Create a Hugging Face inference endpoint | Elasticsearch API documentation (v9)

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.

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

Required authorization

Cluster privileges: manage_inference

Path parameters

task_type string

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

Chunking configuration 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).
  
  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.
- strategy string
  
  The chunking strategy: sentence or word.
  
  Default value is sentence.
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
  
  This setting helps to minimize the number of rate limit errors returned from the service.
  Hide rate_limit attribute Show rate_limit attribute object
  
  requests_per_minute number
  
  The number of requests allowed per minute. By default, the number of requests allowed per minute is set by each service as follows:
  
  alibabacloud-ai-search service: 1000
  
  anthropic service: 50
  
  azureaistudio service: 240
  
  azureopenai service and task type text_embedding: 1440
  
  azureopenai service and task type completion: 120
  
  cohere service: 10000
  
  elastic service and task type chat_completion: 240
  
  googleaistudio service: 360
  
  googlevertexai service: 30000
  
  hugging_face service: 3000
  
  jinaai service: 2000
  
  mistral service: 240
  
  openai service and task type text_embedding: 3000
  
  openai service and task type completion: 500
  
  voyageai service: 2000
  
  watsonxai service: 120
- 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
  
  Chunking configuration 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).
  
  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.
  
  strategy string
  
  The chunking strategy: sentence or word.
  
  Default value is sentence.
- service string Required
  
  The service type
- service_settings object Required
- task_settings object
- inference_id string Required
  
  The inference Id
- task_type string Required
  
  Value is text_embedding.

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" } }

resp = client.inference.put( task_type="text_embedding", inference_id="hugging-face-embeddings", inference_config={ "service": "hugging_face", "service_settings": { "api_key": "hugging-face-access-token", "url": "url-endpoint" } }, )

const response = await client.inference.put({ task_type: "text_embedding", inference_id: "hugging-face-embeddings", inference_config: { service: "hugging_face", service_settings: { api_key: "hugging-face-access-token", url: "url-endpoint", }, }, });

response = client.inference.put( task_type: "text_embedding", inference_id: "hugging-face-embeddings", body: { "service": "hugging_face", "service_settings": { "api_key": "hugging-face-access-token", "url": "url-endpoint" } } )

$resp = $client->inference()->put([ "task_type" => "text_embedding", "inference_id" => "hugging-face-embeddings", "body" => [ "service" => "hugging_face", "service_settings" => [ "api_key" => "hugging-face-access-token", "url" => "url-endpoint", ], ], ]);

curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"hugging_face","service_settings":{"api_key":"hugging-face-access-token","url":"url-endpoint"}}' "$ELASTICSEARCH_URL/_inference/text_embedding/hugging-face-embeddings"

client.inference().put(p -> p .inferenceId("hugging-face-embeddings") .taskType(TaskType.TextEmbedding) .inferenceConfig(i -> i .service("hugging_face") .serviceSettings(JsonData.fromJson("{\"api_key\":\"hugging-face-access-token\",\"url\":\"url-endpoint\"}")) ) );

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" } }