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2 changes: 2 additions & 0 deletions docs/reference/inference/inference-apis.asciidoc
Original file line number Diff line number Diff line change
Expand Up @@ -19,6 +19,7 @@ the following APIs to manage {infer} models and perform {infer}:
* <<get-inference-api>>
* <<post-inference-api>>
* <<put-inference-api>>
* <<stream-inference-api>>
* <<update-inference-api>>

[[inference-landscape]]
Expand Down Expand Up @@ -56,6 +57,7 @@ include::delete-inference.asciidoc[]
include::get-inference.asciidoc[]
include::post-inference.asciidoc[]
include::put-inference.asciidoc[]
include::stream-inference.asciidoc[]
include::update-inference.asciidoc[]
include::service-alibabacloud-ai-search.asciidoc[]
include::service-amazon-bedrock.asciidoc[]
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122 changes: 122 additions & 0 deletions docs/reference/inference/stream-inference.asciidoc
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@@ -0,0 +1,122 @@
[role="xpack"]
[[stream-inference-api]]
=== Stream inference API

Streams a chat completion response.

IMPORTANT: The {infer} APIs enable you to use certain services, such as built-in {ml} models (ELSER, E5), models uploaded through Eland, Cohere, OpenAI, Azure, Google AI Studio, Google Vertex AI, Anthropic, Watsonx.ai, or Hugging Face.
For built-in models and models uploaded through Eland, the {infer} APIs offer an alternative way to use and manage trained models.
However, if you do not plan to use the {infer} APIs to use these models or if you want to use non-NLP models, use the <<ml-df-trained-models-apis>>.


[discrete]
[[stream-inference-api-request]]
==== {api-request-title}

`POST /_inference/<inference_id>/_stream`

`POST /_inference/<task_type>/<inference_id>/_stream`


[discrete]
[[stream-inference-api-prereqs]]
==== {api-prereq-title}

* Requires the `monitor_inference` <<privileges-list-cluster,cluster privilege>>
(the built-in `inference_admin` and `inference_user` roles grant this privilege)
* You must use a client that supports streaming.


[discrete]
[[stream-inference-api-desc]]
==== {api-description-title}

The stream {infer} API enables real-time responses for completion tasks by delivering answers incrementally, reducing response times during computation.
It only works with the `completion` task type.


[discrete]
[[stream-inference-api-path-params]]
==== {api-path-parms-title}

`<inference_id>`::
(Required, string)
The unique identifier of the {infer} endpoint.


`<task_type>`::
(Optional, string)
The type of {infer} task that the model performs.


[discrete]
[[stream-inference-api-request-body]]
==== {api-request-body-title}

`input`::
(Required, string or array of strings)
The text on which you want to perform the {infer} task.
`input` can be a single string or an array.
+
--
[NOTE]
====
Inference endpoints for the `completion` task type currently only support a
single string as input.
====
--


[discrete]
[[stream-inference-api-example]]
==== {api-examples-title}

The following example performs a completion on the example question with streaming.


[source,console]
------------------------------------------------------------
POST _inference/completion/openai-completion/_stream
{
"input": "What is Elastic?"
}
------------------------------------------------------------
// TEST[skip:TBD]


The API returns the following response:


[source,txt]
------------------------------------------------------------
event: message
data: {
"completion":[{
"delta":"Elastic"
}]
}

event: message
data: {
"completion":[{
"delta":" is"
},
{
"delta":" a"
}
]
}

event: message
data: {
"completion":[{
"delta":" software"
},
{
"delta":" company"
}]
}

(...)
------------------------------------------------------------
// NOTCONSOLE
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