下载：mac或liunx使用
参考：https://blog.csdn.net/weixin_42357472/article/details/109243957
使用参考
https://zhuanlan.zhihu.com/p/269907779
https://blog.csdn.net/zlb872551601/article/details/103704874
注意点
faiss是用的二维ndarray，需要不对的需要reshape
1、再add数据时候
ValueError: too many values to unpack (expected 2)
解决方法就是reshape
np.reshape(np.array(imgs2),(-1,512)) ##512是你向量维度大小
2、search的时候
TypeError: in method ‘IndexFlat_search’, argument 3 of type ‘float const *’
解决方法是把输入转化成float32
a1.reshape(-1,512).astype(‘float32’)
代码
import faiss
import numpy as np
import torch
## 加载torch tensor数据
imgs1 = np.load("/mn***es_embs.npy",allow_pickle=True)
## 转化成array，然后再转成二维
imgs2=[(item / item.norm(dim=-1, keepdim=True)).cpu().numpy() for item in imgs1]
imgs3 = np.reshape(np.array(imgs2),(-1,512))
### 创建faiss索引
dim = 512# 向量维度
k = 10 # 定义召回向量个数
index = faiss.IndexFlatL2(dim) # L2距离，即欧式距离（越小越好）
# index=faiss.IndexFlatIP(dim) # 点乘，归一化的向量点乘即cosine相似度（越大越好）
### 索引里添加数据
index.add(imgs3) # 添加训练时的样本
faiss.write_index(index, '/***/tests1.faiss')
## 查询query，也转成二维
a1 = np.array([-1.6135e-03, 6.1526e-03, 2.5185e-02, 1.4978e-02, 6.2750e-03,
-4.5656e-03, -7.8408e-03, -1.1010e-01, -1.7680e-02, 1.1410e-02,
1.9577e-02, -1.4683e-02, 1.2894e-02, -9.9095e-03, 1.5662e-02,
-9.2513e-03, 3.5585e-02, 8.1605e-03, 1.4446e-02, -1.9918e-02,
1.5668e-02, -1.3067e-02, -6.1856e-03, -6.8425e-03, -1.6277e-02,
-1.5883e-03, 6.0463e-03, -8.4852e-03, 4.3100e-03, 1.3474e-02,
-1.8023e-03, -8.6439e-03, -1.6139e-02, 7.0169e-03, -8.7095e-03,
1.2031e-02, 1.6851e-02, -1.2659e-02, -1.1850e-03, -2.1572e-02,
3.4236e-03, -1.2205e-02, 4.9338e-04, -1.1429e-02, 1.3062e-03,
4.7159e-02, -9.6514e-03, -2.1358e-03, 1.7201e-02, -5.9182e-03,
-1.9796e-03, -2.8365e-03, -1.3234e-02, -2.9237e-02, 1.8063e-02,
7.7699e-03, -6.7131e-04, -5.1597e-04, -3.6758e-02, -3.2200e-03,
4.2009e-03, -1.1084e-02, 6.1378e-03, 5.5161e-03, -1.5331e-02,
-2.4345e-02, 6.9667e-03, 5.2047e-03, 4.0733e-03, -3.5713e-03,
3.1985e-03, -2.2585e-02, 1.4869e-03, 5.5577e-03, -1.1904e-02,
-5.9605e-03, 8.5666e-06, 9.3678e-04, -1.7986e-02, 3.5209e-03,
2.6181e-03, -4.0341e-03, -3.3970e-03, 1.9925e-02, 2.1615e-03,
2.5095e-02, -1.7045e-02, -1.5802e-02, 1.9989e-04, 2.9729e-04,
1.2976e-02, -2.6175e-03, -1.4921e-01, 8.0902e-03, 1.9631e-03,
1.8781e-02, 1.8078e-02, 1.2302e-02, -5.2962e-04, 1.7031e-02,
-5.8983e-03, 4.9108e-03, 7.3916e-03, 1.5125e-03, -2.7008e-02,
