Faiss (pronounced “face”) is a widely used open-source library developed by Facebook AI Research. It focuses on efficiently searching and clustering dense vectors in large-scale applications.
Where to use
Faiss is most effective for fast similarity search and clustering of dense vectors in large-scale applications requiring efficient processing.
Features
- Speed and efficiency: Optimized for fast similarity search and clustering, even for massive datasets exceeding RAM limitations.
- Scalability: Handles datasets of various sizes, from small to extremely large.
- Multiple nearest neighbors: Retrieves not just the closest match, but also the “k” closest neighbors.
- Batch processing: Searches for similarities for multiple queries at once, accelerating performance.
- Distance metrics: Supports different distance measures (e.g., Euclidean, dot product) for various use cases.
- Disk storage: Allows storing and loading pre-computed indexes for faster future searches.
Benefits
- Open-source and freely available: No licensing fees or restrictions for usage and customization.
- Scalable and efficient: Handles large datasets and complex searches effectively.
- Versatile with various applications: Adaptable to diverse tasks in different domains.
- Supported by Facebook AI Research: Benefits from ongoing development and community support.