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		<title>Postgres on AIBussin — AI applications, systems and books</title>
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				<title>PostgreSQL for AI: Storing and Searching Embeddings with pgvector</title>
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				<pubDate>Mon, 10 Mar 2025 21:35:37 +0000</pubDate>
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				<description>&lt;h2 id=&#34;summary&#34;&gt;Summary&lt;/h2&gt;&#xA;&lt;p&gt;Vector databases are essential for modern AI applications like semantic search, recommendation systems, and natural language processing. They allow us to store and query high-dimensional vectors efficiently. With the &lt;a href=&#34;https://github.com/pgvector/pgvector&#34;&gt;pgvector&lt;/a&gt; extension &lt;a href=&#34;https://www.postgresql.org/download/&#34;&gt;PostgreSQL&lt;/a&gt; becomes a powerful vector database, enabling you to combine traditional relational data with vector-based operations.&lt;/p&gt;&#xA;&lt;p&gt;In this post, we will walk through the full process:&lt;/p&gt;&#xA;&lt;p&gt;Installing PostgreSQL and pgvector&#xA;Setting up a vector-enabled database&#xA;Generating embeddings using Ollama&#xA;Running similarity queries with Python&#xA;By the end, you&amp;rsquo;ll be able to store, query, and compare high-dimensional vectors in PostgreSQL, opening up new possibilities for AI-powered applications.&lt;/p&gt;</description>
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