把embedding api移入公式库

bionicmemory/core/chroma_service.py

@@ -15,7 +15,7 @@ from bionicmemory.utils.logging_config import get_logger
 logger = get_logger(__name__)
 
 # 在文件顶部添加导入
-from bionicmemory.services.api_embedding_service import get_embedding_service
+from utils.api_embedding_service import get_embedding_service
 
 
 class ChromaService:

bionicmemory/core/memory_system.py

@@ -17,7 +17,7 @@ from bionicmemory.algorithms.newton_cooling_helper import NewtonCoolingHelper, C
 from bionicmemory.core.chroma_service import ChromaService
 from bionicmemory.services.summary_service import SummaryService
 from bionicmemory.algorithms.clustering_suppression import ClusteringSuppression
-from bionicmemory.services.api_embedding_service import get_embedding_service
+from utils.api_embedding_service import get_embedding_service
 
 # 使用统一日志配置
 from bionicmemory.utils.logging_config import get_logger
@@ -1485,4 +1485,4 @@ if __name__ == "__main__":
        stats_after['short_term_memory']['total_records'] == 0:
         logger.info(f"✅ 用户 {target_user_id} (key: {target_key[:10]}...) 历史记录清除成功！")
     else:
-        logger.warning(f"⚠️ 用户 {target_user_id} (key: {target_key[:10]}...) 历史记录可能未完全清除")
+        logger.warning(f"⚠️ 用户 {target_user_id} (key: {target_key[:10]}...) 历史记录可能未完全清除")

