$meta - Amazon DocumentDB
示例（MongoDB 外壳）代码示例

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$meta

$meta聚合运算符访问与聚合管道中的文档关联的元数据。它通常用于检索文本搜索分数和按相关性对结果进行排序。

参数

  • textScore：检索文本搜索分数，该分数表示文档与搜索查询的相关性。

示例（MongoDB 外壳）

以下示例演示了在聚合管道中使用$meta运算符来检索文本搜索分数并按文本搜索分数进行排序。

创建示例文档

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." }
]);

查询示例

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

输出

[
  {
    _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
  }
]

代码示例

要查看使用$meta聚合运算符的代码示例，请选择要使用的语言的选项卡：

Node.js
const { MongoClient } = require('mongodb');

async function 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()