import faiss
from usearch.index import Index

import numpy as np

# Set the seed for reproducibility
np.random.seed(42)

# Generate 500 random vectors of length 1024 with values between 0 and 1
vectors = np.random.rand(500, 1024)
vector=np.random.rand(1024)

%%timeit
# FAISS

indexf = faiss.IndexFlatL2(vectors.shape[-1])
indexf.add(np.array(vectors).astype(np.float32))
D, I = indexf.search(np.array([vector]), 50)
# 1.14 ms ± 4.34 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)


%%timeit

index = Index(
    ndim=vectors.shape[-1], # Define the number of dimensions in input vectors
    metric='l2_sq', # Choose 'cos', 'l2sq', 'haversine' or other metric, default = 'ip'
  
   )
index.add(labels= np.arange(len(vectors)), vectors=vectors)
matches, distances, count = index.search(vector, 50, exact=True)
  
# 94.7 ms ± 20.9 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)