Embeddings

Download schema

A vector representation of an input. Similar vectors corresponds to semantically similar inputs.

See How to query embedding modelsOpen in new context for code snippets using openai Python client.

Create an embedding

POST

https://api.scaleway.ai

/{project_id}/v1/embeddings

Generate an embedding.

Create an embedding › path Parameters

project_id

ProjectId · required

The ID of the Project you want to target. If this value is not provided, your default Project will be used.

Specifying this value allows you to limit access through IAM policies, or to allocate consumption and billing to a specific project.

Create an embedding › Request Body

input

string · required

String or Array of strings to represent as embedding vector. Maximum array items: 2048

model

string · required

Unique identifier of the model, such as bge-multilingual-gemma2. Refer to our supported modelsOpen in new context list or /models endpoint for available models.

encoding_format

string · enum

Format of the embedding representation.

Enum values:

float

base64

Default: float

dimensions

integer

Number of dimensions to use for the embedding vector representation. Currently, the only supported value is that of the maximum dimensions of a modelOpen in new context. Lower values are not supported and vectors should not be trimmed, since available models do not support matryoshka embeddingsOpen in new context.

Create an embedding › Responses

200

CreateEmbeddingResponse

id

integer

UUID of the response.

object

string · enum

Type of response object, always set to list.

Enum values:

list

created

integer

Timestamp when the response was generated (Unix format, in seconds).

model

string

Unique identifier of the model.

Embedding[]

List of embeddings.

object

Usage information generated by this request.

Responses Rerank