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"></a>

`$meta` 運算子用於存取與目前查詢執行相關聯的中繼資料。此運算子主要用於文字搜尋操作，其中中繼資料可以提供有關相符文件相關性的資訊。

**參數**
+ `textScore`：擷取文件的文字搜尋分數。此分數表示文件與文字搜尋查詢的相關性。

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

下列範例示範如何使用 `$meta`運算子擷取符合文字搜尋查詢之文件的文字搜尋分數。

**建立範例文件**

```
db.documents.insertMany([
  { _id: 1, title: "Coffee Basics", content: "Coffee is a popular beverage made from roasted coffee beans." },
  { _id: 2, title: "Coffee Culture", content: "Coffee coffee coffee - the ultimate guide to coffee brewing and coffee preparation." },
  { _id: 3, title: "Tea vs Coffee", content: "Many people prefer tea over coffee for its health benefits." }
]);
```

**建立文字索引**

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

**查詢範例**

```
db.documents.find(
  { $text: { $search: "coffee" } },
  { _id: 0, title: 1, content: 1, score: { $meta: "textScore" } }
).sort({ score: { $meta: "textScore" } });
```

**輸出**

```
[
  {
    title: 'Coffee Culture',
    content: 'Coffee coffee coffee - the ultimate guide to coffee brewing and coffee preparation.',
    score: 0.8897688388824463
  },
  {
    title: 'Coffee Basics',
    content: 'Coffee is a popular beverage made from roasted coffee beans.',
    score: 0.75990891456604
  },
  {
    title: 'Tea vs Coffee',
    content: 'Many people prefer tea over coffee for its health benefits.',
    score: 0.6079270839691162
  }
]
```

## 程式碼範例
<a name="meta-code"></a>

若要檢視使用 `$meta`命令的程式碼範例，請選擇您要使用的語言標籤：

------
#### [ Node.js ]

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

async function findWithTextScore() {
  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('documents');

  const result = await collection.find(
    { $text: { $search: "coffee" } },
    { projection: { _id: 0, title: 1, content: 1, score: { $meta: "textScore" } } }
  ).sort({ score: { $meta: "textScore" } }).toArray();

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

findWithTextScore();
```

------
#### [ Python ]

```
from pymongo import MongoClient

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['documents']

for doc in collection.find(
    {'$text': {'$search': 'coffee'}},
    {'_id': 0, 'title': 1, 'content': 1, 'score': {'$meta': 'textScore'}}
).sort([('score', {'$meta': 'textScore'})]):
    print(doc)

client.close()
```

------