usearch

import numpy as np from usearch.index import Index, Matches # Initialize an index for 3-dimensional vectors using cosine similarity index = Index( ndim=3, metric='cos', # Options include 'l2sq', 'haversine', 'ip' (inner product) dtype='f32', # Quantization to 'f16' (half-precision) or 'i8' (int8) for efficiency connectivity=16, expansion_add=128, expansion_search=64 ) # Create some example vectors vector1 = np.array([0.2, 0.6, 0.4], dtype=np.float32) vector2 = np.array([0.1, 0.7, 0.3], dtype=np.float32) vector3 = np.array([0.9, 0.1, 0.5], dtype=np.float32) # Add vectors to the index with unique keys index.add(key=42, vector=vector1) index.add(key=43, vector=vector2) index.add(key=44, vector=vector3) print(f"Index contains {len(index)} vectors.") # Query the index for the 2 nearest neighbors of vector1 query_vector = np.array([0.21, 0.61, 0.41], dtype=np.float32) matches: Matches = index.search(query_vector, 2) print(f"Found {len(matches)} matches for the query:") for i, match in enumerate(matches): print(f" Match {i+1}: Key={match.key}, Distance={match.distance:.4f}") # Access a vector by key retrieved_vector = index[42] print(f"Retrieved vector for key 42: {retrieved_vector}") assert np.allclose(retrieved_vector, vector1)