faiss

What problem does it solve?

This Skill addresses the critical need for ultra-fast similarity searches on massive datasets of dense vectors, often involving millions or billions of items. It provides a high-performance solution for applications where speed and scale are paramount, without the overhead of a full database.

Core Features & Use Cases

• Billion-Scale Search: Efficiently search and cluster billions of vectors, enabling large-scale applications like image retrieval, data deduplication, or recommendation systems.
• GPU Acceleration: Achieve 10-100× faster search speeds with robust GPU support, drastically reducing query latency for demanding workloads.
• Diverse Index Types: Choose from various index types (Flat for exact, IVF for approximate, HNSW for best quality/speed) to optimize for your specific performance and accuracy needs.
• High Performance: Ideal for applications demanding high throughput and low latency pure similarity search, without needing metadata filtering capabilities.
• Use Case: Build a recommendation system that finds similar items from a catalog of millions in milliseconds, or perform large-scale data deduplication across petabytes of data.

Quick Start

Create a NumPy array of 1000 random 128-dimensional vectors. Initialize a faiss.IndexFlatL2 index, add the vectors, then search for the 5 nearest neighbors to a query vector.