
    Amazon OpenSearch introduces ML connectors that enable seamless integration with Amazon Comprehend for language detection and Amazon Bedrock for multilingual semantic vector search, simplifying data augmentation and embedding processes.


<div>
<p>
This article discusses using Amazon OpenSearch ML connector APIs to augment data ingestion with machine learning capabilities, focusing on two primary use cases:
</p>
<ul>
<li>Language detection using Amazon Comprehend</li>
<li>Multilingual semantic search using Amazon Titan Text Embeddings v2</li>
</ul>
<p>
Key technical steps include:
</p>
<ul>
<li>Creating ML connectors to Amazon Comprehend and Amazon Bedrock</li>
<li>Registering models within OpenSearch</li>
<li>Creating ingest pipelines to process documents</li>
<li>Generating vector embeddings for semantic search</li>
</ul>
<p>
Benefits of using ML connectors include simplified architecture, operational efficiency, and cost-effectiveness compared to traditional model deployment methods.
</p>
</div>


Using Amazon OpenSearch ML connector APIs

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