$meta

The $meta aggregation operator accesses metadata associated with documents in an aggregation pipeline. It is commonly used to retrieve text search scores and sort results by relevance.

Parameters

Example (MongoDB Shell)

The following example demonstrates using the $meta operator in an aggregation pipeline to retrieve and sort by text search scores.

Create sample documents

db.articles.createIndex({ content: "text" });

db.articles.insertMany([
  { _id: 1, title: "Python Programming", content: "Python is a versatile programming language used for web development." },
  { _id: 2, title: "Python Guide", content: "Learn Python programming with Python tutorials and Python examples." },
  { _id: 3, title: "Java Basics", content: "Java is another popular programming language." }
]);

Query example

db.articles.aggregate([
  { $match: { $text: { $search: "Python" } } },
  { $addFields: { score: { $meta: "textScore" } } },
  { $sort: { score: -1 } }
]);

Output

[
  {
    _id: 2,
    title: 'Python Guide',
    content: 'Learn Python programming with Python tutorials and Python examples.',
    score: 1.5
  },
  {
    _id: 1,
    title: 'Python Programming',
    content: 'Python is a versatile programming language used for web development.',
    score: 0.75
  }
]

Code examples

To view a code example for using the $meta aggregation operator, choose the tab for the language that you want to use:

Node.js

const { MongoClient } = require('mongodb');

async function example() {
  const client = await MongoClient.connect('mongodb://<username>:<password>@<cluster-endpoint>:27017/?tls=true&tlsCAFile=global-bundle.pem&replicaSet=rs0&readPreference=secondaryPreferred&retryWrites=false');
  const db = client.db('test');
  const collection = db.collection('articles');

  const result = await collection.aggregate([
    { $match: { $text: { $search: "Python" } } },
    { $addFields: { score: { $meta: "textScore" } } },
    { $sort: { score: -1 } }
  ]).toArray();

  console.log(result);
  await client.close();
}

example();

Python

from pymongo import MongoClient

def example():
    client = MongoClient('mongodb://<username>:<password>@<cluster-endpoint>:27017/?tls=true&tlsCAFile=global-bundle.pem&replicaSet=rs0&readPreference=secondaryPreferred&retryWrites=false')
    db = client['test']
    collection = db['articles']

    result = list(collection.aggregate([
        { '$match': { '$text': { '$search': 'Python' } } },
        { '$addFields': { 'score': { '$meta': 'textScore' } } },
        { '$sort': { 'score': -1 } }
    ]))

    print(result)
    client.close()

example()