v0.4.1

修复了数据库命名问题
修复了嵌入模型未定义问题

.env

@@ -1,26 +1,2 @@
 # 您不应该修改默认值，这个文件被仓库索引，请修改.env.prod
-ENVIRONMENT=prod
+ENVIRONMENT=.dev
-# HOST=127.0.0.1
-# PORT=8080
-
-# 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

.env.prod

@@ -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=

bot.py

@@ -15,25 +15,22 @@ for i, char in enumerate(text):
 print(rainbow_text)
 '''彩蛋'''
 
-# 首先加载基础环境变量
+# 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):
+if os.path.exists(".env.dev"):
     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
+    load_dotenv(".env.dev", override=True)  # override=True 允许覆盖已存在的环境变量
 elif os.path.exists(".env.prod"):
     logger.success("加载环境变量配置")
     load_dotenv(".env.prod", override=True)  # override=True 允许覆盖已存在的环境变量
 else:
-    logger.error(f"{env}对应的环境配置文件{env_file}不存在,请修改.env文件中的ENVIRONMENT变量为 prod.")
+    logger.error(f".env对应的环境配置文件不存在,请修改.env文件中的ENVIRONMENT变量为 prod.")
     exit(1)
 
 # 获取所有环境变量

src/plugins/chat/__init__.py

@@ -17,12 +17,12 @@ driver = get_driver()
 config = driver.config
 
 Database.initialize(
-        host= config.mongodb_host,
+        host= config.MONGODB_HOST,
-        port= int(config.mongodb_port),
+        port= int(config.MONGODB_PORT),
-        db_name= config.database_name,
+        db_name= config.DATABASE_NAME,
-        username= config.mongodb_username,
+        username= config.MONGODB_USERNAME,
-        password= config.mongodb_password,
+        password= config.MONGODB_PASSWORD,
-        auth_source= config.mongodb_auth_source
+        auth_source= config.MONGODB_AUTH_SOURCE
 )
 print("\033[1;32m[初始化数据库完成]\033[0m")
 

src/plugins/chat/config.py

@@ -117,6 +117,9 @@ class BotConfig:
                 if "vlm" in model_config:
                     config.vlm = model_config["vlm"]
                     
+                if "embedding" in model_config:
+                    config.embedding = model_config["embedding"]
+                
             # 消息配置
             if "message" in toml_dict:
                 msg_config = toml_dict["message"]
@@ -152,31 +155,13 @@ 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()
     pass

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
@@ -64,25 +65,9 @@ def is_mentioned_bot_in_txt(message: str) -> bool:
     return False
 
 def get_embedding(text):
-    url = "https://api.siliconflow.cn/v1/embeddings"
+    """获取文本的embedding向量"""
-    payload = {
+    llm = LLM_request(model=global_config.embedding)
-        "model": "BAAI/bge-m3",
+    return llm.get_embedding_sync(text)
-        "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']
 
 def cosine_similarity(v1, v2):
     dot_product = np.dot(v1, v2)

src/plugins/knowledege/knowledge_library.py

@@ -17,12 +17,12 @@ from src.plugins.chat.config import llm_config
 
 # 直接配置数据库连接信息
 Database.initialize(
-    host= config.mongodb_host,
+    host= config.MONGODB_HOST,
-    port= int(config.mongodb_port),
+    port= int(config.MONGODB_PORT),
-    db_name=  config.database_name,
+    db_name=  config.DATABASE_NAME,
-    username= config.mongodb_username,
+    username= config.MONGODB_USERNAME,
-    password= config.mongodb_password,
+    password= config.MONGODB_PASSWORD,
-    auth_source=config.mongodb_auth_source
+    auth_source=config.MONGODB_AUTH_SOURCE
 )
 
 class KnowledgeLibrary:

src/plugins/memory_system/llm_module_memory_make.py

@@ -66,7 +66,7 @@ class LLMModel:
             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)}")
+                    print(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:
                     return f"请求失败: {str(e)}", ""

src/plugins/memory_system/memory.py

@@ -259,12 +259,12 @@ config = driver.config
 start_time = time.time()
 
 Database.initialize(
-    host= config.mongodb_host,
+    host= config.MONGODB_HOST,
-    port= int(config.mongodb_port),
+    port= config.MONGODB_PORT,
-    db_name=  config.database_name,
+    db_name=  config.DATABASE_NAME,
-    username= config.mongodb_username,
+    username= config.MONGODB_USERNAME,
-    password= config.mongodb_password,
+    password= config.MONGODB_PASSWORD,
-    auth_source=config.mongodb_auth_source
+    auth_source=config.MONGODB_AUTH_SOURCE
 )
 #创建记忆图
 memory_graph = Memory_graph()

src/plugins/models/utils_model.py

@@ -9,7 +9,7 @@ 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"])
@@ -61,7 +61,7 @@ class LLM_request:
             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)}")
+                    print(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:
                     return f"请求失败: {str(e)}", ""
@@ -126,7 +126,7 @@ class LLM_request:
             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)}")
+                    print(f"[image回复]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)}", ""
@@ -191,9 +191,119 @@ class LLM_request:
             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)}")
+                    print(f"[image_sync回复]请求失败，等待{wait_time}秒后重试... 错误: {str(e)}")
                     time.sleep(wait_time)
                 else:
                     return f"请求失败: {str(e)}", ""
         
         return "达到最大重试次数，请求仍然失败", ""
+
+    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
+        
+        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"
+        
+        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)
+                    print(f"遇到请求限制(429)，等待{wait_time}秒后重试...")
+                    time.sleep(wait_time)
+                    continue
+                    
+                response.raise_for_status()
+                
+                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)
+                    print(f"[embedding_sync]请求失败，等待{wait_time}秒后重试... 错误: {str(e)}")
+                    time.sleep(wait_time)
+                else:
+                    print(f"embedding请求失败: {str(e)}")
+                    return None
+        
+        print("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"
+        
+        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)
+                            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()
+                        
+                        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)
+                    print(f"[embedding]请求失败，等待{wait_time}秒后重试... 错误: {str(e)}")
+                    await asyncio.sleep(wait_time)
+                else:
+                    print(f"embedding请求失败: {str(e)}")
+                    return None
+        
+        print("达到最大重试次数，embedding请求仍然失败")
+        return None

src/plugins/schedule/schedule_generator.py

@@ -11,12 +11,12 @@ config = driver.config
 
 
 Database.initialize(
-            host= config.mongodb_host,
+            host= config.MONGODB_HOST,
-            port= int(config.mongodb_port),
+            port= int(config.MONGODB_PORT),
-            db_name=  config.database_name,
+            db_name=  config.DATABASE_NAME,
-            username= config.mongodb_username,
+            username= config.MONGODB_USERNAME,
-            password= config.mongodb_password,
+            password= config.MONGODB_PASSWORD,
-            auth_source=config.mongodb_auth_source
+            auth_source=config.MONGODB_AUTH_SOURCE
         )
 
 class ScheduleGenerator: