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		<title>Code Search on AIBussin — AI applications, systems and books</title>
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				<title>RELATE: Searching Embeddings by Relation, Not Just Similarity</title>
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				<pubDate>Wed, 05 Aug 2026 23:24:45 +0100</pubDate>
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				<description>&lt;p&gt;Embeddings are everywhere in modern AI.&lt;/p&gt;&#xA;&lt;p&gt;They power semantic search, retrieval-augmented generation, recommendations, clustering, duplicate detection, code search, memory systems, and many of the mechanisms through which an AI system decides what information is relevant.&lt;/p&gt;&#xA;&lt;p&gt;Yet most systems interrogate embeddings in essentially the same way:&lt;/p&gt;&#xA;&lt;blockquote&gt;&#xA;&lt;p&gt;Take two vectors and calculate cosine similarity.&lt;/p&gt;&#xA;&lt;/blockquote&gt;&#xA;&lt;p&gt;That is useful. But it also makes a strong assumption.&lt;/p&gt;&#xA;&lt;p&gt;It assumes that the information we care about is expressed directly through the default geometry of the embedding space.&lt;/p&gt;</description>
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