スコアの詳細を返す

項目一覧

構文
オプション
出力
スコアに貢献する要因
例
演算子の例
カスタムスコアの例

$searchステージで scoreDetailsブール値オプションを使用すると、クエリ結果内の各ドキュメントのスコアの詳細な内訳を示すことができます。メタデータを表示するには、ステージで $project$meta 式を使用する必要があります。

構文

{
  "$search": {
    "<operator>": {
      <operator-specification>
    },
    "scoreDetails": true | false
  }
},
{
  "$project": {
    "scoreDetails": {"$meta": "searchScoreDetails"}
  }
}

オプション

$search ステージでは、 scoreDetailsブール値オプションは次のいずれかの値を取ります。

true - 結果にドキュメントのスコアの詳細を含める場合。 trueに設定すると、Atlas Search は結果の各ドキュメントのスコアの詳細な内訳を返します。詳細については、「出力」を参照してください。
false - 結果のスコア内訳の詳細を除外します。（デフォルト）

省略した場合、 scoreDetailsオプションはデフォルトでfalseになります。

$project ステージでは、 scoreDetailsフィールドは$meta式。これには、次の値が必要です。

`searchScoreDetails`	結果内の各ドキュメントのスコアの詳細な内訳を返します。

出力

scoreDetailsオプションは、結果内の各ドキュメントのscoreDetailsオブジェクト内のdetails配列の次のフィールドを返します。

フィールド	タイプ	説明
`value`	float	スコアリング式のサブセットによるスコアへの貢献。最上位の`value`は結果ドキュメントのスコア全体を表示し、 `$searchScore`の値と等しくなります。スコアリング式は、クエリで使用される演算子によって異なります。たとえば、Atlas Search では、テキスト演算子と近似演算子を含む複合クエリに次のスコアリング式が使用されます`BM25Similarity + distance decay function` 。
`description`	string	ドキュメントがスコア付けされた方法と、スコアの計算に考慮された要素に関する詳細を含む、スコアリング式のサブセット。最上位の`description`は、ドキュメントをスコアリングするために使用されるスコアリング式全体を示します。詳細については、「スコアに貢献する要因」を参照してください。
`details`	オブジェクトの配列	スコアリング式のサブセットに基づく、ドキュメント内の各一致のスコアの内訳。値は、構造内で再帰的なスコア詳細オブジェクトの配列です。

スコアに貢献する要因

BM25Similarityの場合、スコアはboost * idf * tfとして計算されます。 Atlas Search は、スコアを計算するために次のBM25Similarity要素を考慮します。

boost

タームの重要性を高めます。

freq

クエリ用語の頻度。

idf

クエリの逆ドキュメント頻度。 Atlas Search は、次の式を使用して頻度を計算します。

log(1 + (N - n + 0.5) / (n + 0.5))

以下の条件に一致するもの。

N は、フィールドを持つドキュメントの合計数です。
n は、タームを含むドキュメントの数です。

tf

ターム頻度。 Atlas Search は、次の式を使用して頻度を計算します。

freq / (freq + k1 * (1 - b + b * dl / avgdl))

以下の条件に一致するもの。

freq は、ドキュメント内のタームの出現の数です。
k1 は、内部的に指定される用語同期パラメーターです。これは、タームが再発生するたびにスコアがどの程度増加するかに影響します。
avgdl は、すべてのドキュメントにおけるフィールドの平均長です。
dl は、ドキュメントのフィールドの長さです。
b は、内部的に設定されている長さ正規化パラメーターです。 bにはdlとavgdlの比率が乗じられます。 bが増加すると、 dlとavgdlの比率の影響が大きくなります。

距離減少関数の場合、スコアはpivot / (pivot + abs(fieldValue - origin))として計算されます。 Atlas Search は、スコアを計算するために次の要素を考慮します。

`origin`	近くで検索する値。これは、結果の近接性が測定される基準点です。
`fieldValue`	ドキュメント内のクエリ対象フィールドの値。 `fieldValue`が`origin`に近づくほど、近似クエリのスコアが高くなります。
`pivot`	`fieldValue`と`origin`の距離がそれに等しい場合に、スコアを`0.5`と等しくするための参照点として指定される値。これは、 `fieldValue`と`origin`の距離が大きくなるにつれてスコアがどの程度早く低下するかを定義します。 `fieldValue`と`origin`の間の特定の距離の場合、 `pivot`が減少すると、スコアも減少します。

例

次の例は、次のの結果内のスコアの詳細を取得する方法を示しています。

クエリは、テキスト、ほぼ、複合、および埋め込みドキュメント演算子を使用して実行されます。
functionオプション式を使用してスコアが変更されたクエリ。

Tip

オブジェクトの配列で再帰的にスコアの詳細を表示するには、次のコマンドを実行してmongoshの設定を構成します。

config.set('inspectDepth', Infinity)

演算子の例

次の例は、$searchscoreDetails テキスト、近似、複合、 embeddedDocument 演算子クエリの結果内のドキュメントに対してオプションを使用してスコアの内訳を検索する方法を示しています。

次の例では、テキスト演算子を使用して、 sample_mflix.moviesコレクション内のtitleフィールドでautumnというタームをクエリします。クエリでは、結果内の各ドキュメントのスコアの詳細な内訳を取得するために、 $searchステージでscoreDetailsオプションを指定します。クエリは、 $limitステージを使用して結果を 3 つのドキュメントに制限し、 $projectステージを使用して次の操作を行います。

_idフィールドを除外します。
titleフィールドのみを含めます。
結果にscoreフィールドを追加するとドキュメントのスコアが返され、 scoreDetailsフィールドを結果に追加するとドキュメントのスコアの詳細な内訳が返されます。

1 db.movies.aggregate([
2   {
3     "$search": {
4       "text": {
5         "path": "title",
6         "query": "autumn"
7       },
8       "scoreDetails": true
9     }
10   },
11   {
12     "$limit": 3
13   },
14   {
15     "$project": {
16       "_id": 0,
17       "title": 1,
18       "score": { "$meta": "searchScore" },
19       "scoreDetails": { "$meta": "searchScoreDetails" }
20     }
21   }
22 ])

