ScaNN
A library by Google Research for efficient vector similarity search, suitable for large-scale nearest neighbor applications in AI.
About this tool
ScaNN
ScaNN (Scalable Nearest Neighbors) is an open-source library developed by Google Research for efficient vector similarity search, particularly designed for large-scale nearest neighbor search applications in AI and machine learning. It is optimized for high-dimensional vector data, such as embeddings generated from text, images, or other modalities.
Features
- Efficient Approximate Nearest Neighbor Search: Enables fast similarity search in large datasets of high-dimensional vectors, suitable for millions or billions of items.
- Anisotropic Vector Quantization: Implements a novel quantization technique that penalizes error in the direction parallel to the original vector, improving accuracy for maximum inner-product search (MIPS).
- Optimized for MIPS: Specifically designed to accelerate maximum inner-product search, a common operation in embedding-based retrieval tasks.
- High Performance: Outperforms other vector similarity search libraries on standard benchmarks (e.g., ann-benchmarks.com), achieving up to twice the query throughput for a given accuracy.
- Open Source: Available for direct installation via Pip, with source code and documentation on GitHub.
- Flexible Interfaces: Supports both TensorFlow and Numpy inputs for easy integration into various machine learning workflows.
- Scalable: Handles very large datasets, making it suitable for production-scale AI systems.
Category
SDKs & Libraries
Tags
open-source, ann, vector-search, ai
Pricing
ScaNN is open-source software and is available for free.
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