Create an AlibabaCloud AI Search inference endpoint Added in 8.16.0

PUT /_inference/{task_type}/{alibabacloud_inference_id}

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

When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running. After creating the endpoint, wait for the model deployment to complete before using it. To verify the deployment status, use the get trained model statistics API. Look for "state": "fully_allocated" in the response and ensure that the "allocation_count" matches the "target_allocation_count". Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.

Path parameters

  • task_type string Required

    The type of the inference task that the model will perform.

    Values are completion, rerank, space_embedding, or text_embedding.

  • The unique identifier of the inference endpoint.

application/json

Body

  • Hide chunking_settings attributes Show chunking_settings attributes object
    • service string Required

      The service type

    • service_settings object Required
    • The maximum size of a chunk in words. This value cannot be higher than 300 or lower than 20 (for sentence strategy) or 10 (for word strategy).

    • overlap number

      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.

    • The number of overlapping sentences for chunks. It is applicable only for a sentence chunking strategy. It can be either 1 or 0.

    • strategy string

      The chunking strategy: sentence or word.

  • service string Required

    Value is alibabacloud-ai-search.

  • service_settings object Required
    Hide service_settings attributes Show service_settings attributes object
    • 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.

    • Hide rate_limit attribute Show rate_limit attribute object
    • 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.

  • Hide task_settings attributes Show task_settings attributes object
    • 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.
    • 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.

Responses

  • 200 application/json
    Hide response attributes Show response attributes object
    • Hide attributes Show attributes object
      • The maximum size of a chunk in words. This value cannot be higher than 300 or lower than 20 (for sentence strategy) or 10 (for word strategy).

      • overlap number

        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.

      • The number of overlapping sentences for chunks. It is applicable only for a sentence chunking strategy. It can be either 1 or 0.

      • strategy string

        The chunking strategy: sentence or word.

    • service string Required

      The service type

    • service_settings object Required
    • inference_id string Required

      The inference Id

    • task_type string Required

      Values are sparse_embedding, text_embedding, rerank, completion, or chat_completion.

PUT /_inference/{task_type}/{alibabacloud_inference_id}
curl \
 --request PUT 'http://api.example.com/_inference/{task_type}/{alibabacloud_inference_id}' \
 --header "Authorization: $API_KEY" \
 --header "Content-Type: application/json" \
 --data '"{\n    \"service\": \"alibabacloud-ai-search\",\n    \"service_settings\": {\n        \"host\" : \"default-j01.platform-cn-shanghai.opensearch.aliyuncs.com\",\n        \"api_key\": \"AlibabaCloud-API-Key\",\n        \"service_id\": \"ops-qwen-turbo\",\n        \"workspace\" : \"default\"\n    }\n}"'
Run `PUT _inference/completion/alibabacloud_ai_search_completion` to create an inference endpoint that performs a completion task.
{
    "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"
    }
}
Run `PUT _inference/rerank/alibabacloud_ai_search_rerank` to create an inference endpoint that performs a rerank task.
{
    "service": "alibabacloud-ai-search",
    "service_settings": {
        "api_key": "AlibabaCloud-API-Key",
        "service_id": "ops-bge-reranker-larger",
        "host": "default-j01.platform-cn-shanghai.opensearch.aliyuncs.com",
        "workspace": "default"
    }
}
Run `PUT _inference/sparse_embedding/alibabacloud_ai_search_sparse` to create an inference endpoint that performs perform a sparse embedding task.
{
    "service": "alibabacloud-ai-search",
    "service_settings": {
        "api_key": "AlibabaCloud-API-Key",
        "service_id": "ops-text-sparse-embedding-001",
        "host": "default-j01.platform-cn-shanghai.opensearch.aliyuncs.com",
        "workspace": "default"
    }
}
Run `PUT _inference/text_embedding/alibabacloud_ai_search_embeddings` to create an inference endpoint that performs a text embedding task.
{
    "service": "alibabacloud-ai-search",
    "service_settings": {
        "api_key": "AlibabaCloud-API-Key",
        "service_id": "ops-text-embedding-001",
        "host": "default-j01.platform-cn-shanghai.opensearch.aliyuncs.com",
        "workspace": "default"
    }
}