# \$1meta
<a name="meta-aggregation"></a>

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**
+ `textScore`: Retrieves the text search score indicating document relevance to the search query.

## Example (MongoDB Shell)
<a name="meta-aggregation-examples"></a>

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
<a name="meta-aggregation-code"></a>

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()
```

------