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4. Score Documents

Return the Score Details

On this page

  • Syntax
  • Options
  • Output
  • Factors That Contribute to the Score
  • Examples
  • Operator Examples
  • Custom Score Examples

You can use the scoreDetails boolean option in your $search stage for a detailed breakdown of the score for each document in the query results. To view the metadata, you must use the $meta expression in the $project stage.

Syntax

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

Options

In the $search stage, the scoreDetails boolean option takes one of the following values:

  • true - to include details of the score for the documents in the results. If set to true, Atlas Search returns a detailed breakdown of the score for each document in the result. To learn more, see Output.

  • false - to exclude details of the score breakdown for the results. (Default)

If omitted, the scoreDetails option defaults to false.

In the $project stage, the scoreDetails field takes the $meta expression, which requires the following value:

searchScoreDetails
Returns a detailed breakdown of the score for each document in the results.

Output

The scoreDetails option returns the following fields in the details array inside the scoreDetails object for each document in the result:

Field
Type
Description
value
float

Contribution towards the score by a subset of the scoring formula. The top-level value shows the entire score of the result document, and is equal to the value of $searchScore.

The scoring formula varies based on the operator used in the query. For example, Atlas Search uses the following scoring formula for a compound query with text and near operators: BM25Similarity + distance decay function.

description
string

Subset of the scoring formula including details about how the document was scored and factors considered in calculating the score. The top-level description shows the entire scoring formula used to score the document.

To learn more, see Factors That Contribute to the Score.

details
array of objects
Breakdown of the score for each match in the document based on the subset of the scoring formula. The value is an array of score details objects, recursive in structure.

Factors That Contribute to the Score

For BM25Similarity, the score is computed as boost * idf * tf. Atlas Search takes into account the following BM25Similarity factors for calculating the score:

boost
Increase the importance of the term.
freq
Frequency of the query term.
idf

Inverse document frequency of the query. Atlas Search computes the frequency using the following formula:

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

where:

  • N is the total number of documents with the field.

  • n is the number of documents containing the term.

tf

Term frequency. Atlas Search computes the frequency using the following formula:

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

where:

  • freq is the number of occurrences of the term within the document.

  • k1 is the term saturation parameter that is specified internally. It affects how much the score increases with each reoccurrence of the term.

  • avgdl is the average length of the field across all documents.

  • dl is the length of the field in the document.

  • b is the length normalization parameter that is also set internally. b is multiplied by the ratio of dl to avgdl. If b increases, the effects of the ratio of dl to avgdl is amplified.

For distance decay function, the score is computed as pivot / (pivot + abs(fieldValue - origin)). Atlas Search takes into account the following factors for calculating the score:

origin
Value to search near. This is the origin point from which the proximity of the results are measured.
fieldValue
Value of the field that you are querying in the document. The closer the fieldValue is to origin, the higher the score of the near query.
pivot
Value specified as a reference point to make the score equal to 0.5 if the distance between fieldValue and origin is equal to it. This defines how quickly the score decays as the distance between fieldValue and origin grows. For a given distance between fieldValue and origin, if pivot decreases, the score also decreases.

Examples

The following examples show how to retrieve the details of the scores in the results for the following:

Tip

To view details of the score recursively in the arrays of objects, configure the settings in mongosh by running the following:

config.set('inspectDepth', Infinity)

Operator Examples

The following examples demonstrate how to retrieve a breakdown of the score using the $search scoreDetails option for the documents in the results for the text, near, compound, and embeddedDocument operator queries.

The following example uses the text operator to query the title field in the sample_mflix.movies collection for the term autumn. The query specifies the scoreDetails option in the $search stage to retrieve a detailed breakdown of the score for each document in the results. The query uses the $limit stage to limit the results to three documents and the $project stage to do the following:

  • Exclude the _id field.

  • Include only the title field.

  • Add the score field to the results to return the score of the document and the scoreDetails field to the results to return a detailed breakdown of the score for the document.

1db.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 takes into account the following BM25Similarity factors for calculating the score:

boost
Increase the importance of the term.
freq
Frequency of the query term.
idf

Inverse document frequency of the query. Atlas Search computes the frequency using the following formula:

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

where:

  • N is the total number of documents with the field.

