# Semantic search - Cohere on Azure AI Foundry

> A guide for performing text semantic search with Cohere's Embed models on Azure AI Foundry (API v2).

[Open in GitHub](https://github.com/cohere-ai/cohere-developer-experience/blob/main/notebooks/guides/cohere-on-azure/v2/azure-ai-sem-search.ipynb)

In this tutorial, we'll explore semantic search using Cohere's Embed modelon Azure AI Foundry.

Semantic search enables search systems to capture the meaning and context of search queries, going beyond simple keyword matching to find relevant results based on semantic similarity.

With the Embed model, you can do this across languages. This is particularly powerful for multilingual applications where the same meaning can be expressed in different languages.

In this tutorial, we'll cover:

* Setting up the Cohere client
* Embedding text data
* Building a search index
* Performing semantic search queries

We'll use Cohere's Embed model deployed on Azure to demonstrate these capabilities and help you understand how to effectively implement semantic search in your applications.

## Setup

First, you will need to deploy the Embed model on Azure via Azure AI Foundry. The deployment will create a serverless API with pay-as-you-go token based billing. You can find more information on how to deploy models in the [Azure documentation](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deploy-models-serverless?tabs=azure-ai-studio).

In the example below, we are deploying the Embed 4 model.

Once the model is deployed, you can access it via Cohere's Python SDK. Let's now install the Cohere SDK and set up our client.

To create a client, you need to provide the API key and the model's base URL for the Azure endpoint. You can get these information from the Azure AI Foundry platform where you deployed the model.

```python PYTHON