For AI agents: a documentation index is available at https://www.mongodb.com/docs/llms.txt — markdown versions of all pages are available by appending .md to any URL path.

Customize the Score of the Documents in the Results

You can customize the score of the documents in the results. By adjusting how scores are calculated, you can ensure that the most pertinent documents are ranked higher in the search results. To learn more about the different ways in which you can customize the score, see Score the Documents in the Results. This page demonstrates how to:

  • Modify the score of the documents in the results to boost or bury the results.

  • Normalize your $search query score in the range from 0 to 1 in the subsequent stages of your aggregation pipeline.

Modify the Score of the Documents in the Results

A MongoDB Search query assigns every returned document a score based on its relevance. The documents included in a result set return in order from highest to lowest score. To learn more, see Score the Documents in the Results.

You can use the following options with all operators to modify the default scoring behavior. For details and examples, click any of the following options:

This section demonstrates how to add weights to your search fields to boost or bury the documents in the results or a category of results. Specifically, it demonstrates how to assign one or more values to a field to return results with an increased or decreased score.

Example Index

You can set up an index with dynamic mappings enabled to index all the fields in the collection. Alternatively, use static mappings on the fields that you want to query and sort the results by. To learn more about creating MongoDB Search indexes, see Manage MongoDB Search Indexes.

Example Queries

Interface

The sample queries demonstrate how to boost or bury the documents in the results. They use the compound operator to combine two or more operators into a single query.

Normalize the Score

You can normalize your $search query score in the range from 0 to 1 in the subsequent stages of your aggregation pipeline. You can use the following stages after your $search stage in the following order to normalize the score:

  • $addFields

    {  "$addFields": {  "score": {     "$meta": "searchScore"  } }}

  • $setWindowFields

    {  "$setWindowFields": {    "output": {      "maxScore": {        "$max": "$score"      }    }  }}

  • $addFields

    {  "$addFields": {    "normalizedScore": {      "$divide": [        "$score", "$maxScore"      ]    }  }}
db.movies.aggregate([{    "$search": {      "text": {        "query": "Helsinki",        "path": "plot"      }    }  },  {    "$limit": 5  },  {    "$project": {      "_id": 0,      "title": 1,      "score": 1,      "maxScore": 1,      "normalizedScore": 1    }  },  {    "$addFields": {      "score": {        "$meta": "searchScore"      }    }  },  {    "$setWindowFields": {      "output": {        "maxScore": {          "$max": "$score"        }      }    }  },  {    "$addFields": {      "normalizedScore": {        "$divide": [          "$score", "$maxScore"        ]      }    }}])
[    {      title: 'Drifting Clouds',      score: 4.5660295486450195,      maxScore: 4.5660295486450195,      normalizedScore: 1    },    {      title: 'Sairaan kaunis maailma',      score: 4.041563034057617,      maxScore: 4.5660295486450195,      normalizedScore: 0.8851372929150143    },    {      title: 'Bad Luck Love',      score: 3.6251673698425293,      maxScore: 4.5660295486450195,      normalizedScore: 0.79394303764817    },    {      title: 'Bad Luck Love',      score: 3.6251673698425293,      maxScore: 4.5660295486450195,      normalizedScore: 0.79394303764817    },    {      title: 'Forbidden Fruit',      score: 3.6251673698425293,      maxScore: 4.5660295486450195,      normalizedScore: 0.79394303764817    }]

The MongoDB Search results contain the following scores:

  • The modified score for the $search query in the score field from the $addFields stage.

  • The maximum score assigned to the documents in the results in the maxScore field from the $setWindowFields stage.

  • The normalized score in the normalizedScore field from the $addFields stage. MongoDB computes this score by dividing the modified score in $score by the maximum score in $maxScore using $divide.

Continue Learning

To learn more about compound queries using MongoDB Search, take Unit 9 of the Intro To MongoDB Course on MongoDB University. The 1.5-hour unit includes an overview of MongoDB Search. The unit also covers creating MongoDB Search indexes, running $search queries using compound operators, and grouping results using facet (MongoDB Search Operator).

Back
Improve Accuracy
Next
Hybrid Search
Rate this page

On this page