OpenSearch:Multi-query

URL

/vector-service/multi-query

• The sample URL omits information such as the request headers and the encoding method.

• The sample URL also omits the endpoint that is used to connect to an OpenSearch application.

• For more information about the definitions, usage, and example values of all the request parameters that are concatenated in the preceding URL, see the "Request parameters" section of this topic.

Protocol

HTTP

Request method

POST

Supported format

JSON

Signature method

You can use the following method to calculate the request signature. The request signature is stored in the authorization header.

import com.aliyun.darabonba.encode.Encoder;
import com.aliyun.darabonbastring.Client;

public class GenerateAuthorization {

 public static void main(String[] args) throws Exception {
 String accessUserName = "username";
 String accessPassWord = "password";
 String realmStr = "" + accessUserName + ":" + accessPassWord + "";
 String authorization = Encoder.base64EncodeToString(Client.toBytes(realmStr, "UTF-8"));
 System.out.println(authorization);
 }
}

Valid format for the value of the authorization header:

cm9vdDp******mdhbA==

You must add the Basic prefix when you specify the authorization header in an HTTP request.

Example:

authorization: Basic cm9vdDp******mdhbA==

Request parameters

• SearchRequest

• Query

• SparseData

Response parameters

• Returned results

• Item

Example

Query by using multiple namespaces

Sample request:

{
    "tableName": "gist",
    "indexName": "vec_index",
    "queries": [
        {
            "vector": [
                0.1,
                0.2,
                0.3
            ],
            "namespace": "space_a"
        },
        {
            "vector": [
                0.4,
                0.5,
                0.6
            ],
            "namespace": "space_b"
        }
    ],
    "topK": 3,
    "includeVector": true
}

Sample response:

{
    "result": [
        {
            "id": 1,
            "score": 1.0508723258972169,
            "vector": [
                0.1,
                0.2,
                0.3
            ],
            "namespace": "space_a"
        },
        {
            "id": 2,
            "score": 1.0329746007919312,
            "vector": [
                0.2,
                0.2,
                0.3
            ],
            "namespace": "space_b"
        },
        {
            "id": 3,
            "score": 0.980593204498291,
            "vector": [
                0.3,
                0.2,
                0.3
            ],
            "namespace": "space_a"
        }
    ],
    "totalCount": 3,
    "totalTime": 2.943
}

Hybrid query by using multiple indexes

Sample request:

{
    "tableName": "gist",
    "queries": [
        {
            "indexName": "content_vec",
            "vector": [
                0.1,
                0.2,
                0.3
            ],
            "sparseData": {
                "count": [
                    3
                ],
                "indices": [
                    102,
                    405,
                    503
                ],
                "values": [
                    0.32,
                    0.94,
                    0.25
                ]
            },
            "weight": 0.7,
            "namespace": "space_a"
        },
        {
            "indexName": "title_vec",
            "vector": [
                0.4,
                0.5,
                0.6
            ],
            "sparseData": {
                "count": [
                    2
                ],
                "indices": [
                    203,
                    709
                ],
                "values": [
                    0.98,
                    0.08
                ]
            },
            "weight": 0.3,
            "namespace": "space_b"
        }
    ],
    "topK": 3,
    "includeVector": true
}