bionicmemory/services/api_embedding_service.py

@@ -1,228 +0,0 @@
-import logging
-import re
-import requests
-from typing import List, Optional
-import threading
-import os
-import sys
-
-# 添加项目根目录到路径
-project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '../..'))
-if project_root not in sys.path:
-    sys.path.insert(0, project_root)
-
-try:
-    import utils.config_util as cfg
-    CONFIG_UTIL_AVAILABLE = True
-except ImportError as e:
-    CONFIG_UTIL_AVAILABLE = False
-    cfg = None
-
-# 使用统一日志配置
-from bionicmemory.utils.logging_config import get_logger
-logger = get_logger(__name__)
-
-if not CONFIG_UTIL_AVAILABLE:
-    logger.warning("无法导入 config_util，将使用环境变量配置")
-
-def _sanitize_text(text: str) -> str:
-    if not isinstance(text, str) or not text:
-        return text
-    cleaned = re.sub(r'<think>[\s\S]*?</think>', '', text, flags=re.IGNORECASE)
-    cleaned = re.sub(r'</?think>', '', cleaned, flags=re.IGNORECASE)
-    return cleaned
-
-class ApiEmbeddingService:
-    """API Embedding服务 - 单例模式，调用 OpenAI 兼容的 API"""
-
-    _instance = None
-    _lock = threading.Lock()
-    _initialized = False
-
-    def __new__(cls):
-        if cls._instance is None:
-            with cls._lock:
-                if cls._instance is None:
-                    cls._instance = super().__new__(cls)
-        return cls._instance
-
-    def __init__(self):
-        if not self._initialized:
-            with self._lock:
-                if not self._initialized:
-                    self._initialize_config()
-                    ApiEmbeddingService._initialized = True
-
-    def _initialize_config(self):
-        """初始化配置，只执行一次"""
-        try:
-            # 优先从 system.conf 读取配置
-            api_base_url = None
-            api_key = None
-            model_name = None
-
-            if CONFIG_UTIL_AVAILABLE and cfg:
-                try:
-                    # 确保配置已加载
-                    if cfg.config is None:
-                        cfg.load_config()
-
-                    # 从 config_util 获取配置（自动复用 LLM 配置）
-                    api_base_url = cfg.embedding_api_base_url
-                    api_key = cfg.embedding_api_key
-                    model_name = cfg.embedding_api_model
-
-                    logger.info(f"从 system.conf 读取配置:")
-                    logger.info(f"  - embedding_api_model: {model_name}")
-                    logger.info(f"  - embedding_api_base_url: {api_base_url}")
-                    logger.info(f"  - embedding_api_key: {'已配置' if api_key else '未配置'}")
-                except Exception as e:
-                    logger.warning(f"从 system.conf 读取配置失败: {e}")
-
-            # 验证必需配置并提供更好的错误提示
-            if not api_base_url:
-                api_base_url = os.getenv('EMBEDDING_API_BASE_URL')
-                if not api_base_url:
-                    error_msg = ("未配置 embedding_api_base_url！\n"
-                                "请确保 system.conf 中配置了 gpt_base_url，"
-                                "或设置环境变量 EMBEDDING_API_BASE_URL")
-                    logger.error(error_msg)
-                    raise ValueError(error_msg)
-                logger.warning(f"使用环境变量配置: base_url={api_base_url}")
-
-            if not api_key:
-                api_key = os.getenv('EMBEDDING_API_KEY')
-                if not api_key:
-                    error_msg = ("未配置 embedding_api_key！\n"
-                                "请确保 system.conf 中配置了 gpt_api_key，"
-                                "或设置环境变量 EMBEDDING_API_KEY")
-                    logger.error(error_msg)
-                    raise ValueError(error_msg)
-                logger.warning("使用环境变量配置: api_key")
-
-            if not model_name:
-                model_name = os.getenv('EMBEDDING_API_MODEL', 'text-embedding-ada-002')
-                logger.warning(f"未配置 embedding_api_model，使用默认值: {model_name}")
-
-            # 保存配置信息
-            self.api_base_url = api_base_url.rstrip('/')  # 移除末尾的斜杠
-            self.api_key = api_key
-            self.model_name = model_name
-            self.embedding_dim = None  # 将在首次调用时动态获取
-            self.timeout = 60  # API 请求超时时间（秒），默认 60 秒
-            self.max_retries = 2  # 最大重试次数
-
-            logger.info(f"API Embedding 服务初始化完成")
-            logger.info(f"模型: {self.model_name}")
