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Compression methods that decompose high-dimensional vectors into compact codebook representations.
Distinguishing note: Focuses on memory footprint reduction via vector decomposition.
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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 mana
Reduces memory footprint by decomposing high-dimensional vectors into smaller sub-vectors represented by compact codebook indices.