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Run a MongoDB Vector Search Query

Overview

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

You can use MongoDB Vector Search to perform a vector search on your data stored in MongoDB. 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 MongoDB Vector Search, see the MongoDB 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 MongoDB Search and MongoDB 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 MongoDB Get Started 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 = [
            {
              '$vectorSearch': {
                'index': 'vector_index',
                'path': 'plot_embedding',
                'queryVector': Binary.fromFloat32Array(Float32Array.from([
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                ])),
                'numCandidates': 150,
                'limit': 10
              }
            }, {
              '$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);

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 MongoDB Vector Search tutorials for the Node.js driver, see the MongoDB 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 MongoDB 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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