elasticsearch/docs/reference/inference/service-alibabacloud-ai-search.asciidoc

[[infer-service-alibabacloud-ai-search]]
=== AlibabaCloud AI Search {infer} service

Creates an {infer} endpoint to perform an {infer} task with the `alibabacloud-ai-search` service.

[discrete]
[[infer-service-alibabacloud-ai-search-api-request]]
==== {api-request-title}

`PUT /_inference/<task_type>/<inference_id>`

[discrete]
[[infer-service-alibabacloud-ai-search-api-path-params]]
==== {api-path-parms-title}

`<inference_id>`::
(Required, string)
include::inference-shared.asciidoc[tag=inference-id]

`<task_type>`::
(Required, string)
include::inference-shared.asciidoc[tag=task-type]
+
--
Available task types:

* `text_embedding`,
* `sparse_embedding`,
* `rerank`.
--

[discrete]
[[infer-service-alibabacloud-ai-search-api-request-body]]
==== {api-request-body-title}

`service`::
(Required, string) The type of service supported for the specified task type.
In this case,
`alibabacloud-ai-search`.

`service_settings`::
(Required, object)
include::inference-shared.asciidoc[tag=service-settings]
+
--
These settings are specific to the `alibabacloud-ai-search` service.
--

`api_key`:::
(Required, string)
A valid API key for the AlibabaCloud AI Search API.

`service_id`:::
(Required, string)
The name of the model service to use for the {infer} task.
+
--
Available service_ids for the `text_embedding` task:

* `ops-text-embedding-001`
* `ops-text-embedding-zh-001`
* `ops-text-embedding-en-001`
* `ops-text-embedding-002`

For the supported `text_embedding` service_ids, refer to the https://help.aliyun.com/zh/open-search/search-platform/developer-reference/text-embedding-api-details[documentation].

Available service_id for the `sparse_embedding` task:

* `ops-text-sparse-embedding-001`

For the supported `sparse_embedding` service_id, refer to the https://help.aliyun.com/zh/open-search/search-platform/developer-reference/text-sparse-embedding-api-details[documentation].

Available service_id for the `rerank` task is:

* `ops-bge-reranker-larger`

For the supported `rerank` service_id, refer to the https://help.aliyun.com/zh/open-search/search-platform/developer-reference/ranker-api-details[documentation].
--

`host`:::
(Required, string)
The name of the host address used for the {infer} task. You can find the host address at https://opensearch.console.aliyun.com/cn-shanghai/rag/api-key[ the API keys section] of the documentation.

`workspace`:::
(Required, string)
The name of the workspace used for the {infer} task.

`rate_limit`:::
(Optional, object)
By default, the `alibabacloud-ai-search` service sets the number of requests allowed per minute to `1000`.
This helps to minimize the number of rate limit errors returned from AlibabaCloud AI Search.
To modify this, set the `requests_per_minute` setting of this object in your service settings:
+
--
include::inference-shared.asciidoc[tag=request-per-minute-example]
--


`task_settings`::
(Optional, object)
include::inference-shared.asciidoc[tag=task-settings]
+
.`task_settings` for the `text_embedding` task type
[%collapsible%closed]
=====
`input_type`:::
(Optional, string)
Specifies the type of input passed to the model.
Valid values are:
* `ingest`: for storing document embeddings in a vector database.
* `search`: for storing embeddings of search queries run against a vector database to find relevant documents.
=====
+
.`task_settings` for the `sparse_embedding` task type
[%collapsible%closed]
=====
`input_type`:::
(Optional, string)
Specifies the type of input passed to the model.
Valid values are:
* `ingest`: for storing document embeddings in a vector database.
* `search`: for storing embeddings of search queries run against a vector database to find relevant documents.

`return_token`:::
(Optional, boolean)
If `true`, the token name will be returned in the response. Defaults to `false` which means only the token ID will be returned in the response.
=====

[discrete]
[[inference-example-alibabacloud-ai-search]]
==== AlibabaCloud AI Search service examples

The following example shows how to create an {infer} endpoint called `alibabacloud_ai_search_embeddings` to perform a `text_embedding` task type.

[source,console]
------------------------------------------------------------
PUT _inference/text_embedding/alibabacloud_ai_search_embeddings
{
    "service": "alibabacloud-ai-search",
    "service_settings": {
        "api_key": "<api_key>",
        "service_id": "ops-text-embedding-001",
        "host": "default-j01.platform-cn-shanghai.opensearch.aliyuncs.com",
        "workspace": "default"
    }
}
------------------------------------------------------------
// TEST[skip:TBD]

The following example shows how to create an {infer} endpoint called
`alibabacloud_ai_search_sparse` to perform a `sparse_embedding` task type.

[source,console]
------------------------------------------------------------
PUT _inference/sparse_embedding/alibabacloud_ai_search_sparse
{
    "service": "alibabacloud-ai-search",
    "service_settings": {
        "api_key": "<api_key>",
        "service_id": "ops-text-sparse-embedding-001",
        "host": "default-j01.platform-cn-shanghai.opensearch.aliyuncs.com",
        "workspace": "default"
    }
}
------------------------------------------------------------
// TEST[skip:TBD]

The next example shows how to create an {infer} endpoint called
`alibabacloud_ai_search_rerank` to perform a `rerank` task type.

[source,console]
------------------------------------------------------------
PUT _inference/rerank/alibabacloud_ai_search_rerank
{
    "service": "alibabacloud-ai-search",
    "service_settings": {
        "api_key": "<api_key>",
        "service_id": "ops-bge-reranker-larger",
        "host": "default-j01.platform-cn-shanghai.opensearch.aliyuncs.com",
        "workspace": "default"
    }
}
------------------------------------------------------------
// TEST[skip:TBD]