Create a Hugging Face inference endpoint

Auth

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

For Elastic's rerank task: The selected model must support the sentence-ranking task and expose OpenAI API. HuggingFace supports only dedicated (not serverless) endpoints for Rerank so far. After the endpoint is initialized, copy the full endpoint URL for use. Tested models for rerank task:

  • bge-reranker-base
  • jina-reranker-v1-turbo-en-GGUF

Required authorization

Cluster privileges manage_inference

Path parameters

task_type enum required

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

Values: chat_completion completion rerank text_embedding
huggingface_inference_id string required

The unique identifier of the inference endpoint.

Query parameters

timeout string · default: 30s

Specifies the amount of time to wait for the inference endpoint to be created.

Request body

chunking_settings object

The chunking configuration object.

Reference
max_chunk_size number · default: 250

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 · default: 100

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 · default: 1

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 · default: sentence

The chunking strategy: sentence or word.

service enum required

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

Values: hugging_face
service_settings object required

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

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.

External docs
rate_limit object

This setting helps to minimize the number of rate limit errors returned from Hugging Face. By default, the hugging_face service sets the number of requests allowed per minute to 3000 for all supported tasks. Hugging Face does not publish a universal rate limit — actual limits may vary. It is recommended to adjust this value based on the capacity and limits of your specific deployment environment.

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 docs
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.

task_settings object

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

return_documents boolean

For a rerank task, return doc text within the results.

top_n number

For a rerank task, the number of most relevant documents to return. It defaults to the number of the documents.

Response

application/json
chunking_settings object

Chunking configuration object

max_chunk_size number · default: 250

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 · default: 100

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 · default: 1

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 · default: sentence

The chunking strategy: sentence or word.

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 enum required

The task type

Values: text_embedding