Merge branch 'debug' into feature

src/plugins/chat/bot.py

@@ -97,8 +97,13 @@ class ChatBot:
         
         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}")
+        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)
+        print(f"\033[1;32m[主题识别]\033[0m 使用jieba主题: {topic1}")
+        print(f"\033[1;32m[主题识别]\033[0m 使用llm主题: {topic2}")
+        print(f"\033[1;32m[主题识别]\033[0m 使用snownlp主题: {topic3}")
+        topic = topic3
         
         all_num = 0
         interested_num = 0

src/plugins/chat/topic_identifier.py

@@ -4,19 +4,18 @@ 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_normal)
         
-    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。
@@ -24,33 +23,20 @@ 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()]
+        return topic_list if topic_list else None
 
     def identify_topic_jieba(self, text: str) -> Optional[str]:
         """使用jieba识别主题"""
@@ -80,9 +66,12 @@ class TopicIdentifier:
         filtered_words = []
         for word in words:
             if word not in stop_words and not word.strip() in {
-                '。', '，', '、', '：', '；', '！', '？', '"', '"', ''', ''', 
-                '（', '）', '【', '】', '《', '》', '…', '—', '·', '、', '~', 
-                '～', '+', '=', '-','[',']'
+                '。', '，', '、', '：', '；', '！', '？', '"', '"', ''', ''',
+                '（', '）', '【', '】', '《', '》', '…', '—', '·', '、', '~',
+                '～', '+', '=', '-', '/', '\\', '|', '*', '#', '@', '$', '%',
+                '^', '&', '[', ']', '{', '}', '<', '>', '`', '_', '.', ',',
+                ';', ':', '\'', '"', '(', ')', '?', '!', '±', '×', '÷', '≠',
+                '≈', '∈', '∉', '⊆', '⊇', '⊂', '⊃', '∪', '∩', '∧', '∨'
             }:
                 filtered_words.append(word)
         
@@ -97,4 +86,25 @@ class TopicIdentifier:
         
         return top_words if top_words else None
 
-topic_identifier = TopicIdentifier()
+    def identify_topic_snownlp(self, text: str) -> Optional[List[str]]:
+        """使用 SnowNLP 进行主题识别
+        
+        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(3)
+            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()

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 +9,76 @@ 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 LLMModel:
+    def __init__(self, model_name=os.getenv("SILICONFLOW_MODEL_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")
+
+    async def generate_response(self, prompt: str) -> Tuple[str, str]:
+        """根据输入的提示生成模型的响应"""
+        headers = {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json"
+        }
+        
+        # 构建请求体
+        data = {
+            "model": self.model_name,
+            "messages": [{"role": "user", "content": prompt}],
+            "temperature": 0.5,
+            **self.params
+        }
+        
+        # 发送请求到完整的chat/completions端点
+        api_url = f"{self.base_url.rstrip('/')}/chat/completions"
+        
+        max_retries = 3
+        base_wait_time = 15
+        
+        for retry in range(max_retries):
+            try:
+                async with aiohttp.ClientSession() as session:
+                    async with session.post(api_url, headers=headers, json=data) as response:
+                        if response.status == 429:
+                            wait_time = base_wait_time * (2 ** retry)  # 指数退避
+                            print(f"遇到请求限制(429)，等待{wait_time}秒后重试...")
+                            await asyncio.sleep(wait_time)
+                            continue
+                            
+                        response.raise_for_status()  # 检查其他响应状态
+                        
+                        result = await response.json()
+                        if "choices" in result and len(result["choices"]) > 0:
+                            content = result["choices"][0]["message"]["content"]
+                            reasoning_content = result["choices"][0]["message"].get("reasoning_content", "")
+                            return content, reasoning_content
+                        return "没有返回结果", ""
+                
+            except Exception as e:
+                if retry < max_retries - 1:  # 如果还有重试机会
+                    wait_time = base_wait_time * (2 ** retry)
+                    print(f"请求失败，等待{wait_time}秒后重试... 错误: {str(e)}")
+                    await asyncio.sleep(wait_time)
+                else:
+                    return f"请求失败: {str(e)}", ""
+        
+        return "达到最大重试次数，请求仍然失败", ""
+
    
 class Memory_graph:
     def __init__(self):
@@ -158,12 +223,12 @@ 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()
@@ -185,11 +250,14 @@ def main():
         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。")