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143 lines
3.8 KiB
Text
143 lines
3.8 KiB
Text
[role="xpack"]
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[[post-inference-api]]
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=== Perform inference API
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experimental[]
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Performs an inference task on an input text by using an {infer} model.
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IMPORTANT: The {infer} APIs enable you to use certain services, such as ELSER,
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OpenAI, or Hugging Face, in your cluster. This is not the same feature that you
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can use on an ML node with custom {ml} models. If you want to train and use your
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own model, use the <<ml-df-trained-models-apis>>.
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[discrete]
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[[post-inference-api-request]]
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==== {api-request-title}
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`POST /_inference/<inference_id>`
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`POST /_inference/<task_type>/<inference_id>`
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[discrete]
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[[post-inference-api-prereqs]]
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==== {api-prereq-title}
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* Requires the `monitor_inference` <<privileges-list-cluster,cluster privilege>>
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(the built-in `inference_admin` and `inference_user` roles grant this privilege)
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[discrete]
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[[post-inference-api-desc]]
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==== {api-description-title}
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The perform {infer} API enables you to use {ml} models to perform specific tasks
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on data that you provide as an input. The API returns a response with the
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results of the tasks. The {infer} model you use can perform one specific task
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that has been defined when the model was created with the <<put-inference-api>>.
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[discrete]
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[[post-inference-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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The unique identifier of the {infer} endpoint.
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`<task_type>`::
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(Optional, string)
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The type of {infer} task that the model performs.
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[discrete]
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[[post-inference-api-request-body]]
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== {api-request-body-title}
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`input`::
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(Required, array of strings)
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The text on which you want to perform the {infer} task.
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`input` can be a single string or an array.
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[NOTE]
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====
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Inference endpoints for the `completion` task type currently only support a single string as input.
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====
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[discrete]
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[[post-inference-api-example]]
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==== {api-examples-title}
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The following example performs sparse embedding on the example sentence.
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[source,console]
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------------------------------------------------------------
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POST _inference/sparse_embedding/my-elser-model
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{
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"input": "The sky above the port was the color of television tuned to a dead channel."
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}
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------------------------------------------------------------
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// TEST[skip:TBD]
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The API returns the following response:
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[source,console-result]
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------------------------------------------------------------
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{
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"sparse_embedding": [
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{
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"port": 2.1259406,
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"sky": 1.7073475,
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"color": 1.6922266,
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"dead": 1.6247464,
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"television": 1.3525393,
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"above": 1.2425821,
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"tuned": 1.1440028,
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"colors": 1.1218185,
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"tv": 1.0111054,
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"ports": 1.0067928,
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"poem": 1.0042328,
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"channel": 0.99471164,
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"tune": 0.96235967,
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"scene": 0.9020516,
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(...)
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},
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(...)
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]
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}
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------------------------------------------------------------
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// NOTCONSOLE
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The next example performs a completion on the example question.
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[source,console]
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------------------------------------------------------------
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POST _inference/completion/openai_chat_completions
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{
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"input": "What is Elastic?"
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}
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------------------------------------------------------------
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// TEST[skip:TBD]
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The API returns the following response:
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[source,console-result]
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------------------------------------------------------------
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{
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"completion": [
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{
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"result": "Elastic is a company that provides a range of software solutions for search, logging, security, and analytics. Their flagship product is Elasticsearch, an open-source, distributed search engine that allows users to search, analyze, and visualize large volumes of data in real-time. Elastic also offers products such as Kibana, a data visualization tool, and Logstash, a log management and pipeline tool, as well as various other tools and solutions for data analysis and management."
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}
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]
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}
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------------------------------------------------------------
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// NOTCONSOLE
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