Lint the openai extension

extensions/openai/embeddings.py

@@ -1,8 +1,9 @@
 import os
-from sentence_transformers import SentenceTransformer
+
 import numpy as np
-from extensions.openai.utils import float_list_to_base64, debug_msg
-from extensions.openai.errors import *
+from extensions.openai.errors import ServiceUnavailableError
+from extensions.openai.utils import debug_msg, float_list_to_base64
+from sentence_transformers import SentenceTransformer
 
 st_model = os.environ["OPENEDAI_EMBEDDING_MODEL"] if "OPENEDAI_EMBEDDING_MODEL" in os.environ else "all-mpnet-base-v2"
 embeddings_model = None
@@ -11,10 +12,11 @@ embeddings_device = os.environ.get("OPENEDAI_EMBEDDING_DEVICE", "cpu")
 if embeddings_device.lower() == 'auto':
     embeddings_device = None
 
+
 def load_embedding_model(model: str) -> SentenceTransformer:
     global embeddings_device, embeddings_model
     try:
-        embeddings_model = 'loading...' # flag
+        embeddings_model = 'loading...'  # flag
         # 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
@@ -41,6 +43,7 @@ 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)
 
+
 def embeddings(input: list, encoding_format: str) -> dict:
 
     embeddings = get_embeddings(input)