瞭解如何調整下列向量索引,在 PostgreSQL 適用的 AlloyDB 中提升查詢效能並改善召回率:
您也可以分析查詢並查看向量索引指標,藉此監控及提升查詢效能。
分析查詢
使用 EXPLAIN ANALYZE
指令分析查詢洞察,如下列 SQL 查詢範例所示。
EXPLAIN ANALYZE SELECT result-column
FROM my-table
ORDER BY EMBEDDING_COLUMN <=> embedding('text-embedding-005', 'What is a database?')::vector
LIMIT 1;
範例回應 QUERY PLAN
包含所花時間、掃描或傳回的資料列數,以及使用的資源等資訊。
Limit (cost=0.42..15.27 rows=1 width=32) (actual time=0.106..0.132 rows=1 loops=1)
-> Index Scan using my-scann-index on my-table (cost=0.42..858027.93 rows=100000 width=32) (actual time=0.105..0.129 rows=1 loops=1)
Order By: (embedding_column <=> embedding('text-embedding-005', 'What is a database?')::vector(768))
Limit value: 1
Planning Time: 0.354 ms
Execution Time: 0.141 ms
查看向量索引指標
您可以運用向量索引指標查看向量索引的成效、找出可改進的部分,並視需要根據指標調整索引。
如要查看所有向量索引指標,請執行下列 SQL 查詢,其中會使用 pg_stat_ann_indexes
檢視區塊:
SELECT * FROM pg_stat_ann_indexes;
畫面會顯示類似以下內容的輸出:
-[ RECORD 1 ]----------+---------------------------------------------------------------------------
relid | 271236
indexrelid | 271242
schemaname | public
relname | t1
indexrelname | t1_ix1
indextype | scann
indexconfig | {num_leaves=100,quantizer=SQ8}
indexsize | 832 kB
indexscan | 0
insertcount | 250
deletecount | 0
updatecount | 0
partitioncount | 100
distribution | {"average": 3.54, "maximum": 37, "minimum": 0, "outliers": [37, 12, 11, 10, 10, 9, 9, 9, 9, 9]}
distributionpercentile |{"10": { "num_vectors": 0, "num_partitions": 0 }, "25": { "num_vectors": 0, "num_partitions": 30 }, "50": { "num_vectors": 3, "num_partitions": 30 }, "75": { "num_vectors": 5, "num_partitions": 19 }, "90": { "num_vectors": 7, "num_partitions": 11 }, "95": { "num_vectors": 9, "num_partitions": 5 }, "99": { "num_vectors": 12, "num_partitions": 4 }, "100": { "num_vectors": 37, "num_partitions": 1 }}
如要進一步瞭解完整的指標清單,請參閱「向量索引指標」。