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    <title>PyPI recent updates for docdistance</title>
    <link>https://pypi.org/project/docdistance/</link>
    <description>Recent updates to the Python Package Index for docdistance</description>
    <language>en</language>    <item>
      <title>1.1.3</title>
      <link>https://pypi.org/project/docdistance/1.1.3/</link>
      <description>Project that uses theory of From Word Embeddings To Document Distances / Optimal Transport to give meaningful distance from one document to another, useful if building agentic projects that convert or extract information from one document to another using frontier models but without the ability to calculate KL divergence from logits</description>
      <pubDate>Wed, 01 Jul 2026 18:38:04 GMT</pubDate>
    </item>    <item>
      <title>1.1.2</title>
      <link>https://pypi.org/project/docdistance/1.1.2/</link>
      <description>Project that uses theory of From Word Embeddings To Document Distances / Optimal Transport to give meaningful distance from one document to another, useful if building agentic projects that convert or extract information from one document to another using frontier models but without the ability to calculate KL divergence from logits</description>
      <pubDate>Tue, 23 Jun 2026 08:51:24 GMT</pubDate>
    </item>    <item>
      <title>1.1.1</title>
      <link>https://pypi.org/project/docdistance/1.1.1/</link>
      <description>Project that uses theory of From Word Embeddings To Document Distances / Optimal Transport to give meaningful distance from one document to another, useful if building agentic projects that convert or extract information from one document to another using frontier models but without the ability to calculate KL divergence from logits</description>
      <pubDate>Tue, 23 Jun 2026 06:38:38 GMT</pubDate>
    </item>    <item>
      <title>1.1.0</title>
      <link>https://pypi.org/project/docdistance/1.1.0/</link>
      <description>Project that uses theory of From Word Embeddings To Document Distances / Optimal Transport to give meaningful distance from one document to another, useful if building agentic projects that convert or extract information from one document to another using frontier models but without the ability to calculate KL divergence from logits</description>
      <pubDate>Mon, 22 Jun 2026 23:45:33 GMT</pubDate>
    </item>    <item>
      <title>1.0.17</title>
      <link>https://pypi.org/project/docdistance/1.0.17/</link>
      <description>Project that uses theory of From Word Embeddings To Document Distances / Optimal Transport to give meaningful distance from one document to another, useful if building agentic projects that convert or extract information from one document to another using frontier models but without the ability to calculate KL divergence from logits</description>
      <pubDate>Mon, 22 Jun 2026 18:39:34 GMT</pubDate>
    </item>    <item>
      <title>1.0.16</title>
      <link>https://pypi.org/project/docdistance/1.0.16/</link>
      <description>Project that uses theory of From Word Embeddings To Document Distances / Optimal Transport to give meaningful distance from one document to another, useful if building agentic projects that convert or extract information from one document to another using frontier models but without the ability to calculate KL divergence from logits</description>
      <pubDate>Fri, 19 Jun 2026 02:33:56 GMT</pubDate>
    </item>    <item>
      <title>1.0.15</title>
      <link>https://pypi.org/project/docdistance/1.0.15/</link>
      <description>Project that uses theory of From Word Embeddings To Document Distances / Optimal Transport to give meaningful distance from one document to another, useful if building agentic projects that convert or extract information from one document to another using frontier models but without the ability to calculate KL divergence from logits</description>
      <pubDate>Thu, 18 Jun 2026 12:07:20 GMT</pubDate>
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