Faiss
Faiss (Facebook AI Similarity Search) is an open-source library developed by Facebook AI Research for efficient similarity search and clustering of dense vectors. It is optimized for high-dimensional data and supports various indexing methods, such as IVF, HNSW, and Product Quantization, to enable fast nearest neighbor searches in large datasets. It is widely used in machine learning applications like image retrieval, recommendation systems, and natural language processing.
Developers should learn Faiss when working with large-scale vector databases or applications requiring fast similarity searches, such as building recommendation engines, image search systems, or semantic search in NLP. It is particularly useful in production environments where low-latency querying of high-dimensional embeddings (e.g., from deep learning models) is critical, as it outperforms brute-force methods in speed and memory efficiency.