1 [
2   {
3     title: 'Autumn Leaves',
4     score: 3.834893226623535,
5     scoreDetails: {
6       value: 3.834893226623535,
7       description: '$type:string/title:autumn [BM25Similarity], result of:',
8       details: [
9         {
10           value: 3.834893226623535,
11           description: 'score(freq=1.0), computed as boost * idf * tf from:',
12           details: [
13             {
14               value: 7.39188289642334,
15               description: 'idf, computed as log(1 + (N - n + 0.5) / (n + 0.5)) from:',
16               details: [
17                 {
18                   value: 14,
19                   description: 'n, number of documents containing term',
20                   details: []
21                 },
22                 {
23                   value: 23529,
24                   description: 'N, total number of documents with field',
25                   details: []
26                 }
27               ]
28             },
29             {
30               value: 0.5187978744506836,
31               description: 'tf, computed as freq / (freq + k1 * (1 - b + b * dl / avgdl)) from:',
32               details: [
33                 {
34                   value: 1,
35                   description: 'freq, occurrences of term within document',
36                   details: []
37                 },
38                 {
39                   value: 1.2000000476837158,
40                   description: 'k1, term saturation parameter',
41                   details: []
42                 },
43                 {
44                   value: 0.75,
45                   description: 'b, length normalization parameter',
46                   details: []
47                 },
48                 {
49                   value: 2,
50                   description: 'dl, length of field',
51                   details: []
52                 },
53                 {
54                   value: 2.868375301361084,
55                   description: 'avgdl, average length of field',
56                   details: []
57                 }
58               ]
59             }
60           ]
61         }
62       ]
63     }
64   },
65   {
66     title: 'Late Autumn',
67     score: 3.834893226623535,
68     scoreDetails: {
69       value: 3.834893226623535,
70       description: '$type:string/title:autumn [BM25Similarity], result of:',
71       details: [
72         {
73           value: 3.834893226623535,
74           description: 'score(freq=1.0), computed as boost * idf * tf from:',
75           details: [
76             {
77               value: 7.39188289642334,
78               description: 'idf, computed as log(1 + (N - n + 0.5) / (n + 0.5)) from:',
79               details: [
80                 {
81                   value: 14,
82                   description: 'n, number of documents containing term',
83                   details: []
84                 },
85                 {
86                   value: 23529,
87                   description: 'N, total number of documents with field',
88                   details: []
89                 }
90               ]
91             },
92             {
93               value: 0.5187978744506836,
94               description: 'tf, computed as freq / (freq + k1 * (1 - b + b * dl / avgdl)) from:',
95               details: [
96                 {
97                   value: 1,
98                   description: 'freq, occurrences of term within document',
99                   details: []
100                 },
101                 {
102                   value: 1.2000000476837158,
103                   description: 'k1, term saturation parameter',
104                   details: []
105                 },
106                 {
107                   value: 0.75,
108                   description: 'b, length normalization parameter',
109                   details: []
110                 },
111                 {
112                   value: 2,
113                   description: 'dl, length of field',
114                   details: []
115                 },
116                 {
117                   value: 2.868375301361084,
118                   description: 'avgdl, average length of field',
119                   details: []
120                 }
121               ]
122             }
123           ]
124         }
125       ]
126     }
127   },
128   {
129     title: 'Cheyenne Autumn',
130     score: 3.834893226623535,
131     scoreDetails: {
132       value: 3.834893226623535,
133       description: '$type:string/title:autumn [BM25Similarity], result of:',
134       details: [
135         {
136           value: 3.834893226623535,
137           description: 'score(freq=1.0), computed as boost * idf * tf from:',
138           details: [
139             {
140               value: 7.39188289642334,
141               description: 'idf, computed as log(1 + (N - n + 0.5) / (n + 0.5)) from:',
142               details: [
143                 {
144                   value: 14,
145                   description: 'n, number of documents containing term',
146                   details: []
147                 },
148                 {
149                   value: 23529,
150                   description: 'N, total number of documents with field',
151                   details: []
152                 }
153               ]
154             },
155             {
156               value: 0.5187978744506836,
157               description: 'tf, computed as freq / (freq + k1 * (1 - b + b * dl / avgdl)) from:',
158               details: [
159                 {
160                   value: 1,
161                   description: 'freq, occurrences of term within document',
162                   details: []
163                 },
164                 {
165                   value: 1.2000000476837158,
166                   description: 'k1, term saturation parameter',
167                   details: []
168                 },
169                 {
170                   value: 0.75,
171                   description: 'b, length normalization parameter',
172                   details: []
173                 },
174                 {
175                   value: 2,
176                   description: 'dl, length of field',
177                   details: []
178                 },
179                 {
180                   value: 2.868375301361084,
181                   description: 'avgdl, average length of field',
182                   details: []
183                 }
184               ]
185             }
186           ]
187         }
188       ]
189     }
190   }
191 ]

Atlas Search は、スコアを計算するために次のBM25Similarity要素を考慮します。

boost

タームの重要性を高めます。

freq

クエリ用語の頻度。

idf

クエリの逆ドキュメント頻度。 Atlas Search は、次の式を使用して頻度を計算します。

log(1 + (N - n + 0.5) / (n + 0.5))

以下の条件に一致するもの。

N は、フィールドを持つドキュメントの合計数です。
n は、タームを含むドキュメントの数です。

tf

ターム頻度。 Atlas Search は、次の式を使用して頻度を計算します。

freq / (freq + k1 * (1 - b + b * dl / avgdl))

以下の条件に一致するもの。

freq は、ドキュメント内のタームの出現の数です。
k1 は、内部的に指定される用語同期パラメーターです。これは、タームが再発生するたびにスコアがどの程度増加するかに影響します。
avgdl は、すべてのドキュメントにおけるフィールドの平均長です。
dl は、ドキュメントのフィールドの長さです。
b は、内部的に設定されている長さ正規化パラメーターです。 bにはdlとavgdlの比率が乗じられます。 bが増加すると、 dlとavgdlの比率の影響が大きくなります。

次の例では、 near演算子を使用して、 sample_mflix.moviesコレクション内のreleasedフィールドで、 01と2010の前後にリリースされた映画をクエリします。クエリでは、結果内の各ドキュメントのスコアの詳細な内訳を取得するために、 $searchステージでscoreDetailsオプションを指定します。クエリは、 $limitステージを使用して結果を 3 つのドキュメントに制限し、 $projectステージを使用して次の操作を行います。

_idフィールドを除外します。
titlereleasedフィールドとフィールドのみを含めます。
結果にscoreフィールドを追加するとドキュメントのスコアが返され、 scoreDetailsフィールドを結果に追加するとドキュメントのスコアの詳細な内訳が返されます。

1 db.movies.aggregate([
2   {
3     "$search": {
4       "near": {
5         "path": "released",
6         "origin": ISODate("2010-01-01T00:00:00.000+00:00"),
7         "pivot": 7776000000
8       },
9       "scoreDetails": true
10     }
11   },
12   {
13      "$limit": 3
14   },
15   {
16      "$project": {
17         "_id": 0,
18         "title": 1,
19         "released": 1,
20         "score": { "$meta": "searchScore" },
21         "scoreDetails": { "$meta": "searchScoreDetails" }
22      }
23   }
24 ])

1 [
2   {
3     title: 'Tony',
4     released: ISODate("2010-01-01T00:00:00.000Z"),
5     score: 1,
6     scoreDetails: {
7       value: 1,
8       description: 'Distance score, computed as weight * pivotDistance / (pivotDistance + abs(value - origin)) from:',
9       details: [
10         { value: 1, description: 'weight', details: [] },
11         {
12           value: 7776000000,
13           description: 'pivotDistance',
14           details: []
15         },
16         { value: 1262303969280, description: 'origin', details: [] },
17         {
18           value: 1262303969280,
19           description: 'current value',
20           details: []
21         }
22       ]
23     }
24   },
25  {
26     title: 'And Everything Is Going Fine',
27     released: ISODate("2010-01-01T00:00:00.000Z"),
28     score: 1,
29     scoreDetails: {
30       value: 1,
31       description: 'Distance score, computed as weight * pivotDistance / (pivotDistance + abs(value - origin)) from:',
32       details: [
33         { value: 1, description: 'weight', details: [] },
34         {
35           value: 7776000000,
36           description: 'pivotDistance',
37           details: []
38         },
39         { value: 1262303969280, description: 'origin', details: [] },
40         {
41           value: 1262303969280,
42           description: 'current value',
43           details: []
44         }
45       ]
46     }
47   },
48   {
49     title: 'A Film with Me in It',
50     released: ISODate("2010-01-01T00:00:00.000Z")
51     score: 1,
52     scoreDetails: {
53       value: 1,
54       description: 'Distance score, computed as weight * pivotDistance / (pivotDistance + abs(value - origin)) from:',
55       details: [
56         { value: 1, description: 'weight', details: [] },
57         {
58           value: 7776000000,
59           description: 'pivotDistance',
60           details: []
61         },
62         { value: 1262303969280, description: 'origin', details: [] },
63         {
64           value: 1262303969280,
65           description: 'current value',
66           details: []
67         }
68       ]
69     }
70   }
71 ]

距離スコアの場合、Atlas Search は次の要素を考慮してスコアを計算します。

`origin`	近くで検索する値。これは、結果の近接性が測定される基準点です。
`fieldValue`	ドキュメント内のクエリ対象フィールドの値。 `fieldValue`が`origin`に近づくほど、近似クエリのスコアが高くなります。
`pivot`	`fieldValue`と`origin`の距離がそれに等しい場合に、スコアを`0.5`と等しくするための参照点として指定される値。これは、 `fieldValue`と`origin`の距離が大きくなるにつれてスコアがどの程度早く低下するかを定義します。 `fieldValue`と`origin`の間の特定の距離の場合、 `pivot`が減少すると、スコアも減少します。

