# Introduction to Embeddings at Cohere

> Embeddings transform text into numerical data, enabling language-agnostic similarity searches and efficient storage with compression.

<img src="https://files.buildwithfern.com/cohere.docs.buildwithfern.com/8b7edf88cdfe83a756d674bbb4bd35933670f0c7b6f7ca9e8c93d99af74877cf/assets/images/multi-modal-guide-header.png" alt="embeddings." />

Embeddings are a way to represent the meaning of texts, images, or information as a list of numbers. Using a simple comparison function, we can then calculate a similarity score for two embeddings to figure out whether two pieces of information are about similar things. Common use-cases for embeddings include semantic search, clustering, and classification.

In the example below we use the `embed-v4.0` model to generate embeddings for 3 phrases and compare them using a similarity function. The two similar phrases have a high similarity score, and the embeddings for two unrelated phrases have a low similarity score:

```python PYTHON
import cohere
import numpy as np

co = cohere.ClientV2(api_key="YOUR_API_KEY")