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

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