Python中怎么利用Faiss库实现向量近邻搜索

import pandas as pdimport numpy as npdf = pd.read_csv("./datas/movielens_sparkals_item_embedding.csv")# 提取要使用的电影ID列表，注意要转换成int64ids = df["id"].values.astype(np.int64)# 记录ID列表的大小ids_size = ids.shape[0]# 读取内容embedding数据，转换成二维arrayimport jsonimport numpy as npdatas = []for x in df["features"]:    datas.append(json.loads(x))# 变成二维arraydatas = np.array(datas).astype(np.float32)# 记录数据维度dimension = datas.shape[1]

import faissindex = faiss.IndexFlatL2(dimension)index2 = faiss.IndexIDMap(index)index2.add_with_ids(datas, ids)

# 读取user embedding数据df_user = pd.read_csv("./datas/user_embedding.csv")df_user.head()# 挑选一条user的embedding，转换成1行N列的二维arrayuser_embedding = np.array(json.loads(df_user[df_user["id"] == 10]["features"].iloc[0]))user_embedding = np.expand_dims(user_embedding, axis=0).astype(np.float32)user_embedding# 实现搜索，这里的I就是近邻ID列表tok = 30D, I = index.search(user_embedding, topk)     # actual search

# 把搜索出来的ID变成Seriestarget_ids = pd.Series(I[0], name="MovieID")# 读取电影数据df_movie = pd.read_csv("./datas/ml-1m/movies.dat",                     sep="::", header=None, engine="python",                     names = "MovieID::Title::Genres".split("::"))# 实现内容JOINdf_result = pd.merge(target_ids, df_movie)