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Run a MongoDB Vector Search Query运行MongoDB矢量搜索查询

Overview概述

In this guide, you can learn how to use the MongoDB Vector Search feature in the Node.js driver.在本指南中，您可以学习如何在Node.js驱动程序中使用MongoDB矢量搜索功能。

You can use MongoDB Vector Search to perform a vector search on your data stored in Atlas. Vector search allows you to query your data based on semantic meaning rather than just keyword matches, which helps you retrieve more relevant search results. It enables your AI-powered applications to support use cases such as semantic search, hybrid search, and generative search, including Retrieval-Augmented Generation (RAG).您可以使用MongoDB矢量搜索对存储在Atlas中的数据执行矢量搜索。矢量搜索允许您根据语义含义而不仅仅是键匹配来查询数据，这有助于您检索更相关的搜索结果。它使您的人工智能应用程序能够支持语义搜索、混合搜索和生成搜索等用例，包括检索增强生成（RAG）。

To learn more about MongoDB Vector Search, see the MongoDB Vector Search guides in the MongoDB Atlas documentation.要了解有关MongoDB矢量搜索的更多信息，请参阅MongoDB Atlas文档中的MongoDB矢量搜索指南。

Important

Feature Compatibility功能兼容性

To learn what versions of MongoDB Atlas support this feature, see Limitations in the MongoDB Atlas documentation.要了解哪些版本的MongoDB Atlas支持此功能，请参阅MongoDB Atlas文档中的限制。

Perform a Vector Search执行矢量搜索

To use this feature, you must create a vector search index and index your vector embeddings. 要使用此功能，您必须创建一个矢量搜索索引并对矢量嵌入进行索引。To learn about how to programmatically create a vector search index, see the MongoDB Search and MongoDB Vector Search Indexes section in the Indexes guide. 要了解如何以编程方式创建矢量搜索索引，请参阅索引指南中的MongoDB搜索和MongoDB矢量搜索索引部分。To learn more about vector embeddings, see How to Create Vector Embeddings in the Atlas documentation.要了解有关矢量嵌入的更多信息，请参阅Atlas文档中的“如何创建矢量嵌入”。

After you create a vector search index on your vector embeddings, you can reference this index in your pipeline stage, as shown in the following example.在矢量嵌入上创建矢量搜索索引后，可以在管道阶段引用该索引，如下例所示。

Sample Data样品数据

The example on this page shows how to build an aggregation pipeline that uses the $vectorSearch stage to perform a vector search on the sample_mflix.embedded_movies collection in the Atlas sample datasets.此页面上的示例显示了如何构建一个聚合管道，该管道使用$vectorSearch阶段对Atlas示例数据集中的sample_mflix.embedded_movies集合执行矢量搜索。

To learn how to create a free MongoDB Atlas cluster and load the sample datasets, see the MongoDB Get Started guide.要了解如何创建免费的MongoDB Atlas集群并加载示例数据集，请参阅MongoDB入门指南。

Vector Search Example矢量搜索示例

You can perform a vector search query by using the $vectorSearch stage in an aggregation pipeline. To perform a vector search on a collection, you must first have a collection with a field that contains vector embeddings and a vector search index that covers that field.您可以使用聚合管道中的$vectorSearch阶段执行矢量搜索查询。要对集合执行矢量搜索，您必须首先拥有一个包含矢量嵌入的字段和覆盖该字段的矢量搜索索引的集合。

In the following example, the aggregation pipeline searches the plot field of each document in the collection for text semantically related to the term "time travel". The queryVector field in the $vectorSearch pipeline is the vector representation of your query.在以下示例中，聚合管道在集合中的每个文档的plot字段中搜索与术语“时间旅行”语义相关的文本。$vectorSearch管道中的queryVector字段是查询的矢量表示。

const { MongoClient } = require("mongodb");

// Replace with the connection string to your Atlas cluster to connect替换为Atlas集群的连接字符串以进行连接
const uri = "<connection-string>";

const client = new MongoClient(uri);

async function run() {
    try {
        await client.connect();