次の例では、複合演算子を使用して、次の句を使用する映画のsample_mflix.moviesコレクションをクエリします。

filter 句を使用して、タイトルにfriendというタームが含まれる映画を検索します。
must 句を使用して、 2000から2015年の間に公開された映画を検索します。
mustNot 句を使用して、 Short 、 Western 、 Biographyジャンルに該当しない映画を検索します。

クエリでは、 $searchステージでscoreDetailsオプションを指定し、結果内の各ドキュメントのスコアの詳細な内訳を取得します。クエリは、 $limitステージを使用して結果を 3 つのドキュメントに制限し、 $projectステージを使用して次の操作を行います。

_idフィールドを除外します。
title 、 released 、 genresフィールドのみを含めます。
結果にscoreフィールドを追加するとドキュメントのスコアが返され、 scoreDetailsフィールドを結果に追加するとドキュメントのスコアの詳細な内訳が返されます。

1 db.movies.aggregate([
2   {
3     "$search": {
4       "compound": {
5         "filter": [{
6           "text": {
7             "query": "friend",
8             "path": "title"
9           }
10         }],
11         "must": [{
12           "range": {
13             "path": "year",
14             "gte": 2000,
15             "lte": 2015
16           }
17         }],
18         "mustNot": [{
19           "text": {
20             "query": ["Short, Western", "Biography"],
21             "path": "genres"
22           }
23         }]
24       },
25       "scoreDetails": true
26     }
27   },
28   {
29     "$limit": 3
30   },
31   {
32     "$project": {
33       "_id": 0,
34       "title": 1,
35       "released": 1,
36       "genres": 1,
37       "score": { "$meta": "searchScore" },
38       "scoreDetails": { "$meta": "searchScoreDetails" }
39     }
40   }
41 ])

1 [
2   {
3     genres: [ 'Comedy', 'Drama', 'Mystery' ],
4     title: 'With a Friend Like Harry...',
5     released: ISODate("2001-06-15T00:00:00.000Z"),
6     score: 1,
7     scoreDetails: {
8       value: 1,
9       description: 'sum of:',
10       details: [
11         {
12           value: 0,
13           description: 'match on required clause, product of:',
14           details: [
15             { value: 0, description: '# clause', details: [] },
16             {
17               value: 1,
18               description: '$type:string/title:friend',
19               details: []
20             }
21           ]
22         },
23         {
24           value: 1,
25           description: 'sum of:',
26           details: [
27             {
28               value: 1,
29               description: 'sum of:',
30               details: [
31                 {
32                   value: 1,
33                   description: '$type:double/year:[4656510908468559872 TO 4656576879166226432]',
34                   details: []
35                 }
36               ]
37             }
38           ]
39         }
40       ]
41     }
42   },
43   {
44     genres: [ 'Drama' ],
45     title: 'My Friend Henry',
46     released: ISODate("2004-08-20T00:00:00.000Z"),
47     score: 1,
48     scoreDetails: {
49       value: 1,
50       description: 'sum of:',
51       details: [
52         {
53           value: 0,
54           description: 'match on required clause, product of:',
55           details: [
56             { value: 0, description: '# clause', details: [] },
57             {
58               value: 1,
59               description: '$type:string/title:friend',
60               details: []
61             }
62           ]
63         },
64         {
65           value: 1,
66           description: 'sum of:',
67           details: [
68             {
69               value: 1,
70               description: 'sum of:',
71               details: [
72                 {
73                   value: 1,
74                   description: '$type:double/year:[4656510908468559872 TO 4656576879166226432]',
75                   details: []
76                 }
77               ]
78             }
79           ]
80         }
81       ]
82     }
83   },
84   {
85     genres: [ 'Comedy', 'Drama' ],
86     title: 'A Friend of Mine',
87     released: ISODate("2006-10-26T00:00:00.000Z"),
88     score: 1,
89     scoreDetails: {
90       value: 1,
91       description: 'sum of:',
92       details: [
93         {
94           value: 0,
95           description: 'match on required clause, product of:',
96           details: [
97             { value: 0, description: '# clause', details: [] },
98             {
99               value: 1,
100               description: '$type:string/title:friend',
101               details: []
102             }
103           ]
104         },
105         {
106           value: 1,
107           description: 'sum of:',
108           details: [
109             {
110               value: 1,
111               description: 'sum of:',
112               details: [
113                 {
114                   value: 1,
115                   description: '$type:double/year:[4656510908468559872 TO 4656576879166226432]',
116                   details: []
117                 }
118               ]
119             }
120           ]
121         }
122       ]
123     }
124   }
125 ]

注意

15行、 56行、および97行の結果の# clauseは、ドキュメントのスコアに貢献しない複合クエリfilter句を表します。

次の例では、 embeddedDocument演算子を使用して、 sample_training.companiesコレクション内のproducts.nameフィールドで、 Basicの前に任意の数の他の文字が続く製品をクエリします。クエリは、返されるスコアが一致するすべての埋め込みドキュメントの合計である必要があることをembeddedDocument演算子内に指定します。このクエリでは、結果内の各ドキュメントのスコアの詳細な内訳を取得するために、 $searchステージでscoreDetailsオプションも指定しています。クエリは、 $limitステージを使用して結果を 3 つのドキュメントに制限し、 $projectステージを使用して次の操作を行います。

_idフィールドを除外します。
products.nameフィールドのみを含めます。
結果にscoreフィールドを追加するとドキュメントのスコアが返され、 scoreDetailsフィールドを結果に追加するとドキュメントのスコアの詳細な内訳が返されます。

1 db.companies.aggregate({
2   "$search": {
3     "embeddedDocument": {
4       "path": "products",
5       "operator": {
6         "wildcard": {
7           "path": "products.name",
8           "query": "*Basic",
9           "allowAnalyzedField": true
10         }
11      },
12       "score": {
13         "embedded": {
14           "aggregate": "sum"
15         }
16       }
17     },
18     "scoreDetails": true
19   }
20 },
21 {
22   "$limit": 3
23 },
24 {
25   "$project": {
26     "_id": 0,
27     "name": 1,
28     "products.name": 1,
29     "score": { "$meta": "searchScore" },
30     "scoreDetails": { "$meta": "searchScoreDetails" }
31   }
32 })

1 [
2   {
3     name: 'Plaxo',
4     products: [
5       { name: 'Plaxo Basic' },
6       { name: 'Plaxo Pulse' },
7       { name: 'Plaxo Personal Assistant' }
8     ],
9     score: 1,
10     scoreDetails: {
11       value: 1,
12       description: 'Score based on 1 child docs in range from 27 to 29, best match:',
13       details: [
14         {
15           value: 1,
16           description: '$embedded:8/products/$type:string/products.name:*Basic',
17           details: []
18         }
19       ]
20     }
21   },
22   {
23     name: 'The Game Creators',
24     products: [
25       { name: 'Dark Basic Professional' },
26       { name: 'FPS Creator' },
27       { name: 'FPS Creator X10' }
28     ],
29     score: 1,
30     scoreDetails: {
31       value: 1,
32       description: 'Score based on 1 child docs in range from 7474 to 7476, best match:',
33       details: [
34         {
35           value: 1,
36           description: '$embedded:8/products/$type:string/products.name:*basic',
37           details: []
38         }
39       ]
40     }
41   },
42   {
43     name: 'Load Impact',
44     products: [
45       { name: 'Load Impact LIGHT' },
46       { name: 'Load Impact BASIC' },
47       { name: 'Load Impact PROFESSIONAL' },
48       { name: 'Load Impact ADVANCED' }
49     ],
50     score: 1,
51     scoreDetails: {
52       value: 1,
53       description: 'Score based on 1 child docs in range from 11545 to 11548, best match:',
54       details: [
55         {
56           value: 1,
57           description: '$embedded:8/products/$type:string/products.name:*basic',
58           details: []
59         }
60       ]
61     }
62   }
63 ]

