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433 lines
13 KiB
Text
433 lines
13 KiB
Text
[role="xpack"]
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[[put-inference-api]]
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=== Create {infer} API
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experimental[]
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Creates an {infer} endpoint to perform an {infer} task.
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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, or
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Hugging Face. For built-in models and models uploaded though
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Eland, the {infer} APIs offer an alternative way to use and manage trained
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models. However, if you do not plan to use the {infer} APIs to use these models
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or if you want to use non-NLP models, use the <<ml-df-trained-models-apis>>.
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[discrete]
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[[put-inference-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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[[put-inference-api-prereqs]]
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==== {api-prereq-title}
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* Requires the `manage_inference` <<privileges-list-cluster,cluster privilege>>
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(the built-in `inference_admin` role grants this privilege)
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[discrete]
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[[put-inference-api-desc]]
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==== {api-description-title}
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The create {infer} API enables you to create an {infer} endpoint and configure a
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{ml} model to perform a specific {infer} task.
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The following services are available through the {infer} API:
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* Cohere
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* ELSER
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* Hugging Face
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* OpenAI
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* Elasticsearch (for built-in models and models uploaded through Eland)
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[discrete]
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[[put-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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(Required, string)
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The type of the {infer} task that the model will perform. Available task types:
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* `sparse_embedding`,
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* `text_embedding`,
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* `completion`
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[discrete]
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[[put-inference-api-request-body]]
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== {api-request-body-title}
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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.
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Available services:
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* `cohere`: specify the `text_embedding` task type to use the Cohere service.
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* `elser`: specify the `sparse_embedding` task type to use the ELSER service.
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* `hugging_face`: specify the `text_embedding` task type to use the Hugging Face
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service.
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* `openai`: specify the `text_embedding` task type to use the OpenAI service.
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* `elasticsearch`: specify the `text_embedding` task type to use the E5
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built-in model or text embedding models uploaded by Eland.
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`service_settings`::
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(Required, object)
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Settings used to install the {infer} model. These settings are specific to the
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`service` you specified.
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+
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.`service_settings` for the `cohere` service
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[%collapsible%closed]
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=====
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`api_key`:::
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(Required, string)
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A valid API key of your Cohere account. You can find your Cohere API keys or you
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can create a new one
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https://dashboard.cohere.com/api-keys[on the API keys settings page].
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IMPORTANT: You need to provide the API key only once, during the {infer} model
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creation. The <<get-inference-api>> does not retrieve your API key. After
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creating the {infer} model, you cannot change the associated API key. If you
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want to use a different API key, delete the {infer} model and recreate it with
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the same name and the updated API key.
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`embedding_type`::
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(Optional, string)
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Specifies the types of embeddings you want to get back. Defaults to `float`.
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Valid values are:
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* `byte`: use it for signed int8 embeddings (this is a synonym of `int8`).
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* `float`: use it for the default float embeddings.
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* `int8`: use it for signed int8 embeddings.
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`model_id`::
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(Optional, string)
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The name of the model to use for the {infer} task. To review the available
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models, refer to the
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https://docs.cohere.com/reference/embed[Cohere docs]. Defaults to
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`embed-english-v2.0`.
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=====
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+
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.`service_settings` for the `elser` service
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[%collapsible%closed]
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=====
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`num_allocations`:::
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(Required, integer)
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The number of model allocations to create. `num_allocations` must not exceed the
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number of available processors per node divided by the `num_threads`.
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`num_threads`:::
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(Required, integer)
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The number of threads to use by each model allocation. `num_threads` must not
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exceed the number of available processors per node divided by the number of
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allocations. Must be a power of 2. Max allowed value is 32.
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=====
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+
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.`service_settings` for the `hugging_face` service
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[%collapsible%closed]
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=====
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`api_key`:::
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(Required, string)
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A valid access token of your Hugging Face account. You can find your Hugging
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Face access tokens or you can create a new one
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https://huggingface.co/settings/tokens[on the settings page].
