@@ -89,19 +89,29 @@ def create_index(self, index_name, mm_type, index_prefix):
8989 )
9090 return 'create_success'
9191
92- def mul_add (self , datas : List [VectorData ], model = None , mm_type = None ):
92+ def add (self , datas : List [VectorData ], model = None , mm_type = None ):
93+ # pipe = self._client.pipeline()
9394 for data in datas :
9495 id : int = data .id
9596 embedding = data .data .astype (np .float32 ).tobytes ()
96-
97- # collection_name = get_mm_index_name(model, mm_type)
97+ # obj = {
98+ # "vector": data.data.astype(np.float32).tobytes(),
99+ # }
100+ # collection_name = self.collection_prefix + '_' + model + '_' + self.table_suffix
101+ # collection_name = get_collection_iat_name(model, iat_type, self.table_suffix)
98102 index_prefix = get_mm_index_prefix (model , mm_type )
103+ # print('collection_name: {}'.format(collection_name))
99104
105+ # id_field_name = collection_name + '_' + "id"
106+ # embedding_field_name = collection_name + '_' + "vec"
100107 id_field_name = "data_id"
101108 embedding_field_name = "data_vector"
102109
103110 obj = {id_field_name : id , embedding_field_name : embedding }
111+ # print('obj: {}'.format(obj))
104112 self ._client .hset (f"{ index_prefix } { id } " , mapping = obj )
113+ # pipe.hset(f"{self.doc_prefix}{key}", mapping=obj)
114+ # pipe.execute()
105115
106116 def search (self , data : np .ndarray , top_k : int = - 1 , model = None , mm_type = None ):
107117 index_name = get_mm_index_name (model , mm_type )
0 commit comments