Quickstart

import numpy as np
import usearch
from usearch import DenseIndex, DenseMetric, Scalar

index = DenseIndex.init_ram(
    dimensions=256,  # Scalars per vector
    metric=DenseMetric.ANGULAR,  # INNER, ANGULAR, EUCLIDEAN, HAMMING or JACCARD
    quantization=Scalar.BF16,  # The stored scalar type: BF16...E2M3, I8, U8, U1X8
    connectivity=16,  # Connections per node; 0 derives it from the geometry
)

vectors = np.random.rand(10_000, 256).astype(np.float32)
index.reserve(len(vectors))  # Optional capacity hint; inserts grow on demand
index.insert(np.arange(len(vectors)), vectors)  # On the calling thread

pool = usearch.Executor.for_ram()  # The workers a receiver names
arena = usearch.Arena(pool)  # The memory results land in, made where the pool computes
matches = index.on(pool).search(vectors[:100], 10, arena)  # Blocking, on the pool
assert len(index) == 10_000
assert np.asarray(matches.distances).shape == (100, 10)
key, distance = matches[0][0]
assert distance < 0.001