In today’s AI-driven world, finding information based on meaning and context rather than exact keywords has become crucial. Good thing we now have vector search, enabling semantic search, personalized recommendations, image similarity, and more. However, building and managing vector search infrastructure can be complex.

Fortunately, AWS offers zero-infrastructure managed services that let you implement powerful vector search capabilities without worrying about servers, scaling, or maintenance. Let’s walk through creating a simple yet effective vector search demo using AWS services within the Free Tier so you can follow along without incurring costs.

What is Vector Search?

Vector search is a groundbreaking technique that converts data (text, photos, etc.) into high-dimensional vectors (numerical representations) that maintain the meaning and context. Instead of searching for exact keyword matches, vector search identifies items whose vectors are closest to the query vector, resulting in more intelligent, semantic search results. 

Imagine a world where you can search for documents not just by shared words but by shared concepts or ideas, or find images visually similar to another photo. Vector search is the key to unlocking this potential.

Example of vector search: a query for ‘monkey’ retrieves the closest related vectors like Orangutan, Mojo Jojo, and Son Goku, while distant vectors such as Ant-Man and AI Bot are less relevant.

How it works:

1. Machine learning models convert unstructured data (text, images, audio, etc.) into numerical representations called vector embeddings or vectors.
2. Vectors capture the semantic relationship and meaning of the data, and data with comparable concepts are represented by vectors that are close together in multi-dimensional space.
3. A user’s search query is also converted into a vector.
4. The system searches for vectors that are mathematically closest to the query vector in the dataset. The closer the numerical representations are to the query’s vector, the more relevant the result.
5. The retrieved vectors correspond to the most relevant data points based on meaning and context, not just exact keyword matches.

Why Use Zero-Infrastructure Vector Search on AWS?

Traditionally, building and maintaining vector search infrastructure requires a lot of technical proficiency and resources. To maintain high availability and security, you must solve scalability issues, configure highly complex databases, maintain specialized hardware, and continuously optimize performance. 

AWS removes these barriers by delivering managed services that handle the tedious backend tasks. Instead of dealing with infrastructure complexity, you can concentrate on developing intelligent applications like recommendation systems, image recognition, and natural language processing using this zero-infrastructure approach. 

Key benefits include:

• No Server Management: AWS handles provisioning, patching, and maintenance automatically
• Automatic Scaling: Your search capabilities grow with your data and query volume without manual intervention
• Built-in Reliability: AWS infrastructure manages disaster recovery and high availability.
• Seamless Integration: Easy connection with other AWS AI and ML services for embedding generation and data processing
• Cost Efficiency: Pay only for what you use, with free tier options for experimentation
• Developer-Friendly APIs: With simple REST APIs and SDKs, you can eliminate the need for specialized vector database knowledge, making the process straightforward and boosting your confidence in using the service.

This approach democratizes advanced search technology, making it accessible to developers of all skill levels. It’s a progressive movement that includes everyone, while providing enterprise-grade performance and reliability.

Step-by-Step Demo: Building Vector Search on AWS

For this guide, we will be using three key AWS services to create our vector search system:

• AWS SageMaker: To generate vector embeddings from your data using built-in or custom ML models
• Amazon Kendra: A fully managed semantic search service supporting vector search for similarity queries
• Amazon S3: To store your sample data and embeddings securely and cost-effectively

With AWS Kendra’s free 30-day trial and the free tier limits of AWS SageMaker and S3, you can keep your experiments cost-free, making this setup powerful and a wise financial choice.

Ensure you have an AWS Account to log in to the AWS Management Console.

Step 1: Prepare Your Sample Data

Begin by creating a small set of text files you’d like to search semantically (such as product descriptions, FAQs, or short articles).

• On your laptop or desktop, create a folder called sample-data.
• Add .txt files, each with a few lines of text inside.

For this demo, I created 4 .txt files containing lines of information related to vector search.

Step 2: Log In to AWS Management Console

Log in using your AWS credentials at https://console.aws.amazon.com/.

You’ll need a root or IAM user with sufficient permissions to use SageMaker, S3, and Kendra.

Step 3: Generate Vector Embeddings with SageMaker

Let’s head on over to Amazon SageMaker and convert our sample data into vector embeddings.

