Learn the essentials of vector search and how to apply them in Faiss.| www.pinecone.io
Similarity search is one of the fastest-growing domains in AI and machine learning. At its core, it is the process of matching relevant pieces of information together.| www.pinecone.io
Vector embeddings are one of the most fascinating and useful concepts in machine learning. They are central to many NLP, recommendation, and search algorithms. If you’ve ever used things like recommendation engines, voice assistants, language translators, you’ve come across systems that rely on embeddings.| www.pinecone.io
With similarity search, we can work with semantic representations of our data and find similar items fast. And in the sections below we will discuss how exactly it works.| www.pinecone.io
Hierarchical Navigable Small World (HNSW) graphs are among the top-performing indexes for vector similarity search[1]. HNSW is a hugely popular technology that time and time again produces state-of-the-art performance with super fast search speeds and fantastic recall.| www.pinecone.io