# Semantic Search with Cohere Embeddings

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## Calculating the Similarity Between a Pair of Embeddings

Using a simple comparison function, we can calculate a similarity score for two embeddings to figure out whether two texts are talking about similar things.

There are a number of ways to do this, with two of the most common methods being Euclidean distance and Cosine similarity. Let's walk through an example of the latter approach.

First, if you don't already have the SDK installed do that:

## Related pages

- [Changelog](../changelog.md)
- [Cohere](../index.md)
- [Cohere API](./cohere-api-index.md)
- [Cohere Labs](./cohere-labs-index.md)
- [Cohere Platform](./cohere-platform-index.md)
- [Cookbooks](./cookbooks-index.md)
- [Deployment Options](./deployment-options-index.md)
- [Embeddings (Vectors, Search, Retrieval)](./embeddings-vectors-search-retrieval-index.md)
- [Get Started](./get-started-index.md)
- [Going to Production](./going-to-production-index.md)

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