Make OpenAI API the default API (#4430)

extensions/openai/embeddings.py

@@ -6,9 +6,13 @@ from extensions.openai.utils import debug_msg, float_list_to_base64
 from sentence_transformers import SentenceTransformer
 
 embeddings_params_initialized = False
-# using 'lazy loading' to avoid circular import
-# so this function will be executed only once
+
+
 def initialize_embedding_params():
+    '''
+    using 'lazy loading' to avoid circular import
+    so this function will be executed only once
+    '''
     global embeddings_params_initialized
     if not embeddings_params_initialized:
         global st_model, embeddings_model, embeddings_device
@@ -26,7 +30,7 @@ def load_embedding_model(model: str) -> SentenceTransformer:
     initialize_embedding_params()
     global embeddings_device, embeddings_model
     try:
-        print(f"\Try embedding model: {model} on {embeddings_device}")
+        print(f"Try embedding model: {model} on {embeddings_device}")
         # see: https://www.sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer
         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
@@ -54,7 +58,7 @@ def get_embeddings(input: list) -> np.ndarray:
     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
+    debug_msg(f"embedding result : {embedding}")  # might be too long even for debug, use at you own will
     return embedding