Support async client (#1980)

mem0/embeddings/gemini.py

@@ -1,5 +1,6 @@
 import os
 from typing import Optional
+
 import google.generativeai as genai
 
 from mem0.configs.embeddings.base import BaseEmbedderConfig
@@ -9,7 +10,7 @@ from mem0.embeddings.base import EmbeddingBase
 class GoogleGenAIEmbedding(EmbeddingBase):
     def __init__(self, config: Optional[BaseEmbedderConfig] = None):
         super().__init__(config)
-        
+
         self.config.model = self.config.model or "models/text-embedding-004"
         self.config.embedding_dims = self.config.embedding_dims or 768
 
@@ -27,4 +28,4 @@ class GoogleGenAIEmbedding(EmbeddingBase):
         """
         text = text.replace("\n", " ")
         response = genai.embed_content(model=self.config.model, content=text)
-        return response['embedding']
+        return response["embedding"]

mem0/embeddings/huggingface.py

@@ -14,7 +14,7 @@ class HuggingFaceEmbedding(EmbeddingBase):
 
         self.model = SentenceTransformer(self.config.model, **self.config.model_kwargs)
 
-        self.config.embedding_dims = self.config.embedding_dims  or self.model.get_sentence_embedding_dimension()
+        self.config.embedding_dims = self.config.embedding_dims or self.model.get_sentence_embedding_dimension()
 
     def embed(self, text):
         """
@@ -26,4 +26,4 @@ class HuggingFaceEmbedding(EmbeddingBase):
         Returns:
             list: The embedding vector.
         """
-        return self.model.encode(text, convert_to_numpy = True).tolist()
+        return self.model.encode(text, convert_to_numpy=True).tolist()