新增了知识库一键启动漂亮脚本

.gitignore

@@ -1,4 +1,5 @@
 data/
+data1/
 mongodb/
 NapCat.Framework.Windows.Once/
 log/

run.py

@@ -128,13 +128,17 @@ if __name__ == "__main__":
     )
     os.system("cls")
     if choice == "1":
-        install_napcat()
+        confirm = input("首次安装将下载并配置所需组件\n1.确认\n2.取消\n")
-        install_mongodb()
+        if confirm == "1":
+            install_napcat()
+            install_mongodb()
+        else:
+            print("已取消安装")
     elif choice == "2":
         run_maimbot()
-        choice = input("是否启动推理可视化？（y/N）").upper()
+        choice = input("是否启动推理可视化？（未完善）（y/N）").upper()
         if choice == "Y":
             run_cmd(r"python src\gui\reasoning_gui.py")
-        choice = input("是否启动记忆可视化？（y/N）").upper()
+        choice = input("是否启动记忆可视化？（未完善）（y/N）").upper()
         if choice == "Y":
             run_cmd(r"python src/plugins/memory_system/memory_manual_build.py")

src/common/database.py

@@ -1,8 +1,6 @@
 from typing import Optional
-
 from pymongo import MongoClient
 
-
 class Database:
     _instance: Optional["Database"] = None
     
@@ -50,25 +48,4 @@ class Database:
     def get_instance(cls) -> "Database":
         if cls._instance is None:
             raise RuntimeError("Database not initialized")
-        return cls._instance
+        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

src/plugins/chat/bot.py

@@ -7,7 +7,6 @@ from nonebot.adapters.onebot.v11 import Bot, GroupMessageEvent
 from ..memory_system.memory import hippocampus
 from ..moods.moods import MoodManager  # 导入情绪管理器
 from .config import global_config
-from .cq_code import CQCode, cq_code_tool  # 导入CQCode模块
 from .emoji_manager import emoji_manager  # 导入表情包管理器
 from .llm_generator import ResponseGenerator
 from .message import MessageSending, MessageRecv, MessageThinking, MessageSet
@@ -218,7 +217,7 @@ class ChatBot:
 
             # 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")
-            print(f"添加message_set到message_manager")
+            print("添加message_set到message_manager")
             message_manager.add_message(message_set)
 
             bot_response_time = thinking_time_point

src/plugins/chat/llm_generator.py

@@ -8,7 +8,7 @@ from loguru import logger
 from ...common.database import Database
 from ..models.utils_model import LLM_request
 from .config import global_config
-from .message import MessageRecv, MessageThinking, MessageSending,Message
+from .message import MessageRecv, MessageThinking, Message
 from .prompt_builder import prompt_builder
 from .relationship_manager import relationship_manager
 from .utils import process_llm_response

src/plugins/chat/message.py

@@ -3,14 +3,14 @@ import html
 import re
 import json
 from dataclasses import dataclass
-from typing import Dict, ForwardRef, List, Optional, Union
+from typing import Dict, List, Optional
 
 import urllib3
 from loguru import logger
 
 from .utils_image import image_manager
-from .message_base import Seg, GroupInfo, UserInfo, BaseMessageInfo, MessageBase
+from .message_base import Seg, UserInfo, BaseMessageInfo, MessageBase
-from .chat_stream import ChatStream, chat_manager
+from .chat_stream import ChatStream
 # 禁用SSL警告
 urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
 

src/plugins/chat/message_base.py

@@ -1,5 +1,5 @@
 from dataclasses import dataclass, asdict
-from typing import List, Optional, Union, Any, Dict
+from typing import List, Optional, Union, Dict
 
 @dataclass
 class Seg:

src/plugins/chat/message_cq.py

@@ -1,12 +1,12 @@
 import time
 from dataclasses import dataclass
-from typing import Dict, ForwardRef, List, Optional, Union
+from typing import Dict, Optional
 
 import urllib3
 
-from .cq_code import CQCode, cq_code_tool
+from .cq_code import cq_code_tool
 from .utils_cq import parse_cq_code
-from .utils_user import get_groupname, get_user_cardname, get_user_nickname
+from .utils_user import get_groupname
 from .message_base import Seg, GroupInfo, UserInfo, BaseMessageInfo, MessageBase
 # 禁用SSL警告
 urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)

src/plugins/chat/message_sender.py

@@ -5,12 +5,10 @@ from typing import Dict, List, Optional, Union
 from loguru import logger
 from nonebot.adapters.onebot.v11 import Bot
 