8.3710e-03, 1.0673e-03, 3.0579e-03, -1.2527e-02, -6.4894e-03,
6.5960e-03, 5.5670e-02, -3.5310e-03, 2.0328e-04, 1.2250e-02,
-1.0867e-02, 8.9006e-04, 1.3119e-02, -1.9625e-03, 9.7306e-03,
6.5921e-03, -2.3217e-02, -2.8782e-02, 1.3316e-03, 1.8779e-02,
4.0654e-03, 1.4688e-02, 4.4120e-03, 1.7675e-02, 7.8542e-03,
-4.9073e-03, 1.0992e-02, -9.8296e-03, 6.3323e-01, -2.0654e-02,
3.5992e-02, -1.8231e-03, -2.3037e-02, 2.7939e-03, 1.0834e-03,
-2.9149e-02, -1.9654e-02, -2.1427e-03, 1.7366e-02, 5.6344e-03,
-7.1132e-03, -5.0703e-03, 1.2484e-02, -1.6282e-02, 1.3723e-02,
1.9881e-02, -1.1774e-02, 2.7111e-02, -1.3049e-02, -1.6459e-04,
7.6603e-03, 4.0284e-03, 8.9308e-03, -2.3976e-02, 9.5381e-03,
-2.5485e-02, -6.8038e-03, 9.3994e-03, -2.9602e-03, -9.1713e-03,
4.1727e-03, -1.1986e-02, -2.4143e-03, 2.5649e-02, -1.1914e-02,
1.2253e-02, 5.4045e-03, -4.6767e-04, -8.0852e-03, 1.7791e-02,
9.1432e-03, 6.2547e-03, 1.3582e-02, 7.6576e-05, -1.3660e-02,
1.4324e-03, 1.1834e-02, -6.3317e-03, 1.8725e-02, -5.7790e-03,
7.2861e-03, -5.6955e-03, -1.9022e-02, -1.4089e-02, 1.5589e-02,
-2.4922e-03, -1.2894e-02, -3.0975e-04, 1.4137e-02, 1.2805e-02,
-1.0152e-03, 8.2071e-03, 7.5843e-03, -7.0602e-03, -4.5651e-03,
2.3972e-03, 1.0509e-02, 4.9439e-03, -1.1613e-02, -1.0624e-02,
4.7919e-03, -1.4091e-02, -2.3123e-03, -8.9199e-03, -9.8764e-03,
6.8790e-03, 2.5924e-02, -2.0357e-02, -1.2985e-02, -1.6051e-02,
7.6852e-03, 1.5877e-02, -1.1363e-02, 3.9967e-03, 5.9453e-04,
9.1136e-03, 8.8958e-03, 1.2909e-02, -1.3445e-02, -7.0824e-03,
-1.5924e-02, 2.1556e-02, 9.8549e-03, 2.4709e-03, -4.1877e-04,
1.3899e-02, 1.4676e-02, -1.4603e-03, 8.9065e-03, 1.2485e-02,
-1.2827e-04, -4.3024e-03, -1.8105e-02, -6.6219e-03, 9.7098e-03,
1.2541e-02, 1.0112e-02, -1.2859e-02, 8.5914e-04, 8.3273e-03,
1.0884e-02, -1.2448e-02, 2.7684e-02, 7.7927e-03, -4.6589e-04,
-2.1644e-02, 8.4616e-03, -5.2601e-03, -5.0115e-04, -5.1695e-03,
-1.2965e-03, 8.2417e-03, -1.5047e-02, -2.4089e-03, -8.4488e-03,
1.8661e-02, -8.7175e-03, 5.4111e-03, 1.0800e-02, 1.1520e-02,
1.0093e-02, 1.3177e-03, -1.2543e-03, -6.5828e-03, -1.2601e-03,
-1.2253e-02, -4.8685e-03, 1.9724e-02, -1.6586e-02, 9.9319e-03,
-4.9152e-03, -2.7814e-03, 4.8142e-04, -8.2368e-03, -6.8973e-03,
2.3372e-02, 1.5286e-02, -5.5229e-04, -1.2409e-03, 1.4874e-02,
-2.3169e-03, 1.4013e-02, -2.8084e-02, -3.2836e-03, 1.3919e-03,
-5.0962e-03, -1.1291e-02, 3.4799e-03, 2.6452e-02, 5.2030e-03,
-1.8576e-02, -1.6978e-02, -1.3849e-02, -2.7388e-03, 1.2103e-02,
