Definition
FAISS (Facebook AI Similarity Search) is a specialized library for efficient similarity search and clustering of dense vectors, serving as the high-performance retrieval engine in RAG pipelines. It allows for the rapid identification of relevant context by comparing query embeddings against a large-scale vector store, utilizing optimized indexing techniques to manage the trade-off between search accuracy and computational speed.
A computational library for vector indexing, not a standalone managed database service.
"A high-speed postal sorting facility that uses laser-guided coordinates to instantly group millions of packages based on physical similarity rather than address labels."
- Vector Embeddings(Prerequisite)
- Approximate Nearest Neighbor (ANN)(Underlying Technique)
- HNSW (Hierarchical Navigable Small World)(Indexing Algorithm)
- Cosine Similarity(Distance Metric)
Conceptual Overview
FAISS (Facebook AI Similarity Search) is a specialized library for efficient similarity search and clustering of dense vectors, serving as the high-performance retrieval engine in RAG pipelines. It allows for the rapid identification of relevant context by comparing query embeddings against a large-scale vector store, utilizing optimized indexing techniques to manage the trade-off between search accuracy and computational speed.
Disambiguation
A computational library for vector indexing, not a standalone managed database service.
Visual Analog
A high-speed postal sorting facility that uses laser-guided coordinates to instantly group millions of packages based on physical similarity rather than address labels.