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	<title>Jeffrey Wang &#8211; Noise</title>
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		<title>How Amplitude implemented natural language-powered analytics using Amazon OpenSearch Service as a vector database</title>
		<link>https://noise.getoto.net/2026/03/04/how-amplitude-implemented-natural-language-powered-analytics-using-amazon-opensearch-service-as-a-vector-database/</link>
		
		<dc:creator><![CDATA[Jeffrey Wang]]></dc:creator>
		<pubDate>Wed, 04 Mar 2026 20:06:46 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon OpenSearch Service]]></category>
		<category><![CDATA[Customer Solutions]]></category>
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					<description><![CDATA[Amplitude is a product and customer journey analytics platform. Our customers wanted to ask deep questions about their product usage. Ask Amplitude is an AI assistant that uses large language models (LLMs). It combines schema search and content search to provide a customized, accurate, low latency, natural language-based visualization experience to end customers. Amplitude’s search architecture evolved to scale, simplify, and cost-optimize for our customers, by implementing semantic search and Retrieval Augmented Generation (RAG) powered by Amazon OpenSearch Service. In this post, we walk you through Amplitude’s iterative architectural journey and explore how we address several critical challenges in building a scalable semantic search and analytics platform.]]></description>
		
		
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