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    <title>InfoQ - vector databases</title>
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    <description>InfoQ vector databases feed</description>
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      <title>AWS Introduces Native Vector Search for DynamoDB</title>
      <link>https://www.infoq.com/news/2026/08/aws-dynamodb-vector-search/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=vector+databases</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/08/aws-dynamodb-vector-search/en/headerimage/generatedHeaderImage-1786173983449.jpg"/&gt;&lt;p&gt;Amazon DynamoDB recently introduced native vector search, allowing developers to store embeddings alongside application data and run approximate nearest-neighbor queries directly from DynamoDB without using a separate vector database. The feature supports filtered similarity searches and configurable vector indexes for semantic search workloads.&lt;/p&gt; &lt;i&gt;By Renato Losio&lt;/i&gt;</description>
      <category>AWS</category>
      <category>NoSQL</category>
      <category>Generative AI</category>
      <category>Dynamo DB</category>
      <category>Cloud</category>
      <category>Search</category>
      <category>vector databases</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Sun, 16 Aug 2026 07:21:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/08/aws-dynamodb-vector-search/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=vector+databases</guid>
      <dc:creator>Renato Losio</dc:creator>
      <dc:date>2026-08-16T07:21:00Z</dc:date>
      <dc:identifier>/news/2026/08/aws-dynamodb-vector-search/en</dc:identifier>
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    <item>
      <title>Presentation: From Models to Agents: Building Context-Aware Consumer AI at Scale at DoorDash</title>
      <link>https://www.infoq.com/presentations/ai-agentic-recommendations-semantic-ids/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=vector+databases</link>
      <description>&lt;img src="https://res.infoq.com/presentations/ai-agentic-recommendations-semantic-ids/en/mediumimage/sudeep-das-medium-1785845550765.jpeg"/&gt;&lt;p&gt;Sudeep Das shares how DoorDash shifts from legacy one-shot predictions to an agentic recommendation platform. He discusses leveraging language-native consumer memory, RQ-VAE semantic IDs for catalog representation, and grounded search to dramatically boost relevance and conversion metrics.&lt;/p&gt; &lt;i&gt;By Sudeep Das&lt;/i&gt;</description>
      <category>Generative AI</category>
      <category>E-Commerce</category>
      <category>Large language models</category>
      <category>Agents</category>
      <category>QCon AI Boston 2026</category>
      <category>AI Architecture</category>
      <category>Rankings</category>
      <category>Agentic AI Architecture</category>
      <category>vector databases</category>
      <category>Search</category>
      <category>Transcripts</category>
      <category>Machine Learning</category>
      <category>Architecture &amp; Design</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Sat, 15 Aug 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/ai-agentic-recommendations-semantic-ids/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=vector+databases</guid>
      <dc:creator>Sudeep Das</dc:creator>
      <dc:date>2026-08-15T11:00:00Z</dc:date>
      <dc:identifier>/presentations/ai-agentic-recommendations-semantic-ids/en</dc:identifier>
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