1.3315e-02, 7.6825e-03, -8.8126e-03, 6.0293e-03, -1.5018e-02,
1.0982e-02, 1.4156e-03, -1.1665e-02, -1.9467e-02, 1.7890e-03,
2.4781e-02, 8.0220e-03, 6.3223e-01, -1.5195e-02, -8.7269e-03,
2.0717e-02, 1.2941e-02, -1.1308e-02, 1.7480e-03, 3.8277e-02,
1.5216e-02, 7.4206e-03, 6.3749e-03, 2.6863e-03, -9.5829e-03,
4.0324e-04, -1.5226e-02, -6.5413e-03, -4.6104e-03, -2.6882e-01,
-8.8758e-03, 1.1077e-02, -1.2184e-02, -6.9019e-03, -5.4372e-03,
-1.4190e-03, -1.0375e-02, 1.5531e-02, 1.9881e-03, 8.4441e-03,
1.0767e-02, -1.6926e-02, -5.2742e-03, -7.1148e-04, -9.5662e-03,
2.6844e-03, 1.0767e-02, 2.0270e-02, 1.1346e-02, 6.9011e-03,
2.2358e-02, 9.1316e-03, -2.0739e-03, -7.3354e-03, 2.4359e-02,
2.7133e-02, 4.5612e-03, 8.0810e-03, -2.2221e-03, 3.1086e-02,
-8.4999e-03, -1.0824e-03, 6.3306e-03, 5.5410e-03, -1.1954e-02,
-1.1339e-02, 2.2676e-02, 2.2205e-02, -9.8124e-03, 2.0691e-02,
1.5414e-02, -1.6097e-02, 1.2325e-02, 1.9335e-03, -2.8254e-02,
2.9626e-02, -2.3636e-02, -9.5312e-03, -2.1991e-02, 2.6658e-03,
-2.0500e-02, -4.0802e-03, -5.6075e-03, 1.5054e-02, -2.4843e-03,
1.8199e-02, -1.9825e-02, -8.2771e-03, 2.2264e-02, 1.5191e-02,
-4.6921e-03, -1.5965e-02, 8.4242e-03, 1.7309e-02, 2.7541e-02,
-5.1904e-03, 1.6142e-02, -1.5974e-02, -1.8812e-03, -6.3679e-04,
-8.4971e-03, -3.8564e-03, -2.0782e-02, -5.1108e-04, -4.5805e-03,
-3.2035e-02, 1.9102e-02, 6.1913e-03, 3.0655e-03, 2.9454e-03,
4.2798e-03, -1.6380e-02, -1.2336e-02, -1.2465e-02, -1.0507e-02,
-4.0164e-03, -1.8573e-02, -8.5779e-03, -1.5583e-02, 1.5603e-02,
-1.2849e-02, -1.7189e-02, -9.5142e-03, 6.3270e-03, -1.6096e-02,
8.0913e-03, 6.3659e-03, -1.1888e-02, 9.0989e-03, 9.7781e-03,
-7.4295e-03, -1.2599e-02, -4.6298e-03, -9.7783e-03, -1.7343e-02,
3.0361e-03, -4.4915e-02, 4.1937e-03, 5.8446e-03, 5.7702e-03,
1.2147e-02, 2.5410e-02, 2.7014e-02, 8.7954e-03, 1.4540e-02,
-3.7720e-03, -5.4728e-03, -1.5467e-02, 5.8019e-04, 4.7111e-04,
1.0072e-02, -1.2770e-02, 2.1306e-02, 3.4574e-03, 1.1501e-02,
9.2801e-03, 1.2011e-02, -2.9342e-03, -3.9477e-04, 3.7866e-03,
-5.4005e-03, 1.5335e-02, 1.1379e-02, -4.0515e-03, 3.5547e-04,
2.1120e-03, -6.9303e-03, -7.9791e-03, -2.0967e-02, 7.7576e-03,
1.2412e-02, -2.5669e-03, -5.3622e-03, -7.9149e-03, 9.7494e-03,
4.7953e-03, 1.7233e-03, 9.6419e-03, 4.4269e-03, 1.0419e-02,
-2.5379e-03, -7.3149e-02, 3.8080e-03, -7.7298e-03, -1.4290e-02,
9.8190e-03, 2.7394e-03, 8.3593e-03, 2.2444e-04, -9.2171e-03,
