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.


  • 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.


  • 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.

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