diff --git a/docs/changelog/112273.yaml b/docs/changelog/112273.yaml new file mode 100644 index 0000000000000..3182a1884a145 --- /dev/null +++ b/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: [ ] diff --git a/docs/reference/inference/inference-apis.asciidoc b/docs/reference/inference/inference-apis.asciidoc index 33db148755d8e..8fdf8aecc2ae5 100644 --- a/docs/reference/inference/inference-apis.asciidoc +++ b/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[] diff --git a/docs/reference/inference/put-inference.asciidoc b/docs/reference/inference/put-inference.asciidoc index 57485e0720cca..ba26a563541fc 100644 --- a/docs/reference/inference/put-inference.asciidoc +++ b/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: +* <> * <> * <> * <> diff --git a/docs/reference/inference/service-alibabacloud-ai-search.asciidoc b/docs/reference/inference/service-alibabacloud-ai-search.asciidoc new file mode 100644 index 0000000000000..df5220573d9e4 --- /dev/null +++ b/docs/reference/inference/service-alibabacloud-ai-search.asciidoc @@ -0,0 +1,184 @@ +[[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//` + +[discrete] +[[infer-service-alibabacloud-ai-search-api-path-params]] +==== {api-path-parms-title} + +``:: +(Required, string) +include::inference-shared.asciidoc[tag=inference-id] + +``:: +(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": "", + "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": "", + "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": "", + "service_id": "ops-bge-reranker-larger", + "host": "default-j01.platform-cn-shanghai.opensearch.aliyuncs.com", + "workspace": "default" + } +} +------------------------------------------------------------ +// TEST[skip:TBD] diff --git a/docs/reference/search/search-your-data/semantic-search-inference.asciidoc b/docs/reference/search/search-your-data/semantic-search-inference.asciidoc index f74bc65e31bf0..719aeb070fc7c 100644 --- a/docs/reference/search/search-your-data/semantic-search-inference.asciidoc +++ b/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. diff --git a/docs/reference/tab-widgets/inference-api/infer-api-ingest-pipeline-widget.asciidoc b/docs/reference/tab-widgets/inference-api/infer-api-ingest-pipeline-widget.asciidoc index 997dbbe8a20e6..3a686e27cf580 100644 --- a/docs/reference/tab-widgets/inference-api/infer-api-ingest-pipeline-widget.asciidoc +++ b/docs/reference/tab-widgets/inference-api/infer-api-ingest-pipeline-widget.asciidoc @@ -49,6 +49,12 @@ id="infer-api-ingest-amazon-bedrock"> Amazon Bedrock +
+
diff --git a/docs/reference/tab-widgets/inference-api/infer-api-ingest-pipeline.asciidoc b/docs/reference/tab-widgets/inference-api/infer-api-ingest-pipeline.asciidoc index 6adf3d2ebbf46..6678b60fabc40 100644 --- a/docs/reference/tab-widgets/inference-api/infer-api-ingest-pipeline.asciidoc +++ b/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 +<>, 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[] diff --git a/docs/reference/tab-widgets/inference-api/infer-api-mapping-widget.asciidoc b/docs/reference/tab-widgets/inference-api/infer-api-mapping-widget.asciidoc index 4e3a453a7bbea..66b790bdd57a5 100644 --- a/docs/reference/tab-widgets/inference-api/infer-api-mapping-widget.asciidoc +++ b/docs/reference/tab-widgets/inference-api/infer-api-mapping-widget.asciidoc @@ -49,6 +49,12 @@ id="infer-api-mapping-amazon-bedrock"> Amazon Bedrock +
+
diff --git a/docs/reference/tab-widgets/inference-api/infer-api-mapping.asciidoc b/docs/reference/tab-widgets/inference-api/infer-api-mapping.asciidoc index abeeb87f03e75..c86538ceb9c87 100644 --- a/docs/reference/tab-widgets/inference-api/infer-api-mapping.asciidoc +++ b/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[] diff --git a/docs/reference/tab-widgets/inference-api/infer-api-reindex-widget.asciidoc b/docs/reference/tab-widgets/inference-api/infer-api-reindex-widget.asciidoc index 45cb9fc51b9f1..86f52fee2063c 100644 --- a/docs/reference/tab-widgets/inference-api/infer-api-reindex-widget.asciidoc +++ b/docs/reference/tab-widgets/inference-api/infer-api-reindex-widget.asciidoc @@ -49,6 +49,12 @@ id="infer-api-reindex-amazon-bedrock"> Amazon Bedrock +
+