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IMPORTANT: You need to provide the API key only once, during the {infer} model
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creation. The <<get-inference-api>> does not retrieve your API key. After
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creating the {infer} model, you cannot change the associated API key. If you
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want to use a different API key, delete the {infer} model and recreate it with
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the same name and the updated API key.
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`url`:::
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(Required, string)
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The URL endpoint to use for the requests.
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=====
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+
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.`service_settings` for the `openai` service
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[%collapsible%closed]
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=====
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`api_key`:::
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(Required, string)
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A valid API key of your OpenAI account. You can find your OpenAI API keys in
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your OpenAI account under the
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https://platform.openai.com/api-keys[API keys section].
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IMPORTANT: You need to provide the API key only once, during the {infer} model
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creation. The <<get-inference-api>> does not retrieve your API key. After
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creating the {infer} model, you cannot change the associated API key. If you
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want to use a different API key, delete the {infer} model and recreate it with
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the same name and the updated API key.
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`model_id`:::
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(Required, string)
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The name of the model to use for the {infer} task. Refer to the
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https://platform.openai.com/docs/guides/embeddings/what-are-embeddings[OpenAI documentation]
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for the list of available text embedding models.
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`organization_id`:::
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(Optional, string)
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The unique identifier of your organization. You can find the Organization ID in
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your OpenAI account under
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https://platform.openai.com/account/organization[**Settings** > **Organizations**].
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`url`:::
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(Optional, string)
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The URL endpoint to use for the requests. Can be changed for testing purposes.
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Defaults to `https://api.openai.com/v1/embeddings`.
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=====
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+
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.`service_settings` for the `elasticsearch` service
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[%collapsible%closed]
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=====
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`model_id`:::
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(Required, string)
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The name of the model to use for the {infer} task. It can be the
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ID of either a built-in model (for example, `.multilingual-e5-small` for E5) or
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a text embedding model already
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{ml-docs}/ml-nlp-import-model.html#ml-nlp-import-script[uploaded through Eland].
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`num_allocations`:::
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(Required, integer)
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The number of model allocations to create. `num_allocations` must not exceed the
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number of available processors per node divided by the `num_threads`.
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`num_threads`:::
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(Required, integer)
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The number of threads to use by each model allocation. `num_threads` must not
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exceed the number of available processors per node divided by the number of
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allocations. Must be a power of 2. Max allowed value is 32.
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=====
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`task_settings`::
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(Optional, object)
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Settings to configure the {infer} task. These settings are specific to the
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`<task_type>` you specified.
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+
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.`task_settings` for the `text_embedding` task type
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[%collapsible%closed]
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=====
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`input_type`:::
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(optional, string)
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For `cohere` service only. Specifies the type of input passed to the model.
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Valid values are:
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* `classification`: use it for embeddings passed through a text classifier.
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* `clusterning`: use it for the embeddings run through a clustering algorithm.
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* `ingest`: use it for storing document embeddings in a vector database.
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* `search`: use it for storing embeddings of search queries run against a
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vector data base to find relevant documents.
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`truncate`:::
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(Optional, string)
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For `cohere` service only. Specifies how the API handles inputs longer than the
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maximum token length. Defaults to `END`. Valid values are:
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* `NONE`: when the input exceeds the maximum input token length an error is
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returned.
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* `START`: when the input exceeds the maximum input token length the start of
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the input is discarded.
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* `END`: when the input exceeds the maximum input token length the end of
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the input is discarded.
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`user`:::
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(optional, string)
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For `openai` service only. Specifies the user issuing the request, which can be used for abuse detection.
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=====
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+
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.`task_settings` for the `completion` task type
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[%collapsible%closed]
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=====
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`user`:::
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(optional, string)
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For `openai` service only. Specifies the user issuing the request, which can be used for abuse detection.
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=====
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[discrete]
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[[put-inference-api-example]]
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==== {api-examples-title}
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This section contains example API calls for every service type.
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[discrete]
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[[inference-example-cohere]]
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===== Cohere service
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The following example shows how to create an {infer} endpoint called
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`cohere_embeddings` to perform a `text_embedding` task type.
