How to Integrate Vector Search in MongoDB-Compatible Databases With Amazon DocumentDB?

Introduction

In this guide, we will demonstrate how to integrate vector search capabilities in MongoDB-compatible databases using Amazon DocumentDB. Amazon DocumentDB is a fully managed document database service that is compatible with MongoDB and is designed to store, query, and index JSON data. By integrating vector search, you can perform similarity searches on high-dimensional data, which is useful for applications such as recommendation systems, image search, and natural language processing.

Step-by-Step Explanation

Step 1: Set Up Amazon DocumentDB Cluster

1. Create a VPC: Amazon DocumentDB requires a VPC to deploy the cluster. If you don’t have a VPC, you can create one using Pulumi.
2. Create Security Group: Set up a security group to control access to the DocumentDB cluster.
3. Create DocumentDB Cluster: Deploy an Amazon DocumentDB cluster within the VPC.

Step 2: Configure Vector Search

1. Install Required Libraries: Ensure you have the necessary libraries for vector search, such as annoy or faiss.
2. Store Vectors in DocumentDB: Store your high-dimensional vectors as documents in the DocumentDB collection.
3. Implement Vector Search Logic: Use the installed libraries to perform similarity searches on the stored vectors.

Step 3: Querying and Indexing

1. Create Indexes: Create indexes on the vector fields to optimize search performance.
2. Perform Searches: Execute vector search queries to find similar items based on the stored vectors.

Summary

By following this guide, you can integrate vector search capabilities into your MongoDB-compatible databases using Amazon DocumentDB. This allows you to perform efficient similarity searches on high-dimensional data, enabling advanced applications such as recommendation systems and image search.

For more detailed information, refer to the Amazon DocumentDB documentation and the Pulumi AWS SDK.

Full Code Example

import * as pulumi from "@pulumi/pulumi";
import * as aws from "@pulumi/aws";

// Create a VPC
const vpc = new aws.ec2.Vpc("documentdb-vpc", {
    cidrBlock: "10.0.0.0/16",
});

// Create Subnets
const subnet1 = new aws.ec2.Subnet("documentdb-subnet-1", {
    vpcId: vpc.id,
    cidrBlock: "10.0.1.0/24",
    availabilityZone: "us-west-2a",
});

const subnet2 = new aws.ec2.Subnet("documentdb-subnet-2", {
    vpcId: vpc.id,
    cidrBlock: "10.0.2.0/24",
    availabilityZone: "us-west-2b",
});

// Create a Security Group
const securityGroup = new aws.ec2.SecurityGroup("documentdb-sg", {
    vpcId: vpc.id,
    ingress: [{
        protocol: "tcp",
        fromPort: 27017,
        toPort: 27017,
        cidrBlocks: ["0.0.0.0/0"],
    }],
    egress: [{
        protocol: "-1",
        fromPort: 0,
        toPort: 0,
        cidrBlocks: ["0.0.0.0/0"],
    }],
});

// Create a DocumentDB Subnet Group
const subnetGroup = new aws.docdb.SubnetGroup("documentdb-subnet-group", {
    subnetIds: [subnet1.id, subnet2.id],
});

// Create a DocumentDB Cluster
const cluster = new aws.docdb.Cluster("documentdb-cluster", {
    masterPassword: pulumi.secret("your-master-password"),
    masterUsername: "your-master-username",
    backupRetentionPeriod: 5,
    clusterIdentifier: "documentdb-cluster",
    dbSubnetGroupName: subnetGroup.name,
    vpcSecurityGroupIds: [securityGroup.id],
});

// Export the VPC ID, Security Group ID, and Cluster Endpoint
export const vpcId = vpc.id;
export const securityGroupId = securityGroup.id;
export const clusterEndpoint = cluster.endpoint;