Faiss (Facebook AI Similarity Search) is an open-source library developed by Meta (formerly Facebook) for efficient similarity search and clustering of dense vectors. It’s widely used in AI applications where fast nearest neighbor searches are essential, such as recommendation systems, image retrieval, and natural language processing. Faiss is optimized for handling large datasets of vectors, making it a powerful tool for searching millions—or even billions—of high-dimensional data points. It achieves this efficiency through indexing techniques such as hierarchical clustering, product quantization, and approximate nearest neighbor (ANN) search. These methods significantly reduce computational overhead while maintaining high accuracy. One of Faiss’s standout features is its GPU acceleration, allowing massive datasets to be processed quickly using NVIDIA GPUs. Developers frequently pair Faiss with vector databases like Milvus to manage and search embeddings effectively.
What is Faiss?
- Evaluating Your RAG Applications: Methods and Metrics
- The Definitive Guide to Building RAG Apps with LlamaIndex
- GenAI Ecosystem
- Natural Language Processing (NLP) Basics
- AI & Machine Learning
- All learn series →
Recommended AI Learn Series
VectorDB for GenAI Apps
Zilliz Cloud is a managed vector database perfect for building GenAI applications.
Try Zilliz Cloud for FreeKeep Reading
What are attention mechanisms in reasoning models?
Attention mechanisms in reasoning models are techniques that allow a model to focus on specific parts of an input when m
What are face recognition solutions?
Face recognition solutions are systems or technologies designed to identify or verify individuals based on their facial
What are the limitations of training LLMs?
Training LLMs comes with several limitations, primarily related to data, computational resources, and ethical considerat