patann 0.1.1

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 - Pattern-Aware Vector Database / ANN

Overview

PatANN is a pattern-aware, massively parallel, and distributed vector search framework designed for scalable and efficient nearest neighbor search, operating both in-memory and on-disk. Unlike conventional algorithms, PatANN leverages macro and micro patterns within vectors to drastically reduce search space before performing costly distance computations.

Refer to the website for technical details, algorithm overview, key innovations, benchmarks, and tutorials.

Website: https://patann.dev

While still in beta, PatANN's pattern-first approach delivers unprecedented performance advantages. As shown in our benchmarks (Figure 1), PatANN consistently outperforms leading ANN libraries including HNSW (hnswlib), Google ScaNN, Facebook FAISS variants, and others in the critical recall-throughput tradeoff.

Installation

Install PatANN using pip:

pip install patann

Usage

PatANN provides Python API as well as a command-line interface to download example code.

Command Line Interface

patann --help

Output:

PatANN Version: 0.1.0
Python Version: 3.11.5
PatANN - https://github.com/mesibo/patann
usage: patann [-h] [--help-examples] [--list-examples] [--copy-examples [DEST]]
PatANN: Pattern-Aware Approximate Nearest Neighbors Search
options:
  -h, --help            show this help message and exit
  --help-examples       Show information about examples
  --list-examples       List available examples
  --copy-examples [DEST]
                        Copy examples to specified directory (default: current directory)

Python API

PatANN supports both synchronous (blocking) and asynchronous (non-blocking) operation modes:

1. Synchronous Mode: Simple sequential operations, ideal for straightforward applications
2. Asynchronous Mode: High-throughput parallel operations with callbacks, ideal for production systems

Platforms

• Linux
• macOS (Apple Silicon)
• Windows
• Android
• iOS

Code Examples

Code examples for all platforms (Python, Kotlin, Java, Objective-C, Swift) in both asynchronous and synchronous modes are available at https://github.com/mesibo/patann

Key Distinguishing Features

- Novel pattern-based probing for ANN search
- In-Memory, On-Disk, and Hybrid Index
- Fully asynchronous operations with built-in support for conventional synchronous execution
- Refined search, filtering, and pagination algorithms
- Unlimited scalability without pre-specified capacity
- Dynamic sharding to load balance across servers
- Cloud (in-progress) and Serverless
- SIMD-Accelerated for both x86_64 (SSE*, AVX2, AVX-512) and ARM (NEON, SVE) platforms

• OS-optimized I/O—huge pages (Linux), large pages (Windows), and super pages (macOS)
  • NUMA-aware architecture