import faiss
import numpy as np

# 数据归一化函数
def normalize_vectors(vectors):
    """对向量进行L2归一化"""
    norms = np.linalg.norm(vectors, axis=1, keepdims=True)
    # 避免除零
    norms = np.where(norms == 0, 1, norms)
    return vectors / norms


data = np.array([[2, 3], [2, 4], [3, 7]], dtype='float32')

# 归一化数据
data_normalized = normalize_vectors(data)
print("原始数据:")
print(data)
print("归一化后数据:")
print(data_normalized)

# 普通索引
# base_index = faiss.IndexFlatL2(2)
base_index = faiss.IndexFlatIP(2)

# 包一层 IDMap
index = faiss.IndexIDMap(base_index)

# 指定 ID
ids = np.array([101, 102, 103])  # 自定义 ID
# index.add_with_ids(data, ids)
index.add_with_ids(data_normalized, ids)

# 查询
query = np.array([[3, 4.5]], dtype='float32')
query_normalized = normalize_vectors(query)
# D, I = index.search(query, k=2)
D, I = index.search(query_normalized, k=2)
print(D)
print(I)  # 可能输出 [[101 102]]