diff --git a/docs/reference/tab-widgets/inference-api/infer-api-reindex.asciidoc b/docs/reference/tab-widgets/inference-api/infer-api-reindex.asciidoc index d961ec8bd39bd..25d4023c650c0 100644 --- a/docs/reference/tab-widgets/inference-api/infer-api-reindex.asciidoc +++ b/docs/reference/tab-widgets/inference-api/infer-api-reindex.asciidoc @@ -200,3 +200,26 @@ number makes the update of the reindexing process quicker which enables you to follow the progress closely and detect errors early. // end::amazon-bedrock[] + +// tag::alibabacloud-ai-search[] + +[source,console] +---- +POST _reindex?wait_for_completion=false +{ + "source": { + "index": "test-data", + "size": 50 <1> + }, + "dest": { + "index": "alibabacloud-ai-search-embeddings", + "pipeline": "alibabacloud_ai_search_embeddings" + } +} +---- +// TEST[skip:TBD] +<1> The default batch size for reindexing is 1000. Reducing `size` to a smaller +number makes the update of the reindexing process quicker which enables you to +follow the progress closely and detect errors early. + +// end::alibabacloud-ai-search[] diff --git a/docs/reference/tab-widgets/inference-api/infer-api-requirements-widget.asciidoc b/docs/reference/tab-widgets/inference-api/infer-api-requirements-widget.asciidoc index c867b39b88e3b..fb686a2d8be12 100644 --- a/docs/reference/tab-widgets/inference-api/infer-api-requirements-widget.asciidoc +++ b/docs/reference/tab-widgets/inference-api/infer-api-requirements-widget.asciidoc @@ -49,6 +49,12 @@ id="infer-api-requirements-amazon-bedrock"> Amazon Bedrock +
+
diff --git a/docs/reference/tab-widgets/inference-api/infer-api-requirements.asciidoc b/docs/reference/tab-widgets/inference-api/infer-api-requirements.asciidoc index 603cd85a8f93d..c9e7ca8b80ba6 100644 --- a/docs/reference/tab-widgets/inference-api/infer-api-requirements.asciidoc +++ b/docs/reference/tab-widgets/inference-api/infer-api-requirements.asciidoc @@ -52,3 +52,9 @@ You can apply for access to Azure OpenAI by completing the form at https://aka.m * A pair of access and secret keys used to access Amazon Bedrock // end::amazon-bedrock[] + +// tag::alibabacloud-ai-search[] +* An AlibabaCloud Account with https://console.aliyun.com[AlibabaCloud] access +* An API key generated for your account from the https://opensearch.console.aliyun.com/cn-shanghai/rag/api-key[API keys section] + +// end::alibabacloud-ai-search[] diff --git a/docs/reference/tab-widgets/inference-api/infer-api-search-widget.asciidoc b/docs/reference/tab-widgets/inference-api/infer-api-search-widget.asciidoc index fa4a11c59a158..996148d80a4bd 100644 --- a/docs/reference/tab-widgets/inference-api/infer-api-search-widget.asciidoc +++ b/docs/reference/tab-widgets/inference-api/infer-api-search-widget.asciidoc @@ -49,6 +49,12 @@ id="infer-api-search-amazon-bedrock"> Amazon Bedrock +
+
diff --git a/docs/reference/tab-widgets/inference-api/infer-api-search.asciidoc b/docs/reference/tab-widgets/inference-api/infer-api-search.asciidoc index f23ed1dfef05d..fe1f58b6bd1a9 100644 --- a/docs/reference/tab-widgets/inference-api/infer-api-search.asciidoc +++ b/docs/reference/tab-widgets/inference-api/infer-api-search.asciidoc @@ -531,3 +531,68 @@ query from the `amazon-bedrock-embeddings` index sorted by their proximity to th // NOTCONSOLE // end::amazon-bedrock[] + +// tag::alibabacloud-ai-search[] + +[source,console] +-------------------------------------------------- +GET alibabacloud-ai-search-embeddings/_search +{ + "knn": { + "field": "content_embedding", + "query_vector_builder": { + "text_embedding": { + "model_id": "alibabacloud_ai_search_embeddings", + "model_text": "Calculate fuel cost" + } + }, + "k": 10, + "num_candidates": 100 + }, + "_source": [ + "id", + "content" + ] +} +-------------------------------------------------- +// TEST[skip:TBD] + +As a result, you receive the top 10 documents that are closest in meaning to the +query from the `alibabacloud-ai-search-embeddings` index sorted by their proximity to the query: + +[source,consol-result] +-------------------------------------------------- +"hits": [ + { + "_index": "alibabacloud-ai-search-embeddings", + "_id": "DDd5OowBHxQKHyc3TDSC", + "_score": 0.83704096, + "_source": { + "id": 862114, + "body": "How to calculate fuel cost for a road trip. By Tara