diff --git a/AgentReact/utils/VectorDatabase.py b/AgentReact/utils/VectorDatabase.py
index c1d9f0e..4e944b2 100644
--- a/AgentReact/utils/VectorDatabase.py
+++ b/AgentReact/utils/VectorDatabase.py
@@ -3,43 +3,21 @@ from langchain_chroma import Chroma # TODO plus tard, ramplacer par PG Vector
 import sys
 from pathlib import Path
 
-# Permet de garder ChromaDB en mémoire.
-# Cette classe est un Singleton, il n'y en aura qu'une seule et unique instance à tout moment
-# https://refactoring.guru/design-patterns/singleton
-class VectorDatabase:
-    instance = None
+base_dir:str = Path(sys.argv[0]).resolve().parent.as_posix() # Récupérer le chemin vers le point d'entrée du programme
+bdd_path:str = base_dir + "/../chroma_db/"
 
-    def __new__(cls): # Selon https://www.geeksforgeeks.org/python/singleton-pattern-in-python-a-complete-guide/
-        if cls.instance is None:
-            cls.instance = super().__new__(cls)
-            # J'initialise les attributs à None ici, permet de tester si la classe a déjà été init une première fois ou non
-            cls.instance.__embeddings = None
-            cls.instance.__chroma = None
-        return cls.instance
-
-    def __init__(self):
-        if self.__embeddings is not None: return
-
-        base_dir:str = Path(sys.argv[0]).resolve().parent.as_posix() # Récupérer le chemin vers le point d'entrée du programme
-        bdd_path:str = base_dir + "/chroma_db/"
-
-        self.__embeddings = HuggingFaceEmbeddings(model_name="jinaai/jina-embeddings-v3", model_kwargs={"trust_remote_code": True})
-        self.__chroma = Chroma(
+EMBEDDINGS = HuggingFaceEmbeddings(model_name="intfloat/multilingual-e5-large", model_kwargs={"trust_remote_code": True})
+CHROMA = Chroma(
             persist_directory=bdd_path,
-            embedding_function=self.__embeddings
+            embedding_function=EMBEDDINGS
         )
 
-    def getChroma(self)->Chroma:
-        return self.__chroma
+class VectorDatabase: # Classe pour récupérer la BDD
 
-    def getEmbeddings(self)->'Embeddings Hugging Face':
-        return self.__embeddings
+    @staticmethod
+    def getChroma()->Chroma:
+        return CHROMA
 
-if __name__ == "__main__":
-
-    test1 = VectorDatabase()
-    print('TEST 1 INIT')
-    test2 = VectorDatabase()
-
-    print(test1 is test2)
-    assert test1 is test2
\ No newline at end of file
+    @staticmethod
+    def getEmbeddings()->'Embeddings Hugging Face':
+        return EMBEDDINGS
\ No newline at end of file
diff --git a/RAG/init.py b/RAG/init.py
index a328e71..40e52c6 100644
--- a/RAG/init.py
+++ b/RAG/init.py
@@ -43,7 +43,7 @@ print("===")
 # Création du modèle d'embeddings 
 # https://docs.langchain.com/oss/python/integrations/text_embedding/huggingfacehub
 # https://huggingface.co/jinaai/jina-clip-v2
-embeddings = HuggingFaceEmbeddings(model_name="jinaai/jina-embeddings-v3", model_kwargs={"trust_remote_code": True})
+embeddings = HuggingFaceEmbeddings(model_name="intfloat/multilingual-e5-large", model_kwargs={"trust_remote_code": True})
 
 # Stockage des embeddings dans ChromaDB dans un dossier local "chroma_db"
 vectorstore = Chroma.from_documents(documents=chunks,embedding=embeddings, persist_directory=base_dir.as_posix()+"/chroma_db/",) # https://docs.langchain.com/oss/python/integrations/vectorstores/chroma