VECTOR

VECTOR is a lightweight vector database library for Python that brings mathematical elegance to data organization. Built with clean domain-driven architecture principles, VECTOR organizes data using coordinate systems where every table has an X-axis as the primary key, with other attributes representing relationships between dimensional spaces.

Key Features:

Mathematical Foundation: Data organized around coordinate system principles, with X-axis as the primary key and dimensional spaces for relationships
O(1) Performance: Instant lookup, insert, and update operations through coordinate-based indexing
Clean Architecture: Domain-driven design with clear separation between domain logic, application services, and infrastructure
Storage Optimization: Automatic value deduplication and MessagePack serialization with gzip compression for optimal files’ size
N-Dimensional Scalability: Add new dimensions dynamically without structural changes or migrations
Developer-Friendly: Context managers for automatic resource management, batch operations and LRU caching

What makes VECTOR unique is its coordinate-based approach to data organization. Instead of traditional table structures, VECTOR treats data as points in a coordinate space, where the X-axis uniquely identifies each record and other dimensions represent relationships. This mathematical model enables both elegant code design and exceptional performance, because everything is already indexed, and each attribute axis can share one value with several records by simply assigning a coordinate, optimizing space and computing resources.

VECTOR demonstrates that database design can be both mathematically rigorous and practically efficient, combining clean architecture principles with O(1) operations. The library is perfect for embedded systems, lightweight applications, or anyone exploring database internals through a lens of mathematical clarity.