AnalyticDB for PostgreSQL:Java

Prerequisites

• An AnalyticDB for PostgreSQL V6.0 instance in elastic storage mode is created. For more information, see Create an instance.

• Vector search engine optimization is enabled. For more information, see Enable or disable vector search engine optimization.

• A privileged account is created. For more information, see Create a database account.

Procedure

1. Initialize the vector database.

2. Create a namespace.

3. Create a collection.

4. Upload vector data.

5. Retrieve vector data.

Initialize the vector database

Before you use vector search, you must initialize the knowledgebase database and the full-text search feature.

The following sample code provides an example on how to call an API operation:

InitVectorDatabaseRequest request = new InitVectorDatabaseRequest();
request.setDBInstanceId("gp-bp1c62r3l489****");
request.setManagerAccount("myaccount");
request.setManagerAccountPassword("myaccount_password");
request.setRegionId("ap-southeast-1");
InitVectorDatabaseResponse response = client.getAcsResponse(request);
System.out.println(new Gson().toJson(response));

For information about the relevant parameters, see InitVectorDatabase.

Create a namespace

Namespaces are used to separate schemas. Before you use vectors, you must create at least one namespace or use the public namespace.

The following sample code provides an example on how to call an API operation:

CreateNamespaceRequest request = new CreateNamespaceRequest();
request.setDBInstanceId("gp-bp1c62r3l489****");
request.setManagerAccount("myaccount");
request.setManagerAccountPassword("myaccount_password");
request.setNamespace("vector_test");
request.setNamespacePassword("vector_test_password");
request.setRegionId("ap-southeast-1");
CreateNamespaceResponse response = client.getAcsResponse(request);
System.out.println(new Gson().toJson(response));

For information about the relevant parameters, see CreateNamespace.

After you create a namespace, you can query the corresponding schema in the knowledgebase database of the instance.

SELECT schema_name FROM information_schema.schemata;

Create a collection

Collections are used to store vector data and are separated by namespaces.

The following sample code provides an example on how to call an API operation:

Map<String,String> metadata = new HashMap<>();
metadata.put("title", "text");
metadata.put("link", "text");
metadata.put("content", "text");
metadata.put("pv", "int");
List<String> fullTextRetrievalFields = Arrays.asList("title", "content");

CreateCollectionRequest request = new CreateCollectionRequest();
request.setDBInstanceId("gp-bp1c62r3l489****");
request.setManagerAccount("myaccount");
request.setManagerAccountPassword("myaccount_password");
request.setNamespace("vector_test");
request.setCollection("document");
request.setDimension(10L);
request.setFullTextRetrievalFields(StringUtils.join(fullTextRetrievalFields, ","));
request.setMetadata(new Gson().toJson(metadata));
request.setParser("zh_ch");
request.setRegionId("ap-southeast-1");
CreateCollectionResponse response = client.getAcsResponse(request);
System.out.println(new Gson().toJson(response));

After you create a collection, you can query the corresponding table in the knowledgebase database of the instance.

SELECT tablename FROM pg_tables WHERE schemaname='vector_test';

Upload vector data

Upload the prepared embedding vector data to the corresponding collection.

The following sample code provides an example on how to call an API operation:

UpsertCollectionDataRequest request = new UpsertCollectionDataRequest();
request.setDBInstanceId("gp-bp1c62r3l489****");
request.setCollection("document");
request.setNamespace("vector_test");
request.setNamespacePassword("vector_test_password");
request.setRegionId("ap-southeast-1");

List<UpsertCollectionDataRequest.UpsertCollectionDataRequestRows> rows = new ArrayList<>();
UpsertCollectionDataRequest.UpsertCollectionDataRequestRows row = new UpsertCollectionDataRequest.UpsertCollectionDataRequestRows();
row.setId("0CB55798-ECF5-4064-B81E-FE35B19E01A6");
row.setVector(Arrays.asList(0.2894745251078251,0.5364747050266715,0.1276845661831275,0.22528871956822372,0.7009319238651552,0.40267406135256123,0.8873626696379067,0.1248525955774931,0.9115507046412368,0.2450859133174706));
Map<String, String> rowsMetadata = new HashMap<>();
rowsMetadata.put("title", "Test document");
rowsMetadata.put("content","Test content");
rowsMetadata.put("link","http://127.0.0.1/document1");
rowsMetadata.put("pv","1000");
row.setMetadata(rowsMetadata);
rows.add(row);
request.setRows(rows);
UpsertCollectionDataResponse response = client.getAcsResponse(request);
System.out.println(new Gson().toJson(response));

For information about the relevant parameters, see UpsertCollectionData.

After you upload vector data, you can query the data in the knowledgebase database of the instance.

SELECT * FROM vector_test.document;

Retrieve vector data

Use the prepared vectors or full-text search fields to retrieve vector data.

The following sample code provides an example on how to call an API operation:

QueryCollectionDataRequest request = new QueryCollectionDataRequest();
request.setDBInstanceId("gp-bp1c62r3l489****");
request.setCollection("document");
request.setNamespace("vector_test");
request.setNamespacePassword("vector_test_password");
request.setContent("Test");
request.setFilter("pv > 10");
request.setTopK(10L);
request.setVector(Arrays.asList(0.7152607422256894,0.5524872066437732,0.1168505269851303,0.704130971473022,0.4118874999967596,0.2451574619214022,0.18193414783144812,0.3050522957905741,0.24846180714868163,0.0549715380856951));
request.setRegionId("ap-southeast-1");
QueryCollectionDataResponse response = client.getAcsResponse(request);
System.out.println(new Gson().toJson(response));

Sample result:

{
  "Matches": {
    "match": [
      {
        "Id": "0CB55798-ECF5-4064-B81E-FE35B19E01A6",
        "Metadata": {
          "title":"Test document",
          "content":"Test content",
          "link":"http://127.0.0.1/document1",
          "pv":"1000"
        },
        "Values": [
           0.2894745251078251,
           0.5364747050266715,
           0.1276845661831275,
           0.22528871956822372,
           0.7009319238651552,
           0.40267406135256123,
           0.8873626696379067,
           0.1248525955774931,
           0.9115507046412368,
           0.2450859133174706
        ]
      }
    ]
  },
  "RequestId": "ABB39CC3-4488-4857-905D-2E4A051D0521",
  "Status": "success"
}

References

Perform vector search