Create a Cohere inference endpoint Generally available; Added in 8.13.0

PUT /_inference/{task_type}/{cohere_inference_id}

Create an inference endpoint to perform an inference task with the cohere service.

Required authorization

  • Cluster privileges: manage_inference

Path parameters

  • task_type string Required

    The type of the inference task that the model will perform.

    Values are completion, rerank, or text_embedding.

  • cohere_inference_id string Required

    The unique identifier of the inference endpoint.

application/json

Body

  • chunking_settings object

    Chunking configuration object

    Hide chunking_settings attributes Show chunking_settings attributes object
    • max_chunk_size number

      The maximum size of a chunk in words. This value cannot be higher than 300 or lower than 20 (for sentence strategy) or 10 (for word strategy).

    • overlap number

      The number of overlapping words for chunks. It is applicable only to a word chunking strategy. This value cannot be higher than half the max_chunk_size value.

    • sentence_overlap number

      The number of overlapping sentences for chunks. It is applicable only for a sentence chunking strategy. It can be either 1 or 0.

    • strategy string

      The chunking strategy: sentence or word.

  • service string Required

    Value is cohere.

  • service_settings object Required
    Hide service_settings attributes Show service_settings attributes object
    • api_key string Required

      A valid API key for your Cohere account. You can find or create your Cohere API keys on the Cohere API key settings page.

      IMPORTANT: You need to provide the API key only once, during the inference model creation. The get inference endpoint API does not retrieve your API key. After creating the inference model, you cannot change the associated API key. If you want to use a different API key, delete the inference model and recreate it with the same name and the updated API key.

      External documentation
    • embedding_type string

      Values are binary, bit, byte, float, or int8.

    • model_id string

      For a completion, rerank, or text_embedding task, the name of the model to use for the inference task.

      The default value for a text embedding task is embed-english-v2.0.

    • rate_limit object

      This setting helps to minimize the number of rate limit errors returned from the service.

      Hide rate_limit attribute Show rate_limit attribute object
      • requests_per_minute number

        The number of requests allowed per minute. By default, the number of requests allowed per minute is set by each service as follows:

        • alibabacloud-ai-search service: 1000
        • anthropic service: 50
        • azureaistudio service: 240
        • azureopenai service and task type text_embedding: 1440
        • azureopenai service and task type completion: 120
        • cohere service: 10000
        • elastic service and task type chat_completion: 240
        • googleaistudio service: 360
        • googlevertexai service: 30000
        • hugging_face service: 3000
        • jinaai service: 2000
        • mistral service: 240
        • openai service and task type text_embedding: 3000
        • openai service and task type completion: 500
        • voyageai service: 2000
        • watsonxai service: 120
    • similarity string

      Values are cosine, dot_product, or l2_norm.

  • task_settings object
    Hide task_settings attributes Show task_settings attributes object
    • input_type string

      Values are classification, clustering, ingest, or search.

    • return_documents boolean

      For a rerank task, return doc text within the results.

    • top_n number

      For a rerank task, the number of most relevant documents to return. It defaults to the number of the documents. If this inference endpoint is used in a text_similarity_reranker retriever query and top_n is set, it must be greater than or equal to rank_window_size in the query.

    • truncate string

      Values are END, NONE, or START.

Responses

  • 200 application/json
    Hide response attributes Show response attributes object
    • chunking_settings object

      Chunking configuration object

      Hide chunking_settings attributes Show chunking_settings attributes object
      • max_chunk_size number

        The maximum size of a chunk in words. This value cannot be higher than 300 or lower than 20 (for sentence strategy) or 10 (for word strategy).

      • overlap number

        The number of overlapping words for chunks. It is applicable only to a word chunking strategy. This value cannot be higher than half the max_chunk_size value.

      • sentence_overlap number

        The number of overlapping sentences for chunks. It is applicable only for a sentence chunking strategy. It can be either 1 or 0.

      • strategy string

        The chunking strategy: sentence or word.

    • service string Required

      The service type

    • service_settings object Required
    • task_settings object
    • inference_id string Required

      The inference Id

    • task_type string Required

      Values are text_embedding, rerank, or completion.

PUT /_inference/{task_type}/{cohere_inference_id}
PUT _inference/text_embedding/cohere-embeddings
{
    "service": "cohere",
    "service_settings": {
        "api_key": "Cohere-Api-key",
        "model_id": "embed-english-light-v3.0",
        "embedding_type": "byte"
    }
}
resp = client.inference.put(
    task_type="text_embedding",
    inference_id="cohere-embeddings",
    inference_config={
        "service": "cohere",
        "service_settings": {
            "api_key": "Cohere-Api-key",
            "model_id": "embed-english-light-v3.0",
            "embedding_type": "byte"
        }
    },
)
const response = await client.inference.put({
  task_type: "text_embedding",
  inference_id: "cohere-embeddings",
  inference_config: {
    service: "cohere",
    service_settings: {
      api_key: "Cohere-Api-key",
      model_id: "embed-english-light-v3.0",
      embedding_type: "byte",
    },
  },
});
response = client.inference.put(
  task_type: "text_embedding",
  inference_id: "cohere-embeddings",
  body: {
    "service": "cohere",
    "service_settings": {
      "api_key": "Cohere-Api-key",
      "model_id": "embed-english-light-v3.0",
      "embedding_type": "byte"
    }
  }
)
$resp = $client->inference()->put([
    "task_type" => "text_embedding",
    "inference_id" => "cohere-embeddings",
    "body" => [
        "service" => "cohere",
        "service_settings" => [
            "api_key" => "Cohere-Api-key",
            "model_id" => "embed-english-light-v3.0",
            "embedding_type" => "byte",
        ],
    ],
]);
curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"cohere","service_settings":{"api_key":"Cohere-Api-key","model_id":"embed-english-light-v3.0","embedding_type":"byte"}}' "$ELASTICSEARCH_URL/_inference/text_embedding/cohere-embeddings"
Request examples
Run `PUT _inference/text_embedding/cohere-embeddings` to create an inference endpoint that performs a text embedding task.
{
    "service": "cohere",
    "service_settings": {
        "api_key": "Cohere-Api-key",
        "model_id": "embed-english-light-v3.0",
        "embedding_type": "byte"
    }
}
Run `PUT _inference/rerank/cohere-rerank` to create an inference endpoint that performs a rerank task.
{
    "service": "cohere",
    "service_settings": {
        "api_key": "Cohere-API-key",
        "model_id": "rerank-english-v3.0"
    },
    "task_settings": {
        "top_n": 10,
        "return_documents": true
    }
}