const Module

Enums

DistanceFunction

Distance functions for similarity search. Cosine Similarity the cosine (angular) similarity between two vectors measures only the angle between the two vectors, without taking into account the length of the vectors Cosine Similarity = 1 - Cosine Distance -1 means vectors are opposite 0 means vectors are orthogonal 1 means vectors are identical Cosine Distance the cosine (angular) distance between two vectors measures only the angle between the two vectors, without taking into account the length of the vectors Cosine Distance = 1 - Cosine Similarity 2 means vectors are opposite 1 means vectors are orthogonal 0 means vectors are identical Dot Product measures both the length and angle between two vectors same as cosine similarity if the vectors are the same length, but more performant Euclidean Distance measures the Euclidean distance between two vectors also known as l2-norm Euclidean Squared Distance measures the Euclidean squared distance between two vectors also known as l2-squared Manhattan measures the Manhattan distance between two vectors Hamming number of differences between vectors at each dimensions

IndexKind

Index kinds for similarity search. HNSW Hierarchical Navigable Small World which performs an approximate nearest neighbor (ANN) search. Lower accuracy than exhaustive k nearest neighbor, but faster and more efficient. Flat Does a brute force search to find the nearest neighbors. Calculates the distances between all pairs of data points, so has a linear time complexity, that grows directly proportional to the number of points. Also referred to as exhaustive k nearest neighbor in some databases. High recall accuracy, but slower and more expensive than HNSW. Better with smaller datasets. IVF Flat Inverted File with Flat Compression. Designed to enhance search efficiency by narrowing the search area through the use of neighbor partitions or clusters. Also referred to as approximate nearest neighbor (ANN) search. Disk ANN Disk-based Approximate Nearest Neighbor algorithm designed for efficiently searching for approximate nearest neighbors (ANN) in high-dimensional spaces. The primary focus of DiskANN is to handle large-scale datasets that cannot fit entirely into memory, leveraging disk storage to store the data while maintaining fast search times. Quantized Flat Index that compresses vectors using DiskANN-based quantization methods for better efficiency in the kNN search. Dynamic Dynamic index allows to automatically switch from FLAT to HNSW indexes.