diff --git a/app/chatbot/model.py b/app/chatbot/model.py
index c86c7fe..b8a264a 100644
--- a/app/chatbot/model.py
+++ b/app/chatbot/model.py
@@ -7,35 +7,16 @@
 from app.embeddings.chunk_utils import *
 from app.utils.constants import ENGINE_NAME
 import pinecone
-
-
-# Store the value of the VECTOR_DB environment variable in a variable
-vector_db = os.getenv('VECTOR_DB').upper()
-
-if vector_db == "MILVUS":
+if os.getenv('VECTOR_DB').upper() == "MILVUS":
     from pymilvus import connections, Collection
     import app.utils.vectordb.start_milvus as vector_db
     import app.embeddings.embeddings_utils as model_embedding
 
-if vector_db == "PINECONE":
+if os.getenv('VECTOR_DB').upper() == "PINECONE":
     from sentence_transformers import SentenceTransformer
 
-class LazyModel:
-    def __init__(self, load_model_func):
-        self.load_model_func = load_model_func
-        self._model = None
-
-    @property
-    def model(self):
-        if self._model is None:
-            self._model = self.load_model_func()  # Model is loaded here if it hasn't been loaded already
-        return self._model
-
-    def __call__(self, *args, **kwargs):
-        return self.model(*args, **kwargs)
 
-# This creates a LazyModel instance that will load the model the first time it's used
-llama2_model = LazyModel(load_llama_model)
+llama2_model = load_llama_model()
 
 class TextInput(BaseModel):
     inputs: str
@@ -78,7 +59,7 @@ def get_responses(engine, temperature, token_count, question):
     if token_count == "" or token_count is None:
       token_count = 100
 
-    if vector_db == "MILVUS":
+    if os.getenv('VECTOR_DB').upper() == "MILVUS":
         # Load Milvus Vector DB collection
         vector_db_collection = Collection('cloudera_docs')
         vector_db_collection.load()
@@ -86,11 +67,11 @@ def get_responses(engine, temperature, token_count, question):
     # Phase 1: Get nearest knowledge base chunk for a user question from a vector db
     vdb_question = question
     
-    if vector_db == "MILVUS":
+    if os.getenv('VECTOR_DB').upper() == "MILVUS":
         context_chunk = get_nearest_chunk_from_milvus_vectordb(vector_db_collection, vdb_question)
         vector_db_collection.release()
 
-    if vector_db == "PINECONE":
+    if os.getenv('VECTOR_DB').upper() == "PINECONE":
         PINECONE_API_KEY = os.getenv('PINECONE_API_KEY')
         PINECONE_ENVIRONMENT = os.getenv('PINECONE_ENVIRONMENT')
         PINECONE_INDEX = os.getenv('PINECONE_INDEX')