diff --git a/backend/config.py b/backend/config.py
index 364f498e3..81582ab51 100644
--- a/backend/config.py
+++ b/backend/config.py
@@ -397,9 +397,10 @@ CHROMA_DATA_PATH = f"{DATA_DIR}/vector_db"
 # this uses the model defined in the Dockerfile ENV variable. If you dont use docker or docker based deployments such as k8s, the default embedding model will be used (all-MiniLM-L6-v2)
 RAG_EMBEDDING_MODEL = os.environ.get("RAG_EMBEDDING_MODEL", "all-MiniLM-L6-v2")
 log.info(f"Embedding model set: {RAG_EMBEDDING_MODEL}"),
-RAG_EMBEDDING_MODEL_AUTO_UPDATE = False
-if os.environ.get("RAG_EMBEDDING_MODEL_AUTO_UPDATE", "").lower() == "true":
-    RAG_EMBEDDING_MODEL_AUTO_UPDATE = True
+
+RAG_EMBEDDING_MODEL_AUTO_UPDATE = (
+    os.environ.get("RAG_EMBEDDING_MODEL_AUTO_UPDATE", "").lower() == "true"
+)
 
 
 # device type ebbeding models - "cpu" (default), "cuda" (nvidia gpu required) or "mps" (apple silicon) - choosing this right can lead to better performance