Vector embeddings have proven to be an effective tool in a variety of fields, including natural language processing and computer vision. Comparing vector embeddings and determining their similarity is an essential part of semantic search, recommendation systems, anomaly detection, and much more.
In fact, this is one of the primary determining factors in how Pinecone produces its results. In this article, we will look at three common vector similarity metrics: Euclidean distance, cosine similarity, and dot product similarity. Understanding the benefits and drawbacks of each metric will enable you to make more informed decisions when deciding on the best similarity metric for your use case.
This is a companion discussion topic for the original entry at https://www.pinecone.io/learn/vector-similarity/