Baukus Mello • Bankrate.com. Dear Driving for Dollars, My family is considering taking a long road trip to finish off the end of the summer, but I'm a little worried about gas prices and our overall fuel cost.It doesn't seem easy to calculate since we'll be traveling through many states and we are considering several routes.y family is considering taking a long road trip to finish off the end of the summer, but I'm a little worried about gas prices and our overall fuel cost. It doesn't seem easy to calculate since we'll be traveling through many states and we are considering several routes." + } + }, + { + "_index": "alibabacloud-ai-search-embeddings", + "_id": "ajd5OowBHxQKHyc3TDSC", + "_score": 0.8345704, + "_source": { + "id": 820622, + "body": "Home Heating Calculator. Typically, approximately 50% of the energy consumed in a home annually is for space heating. When deciding on a heating system, many factors will come into play: cost of fuel, installation cost, convenience and life style are all important.This calculator can help you estimate the cost of fuel for different heating appliances.hen deciding on a heating system, many factors will come into play: cost of fuel, installation cost, convenience and life style are all important. This calculator can help you estimate the cost of fuel for different heating appliances." + } + }, + { + "_index": "alibabacloud-ai-search-embeddings", + "_id": "Djd5OowBHxQKHyc3TDSC", + "_score": 0.8327426, + "_source": { + "id": 8202683, + "body": "Fuel is another important cost. This cost will depend on your boat, how far you travel, and how fast you travel. A 33-foot sailboat traveling at 7 knots should be able to travel 300 miles on 50 gallons of diesel fuel.If you are paying $4 per gallon, the trip would cost you $200.Most boats have much larger gas tanks than cars.uel is another important cost. This cost will depend on your boat, how far you travel, and how fast you travel. A 33-foot sailboat traveling at 7 knots should be able to travel 300 miles on 50 gallons of diesel fuel." + } + }, + (...) + ] +-------------------------------------------------- +// NOTCONSOLE + +// end::alibabacloud-ai-search[] diff --git a/docs/reference/tab-widgets/inference-api/infer-api-task-widget.asciidoc b/docs/reference/tab-widgets/inference-api/infer-api-task-widget.asciidoc index f12be341d866d..1dfa6077553fe 100644 --- a/docs/reference/tab-widgets/inference-api/infer-api-task-widget.asciidoc +++ b/docs/reference/tab-widgets/inference-api/infer-api-task-widget.asciidoc @@ -49,6 +49,12 @@ id="infer-api-task-amazon-bedrock"> Amazon Bedrock +
+
diff --git a/docs/reference/tab-widgets/inference-api/infer-api-task.asciidoc b/docs/reference/tab-widgets/inference-api/infer-api-task.asciidoc index b186b2c58ccc5..2b4aa1a200102 100644 --- a/docs/reference/tab-widgets/inference-api/infer-api-task.asciidoc +++ b/docs/reference/tab-widgets/inference-api/infer-api-task.asciidoc @@ -223,3 +223,32 @@ PUT _inference/text_embedding/amazon_bedrock_embeddings <1> <6> The model ID or ARN of the model to use. // end::amazon-bedrock[] + +// tag::alibabacloud-ai-search[] + +[source,console] +------------------------------------------------------------ +PUT _inference/text_embedding/alibabacloud_ai_search_embeddings <1> +{ + "service": "alibabacloud-ai-search", + "service_settings": { + "api_key": "", <2> + "service_id": "", <3> + "host": "", <4> + "workspace": "" <5> + } +} +------------------------------------------------------------ +// TEST[skip:TBD] +<1> The task type is `text_embedding` in the path and the `inference_id` which is the unique identifier of the {infer} endpoint is `alibabacloud_ai_search_embeddings`. +<2> The API key for accessing the AlibabaCloud AI Search API. You can find your API keys in +your AlibabaCloud account under the +https://opensearch.console.aliyun.com/cn-shanghai/rag/api-key[API keys section]. You need to provide +your API key only once. The <> does not return your API +key. +<3> The AlibabaCloud AI Search embeddings model name, for example `ops-text-embedding-zh-001`. +<4> The name our your AlibabaCloud AI Search host address. +<5> The name our your AlibabaCloud AI Search workspace. + +// end::alibabacloud-ai-search[] +