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6.1 KiB
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192 lines
No EOL
6.1 KiB
Text
[[infer-service-elser]]
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=== ELSER {infer} integration
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.New API reference
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[sidebar]
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--
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For the most up-to-date API details, refer to {api-es}/group/endpoint-inference[{infer-cap} APIs].
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--
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Creates an {infer} endpoint to perform an {infer} task with the `elser` service.
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You can also deploy ELSER by using the <<infer-service-elasticsearch>>.
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[NOTE]
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====
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* Your {es} deployment contains <<default-enpoints,a preconfigured ELSER {infer} endpoint>>, you only need to create the enpoint using the API if you want to customize the settings.
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* The API request will automatically download and deploy the ELSER model if it isn't already downloaded.
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====
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[WARNING]
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.Deprecated in 8.16
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====
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The `elser` service is deprecated and will be removed in a future release.
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Use the <<infer-service-elasticsearch>> instead, with `model_id` included in the `service_settings`.
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====
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[discrete]
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[[infer-service-elser-api-request]]
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==== {api-request-title}
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`PUT /_inference/<task_type>/<inference_id>`
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[discrete]
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[[infer-service-elser-api-path-params]]
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==== {api-path-parms-title}
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`<inference_id>`::
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(Required, string)
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include::inference-shared.asciidoc[tag=inference-id]
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`<task_type>`::
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(Required, string)
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include::inference-shared.asciidoc[tag=task-type]
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+
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--
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Available task types:
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* `sparse_embedding`.
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--
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[discrete]
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[[infer-service-elser-api-request-body]]
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==== {api-request-body-title}
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`chunking_settings`::
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(Optional, object)
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include::inference-shared.asciidoc[tag=chunking-settings]
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`max_chunk_size`:::
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(Optional, integer)
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include::inference-shared.asciidoc[tag=chunking-settings-max-chunking-size]
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`overlap`:::
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(Optional, integer)
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include::inference-shared.asciidoc[tag=chunking-settings-overlap]
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`sentence_overlap`:::
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(Optional, integer)
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include::inference-shared.asciidoc[tag=chunking-settings-sentence-overlap]
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`strategy`:::
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(Optional, string)
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include::inference-shared.asciidoc[tag=chunking-settings-strategy]
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`service`::
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(Required, string)
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The type of service supported for the specified task type. In this case,
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`elser`.
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`service_settings`::
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(Required, object)
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include::inference-shared.asciidoc[tag=service-settings]
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+
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--
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These settings are specific to the `elser` service.
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--
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`adaptive_allocations`:::
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(Optional, object)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=adaptive-allocation]
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`enabled`::::
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(Optional, Boolean)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=adaptive-allocation-enabled]
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`max_number_of_allocations`::::
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(Optional, integer)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=adaptive-allocation-max-number]
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`min_number_of_allocations`::::
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(Optional, integer)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=adaptive-allocation-min-number]
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`num_allocations`:::
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(Required, integer)
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The total number of allocations this model is assigned across machine learning nodes.
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Increasing this value generally increases the throughput.
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If `adaptive_allocations` is enabled, do not set this value, because it's automatically set.
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`num_threads`:::
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(Required, integer)
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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.
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Must be a power of 2. Max allowed value is 32.
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[discrete]
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[[inference-example-elser-adaptive-allocation]]
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==== ELSER service example with adaptive allocations
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When adaptive allocations are enabled, the number of allocations of the model is set automatically based on the current load.
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NOTE: For more information on how to optimize your ELSER endpoints, refer to {ml-docs}/ml-nlp-elser.html#elser-recommendations[the ELSER recommendations] section in the model documentation.
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To learn more about model autoscaling, refer to the {ml-docs}/ml-nlp-auto-scale.html[trained model autoscaling] page.
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The following example shows how to create an {infer} endpoint called `my-elser-model` to perform a `sparse_embedding` task type and configure adaptive allocations.
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The request below will automatically download the ELSER model if it isn't already downloaded and then deploy the model.
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[source,console]
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------------------------------------------------------------
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PUT _inference/sparse_embedding/my-elser-model
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{
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"service": "elser",
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"service_settings": {
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"adaptive_allocations": {
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"enabled": true,
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"min_number_of_allocations": 3,
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"max_number_of_allocations": 10
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},
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"num_threads": 1
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}
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}
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------------------------------------------------------------
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// TEST[skip:TBD]
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[discrete]
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[[inference-example-elser]]
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==== ELSER service example without adaptive allocations
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The following example shows how to create an {infer} endpoint called `my-elser-model` to perform a `sparse_embedding` task type.
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Refer to the {ml-docs}/ml-nlp-elser.html[ELSER model documentation] for more info.
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NOTE: If you want to optimize your ELSER endpoint for ingest, set the number of threads to `1` (`"num_threads": 1`).
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If you want to optimize your ELSER endpoint for search, set the number of threads to greater than `1`.
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The request below will automatically download the ELSER model if it isn't already downloaded and then deploy the model.
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[source,console]
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------------------------------------------------------------
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PUT _inference/sparse_embedding/my-elser-model
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{
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"service": "elser",
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"service_settings": {
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"num_allocations": 1,
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"num_threads": 1
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}
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}
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------------------------------------------------------------
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// TEST[skip:TBD]
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Example response:
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[source,console-result]
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------------------------------------------------------------
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{
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"inference_id": "my-elser-model",
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"task_type": "sparse_embedding",
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"service": "elser",
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"service_settings": {
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"num_allocations": 1,
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"num_threads": 1
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},
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"task_settings": {}
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}
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------------------------------------------------------------
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// NOTCONSOLE
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[NOTE]
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====
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You might see a 502 bad gateway error in the response when using the {kib} Console.
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This error usually just reflects a timeout, while the model downloads in the background.
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You can check the download progress in the {ml-app} UI.
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If using the Python client, you can set the `timeout` parameter to a higher value.
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==== |