pip install usearch

import numpy as np from usearch.index import Index

index = Index(ndim=3) # Default settings for 3D vectors vector = np.array([0.2, 0.6, 0.4]) # Can be a matrix for batch operations index.add(42, vector) # Add one or many vectors in parallel matches = index.search(vector, 10) # Find 10 nearest neighbors

assert matches[0].key == 42 assert matches[0].distance <= 0.001 assert np.allclose(index[42], vector, atol=0.1) # Ensure high tolerance in mixed-precision comparisons

More settings are always available, and the API is designed to be as flexible as possible.
The default storage/quantization level is hardware-dependant for efficiency, but `bf16` is recommended for most modern CPUs.

```py
index = Index(
    ndim=3, # Define the number of dimensions in input vectors
    metric='cos', # Choose 'l2sq', 'ip', 'haversine' or other metric, default = 'cos'
    dtype='bf16', # Store as 'f64', 'f32', 'bf16', 'f16', 'e5m2', 'e4m3', 'e3m2', 'e2m3', 'u8', 'i8', 'b1'..., default = None
    connectivity=16, # Optional: Limit number of neighbors per graph node
    expansion_add=128, # Optional: Control the recall of indexing
    expansion_search=64, # Optional: Control the quality of the search
    multi=False, # Optional: Allow multiple vectors per key, default = False
)