---
type: api
title: Create an AlibabaCloud AI Search inference endpoint
description: Create an inference endpoint to perform an inference task with the `alibabacloud-ai-search` service.
url: https://www.elastic.co/elastic/docs-builder/docs/4371/api/doc/elasticsearch/operation/operation-inference-put-alibabacloud
resource: https://www.elastic.co/elastic/docs-builder/docs/4371/api/doc/elasticsearch/operation/operation-inference-put-alibabacloud
products:
  - Elasticsearch
---

# Create an AlibabaCloud AI Search inference endpoint

Availability: 8.16+

## Paths

- `PUT` `/_inference/{task_type}/{alibabacloud_inference_id}`

- [Api key auth](/elastic/docs-builder/docs/4371/api/doc/elasticsearch/authentication#apikeyauth)
- [Basic auth](/elastic/docs-builder/docs/4371/api/doc/elasticsearch/authentication#basicauth)
- [Bearer auth](/elastic/docs-builder/docs/4371/api/doc/elasticsearch/authentication#bearerauth)

#### Path Parameters

- `task_type` (enum AlibabaCloudTaskType)
  The type of the inference task that the model will perform.
  Values: `completion`, `rerank`, `sparse_embedding`, `text_embedding`
- `alibabacloud_inference_id` (string Id)
  The unique identifier of the inference endpoint.

## Description

Create an inference endpoint to perform an inference task with the `alibabacloud-ai-search` service.

## Required authorization

* Cluster privileges: `manage_inference`

## Query String Parameters

- `timeout` (string Duration · default: 30s)
  Specifies the amount of time to wait for the inference endpoint to be created.

## Request Body

`application/json`

- `chunking_settings` ({} InferenceChunkingSettings) — optional
  The chunking configuration object.
Applies only to the `sparse_embedding` or `text_embedding` task types.
Not applicable to the `rerank` or `completion` task types.
  - `max_chunk_size` (number · default: 250) — optional
    The maximum size of a chunk in words.
This value cannot be lower than `20` (for `sentence` strategy) or `10` (for `word` strategy).
This value should not exceed the window size for the associated model.
  - `overlap` (number · default: 100) — optional
    The number of overlapping words for chunks.
It is applicable only to a `word` chunking strategy.
This value cannot be higher than half the `max_chunk_size` value.
  - `sentence_overlap` (number · default: 1) — optional
    The number of overlapping sentences for chunks.
It is applicable only for a `sentence` chunking strategy.
It can be either `1` or `0`.
  - `separator_group` (string) — optional
    Only applicable to the `recursive` strategy and required when using it.

Sets a predefined list of separators in the saved chunking settings based on the selected text type.
Values can be `markdown` or `plaintext`.

Using this parameter is an alternative to manually specifying a custom `separators` list.
  - `separators` ([] string) — optional
    Only applicable to the `recursive` strategy and required when using it.

A list of strings used as possible split points when chunking text.

Each string can be a plain string or a regular expression (regex) pattern.
The system tries each separator in order to split the text, starting from the first item in the list.

After splitting, it attempts to recombine smaller pieces into larger chunks that stay within
the `max_chunk_size` limit, to reduce the total number of chunks generated.
  - `strategy` (string · default: sentence) — optional
    The chunking strategy: `sentence`, `word`, `none` or `recursive`.

 * If `strategy` is set to `recursive`, you must also specify:

- `max_chunk_size`
- either `separators` or`separator_group`

Learn more about different chunking strategies in the linked documentation.
- `service` (enum AlibabaCloudServiceType) — required
  The type of service supported for the specified task type. In this case, `alibabacloud-ai-search`.
  Values: `alibabacloud-ai-search`
- `service_settings` ({} AlibabaCloudServiceSettings) — required
  Settings used to install the inference model. These settings are specific to the `alibabacloud-ai-search` service.
  - `api_key` (string) — required
    A valid API key for the AlibabaCloud AI Search API.
  - `host` (string) — required
    The name of the host address used for the inference task.
You can find the host address in the API keys section of the documentation.
  - `rate_limit` ({} RateLimitSetting) — optional
    This setting helps to minimize the number of rate limit errors returned from AlibabaCloud AI Search.
By default, the `alibabacloud-ai-search` service sets the number of requests allowed per minute to `1000`.
    - `requests_per_minute` (number) — optional
      The number of requests allowed per minute.
By default, the number of requests allowed per minute is set by each service as follows:

