Create a Hugging Face inference endpoint | Elasticsearch API documentation (8x-unreleased)

Create a Hugging Face inference endpoint Generally available; Added in 8.12.0

PUT /_inference/{task_type}/{huggingface_inference_id}

Create an inference endpoint to perform an inference task with the hugging_face service. Supported tasks include: text_embedding, completion, and chat_completion.

To configure the endpoint, first visit the Hugging Face Inference Endpoints page and create a new endpoint. Select a model that supports the task you intend to use.

For Elastic's text_embedding task: The selected model must support the Sentence Embeddings task. On the new endpoint creation page, select the Sentence Embeddings task under the Advanced Configuration section. After the endpoint has initialized, copy the generated endpoint URL. Recommended models for text_embedding task:

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

For Elastic's chat_completion and completion tasks: The selected model must support the Text Generation task and expose OpenAI API. HuggingFace supports both serverless and dedicated endpoints for Text Generation. When creating dedicated endpoint select the Text Generation task. After the endpoint is initialized (for dedicated) or ready (for serverless), ensure it supports the OpenAI API and includes /v1/chat/completions part in URL. Then, copy the full endpoint URL for use. Recommended models for chat_completion and completion tasks:

Mistral-7B-Instruct-v0.2
QwQ-32B
Phi-3-mini-128k-instruct ##Required authorization
Cluster privileges: manage_inference

Path parameters

task_type string Required

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

Values are chat_completion, completion, or 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
  
  requests_per_minute number
  
  The number of requests allowed per minute.
- url string Required
  
  The URL endpoint to use for the requests. For completion and chat_completion tasks, the deployed model must be compatible with the Hugging Face Chat Completion interface (see the linked external documentation for details). The endpoint URL for the request must include /v1/chat/completions. If the model supports the OpenAI Chat Completion schema, a toggle should appear in the interface. Enabling this toggle doesn't change any model behavior, it reveals the full endpoint URL needed (which should include /v1/chat/completions) when configuring the inference endpoint in Elasticsearch. If the model doesn't support this schema, the toggle may not be shown.
  
  External documentation
- model_id string
  
  The name of the HuggingFace model to use for the inference task. For completion and chat_completion tasks, this field is optional but may be required for certain models — particularly when using serverless inference endpoints. For the text_embedding task, this field should not be included. Otherwise, the request will fail.

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 text_embedding, chat_completion, or completion.

PUT /_inference/{task_type}/{huggingface_inference_id}

PUT _inference/text_embedding/hugging-face-embeddings
{
    "service": "hugging_face",
    "service_settings": {
        "api_key": "hugging-face-access-token", 
        "url": "url-endpoint" 
    }
}

curl \
 --request PUT 'http://api.example.com/_inference/{task_type}/{huggingface_inference_id}' \
 --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" 
    }
}

Documentation preview

This is a preview of your version @2025-06-09 which is not yet released.

View current documentation