本文為英文版的機器翻譯版本，如內容有任何歧義或不一致之處，概以英文版為準。

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

`$meta` 彙總運算子會存取與彙總管道中文件相關聯的中繼資料。它通常用於擷取文字搜尋分數，並依相關性排序結果。

**參數**
+ `textScore`：擷取文字搜尋分數，指出與搜尋查詢的文件相關性。

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

下列範例示範在彙總管道中使用 `$meta` 運算子，依文字搜尋分數擷取和排序。

**建立範例文件**

```
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." }
]);
```

**查詢範例**

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

**輸出**

```
[
  {
    _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
  }
]
```

## 程式碼範例
<a name="meta-aggregation-code"></a>

若要檢視使用`$meta`彙總運算子的程式碼範例，請選擇您要使用的語言標籤：

------
#### [ 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()
```

------