-5.2412e-03, 1.5812e-02, 3.6178e-03, 8.2209e-04, 3.9101e-03,
-1.3362e-02, 2.2686e-02, -6.1439e-03, 5.5911e-03, 3.1641e-03,
-2.4614e-02, -7.6460e-03, 1.8715e-02, -1.5930e-02, -7.7982e-04,
-9.6896e-03, 9.1894e-03, -5.3417e-03, -1.7233e-02, 6.3065e-03,
1.4615e-02, 1.5300e-02])
a2 = a1.reshape(-1,512).astype('float32')
## 搜索最近cosine
D, I = index.search(a2, k) # 寻找相似向量， I表示相似用户ID矩阵， D表示距离矩阵
与直接tensor求结果一致
指定自定义id
参考：https://www.cnblogs.com/houkai/p/9316155.html
https://www.cnblogs.com/yhzhou/p/10568728.html
自定义id：ids为整数字符串不可以
最后结果索引I ：为什么很多0和很大内存值？，间隔，其他加100000能对应上
### 测试自定义id
ids = np.arange(100000, 137843)
index1 = faiss.IndexFlatL2(dim)
index3 = faiss.IndexIDMap(index1)
index3.add_with_ids(imgs3, ids)
index1.add(imgs3)
D, I = index3.search(a1.reshape(-1,512).astype('float32'), 20) # 寻找相似向量， I表示相似用户ID矩阵， D表示距离矩阵
对外serving http接口
参考：https://github.com/scatterlab/faiss-serving
生成faiss 文件
faiss.write_index(index, '/***/tests1.faiss')
docker 运行
docker run -p 8080:8080 -v D:/t**ex/tests1.faiss:/index/tests1.faiss scatterlab/faiss-serving --index-file /index/tests1.faiss
curl 验证
curl localhost:8080/v1/search \
-d '{"queries":[[ 7.3085e-03, -4.6353e-03, -4.4408e-04, -3.3634e-04, -1.4829e-02,
-2.7456e-02, -1.0324e-02, -1.4206e-01, 8.3942e-03, 4.9810e-03,
1.2072e-02, -3.7897e-03, -2.8862e-03, 3.2950e-03, -2.6619e-03,
-4.8818e-03, 2.2135e-02, -3.2677e-04, -1.5755e-02, -2.0045e-02,
9.9448e-03, -3.3146e-03, 1.1625e-02, 1.8703e-02, -6.4526e-03,
1.0457e-03, -5.9497e-03, -9.7896e-03, 1.9700e-03, -6.4454e-03,
-2.5071e-02, -1.6847e-02, 2.8318e-03, 9.6513e-03, -1.1618e-02,
1.0626e-02, 1.2928e-02, 2.1819e-03, -1.3969e-02, 1.3454e-02,
-4.5545e-03, 5.0375e-03, 3.2696e-03, -1.0707e-02, -4.0825e-03,
3.2296e-02, 1.2704e-02, 2.6181e-02, 2.1077e-02, 1.7445e-02,
1.4241e-02, -5.4372e-03, 1.3411e-02, -3.0425e-02, -4.2241e-03,
7.4228e-03, -1.8870e-03, 4.8402e-03, -1.9162e-02, -4.9534e-03,
1.5652e-02, 6.7666e-03, 9.0648e-03, -4.9025e-03, -4.5077e-03,
-1.5952e-02, 5.8742e-03, -2.5665e-02, 1.6273e-02, -3.7294e-04,
5.2161e-03, 6.6520e-03, -2.6140e-02, 8.4355e-03, 3.0880e-05,
-1.7359e-02, 2.8472e-03, -4.2389e-03, -1.4569e-02, -1.7418e-02,
-1.7773e-02, -3.1625e-03, -2.5610e-02, 2.9700e-03, 3.1136e-03,
7.7301e-03, 1.7498e-02, -1.8270e-02, 2.8468e-02, -5.7182e-03,
-1.7443e-03, 8.3888e-03, -1.5365e-01, 9.2144e-03, 7.2510e-03,
1.1288e-02, -5.9218e-03, 2.1798e-02, -1.2795e-02, -1.7796e-02,
