microsoft/SPTAG
SPTAG
SPTAG is a vector approximate nearest neighbor search library and distributed vector search engine. It provides a large-scale vector index designed to organize and retrieve similar vectors from massive datasets using high-performance similarity search and proximity queries.
The system functions as a dynamic vector index manager, supporting incremental updates, insertions, and deletions of vectors without requiring a full index rebuild. It scales search operations across multiple machines to handle large-scale datasets and high volumes of online requests through distributed search request handling.
The project implements search and indexing capabilities using space-partition trees and relative neighborhood graphs. It performs approximate nearest neighbor searches via iterative graph traversal and distance metric calculations to locate the closest vectors to a query point.
Features
- Billion-Scale Vector Search - Provides high-performance approximate nearest neighbor search optimized for massive, billion-scale vector datasets.
- Approximate Nearest Neighbor Search - Provides a library for high-performance retrieval using approximate nearest neighbor search algorithms.
- Distributed Vector Stores - Scales vector search operations across multiple machines to handle extremely large datasets and online requests.
- Graph Traversal - Uses iterative graph traversal to navigate connected vector nodes and find the nearest neighbors.
- Vector Search Indexes - Implements high-performance data structures optimized for similarity search in high-dimensional vector spaces.
- Vector Indexing - Provides tools for building and managing vector indices that support incremental updates without full rebuilds.
- Dynamic Index Management - Supports incremental updates, insertions, and deletions of vectors without requiring a full index rebuild.
- Distributed Engines - Functions as a distributed engine that scales horizontally across multiple nodes for massive datasets.
- Vector Similarity Search - Retrieves vectors closest to a query point by calculating distance metrics through iterative traversal.
- High-Dimensional - Divides high-dimensional space into recursive regions to narrow the search area for candidate vector matches.
- Proximity Graphs - Builds relative neighborhood graphs to enable efficient greedy traversal during similarity searches.
- Distributed Search Engines - Utilizes a distributed cluster architecture to scale similarity search operations for massive datasets.
- Incremental Indexing Engines - Supports dynamic updates to large-scale vector indices without requiring a full re-indexing process.
- Distributed Search Query Routing - Implements query routing across a cluster of machines to handle massive datasets while maintaining low latency.
- Similarity Search - Library for fast approximate nearest neighbor search.
Star history
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