* `alibabacloud-ai-search` service: `1000`
* `amazonbedrock` service: `240`
* `anthropic` service: `50`
* `azureaistudio` service: `240`
* `azureopenai` service and task type `text_embedding`: `1440`
* `azureopenai` service and task types `completion` or `chat_completion`: `120`
* `cohere` service: `10000`
* `contextualai` service: `1000`
* `elastic` service and task type `chat_completion`: `240`
* `fireworksai` service: `6000`
* `googleaistudio` service: `360`
* `googlevertexai` service: `30000`
* `hugging_face` service: `3000`
* `jinaai` service: `2000`
* `llama` service: `3000`
* `mistral` service: `240`
* `openai` service and task type `text_embedding`: `3000`
* `openai` service and task type `completion`: `500`
* `openshift_ai` service: `3000`
* `voyageai` service: `2000`
* `watsonxai` service: `120`
  - `service_id` (string) — required
    The name of the model service to use for the inference task.
The following service IDs are available for the `completion` task:

* `ops-qwen-turbo`
* `qwen-turbo`
* `qwen-plus`
* `qwen-max ÷ qwen-max-longcontext`

The following service ID is available for the `rerank` task:

* `ops-bge-reranker-larger`

The following service ID is available for the `sparse_embedding` task:

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

The following service IDs are available for the `text_embedding` task:

`ops-text-embedding-001`
`ops-text-embedding-zh-001`
`ops-text-embedding-en-001`
`ops-text-embedding-002`
  - `workspace` (string) — required
    The name of the workspace used for the inference task.
- `task_settings` ({} AlibabaCloudTaskSettings) — optional
  Settings to configure the inference task.
These settings are specific to the task type you specified.
  - `input_type` (string) — optional
    For a `sparse_embedding` or `text_embedding` task, specify 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` (boolean) — optional
    For a `sparse_embedding` task, it affects whether the token name will be returned in the response.
It defaults to `false`, which means only the token ID will be returned in the response.

## Response

`application/json`

- `chunking_settings` ({} InferenceChunkingSettings)
  The chunking configuration object.
Applies only to the `embedding`, `sparse_embedding` and `text_embedding` task types.
Not applicable to the `rerank`, `completion`, or `chat_completion` task types.
  - `max_chunk_size` (number · default: 250)
    The maximum size of a chunk in words.
This value cannot be lower than `20` (for `sentence` strategy) or `10` (for `word` strategy).
This value should not exceed the window size for the associated model.
  - `overlap` (number · default: 100)
    The number of overlapping words for chunks.
It is applicable only to a `word` chunking strategy.
This value cannot be higher than half the `max_chunk_size` value.
  - `sentence_overlap` (number · default: 1)
    The number of overlapping sentences for chunks.
It is applicable only for a `sentence` chunking strategy.
It can be either `1` or `0`.
  - `separator_group` (string)
    Only applicable to the `recursive` strategy and required when using it.

Sets a predefined list of separators in the saved chunking settings based on the selected text type.
Values can be `markdown` or `plaintext`.

Using this parameter is an alternative to manually specifying a custom `separators` list.
  - `separators` ([] string)
    Only applicable to the `recursive` strategy and required when using it.

A list of strings used as possible split points when chunking text.

Each string can be a plain string or a regular expression (regex) pattern.
The system tries each separator in order to split the text, starting from the first item in the list.

After splitting, it attempts to recombine smaller pieces into larger chunks that stay within
the `max_chunk_size` limit, to reduce the total number of chunks generated.
  - `strategy` (string · default: sentence)
    The chunking strategy: `sentence`, `word`, `none` or `recursive`.