注意

範囲内の子ドキュメントに基づくスコアの場合、範囲内の数値は、フードの下で Lucene によってインデックス付けされた親ドキュメントと子ドキュメントの ID を表します。子ドキュメント（ 16行、 36行、および57行）のdescriptionは、パスの内部表現を示します。

カスタムスコアの例

$searchscoreDetails次の例は、sample_mflix.movies コレクションに対する関数式のサンプルクエリの結果内のドキュメントに対してオプションを使用してスコアの内訳を取得する方法を示しています。

1 db.movies.aggregate([{
2   "$search": {
3     "text": {
4       "path": "title",
5       "query": "men",
6       "score": {
7         "function":{
8           "multiply":[
9             {
10               "path": {
11                 "value": "imdb.rating",
12                 "undefined": 2
13               }
14             },
15             {
16               "score": "relevance"
17             }
18           ]
19         }
20       }
21     },
22     "scoreDetails": true
23   }
24 },
25 {
26   $limit: 5
27 },
28 {
29   $project: {
30     "_id": 0,
31     "title": 1,
32     "score": { "$meta": "searchScore" },
33     "scoreDetails": {"$meta": "searchScoreDetails"}
34   }
35 }])

[
  {
    title: 'Men...',
    score: 23.431293487548828,
    scoreDetails: {
      value: 23.431293487548828,
      description: 'FunctionScoreQuery($type:string/title:men, scored by (imdb.rating * scores)) [BM25Similarity], result of:',
      details: [
        {
          value: 23.431293487548828,
          description: '(imdb.rating * scores)',
          details: []
        }
      ]
    }
  },
  {
    title: '12 Angry Men',
    score: 22.080968856811523,
    scoreDetails: {
      value: 22.080968856811523,
      description: 'FunctionScoreQuery($type:string/title:men, scored by (imdb.rating * scores)) [BM25Similarity], result of:',
      details: [
        {
          value: 22.080968856811523,
          description: '(imdb.rating * scores)',
          details: []
        }
      ]
    }
  },
  {
    title: 'X-Men',
    score: 21.34803581237793,
    scoreDetails: {
      value: 21.34803581237793,
      description: 'FunctionScoreQuery($type:string/title:men, scored by (imdb.rating * scores)) [BM25Similarity], result of:',
      details: [
        {
          value: 21.34803581237793,
          description: '(imdb.rating * scores)',
          details: []
        }
      ]
    }
  },
  {
    title: 'X-Men',
    score: 21.34803581237793,
    scoreDetails: {
      value: 21.34803581237793,
      description: 'FunctionScoreQuery($type:string/title:men, scored by (imdb.rating * scores)) [BM25Similarity], result of:',
      details: [
        {
          value: 21.34803581237793,
          description: '(imdb.rating * scores)',
          details: []
        }
      ]
    }
  },
  {
    title: 'Matchstick Men',
    score: 21.05954933166504,
    scoreDetails: {
      value: 21.05954933166504,
      description: 'FunctionScoreQuery($type:string/title:men, scored by (imdb.rating * scores)) [BM25Similarity], result of:',
      details: [
        {
          value: 21.05954933166504,
          description: '(imdb.rating * scores)',
          details: []
        }
      ]
    }
  }
]

1 db.movies.aggregate([
2   {
3     "$search": {
4       "text": {
5         "path": "title",
6         "query": "men",
7         "score": {
8           "function":{
9             "constant": 3
10           }
11         }
12       },
13       "scoreDetails": true
14     }
15   },
16   {
17     $limit: 5
18   },
19   {
20     $project: {
21       "_id": 0,
22       "title": 1,
23       "score": { "$meta": "searchScore" },
24       "scoreDetails": {"$meta": "searchScoreDetails"}
25     }
26   }
27 ])

[
  {
    title: 'Men Without Women',
    score: 3,
    scoreDetails: {
      value: 3,
      description: 'FunctionScoreQuery($type:string/title:men, scored by constant(3.0)) [BM25Similarity], result of:',
      details: [ { value: 3, description: 'constant(3.0)', details: [] } ]
    }
  },
  {
    title: 'One Hundred Men and a Girl',
    score: 3,
    scoreDetails: {
      value: 3,
      description: 'FunctionScoreQuery($type:string/title:men, scored by constant(3.0)) [BM25Similarity], result of:',
      details: [ { value: 3, description: 'constant(3.0)', details: [] } ]
    }
  },
  {
    title: 'Of Mice and Men',
    score: 3,
    scoreDetails: {
      value: 3,
      description: 'FunctionScoreQuery($type:string/title:men, scored by constant(3.0)) [BM25Similarity], result of:',
      details: [ { value: 3, description: 'constant(3.0)', details: [] } ]
    }
  },
  {
    title: "All the King's Men",
    score: 3,
    scoreDetails: {
      value: 3,
      description: 'FunctionScoreQuery($type:string/title:men, scored by constant(3.0)) [BM25Similarity], result of:',
      details: [ { value: 3, description: 'constant(3.0)', details: [] } ]
    }
  },
  {
    title: 'The Men',
    score: 3,
    scoreDetails: {
      value: 3,
      description: 'FunctionScoreQuery($type:string/title:men, scored by constant(3.0)) [BM25Similarity], result of:',
      details: [ { value: 3, description: 'constant(3.0)', details: [] } ]
    }
  }
]

1 db.movies.aggregate([
2   {
3     "$search": {
4       "text": {
5         "path": "title",
6         "query": "shop",
7         "score": {
8           "function":{
9             "gauss": {
10               "path": {
11                 "value": "imdb.rating",
12                 "undefined": 4.6
13               },
14               "origin": 9.5,
15               "scale": 5,
16               "offset": 0,
17               "decay": 0.5
18             }
19           }
20         }
21       },
22       "scoreDetails": true
23     }
24   },
25   {
26     "$limit": 10
27   },
28   {
29     "$project": {
30       "_id": 0,
31       "title": 1,
32       "score": { "$meta": "searchScore" },
33       "scoreDetails": {"$meta": "searchScoreDetails"}
34     }
35   }
36 ])

