schedule_text) + print(f"解析日程失败: {str(e)}") + return False def _parse_time(self, time_str: str) -> str: """解析时间字符串，转换为时间""" @@ -105,6 +104,8 @@ class ScheduleGenerator: min_diff = float('inf') # 检查今天的日程 + if not self.today_schedule.keys(): + return "摸鱼" for time_str in self.today_schedule.keys(): diff = abs(self._time_diff(current_time, time_str)) if closest_time is None or diff < min_diff: @@ -128,6 +129,10 @@ class ScheduleGenerator: def _time_diff(self, time1: str, time2: str) -> int: """计算两个时间字符串之间的分钟差""" + if time1=="24:00": + time1="23:59" + if time2=="24:00": + time2="23:59" t1 = datetime.datetime.strptime(time1, "%H:%M") t2 = datetime.datetime.strptime(time2, "%H:%M") diff = int((t2 - t1).total_seconds() / 60) @@ -141,11 +146,14 @@ class ScheduleGenerator: def print_schedule(self): """打印完整的日程安排""" - - print("\n=== 今日日程安排 ===") - for time_str, activity in self.today_schedule.items(): - print(f"时间[{time_str}]: 活动[{activity}]") - print("==================\n") + if not self._parse_schedule(self.today_schedule_text): + print("今日日程有误，将在下次运行时重新生成") + self.db.db.schedule.delete_one({"date": datetime.datetime.now().strftime("%Y-%m-%d")}) + else: + print("\n=== 今日日程安排 ===") + for time_str, activity in self.today_schedule.items(): + print(f"时间[{time_str}]: 活动[{activity}]") + print("==================\n") # def main(): # # 使用示例 @@ -165,4 +173,4 @@ class ScheduleGenerator: # if __name__ == "__main__": # main() -bot_schedule = ScheduleGenerator() \ No newline at end of file +bot_schedule = ScheduleGenerator() diff --git a/src/test/emotion_cal.py b/src/test/emotion_cal.py deleted file mode 100644 index eaf0cbcf0..000000000 --- a/src/test/emotion_cal.py +++ /dev/null @@ -1,70 +0,0 @@ -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 deleted file mode 100644 index 7469e64d4..000000000 --- a/src/test/emotion_cal_bert.py +++ /dev/null @@ -1,74 +0,0 @@ -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 deleted file mode 100644 index 072dc7126..000000000 --- a/src/test/emotion_cal_hanlp.py +++ /dev/null @@ -1,62 +0,0 @@ -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/typo_creator.py b/src/test/typo_creator.py new file mode 100644 index 000000000..c452589ce --- /dev/null +++ b/src/test/typo_creator.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 deleted file mode 100644 index b6982c0ed..000000000 --- a/src/test/typo_word.py +++ /dev/null @@ -1,301 +0,0 @@ -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() diff --git a/.env.prod b/template.env similarity index 93% rename from .env.prod rename to template.env index f00cd5169..d70bba206 100644 --- a/.env.prod +++ b/template.env @@ -1,8 +1,6 @@ HOST=127.0.0.1 PORT=8080 -COMMAND_START=["/"] - # 插件配置 PLUGINS=["src2.plugins.chat"] @@ -16,11 +14,11 @@ MONGODB_PASSWORD = "" # 默认空值 MONGODB_AUTH_SOURCE = "" # 默认空值 #key and url - 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 +#定义你要用的api的base_url DEEP_SEEK_KEY= CHAT_ANY_WHERE_KEY= SILICONFLOW_KEY= \ No newline at end of file