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[source,console]
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------------------------------------------------------------
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PUT _inference/text_embedding/cohere-embeddings
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{
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"service": "cohere",
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"service_settings": {
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"api_key": "<api_key>",
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"model_id": "embed-english-light-v3.0",
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"embedding_type": "byte"
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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-e5]]
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===== E5 via the elasticsearch service
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The following example shows how to create an {infer} endpoint called
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`my-e5-model` to perform a `text_embedding` task type.
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[source,console]
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------------------------------------------------------------
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PUT _inference/text_embedding/my-e5-model
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{
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"service": "elasticsearch",
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"service_settings": {
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"num_allocations": 1,
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"num_threads": 1,
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"model_id": ".multilingual-e5-small" <1>
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}
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}
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------------------------------------------------------------
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// TEST[skip:TBD]
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<1> The `model_id` must be the ID of one of the built-in E5 models. Valid values
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are `.multilingual-e5-small` and `.multilingual-e5-small_linux-x86_64`. For
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further details, refer to the {ml-docs}/ml-nlp-e5.html[E5 model documentation].
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[discrete]
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[[inference-example-elser]]
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===== ELSER service
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The following example shows how to create an {infer} endpoint called
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`my-elser-model` to perform a `sparse_embedding` task type.
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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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[discrete]
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[[inference-example-hugging-face]]
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===== Hugging Face service
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The following example shows how to create an {infer} endpoint called
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`hugging-face_embeddings` to perform a `text_embedding` task type.
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[source,console]
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------------------------------------------------------------
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PUT _inference/text_embedding/hugging-face-embeddings
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{
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"service": "hugging_face",
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"service_settings": {
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"api_key": "<access_token>", <1>
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"url": "<url_endpoint>" <2>
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}
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}
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------------------------------------------------------------
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// TEST[skip:TBD]
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<1> A valid Hugging Face access token. You can find on the
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https://huggingface.co/settings/tokens[settings page of your account].
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<2> The {infer} endpoint URL you created on Hugging Face.
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Create a new {infer} endpoint on
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https://ui.endpoints.huggingface.co/[the Hugging Face endpoint page] to get an
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endpoint URL. Select the model you want to use on the new endpoint creation page
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- for example `intfloat/e5-small-v2` - then select the `Sentence Embeddings`
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task under the Advanced configuration section. Create the endpoint. Copy the URL
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after the endpoint initialization has been finished.
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[discrete]
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[[inference-example-eland]]
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===== Models uploaded by Eland via the elasticsearch service
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The following example shows how to create an {infer} endpoint called
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`my-msmarco-minilm-model` to perform a `text_embedding` task type.
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[source,console]
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------------------------------------------------------------
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PUT _inference/text_embedding/my-msmarco-minilm-model
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{
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"service": "elasticsearch",
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"service_settings": {
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"num_allocations": 1,
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"num_threads": 1,
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"model_id": "msmarco-MiniLM-L12-cos-v5" <1>
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}
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}
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------------------------------------------------------------
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// TEST[skip:TBD]
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<1> The `model_id` must be the ID of a text embedding model which has already
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been
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{ml-docs}/ml-nlp-import-model.html#ml-nlp-import-script[uploaded through Eland].
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[discrete]
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[[inference-example-openai]]
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===== OpenAI service
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The following example shows how to create an {infer} endpoint called
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`openai_embeddings` to perform a `text_embedding` task type.
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[source,console]
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------------------------------------------------------------
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PUT _inference/text_embedding/openai_embeddings
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{
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"service": "openai",
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"service_settings": {
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"api_key": "<api_key>",
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"model_id": "text-embedding-ada-002"
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}
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}
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------------------------------------------------------------
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// TEST[skip:TBD]
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The next example shows how to create an {infer} endpoint called
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`openai_completion` to perform a `completion` task type.
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[source,console]
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------------------------------------------------------------
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PUT _inference/completion/openai_completion
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{
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"service": "openai",
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"service_settings": {
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"api_key": "<api_key>",
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"model_id": "gpt-3.5-turbo"
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}
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}
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------------------------------------------------------------
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// TEST[skip:TBD]
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