-from .cq_code import cq_code_tool
 from .message_cq import MessageSendCQ
-from .message import MessageSending, MessageThinking, MessageRecv,MessageSet
+from .message import MessageSending, MessageThinking, MessageSet
 from .storage import MessageStorage
 from .config import global_config
-from .chat_stream import chat_manager
 
 
 class Message_Sender:

src/plugins/chat/prompt_builder.py

@@ -9,7 +9,7 @@ from ..moods.moods import MoodManager
 from ..schedule.schedule_generator import bot_schedule
 from .config import global_config
 from .utils import get_embedding, get_recent_group_detailed_plain_text
-from .chat_stream import ChatStream, chat_manager
+from .chat_stream import chat_manager
 
 
 class PromptBuilder:

src/plugins/chat/relationship_manager.py

@@ -1,6 +1,5 @@
 import asyncio
-from typing import Optional, Union
+from typing import Optional
-from typing import Optional, Union
 from loguru import logger
 
 from ...common.database import Database

src/plugins/chat/storage.py

@@ -1,8 +1,6 @@
 from typing import Optional, Union
-from typing import Optional, Union
 
 from ...common.database import Database
-from .message_base import MessageBase
 from .message import MessageSending, MessageRecv
 from .chat_stream import ChatStream
 from loguru import logger

src/plugins/chat/utils.py

@@ -12,8 +12,8 @@ from loguru import logger
 from ..models.utils_model import LLM_request
 from ..utils.typo_generator import ChineseTypoGenerator
 from .config import global_config
-from .message import MessageThinking, MessageRecv,MessageSending,MessageProcessBase,Message
+from .message import MessageRecv,Message
-from .message_base import MessageBase,BaseMessageInfo,UserInfo,GroupInfo
+from .message_base import UserInfo
 from .chat_stream import ChatStream
 from ..moods.moods import MoodManager
 

src/plugins/chat/utils_image.py

@@ -1,16 +1,12 @@
 import base64
-import io
 import os
 import time
-import zlib
 import aiohttp
 import hashlib
-from typing import Optional, Tuple, Union
+from typing import Optional, Union
-from urllib.parse import urlparse
 
 from loguru import logger
 from nonebot import get_driver
-from PIL import Image
 
 from ...common.database import Database
 from ..chat.config import global_config

src/plugins/chat/willing_manager.py

@@ -1,13 +1,9 @@
 import asyncio
 from typing import Dict
-from loguru import logger
 
-from typing import Dict
-from loguru import logger
 
 from .config import global_config
-from .message_base import UserInfo, GroupInfo
+from .chat_stream import ChatStream
-from .chat_stream import chat_manager,ChatStream
 
 
 class WillingManager:

src/plugins/knowledege/knowledge_library.py

@@ -1,199 +0,0 @@
-import os
-import sys
-import time
-
-import requests
-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)
-
-# 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)
-
-from src.common.database import Database
-
-# 从环境变量获取配置
-Database.initialize(
-    uri=os.getenv("MONGODB_URI"),
-    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"),
-)
-
-class KnowledgeLibrary:
-    def __init__(self):
-        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"""
-        os.makedirs(self.raw_info_dir, exist_ok=True)
-        
-    def get_embedding(self, text: str) -> list:
-        """获取文本的embedding向量"""
-        url = "https://api.siliconflow.cn/v1/embeddings"
-        payload = {
-            "model": "BAAI/bge-m3",
-            "input": text,
-            "encoding_format": "float"
-        }
-        headers = {
-            "Authorization": f"Bearer {self.api_key}",
-            "Content-Type": "application/json"
-        }
-        
-        response = requests.post(url, json=payload, headers=headers)
-        if response.status_code != 200:
-            print(f"获取embedding失败: {response.text}")
-            return None
-            
-        return response.json()['data'][0]['embedding']
-        
-    def process_files(self):
-        """处理raw_info目录下的所有txt文件"""
-        for filename in os.listdir(self.raw_info_dir):
-            if filename.endswith('.txt'):
-                file_path = os.path.join(self.raw_info_dir, filename)
-                self.process_single_file(file_path)
-                
-    def process_single_file(self, file_path: str):
-        """处理单个文件"""
-        try:
-            # 检查文件是否已处理
-            if self.db.db.processed_files.find_one({"file_path": file_path}):
-                print(f"文件已处理过，跳过: {file_path}")
-                return
-                
-            with open(file_path, 'r', encoding='utf-8') as f:
-                content = f.read()
-                
-            # 按1024字符分段
-            segments = [content[i:i+600] for i in range(0, len(content), 300)]
-            
-            # 处理每个分段
-            for segment in segments:
-                if not segment.strip():  # 跳过空段
-                    continue
-                    
-                # 获取embedding
-                embedding = self.get_embedding(segment)
-                if not embedding:
-                    continue
-                    
-                # 存储到数据库
-                doc = {
-                    "content": segment,
-                    "embedding": embedding,
-                    "file_path": file_path,
-                    "segment_length": len(segment)
-                }
-                
-                # 使用文本内容的哈希值作为唯一标识
-                content_hash = hash(segment)
-                
-                # 更新或插入文档
-                self.db.db.knowledges.update_one(
-                    {"content_hash": content_hash},
-                    {"$set": doc},
-                    upsert=True
-                )
-                
-            # 记录文件已处理
-            self.db.db.processed_files.insert_one({
-                "file_path": file_path,
-                "processed_time": time.time()
-            })
-                
-            print(f"成功处理文件: {file_path}")
-            
-        except Exception as e:
-            print(f"处理文件 {file_path} 时出错: {str(e)}")
-            
-    def search_similar_segments(self, query: str, limit: int = 5) -> list:
-        """搜索与查询文本相似的片段"""
-        query_embedding = self.get_embedding(query)
-        if not query_embedding:
-            return []
-            
-        # 使用余弦相似度计算
-        pipeline = [
-            {
-                "$addFields": {
-                    "dotProduct": {
-                        "$reduce": {
-                            "input": {"$range": [0, {"$size": "$embedding"}]},
-                            "initialValue": 0,
-                            "in": {
-                                "$add": [
-                                    "$$value",
-                                    {"$multiply": [
-                                        {"$arrayElemAt": ["$embedding", "$$this"]},
-                                        {"$arrayElemAt": [query_embedding, "$$this"]}
-                                    ]}
-                                ]
-                            }
-                        }
-                    },
-                    "magnitude1": {
-                        "$sqrt": {
-                            "$reduce": {
-                                "input": "$embedding",
-                                "initialValue": 0,
-                                "in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]}
-                            }
-                        }
-                    },
-                    "magnitude2": {
-                        "$sqrt": {
-                            "$reduce": {
-                                "input": query_embedding,
-                                "initialValue": 0,
-                                "in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]}
-                            }
-                        }
-                    }
-                }
-            },
-            {
-                "$addFields": {
-                    "similarity": {
-                        "$divide": ["$dotProduct", {"$multiply": ["$magnitude1", "$magnitude2"]}]
-                    }
-                }
-            },
-            {"$sort": {"similarity": -1}},
-            {"$limit": limit},
-            {"$project": {"content": 1, "similarity": 1, "file_path": 1}}
-        ]
-        
-        results = list(self.db.db.knowledges.aggregate(pipeline))
-        return results
-
-# 创建单例实例
-knowledge_library = KnowledgeLibrary()
-
-if __name__ == "__main__":
-    # 测试知识库功能
-    print("开始处理知识库文件...")
-    knowledge_library.process_files()
-    
-    # 测试搜索功能
-    test_query = "麦麦评价一下僕と花"
-    print(f"\n搜索与'{test_query}'相似的内容:")
-    results = knowledge_library.search_similar_segments(test_query)
-    for result in results:
-        print(f"相似度: {result['similarity']:.4f}")
-        print(f"内容: {result['content'][:100]}...")
-        print("-" * 50)

src/plugins/memory_system/memory_manual_build.py

@@ -10,7 +10,6 @@ from pathlib import Path
 
 import matplotlib.pyplot as plt
 import networkx as nx
-import pymongo
 from dotenv import load_dotenv
 from loguru import logger
 import jieba