SageMaker Domain

• Navigate to Amazon SageMaker in the AWS Console.
• Set up a SageMaker AI Domain to enable Studio and related features.

It is recommended that you choose the Quick setup option when setting up your domain when you’re getting started with SageMaker.

SageMaker JumpStart

• Next, go to JumpStart, then choose Natural language processing models.
• Search for a transformer or “text embedding” model that is available without a subscription.

SageMaker Studio

• Open the chosen model in SageMaker Studio (directly accessible for free-tier and demo use).
• Deploy the pre-trained model to a SageMaker endpoint, choosing the optimal instance size (the smallest instance is fine for small-scale prototyping and in my case, for demo purposes).

• Enter the JupyterLab application in SageMaker Studio.

JupyterLab Application

• Start a Space Template using “Quick start”—this is optimal for notebook prototyping, small data sets, and hands-on learning exercises.

• Open a new Python 3 (ipykernel) notebook.

• Use this Python code to test embedding generation:

After running the code, your sample test should be converted into a vector embedding via a result showing the numerical representation of your data.

Alright! Now we can add the folder that we made locally (Step 1) into the notebook and use this following code to convert them into vector embeddings:

Then we can compile these embeddings and convert them into a single json file.

• Run the code to process your text files from the sample-data folder, generate embedding vectors for each, and save output to embeddings.json

You can also upload your embeddings directly to S3 by using this code:

Step 4: Store Data and Embeddings in Amazon S3

Now we can head on to Amazon S3 to store our data

• Create a new S3 bucket in the AWS Console.
• Upload your original .txt files, plus the embeddings (json file) you generated with SageMaker.

Step 5: Create an Amazon Kendra Index

• In the AWS Console, open Amazon Kendra.
• Start a new index.

• Set up a data source pointing to your S3 bucket or upload files manually.

• Configure indexing options and permissions.
• Launch the index build and Sync now to index content for searching.

Step 6: Query Your Kendra Index for Vector Search

Let’s see if our Index is working properly.

• Navigate to Search indexed content.

• Try queries like “Explain vector search” or “How does similarity retrieval work?”
• Amazon Kendra will return results ranked by semantic relevance.

Well done! Using advanced vector search technology, you’ve successfully converted unstructured data into intelligent, searchable knowledge. You’ve generated embeddings, used Amazon Kendra to index them, and run semantic queries that can comprehend the intent behind your searches rather than just terms. What you’ve accomplished is no small feat: you’ve created a working zero-infrastructure vector search using AWS managed services, laying the groundwork for knowledgeable search applications.

Step 7: Clean Up to Avoid Charges (Optimal but Recommended)

Now that you’ve successfully completed your demo and seen the power of vector search in action, you may want to clean up your AWS resources.

Consider doing these after you’re done with your work to prevent any unexpected charges:

• Stop and delete the SageMaker endpoint.
• Delete the Kendra index (or let the trial expire).
• Remove files from S3 as needed.
• Check the AWS Free Tier dashboard to confirm you’ve stayed within limits.

Cleaning up is optional, but it keeps your AWS environment neat and helps keep costs under control, especially if you’re eager to keep playing with your new creation.

Managing Performance and Costs

Managing performance and costs effectively is crucial when deploying vector search solutions on AWS. Although AWS’s managed services handle complexity, optimizing performance and controlling expenses can elevate your application’s reliability and scalability.

For example, choosing the right vector embedding size plays a significant role in performance. Faster searches are usually achieved with lower-dimensional embeddings without a considerable accuracy trade-off. Moreover, queries can be significantly accelerated while yielding pertinent answers using approximate nearest neighbor (ANN) search methods. 

Monitoring your AWS resources with CloudWatch allows you to monitor query latency, throughput, and usage patterns, helping you spot bottlenecks before they impact users. Batching updates and indexing operations can reduce overhead and improve efficiency when working with large or frequently updated datasets.

Cost control is equally important. You can test stuff without paying for it if you stay inside the AWS Free Tier limits while developing. Don’t forget to remove Amazon Kendra indexes and terminate SageMaker endpoints when not in use. Storage considerations are also important. Amazon S3 is a very affordable way to store your data and embeddings, but you may further reduce costs by selecting the appropriate storage class and lifecycle policies. 

By balancing these performance and cost factors, you can create a robust, scalable vector search system that maximizes AWS’s cloud capabilities.

