patann 0.0.2

PatANN is a massively parallel, distributed, and scalable in-memory/on-disk vector database library for efficient nearest neighbor search across large-scale datasets by finding vector patterns.

PatANN

Overview

PatANN is a massively parallel, distributed, and scalable in-memory/on-disk vector database library for efficient nearest neighbor search across large-scale datasets by finding vector patterns.

PatANN leverages hashing for data partitioning like Google ScANN, implements disk-based I/O similar to DiskANN, and employs search techniques akin to HNSWlib, resulting in an algorithm that synthesizes the best features to outperform existing approaches.

Status

Beta Version: Currently uploaded for benchmarking purposes. Complete documentation and updates are under development. Not for production use yet.

Key Features

• Faster Index building and Searching
• Supports both in-memory and on-disk operations
• Dynamic sharding and load balancing across servers
• Advanced search, filtering and pagination
• Unlimited scalability without pre-specified capacity

Algorithmic Approach

• Combines NSW (Navigable Small World) graph with a novel hashing and partitioning algorithm
• Preliminary results show phenomenal performance in building index and searching
• Potential slight variations in lower-end matching

Contributions Welcome

We are seeking help to:

• Run additional datasets
• Validate and improve the algorithm

Contact

For queries, please contact: [e-mail]

Disclaimer

Results may vary. Detailed research paper forthcoming.