-            logger.info(f"API 地址: {self.api_base_url}")
-
-        except Exception as e:
-            logger.error(f"API Embedding 服务初始化失败: {e}")
-            raise
-
-    def encode_text(self, text: str) -> List[float]:
-        """编码单个文本（带重试机制）"""
-        import time
-
-        text = _sanitize_text(text)
-        last_error = None
-        for attempt in range(self.max_retries + 1):
-            try:
-                # 调用 API 进行编码
-                url = f"{self.api_base_url}/embeddings"
-                headers = {
-                    "Content-Type": "application/json",
-                    "Authorization": f"Bearer {self.api_key}"
-                }
-                payload = {
-                    "model": self.model_name,
-                    "input": text
-                }
-
-                # 记录请求信息
-                text_preview = text[:50] + "..." if len(text) > 50 else text
-                logger.info(f"发送 embedding 请求 (尝试 {attempt + 1}/{self.max_retries + 1}): 文本长度={len(text)}, 预览='{text_preview}'")
-
-                response = requests.post(url, json=payload, headers=headers, timeout=self.timeout)
-                response.raise_for_status()
-
-                result = response.json()
-                embedding = result['data'][0]['embedding']
-
-                # 首次调用时获取实际维度
-                if self.embedding_dim is None:
-                    self.embedding_dim = len(embedding)
-                    logger.info(f"动态获取 embedding 维度: {self.embedding_dim},与原记忆节点不一致，将重新生成记忆节点的 embedding维度")
-
-                logger.info(f"embedding 生成成功")
-                return embedding
-
-            except requests.exceptions.Timeout as e:
-                last_error = e
-                logger.warning(f"请求超时 (尝试 {attempt + 1}/{self.max_retries + 1}): {e}")
-                if attempt < self.max_retries:
-                    wait_time = 2 ** attempt  # 指数退避: 1s, 2s, 4s
-                    logger.info(f"等待 {wait_time} 秒后重试...")
-                    time.sleep(wait_time)
-                else:
-                    logger.error(f"所有重试均失败，文本长度: {len(text)}")
-                    raise
-
-            except Exception as e:
-                last_error = e
-                logger.error(f"文本编码失败 (尝试 {attempt + 1}/{self.max_retries + 1}): {e}")
-                if attempt < self.max_retries:
-                    wait_time = 2 ** attempt
-                    logger.info(f"等待 {wait_time} 秒后重试...")
-                    time.sleep(wait_time)
-                else:
-                    raise
-
-    def encode_texts(self, texts: List[str]) -> List[List[float]]:
-        """批量编码文本"""
-        try:
-            texts = [_sanitize_text(text) for text in texts]
-            # 调用 API 进行批量编码
-            url = f"{self.api_base_url}/embeddings"
-            headers = {
-                "Content-Type": "application/json",
-                "Authorization": f"Bearer {self.api_key}"
-            }
-            payload = {
-                "model": self.model_name,
-                "input": texts
-            }
-
-            # 批量请求使用更长的超时时间
-            batch_timeout = self.timeout * 2  # 批量请求超时时间加倍
-            logger.info(f"发送批量 embedding 请求: 文本数={len(texts)}, 超时={batch_timeout}秒")
-            response = requests.post(url, json=payload, headers=headers, timeout=batch_timeout)
-            response.raise_for_status()
-
-            result = response.json()
-            embeddings = [item['embedding'] for item in result['data']]
-
-            return embeddings
-        except Exception as e:
-            logger.error(f"批量文本编码失败: {e}")
-            raise
-
-    def get_model_info(self) -> dict:
-        """获取模型信息"""
-        return {
-            "model_name": self.model_name,
-            "embedding_dim": self.embedding_dim,
-            "api_base_url": self.api_base_url,
-            "initialized": self._initialized,
-            "service_type": "api"
-        }
-
-# 全局实例
-_global_embedding_service = None
-
-def get_embedding_service() -> ApiEmbeddingService:
-    """获取全局embedding服务实例"""
-    global _global_embedding_service
-    if _global_embedding_service is None:
-        _global_embedding_service = ApiEmbeddingService()
-    return _global_embedding_service

