> ## Documentation Index
> Fetch the complete documentation index at: https://docs.neum.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# AzureOpenAIEmbed

> The AzureOpenAIEmbed connector is designed to generate embeddings for text data using Azure OpenAI’s models, providing a bridge between Neum AI documents and OpenAI’s powerful embedding capabilities.

The `AzureOpenAIEmbed` class serves as an interface to Azure's OpenAI embedding services. It is designed to create embeddings for textual data, which is essential for various machine learning tasks.

## Properties

Required properties:

* `deployment_name`: The specific deployment of the OpenAI model to be used.
* `api_key`: The API key for authentication with Azure OpenAI services.
* `endpoint`: The endpoint URL for the Azure OpenAI API.

<CodeGroup>
  ```python Local Development
  from neumai.EmbedConnectors import AzureOpenAIEmbed

  azure_openai_embed = AzureOpenAIEmbed(
      deployment_name = "<Insert AZURE OPEN AI DEPLOYMENT NAME>",
      api_key = "<Insert Azure OPEN AI API Key>",
      endpoint = "<Insert AZURE OPEN AI ENDPOINT>"
  )

  ```

  ```json Cloud
  {
      # Add source connectors
      "embed": {
          "embed_name":"AzureOpenAIEmbed",
          "embed_information":{
              "deployment_name": "<Insert AZURE OPEN AI DEPLOYMENT NAME>",
              "api_key": "<Insert Azure OPEN AI API Key>",
              "endpoint": "<Insert AZURE OPEN AI ENDPOINT>"
          }
      }
      # Add sink connector
  }
  ```
</CodeGroup>