elasticsearch/docs/reference/inference/service-elasticsearch.asciidoc

[[infer-service-elasticsearch]]
=== Elasticsearch {infer} service

Creates an {infer} endpoint to perform an {infer} task with the `elasticsearch`
service.

NOTE: If you use the E5 model through the `elasticsearch` service, the API
request will automatically download and deploy the model if it isn't downloaded
yet.


[discrete]
[[infer-service-elasticsearch-api-request]]
==== {api-request-title}

`PUT /_inference/<task_type>/<inference_id>`

[discrete]
[[infer-service-elasticsearch-api-path-params]]
==== {api-path-parms-title}

`<inference_id>`::
(Required, string)
include::inference-shared.asciidoc[tag=inference-id]

`<task_type>`::
(Required, string)
include::inference-shared.asciidoc[tag=task-type]
+
--
Available task types:

* `rerank`,
* `text_embedding`.
--

[discrete]
[[infer-service-elasticsearch-api-request-body]]
==== {api-request-body-title}

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

`service_settings`::
(Required, object)
include::inference-shared.asciidoc[tag=service-settings]
+
--
These settings are specific to the `elasticsearch` service.
--

`adaptive_allocations`:::
(Optional, object)
include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=adaptive-allocation]

`enabled`::::
(Optional, Boolean)
include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=adaptive-allocation-enabled]

`max_number_of_allocations`::::
(Optional, integer)
include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=adaptive-allocation-max-number]

`min_number_of_allocations`::::
(Optional, integer)
include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=adaptive-allocation-min-number]

`model_id`:::
(Required, string)
The name of the model to use for the {infer} task.
It can be the ID of either a built-in model (for example, `.multilingual-e5-small` for E5) or a text embedding model already
{ml-docs}/ml-nlp-import-model.html#ml-nlp-import-script[uploaded through Eland].

`num_allocations`:::
(Required, integer)
The total number of allocations this model is assigned across machine learning nodes.
Increasing this value generally increases the throughput.
If `adaptive_allocations` is enabled, do not set this value, because it's automatically set.

`num_threads`:::
(Required, integer)
Sets the number of threads used by each model allocation during inference. This generally increases the speed per inference request. The inference process is a compute-bound process; `threads_per_allocations` must not exceed the number of available allocated processors per node.
Must be a power of 2. Max allowed value is 32.

`task_settings`::
(Optional, object)
include::inference-shared.asciidoc[tag=task-settings]
+
.`task_settings` for the `rerank` task type
[%collapsible%closed]
=====
`return_documents`:::
(Optional, Boolean)
Returns the document instead of only the index. Defaults to `true`.
=====


[discrete]
[[inference-example-elasticsearch]]
==== E5 via the `elasticsearch` service

The following example shows how to create an {infer} endpoint called
`my-e5-model` to perform a `text_embedding` task type.

The API request below will automatically download the E5 model if it isn't
already downloaded and then deploy the model.

[source,console]
------------------------------------------------------------
PUT _inference/text_embedding/my-e5-model
{
  "service": "elasticsearch",
  "service_settings": {
    "num_allocations": 1,
    "num_threads": 1,
    "model_id": ".multilingual-e5-small" <1>
  }
}
------------------------------------------------------------
// TEST[skip:TBD]
<1> The `model_id` must be the ID of one of the built-in E5 models.
Valid values are `.multilingual-e5-small` and `.multilingual-e5-small_linux-x86_64`.
For further details, refer to the {ml-docs}/ml-nlp-e5.html[E5 model documentation].

[NOTE]
====
You might see a 502 bad gateway error in the response when using the {kib} Console.
This error usually just reflects a timeout, while the model downloads in the background.
You can check the download progress in the {ml-app} UI.
If using the Python client, you can set the `timeout` parameter to a higher value.
====

[discrete]
[[inference-example-eland]]
==== Models uploaded by Eland via the elasticsearch service

The following example shows how to create an {infer} endpoint called
`my-msmarco-minilm-model` to perform a `text_embedding` task type.

[source,console]
------------------------------------------------------------
PUT _inference/text_embedding/my-msmarco-minilm-model <1>
{
  "service": "elasticsearch",
  "service_settings": {
    "num_allocations": 1,
    "num_threads": 1,
    "model_id": "msmarco-MiniLM-L12-cos-v5" <2>
  }
}
------------------------------------------------------------
// TEST[skip:TBD]
<1> Provide an unique identifier for the inference endpoint. The `inference_id` must be unique and must not match the `model_id`.
<2> The `model_id` must be the ID of a text embedding model which has already been
{ml-docs}/ml-nlp-import-model.html#ml-nlp-import-script[uploaded through Eland].

[discrete]
[[inference-example-adaptive-allocation]]
==== Setting adaptive allocation for E5 via the `elasticsearch` service

The following example shows how to create an {infer} endpoint called
`my-e5-model` to perform a `text_embedding` task type and configure adaptive
allocations.

The API request below will automatically download the E5 model if it isn't
already downloaded and then deploy the model.

[source,console]
------------------------------------------------------------
PUT _inference/text_embedding/my-e5-model
{
  "service": "elasticsearch",
  "service_settings": {
    "adaptive_allocations": {
      "enabled": true,
      "min_number_of_allocations": 3,
      "max_number_of_allocations": 10
    },
    "model_id": ".multilingual-e5-small"
  }
}
------------------------------------------------------------
// TEST[skip:TBD]