Fix ragflow may encounter an OOM (Out Of Memory) when there are a lot of conversations (#1292)

### What problem does this PR solve?

Fix ragflow may encounter an OOM (Out Of Memory) when there are a lot of
conversations.
#1288

### Type of change

- [ ] Bug Fix (non-breaking change which fixes an issue)

Co-authored-by: zhuhao <zhuhao@linklogis.com>

rag/llm/embedding_model.py

 #
 import re
 from typing import Optional
+import  threading
 import requests
 from huggingface_hub import snapshot_download
 from zhipuai import ZhipuAI
 
 class DefaultEmbedding(Base):
     _model = None
-
+    _model_lock = threading.Lock()
     def __init__(self, key, model_name, **kwargs):
         """
         If you have trouble downloading HuggingFace models, -_^ this might help!!
 
         """
         if not DefaultEmbedding._model:
-            try:
-                self._model = FlagModel(os.path.join(get_home_cache_dir(), re.sub(r"^[a-zA-Z]+/", "", model_name)),
-                                        query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
-                                        use_fp16=torch.cuda.is_available())
-            except Exception as e:
-                model_dir = snapshot_download(repo_id="BAAI/bge-large-zh-v1.5",
-                                                local_dir=os.path.join(get_home_cache_dir(), re.sub(r"^[a-zA-Z]+/", "", model_name)),
-                                                local_dir_use_symlinks=False)
-                self._model = FlagModel(model_dir,
-                                        query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
-                                        use_fp16=torch.cuda.is_available())
+            with DefaultEmbedding._model_lock:
+                if not DefaultEmbedding._model:
+                    try:
+                        DefaultEmbedding._model = FlagModel(os.path.join(get_home_cache_dir(), re.sub(r"^[a-zA-Z]+/", "", model_name)),
+                                                            query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
+                                                            use_fp16=torch.cuda.is_available())
+                    except Exception as e:
+                        model_dir = snapshot_download(repo_id="BAAI/bge-large-zh-v1.5",
+                                                      local_dir=os.path.join(get_home_cache_dir(), re.sub(r"^[a-zA-Z]+/", "", model_name)),
+                                                      local_dir_use_symlinks=False)
+                        DefaultEmbedding._model = FlagModel(model_dir,
+                                                            query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
+                                                            use_fp16=torch.cuda.is_available())
+        self._model = DefaultEmbedding._model
 
     def encode(self, texts: list, batch_size=32):
         texts = [truncate(t, 2048) for t in texts]

rag/llm/rerank_model.py

 #  limitations under the License.
 #
 import re
+import  threading
 import requests
 import torch
 from FlagEmbedding import FlagReranker
 
 class DefaultRerank(Base):
     _model = None
-
+    _model_lock = threading.Lock()
     def __init__(self, key, model_name, **kwargs):
         """
         If you have trouble downloading HuggingFace models, -_^ this might help!!
 
         """
         if not DefaultRerank._model:
-            try:
-                self._model = FlagReranker(os.path.join(get_home_cache_dir(), re.sub(r"^[a-zA-Z]+/", "", model_name)),
-                                           use_fp16=torch.cuda.is_available())
-            except Exception as e:
-                self._model = snapshot_download(repo_id=model_name,
-                                                local_dir=os.path.join(get_home_cache_dir(),
-                                                                       re.sub(r"^[a-zA-Z]+/", "", model_name)),
-                                                local_dir_use_symlinks=False)
-                self._model = FlagReranker(os.path.join(get_home_cache_dir(), model_name),
-                                           use_fp16=torch.cuda.is_available())
+            with DefaultRerank._model_lock:
+                if not DefaultRerank._model:
+                    try:
+                        DefaultRerank._model = FlagReranker(os.path.join(get_home_cache_dir(), re.sub(r"^[a-zA-Z]+/", "", model_name)), use_fp16=torch.cuda.is_available())
+                    except Exception as e:
+                        model_dir = snapshot_download(repo_id= model_name,
+                                                      local_dir=os.path.join(get_home_cache_dir(), re.sub(r"^[a-zA-Z]+/", "", model_name)),
+                                                      local_dir_use_symlinks=False)
+                        DefaultRerank._model = FlagReranker(model_dir, use_fp16=torch.cuda.is_available())
+        self._model = DefaultRerank._model
 
     def similarity(self, query: str, texts: list):
         pairs = [(query,truncate(t, 2048)) for t in texts]