Text embedding - OpenSearch - Alibaba Cloud Documentation Center

The AI Search Open Platform supports calling text embedding services via API. You can use these services to convert text data into dense vector representations, suitable for scenarios such as information retrieval, text classification, and similarity comparison.

Service name	Service ID	Service description	QPS limit for API calls (Alibaba Cloud account and RAM users)
OpenSearch text vectorization service -001	ops-text-embedding-001	Supported languages: Multilingual (40+) Maximum input text length: 300 Output vector dimension: 1536	50 Note To apply for higher QPS, submit a ticket.
OpenSearch Text Embedding Service-Chinese-001	ops-text-embedding-zh-001	Supported languages: Chinese Maximum input text length: 1024 Output vector dimension: 768
OpenSearch Text Embedding Service-English-001	ops-text-embedding-en-001	Supported languages: English Maximum input text length: 512 Output vector dimension: 768
OpenSearch General Text Embedding Service-002	ops-text-embedding-002	This model offers enhanced language support and improved performance in retrieval tasks compared to the 001 model. Supported languages: Multilingual (100+) Maximum input text length: 8192 Output vector dimension: 1024
GTE Text Embedding-Multilingual-Base	ops-gte-sentence-embedding-multilingual-base	Supported languages: Multilingual (70+) Maximum input text length: 8192 Output vector dimension: 768
Qwen3 Text Embedding-0.6B	ops-qwen3-embedding-0.6b	Qwen3 series multilingual (100+) text embedding service. Maximum input length: 32k Output vector dimension: 1024 Parameter count: 0.6B

Prerequisites

The authentication information is obtained.
When you call an AI Search Open Platform service by using an API, you need to authenticate the caller's identity.
The service access address is obtained.
You can call a service over the Internet or a virtual private cloud (VPC). For more information, see Get service registration address.

Request description

General description

The maximum request body size must not exceed 8 MB.

Request method

POST

URL

{host}/v3/openapi/workspaces/{workspace_name}/text-embedding/{service_id}

host: The service endpoint, which supports API calls over the Internet and through a VPC. For more information, see Obtain a service endpoint.
workspace_name: The name of the workspace, such as 'default'.
service_id: The system built-in service ID, such as 'ops-text-embedding-001'.

Request parameters

Header parameters

API-KEY authentication

Parameter	Type	Required	Description	Example value
Content-Type	String	Yes	Request type: application/json	application/json
Authorization	String	Yes	API-Key	Bearer OS-d1**2a

Body parameters

Parameter

Type

Required

Description

Example value

input

Array/String

Yes

The content to be processed. It supports multiple text inputs, with a maximum of 32 per request. The length of each input is model-dependent. Empty strings are not accepted.

["Science and technology are the primary productive forces","opensearch product documentation"]

input_type

String

The data type of the input

query
document, default value

document

Response parameters

Parameter	Type	Description	Example value
request_id	String	The request ID.	B4AB89C8-B135-****-A6F8-2BAB801A2CE4
latency	Float/Int	The request duration in milliseconds.	10
usage	Object	Metering information generated by this call.	"usage": { "token_count": 3072 }
usage.token_count	Int	The number of tokens.	3072
result.embeddings	List	The output embedding content, an array of results.	[{ "index": 0, "embedding": [0.003143,0.009750,...,-0.017395] }, {}]
result.embeddings[].index	Int	The ordinal number corresponding to the input text.	0
result.embeddings[].embedding	List(Float)	The vectorized result.	[0.003143,0.009750,...,-0.017395]

Curl request example

curl -XPOST -H"Content-Type: application/json" 
"http://****-hangzhou.opensearch.aliyuncs.com/v3/openapi/workspaces/default/text-embedding/ops-text-embedding-001" 
-H "Authorization: Bearer your API-KEY" 
-d "{
    \"input\": [
          \"Science and technology are the primary productive forces\", 
          \"opensearch product documentation\"
    ], 
    \"input_type\": \"query\"
}"

Response example

Normal response example

{
    "request_id": "B4AB89C8-B135-****-A6F8-2BAB801A2CE4",
    "latency": 38,
    "usage": {
        "token_count": 3072
    },
    "result": {
        "embeddings": [
            {
                "index": 0,
                "embedding": [
                    -0.02868066355586052,
                    0.022033605724573135,
                    -0.0417383536696434,
                    -0.044081952422857285,
                    0.02141784131526947,
                    -8.240503375418484E-4,
                    -0.01309406291693449,
                    -0.02169642224907875,
                    -0.03996409475803375,
                    0.008053945377469063,
                    ...
                    -0.05131729692220688,
                    -0.016595875844359398
                ]
            }
        ]
    }
}

Abnormal response example

If the request encounters an error, the response will detail the cause with a specific code and message.

{
    "request_id": "651B3087-8A07-****-B931-9C4E7B60F52D",
    "latency": 0,
    "code": "InvalidParameter",
    "message": "JSON parse error: Cannot deserialize value of type `InputType` from String \"xxx\""
}

Status code description

For more information, see Status codes.