vamana

🧬🔍 Vecgo is a pure Go, embeddable, hybrid vector database designed for high-performance production workloads. It combines commit-oriented durability with HNSW + DiskANN indexing for best-in-class performance.

A university project implementing Vamana-Indexing-Algorithm (VIA) for Approximate-Nearest-Neighbors (ANN) problem.

Efficient and robust genome similarity search with evolving disk-based proximity graphs

A C++ implementation of the Vamana indexing algorithm for Approximate Nearest Neighbor search, exploring graph-based vector indexing techniques for fast similarity retrieval in high-dimensional spaces.

High-performance C++ implementations of HNSW and Vamana with SIMD vectorization and OpenMP

Vamana Indexing and Approximate Nearest Neighbor (ANN) Search Experimentation — Developed as part of the K23a Software Development for Information Systems Course at NKUA.

A minimalistic, pure-Python vector database for semantic search and RAG applications, featuring Vamana graph indexing and persistence.

Interactive visualization of how Vamana builds its proximity graph

Implementation of Vamana-based algorithms for efficient k-nearest neighbor (KNN) search. Designed for optimized indexing and query performance in high-dimensional data.

🚀 Vamana-based approximate k-nearest neighbor search with filtered extensions. Implements SIGMOD 2023 contest specifications with performance optimizations.