[Inference API] Add Docs for AlibabaCloud AI Search Support for the Inference API

docs/changelog/112273.yaml

@@ -0,0 +1,5 @@
+pr: 111181
+summary: "[Inference API] Add Docs for AlibabaCloud AI Search Support for the Inference API"
+area: Machine Learning
+type: enhancement
+issues: [ ]

docs/reference/inference/inference-apis.asciidoc

@@ -39,6 +39,7 @@ include::delete-inference.asciidoc[]
 include::get-inference.asciidoc[]
 include::post-inference.asciidoc[]
 include::put-inference.asciidoc[]
+include::service-alibabacloud-ai-search.asciidoc[]
 include::service-amazon-bedrock.asciidoc[]
 include::service-anthropic.asciidoc[]
 include::service-azure-ai-studio.asciidoc[]

docs/reference/inference/put-inference.asciidoc

@@ -39,6 +39,7 @@ The create {infer} API enables you to create an {infer} endpoint and configure a
 
 The following services are available through the {infer} API, click the links to review the configuration details of the services:
 
+* <<infer-service-alibabacloud-ai-search,AlibabaCloud AI Search>>
 * <<infer-service-amazon-bedrock,Amazon Bedrock>>
 * <<infer-service-anthropic,Anthropic>>
 * <<infer-service-azure-ai-studio,Azure AI Studio>>

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

@@ -0,0 +1,193 @@
+[[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.
+
+The service_id available for the `text_embedding` task are:
+
+* `ops-text-embedding-001`
+* `ops-text-embedding-zh-001`
+* `ops-text-embedding-en-001`
+* `ops-text-embedding-002`
+
+You can find the supported `text_embedding` service_id at https://help.aliyun.com/zh/open-search/search-platform/developer-reference/text-embedding-api-details[embedding detail]
+
+for the `sparse_embedding` task are:
+
+* `ops-text-sparse-embedding-001`
+
+You can find the supported `sparse_embedding` service_id at https://help.aliyun.com/zh/open-search/search-platform/developer-reference/text-sparse-embedding-api-details[sparse embedding detail]
+
+for the `rerank` task are:
+
+* `ops-bge-reranker-larger`
+
+You can find the supported `rerank` service_id at https://help.aliyun.com/zh/open-search/search-platform/developer-reference/ranker-api-details[rerank detail]
+
+`host`:::
+(Required, string)
+The name of the host address use for the {infer} task. You can find the host address at https://opensearch.console.aliyun.com/cn-shanghai/rag/api-key[API keys section]
+
+`workspace`:::
+(Required, string)
+The name of the workspace use 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`: use it for storing document embeddings in a vector database.
+* `search`: use it 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`: use it for storing document embeddings in a vector database.
+* `search`: use it for storing embeddings of search queries run against a vector database to find relevant documents.
++
+
+`return_token`:::
+(Optional, boolean)
+Specify Whether to return the token name, the default value is false, it means that it will return the token id.
+
+=====
++
+.`task_settings` for the `rerank` task type
+[%collapsible%closed]
+=====
+
+There are no `task_settings` available for the `rerank` task type.
+
+=====
+
+[discrete]
+[[inference-example-alibabacloud-ai-search]]
+==== AlibabaCloud AI Search service example
+
+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]

docs/reference/search/search-your-data/semantic-search-inference.asciidoc

@@ -17,6 +17,7 @@ Azure based examples use models available through https://ai.azure.com/explore/m
 or https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models[Azure OpenAI].
 Mistral examples use the `mistral-embed` model from https://docs.mistral.ai/getting-started/models/[the Mistral API].
 Amazon Bedrock examples use the `amazon.titan-embed-text-v1` model from https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html[the Amazon Bedrock base models].
+AlibabaCloud AI Search examples use the `ops-text-embedding-zh-001` model from https://help.aliyun.com/zh/open-search/search-platform/developer-reference/text-embedding-api-details[the AlibabaCloud AI Search base models].
 
 Click the name of the service you want to use on any of the widgets below to review the corresponding instructions.
 

docs/reference/tab-widgets/inference-api/infer-api-ingest-pipeline-widget.asciidoc

@@ -49,6 +49,12 @@
             id="infer-api-ingest-amazon-bedrock">
       Amazon Bedrock
     </button>
+    <button role="tab"
+            aria-selected="false"
+            aria-controls="infer-api-ingest-alibabacloud-ai-search-tab"
+            id="infer-api-ingest-alibabacloud-ai-search">
+      AlibabaCloud AI Search
+    </button>
   </div>
   <div tabindex="0"
        role="tabpanel"
@@ -135,6 +141,17 @@ include::infer-api-ingest-pipeline.asciidoc[tag=mistral]
 
 include::infer-api-ingest-pipeline.asciidoc[tag=amazon-bedrock]
 
+++++
+  </div>
+  <div tabindex="0"
+       role="tabpanel"
+       id="infer-api-ingest-alibabacloud-ai-search-tab"
+       aria-labelledby="infer-api-ingest-alibabacloud-ai-search"
+       hidden="">
+++++
+
+include::infer-api-ingest-pipeline.asciidoc[tag=alibabacloud-ai-search]
+
 ++++
   </div>
 </div>

docs/reference/tab-widgets/inference-api/infer-api-ingest-pipeline.asciidoc

@@ -216,3 +216,29 @@ PUT _ingest/pipeline/amazon_bedrock_embeddings
 and the `output_field` that will contain the {infer} results.
 
