> For the complete documentation index, see [llms.txt](https://docs.n8n.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.embeddingsazureopenai.md).

# Microsoft Foundry Embeddings

Learn how to use the Microsoft Foundry Embeddings node in n8n. Follow technical documentation to integrate Microsoft Foundry Embeddings node into your workflows.

Use the Microsoft Foundry Embeddings node to generate embeddings[^1] for a given text.

This node was previously called the **Embeddings Azure OpenAI** node.

On this page, you'll find the node parameters for the Microsoft Foundry Embeddings node, and links to more resources.

{% hint style="info" %}
**Credentials**

Refer to the [Microsoft Foundry credentials documentation](/integrations/builtin/credentials/azureopenai.md) for authentication information for this node.
{% endhint %}

{% hint style="info" %}
**Parameter resolution in sub-nodes**

Sub-nodes behave differently to other nodes when processing multiple items using an expression.

Most nodes, including root nodes, take any number of items as input, process these items, and output the results. You can use expressions to refer to input items, and the node resolves the expression for each item in turn. For example, given an input of five `name` values, the expression `{{ $json.name }}` resolves to each name in turn.

In sub-nodes, the expression always resolves to the first item. For example, given an input of five `name` values, the expression `{{ $json.name }}` always resolves to the first name.
{% endhint %}

## Node parameters <a href="#node-parameters" id="node-parameters"></a>

* **Authentication**: Choose how the node signs in to Azure. Each option uses its own credential.
  * **API Key**: Use a stored API key. This is the default. Refer to [Using API key](/integrations/builtin/credentials/azureopenai.md#using-api-key).
  * **Azure Entra ID (OAuth2)**: Sign in as your Entra application with a client ID and secret. Use this when your organization doesn't allow stored API keys. No API key is stored in n8n. Refer to [Using Microsoft Foundry (Entra ID)](/integrations/builtin/credentials/azureopenai.md#using-azure-entra-id-oauth2).

Both options work with the **Classic** and the **Microsoft Foundry** endpoint types.

## Node options <a href="#node-options" id="node-options"></a>

* **Model (Deployment) Name**: Select the model (deployment) to use for generating embeddings.
* **Batch Size**: Enter the maximum number of documents to send in each request.
* **Strip New Lines**: Select whether to remove new line characters from input text (turned on) or not (turned off). n8n enables this by default.
* **Timeout**: Enter the maximum amount of time a request can take in seconds. Set to `-1` for no timeout.

## Templates and examples <a href="#templates-and-examples" id="templates-and-examples"></a>

[Browse Microsoft Foundry Embeddings node documentation integration templates](https://n8n.io/integrations/embeddings-azure-openai) or [search all templates](https://n8n.io/workflows/)

## Related resources <a href="#related-resources" id="related-resources"></a>

Refer to [LangChain's Azure OpenAI embeddings documentation](https://docs.langchain.com/oss/javascript/integrations/embeddings/azure_openai) for more information about the service.

View n8n's [Advanced AI](/build/integrate-ai.md) documentation.

[^1]: Embeddings are numerical representations of data using vectors. They're used by AI to interpret complex data and relationships by mapping values across many dimensions. Vector databases, or vector stores, are databases designed to store and access embeddings.