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243 lines
7.3 KiB
Text
243 lines
7.3 KiB
Text
[[infer-service-alibabacloud-ai-search]]
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=== AlibabaCloud AI Search {infer} integration
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.New API reference
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[sidebar]
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--
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For the most up-to-date API details, refer to {api-es}/group/endpoint-inference[{infer-cap} APIs].
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--
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Creates an {infer} endpoint to perform an {infer} task with the `alibabacloud-ai-search` service.
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[discrete]
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[[infer-service-alibabacloud-ai-search-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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[[infer-service-alibabacloud-ai-search-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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include::inference-shared.asciidoc[tag=inference-id]
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`<task_type>`::
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(Required, string)
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include::inference-shared.asciidoc[tag=task-type]
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+
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--
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Available task types:
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* `completion`,
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* `rerank`
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* `sparse_embedding`,
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* `text_embedding`.
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--
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[discrete]
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[[infer-service-alibabacloud-ai-search-api-request-body]]
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==== {api-request-body-title}
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`chunking_settings`::
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(Optional, object)
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include::inference-shared.asciidoc[tag=chunking-settings]
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`max_chunk_size`:::
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(Optional, integer)
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include::inference-shared.asciidoc[tag=chunking-settings-max-chunking-size]
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`overlap`:::
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(Optional, integer)
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include::inference-shared.asciidoc[tag=chunking-settings-overlap]
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`sentence_overlap`:::
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(Optional, integer)
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include::inference-shared.asciidoc[tag=chunking-settings-sentence-overlap]
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`strategy`:::
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(Optional, string)
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include::inference-shared.asciidoc[tag=chunking-settings-strategy]
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`service`::
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(Required, string) The type of service supported for the specified task type.
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In this case,
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`alibabacloud-ai-search`.
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`service_settings`::
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(Required, object)
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include::inference-shared.asciidoc[tag=service-settings]
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+
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--
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These settings are specific to the `alibabacloud-ai-search` service.
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--
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`api_key`:::
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(Required, string)
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A valid API key for the AlibabaCloud AI Search API.
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`service_id`:::
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(Required, string)
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The name of the model service to use for the {infer} task.
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--
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Available service_ids for the `completion` task:
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* `ops-qwen-turbo`
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* `qwen-turbo`
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* `qwen-plus`
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* `qwen-max`
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÷ `qwen-max-longcontext`
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For the supported `completion` service_ids, refer to the https://help.aliyun.com/zh/open-search/search-platform/developer-reference/text-generation-api-details[documentation].
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Available service_id for the `rerank` task is:
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* `ops-bge-reranker-larger`
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For the supported `rerank` service_id, refer to the https://help.aliyun.com/zh/open-search/search-platform/developer-reference/ranker-api-details[documentation].
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Available service_id for the `sparse_embedding` task:
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* `ops-text-sparse-embedding-001`
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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].
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Available service_ids for the `text_embedding` task:
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* `ops-text-embedding-001`
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* `ops-text-embedding-zh-001`
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* `ops-text-embedding-en-001`
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* `ops-text-embedding-002`
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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].
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--
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`host`:::
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(Required, string)
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The name of the host address used for the {infer} task.
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You can find the host address at https://opensearch.console.aliyun.com/cn-shanghai/rag/api-key[the API keys section] of the documentation.
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`workspace`:::
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(Required, string)
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The name of the workspace used for the {infer} task.
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`rate_limit`:::
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(Optional, object)
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By default, the `alibabacloud-ai-search` service sets the number of requests allowed per minute to `1000`.
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This helps to minimize the number of rate limit errors returned from AlibabaCloud AI Search.
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To modify this, set the `requests_per_minute` setting of this object in your service settings:
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+
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--
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include::inference-shared.asciidoc[tag=request-per-minute-example]
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--
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`task_settings`::
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(Optional, object)
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include::inference-shared.asciidoc[tag=task-settings]
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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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Specifies the type of input passed to the model.
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Valid values are:
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* `ingest`: for storing document embeddings in a vector database.
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* `search`: for storing embeddings of search queries run against a vector database to find relevant documents.
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=====
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+
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.`task_settings` for the `sparse_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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Specifies the type of input passed to the model.
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Valid values are:
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* `ingest`: for storing document embeddings in a vector database.
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* `search`: for storing embeddings of search queries run against a vector database to find relevant documents.
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`return_token`:::
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(Optional, boolean)
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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.
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=====
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[discrete]
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[[inference-example-alibabacloud-ai-search]]
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==== AlibabaCloud AI Search service examples
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The following example shows how to create an {infer} endpoint called `alibabacloud_ai_search_completion` to perform a `completion` task type.
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[source,console]
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------------------------------------------------------------
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PUT _inference/completion/alibabacloud_ai_search_completion
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{
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"service": "alibabacloud-ai-search",
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"service_settings": {
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"host" : "default-j01.platform-cn-shanghai.opensearch.aliyuncs.com",
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"api_key": "{{API_KEY}}",
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"service_id": "ops-qwen-turbo",
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"workspace" : "default"
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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 `alibabacloud_ai_search_rerank` to perform a `rerank` task type.
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[source,console]
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------------------------------------------------------------
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PUT _inference/rerank/alibabacloud_ai_search_rerank
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{
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"service": "alibabacloud-ai-search",
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"service_settings": {
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"api_key": "<api_key>",
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"service_id": "ops-bge-reranker-larger",
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"host": "default-j01.platform-cn-shanghai.opensearch.aliyuncs.com",
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"workspace": "default"
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}
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}
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------------------------------------------------------------
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// TEST[skip:TBD]
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The following example shows how to create an {infer} endpoint called `alibabacloud_ai_search_sparse` 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/alibabacloud_ai_search_sparse
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{
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"service": "alibabacloud-ai-search",
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"service_settings": {
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"api_key": "<api_key>",
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"service_id": "ops-text-sparse-embedding-001",
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"host": "default-j01.platform-cn-shanghai.opensearch.aliyuncs.com",
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"workspace": "default"
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}
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}
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------------------------------------------------------------
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// TEST[skip:TBD]
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The following example shows how to create an {infer} endpoint called `alibabacloud_ai_search_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/alibabacloud_ai_search_embeddings
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{
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"service": "alibabacloud-ai-search",
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"service_settings": {
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"api_key": "<api_key>",
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"service_id": "ops-text-embedding-001",
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"host": "default-j01.platform-cn-shanghai.opensearch.aliyuncs.com",
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"workspace": "default"
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}
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}
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------------------------------------------------------------
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// TEST[skip:TBD]
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