[
  {
    title: 'The Shop Around the Corner',
    score: 0.9471074342727661,
    scoreDetails: {
      value: 0.9471074342727661,
      description: 'FunctionScoreQuery($type:string/title:shop, scored by exp((max(0, |imdb.rating - 9.5| - 0.0)^2) / 2 * (5.0^2 / 2 * ln(0.5)))) [BM25Similarity], result of:',
      details: [
        {
          value: 0.9471074342727661,
          description: 'exp((max(0, |imdb.rating - 9.5| - 0.0)^2) / 2 * (5.0^2 / 2 * ln(0.5)))',
          details: []
        }
      ]
    }
  },
  {
    title: 'Exit Through the Gift Shop',
    score: 0.9471074342727661,
    scoreDetails: {
      value: 0.9471074342727661,
      description: 'FunctionScoreQuery($type:string/title:shop, scored by exp((max(0, |imdb.rating - 9.5| - 0.0)^2) / 2 * (5.0^2 / 2 * ln(0.5)))) [BM25Similarity], result of:',
      details: [
        {
          value: 0.9471074342727661,
          description: 'exp((max(0, |imdb.rating - 9.5| - 0.0)^2) / 2 * (5.0^2 / 2 * ln(0.5)))',
          details: []
        }
      ]
    }
  },
  {
    title: 'The Shop on Main Street',
    score: 0.9395227432250977,
    scoreDetails: {
      value: 0.9395227432250977,
      description: 'FunctionScoreQuery($type:string/title:shop, scored by exp((max(0, |imdb.rating - 9.5| - 0.0)^2) / 2 * (5.0^2 / 2 * ln(0.5)))) [BM25Similarity], result of:',
      details: [
        {
          value: 0.9395227432250977,
          description: 'exp((max(0, |imdb.rating - 9.5| - 0.0)^2) / 2 * (5.0^2 / 2 * ln(0.5)))',
          details: []
        }
      ]
    }
  },
  {
    title: 'Chop Shop',
    score: 0.8849083781242371,
    scoreDetails: {
      value: 0.8849083781242371,
      description: 'FunctionScoreQuery($type:string/title:shop, scored by exp((max(0, |imdb.rating - 9.5| - 0.0)^2) / 2 * (5.0^2 / 2 * ln(0.5)))) [BM25Similarity], result of:',
      details: [
        {
          value: 0.8849083781242371,
          description: 'exp((max(0, |imdb.rating - 9.5| - 0.0)^2) / 2 * (5.0^2 / 2 * ln(0.5)))',
          details: []
        }
      ]
    }
  },
  {
    title: 'Little Shop of Horrors',
    score: 0.8290896415710449,
    scoreDetails: {
      value: 0.8290896415710449,
      description: 'FunctionScoreQuery($type:string/title:shop, scored by exp((max(0, |imdb.rating - 9.5| - 0.0)^2) / 2 * (5.0^2 / 2 * ln(0.5)))) [BM25Similarity], result of:',
      details: [
        {
          value: 0.8290896415710449,
          description: 'exp((max(0, |imdb.rating - 9.5| - 0.0)^2) / 2 * (5.0^2 / 2 * ln(0.5)))',
          details: []
        }
      ]
    }
  },
  {
    title: 'The Suicide Shop',
    score: 0.7257778644561768,
    scoreDetails: {
      value: 0.7257778644561768,
      description: 'FunctionScoreQuery($type:string/title:shop, scored by exp((max(0, |imdb.rating - 9.5| - 0.0)^2) / 2 * (5.0^2 / 2 * ln(0.5)))) [BM25Similarity], result of:',
      details: [
        {
          value: 0.7257778644561768,
          description: 'exp((max(0, |imdb.rating - 9.5| - 0.0)^2) / 2 * (5.0^2 / 2 * ln(0.5)))',
          details: []
        }
      ]
    }
  },
  {
    title: 'A Woman, a Gun and a Noodle Shop',
    score: 0.6559237241744995,
    scoreDetails: {
      value: 0.6559237241744995,
      description: 'FunctionScoreQuery($type:string/title:shop, scored by exp((max(0, |imdb.rating - 9.5| - 0.0)^2) / 2 * (5.0^2 / 2 * ln(0.5)))) [BM25Similarity], result of:',
      details: [
        {
          value: 0.6559237241744995,
          description: 'exp((max(0, |imdb.rating - 9.5| - 0.0)^2) / 2 * (5.0^2 / 2 * ln(0.5)))',
          details: []
        }
      ]
    }
  },
  {
    title: 'Beauty Shop',
    score: 0.6274620294570923,
    scoreDetails: {
      value: 0.6274620294570923,
      description: 'FunctionScoreQuery($type:string/title:shop, scored by exp((max(0, |imdb.rating - 9.5| - 0.0)^2) / 2 * (5.0^2 / 2 * ln(0.5)))) [BM25Similarity], result of:',
      details: [
        {
          value: 0.6274620294570923,
          description: 'exp((max(0, |imdb.rating - 9.5| - 0.0)^2) / 2 * (5.0^2 / 2 * ln(0.5)))',
          details: []
        }
      ]
    }
  }
]

1 db.movies.aggregate([{
2   "$search": {
3     "text": {
4       "path": "title",
5       "query": "men",
6       "score": {
7         "function":{
8           "path": {
9             "value": "imdb.rating",
10             "undefined": 4.6
11           }
12         }
13       }
14     },
15     "scoreDetails": true
16   }
17 },
18 {
19   $limit: 5
20 },
21 {
22   $project: {
23     "_id": 0,
24     "title": 1,
25     "score": { "$meta": "searchScore" },
26     "scoreDetails": {"$meta": "searchScoreDetails"}
27   }
28 }])

[
  {
    title: '12 Angry Men',
    score: 8.899999618530273,
    scoreDetails: {
      value: 8.899999618530273,
      description: 'FunctionScoreQuery($type:string/title:men, scored by imdb.rating) [BM25Similarity], result of:',
      details: [
        {
          value: 8.899999618530273,
          description: 'imdb.rating',
          details: []
        }
      ]
    }
  },
  {
    title: 'The Men Who Built America',
    score: 8.600000381469727,
    scoreDetails: {
      value: 8.600000381469727,
      description: 'FunctionScoreQuery($type:string/title:men, scored by imdb.rating) [BM25Similarity], result of:',
      details: [
        {
          value: 8.600000381469727,
          description: 'imdb.rating',
          details: []
        }
      ]
    }
  },
  {
    title: 'No Country for Old Men',
    score: 8.100000381469727,
    scoreDetails: {
      value: 8.100000381469727,
      description: 'FunctionScoreQuery($type:string/title:men, scored by imdb.rating) [BM25Similarity], result of:',
      details: [
        {
          value: 8.100000381469727,
          description: 'imdb.rating',
          details: []
        }
      ]
    }
  },
  {
    title: 'X-Men: Days of Future Past',
    score: 8.100000381469727,
    scoreDetails: {
      value: 8.100000381469727,
      description: 'FunctionScoreQuery($type:string/title:men, scored by imdb.rating) [BM25Similarity], result of:',
      details: [
        {
          value: 8.100000381469727,
          description: 'imdb.rating',
          details: []
        }
      ]
    }
  },
  {
    title: 'The Best of Men',
    score: 8.100000381469727,
    scoreDetails: {
      value: 8.100000381469727,
      description: 'FunctionScoreQuery($type:string/title:men, scored by imdb.rating) [BM25Similarity], result of:',
      details: [
        {
          value: 8.100000381469727,
          description: 'imdb.rating',
          details: []
        }
      ]
    }
  }
]

1 db.movies.aggregate([{
2   "$search": {
3     "text": {
4       "path": "title",
5       "query": "men",
6       "score": {
7         "function":{
8           "score": "relevance"
9         }
10       }
11     },
12     "scoreDetails": true
13   }
14 },
15 {
16   $limit: 5
17 },
18 {
19   $project: {
20     "_id": 0,
21     "title": 1,
22     "score": { "$meta": "searchScore" },
23     "scoreDetails": {"$meta": "searchScoreDetails"}
24   }
25 }])

