Run an Atlas Vector Search Query

On this page

Overview

Perform a Vector Search
Sample Data
Vector Search Example
Additional Information
API Documentation

Overview

In this guide, you can learn how to use the Atlas Vector Search feature in the Node.js driver.

You can use Atlas 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).

To learn more about Atlas Vector Search, see the Atlas Vector Search guides in the MongoDB Atlas documentation.

Important

Feature Compatibility

To learn what versions of MongoDB Atlas support this feature, see Limitations in the MongoDB Atlas documentation.

Perform a Vector Search

To use this feature, you must create a vector search index and index your vector embeddings. To learn about how to programmatically create a vector search index, see the Atlas Search and Atlas Vector Search Indexes section in the Indexes guide. To learn more about vector embeddings, see How to Create Vector Embeddings in the Atlas documentation.

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.

Sample Data

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.

To learn how to create a free MongoDB Atlas cluster and load the sample datasets, see the Get Started with Atlas guide.

Vector Search Example

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.

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.

const { MongoClient } = require("mongodb");
// Replace with the connection string to your Atlas cluster to connect
const uri = "<connection-string>";
const client = new MongoClient(uri);
async function run() {
    try {
        await client.connect();
        // Sets the namespace
        const database = client.db("sample_mflix");
        const coll = database.collection("embedded_movies");
        // Defines the pipeline
        const agg = [
            {
              '$vectorSearch': {
                'index': 'vector_index',
                'path': 'plot_embedding',
                'queryVector': Binary.fromFloat32Array(Float32Array.from([
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0.042732596, -0.011649498, -0.011982721, -0.016967745, -0.0060913274, -0.007130985, -0.013109017, -0.009710136
                ])),
                'numCandidates': 150,
                'limit': 10
              }
            }, {
              '$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:

Perform an Approximate Nearest Neighbor (ANN) vector search
Search for the specified term in the plot_embedding field
Set the number of nearest neighbors used in the search to 150 by using the numCandidates option
Return a maximum of 10 documents from the query using the limit option

It uses the $project stage to:

Only include the movie plot and title fields in the results
Add a score field to show the relevance of each result to the search term

Additional Information

To see more Atlas Vector Search tutorials for the Node.js driver, see the Atlas Vector Search tutorials in the Atlas documentation.

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 Atlas Vector Search section of the Atlas documentation.

API Documentation

To learn more about the aggregate() method, see aggregate() in the API documentation.

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Atlas Search

Monitoring and Logging