Create a Hugging Face inference endpoint Added in 8.12.0

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

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

You must first create an inference endpoint on the Hugging Face endpoint page to get an endpoint URL. Select the model you want to use on the new endpoint creation page (for example intfloat/e5-small-v2), then select the sentence embeddings task under the advanced configuration section. Create the endpoint and copy the URL after the endpoint initialization has been finished.

The following models are recommended for the Hugging Face service:

  • all-MiniLM-L6-v2
  • all-MiniLM-L12-v2
  • all-mpnet-base-v2
  • e5-base-v2
  • e5-small-v2
  • multilingual-e5-base
  • multilingual-e5-small

When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running. After creating the endpoint, wait for the model deployment to complete before using it. To verify the deployment status, use the get trained model statistics API. Look for "state": "fully_allocated" in the response and ensure that the "allocation_count" matches the "target_allocation_count". Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.

Path parameters

  • task_type string Required

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

    Value is text_embedding.

  • huggingface_inference_id string Required

    The unique identifier of the inference endpoint.

application/json

Body

  • chunking_settings 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 hugging_face.

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

      A valid access token for your HuggingFace account. You can create or find your access tokens on the HuggingFace 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
    • rate_limit object
      Hide rate_limit attribute Show rate_limit attribute object
    • url string Required

      The URL endpoint to use for the requests.

Responses

  • 200 application/json
    Hide response attributes Show response attributes object
    • chunking_settings 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 sparse_embedding, text_embedding, rerank, completion, or chat_completion.
PUT /_inference/{task_type}/{huggingface_inference_id} 
curl \
 --request PUT 'http://api.example.com/_inference/{task_type}/{huggingface_inference_id}' \
 --header "Authorization: $API_KEY" \
 --header "Content-Type: application/json" \
 --data '"{\n    \"service\": \"hugging_face\",\n    \"service_settings\": {\n        \"api_key\": \"hugging-face-access-token\", \n        \"url\": \"url-endpoint\" \n    }\n}"'
Request example
Run `PUT _inference/text_embedding/hugging-face-embeddings` to create an inference endpoint that performs a `text_embedding` task type. 
{
    "service": "hugging_face",
    "service_settings": {
        "api_key": "hugging-face-access-token", 
        "url": "url-endpoint" 
    }
}
Response examples (200) 
{
  "chunking_settings": {
    "max_chunk_size": 42.0,
    "overlap": 42.0,
    "sentence_overlap": 42.0,
    "strategy": "string"
  },
  "service": "string",
  "service_settings": {},
  "task_settings": {},
  "inference_id": "string",
  "task_type": "sparse_embedding"
}