elasticsearch/docs/reference/inference/service-elasticsearch.asciidoc
2024-07-01 10:12:16 +02:00

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[[infer-service-elasticsearch]]
=== Elasticsearch {infer} service
Creates an {infer} endpoint to perform an {infer} task with the `elasticsearch` service.
[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.
--
`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.
`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.
[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].
[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
{
"service": "elasticsearch",
"service_settings": {
"num_allocations": 1,
"num_threads": 1,
"model_id": "msmarco-MiniLM-L12-cos-v5" <1>
}
}
------------------------------------------------------------
// TEST[skip:TBD]
<1> 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].