max_chunking_size: (Optional, integer) Specifies the maximum size of a chunk in words. Defaults to 250. This value cannot be higher than 300 or lower than 20 (for sentence strategy) or 10 (for word strategy).
overlap: (Optional, integer) Only for word chunking strategy. Specifies the number of overlapping words for chunks. Defaults to 100. This value cannot be higher than the half of max_chunking_size.
sentence_overlap: (Optional, integer) Only for sentence chunking strategy. Specifies the numnber of overlapping sentences for chunks. It can be either 1 or 0. Defaults to 1.
strategy: (Optional, string) Specifies the chunking strategy. It could be either sentence or word.

service

(Required, string) The type of service supported for the specified task type. In this case, hugging_face.

service_settings

(Required, object) Settings used to install the inference model.

These settings are specific to the hugging_face service.

api_key

(Required, string) A valid access token of your Hugging Face account. You can find your Hugging Face access tokens or you can create a new one on the settings page.

You need to provide the API key only once, during the inference model creation. The Get inference 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.

url

(Required, string) The URL endpoint to use for the requests.

rate_limit

(Optional, object) By default, the huggingface service sets the number of requests allowed per minute to 3000. This helps to minimize the number of rate limit errors returned from Hugging Face. To modify this, set the requests_per_minute setting of this object in your service settings:

"rate_limit": {
    "requests_per_minute": <<number_of_requests>>
}

Hugging Face service example

edit

The following example shows how to create an inference endpoint called hugging-face-embeddings to perform a text_embedding task type.

resp = client.inference.put(
    task_type="text_embedding",
    inference_id="hugging-face-embeddings",
    inference_config={
        "service": "hugging_face",
        "service_settings": {
            "api_key": "<access_token>",
            "url": "<url_endpoint>"
        }
    },
)
print(resp)

const response = await client.inference.put({
  task_type: "text_embedding",
  inference_id: "hugging-face-embeddings",
  inference_config: {
    service: "hugging_face",
    service_settings: {
      api_key: "<access_token>",
      url: "<url_endpoint>",
    },
  },
});
console.log(response);

PUT _inference/text_embedding/hugging-face-embeddings
{
  "service": "hugging_face",
  "service_settings": {
    "api_key": "<access_token>", 
    "url": "<url_endpoint>" 
  }
}

	A valid Hugging Face access token. You can find on the settings page of your account.
	The inference endpoint URL you created on Hugging Face.

Create a new 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. Copy the URL after the endpoint initialization has been finished.

The list of recommended models for the Hugging Face service:

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