# from faiss import IndexFlatL2,write_index,read_index
# import numpy as np
# from utils.convert_embedding import GetEmbedding


# class VectorStore:
#     def __init__(self):
#         pass

#     def store_vectors(self,data:list,embedding_space_name:str = 'faiss_index.index',numpy_emb_space:str = 'embeddings.npy' ):
#         try:
#             embeddings = GetEmbedding(data=data).convert_emb()
#             diamension = embeddings.shape[1]
#             print("Diamension",diamension)
#             # Create L2 distance index
#             index = IndexFlatL2(diamension)

#             index.add(embeddings)

#             write_index(index, embedding_space_name)

#             # Save embeddings to file
#             np.save(numpy_emb_space, embeddings)
#             return True
#         except Exception as e:
#             print(e)
#             return False

#     def get_similary_search(self,query,embedding_space_name:str = 'faiss_index.index',numpy_emb_space:str = 'embeddings.npy',K:int= 1):
#         # Load the FAISS index
#         index = read_index('faiss_index.index')

#         # Load the embeddings
#         embeddings_np = np.load('embeddings.npy')

#         # Now you can perform similarity searches on the index
#         query = "What is photosynthesis?"
#         query_embedding = GetEmbedding([query]).convert_emb()
#         query_embedding = query_embedding.detach().numpy()
#         # query_embedding = np.array(query_embedding)  # Convert to numpy array
#         # query_embedding = query_embedding.reshape(1, -1)
#         # print("shape")
#         # print(query_embedding.shape)
#         # Perform search
#         distances, indices = index.search(query_embedding, k = K)

#         return indices