[Thank Goodness its Search] Automatic Semantic Enhancement in Amazon OpenSearch Service

Welcome to Thank Goodness It’s Search series—your Friday fix of OpenSearch learnings, feature drops, and real-world solutions. I will keep it short, sharp, and search-focused—so you can end your week a little more knowledge on Search than you started.

In today's article, you will learn about yet another exciting feature launched in Amazon OpenSearch Service at re:Invent 2025: Automatic Semantic Enhancement. This feature is designed to improve search relevance by leveraging AI to better understand the meaning behind text data, making it easier to deliver accurate results to users.

What we announced

We are excited to announce the launch of Automatic Semantic Enhancement for Amazon OpenSearch Service. This new feature leverages advanced AI techniques to automatically enhance the semantic understanding of your text data, resulting in more accurate and relevant search results. With this feature, you can easily improve your search capabilities without the need for complex configurations or AI model management. Simply enable the feature, specify the text fields to enhance, and let the system handle the rest. Experience the benefits of enhanced search relevance and user satisfaction with minimal effort.

Introduction

Amazon OpenSearch Service now includes a powerful new feature that makes search results more accurate and relevant. This feature combines traditional keyword matching with advanced AI-powered understanding of language meaning. It works by automatically analyzing and enhancing your text data during upload, without requiring any complex setup or configuration.

When you add documents to Amazon OpenSearch service, the system automatically processes the text using AI to better understand its meaning. This enhanced understanding helps match searches to the most relevant results, even when the exact keywords don't match perfectly. You only need to specify which text fields you want to enhance - the system handles everything else automatically. The feature uses pre-built AI models that work well out of the box, with no need for customization. When you add documents, the system analyzes them to capture both the words used and their meaning. This information is stored efficiently to enable fast searching. To keep things quick and cost-effective, the heavy processing only happens when adding documents, not during searches.

This enhancement makes it much easier to build better search experiences. You don't need to manage any AI models or complex configurations - just enable the feature and let the system do the work. The result is more accurate and relevant search results that better understand what users are looking for.

Key benefits include

• Significant search improvement of 10-20% without impacting latency
• Simplified implementation with out-of-the-box semantic indexes requiring no ML model management
• Cost-effective pay-as-you-use pricing with charges only during ingestion
• Comprehensive multi-language support covering 15 languages including English, Chinese, Arabic, and Spanish

How to enable this feature?

Automatic Semantic Enhancement is automatically enabled with Amazon OpenSearch Service. When creating an index, make sure to select automatic semantic enrichment as part of your index settings. Here’s how to enable it through the Amazon OpenSearch Service Console:

1. Navigate to AWS Console: Go to the Amazon OpenSearch Service section in the AWS Management Console and choose the domain or collection.

2. Create an Index: Navigate to the Indexes tab on your domain or collection and select Create Index button. Begin configuring the index mappings.

3. Configure Fields: In the index mapping, identify and mark the text fields that require semantic enhancement by enabling the appropriate settings.

4. Save and Apply: Click Create to save your settings to apply the semantic enhancement configuration.

5. Verify Index Mapping: Review Field mappings in both the AWS Console and the Amazon OpenSearch Service dev console.

6. Validate Pipeline: Inspect the index and search pipeline to verify model configurations and semantic processors are set up correctly.

7. Ingest Data: Ingest multilingual data

8. Review Index: Review the ingested documents to verify that Sparse tokens have been generated

9. Perform Search: Complete the process by executing a sample search for term "good bag for short trip"

Conclusion

Automatic Semantic Enhancement in Amazon OpenSearch Service helps you improve search accuracy and relevance with minimal effort. By using managed sparse encoding techniques, this feature enhances semantic understanding of text data to deliver better search results and user experience. You can now avoid manually managing semantic workflows or deploying models on Amazon SageMaker or Amazon Bedrock, as the entire process is automated through the Amazon OpenSearch Service console page. The straightforward implementation and cost-effective pricing make Automatic Semantic Enhancement a valuable addition to any search application

Next Steps

To get started with Automatic Semantic Enhancement in Amazon OpenSearch Service, refer to the official documentation and tutorials available on the AWS website. Explore how this feature can transform your search applications and drive innovation in your organization. Here are some useful links to get you started:

• Automatic semantic enrichment for Amazon OpenSearch Serverless
• Automatic semantic enrichment for Amazon OpenSearch Service
• Getting Started with Automatic Semantic Enhancement