utils/convert_embedding.py

# from sentence_transformers import SentenceTransformer

# class GetEmbedding:
#     def __init__(self,data:list):
#         self.data = data
#     def user_query_emb(self,model_name:str = 'paraphrase-MiniLM-L6-v2'):
#         try:
#             model = SentenceTransformer(model_name_or_path=model_name)
#             embedding = model.encode(self.data)
#             return embedding
#         except Exception as e:
#             print(e)

#     def convert_data(self,model_name:str = 'paraphrase-MiniLM-L6-v2'):
#         try:
#             model = SentenceTransformer(model_name)
#             embeddings = model.encode(self.data)
#             return embeddings
#         except Exception as e:
#             print(e)

# if __name__ == "__main__":
#     emb = GetEmbedding("lalit")
#     print( emb)