 // end::amazon-bedrock[]
+
+// tag::alibabacloud-ai-search[]
+
+[source,console]
+--------------------------------------------------
+PUT _ingest/pipeline/alibabacloud_ai_search_embeddings
+{
+  "processors": [
+    {
+      "inference": {
+        "model_id": "alibabacloud_ai_search_embeddings", <1>
+        "input_output": { <2>
+          "input_field": "content",
+          "output_field": "content_embedding"
+        }
+      }
+    }
+  ]
+}
+--------------------------------------------------
+<1> The name of the inference endpoint you created by using the
+<<put-inference-api>>, it's referred to as `inference_id` in that step.
+<2> Configuration object that defines the `input_field` for the {infer} process
+and the `output_field` that will contain the {infer} results.
+
+// end::alibabacloud-ai-search[]

docs/reference/tab-widgets/inference-api/infer-api-mapping-widget.asciidoc

@@ -49,6 +49,12 @@
             id="infer-api-mapping-amazon-bedrock">
       Amazon Bedrock
     </button>
+    <button role="tab"
+            aria-selected="false"
+            aria-controls="infer-api-mapping-alibabacloud-ai-search-tab"
+            id="infer-api-mapping-alibabacloud-ai-search">
+      AlibabaCloud AI Search
+    </button>
   </div>
   <div tabindex="0"
        role="tabpanel"
@@ -135,6 +141,17 @@ include::infer-api-mapping.asciidoc[tag=mistral]
 
 include::infer-api-mapping.asciidoc[tag=amazon-bedrock]
 
+++++
+  </div>
+  <div tabindex="0"
+       role="tabpanel"
+       id="infer-api-mapping-alibabacloud-ai-search-tab"
+       aria-labelledby="infer-api-mapping-alibabacloud-ai-search"
+       hidden="">
+++++
+
+include::infer-api-mapping.asciidoc[tag=alibabacloud-ai-search]
+
 ++++
   </div>
 </div>

docs/reference/tab-widgets/inference-api/infer-api-mapping.asciidoc

@@ -270,3 +270,35 @@ the {infer} pipeline configuration in the next step.
 <6> The field type which is text in this example.
 
 // end::amazon-bedrock[]
+
+// tag::alibabacloud-ai-search[]
+
+[source,console]
+--------------------------------------------------
+PUT alibabacloud-ai-search-embeddings
+{
+  "mappings": {
+    "properties": {
+      "content_embedding": { <1>
+        "type": "dense_vector", <2>
+        "dims": 1024, <3>
+        "element_type": "float"
+      },
+      "content": { <4>
+        "type": "text" <5>
+      }
+    }
+  }
+}
+--------------------------------------------------
+<1> The name of the field to contain the generated tokens. It must be referenced
+in the {infer} pipeline configuration in the next step.
+<2> The field to contain the tokens is a `dense_vector` field.
+<3> The output dimensions of the model. This value may be different depending on the underlying model used.
+See the https://help.aliyun.com/zh/open-search/search-platform/developer-reference/text-embedding-api-details[AlibabaCloud AI Search embedding model] documentation.
+<4> The name of the field from which to create the dense vector representation.
+In this example, the name of the field is `content`. It must be referenced in
+the {infer} pipeline configuration in the next step.
+<5> The field type which is text in this example.
+
+// end::alibabacloud-ai-search[]

docs/reference/tab-widgets/inference-api/infer-api-reindex-widget.asciidoc

@@ -49,6 +49,12 @@
             id="infer-api-reindex-amazon-bedrock">
       Amazon Bedrock
     </button>
+    <button role="tab"
+            aria-selected="false"
+            aria-controls="infer-api-reindex-alibabacloud-ai-search-tab"
+            id="infer-api-reindex-alibabacloud-ai-search">
+      AlibabaCloud AI Search
+    </button>
   </div>
   <div tabindex="0"
        role="tabpanel"
@@ -135,6 +141,17 @@ include::infer-api-reindex.asciidoc[tag=mistral]
 
 include::infer-api-reindex.asciidoc[tag=amazon-bedrock]
 
+++++
+  </div>
+  <div tabindex="0"
+       role="tabpanel"
+       id="infer-api-reindex-alibabacloud-ai-search-tab"
+       aria-labelledby="infer-api-reindex-alibabacloud-ai-search"
+       hidden="">
+++++
+
+include::infer-api-reindex.asciidoc[tag=alibabacloud-ai-search]
+
 ++++
   </div>
 </div>