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Copy file name to clipboardExpand all lines: docs/reference/inference/inference-apis.asciidoc
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@@ -35,21 +35,21 @@ Elastic –, then create an {infer} endpoint by the <<put-inference-api>>.
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Now use <<semantic-search-semantic-text, semantic text>> to perform
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<<semantic-search, semantic search>> on your data.
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[discrete]
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[[default-enpoints]]
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=== Default {infer} endpoints
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//[discrete]
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//[[default-enpoints]]
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//=== Default {infer} endpoints
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Your {es} deployment contains some preconfigured {infer} endpoints that makes it easier for you to use them when defining `semantic_text` fields or {infer} processors.
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The following list contains the default {infer} endpoints listed by `inference_id`:
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//Your {es} deployment contains some preconfigured {infer} endpoints that makes it easier for you to use them when defining `semantic_text` fields or {infer} processors.
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//The following list contains the default {infer} endpoints listed by `inference_id`:
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* `.elser-2-elasticsearch`: uses the {ml-docs}/ml-nlp-elser.html[ELSER] built-in trained model for `sparse_embedding` tasks (recommended for English language texts)
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* `.multilingual-e5-small-elasticsearch`: uses the {ml-docs}/ml-nlp-e5.html[E5] built-in trained model for `text_embedding` tasks (recommended for non-English language texts)
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//* `.elser-2-elasticsearch`: uses the {ml-docs}/ml-nlp-elser.html[ELSER] built-in trained model for `sparse_embedding` tasks (recommended for English language texts)
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//* `.multilingual-e5-small-elasticsearch`: uses the {ml-docs}/ml-nlp-e5.html[E5] built-in trained model for `text_embedding` tasks (recommended for non-English language texts)
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Use the `inference_id` of the endpoint in a <<semantic-text,`semantic_text`>> field definition or when creating an <<inference-processor,{infer} processor>>.
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The API call will automatically download and deploy the model which might take a couple of minutes.
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Default {infer} enpoints have {ml-docs}/ml-nlp-auto-scale.html#nlp-model-adaptive-allocations[adaptive allocations] enabled.
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For these models, the minimum number of allocations is `0`.
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If there is no {infer} activity that uses the endpoint, the number of allocations will scale down to `0` automatically after 15 minutes.
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//Use the `inference_id` of the endpoint in a <<semantic-text,`semantic_text`>> field definition or when creating an <<inference-processor,{infer} processor>>.
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//The API call will automatically download and deploy the model which might take a couple of minutes.
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//Default {infer} enpoints have {ml-docs}/ml-nlp-auto-scale.html#nlp-model-adaptive-allocations[adaptive allocations] enabled.
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//For these models, the minimum number of allocations is `0`.
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//If there is no {infer} activity that uses the endpoint, the number of allocations will scale down to `0` automatically after 15 minutes.
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