Tablestore:KNN vector query

Prerequisites

• An OTSClient instance is initialized. For more information, see Initialize an OTSClient instance.

• A data table is created and data is written to the data table. For more information, see Create data tables and Write data.

• A search index is created and the vector field is configured on the data table. For more information, see Create search indexes.

Usage notes

• The KNN vector query feature is supported by Tablestore SDK for Node.js of version 5.5.0 or later. Make sure that a supported version of Tablestore SDK for Node.js is installed.

  Note

  For information about the version history of Tablestore SDK for Node.js, see Version history of Tablestore SDK for Java.

• Limits are imposed on the number of vector fields and the number of dimensions for a vector field. For more information, see Search index limits.

• The search index server has multiple partitions. Each partition of the search index server returns the top k nearest neighbors to the vector that you want to query. The top k nearest neighbors returned by the partitions are aggregated on the client node. If you want to use tokens to query all data by page, the total number of rows in the response is related to the number of partitions of the search index server.

Parameters

Example

The following sample code provides an example on how to query the ten nearest neighbors of a specific vector in a data table. In this example, the value of the col_keyword column of the nearest-neighbor vectors must be equal to "0" and the value of the col_long column of the nearest-neighbor vectors must be between 0 and 50.

const tableName = "<TABLE_ANME>"
const indexName = "<SEARCH_INDEX_NAME>"

async function knnVectorQuery() {
    return new Promise(function (resolve, reject) {
        let params = {
            tableName: tableName,
            indexName: indexName,
            searchQuery: {
                offset: 0,
                limit: 10,
                query: {
                    queryType: TableStore.QueryType.KNN_VECTOR_QUERY,
                    query: {
                        fieldName: "col_vector",
                        topK: TableStore.Long.fromNumber(10),
                        float32QueryVector: [1.0, 1.1, 1.2, -1.3],
                        filter: {
                            queryType: TableStore.QueryType.BOOL_QUERY,
                            query: {
                                mustQueries: [
                                    {
                                        queryType: TableStore.QueryType.RANGE_QUERY,
                                        query: {
                                            fieldName: "col_long",
                                            rangeFrom: TableStore.Long.fromNumber(0),
                                            includeLower: true,
                                            rangeTo: TableStore.Long.fromNumber(50),
                                            includeUpper: true,
                                        }
                                    },
                                    {
                                        queryType: TableStore.QueryType.TERM_QUERY,
                                        query: {
                                            fieldName: "col_keyword",
                                            term: "0",
                                        }
                                    },
                                ],
                            }
                        },
                    },
                },
                sort: {
                    sorters: [
                        {
                            scoreSort: {
                                order: TableStore.SortOrder.SORT_ORDER_DESC // Sort the query results based on the scores in descending order.
                            }
                        }
                    ],
                },
                getTotalCount: false,
            },
            columnToGet: {
                returnType: TableStore.ColumnReturnType.RETURN_SPECIFIED,
                returnNames: ["col_long", "col_keyword"]
            },
            timeoutMs: 10000,
        }
        client.search(params, function (err, data) {
            if (err) {
                console.log('search error:', err.toString());
                reject(err);
            } else {
                console.log('RequestId:', data.RequestId);
                for (let i = 0; i < data.searchHits.length; i++) {
                    let hit = data.searchHits[i]
                    console.log('Score:', hit.score, 'Row:', hit.row);
                }
                resolve(data)
            }
        });
    })
}
knnVectorQuery();

References

• The following query types are supported by search indexes: term query, terms query, match all query, match query, match phrase query, prefix query, range query, wildcard query, Boolean query, geo query, nested query, vector query, and exists query. You can select a query type to query data based on your business requirements.

  If you want to sort or paginate the rows that meet the query conditions, you can use the sorting and paging feature. For more information, see Sorting and paging.

  If you want to collapse the result set based on a specific column, you can use the collapse (distinct) feature. This way, data of the specified type appears only once in the query results. For more information, see Collapse (distinct).

• If you want to analyze data in a data table, such as obtaining the extreme values, sum, and total number of rows, you can perform aggregation operations or execute SQL statements. For more information, see Aggregation and SQL query.

• If you want to quickly obtain all rows that meet the query conditions without the need to sort the rows, you can call the ParallelScan and ComputeSplits operations to use the parallel scan feature. For more information, see Parallel scan.