Create a Hugging Face inference endpoint | Elasticsearch API documentation

Path parameters

task_typestring Required
The type of the inference task that the model will perform.
Values are chat_completion, completion, rerank, or text_embedding.
huggingface_inference_idstring Required
The unique identifier of the inference endpoint.

application/json

Body

chunking_settingsobject
Chunking configuration object
Hide chunking_settings attributes Show chunking_settings attributes object
- max_chunk_sizenumber
  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).
- overlapnumber
  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_overlapnumber
  The number of overlapping sentences for chunks. It is applicable only for a sentence chunking strategy. It can be either 1 or 0.
- strategystring
  The chunking strategy: sentence or word.
servicestring Required
Value is hugging_face.
service_settingsobject Required
Hide service_settings attributes Show service_settings attributes object
- api_keystring 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_limitobject
  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_minutenumber
  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
- urlstring 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_idstring
  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_settingsobject
Hide task_settings attributes Show task_settings attributes object
- return_documentsboolean
  For a rerank task, return doc text within the results.
- top_nnumber
  For a rerank task, the number of most relevant documents to return. It defaults to the number of the documents.

Responses

200 application/json
Hide response attributes Show response attributes object
- chunking_settingsobject
  Chunking configuration object
  Hide chunking_settings attributes Show chunking_settings attributes object
  max_chunk_sizenumber
  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).
  overlapnumber
  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_overlapnumber
  The number of overlapping sentences for chunks. It is applicable only for a sentence chunking strategy. It can be either 1 or 0.
  strategystring
  The chunking strategy: sentence or word.
- servicestring Required
  The service type
- service_settingsobject Required
- task_settingsobject
- inference_idstring Required
  The inference Id
- task_typestring Required
  Values are chat_completion, completion, rerank, or 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"

Request examples

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" 
    }
}

Run `PUT _inference/rerank/hugging-face-rerank` to create an inference endpoint that performs a `rerank` task type.

{
    "service": "hugging_face",
    "service_settings": {
        "api_key": "hugging-face-access-token", 
        "url": "url-endpoint" 
    },
    "task_settings": {
        "return_documents": true,
        "top_n": 3
    }
}