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	<title>Yuanbo Li &#8211; Noise</title>
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		<title>Integrate sparse and dense vectors to enhance knowledge retrieval in RAG using Amazon OpenSearch Service</title>
		<link>https://noise.getoto.net/2024/09/05/integrate-sparse-and-dense-vectors-to-enhance-knowledge-retrieval-in-rag-using-amazon-opensearch-service/</link>
		
		<dc:creator><![CDATA[Yuanbo Li]]></dc:creator>
		<pubDate>Thu, 05 Sep 2024 20:09:52 +0000</pubDate>
				<category><![CDATA[Amazon OpenSearch Service]]></category>
		<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[Best practices]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[In this post, instead of using the BM25 algorithm, we introduce sparse vector retrieval. This approach offers improved term expansion while maintaining interpretability. We walk through the steps of integrating sparse and dense vectors for knowledge retrieval using Amazon OpenSearch Service and run some experiments on some public datasets to show its advantages.]]></description>
		
		
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