src/plugins/zhishi/knowledge_library.py

@@ -0,0 +1,383 @@
+import os
+import sys
+import time
+import requests
+from dotenv import load_dotenv
+import hashlib
+from datetime import datetime
+from tqdm import tqdm
+from rich.console import Console
+from rich.table import Table
+
+# 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)
+
+# 现在可以导入src模块
+from src.common.database import Database
+
+# 加载根目录下的env.edv文件
+env_path = os.path.join(root_path, ".env.prod")
+if not os.path.exists(env_path):
+    raise FileNotFoundError(f"配置文件不存在: {env_path}")
+load_dotenv(env_path)
+
+class KnowledgeLibrary:
+    def __init__(self):
+        # 初始化数据库连接
+        if Database._instance is None:
+            Database.initialize(
+                uri=os.getenv("MONGODB_URI"),
+                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"),
+            )
+        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")
+        self.console = Console()
+        
+    def _ensure_dirs(self):
+        """确保必要的目录存在"""
+        os.makedirs(self.raw_info_dir, exist_ok=True)
+        
+    def read_file(self, file_path: str) -> str:
+        """读取文件内容"""
+        with open(file_path, 'r', encoding='utf-8') as f:
+            return f.read()
+            
+    def split_content(self, content: str, max_length: int = 512) -> list:
+        """将内容分割成适当大小的块，保持段落完整性
+        
+        Args:
+            content: 要分割的文本内容
+            max_length: 每个块的最大长度
+            
+        Returns:
+            list: 分割后的文本块列表
+        """
+        # 首先按段落分割
+        paragraphs = [p.strip() for p in content.split('\n\n') if p.strip()]
+        chunks = []
+        current_chunk = []
+        current_length = 0
+        
+        for para in paragraphs:
+            para_length = len(para)
+            
+            # 如果单个段落就超过最大长度
+            if para_length > max_length:
+                # 如果当前chunk不为空，先保存
+                if current_chunk:
+                    chunks.append('\n'.join(current_chunk))
+                    current_chunk = []
+                    current_length = 0
+                
+                # 将长段落按句子分割
+                sentences = [s.strip() for s in para.replace('。', '。\n').replace('！', '！\n').replace('？', '？\n').split('\n') if s.strip()]
+                temp_chunk = []
+                temp_length = 0
+                
+                for sentence in sentences:
+                    sentence_length = len(sentence)
+                    if sentence_length > max_length:
+                        # 如果单个句子超长，强制按长度分割
+                        if temp_chunk:
+                            chunks.append('\n'.join(temp_chunk))
+                            temp_chunk = []
+                            temp_length = 0
+                        for i in range(0, len(sentence), max_length):
+                            chunks.append(sentence[i:i + max_length])
+                    elif temp_length + sentence_length + 1 <= max_length:
+                        temp_chunk.append(sentence)
+                        temp_length += sentence_length + 1
+                    else:
+                        chunks.append('\n'.join(temp_chunk))
+                        temp_chunk = [sentence]
+                        temp_length = sentence_length
+                
+                if temp_chunk:
+                    chunks.append('\n'.join(temp_chunk))
+                
+            # 如果当前段落加上现有chunk不超过最大长度
+            elif current_length + para_length + 1 <= max_length:
+                current_chunk.append(para)
+                current_length += para_length + 1
+            else:
+                # 保存当前chunk并开始新的chunk
+                chunks.append('\n'.join(current_chunk))
+                current_chunk = [para]
+                current_length = para_length
+        
+        # 添加最后一个chunk
+        if current_chunk:
+            chunks.append('\n'.join(current_chunk))
+        
+        return chunks
+        
+    def get_embedding(self, text: str) -> list:
+        """获取文本的embedding向量"""
+        url = "https://api.siliconflow.cn/v1/embeddings"
+        payload = {
+            "model": "BAAI/bge-m3",
+            "input": text,
+            "encoding_format": "float"
+        }
+        headers = {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json"
+        }
+        
+        response = requests.post(url, json=payload, headers=headers)
+        if response.status_code != 200:
+            print(f"获取embedding失败: {response.text}")
+            return None
+            
+        return response.json()['data'][0]['embedding']
+        
+    def process_files(self, knowledge_length:int=512):
+        """处理raw_info目录下的所有txt文件"""
+        txt_files = [f for f in os.listdir(self.raw_info_dir) if f.endswith('.txt')]
+        
+        if not txt_files:
+            self.console.print("[red]警告：在 {} 目录下没有找到任何txt文件[/red]".format(self.raw_info_dir))
+            self.console.print("[yellow]请将需要处理的文本文件放入该目录后再运行程序[/yellow]")
+            return
+            
+        total_stats = {
+            "processed_files": 0,
+            "total_chunks": 0,
+            "failed_files": [],
+            "skipped_files": []
+        }
+        
+        self.console.print(f"\n[bold blue]开始处理知识库文件 - 共{len(txt_files)}个文件[/bold blue]")
+        
+        for filename in tqdm(txt_files, desc="处理文件进度"):
+            file_path = os.path.join(self.raw_info_dir, filename)
+            result = self.process_single_file(file_path, knowledge_length)
+            self._update_stats(total_stats, result, filename)
+            
+        self._display_processing_results(total_stats)
+                
+    def process_single_file(self, file_path: str, knowledge_length: int = 512):
+        """处理单个文件"""
+        result = {
+            "status": "success",
+            "chunks_processed": 0,
+            "error": None
+        }
+        
+        try:
+            current_hash = self.calculate_file_hash(file_path)
+            processed_record = self.db.db.processed_files.find_one({"file_path": file_path})
+            
+            if processed_record:
+                if processed_record.get("hash") == current_hash:
+                    if knowledge_length in processed_record.get("split_by", []):
+                        result["status"] = "skipped"
+                        return result
+            
+            content = self.read_file(file_path)
+            chunks = self.split_content(content, knowledge_length)
+            
+            for chunk in tqdm(chunks, desc=f"处理 {os.path.basename(file_path)} 的文本块", leave=False):
+                embedding = self.get_embedding(chunk)
+                if embedding:
+                    knowledge = {
+                        "content": chunk,
+                        "embedding": embedding,
+                        "source_file": file_path,
+                        "split_length": knowledge_length,
+                        "created_at": datetime.now()
+                    }
+                    self.db.db.knowledges.insert_one(knowledge)
+                    result["chunks_processed"] += 1
+            
