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

该`$meta`运算符用于访问与当前查询执行相关的元数据。此运算符主要用于文本搜索操作，其中元数据可以提供有关匹配文档相关性的信息。

**参数**
+ `textScore`：检索文档的文本搜索分数。该分数表示文档与文本搜索查询的相关性。

## 示例（MongoDB 外壳）
<a name="meta-examples"></a>

以下示例演示如何使用`$meta`运算符检索与文本搜索查询相匹配的文档的文本搜索分数。

**创建示例文档**

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

**创建文本索引**

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

**查询示例**

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

**输出**

```
[
  {
    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
  }
]
```

## 代码示例
<a name="meta-code"></a>

要查看使用该`$meta`命令的代码示例，请选择要使用的语言的选项卡：

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

------