bionicmemory/services/local_embedding_service.py

@@ -1,199 +1,199 @@
-import logging
-import numpy as np
-from typing import List, Optional
-from sentence_transformers import SentenceTransformer
-import torch
-import hashlib
-import threading
-import os
-import sys
-from dotenv import load_dotenv,find_dotenv
-
-# 设置离线模式，避免访问Hugging Face
-os.environ['TRANSFORMERS_OFFLINE'] = '1'
-os.environ['HF_HUB_OFFLINE'] = '1'
-os.environ['HF_DATASETS_OFFLINE'] = '1'
-
-# 设置国内 Hugging Face 镜像站点（作为备用）
-os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
-
-# 加载环境变量
-load_dotenv()
-
-# 导入配置工具
-# 添加项目根目录到路径
-project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '../..'))
-if project_root not in sys.path:
-    sys.path.insert(0, project_root)
-
-try:
-    import utils.config_util as cfg
-    CONFIG_UTIL_AVAILABLE = True
-except ImportError as e:
-    CONFIG_UTIL_AVAILABLE = False
-    cfg = None
-
-# 使用统一日志配置
-from bionicmemory.utils.logging_config import get_logger
-logger = get_logger(__name__)
-
-if not CONFIG_UTIL_AVAILABLE:
-    logger.warning("无法导入 config_util，将使用 .env 配置")
-
-class LocalEmbeddingService:
-    """本地Embedding服务 - 单例模式，模型驻留内存"""
-    
-    _instance = None
-    _lock = threading.Lock()
-    _initialized = False
-    
-    def __new__(cls):
-        if cls._instance is None:
-            with cls._lock:
-                if cls._instance is None:
-                    cls._instance = super().__new__(cls)
-        return cls._instance
-    
-    def __init__(self):
-        if not self._initialized:
-            with self._lock:
-                if not self._initialized:
-                    self._initialize_model()
-                    LocalEmbeddingService._initialized = True
-    
-    def _initialize_model(self):
-        """初始化模型，只执行一次"""
-        try:
-            # 优先从 system.conf 读取配置
-            user_model_name = None
-            cache_dir_config = None
-
-            if CONFIG_UTIL_AVAILABLE and cfg:
-                try:
-                    # 确保配置已加载
-                    if cfg.config is None:
-                        cfg.load_config()
-
-                    # 从 config_util 获取配置
-                    user_model_name = cfg.embedding_model
-                    cache_dir_config = cfg.embedding_cache_dir
-
-                    if user_model_name:
-                        logger.info(f"从 system.conf 读取配置: embedding_model={user_model_name}")
-                    if cache_dir_config:
-                        logger.info(f"从 system.conf 读取配置: embedding_cache_dir={cache_dir_config}")
-                except Exception as e:
-                    logger.warning(f"从 system.conf 读取配置失败: {e}")
-
-            # 降级到 .env 或默认值
-            if not user_model_name:
-                user_model_name = os.getenv('LOCAL_EMBEDDING_MODEL', 'Qwen/Qwen3-Embedding-0.6B')
-                logger.info(f"使用 .env 或默认配置: embedding_model={user_model_name}")
-
-            if not cache_dir_config:
-                cache_dir_config = os.getenv('LOCAL_EMBEDDING_CACHE_DIR', 'models/embeddings')
-                logger.info(f"使用 .env 或默认配置: embedding_cache_dir={cache_dir_config}")
-
-            # 处理相对路径
-            if not os.path.isabs(cache_dir_config):
-                cache_dir = os.path.join(os.getcwd(), cache_dir_config)
-            else:
-                cache_dir = cache_dir_config
-
-            cache_dir_abs = os.path.abspath(cache_dir)
-
-            # 按规则拼成路径
-            model_path = os.path.join(cache_dir_abs, f"models--{user_model_name.replace('/', '--')}", "snapshots",
-                                    "c54f2e6e80b2d7b7de06f51cec4959f6b3e03418")
-
-            # 转换为绝对路径
-            model_name_abs = os.path.abspath(model_path)
-
-
-            logger.info(f"用户设置的模型名称: {user_model_name}")
-            logger.info(f"按规则拼成的模型路径: {model_path}")
-            logger.info(f"程序实际使用的模型绝对路径: {model_name_abs}")
-            logger.info(f"程序实际使用的缓存绝对路径: {cache_dir_abs}")
-            logger.info(f"模型路径是否存在: {os.path.exists(model_name_abs)}")
-            logger.info(f"缓存路径是否存在: {os.path.exists(cache_dir_abs)}")
-            
-            # 检查路径是否存在，如果不存在则自动下载
-            if not os.path.exists(model_name_abs):
-                logger.info(f"模型路径不存在: {model_name_abs}")
-                logger.info("开始自动下载模型...")
-                
-                # 确保缓存目录存在
-                os.makedirs(cache_dir_abs, exist_ok=True)
-                
-                # 使用 SentenceTransformer 自动下载模型
-                logger.info(f"正在下载模型: {user_model_name}")
-                self.model = SentenceTransformer(user_model_name, cache_folder=cache_dir_abs)
-                logger.info("模型下载完成！")
-            else:
-                logger.info(f"使用本地模型: {model_name_abs}")
