Rerank

Download schema

A ranking is an ordering of documents by relevance, based on a query.

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

Create a reranking

POST

https://api.scaleway.ai

/{project_id}/v1/rerank

Identify most relevant documents to answer a query.

Create a reranking › 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 a reranking › Request Body

query

string · required

Query requiring relevant documents to provide a better answer.

documents

string[] · required

Array of document contents in string format. Maximum array items: 1000

model

string · required

Unique identifier of the model, such as qwen3-embedding-8b. Refer to our supported modelsOpen in new context list or /models endpoint for available models.

top_n

integer

Number of most relevant documents to retrieve.

Create a reranking › Responses

200

CreateRerankResponse

id

integer

UUID of the response.

model

string

Unique identifier of the model.

Ranking[]

List of documents sorted by relevance.

object

Usage information generated by this request.

POST/{project_id}/v1/rerank

curl https://api.scaleway.ai/v1/rerank \
  -H "Authorization: Bearer $SCW_SECRET_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3-embedding-8b",
    "query": "What is the biggest area of water on earth ?",
    "documents": [
      "The Pacific is approximately 165 million km²",
      "Oceans can be sorted by size: Pacific, Atlantic, Indian",
      "The Atlantic is a very large ocean.",
      "The deepest pool on earth is 96 000 m²"
    ],
    "top_n": 3
  }'

Example Request Body

{
  "query": "This is an example query.",
  "documents": [
    "string"
  ],
  "model": "qwen3-embedding-8b",
  "top_n": 3
}

json

Example Responses

{
  "id": 0,
  "model": "model",
  "results": [
    {
      "index": 0,
      "relevance_score": 0,
      "document": {
        "text": "text"
      }
    }
  ],
  "usage": {
    "total_tokens": 0
  }
}

json

application/json

Embeddings Audio