        // Sets the namespace设置命名空间
        const database = client.db("sample_mflix");
        const coll = database.collection("embedded_movies");

        // Defines the pipeline定义管道
        const agg = [
            {
              '$vectorSearch': {
                'index': 'vector_index',
                'path': 'plot_embedding',
                'queryVector': Binary.fromFloat32Array(Float32Array.from([
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  0.023485601, 0.0023342315, -0.011882754, 0.018287312, -0.0068910643, 
  0.003912045, 0.009243623, -0.001355387, -0.028603915, -0.012802451, 
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  0.014475234, -0.028577257, 0.005494857, 0.00011725306, -0.013315615, 
  0.015941417, 0.009376912, 0.0025158382, 0.008743787, 0.023832154, 
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  0.009736794, 0.015274971, -0.0012937407, -0.018087378, -0.0039387033, 
  0.008637156, -0.011189649, -0.00023846315, -0.011582852, 0.0066411467, 
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  0.0034188742, -0.010249958, -0.0007747449, -0.00795738, -0.022192692, 
  0.03910712, 0.032122757, 0.023898797, 0.0076241563, -0.007397564, 
  -0.003655463, 0.011442899, -0.014115352, -0.00505167, -0.031163072, 
  0.030336678, -0.006857742, -0.022259338, 0.004048667, 0.02072651, 
  0.0030156737, -0.0042119464, 0.00041861215, -0.005731446, 0.011103011, 
  0.013822115, 0.021512916, 0.009216965, -0.006537847, -0.027057758, 
  -0.04054665, 0.010403241, -0.0056281467, -0.005701456, -0.002709108, 
  -0.00745088, -0.0024841821, 0.009356919, -0.022659205, 0.004061996, 
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  0.005391558, -0.016781142, -0.0058747325, 0.010309938, -0.013049036, 
  0.01186276, -0.0011246296, 0.0062112883, 0.0028190718, -0.021739509, 
  0.009883412, -0.0073175905, -0.012715813, -0.017181009, -0.016607866, 
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  0.021979429, 0.01332228, -0.026244693, -0.0123492675, -0.003895384, 
  0.0071576433, -0.035454992, -0.00046984528, 0.0033522295, 0.039347045, 
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  0.016861115, -0.0050849924, 0.014235313, 0.0051449724, 0.0025924798, 
  -0.0025741523, 0.04289254, -0.002104307, 0.012969063, -0.008310596, 
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  0.0151016945, -0.017527562, 0.0018727167, 0.0002830318, 0.015168339, 
  0.0144219175, -0.004048667, -0.004358565, 0.011836103, -0.010343261, 
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  0.0017877447, -0.024551915, 0.013155668, 0.023525586, -0.0255116, 
  0.017220996, 0.004358565, -0.00934359, 0.0099967085, 0.011162991, 
  0.03092315, -0.021046404, -0.015514892, 0.0011946067, -0.01816735, 
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  0.0005598157, -0.010469886, 0.0016852784, 0.01716768, -0.008990373, 
  -0.001987679, 0.026417969, 0.023792166, 0.0046917885, -0.0071909656, 
  -0.00032051947, -0.023259008, -0.009170313, 0.02071318, -0.03156294, 
  -0.030869836, -0.006324584, 0.013795458, -0.00047151142, 0.016874444, 
  0.00947688, 0.00985009, -0.029883493, 0.024205362, -0.013522214, 
  -0.015075036, -0.030603256, 0.029270362, 0.010503208, 0.021539574, 
  0.01743426, -0.023898797, 0.022019416, -0.0068777353, 0.027857494, 
  -0.021259667, 0.0025758184, 0.006197959, 0.006447877, -0.00025200035, 
  -0.004941706, -0.021246338, -0.005504854, -0.008390571, -0.0097301295, 
  0.027244363, -0.04446536, 0.05216949, 0.010243294, -0.016008062, 
  0.0122493, -0.0199401, 0.009077012, 0.019753495, 0.006431216, 
  -0.037960835, -0.027377652, 0.016381273, -0.0038620618, 0.022512587, 
  -0.010996379, -0.0015211658, -0.0102233, 0.007071005, 0.008230623, 
  -0.009490209, -0.010083347, 0.024431955, 0.002427534, 0.02828402, 
  0.0035721571, -0.022192692, -0.011882754, 0.010056688, 0.0011904413, 
  -0.01426197, -0.017500903, -0.00010985966, 0.005591492, -0.0077707744, 
  -0.012049366, 0.011869425, 0.00858384, -0.024698535, -0.030283362, 
  0.020140035, 0.011949399, -0.013968734, 0.042732596, -0.011649498, 
  -0.011982721, -0.016967745, -0.0060913274, -0.007130985, -0.013109017, 
  -0.009710136
                ])),
                'numCandidates': 150,
                'limit': 10
              }
            }, {
              '$project': {
                '_id': 0,
                'plot': 1,
                'title': 1,
                'score': {
                  '$meta': 'vectorSearchScore'
                }
              }
            }
          ];
        // Runs pipeline
        const result = coll.aggregate(agg);