-                # 使用绝对路径
-                self.model = SentenceTransformer(model_name_abs, cache_folder=cache_dir_abs)
-            
-            # 设置为评估模式
-            self.model.eval()
-            
-            # 如果支持GPU，使用GPU
-            if torch.cuda.is_available():
-                self.model = self.model.cuda()
-                logger.info("使用GPU加速")
-            else:
-                logger.info("使用CPU")
-            
-            logger.info(f"{model_name_abs}模型加载完成")
-            logger.info(f"模型缓存路径: {cache_dir_abs}")
-            
-            # 保存配置信息
-            self.model_name = user_model_name
-            self.cache_dir = cache_dir
-            
-        except Exception as e:
-            logger.error(f"{model_name_abs}模型加载失败: {e}")
-            raise
-    
-    def encode_text(self, text: str) -> List[float]:
-        """编码单个文本"""
-        try:
-            # 使用驻留的模型进行编码
-            embedding = self.model.encode(text, convert_to_numpy=True)
-            return embedding.tolist()  # 转换为list
-        except Exception as e:
-            logger.error(f"文本编码失败: {e}")
-            raise
-    
-    def encode_texts(self, texts: List[str]) -> List[List[float]]:
-        """批量编码文本"""
-        try:
-            # 使用驻留的模型进行批量编码
-            embeddings = self.model.encode(texts, convert_to_numpy=True)
-            return embeddings.tolist()  # 转换为list
-        except Exception as e:
-            logger.error(f"批量文本编码失败: {e}")
-            raise
-    
-    def get_model_info(self) -> dict:
-        """获取模型信息"""
-        return {
-            "model_name": getattr(self, 'model_name', 'Qwen/Qwen3-Embedding-0.6B'),
-            "embedding_dim": 1024,
-            "device": "cuda" if torch.cuda.is_available() else "cpu",
-            "initialized": self._initialized,
-            "cache_dir": getattr(self, 'cache_dir', os.path.join(os.getcwd(), "ChromaWithForgetting", "models", "embeddings"))
-        }
-
-# 导入 API Embedding 服务
-from bionicmemory.services.api_embedding_service import ApiEmbeddingService
-
-# 全局实例
-_global_embedding_service = None
-
-def get_embedding_service() -> ApiEmbeddingService:
-    """获取全局embedding服务实例（现在返回 API 服务）"""
-    global _global_embedding_service
-    if _global_embedding_service is None:
-        _global_embedding_service = ApiEmbeddingService()
-    return _global_embedding_service
+import logging
+import numpy as np
+from typing import List, Optional
+from sentence_transformers import SentenceTransformer
+import torch
+import hashlib
+import threading
+import os
+import sys
+from dotenv import load_dotenv,find_dotenv
+
+# 设置离线模式，避免访问Hugging Face
+os.environ['TRANSFORMERS_OFFLINE'] = '1'
+os.environ['HF_HUB_OFFLINE'] = '1'
+os.environ['HF_DATASETS_OFFLINE'] = '1'
+
+# 设置国内 Hugging Face 镜像站点（作为备用）
+os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
+
+# 加载环境变量
+load_dotenv()
+
+# 导入配置工具
+# 添加项目根目录到路径
+project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '../..'))
+if project_root not in sys.path:
+    sys.path.insert(0, project_root)
+
+try:
+    import utils.config_util as cfg
+    CONFIG_UTIL_AVAILABLE = True
+except ImportError as e:
+    CONFIG_UTIL_AVAILABLE = False
+    cfg = None
+
+# 使用统一日志配置
+from bionicmemory.utils.logging_config import get_logger
+logger = get_logger(__name__)
+
+if not CONFIG_UTIL_AVAILABLE:
+    logger.warning("无法导入 config_util，将使用 .env 配置")
+
+class LocalEmbeddingService:
+    """本地Embedding服务 - 单例模式，模型驻留内存"""
+    
+    _instance = None
+    _lock = threading.Lock()
+    _initialized = False
+    
+    def __new__(cls):
+        if cls._instance is None:
+            with cls._lock:
+                if cls._instance is None:
+                    cls._instance = super().__new__(cls)
+        return cls._instance
+    
+    def __init__(self):
+        if not self._initialized:
+            with self._lock:
+                if not self._initialized:
+                    self._initialize_model()
+                    LocalEmbeddingService._initialized = True
+    
+    def _initialize_model(self):
+        """初始化模型，只执行一次"""
+        try:
+            # 优先从 system.conf 读取配置
+            user_model_name = None
+            cache_dir_config = None
+
+            if CONFIG_UTIL_AVAILABLE and cfg:
+                try:
+                    # 确保配置已加载
+                    if cfg.config is None:
+                        cfg.load_config()
+
+                    # 从 config_util 获取配置
+                    user_model_name = cfg.embedding_model
+                    cache_dir_config = cfg.embedding_cache_dir
+
+                    if user_model_name:
+                        logger.info(f"从 system.conf 读取配置: embedding_model={user_model_name}")
