Vector Similarity Explained

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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/
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Hi,

I Just started exploring your product, and so far looks amazing! Keep up the good job!

I wanted to raise your awareness that there is duplication in this post:

The basic rule of thumb in selecting the best similarity metric for your Pinecone index is to match it to the one used to train your embedding model. For example, the all-MiniLM-L6-v2 model was trained using cosine similarity — so using cosine similarity for the index will produce the most accurate result. If you used a Euclidean distance measure to train your model, the same similarity metric should be used in the index, etc. Pinecone will be able to use the best algorithms as long as we follow this rule.

For example, the all-MiniLM-L6-v2 model was trained using cosine similarity — so using cosine similarity for the index will produce the most accurate result. If you used a Euclidean distance measure to train your model, the same similarity metric should be used in the index, etc.