[
  {
    title: 'Men...',
    score: 3.4457783699035645,
    scoreDetails: {
      value: 3.4457783699035645,
      description: 'FunctionScoreQuery($type:string/title:men, scored by scores) [BM25Similarity], result of:',
      details: [
        {
          value: 3.4457783699035645,
          description: 'weight($type:string/title:men in 4705) [BM25Similarity], result of:',
          details: [
            {
              value: 3.4457783699035645,
              description: 'score(freq=1.0), computed as boost * idf * tf from:',
              details: [
                {
                  value: 5.5606818199157715,
                  description: 'idf, computed as log(1 + (N - n + 0.5) / (n + 0.5)) from:',
                  details: [
                    {
                      value: 90,
                      description: 'n, number of documents containing term',
                      details: []
                    },
                    {
                      value: 23529,
                      description: 'N, total number of documents with field',
                      details: []
                    }
                  ]
                },
                {
                  value: 0.6196683645248413,
                  description: 'tf, computed as freq / (freq + k1 * (1 - b + b * dl / avgdl)) from:',
                  details: [
                    {
                      value: 1,
                      description: 'freq, occurrences of term within document',
                      details: []
                    },
                    {
                      value: 1.2000000476837158,
                      description: 'k1, term saturation parameter',
                      details: []
                    },
                    {
                      value: 0.75,
                      description: 'b, length normalization parameter',
                     details: []
                    },
                    {
                      value: 1,
                      description: 'dl, length of field',
                      details: []
                    },
                    {
                      value: 2.868375301361084,
                      description: 'avgdl, average length of field',
                      details: []
                    }
                  ]
                }
              ]
            }
          ]
        }
      ]
    }
  },
  {
    title: 'The Men',
    score: 2.8848698139190674,
    scoreDetails: {
      value: 2.8848698139190674,
      description: 'FunctionScoreQuery($type:string/title:men, scored by scores) [BM25Similarity], result of:',
      details: [
        {
          value: 2.8848698139190674,
          description: 'weight($type:string/title:men in 870) [BM25Similarity], result of:',
          details: [
            {
              value: 2.8848698139190674,
              description: 'score(freq=1.0), computed as boost * idf * tf from:',
              details: [
                {
                  value: 5.5606818199157715,
                  description: 'idf, computed as log(1 + (N - n + 0.5) / (n + 0.5)) from:',
                  details: [
                    {
                      value: 90,
                      description: 'n, number of documents containing term',
                      details: []
                    },
                    {
                      value: 23529,
                      description: 'N, total number of documents with field',
                      details: []
                    }
                  ]
                },
                {
                  value: 0.5187978744506836,
                  description: 'tf, computed as freq / (freq + k1 * (1 - b + b * dl / avgdl)) from:',
                  details: [
                    {
                      value: 1,
                      description: 'freq, occurrences of term within document',
                      details: []
                    },
                    {
                      value: 1.2000000476837158,
                      description: 'k1, term saturation parameter',
                      details: []
                    },
                    {
                      value: 0.75,
                      description: 'b, length normalization parameter',
                      details: []
                    },
                    {
                      value: 2,
                      description: 'dl, length of field',
                      details: []
                    },
                    {
                      value: 2.868375301361084,
                      description: 'avgdl, average length of field',
                      details: []
                    }
                  ]
                }
              ]
            }
          ]
        }
      ]
    }
  },
  {
    title: 'Simple Men',
    score: 2.8848698139190674,
    scoreDetails: {
      value: 2.8848698139190674,
      description: 'FunctionScoreQuery($type:string/title:men, scored by scores) [BM25Similarity], result of:',
      details: [
        {
          value: 2.8848698139190674,
          description: 'weight($type:string/title:men in 6371) [BM25Similarity], result of:',
          details: [
            {
              value: 2.8848698139190674,
              description: 'score(freq=1.0), computed as boost * idf * tf from:',
              details: [
                {
                  value: 5.5606818199157715,
                  description: 'idf, computed as log(1 + (N - n + 0.5) / (n + 0.5)) from:',
                  details: [
                    {
                      value: 90,
                      description: 'n, number of documents containing term',
                      details: []
                    },
                    {
                      value: 23529,
                      description: 'N, total number of documents with field',
                      details: []
                    }
                  ]
                },
                {
                  value: 0.5187978744506836,
                  description: 'tf, computed as freq / (freq + k1 * (1 - b + b * dl / avgdl)) from:',
                  details: [
                    {
                      value: 1,
                      description: 'freq, occurrences of term within document',
                      details: []
                    },
                    {
                      value: 1.2000000476837158,
                      description: 'k1, term saturation parameter',
                      details: []
                    },
                    {
                      value: 0.75,
                      description: 'b, length normalization parameter',
                      details: []
                    },
                    {
                      value: 2,
                      description: 'dl, length of field',
                      details: []
                    },
                    {
                      value: 2.868375301361084,
                      description: 'avgdl, average length of field',
                      details: []
                    }
                  ]
                }
              ]
            }
          ]
        }
      ]
    }
  },
  {
    title: 'X-Men',
    score: 2.8848698139190674,
    scoreDetails: {
      value: 2.8848698139190674,
      description: 'FunctionScoreQuery($type:string/title:men, scored by scores) [BM25Similarity], result of:',
      details: [
        {
          value: 2.8848698139190674,
          description: 'weight($type:string/title:men in 8368) [BM25Similarity], result of:',
          details: [
            {
              value: 2.8848698139190674,
              description: 'score(freq=1.0), computed as boost * idf * tf from:',
              details: [
                {
                  value: 5.5606818199157715,
                  description: 'idf, computed as log(1 + (N - n + 0.5) / (n + 0.5)) from:',
                  details: [
                    {
                      value: 90,
                      description: 'n, number of documents containing term',
                      details: []
                    },
                    {
                      value: 23529,
                      description: 'N, total number of documents with field',
                      details: []
                    }
                  ]
                },
                {
                  value: 0.5187978744506836,
                  description: 'tf, computed as freq / (freq + k1 * (1 - b + b * dl / avgdl)) from:',
                  details: [
                    {
                      value: 1,
                      description: 'freq, occurrences of term within document',
                      details: []
                    },
                    {
                      value: 1.2000000476837158,
                      description: 'k1, term saturation parameter',
                      details: []
                    },
                    {
                      value: 0.75,
                      description: 'b, length normalization parameter',
                      details: []
                    },
                    {
                      value: 2,
                      description: 'dl, length of field',
                      details: []
                    },
                    {
                      value: 2.868375301361084,
                      description: 'avgdl, average length of field',
                      details: []
                    }
                  ]
                }
              ]
            }
          ]
        }
      ]
    }
  },
  {
    title: 'Mystery Men',
    score: 2.8848698139190674,
    scoreDetails: {
      value: 2.8848698139190674,
      description: 'FunctionScoreQuery($type:string/title:men, scored by scores) [BM25Similarity], result of:',
      details: [
        {
          value: 2.8848698139190674,
          description: 'weight($type:string/title:men in 8601) [BM25Similarity], result of:',
          details: [
            {
              value: 2.8848698139190674,
              description: 'score(freq=1.0), computed as boost * idf * tf from:',
              details: [
                {
                  value: 5.5606818199157715,
                  description: 'idf, computed as log(1 + (N - n + 0.5) / (n + 0.5)) from:',
                  details: [
                    {
                      value: 90,
                      description: 'n, number of documents containing term',
                      details: []
                    },
                    {
                      value: 23529,
                      description: 'N, total number of documents with field',
                      details: []
                    }
                  ]
                },
                {
                  value: 0.5187978744506836,
                  description: 'tf, computed as freq / (freq + k1 * (1 - b + b * dl / avgdl)) from:',
                  details: [
                    {
                      value: 1,
                      description: 'freq, occurrences of term within document',
                      details: []
                    },
                    {
                      value: 1.2000000476837158,
                      description: 'k1, term saturation parameter',
                      details: []
                    },
                    {
                      value: 0.75,
                      description: 'b, length normalization parameter',
                      details: []
                    },
                    {
                      value: 2,
                      description: 'dl, length of field',
                      details: []
                    },
                    {
                      value: 2.868375301361084,
                      description: 'avgdl, average length of field',
                      details: []
                    }
                  ]
                }
              ]
            }
          ]
        }
      ]
    }
  }
]

1 db.movies.aggregate([{
2   "$search": {
3     "text": {
4       "path": "title",
5       "query": "men",
6       "score": {
7         "function": {
8           "log": {
9             "path": {
10               "value": "imdb.rating",
11               "undefined": 10
12             }
13           }
14         }
15       }
16     },
17     "scoreDetails": true
18   }
19 },
20 {
21   $limit: 5
22 },
23 {
24   $project: {
25     "_id": 0,
26     "title": 1,
27     "score": { "$meta": "searchScore" },
28     "scoreDetails": {"$meta": "searchScoreDetails"}
29   }
30 }])

