This project is a high-performance library designed for the similarity search and clustering of dense vectors across massive datasets. It functions as a vector similarity search engine, providing the necessary tools to organize complex numerical data into specialized structures that facilitate rapid retrieval and efficient querying of millions of records.
The library distinguishes itself through a variety of advanced indexing and compression techniques, including hierarchical navigable small worlds for logarithmic time complexity and inverted file indexing to partition vector spaces into manageable subsets. To handle large-scale data, it employs product quantization to reduce memory footprints and utilizes hardware-level vector instructions to accelerate mathematical operations. For scenarios requiring absolute precision, the system also supports exhaustive brute-force search methods.
Beyond its core indexing capabilities, the library provides a comprehensive framework for the end-to-end vector search workflow, from the initial formatting of floating-point data into row-major matrices to the execution of nearest-neighbor retrieval. It includes support for memory-mapped index storage, allowing for the management of datasets that exceed physical memory capacity, and serves as a foundation for machine learning feature retrieval tasks.
