Perform inference on the service Generally available; Added in 8.11.0

POST /_inference/{task_type}/{inference_id}
Api key auth Basic auth Bearer auth

All methods and paths for this operation:

POST /_inference/{inference_id}

POST /_inference/{task_type}/{inference_id}

This API enables you to use machine learning models to perform specific tasks on data that you provide as an input. It returns a response with the results of the tasks. The inference endpoint you use can perform one specific task that has been defined when the endpoint was created with the create inference API.

For details about using this API with a service, such as Amazon Bedrock, Anthropic, or HuggingFace, refer to the service-specific documentation.


The inference APIs enable you to use certain services, such as built-in machine learning models (ELSER, E5), models uploaded through Eland, Cohere, OpenAI, Azure, Google AI Studio, Google Vertex AI, Anthropic, Watsonx.ai, or Hugging Face. For built-in models and models uploaded through Eland, the inference APIs offer an alternative way to use and manage trained models. However, if you do not plan to use the inference APIs to use these models or if you want to use non-NLP models, use the machine learning trained model APIs.

highlight#highlightFromAnchor" href="#topic-required-authorization"> Required authorization

  • Cluster privileges: monitor_inference

Path parameters

  • task_type string Required

    The type of inference task that the model performs.

    Values are sparse_embedding, text_embedding, rerank, completion, or chat_completion.

  • inference_id string Required

    The unique identifier for the inference endpoint.

Query parameters

  • timeout string

    The amount of time to wait for the inference request to complete.

    Values are -1 or 0.

application/json

Body

Responses

  • 200 application/json
    details#setActive"> Hide response attributes Show response attributes object
    • text_embedding_bytes array[object]

      The text embedding result object for byte representation

      details#setActive"> Hide text_embedding_bytes attribute Show text_embedding_bytes attribute object
      • embedding array[number] Required

        Text Embedding results containing bytes are represented as Dense Vectors of bytes.

    • text_embedding_bits array[object]

      The text embedding result object for byte representation

      details#setActive"> Hide text_embedding_bits attribute Show text_embedding_bits attribute object
      • embedding array[number] Required

        Text Embedding results containing bytes are represented as Dense Vectors of bytes.

    • text_embedding array[object]

      The text embedding result object

      details#setActive"> Hide text_embedding attribute Show text_embedding attribute object
      • embedding array[number] Required

        Text Embedding results are represented as Dense Vectors of floats.

    • sparse_embedding array[object]
      details#setActive"> Hide sparse_embedding attribute Show sparse_embedding attribute object
      • embedding object Required

        Sparse Embedding tokens are represented as a dictionary of string to double.

        details#setActive"> Hide embedding attribute Show embedding attribute object
        • * number Additional properties
    • completion array[object]

      The completion result object

      details#setActive"> Hide completion attribute Show completion attribute object
      • result string Required
    • rerank array[object]

      The rerank result object representing a single ranked document id: the original index of the document in the request relevance_score: the relevance_score of the document relative to the query text: Optional, the text of the document, if requested

      details#setActive"> Hide rerank attributes Show rerank attributes object
      • index number Required
      • relevance_score number Required
      • text string
POST /_inference/{task_type}/{inference_id} 
curl \
 --request POST 'http://api.example.com/_inference/{task_type}/{inference_id}' \
 --header "Authorization: $API_KEY" \
 --header "Content-Type: application/json" \
 --data '{"query":"string","input":"string","input_type":"string","task_settings":{}}'