Create a DeepSeek inference endpoint Generally available

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PUT /_inference/{task_type}/{deepseek_inference_id}
Api key auth

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

Required authorization

  • Cluster privileges: manage_inference

Path parameters

  • task_type string

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

    Values are completion or chat_completion.

  • deepseek_inference_id string Required

    The unique identifier of the inference endpoint.

Query parameters

application/json

Body Required

  • service string Required

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

    Value is deepseek.

  • service_settings object Required

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

    Hide service_settings attributes Show service_settings attributes object
    • api_key string Required

      A valid API key for your DeepSeek account. You can find or create your DeepSeek API keys on the DeepSeek API key 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
    • model_id string Required

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

      For the available completion and chat_completion models, refer to the DeepSeek Models & Pricing docs.

    • url string

      The URL endpoint to use for the requests. Defaults to https://api.deepseek.com/chat/completions.

Responses

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

      The chunking configuration object. Applies only to the sparse_embedding and text_embedding task types. Not applicable to the rerank, completion, or chat_completion task types.

      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 completion or chat_completion.
PUT /_inference/{task_type}/{deepseek_inference_id} 
curl \
 --request PUT 'http://api.example.com/_inference/{task_type}/{deepseek_inference_id}' \
 --header "Authorization: $API_KEY" \
 --header "Content-Type: application/json" \
 --data '{"service":"deepseek","service_settings":{"api_key":"string","model_id":"string","url":"string"}}'