diff --git a/extensions/openai/embeddings.py b/extensions/openai/embeddings.py
index 96f44d91..d6d30721 100644
--- a/extensions/openai/embeddings.py
+++ b/extensions/openai/embeddings.py
@@ -26,23 +26,21 @@ def load_embedding_model(model: str) -> SentenceTransformer:
     initialize_embedding_params()
     global embeddings_device, embeddings_model
     try:
-        embeddings_model = 'loading...'  # flag
+        print(f"\Try embedding model: {model} on {embeddings_device}")
         # see: https://www.sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer
-        emb_model = SentenceTransformer(model, device=embeddings_device)
-        # ... emb_model.device doesn't seem to work, always cpu anyways? but specify cpu anyways to free more VRAM
-        print(f"\nLoaded embedding model: {model} on {emb_model.device} [always seems to say 'cpu', even if 'cuda'], max sequence length: {emb_model.max_seq_length}")
+        embeddings_model = SentenceTransformer(model, device=embeddings_device)
+        # ... embeddings_model.device doesn't seem to work, always cpu anyways? but specify cpu anyways to free more VRAM
+        print(f"\nLoaded embedding model: {model} on {embeddings_model.device} [always seems to say 'cpu', even if 'cuda'], max sequence length: {embeddings_model.max_seq_length}")
     except Exception as e:
         embeddings_model = None
         raise ServiceUnavailableError(f"Error: Failed to load embedding model: {model}", internal_message=repr(e))
 
-    return emb_model
-
 
 def get_embeddings_model() -> SentenceTransformer:
     initialize_embedding_params()
     global embeddings_model, st_model
     if st_model and not embeddings_model:
-        embeddings_model = load_embedding_model(st_model)  # lazy load the model
+        load_embedding_model(st_model)  # lazy load the model
     return embeddings_model
 
 
@@ -53,7 +51,11 @@ def get_embeddings_model_name() -> str:
 
 
 def get_embeddings(input: list) -> np.ndarray:
-    return get_embeddings_model().encode(input, convert_to_numpy=True, normalize_embeddings=True, convert_to_tensor=False, device=embeddings_device)
+    model = get_embeddings_model()
+    debug_msg(f"embedding model : {model}")
+    embedding = model.encode(input, convert_to_numpy=True, normalize_embeddings=True, convert_to_tensor=False)
+    debug_msg(f"embedding result : {embedding}") # might be too long even for debug, use at you own will
+    return embedding
 
 
 def embeddings(input: list, encoding_format: str) -> dict: