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75 lines
3.6 KiB
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75 lines
3.6 KiB
Text
[role="xpack"]
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[[inference-apis]]
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== {infer-cap} APIs
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IMPORTANT: The {infer} APIs enable you to use certain services, such as built-in
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{ml} models (ELSER, E5), models uploaded through Eland, Cohere, OpenAI, Azure,
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Google AI Studio or Hugging Face. For built-in models and models uploaded
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through Eland, the {infer} APIs offer an alternative way to use and manage
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trained models. However, if you do not plan to use the {infer} APIs to use these
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models or if you want to use non-NLP models, use the
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<<ml-df-trained-models-apis>>.
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The {infer} APIs enable you to create {infer} endpoints and use {ml} models of
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different providers - such as Amazon Bedrock, Anthropic, Azure AI Studio,
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Cohere, Google AI, Mistral, OpenAI, or HuggingFace - as a service. Use
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the following APIs to manage {infer} models and perform {infer}:
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* <<delete-inference-api>>
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* <<get-inference-api>>
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* <<post-inference-api>>
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* <<put-inference-api>>
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* <<stream-inference-api>>
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* <<update-inference-api>>
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[[inference-landscape]]
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.A representation of the Elastic inference landscape
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image::images/inference-landscape.jpg[A representation of the Elastic inference landscape,align="center"]
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An {infer} endpoint enables you to use the corresponding {ml} model without
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manual deployment and apply it to your data at ingestion time through
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<<semantic-search-semantic-text, semantic text>>.
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Choose a model from your provider or use ELSER – a retrieval model trained by
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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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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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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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include::delete-inference.asciidoc[]
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include::get-inference.asciidoc[]
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include::post-inference.asciidoc[]
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include::put-inference.asciidoc[]
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include::stream-inference.asciidoc[]
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include::update-inference.asciidoc[]
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include::service-alibabacloud-ai-search.asciidoc[]
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include::service-amazon-bedrock.asciidoc[]
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include::service-anthropic.asciidoc[]
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include::service-azure-ai-studio.asciidoc[]
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include::service-azure-openai.asciidoc[]
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include::service-cohere.asciidoc[]
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include::service-elasticsearch.asciidoc[]
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include::service-elser.asciidoc[]
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include::service-google-ai-studio.asciidoc[]
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include::service-google-vertex-ai.asciidoc[]
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include::service-hugging-face.asciidoc[]
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include::service-mistral.asciidoc[]
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include::service-openai.asciidoc[]
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include::service-watsonx-ai.asciidoc[]
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