A Comparative Study of Vector Indexing Strategies Using Facebook AI Similarity Search as a Case Study
This paper presents a comprehensive experimental evaluation of various Facebook AI Similarity Search (FAISS) indexing strategies, analyzing their trade-offs in accuracy, latency, and memory usage across different distance metrics and quantization techniques to provide practical guidance for large-scale similarity search deployments.