3 Repos
Tools for extracting and processing vector embeddings within database queries.
Distinct from Vector Databases: Distinct from Vector Databases: focuses on the query-time manipulation of vector data rather than the storage engine itself.
Explore 3 awesome GitHub repositories matching data & databases · Vector Manipulations. Refine with filters or upvote what's useful.
LibSQL is a high-performance, distributed SQL database engine that extends SQLite to support remote network access, edge computing, and real-time synchronization. It functions as an embedded database library that integrates directly into application processes while providing the infrastructure to maintain consistency across multiple geographic regions. The platform distinguishes itself by enabling database interaction over standard HTTP protocols, allowing applications to query remote data sources in serverless and edge environments without requiring local filesystem access. It includes nativ
Allows extraction and slicing of vector embeddings directly within database queries for flexible data processing.
This project is a header-only C++ library designed for graphics mathematics, providing a comprehensive suite of vector, matrix, and quaternion types. It is built using template metaprogramming to generate mathematical primitives at compile time, eliminating the need for precompiled binary libraries and allowing for direct integration into existing build systems. The library is distinguished by its strict adherence to the OpenGL Shading Language specification, ensuring that mathematical results remain consistent across both CPU and GPU code. It provides specialized utilities for managing float
Performs component-wise relational comparisons between two vectors to produce a boolean vector indicating the result of each individual operation.
LanceDB is a vector database and columnar data store designed to function as a versioned dataset manager and vector search engine. It serves as a high-performance backend for indexing and retrieving high-dimensional embeddings, providing the foundation for machine learning data pipelines. The system distinguishes itself through a combination of cloud-native object storage and immutable version tracking, allowing for data time-travel and reproducible AI experiments. It integrates hybrid search capabilities, merging dense vector similarity with BM25 full-text search and SQL-like scalar filters
Handles malformed vectors by dropping them or filling missing values with constants.