Create an Contextual AI inference endpoint Generally available; Added in 9.2.0

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

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

To review the available rerank models, refer to https://docs.contextual.ai/api-reference/rerank/rerank#body-model.

Required authorization

  • Cluster privileges: manage_inference

Path parameters

  • task_type string

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

    Value is rerank.

  • contextualai_inference_id string Required

    The unique identifier of the inference endpoint.

Query parameters

application/json

Body

  • chunking_settings object

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

    Value is contextualai.

  • service_settings object Required

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

    Hide service_settings attributes Show service_settings attributes object
    • api_key string Required

      A valid API key for your Contexutual AI account.

      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.

    • model_id string Required

      The name of the model to use for the inference task. Refer to the Contextual AI documentation for the list of available rerank models.

    • rate_limit object

      This setting helps to minimize the number of rate limit errors returned from Contextual AI. The contextualai service sets a default number of requests allowed per minute depending on the task type. For rerank, it is set to 1000.

      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
  • task_settings object

    Settings to configure the inference task. These settings are specific to the task type you specified.

    Hide task_settings attributes Show task_settings attributes object
    • instruction string

      Instructions for the reranking model. Refer to https://docs.contextual.ai/api-reference/rerank/rerank#body-instruction Only for the rerank task type.

    • return_documents boolean

      Whether to return the source documents in the response. Only for the rerank task type.

      Default value is false.

    • top_k number

      The number of most relevant documents to return. If not specified, the reranking results of all documents will be returned. Only for the rerank task type.

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

      Value is rerank.
PUT /_inference/{task_type}/{contextualai_inference_id} 
PUT _inference/rerank/contextualai-rerank
{
    "service": "contextualai",
    "service_settings": {
        "api_key": "ContextualAI-Api-key",
        "model_id": "ctxl-rerank-v2-instruct-multilingual-mini"
    },
    "task_settings": {
        "instruction": "Rerank the following documents based on their relevance to the query.",
        "top_k": 3
    }
}
resp = client.inference.put(
    task_type="rerank",
    inference_id="contextualai-rerank",
    inference_config={
        "service": "contextualai",
        "service_settings": {
            "api_key": "ContextualAI-Api-key",
            "model_id": "ctxl-rerank-v2-instruct-multilingual-mini"
        },
        "task_settings": {
            "instruction": "Rerank the following documents based on their relevance to the query.",
            "top_k": 3
        }
    },
)
const response = await client.inference.put({
  task_type: "rerank",
  inference_id: "contextualai-rerank",
  inference_config: {
    service: "contextualai",
    service_settings: {
      api_key: "ContextualAI-Api-key",
      model_id: "ctxl-rerank-v2-instruct-multilingual-mini",
    },
    task_settings: {
      instruction:
        "Rerank the following documents based on their relevance to the query.",
      top_k: 3,
    },
  },
});
response = client.inference.put(
  task_type: "rerank",
  inference_id: "contextualai-rerank",
  body: {
    "service": "contextualai",
    "service_settings": {
      "api_key": "ContextualAI-Api-key",
      "model_id": "ctxl-rerank-v2-instruct-multilingual-mini"
    },
    "task_settings": {
      "instruction": "Rerank the following documents based on their relevance to the query.",
      "top_k": 3
    }
  }
)
$resp = $client->inference()->put([
    "task_type" => "rerank",
    "inference_id" => "contextualai-rerank",
    "body" => [
        "service" => "contextualai",
        "service_settings" => [
            "api_key" => "ContextualAI-Api-key",
            "model_id" => "ctxl-rerank-v2-instruct-multilingual-mini",
        ],
        "task_settings" => [
            "instruction" => "Rerank the following documents based on their relevance to the query.",
            "top_k" => 3,
        ],
    ],
]);
curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"contextualai","service_settings":{"api_key":"ContextualAI-Api-key","model_id":"ctxl-rerank-v2-instruct-multilingual-mini"},"task_settings":{"instruction":"Rerank the following documents based on their relevance to the query.","top_k":3}}' "$ELASTICSEARCH_URL/_inference/rerank/contextualai-rerank"
Request example
Run `PUT _inference/rerank/contextualai-rerank` to create an inference endpoint for rerank tasks using the Contextual AI service. 
{
    "service": "contextualai",
    "service_settings": {
        "api_key": "ContextualAI-Api-key",
        "model_id": "ctxl-rerank-v2-instruct-multilingual-mini"
    },
    "task_settings": {
        "instruction": "Rerank the following documents based on their relevance to the query.",
        "top_k": 3
    }
}