HVS (Hierarchical Graph Structure)
HVS is a graph-based index structure leveraging Voronoi diagrams for approximate nearest neighbor search in high-dimensional vector spaces. It is directly relevant to vector databases as it provides efficient similarity search capabilities for large-scale vector data.
About this tool
HVS (Hierarchical Graph Structure)
Source: GitHub Repository
Category: Open Source
Tags: open-source, ann, graph-database, similarity-search
Description
HVS is a graph-based index structure that leverages Voronoi diagrams for approximate nearest neighbor (ANN) search in high-dimensional vector spaces. It is designed to provide efficient similarity search capabilities, making it suitable for large-scale vector data, such as those found in vector databases.
Features
- Hierarchical Graph Structure: Organizes data points in a multi-level graph for efficient traversals.
- Voronoi Diagram-Based Partitioning: Utilizes Voronoi diagrams to partition the data space, improving search efficiency.
- Approximate Nearest Neighbor Search: Enables fast and effective ANN queries in high-dimensional spaces.
- Large-Scale Data Support: Designed to handle large datasets, common in vector database applications.
- Command-Line Tools: Provides commands for building the HVS index and performing searches.
- Example Usage: Offers running examples (e.g., on ImageNet) to demonstrate functionality.
- Open Source: Available as open-source software for community use and contribution.
Pricing
HVS is open source and free to use.