---
title: Integrate Generative APIs into n8n workflows
description: Learn how to integrate Scaleway Generative APIs with open-source workflow automation tool n8n
tags: generative-apis ai machine-learning n8n workflow automation
dates:
validation: 2025-08-06
posted: 2025-08-06
---
import Requirements from '@macros/iam/requirements.mdx'
import scwN8nInterface from './assets/scw-n8n-interface.webp'
import scwN8nChatmodel from './assets/scw-n8n-chatmodel.webp'
import scwN8nEmbeddings from './assets/scw-n8n-embeddings.webp'
n8n is an open-source workflow automation tool that allows users to build automated workflows by connecting various AI agents, apps, and services. It provides a visual interface to facilitate the creation of workflows - this document will show you how to use this interface to integrate Scaleway Generative APIs, by:
- Adding a chat model to a workflow
- Adding an embedding model to a workflow
- A Scaleway account logged into the [console](https://console.scaleway.com)
- [Owner](/iam/concepts/#owner) status or [IAM permissions](/iam/concepts/#permission) allowing you to perform actions in the intended Organization
- A valid [API key](/iam/how-to/create-api-keys/) for API authentication
- [Installed n8n](https://docs.n8n.io/hosting/installation/docker/) on your local machine
## Adding a chat model
You can add a chat model to a workflow by adding an **AI Agent** node, and configuring it to use Scaleway Generative APIs. Follow the steps below.
1. Open the n8n visual interface on your local machine.
2. Click `+` > **Workflow** in the top left corner to create a new workflow.
3. Click **Add first step** to define what will trigger this workflow.
4. Type **AI agent** in the search bar, and select that option from the search results.
A form displays prompting you to define parameters and settings for the AI agent. You can come back to this later, if necessary.
5. Click **Back to canvas** in the top left corner.
A canvas displays, visually showing the trigger **When chat message received**, connected to the node **AI agent**.
6. Click **Chat Model +** underneath the AI agent node to define a chat model for the agent.
A list of language models displays.
7. Select **OpenAI Chat Model**.
A form displays prompting you to define parameters and settings for the model.
8. Under **Credential to connect with**, click **Select Credential** > **Create new credential**.
A form displays prompting you to enter the credentials.
9. Fill in the form, to add where prompted:
- The secret part of your [Scaleway API key](https://console.scaleway.com/iam/api-keys)
- The URL `https://api.scaleway.ai/v1` as your base URL
If you are using a Project other than your [default Project](/organizations-and-projects/concepts/#default-project), modify the base URL accordingly: `https://api.scaleway.ai/{project_id}/v1`
10. Click **Save**.
The credentials are saved, and an automatic connection test is carried out, displaying the message **Connected tested successfully**. This confirms that n8n can access the `/v1/models` and `/v1/chat/completions` endpoints.
11. Close the credentials screen to go back to the model parameters.
12. Under **Model**, click your chosen model, e.g., `gemma-4-26b-a4b-it`.
13. Click **Back to canvas** in the top left corner.
Your AI agent node is now configured to use the model you configured via Scaleway Generative APIs.
### Testing the model
You can test the model by opening a chat from the interface, asking it questions and checking the answers.
1. Click the **Open chat** button at the bottom of the screen.
The chat interface displays.
2. Enter your question in the **Chat** box on the left, e.g. `How much is 1 + 1 ?`, and send it.
3. Check the result that displays in the **Output** box on the right, e.g. `I can calculate that for you. The answer is 2.`.
### Adding tools
You can add **tools** to the AI Agent node you created, to define new capabilities and actions for the agent. A "tool" lets the agent, for example, call another n8n workflow, make an HTTP request, or run pre-defined code.
In the example below, we create and add a code tool to provide weather information.
1. Click **Tool +** underneath the AI agent node to define a new tool for the agent.
A list of tools displays.
2. Click **Code Tool**.
A form displays prompting you to define parameters and settings for the tool.
3. In the **Description** box, enter `This tool provides the weather in Paris.`
4. Under **Language**, select **Javascript**.
5. In the **Javascript** code box, edit the return statement in the sample code as follows:
```javascript
return "Paris is sunny with clear skies, and the temperature is 30 degrees"
```
6. Click **Back to canvas** in the top left corner.
Now when you [test the model](#testing-the-model) by asking a question such as `What's the weather in Paris?`, you should see via the workflow that the agent calls the code tool, and ultimately returns the answer `Paris is sunny with clear skies, and the temperature is 30 degrees`.