        // Prints results
        for await (const doc of result) {
            console.log(doc);
        }
    }   finally {
            await client.close();
    }
}
run().catch(console.dir);
{
   plot: 'A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.',
   title: 'Thrill Seekers',
   score: 0.9259490966796875
}
{
   plot: 'At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.',
   title: 'About Time',
   score: 0.9253997802734375
}
{
   plot: 'An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.',
   title: 'Timecop',
   score: 0.922332763671875
}
{
   plot: "After using his mother's newly built time machine, Dolf gets stuck
   involuntary in the year 1212. He ends up in a children's crusade where he confronts
   his new friends with modern techniques...",
   title: 'Crusade in Jeans',
   score: 0.92205810546875
}
{
   plot: 'Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.',
   title: 'The Time Machine',
   score: 0.921875
}
{
   plot: 'A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...',
   title: 'A.P.E.X.',
   score: 0.9202728271484375
}
{
   plot: "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.",
   title: 'Men in Black 3',
   score: 0.9198150634765625
}
{
   plot: 'Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.',
   title: 'Tomorrowland',
   score: 0.91961669921875
}
{
   plot: 'A romantic drama about a Chicago librarian with a gene that causes him to involuntarily time travel, and the complications it creates for his marriage.',
   title: "The Time Traveler's Wife",
   score: 0.9174346923828125
}
{
   plot: 'With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.',
   title: 'Love Story 2050',
   score: 0.9165191650390625
}

This query uses the $vectorSearch stage to:此查询使用$vectorSearch阶段来：

  • Perform an Approximate Nearest Neighbor (ANN) vector search执行近似最近邻（ANN）矢量搜索
  • Search for the specified term in the plot_embedding field在plot_embedding字段中搜索指定的术语
  • Set the number of nearest neighbors used in the search to 150 by using the numCandidates option使用numCandidates选项将搜索中使用的最近邻数量设置为150
  • Return a maximum of 10 documents from the query using the limit option使用limit选项从查询中最多返回10个文档

It uses the $project stage to:它使用$project阶段来：

  • Only include the movie plot and title fields in the results仅在结果中包含plot和title字段
  • Add a score field to show the relevance of each result to the search term添加一个score字段，显示每个结果与搜索词的相关性

Additional Information附加信息

To see more MongoDB Vector Search tutorials for the Node.js driver, see the MongoDB Vector Search tutorials in the Atlas documentation.要查看Node.js驱动程序的更多MongoDB矢量搜索教程，请参阅Atlas文档中的MongoDB矢量搜索指南。

To learn more about the syntax of the $vectorSearch pipeline stage, see the Syntax and Fields sections of the Create and Run Queries guide in the MongoDB Vector Search section of the Atlas documentation.要了解有关$vectorSearch管道阶段语法的更多信息，请参阅Atlas文档的MongoDB矢量搜索部分中的创建和运行查询指南的语法和字段部分。

API Documentation文档

To learn more about the aggregate() method, see aggregate() in the API documentation.要了解有关aggregate()方法的更多信息，请参阅API文档中的aggregate()。