[
  {
    title: '12 Angry Men',
    score: 0.9493899941444397,
    scoreDetails: {
      value: 0.9493899941444397,
      description: 'FunctionScoreQuery($type:string/title:men, scored by log(imdb.rating)) [BM25Similarity], result of:',
      details: [
        {
          value: 0.9493899941444397,
          description: 'log(imdb.rating)',
          details: []
        }
      ]
    }
  },
  {
    title: 'The Men Who Built America',
    score: 0.9344984292984009,
    scoreDetails: {
      value: 0.9344984292984009,
      description: 'FunctionScoreQuery($type:string/title:men, scored by log(imdb.rating)) [BM25Similarity], result of:',
      details: [
        {
          value: 0.9344984292984009,
          description: 'log(imdb.rating)',
          details: []
        }
      ]
    }
  },
  {
    title: 'No Country for Old Men',
    score: 0.9084849953651428,
    scoreDetails: {
      value: 0.9084849953651428,
      description: 'FunctionScoreQuery($type:string/title:men, scored by log(imdb.rating)) [BM25Similarity], result of:',
      details: [
        {
          value: 0.9084849953651428,
          description: 'log(imdb.rating)',
          details: []
        }
      ]
    }
  },
  {
    title: 'X-Men: Days of Future Past',
    score: 0.9084849953651428,
    scoreDetails: {
      value: 0.9084849953651428,
      description: 'FunctionScoreQuery($type:string/title:men, scored by log(imdb.rating)) [BM25Similarity], result of:',
      details: [
        {
          value: 0.9084849953651428,
          description: 'log(imdb.rating)',
          details: []
        }
      ]
    }
  },
  {
    title: 'The Best of Men',
    score: 0.9084849953651428,
    scoreDetails: {
      value: 0.9084849953651428,
      description: 'FunctionScoreQuery($type:string/title:men, scored by log(imdb.rating)) [BM25Similarity], result of:',
      details: [
        {
          value: 0.9084849953651428,
          description: 'log(imdb.rating)',
          details: []
        }
      ]
    }
  }
]

戻る

スコアを修正する

スコアを正規化する

1	db.movies.aggregate([
2	{
3	"$search": {
4	"text": {
5	"path": "title",
6	"query": "autumn"
7	},
8	"scoreDetails": true
9	}
10	},
11	{
12	"$limit": 3
13	},
14	{
15	"$project": {
16	"_id": 0,
17	"title": 1,
18	"score": { "$meta": "searchScore" },
19	"scoreDetails": { "$meta": "searchScoreDetails" }
20	}
21	}
22	])

1	[
2	{
3	title: 'Autumn Leaves',
4	score: 3.834893226623535,
5	scoreDetails: {
6	value: 3.834893226623535,
7	description: '$type:string/title:autumn [BM25Similarity], result of:',
8	details: [
9	{
10	value: 3.834893226623535,
11	description: 'score(freq=1.0), computed as boost * idf * tf from:',
12	details: [
13	{
14	value: 7.39188289642334,
15	description: 'idf, computed as log(1 + (N - n + 0.5) / (n + 0.5)) from:',
16	details: [
17	{
18	value: 14,
19	description: 'n, number of documents containing term',
20	details: []
21	},
22	{
23	value: 23529,
24	description: 'N, total number of documents with field',
25	details: []
26	}
27	]
28	},
29	{
30	value: 0.5187978744506836,
31	description: 'tf, computed as freq / (freq + k1 * (1 - b + b * dl / avgdl)) from:',
32	details: [
33	{
34	value: 1,
35	description: 'freq, occurrences of term within document',
36	details: []
37	},
38	{
39	value: 1.2000000476837158,
40	description: 'k1, term saturation parameter',
41	details: []
42	},
43	{
44	value: 0.75,
45	description: 'b, length normalization parameter',
46	details: []
47	},
48	{
49	value: 2,
50	description: 'dl, length of field',
51	details: []
52	},
53	{
54	value: 2.868375301361084,
55	description: 'avgdl, average length of field',
56	details: []
57	}
58	]
59	}
60	]
61	}
62	]
63	}
64	},
65	{
66	title: 'Late Autumn',
67	score: 3.834893226623535,
68	scoreDetails: {
69	value: 3.834893226623535,
70	description: '$type:string/title:autumn [BM25Similarity], result of:',
71	details: [
72	{
73	value: 3.834893226623535,
74	description: 'score(freq=1.0), computed as boost * idf * tf from:',
75	details: [
76	{
77	value: 7.39188289642334,
78	description: 'idf, computed as log(1 + (N - n + 0.5) / (n + 0.5)) from:',
79	details: [
80	{
81	value: 14,
82	description: 'n, number of documents containing term',
83	details: []
84	},
85	{
86	value: 23529,
87	description: 'N, total number of documents with field',
88	details: []
89	}
90	]
91	},
92	{
93	value: 0.5187978744506836,
94	description: 'tf, computed as freq / (freq + k1 * (1 - b + b * dl / avgdl)) from:',
95	details: [
96	{
97	value: 1,
98	description: 'freq, occurrences of term within document',
99	details: []
100	},
101	{
102	value: 1.2000000476837158,
103	description: 'k1, term saturation parameter',
104	details: []
105	},
106	{
107	value: 0.75,
108	description: 'b, length normalization parameter',
109	details: []
110	},
111	{
112	value: 2,
113	description: 'dl, length of field',
114	details: []
115	},
116	{
117	value: 2.868375301361084,
118	description: 'avgdl, average length of field',
119	details: []
120	}
121	]
122	}
123	]
124	}
125	]
126	}
127	},
128	{
129	title: 'Cheyenne Autumn',
130	score: 3.834893226623535,
131	scoreDetails: {
132	value: 3.834893226623535,
133	description: '$type:string/title:autumn [BM25Similarity], result of:',
134	details: [
135	{
136	value: 3.834893226623535,
137	description: 'score(freq=1.0), computed as boost * idf * tf from:',
138	details: [
139	{
140	value: 7.39188289642334,
141	description: 'idf, computed as log(1 + (N - n + 0.5) / (n + 0.5)) from:',
142	details: [
143	{
144	value: 14,
145	description: 'n, number of documents containing term',
146	details: []
147	},
148	{
149	value: 23529,
150	description: 'N, total number of documents with field',
151	details: []
152	}
153	]
154	},
155	{
156	value: 0.5187978744506836,
157	description: 'tf, computed as freq / (freq + k1 * (1 - b + b * dl / avgdl)) from:',
158	details: [
159	{
160	value: 1,
161	description: 'freq, occurrences of term within document',
162	details: []
163	},
164	{
165	value: 1.2000000476837158,
166	description: 'k1, term saturation parameter',
167	details: []
168	},
169	{
170	value: 0.75,
171	description: 'b, length normalization parameter',
172	details: []
173	},
174	{
175	value: 2,
176	description: 'dl, length of field',
177	details: []
178	},
179	{
180	value: 2.868375301361084,
181	description: 'avgdl, average length of field',
182	details: []
183	}
184	]
185	}
186	]
187	}
188	]
189	}
190	}
191	]

1	db.movies.aggregate([
2	{
3	"$search": {
4	"near": {
5	"path": "released",
6	"origin": ISODate("2010-01-01T00:00:00.000+00:00"),
7	"pivot": 7776000000
8	},
9	"scoreDetails": true
10	}
11	},
12	{
13	"$limit": 3
14	},
15	{
16	"$project": {
17	"_id": 0,
18	"title": 1,
19	"released": 1,
20	"score": { "$meta": "searchScore" },
21	"scoreDetails": { "$meta": "searchScoreDetails" }
22	}
23	}
24	])