+            split_by = processed_record.get("split_by", []) if processed_record else []
+            if knowledge_length not in split_by:
+                split_by.append(knowledge_length)
+            
+            self.db.db.processed_files.update_one(
+                {"file_path": file_path},
+                {
+                    "$set": {
+                        "hash": current_hash,
+                        "last_processed": datetime.now(),
+                        "split_by": split_by
+                    }
+                },
+                upsert=True
+            )
+            
+        except Exception as e:
+            result["status"] = "failed"
+            result["error"] = str(e)
+            
+        return result
+        
+    def _update_stats(self, total_stats, result, filename):
+        """更新总体统计信息"""
+        if result["status"] == "success":
+            total_stats["processed_files"] += 1
+            total_stats["total_chunks"] += result["chunks_processed"]
+        elif result["status"] == "failed":
+            total_stats["failed_files"].append((filename, result["error"]))
+        elif result["status"] == "skipped":
+            total_stats["skipped_files"].append(filename)
+            
+    def _display_processing_results(self, stats):
+        """显示处理结果统计"""
+        self.console.print("\n[bold green]处理完成！统计信息如下：[/bold green]")
+        
+        table = Table(show_header=True, header_style="bold magenta")
+        table.add_column("统计项", style="dim")
+        table.add_column("数值")
+        
+        table.add_row("成功处理文件数", str(stats["processed_files"]))
+        table.add_row("处理的知识块总数", str(stats["total_chunks"]))
+        table.add_row("跳过的文件数", str(len(stats["skipped_files"])))
+        table.add_row("失败的文件数", str(len(stats["failed_files"])))
+        
+        self.console.print(table)
+        
+        if stats["failed_files"]:
+            self.console.print("\n[bold red]处理失败的文件：[/bold red]")
+            for filename, error in stats["failed_files"]:
+                self.console.print(f"[red]- {filename}: {error}[/red]")
+                
+        if stats["skipped_files"]:
+            self.console.print("\n[bold yellow]跳过的文件（已处理）：[/bold yellow]")
+            for filename in stats["skipped_files"]:
+                self.console.print(f"[yellow]- {filename}[/yellow]")
+            
+    def calculate_file_hash(self, file_path):
+        """计算文件的MD5哈希值"""
+        hash_md5 = hashlib.md5()
+        with open(file_path, "rb") as f:
+            for chunk in iter(lambda: f.read(4096), b""):
+                hash_md5.update(chunk)
+        return hash_md5.hexdigest()
+
+    def search_similar_segments(self, query: str, limit: int = 5) -> list:
+        """搜索与查询文本相似的片段"""
+        query_embedding = self.get_embedding(query)
+        if not query_embedding:
+            return []
+            
+        # 使用余弦相似度计算
+        pipeline = [
+            {
+                "$addFields": {
+                    "dotProduct": {
+                        "$reduce": {
+                            "input": {"$range": [0, {"$size": "$embedding"}]},
+                            "initialValue": 0,
+                            "in": {
+                                "$add": [
+                                    "$$value",
+                                    {"$multiply": [
+                                        {"$arrayElemAt": ["$embedding", "$$this"]},
+                                        {"$arrayElemAt": [query_embedding, "$$this"]}
+                                    ]}
+                                ]
+                            }
+                        }
+                    },
+                    "magnitude1": {
+                        "$sqrt": {
+                            "$reduce": {
+                                "input": "$embedding",
+                                "initialValue": 0,
+                                "in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]}
+                            }
+                        }
+                    },
+                    "magnitude2": {
+                        "$sqrt": {
+                            "$reduce": {
+                                "input": query_embedding,
+                                "initialValue": 0,
+                                "in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]}
+                            }
+                        }
+                    }
+                }
+            },
+            {
+                "$addFields": {
+                    "similarity": {
+                        "$divide": ["$dotProduct", {"$multiply": ["$magnitude1", "$magnitude2"]}]
+                    }
+                }
+            },
+            {"$sort": {"similarity": -1}},
+            {"$limit": limit},
+            {"$project": {"content": 1, "similarity": 1, "file_path": 1}}
+        ]
+        
+        results = list(self.db.db.knowledges.aggregate(pipeline))
+        return results
+
+# 创建单例实例
+knowledge_library = KnowledgeLibrary()
+
+if __name__ == "__main__":
+    console = Console()
+    console.print("[bold green]知识库处理工具[/bold green]")
+    
+    while True:
+        console.print("\n请选择要执行的操作：")
+        console.print("[1] 麦麦开始学习")
+        console.print("[2] 麦麦全部忘光光（仅知识）")
+        console.print("[q] 退出程序")
+        
+        choice = input("\n请输入选项: ").strip()
+        
+        if choice.lower() == 'q':
+            console.print("[yellow]程序退出[/yellow]")
+            sys.exit(0)
+        elif choice == '2':
+            confirm = input("确定要删除所有知识吗？这个操作不可撤销！(y/n): ").strip().lower()
+            if confirm == 'y':
+                knowledge_library.db.db.knowledges.delete_many({})
+                console.print("[green]已清空所有知识！[/green]")
+            continue
+        elif choice == '1':
+            if not os.path.exists(knowledge_library.raw_info_dir):
+                console.print(f"[yellow]创建目录：{knowledge_library.raw_info_dir}[/yellow]")
+                os.makedirs(knowledge_library.raw_info_dir, exist_ok=True)
+            
+            # 询问分割长度
+            while True:
+                try:
+                    length_input = input("请输入知识分割长度（默认512，输入q退出，回车使用默认值）: ").strip()
+                    if length_input.lower() == 'q':
+                        break
+                    if not length_input:  # 如果直接回车，使用默认值
+                        knowledge_length = 512
+                        break
+                    knowledge_length = int(length_input)
+                    if knowledge_length <= 0:
+                        print("分割长度必须大于0，请重新输入")
+                        continue
+                    break
+                except ValueError:
+                    print("请输入有效的数字")
+                    continue
+            
+            if length_input.lower() == 'q':
+                continue
+                
+            # 测试知识库功能
+            print(f"开始处理知识库文件，使用分割长度: {knowledge_length}...")
+            knowledge_library.process_files(knowledge_length=knowledge_length)
+        else:
+            console.print("[red]无效的选项，请重新选择[/red]")
+            continue

