---
title: How to query embedding models
description: Learn how to interact with embedding models using Scaleway's Generative APIs service.
tags: generative-apis ai-data embedding-models embeddings-api
dates:
  validation: 2025-11-19
  posted: 2024-08-28
---
import Requirements from '@macros/iam/requirements.mdx'



Scaleway's Generative APIs service allows users to interact with embedding models hosted on the platform.
The embedding API provides a simple interface for generating vector representations (embeddings) based on your input data. 
The embedding service is OpenAI compatible. Refer to OpenAI's [embedding documentation](https://platform.openai.com/docs/api-reference/embeddings) for more detailed information. 

<Requirements />

- 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
- Python 3.7+ installed on your system

## Querying embedding models via API

The embedding model inputs text and outputs a vector (list) of floating point numbers to use for tasks like similarity comparisons and search.
The instructions below show you how to query the model programmatically using the OpenAI SDK.

### Installing the OpenAI SDK

First, ensure you have the OpenAI SDK installed in your development environment. You can install it using pip:

```bash
pip install openai
```

### Initializing the client

Initialize the OpenAI client with your base URL and API key:

<Message type="tip">
    In the case of a dedicated Generative APIs deployment, the `base_url` value is the **Public Endpoint URL** displayed on the Overview tab of the deployment's dashboard.
</Message>

```python
from openai import OpenAI

# Initialize the client with your base URL and API key
client = OpenAI(
    base_url="https://api.scaleway.ai/v1",  # Scaleway's Generative APIs service URL
    api_key="<SCW_API_KEY>"  # Your unique API key from Scaleway
)
```

### Generating embeddings with bge-multilingual-gemma2

You can now generate embeddings using the `bge-multilingual-gemma2` model, as shown in the following example:

```python
# Generate embeddings using the 'bge-multilingual-gemma2' model
embedding_response = client.embeddings.create(
    input= "Artificial Intelligence is transforming the world.",
    model= "bge-multilingual-gemma2"
)

# Output the embedding vector
print(embedding_response.data[0].embedding)
```

This code sends input text to the `bge-multilingual-gemma2` embedding model and returns a vector representation of the text. The `bge-multilingual-gemma2` model is specifically designed for generating high-quality sentence embeddings.

### Model parameters and their effects

The following parameters can be adjusted to influence the output of the embedding model:

- **`input`** (string or array of strings): The text or data you want to convert into vectors.
- **`model`** (string): The specific embedding model to use, find all our [supported models](/generative-apis/reference-content/supported-models).

<Message type="warning">
 If you encounter an error such as `Forbidden 403`, refer to the [API documentation](/generative-apis/api-cli/understanding-errors) for troubleshooting tips.
</Message>