1.0269e-02, 9.1577e-03, 9.0181e-03, 1.0456e-02, 2.4344e-03,
4.6265e-03, 5.6966e-02, -1.0212e-03, -1.1346e-02, -1.2175e-02,
-1.1278e-02, 3.0670e-02, -8.5659e-03, 6.4355e-03, -7.5599e-04,
-4.8674e-03, 1.6917e-02, 2.4903e-02, 3.4145e-02, -6.7437e-03,
1.7535e-02, 5.8753e-03, -9.9181e-03, -6.7377e-04, -8.9304e-03,
-5.2707e-03, 7.7407e-03, -5.6767e-03, 1.2428e-03, 2.2054e-02,
3.1943e-02, 1.6942e-02, -8.7350e-03, 6.3862e-01, 1.2686e-02,
3.0839e-02, -1.7029e-03, -8.3889e-03, -1.8593e-03, 6.1738e-03,
1.7674e-02, -5.5610e-03, 5.0874e-03, 7.0389e-04, -1.9809e-02,
1.1335e-02, 6.7522e-03, -1.6445e-03, -1.2855e-02, -1.2721e-02,
9.9355e-03, -3.1388e-02, -1.6257e-02, -1.9840e-02, -1.8985e-02,
1.0873e-02, -1.2014e-02, 9.1951e-03, -6.0484e-03, 1.4649e-02,
5.5614e-03, 1.0983e-02, 8.5162e-03, -7.3627e-03, -5.1069e-03,
-7.0275e-03, 9.7484e-03, -4.7364e-03, 1.5896e-02, 5.2478e-03,
-1.7393e-02, -1.5866e-02, 4.9922e-03, -2.7392e-02, -1.5921e-02,
1.4461e-02, -2.4244e-02, 1.9156e-02, -1.0526e-02, 4.7988e-03,
7.0213e-04, 7.6714e-03, 1.1333e-02, -1.9360e-03, -5.7769e-03,
-1.0216e-02, 1.7380e-02, -1.6150e-03, 1.2054e-02, -4.2982e-03,
-1.3849e-02, -2.9835e-02, -3.5999e-03, 3.4162e-03, -1.6784e-02,
1.2114e-02, 7.4834e-03, 1.4978e-02, 1.2631e-02, -2.5660e-04,
6.4626e-03, -4.5177e-03, -1.0836e-02, -1.0800e-03, -1.3658e-03,
-1.0902e-03, -2.7910e-02, -4.0745e-03, -5.6879e-03, 5.9956e-03,
3.4220e-02, 2.3980e-03, -1.5363e-03, 2.2087e-03, 5.3201e-04,
6.7227e-03, 1.1851e-02, -1.1044e-03, -5.7496e-03, 5.6624e-03,
-3.9450e-02, -2.1106e-02, 1.1475e-02, -1.1815e-02, 8.4447e-03,
1.9530e-02, 1.5334e-02, 1.9508e-02, -7.6016e-03, -3.5144e-03,
1.5208e-02, 2.2831e-03, -4.2248e-03, 1.2131e-02, -3.0158e-03,
2.3440e-02, -8.5046e-03, 1.5710e-03, -8.0973e-03, -9.9754e-03,
1.3329e-02, 2.4785e-03, 4.1647e-03, -1.6831e-03, 6.0369e-03,
1.4145e-03, 8.8724e-03, 2.6906e-02, -1.0865e-02, 4.5856e-03,
-8.5550e-03, 6.8099e-03, -1.4174e-02, 8.7767e-03, 1.5745e-02,
8.6514e-04, 6.5093e-03, -6.9840e-03, 1.8041e-02, -1.0809e-02,
1.2335e-03, 1.5234e-02, 4.0324e-03, 3.0829e-03, -1.7889e-03,
-1.7815e-05, 1.6386e-02, -2.6454e-03, -1.1920e-02, -3.4991e-03,
1.3945e-03, -2.0398e-02, -1.9783e-03, -3.2363e-02, 1.9394e-02,
-5.3849e-04, -1.8205e-02, -1.5242e-03, 1.5408e-02, 5.0961e-03,
2.3142e-02, -1.4586e-02, 1.6434e-02, 9.6912e-03, -1.6438e-03,
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