 * If `strategy` is set to `recursive`, you must also specify:

- `max_chunk_size`
- either `separators` or`separator_group`

Learn more about different chunking strategies in the linked documentation.
- `service` (string)
  The service type
- `service_settings` (ServiceSettings)
  Settings specific to the service
- `task_settings` (TaskSettings)
  Task settings specific to the service and task type
- `inference_id` (string)
  The inference Id
- `task_type` (enum TaskTypeAlibabaCloudAI)
  The task type
  Values: `text_embedding`, `rerank`, `completion`, `sparse_embedding`
## Code Examples

#### Console

```console
PUT _inference/completion/alibabacloud_ai_search_completion
{
    "service": "alibabacloud-ai-search",
    "service_settings": {
        "host" : "default-j01.platform-cn-shanghai.opensearch.aliyuncs.com",
        "api_key": "AlibabaCloud-API-Key",
        "service_id": "ops-qwen-turbo",
        "workspace" : "default"
    }
}
```

#### JavaScript

```javascript
const response = await client.inference.put({
  task_type: "completion",
  inference_id: "alibabacloud_ai_search_completion",
  inference_config: {
    service: "alibabacloud-ai-search",
    service_settings: {
      host: "default-j01.platform-cn-shanghai.opensearch.aliyuncs.com",
      api_key: "AlibabaCloud-API-Key",
      service_id: "ops-qwen-turbo",
      workspace: "default",
    },
  },
});
```

#### Python

```python
resp = client.inference.put(
    task_type="completion",
    inference_id="alibabacloud_ai_search_completion",
    inference_config={
        "service": "alibabacloud-ai-search",
        "service_settings": {
            "host": "default-j01.platform-cn-shanghai.opensearch.aliyuncs.com",
            "api_key": "AlibabaCloud-API-Key",
            "service_id": "ops-qwen-turbo",
            "workspace": "default"
        }
    },
)
```

#### curl

```curl
curl -X PUT "$ELASTICSEARCH_URL/_inference/completion/alibabacloud_ai_search_completion" \
  -H "Authorization: ApiKey $ELASTIC_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "service": "alibabacloud-ai-search",
    "service_settings": {
      "host": "default-j01.platform-cn-shanghai.opensearch.aliyuncs.com",
      "api_key": "AlibabaCloud-API-Key",
      "service_id": "ops-qwen-turbo",
      "workspace": "default"
    }
  }'
```

#### Java

```java
client.inference().put(p -> p
    .inferenceId("alibabacloud_ai_search_completion")
    .taskType(TaskType.Completion)
    .inferenceConfig(i -> i
        .service("alibabacloud-ai-search")
        .serviceSettings(JsonData.fromJson("""
{"host":"default-j01.platform-cn-shanghai.opensearch.aliyuncs.com","api_key":"AlibabaCloud-API-Key","service_id":"ops-qwen-turbo","workspace":"default"}
"""))
    )
);
```

#### C#

```c#
var response = await client.Inference
    .PutAsync(new PutInferenceRequest()
    {
        InferenceId = "alibabacloud_ai_search_completion",
        TaskType = TaskType.Completion,
        InferenceConfig = new()
        {
            Service = "alibabacloud-ai-search",
            ServiceSettings = JsonSerializer.Deserialize<JsonElement>("""
            {
              "host": "default-j01.platform-cn-shanghai.opensearch.aliyuncs.com",
              "api_key": "AlibabaCloud-API-Key",
              "service_id": "ops-qwen-turbo",
              "workspace": "default"
            }
            """)
        }
    });
```

#### PHP

```php
$resp = $client->inference()->put([
    "task_type" => "completion",
    "inference_id" => "alibabacloud_ai_search_completion",
    "body" => [
        "service" => "alibabacloud-ai-search",
        "service_settings" => [
            "host" => "default-j01.platform-cn-shanghai.opensearch.aliyuncs.com",
            "api_key" => "AlibabaCloud-API-Key",
            "service_id" => "ops-qwen-turbo",
            "workspace" => "default",
        ],
    ],
]);
```

#### Ruby

```ruby
response = client.inference.put(
  task_type: "completion",
  inference_id: "alibabacloud_ai_search_completion",
  body: {
    "service": "alibabacloud-ai-search",
    "service_settings": {
      "host": "default-j01.platform-cn-shanghai.opensearch.aliyuncs.com",
      "api_key": "AlibabaCloud-API-Key",
      "service_id": "ops-qwen-turbo",
      "workspace": "default"
    }
  }
)
```