+                    if cache_dir_config:
+                        logger.info(f"从 system.conf 读取配置: embedding_cache_dir={cache_dir_config}")
+                except Exception as e:
+                    logger.warning(f"从 system.conf 读取配置失败: {e}")
+
+            # 降级到 .env 或默认值
+            if not user_model_name:
+                user_model_name = os.getenv('LOCAL_EMBEDDING_MODEL', 'Qwen/Qwen3-Embedding-0.6B')
+                logger.info(f"使用 .env 或默认配置: embedding_model={user_model_name}")
+
+            if not cache_dir_config:
+                cache_dir_config = os.getenv('LOCAL_EMBEDDING_CACHE_DIR', 'models/embeddings')
+                logger.info(f"使用 .env 或默认配置: embedding_cache_dir={cache_dir_config}")
+
+            # 处理相对路径
+            if not os.path.isabs(cache_dir_config):
+                cache_dir = os.path.join(os.getcwd(), cache_dir_config)
+            else:
+                cache_dir = cache_dir_config
+
+            cache_dir_abs = os.path.abspath(cache_dir)
+
+            # 按规则拼成路径
+            model_path = os.path.join(cache_dir_abs, f"models--{user_model_name.replace('/', '--')}", "snapshots",
+                                    "c54f2e6e80b2d7b7de06f51cec4959f6b3e03418")
+
+            # 转换为绝对路径
+            model_name_abs = os.path.abspath(model_path)
+
+
+            logger.info(f"用户设置的模型名称: {user_model_name}")
+            logger.info(f"按规则拼成的模型路径: {model_path}")
+            logger.info(f"程序实际使用的模型绝对路径: {model_name_abs}")
+            logger.info(f"程序实际使用的缓存绝对路径: {cache_dir_abs}")
+            logger.info(f"模型路径是否存在: {os.path.exists(model_name_abs)}")
+            logger.info(f"缓存路径是否存在: {os.path.exists(cache_dir_abs)}")
+            
+            # 检查路径是否存在，如果不存在则自动下载
+            if not os.path.exists(model_name_abs):
+                logger.info(f"模型路径不存在: {model_name_abs}")
+                logger.info("开始自动下载模型...")
+                
+                # 确保缓存目录存在
+                os.makedirs(cache_dir_abs, exist_ok=True)
+                
+                # 使用 SentenceTransformer 自动下载模型
+                logger.info(f"正在下载模型: {user_model_name}")
+                self.model = SentenceTransformer(user_model_name, cache_folder=cache_dir_abs)
+                logger.info("模型下载完成！")
+            else:
+                logger.info(f"使用本地模型: {model_name_abs}")
+                # 使用绝对路径
+                self.model = SentenceTransformer(model_name_abs, cache_folder=cache_dir_abs)
+            
+            # 设置为评估模式
+            self.model.eval()
+            
+            # 如果支持GPU，使用GPU
+            if torch.cuda.is_available():
+                self.model = self.model.cuda()
+                logger.info("使用GPU加速")
+            else:
+                logger.info("使用CPU")
+            
+            logger.info(f"{model_name_abs}模型加载完成")
+            logger.info(f"模型缓存路径: {cache_dir_abs}")
+            
+            # 保存配置信息
+            self.model_name = user_model_name
+            self.cache_dir = cache_dir
+            
+        except Exception as e:
+            logger.error(f"{model_name_abs}模型加载失败: {e}")
+            raise
+    
+    def encode_text(self, text: str) -> List[float]:
+        """编码单个文本"""
+        try:
+            # 使用驻留的模型进行编码
+            embedding = self.model.encode(text, convert_to_numpy=True)
+            return embedding.tolist()  # 转换为list
+        except Exception as e:
+            logger.error(f"文本编码失败: {e}")
+            raise
+    
+    def encode_texts(self, texts: List[str]) -> List[List[float]]:
+        """批量编码文本"""
+        try:
+            # 使用驻留的模型进行批量编码
+            embeddings = self.model.encode(texts, convert_to_numpy=True)
+            return embeddings.tolist()  # 转换为list
+        except Exception as e:
+            logger.error(f"批量文本编码失败: {e}")
+            raise
+    
+    def get_model_info(self) -> dict:
+        """获取模型信息"""
+        return {
+            "model_name": getattr(self, 'model_name', 'Qwen/Qwen3-Embedding-0.6B'),
+            "embedding_dim": 1024,
+            "device": "cuda" if torch.cuda.is_available() else "cpu",
+            "initialized": self._initialized,
+            "cache_dir": getattr(self, 'cache_dir', os.path.join(os.getcwd(), "ChromaWithForgetting", "models", "embeddings"))
+        }
+
+# 导入 API Embedding 服务
+from utils.api_embedding_service import ApiEmbeddingService
+
+# 全局实例
+_global_embedding_service = None
+
+def get_embedding_service() -> ApiEmbeddingService:
+    """获取全局embedding服务实例（现在返回 API 服务）"""
+    global _global_embedding_service
+    if _global_embedding_service is None:
+        _global_embedding_service = ApiEmbeddingService()
+    return _global_embedding_service