1	[
2	{
3	title: 'Tony',
4	released: ISODate("2010-01-01T00:00:00.000Z"),
5	score: 1,
6	scoreDetails: {
7	value: 1,
8	description: 'Distance score, computed as weight * pivotDistance / (pivotDistance + abs(value - origin)) from:',
9	details: [
10	{ value: 1, description: 'weight', details: [] },
11	{
12	value: 7776000000,
13	description: 'pivotDistance',
14	details: []
15	},
16	{ value: 1262303969280, description: 'origin', details: [] },
17	{
18	value: 1262303969280,
19	description: 'current value',
20	details: []
21	}
22	]
23	}
24	},
25	{
26	title: 'And Everything Is Going Fine',
27	released: ISODate("2010-01-01T00:00:00.000Z"),
28	score: 1,
29	scoreDetails: {
30	value: 1,
31	description: 'Distance score, computed as weight * pivotDistance / (pivotDistance + abs(value - origin)) from:',
32	details: [
33	{ value: 1, description: 'weight', details: [] },
34	{
35	value: 7776000000,
36	description: 'pivotDistance',
37	details: []
38	},
39	{ value: 1262303969280, description: 'origin', details: [] },
40	{
41	value: 1262303969280,
42	description: 'current value',
43	details: []
44	}
45	]
46	}
47	},
48	{
49	title: 'A Film with Me in It',
50	released: ISODate("2010-01-01T00:00:00.000Z")
51	score: 1,
52	scoreDetails: {
53	value: 1,
54	description: 'Distance score, computed as weight * pivotDistance / (pivotDistance + abs(value - origin)) from:',
55	details: [
56	{ value: 1, description: 'weight', details: [] },
57	{
58	value: 7776000000,
59	description: 'pivotDistance',
60	details: []
61	},
62	{ value: 1262303969280, description: 'origin', details: [] },
63	{
64	value: 1262303969280,
65	description: 'current value',
66	details: []
67	}
68	]
69	}
70	}
71	]

1	db.movies.aggregate([
2	{
3	"$search": {
4	"compound": {
5	"filter": [{
6	"text": {
7	"query": "friend",
8	"path": "title"
9	}
10	}],
11	"must": [{
12	"range": {
13	"path": "year",
14	"gte": 2000,
15	"lte": 2015
16	}
17	}],
18	"mustNot": [{
19	"text": {
20	"query": ["Short, Western", "Biography"],
21	"path": "genres"
22	}
23	}]
24	},
25	"scoreDetails": true
26	}
27	},
28	{
29	"$limit": 3
30	},
31	{
32	"$project": {
33	"_id": 0,
34	"title": 1,
35	"released": 1,
36	"genres": 1,
37	"score": { "$meta": "searchScore" },
38	"scoreDetails": { "$meta": "searchScoreDetails" }
39	}
40	}
41	])

1	[
2	{
3	genres: [ 'Comedy', 'Drama', 'Mystery' ],
4	title: 'With a Friend Like Harry...',
5	released: ISODate("2001-06-15T00:00:00.000Z"),
6	score: 1,
7	scoreDetails: {
8	value: 1,
9	description: 'sum of:',
10	details: [
11	{
12	value: 0,
13	description: 'match on required clause, product of:',
14	details: [
15	{ value: 0, description: '# clause', details: [] },
16	{
17	value: 1,
18	description: '$type:string/title:friend',
19	details: []
20	}
21	]
22	},
23	{
24	value: 1,
25	description: 'sum of:',
26	details: [
27	{
28	value: 1,
29	description: 'sum of:',
30	details: [
31	{
32	value: 1,
33	description: '$type:double/year:[4656510908468559872 TO 4656576879166226432]',
34	details: []
35	}
36	]
37	}
38	]
39	}
40	]
41	}
42	},
43	{
44	genres: [ 'Drama' ],
45	title: 'My Friend Henry',
46	released: ISODate("2004-08-20T00:00:00.000Z"),
47	score: 1,
48	scoreDetails: {
49	value: 1,
50	description: 'sum of:',
51	details: [
52	{
53	value: 0,
54	description: 'match on required clause, product of:',
55	details: [
56	{ value: 0, description: '# clause', details: [] },
57	{
58	value: 1,
59	description: '$type:string/title:friend',
60	details: []
61	}
62	]
63	},
64	{
65	value: 1,
66	description: 'sum of:',
67	details: [
68	{
69	value: 1,
70	description: 'sum of:',
71	details: [
72	{
73	value: 1,
74	description: '$type:double/year:[4656510908468559872 TO 4656576879166226432]',
75	details: []
76	}
77	]
78	}
79	]
80	}
81	]
82	}
83	},
84	{
85	genres: [ 'Comedy', 'Drama' ],
86	title: 'A Friend of Mine',
87	released: ISODate("2006-10-26T00:00:00.000Z"),
88	score: 1,
89	scoreDetails: {
90	value: 1,
91	description: 'sum of:',
92	details: [
93	{
94	value: 0,
95	description: 'match on required clause, product of:',
96	details: [
97	{ value: 0, description: '# clause', details: [] },
98	{
99	value: 1,
100	description: '$type:string/title:friend',
101	details: []
102	}
103	]
104	},
105	{
106	value: 1,
107	description: 'sum of:',
108	details: [
109	{
110	value: 1,
111	description: 'sum of:',
112	details: [
113	{
114	value: 1,
115	description: '$type:double/year:[4656510908468559872 TO 4656576879166226432]',
116	details: []
117	}
118	]
119	}
120	]
121	}
122	]
123	}
124	}
125	]

1	db.companies.aggregate({
2	"$search": {
3	"embeddedDocument": {
4	"path": "products",
5	"operator": {
6	"wildcard": {
7	"path": "products.name",
8	"query": "*Basic",
9	"allowAnalyzedField": true
10	}
11	},
12	"score": {
13	"embedded": {
14	"aggregate": "sum"
15	}
16	}
17	},
18	"scoreDetails": true
19	}
20	},
21	{
22	"$limit": 3
23	},
24	{
25	"$project": {
26	"_id": 0,
27	"name": 1,
28	"products.name": 1,
29	"score": { "$meta": "searchScore" },
30	"scoreDetails": { "$meta": "searchScoreDetails" }
31	}
32	})

1	[
2	{
3	name: 'Plaxo',
4	products: [
5	{ name: 'Plaxo Basic' },
6	{ name: 'Plaxo Pulse' },
7	{ name: 'Plaxo Personal Assistant' }
8	],
9	score: 1,
10	scoreDetails: {
11	value: 1,
12	description: 'Score based on 1 child docs in range from 27 to 29, best match:',
13	details: [
14	{
15	value: 1,
16	description: '$embedded:8/products/$type:string/products.name:*Basic',
17	details: []
18	}
19	]
20	}
21	},
22	{
23	name: 'The Game Creators',
24	products: [
25	{ name: 'Dark Basic Professional' },
26	{ name: 'FPS Creator' },
27	{ name: 'FPS Creator X10' }
28	],
29	score: 1,
30	scoreDetails: {
31	value: 1,
32	description: 'Score based on 1 child docs in range from 7474 to 7476, best match:',
33	details: [
34	{
35	value: 1,
36	description: '$embedded:8/products/$type:string/products.name:*basic',
37	details: []
38	}
39	]
40	}
41	},
42	{
43	name: 'Load Impact',
44	products: [
45	{ name: 'Load Impact LIGHT' },
46	{ name: 'Load Impact BASIC' },
47	{ name: 'Load Impact PROFESSIONAL' },
48	{ name: 'Load Impact ADVANCED' }
49	],
50	score: 1,
51	scoreDetails: {
52	value: 1,
53	description: 'Score based on 1 child docs in range from 11545 to 11548, best match:',
54	details: [
55	{
56	value: 1,
57	description: '$embedded:8/products/$type:string/products.name:*basic',
58	details: []
59	}
60	]
61	}
62	}
63	]

1	db.movies.aggregate([{
2	"$search": {
3	"text": {
4	"path": "title",
5	"query": "men",
6	"score": {
7	"function":{
8	"multiply":[
9	{
10	"path": {
11	"value": "imdb.rating",
12	"undefined": 2
13	}
14	},
15	{
16	"score": "relevance"
17	}
18	]
19	}
20	}
21	},
22	"scoreDetails": true
23	}
24	},
25	{
26	$limit: 5
27	},
28	{
29	$project: {
30	"_id": 0,
31	"title": 1,
32	"score": { "$meta": "searchScore" },
33	"scoreDetails": {"$meta": "searchScoreDetails"}
34	}
35	}])

スコアの詳細を返す

構文

オプション

出力

スコアに貢献する要因

例

Tip

演算子の例

注意

注意

カスタム スコアの例

カスタムスコアの例