Create a Watsonx inference endpoint Generally available; Added in 8.16.0

PUT /_inference/{task_type}/{watsonx_inference_id}
Api key auth Basic auth Bearer auth

Create an inference endpoint to perform an inference task with the watsonxai service. You need an IBM Cloud Databases for Elasticsearch deployment to use the watsonxai inference service. You can provision one through the IBM catalog, the Cloud Databases CLI plug-in, the Cloud Databases API, or Terraform.

Required authorization

  • Cluster privileges: manage_inference

Path parameters

  • task_type string

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

    Values are text_embedding, chat_completion, or completion.

  • watsonx_inference_id string Required

    The unique identifier of the inference endpoint.

Query parameters

application/json

Body

  • service string Required

    The type of service supported for the specified task type. In this case, watsonxai.

    Value is watsonxai.

  • service_settings object Required

    Settings used to install the inference model. These settings are specific to the watsonxai service.

    Hide service_settings attributes Show service_settings attributes object
    • api_key string Required

      A valid API key of your Watsonx account. You can find your Watsonx API keys or you can create a new one on the API keys 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
    • api_version string Required

      A version parameter that takes a version date in the format of YYYY-MM-DD. For the active version data parameters, refer to the Wastonx documentation.

      External documentation
    • model_id string Required

      The name of the model to use for the inference task. Refer to the IBM Embedding Models section in the Watsonx documentation for the list of available text embedding models. Refer to the IBM library - Foundation models in Watsonx.ai.

      External documentation
    • project_id string Required

      The identifier of the IBM Cloud project to use for the inference task.

    • rate_limit object

      This setting helps to minimize the number of rate limit errors returned from Watsonx. By default, the watsonxai service sets the number of requests allowed per minute to 120.

      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
        • contextualai service: 1000
        • elastic service and task type chat_completion: 240
        • googleaistudio service: 360
        • googlevertexai service: 30000
        • hugging_face service: 3000
        • jinaai service: 2000
        • llama service: 3000
        • mistral service: 240
        • openai service and task type text_embedding: 3000
        • openai service and task type completion: 500
        • voyageai service: 2000
        • watsonxai service: 120
    • url string Required

      The URL of the inference endpoint that you created on Watsonx.

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 lower than 20 (for sentence strategy) or 10 (for word strategy). This value should not exceed the window size for the associated model.

        Default value is 250.

      • 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.

        Default value is 100.

      • 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.

        Default value is 1.

      • separator_group string

        Only applicable to the recursive strategy and required when using it.

        Sets a predefined list of separators in the saved chunking settings based on the selected text type. Values can be markdown or plaintext.

        Using this parameter is an alternative to manually specifying a custom separators list.

      • separators array[string]

        Only applicable to the recursive strategy and required when using it.

        A list of strings used as possible split points when chunking text.

        Each string can be a plain string or a regular expression (regex) pattern. The system tries each separator in order to split the text, starting from the first item in the list.

        After splitting, it attempts to recombine smaller pieces into larger chunks that stay within the max_chunk_size limit, to reduce the total number of chunks generated.

      • strategy string

        The chunking strategy: sentence, word, none or recursive.

        • If strategy is set to recursive, you must also specify:

          • max_chunk_size
          • either separators orseparator_group

        Learn more about different chunking strategies in the linked documentation.

        Default value is sentence.

        External documentation
    • service string Required

      The service type

    • service_settings object Required

      Settings specific to the service

    • task_settings object

      Task settings specific to the service and task type

    • inference_id string Required

      The inference Id

    • task_type string Required

      The task type

      Values are text_embedding, chat_completion, or completion.
PUT /_inference/{task_type}/{watsonx_inference_id} 
PUT _inference/text_embedding/watsonx-embeddings
{
  "service": "watsonxai",
  "service_settings": {
      "api_key": "Watsonx-API-Key", 
      "url": "Wastonx-URL", 
      "model_id": "ibm/slate-30m-english-rtrvr",
      "project_id": "IBM-Cloud-ID", 
      "api_version": "2024-03-14"
  }
}
resp = client.inference.put(
    task_type="text_embedding",
    inference_id="watsonx-embeddings",
    inference_config={
        "service": "watsonxai",
        "service_settings": {
            "api_key": "Watsonx-API-Key",
            "url": "Wastonx-URL",
            "model_id": "ibm/slate-30m-english-rtrvr",
            "project_id": "IBM-Cloud-ID",
            "api_version": "2024-03-14"
        }
    },
)
const response = await client.inference.put({
  task_type: "text_embedding",
  inference_id: "watsonx-embeddings",
  inference_config: {
    service: "watsonxai",
    service_settings: {
      api_key: "Watsonx-API-Key",
      url: "Wastonx-URL",
      model_id: "ibm/slate-30m-english-rtrvr",
      project_id: "IBM-Cloud-ID",
      api_version: "2024-03-14",
    },
  },
});
response = client.inference.put(
  task_type: "text_embedding",
  inference_id: "watsonx-embeddings",
  body: {
    "service": "watsonxai",
    "service_settings": {
      "api_key": "Watsonx-API-Key",
      "url": "Wastonx-URL",
      "model_id": "ibm/slate-30m-english-rtrvr",
      "project_id": "IBM-Cloud-ID",
      "api_version": "2024-03-14"
    }
  }
)
$resp = $client->inference()->put([
    "task_type" => "text_embedding",
    "inference_id" => "watsonx-embeddings",
    "body" => [
        "service" => "watsonxai",
        "service_settings" => [
            "api_key" => "Watsonx-API-Key",
            "url" => "Wastonx-URL",
            "model_id" => "ibm/slate-30m-english-rtrvr",
            "project_id" => "IBM-Cloud-ID",
            "api_version" => "2024-03-14",
        ],
    ],
]);
curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"watsonxai","service_settings":{"api_key":"Watsonx-API-Key","url":"Wastonx-URL","model_id":"ibm/slate-30m-english-rtrvr","project_id":"IBM-Cloud-ID","api_version":"2024-03-14"}}' "$ELASTICSEARCH_URL/_inference/text_embedding/watsonx-embeddings"
Request example
Run `PUT _inference/text_embedding/watsonx-embeddings` to create an Watonsx inference endpoint that performs a text embedding task. 
{
  "service": "watsonxai",
  "service_settings": {
      "api_key": "Watsonx-API-Key", 
      "url": "Wastonx-URL", 
      "model_id": "ibm/slate-30m-english-rtrvr",
      "project_id": "IBM-Cloud-ID", 
      "api_version": "2024-03-14"
  }
}