Real-Life Use Cases with AWS Vector Search

Vector search transforms how organizations find, analyze, and leverage their data across industries. By understanding meaning rather than just matching keywords, AWS vector search enables intelligent applications that deliver real business value.

Enterprise Knowledge Management

Information silos in large companies cause employees to spend hours searching through documents, wikis, and knowledge bases. Companies like Amazon Kendra have revolutionized their internal search with semantic search by identifying purpose and context. Instead of searching for exact phrases, employees can ask natural questions like “What’s our policy on remote work flexibility?” and receive relevant results even if those words don’t appear in the documents. This dramatically reduces time spent on information retrieval and improves decision-making speed.

Retail Intelligence and Online Shopping

Modern e-commerce systems that leverage vector search to provide customized experiences are revolutionizing consumer shopping. The technology does more than match identical terms when a customer searches for “comfortable winter clothing,” for instance. Instead, it understands the deeper connections between warmth, comfort, and seasonal needs, returning relevant products even when product descriptions use completely different language. This technique is brilliantly demonstrated by Amazon’s recommendation engine, which uses product relationships and browsing behaviors to promote products that customers wish to purchase, resulting in far higher sales and happy customers. 

Medical Research and Healthcare

Healthcare companies use vector search to examine research material, medical imaging, and patient records. To aid diagnosis and treatment planning, radiologists can upload an X-ray and search large medical databases for comparable cases. Medical practitioners can make faster and more accurate clinical choices while protecting patient privacy with a secure AWS infrastructure by using vector search to find patterns in patient data that may point to particular disorders. 

Detecting Fraud in Financial Services

Banks and other financial institutions use vector search to identify questionable activity before it becomes fraud. These systems can identify unusual spending patterns by transforming transaction data, customer behavior, and account information into patterns. Customers can enjoy a safer and more seamless purchasing experience due to the early fraud detection and decreased false alerts that needlessly prevent valid purchases.

These instances show how AWS vector search isn’t a workable answer to business problems, rather than merely a theoretical concept. Vector search already demonstrates its value in increasing productivity, increasing customer happiness, and gaining a competitive advantage. And this is just the beginning; there are many more opportunities across industries when sophisticated search features are combined with AWS’s scalability and dependability.

Conclusion

This article helps you overcome the traditional barriers by showing you how to build a powerful vector search engine with AWS’s free-tier resources, preventing you from learning more about this intriguing topic. By putting the cost and performance management techniques we’ve covered into practice, you can ensure your application expands without hiccups and remains user-responsive.

The field of searching is shifting rapidly. Previously thought to be exclusive to tech giants, cutting-edge technology is now accessible to developers and companies of all sizes. Quickly, semantic and vector-based search features are no longer optional; people now demand intelligent, contextual search experiences that comprehend their meanings and their typing.

AWS gives you the resources and infrastructure you need to fulfill these changing demands. Thanks to the foundation you have established today, you can develop applications that do more than search through data; they actually comprehend and relate to what people seek. Semantic search is the way of the future, and you now possess the abilities to contribute to this change.

Ready to Take Your Vector Search Skills Further?

While this guide gives you a solid foundation using the traditional approach with SageMaker, Kendra, and S3, AWS has recently introduced even more powerful capabilities. If you’re ready for advanced implementation patterns and want to leverage the latest Amazon S3 vector storage features for better performance and integration, check out the comprehensive guide on Zero-Infrastructure Vector Search with Amazon S3 Vectors. This advanced tutorial covers the newest AWS vector bucket capabilities and enterprise-grade optimization techniques that can take your semantic search applications to the next level.

Semantic search is the way of the future, and you now possess the abilities to contribute to this change.

References:

• Amazon Kendra Documentation
• Amazon SageMaker JumpStart for Pre-trained Models
• Amazon S3 Vector Storage
• Vector Search in Amazon OpenSearch Service
• AWS SageMaker
• Vector Search with Amazon OpenSearch Service
• AWS Billing and Cost Management
• How BrainPad fosters internal knowledge sharing with Amazon Kendra
• Revolutionize retail recommendations for ecommerce with Amazon RDS for PostgreSQL and generative AI
• AI in Healthcare: 8 Uses of Vector Databases in Precision Medicine
• Power real-time vector search capabilities with Amazon MemoryDB