You can also specify a schema to define the input the tool receives, and modify the code accordingly. For example, edit the tool description with `This tool provides the weather in a city`, and edit the Javascript as follows:
```
let weather = "unknown";
if (query.city === "Paris") {
weather = "sunny";
} else if (query.city === "London") {
weather = "rainy";
}
return weather;
```
Toggle the **Specify Input Schema** setting on, and under **Schema Type** select **Define using JSON Schema**. Add the following input schema:
```json
{
"type": "object",
"properties": {
"city": {
"type": "string",
"description": "City name"
}
}
}
```
Now when you ask `What's the weather in London?` you should get the output `rainy.`, and `What's the weather in Tokyo` should generate the response `unknown`.
## Adding an embedding model
You can add an embedding model to a workflow by adding a **Simple Vector Store** node, and configuring it to use Scaleway Generative APIs. Follow the steps below to see how to create a workflow that takes a chat message as an input, and outputs an embedding of the message (list of vectors).
1. Open the n8n visual interface on your local machine.
2. Click `+` > **Workflow** in the top left corner to create a new workflow.
3. Click **Add first step** to define what will trigger this workflow.
3. Select **On chat message** as the trigger, then click **Back to canvas**.
4. Test the chat by sending a message in the bottom left. The output is a JSON object containing session ID, action and chatInput fields.
5. Click `+` to the right of your **When chat message received** node to define what happens next.
6. Type **Simple Vector Store** in the search bar, and select that option from the search results.
7. From the **Actions** sub-menu that displays, click **Add documents to vector store**.
A form displays prompting you to define parameters and settings.
8. Click **+** under **Embeddings** at the bottom of the form to add an embedding.
A list of embeddings nodes displays.
9. Click **Embeddings OpenAPI**.
A form displays prompting you to define parameters and settings.
10. Under **Credential to connect with**, select existing credentials and skip to step 15, or click **Select Credential** > **Create new credential**.
A form displays prompting you to enter the credentials.
11. Fill in the form, to add where prompted:
- The secret part of your [Scaleway API key](https://console.scaleway.com/iam/api-keys)
- The URL `https://api.scaleway.ai/v1` as your base URL
If you are using a Project other than your [default Project](/organizations-and-projects/concepts/#default-project), modify the base URL accordingly: `https://api.scaleway.ai/{project_id}/v1`
12. Click **Save**.
The credentials are saved, and an automatic connection test is carried out, displaying the message **Connected tested successfully**. This confirms that n8n can access the `/v1/models` and `/v1/chat/completions` endpoints.
13. Close the credentials screen to go back to the **Parameters** tab.
14. Under **Model**, click your chosen model, e.g., `qwen3-embedding-8b` (other listed models are not supported by Scaleway Generative APIs at the time of writing).
For n8n versions earlier than `1.104.1`, a [known issue](https://github.com/n8n-io/n8n/issues/16716) prevents the model list from displaying. If you cannot upgrade your n8n version, you can still Select **Expression** instead of **Fixed** and type the model name.
15. Click **Back to canvas** in the top left corner.
16. Click **Document +** underneath the Simple Vector Store node.
A list of document loaders displays.
17. Click **Default Data Loader**.
A form displays prompting you to define parameters and settings for the data loader.
18. Under **Mode**, select ** Load Specific Data**.
17. Drag and drop the **chatInput** field from the **Input** panel on the left into the **Data** field of the main form. Alternatively type `{{ $('When chat message received').item.json.chatInput }}` into the **Data** field.
18. Click **Back to canvas**
### Testing the workflow
You can test the workflow by opening a chat from the interface, sending it a message, and checking that the output is an embedding (list of vectors).
1. Click the **Open chat** button at the bottom of the screen.
The chat interface displays.
2. Enter your chat input in the **Chat** box on the left, e.g. `This is a message to embed`, and send it.
3. In the middle **Logs** panel, click **Simple Vector Store** > **Embeddings OpenAI**.
5. Check that the input shows your chat message, and the output is an embedding (list of vectors).
### Going further
You now know how to add a simple embedding model to your n8n workflows. To go further, you can:
- Replace the **Simple Vector Store** node with a persistent storage node, such as **Postgres PGVector Store** or **Qdrant Vector Store**.
- Implement a RAG pipeline using Vector retrieval components, such as **AI Agent** and **Query Data Tool** (using for instance [RAG Starter Template](https://n8n.io/workflows/5010-rag-starter-template-using-simple-vector-stores-form-trigger-and-openai/)).