AnalyticDB:ベクトル解析の実行

前提条件

ベクトルデータがテーブルにインポートされます。 詳細については、「ベクターデータのインポート」をご参照ください。

ベクター解析の実行

この例では、ベクトル分析を実行して、2乗ユークリッド距離およびドット積距離 (またはコサイン類似度) を得る。

二乗ユークリッド距離の取得

ベクトル分析を実行して、2乗ユークリッド距離を取得します。

SELECT id, l2_squared_distance(feature, array[0.495181661387,0.108697291209,0.181728549067,0.109680543346,0.19713082404,0.0197809514512,0.534227452778,0.442411970815,0.409909873031,0.0975687394505]::float4[]) AS distance 
  FROM vector_test.car_info 
  ORDER BY feature <-> array[0.495181661387,0.108697291209,0.181728549067,0.109680543346,0.19713082404,0.0197809514512,0.534227452778,0.442411970815,0.409909873031,0.0975687394505]::float4[] 
  LIMIT 10;

サンプル結果:

  id  |      distance
------+--------------------
    2 |                  0
 1331 | 0.0677967891097069
 1543 |  0.079616591334343
 5606 | 0.0892329216003418
 6423 | 0.0894578248262405
 1667 | 0.0903968289494514
 8215 | 0.0936210229992867
 7801 | 0.0952572822570801
 2581 | 0.0965127795934677
 2645 | 0.0987173467874527
(10 rows)

ドット積距離 (またはコサイン類似度)

ベクトル分析を実行して、内積距離 (または正規化を使用する場合はコサイン類似度) を取得します。

SELECT id, dp_distance(feature, array[0.495181661387,0.108697291209,0.181728549067,0.109680543346,0.19713082404,0.0197809514512,0.534227452778,0.442411970815,0.409909873031,0.0975687394505]::float4[]) AS similarity 
  FROM vector_test.car_info 
  ORDER BY feature <-> array[0.495181661387,0.108697291209,0.181728549067,0.109680543346,0.19713082404,0.0197809514512,0.534227452778,0.442411970815,0.409909873031,0.0975687394505]::float4[] 
  LIMIT 10;

サンプル結果:

  id  |    similarity
------+-------------------
    2 |                 1
 1331 | 0.966101586818695
 1543 | 0.960191607475281
 5606 | 0.955383539199829
 6423 | 0.955271065235138
 1667 | 0.954801559448242
 8215 | 0.953189492225647
 7801 |  0.95237135887146
 2581 | 0.951743602752686
 2645 | 0.950641334056854
(10 rows)

統合クエリ

SQL文を実行して、構造化データと非構造化データの両方を照会します。 例：

SELECT id, dp_distance(feature, array[0.495181661387,0.108697291209,0.181728549067,0.109680543346,0.19713082404,0.0197809514512,0.534227452778,0.442411970815,0.409909873031,0.0975687394505]::float4[]) AS similarity 
  FROM vector_test.car_info 
  WHERE market_time >= '2020-10-30 00:00:00' 
  AND market_time < '2021-01-01 00:00:00' 
  AND color in ('red', 'white', 'blue') 
  AND price < 100 
  ORDER BY feature <-> array[0.495181661387,0.108697291209,0.181728549067,0.109680543346,0.19713082404,0.0197809514512,0.534227452778,0.442411970815,0.409909873031,0.0975687394505]::float4[] 
  LIMIT 10;

サンプル結果:

  id  |    similarity
------+-------------------
 7645 | 0.922723233699799
 8956 | 0.920517802238464
 8219 |  0.91210675239563
 8503 | 0.895939946174622
 5113 | 0.895431876182556
 7680 | 0.893448948860168
 8433 | 0.893425941467285
 3604 |  0.89293098449707
 3945 | 0.891274154186249
 7153 | 0.891128540039062
(10 rows)

関連ドキュメント

ベクトル解析