Faiss (Facebook AI Similarity Search) is a library developed by Facebook AI Research for efficient similarity search and clustering of dense vectors. It is optimized for both CPU and GPU, making it capable of handling large-scale vector search tasks.
It may face scalability issues when dealing with large datasets that exceed available RAM.
FAISS employs vector representations for data points and conducts approximate nearest-neighbor searches to identify similar items, resulting in faster search times and lower memory consumption compared to conventional approaches.
(Perfect for making buy/build decisions or internal reviews.)
