Create an AlibabaCloud AI Search inference endpoint | Elasticsearch API documentation (v8)

Create an AlibabaCloud AI Search inference endpoint Added in 8.16.0

PUT /_inference/{task_type}/{alibabacloud_inference_id}

Api key auth Basic auth Bearer auth

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

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.
alibabacloud_inference_id string Required

The unique identifier of the inference endpoint.

application/json

Body

chunking_settings object
Hide chunking_settings attributes Show chunking_settings attributes object
- max_chunk_size number
  
  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.
- sentence_overlap number
  
  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.
  
  External documentation
- rate_limit object
  Hide rate_limit attribute Show rate_limit attribute object
  
  requests_per_minute number
  
  The number of requests allowed per minute.
- 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 object
Hide task_settings attributes Show task_settings attributes object
- input_type string
  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
  
  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
- chunking_settings object
  
  Hide chunking_settings attributes Show chunking_settings attributes object
  
  max_chunk_size number
  
  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.
  
  sentence_overlap number
  
  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
- task_settings object
- 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}"'

Request examples

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"
    }
}