FAISS

FAISS (Facebook AI Similarity Search) is an efficient similarity search and dense vector clustering library developed by Facebook AI Research. It is designed specifically for handling large-scale vector collections and can quickly perform nearest neighbor searches, which is a core operation for many AI applications such as image retrieval, recommendation systems, and RAG.

FAISS's main advantage lies in its efficient algorithms and optimized implementation. It provides multiple index types, from exact brute-force search to approximate but faster quantization and graph indexes, allowing developers to trade off between speed and accuracy based on application requirements. Additionally, FAISS supports GPU acceleration, further improving search performance.

In RAG systems, FAISS is typically used as the underlying implementation for vector indexing. It can integrate with other tools and frameworks (such as LangChain, LlamaIndex, etc.) to provide efficient vector retrieval functionality. While FAISS itself is not a complete database system, it provides the core components for building high-performance vector search engines.