麦麦开始学习.bat

@@ -0,0 +1,45 @@
+@echo off
+setlocal enabledelayedexpansion
+chcp 65001
+cd /d %~dp0
+
+echo =====================================
+echo 选择Python环境:
+echo 1 - venv (推荐)
+echo 2 - conda
+echo =====================================
+choice /c 12 /n /m "输入数字(1或2): "
+
+if errorlevel 2 (
+    echo =====================================
+    set "CONDA_ENV="
+    set /p CONDA_ENV="请输入要激活的 conda 环境名称: "
+    
+    :: 检查输入是否为空
+    if "!CONDA_ENV!"=="" (
+        echo 错误：环境名称不能为空
+        pause
+        exit /b 1
+    )
+    
+    call conda activate !CONDA_ENV!
+    if errorlevel 1 (
+        echo 激活 conda 环境失败
+        pause
+        exit /b 1
+    )
+    
+    echo Conda 环境 "!CONDA_ENV!" 激活成功
+    python src/plugins/zhishi/knowledge_library.py
+) else (
+    if exist "venv\Scripts\python.exe" (
+        venv\Scripts\python src/plugins/zhishi/knowledge_library.py
+    ) else (
+        echo =====================================
+        echo 错误: venv环境不存在，请先创建虚拟环境
+        pause
+        exit /b 1
+    )
+)
+endlocal
+pause