Perform reranking inference on the service

POST /_inference/rerank/{inference_id}

Auth

Required privileges

Cluster privileges monitor_inference

Path parameters

inference_id string required: The unique identifier for the inference endpoint.

Query parameters

timeout union oneOf · default: 30s: The amount of time to wait for the inference request to complete.; Values: -1 0

Request body

query string required: Query input.
input union string | string[] required: The text on which you want to perform the inference task. It can be a single string or an array.

Note

Inference endpoints for the completion task type currently only support a single string as input.
task_settings object: Task settings for the individual inference request. These settings are specific to the task type you specified and override the task settings specified when initializing the service.

Response

application/json

rerank object[] required

View as Markdown

Examples

Request Console 1 / 8 languages

Run POST _inference/rerank/cohere_rerank to perform reranking on the example input.

Console Kibana Dev Tools

POST _inference/rerank/cohere_rerank
{
  "input": ["luke", "like", "leia", "chewy","r2d2", "star", "wars"],
  "query": "star wars main character"
}

JavaScript Elasticsearch JavaScript Client

const response = await client.inference.rerank({
  inference_id: "cohere_rerank",
  input: ["luke", "like", "leia", "chewy", "r2d2", "star", "wars"],
  query: "star wars main character",
});

Python Elasticsearch Python Client

resp = client.inference.rerank(
    inference_id="cohere_rerank",
    input=[
        "luke",
        "like",
        "leia",
        "chewy",
        "r2d2",
        "star",
        "wars"
    ],
    query="star wars main character",
)

curl Shell

curl -X POST "$ELASTICSEARCH_URL/_inference/rerank/cohere_rerank" \
  -H "Authorization: ApiKey $ELASTIC_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "input": [
      "luke",
      "like",
      "leia",
      "chewy",
      "r2d2",
      "star",
      "wars"
    ],
    "query": "star wars main character"
  }'

Java Elasticsearch Java Client

client.inference().rerank(r -> r
    .inferenceId("cohere_rerank")
    .input(List.of("luke","like","leia","chewy","r2d2","star","wars"))
    .query("star wars main character")
);

C# Elasticsearch .NET Client

var response = await client.Inference
    .RerankAsync(inferenceId: "cohere_rerank", d1 => d1
        .Input("luke", "like", "leia", "chewy", "r2d2", "star", "wars")
        .Query("star wars main character")
    );

PHP Elasticsearch PHP Client

$resp = $client->inference()->rerank([
    "inference_id" => "cohere_rerank",
    "body" => [
        "input" => array(
            "luke",
            "like",
            "leia",
            "chewy",
            "r2d2",
            "star",
            "wars",
        ),
        "query" => "star wars main character",
    ],
]);

Ruby Elasticsearch Ruby Client

response = client.inference.rerank(
  inference_id: "cohere_rerank",
  body: {
    "input": [
      "luke",
      "like",
      "leia",
      "chewy",
      "r2d2",
      "star",
      "wars"
    ],
    "query": "star wars main character"
  }
)

Response

A successful response from POST _inference/rerank/bge-reranker-base-mkn.

{
  "rerank": [
    {
      "index": 6,
      "relevance_score": 0.50955844,
      "text": "wars"
    },
    {
      "index": 5,
      "relevance_score": 0.084341794,
      "text": "star"
    },
    {
      "index": 3,
      "relevance_score": 0.004520818,
      "text": "chewy"
    }
  ]
}