  • n is the number of documents containing the term.

tf

Term frequency. Atlas Search computes the frequency using the following formula:

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

where:

  • freq is the number of occurrences of the term within the document.

  • k1 is the term saturation parameter that is specified internally. It affects how much the score increases with each reoccurrence of the term.

  • avgdl is the average length of the field across all documents.

  • dl is the length of the field in the document.

  • b is the length normalization parameter that is also set internally. b is multiplied by the ratio of dl to avgdl. If b increases, the effects of the ratio of dl to avgdl is amplified.

The following example uses the near operator to query the released field in the sample_mflix.movies collection for movies released near January 01, 2010. The query specifies the scoreDetails option in the $search stage to retrieve a detailed breakdown of the score for each document in the results. The query uses the $limit stage to limit the results to three documents and the $project stage to do the following:

  • Exclude the _id field.

  • Include only the title and released fields.

  • Add the score field to the results to return the score of the document and the scoreDetails field to the results to return a detailed breakdown of the score for the document.

1db.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]

For distance score, Atlas Search takes into account the following factors for calculating the score:

origin
Value to search near. This is the origin point from which the proximity of the results are measured.
fieldValue
Value of the field that you are querying in the document. The closer the fieldValue is to origin, the higher the score of the near query.
pivot
Value specified as a reference point to make the score equal to 0.5 if the distance between fieldValue and origin is equal to it. This defines how quickly the score decays as the distance between fieldValue and origin grows. For a given distance between fieldValue and origin, if pivot decreases, the score also decreases.

The following example uses the compound operator to query the sample_mflix.movies collection for movies using the following clauses:

  • filter clause to find movies that contain the term friend in the title.

  • must clause to find movies released between the years 2000 and 2015.

  • mustNot clause to find movies that are not in the Short, Western, and Biography genres.

The query specifies the scoreDetails option in the $search stage to retrieve a detailed breakdown of the score for each document in the results. The query uses the $limit stage to limit the results to three documents and the $project stage to do the following:

  • Exclude the _id field.

  • Include only the title, released, and genres fields.

  • Add the score field to the results to return the score of the document and the scoreDetails field to the results to return a detailed breakdown of the score for the document.

1db.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]

Note

The # clause in the results on lines 15, 56, and 97 represent the compound query filter clause, which doesn't contribute to the score of the document.

The following example uses the embeddedDocument operator to query the products.name field in the sample_training.companies collection for products that contain the term Basic preceded by any number of other characters. The query specifies inside the embeddedDocument operator that the returned score must be a sum of all the matching embedded documents. The query also specifies the scoreDetails option in the $search stage to retrieve a detailed breakdown of the score for each document in the results. The query uses the $limit stage to limit the results to three documents and the $project stage to do the following:

  • Exclude the _id field.

  • Include only the products.name field.

  • Add the score field to the results to return the score of the document and the scoreDetails field to the results to return a detailed breakdown of the score for the document.

1db.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]

Note

For scores based on child documents in range, the numbers in the range represent the IDs of parent and child documents indexed by Lucene under the hood. The description in the child documents (on lines 16, 36, and 57) show an internal representation of a path.

Custom Score Examples

The following examples demonstrate how to retrieve a breakdown of the score using the $search scoreDetails option for the documents in the results for the function expression example queries against the sample_mflix.movies collection.

1db.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: []
        }
      ]
    }
  }
]
1db.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: [] } ]
    }
  }
]
1db.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($type:string/title:shop, scored by exp((max(0, |imdb.rating - 9.5| - 0.0)^2) / 2 * (5.0^2 / 2 * ln(0.5)))) [BM25Similarity], result of:',
      details: [
        {
          value: 0.6274620294570923,
          description: 'exp((max(0, |imdb.rating - 9.5| - 0.0)^2) / 2 * (5.0^2 / 2 * ln(0.5)))',
          details: []
        }
      ]
    }
  }
]
1db.movies.aggregate([{
2  "$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: []
        }
      ]
    }
  }
]
1db.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: []
                    }
                  ]
                }
              ]
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          ]
        }
      ]
    }
  },
  {
    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: []
                    }
                  ]
                }
              ]
            }
          ]
        }
      ]
    }
  }
]
1db.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: []
        }
      ]
    }
  }
]

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