diff --git a/backend/apps/ai_model/embedding.py b/backend/apps/ai_model/embedding.py
new file mode 100644
index 0000000..315c846
--- /dev/null
+++ b/backend/apps/ai_model/embedding.py
@@ -0,0 +1,63 @@
+import os.path
+import threading
+from typing import Optional
+
+from langchain_core.embeddings import Embeddings
+from langchain_huggingface import HuggingFaceEmbeddings
+from pydantic import BaseModel
+
+from common.core.config import settings
+
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+
+class EmbeddingModelInfo(BaseModel):
+    folder: str
+    name: str
+    device: str = 'cpu'
+
+
+local_embedding_model = EmbeddingModelInfo(folder=settings.LOCAL_MODEL_PATH,
+                                           name=os.path.join(settings.LOCAL_MODEL_PATH, 'embedding',
+                                                             "shibing624_text2vec-base-chinese"))
+
+_lock = threading.Lock()
+locks = {}
+
+_embedding_model: dict[str, Optional[Embeddings]] = {}
+
+
+class EmbeddingModelCache:
+
+    @staticmethod
+    def _new_instance(config: EmbeddingModelInfo = local_embedding_model):
+        return HuggingFaceEmbeddings(model_name=config.name, cache_folder=config.folder,
+                                     model_kwargs={'device': config.device},
+                                     encode_kwargs={'normalize_embeddings': True}
+                                     )
+
+    @staticmethod
+    def _get_lock(key: str = settings.DEFAULT_EMBEDDING_MODEL):
+        lock = locks.get(key)
+        if lock is None:
+            with _lock:
+                lock = locks.get(key)
+                if lock is None:
+                    lock = threading.Lock()
+                    locks[key] = lock
+
+        return lock
+
+    @staticmethod
+    def get_model(key: str = settings.DEFAULT_EMBEDDING_MODEL,
+                  config: EmbeddingModelInfo = local_embedding_model) -> Embeddings:
+        model_instance = _embedding_model.get(key)
+        if model_instance is None:
+            lock = EmbeddingModelCache._get_lock(key)
+            with lock:
+                model_instance = _embedding_model.get(key)
+                if model_instance is None:
+                    model_instance = EmbeddingModelCache._new_instance(config)
+                    _embedding_model[key] = model_instance
+
+        return model_instance