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    <title>PyPI recent updates for agentic-rag-pdf</title>
    <link>https://pypi.org/project/agentic-rag-pdf/</link>
    <description>Recent updates to the Python Package Index for agentic-rag-pdf</description>
    <language>en</language>    <item>
      <title>0.1.5</title>
      <link>https://pypi.org/project/agentic-rag-pdf/0.1.5/</link>
      <description>Agentic RAG over any PDF — grounded, cited answers from research reports, ESG/sustainability disclosures, contracts, manuals, and filings. Multi-agent LangGraph workflow with hybrid retrieval, RAPTOR summaries, NL→SQL over extracted tables, CRAG corrective loops, and a defense-in-depth hallucination stack.</description>
<author>bhagatkumar75@gmail.com</author>      <pubDate>Sun, 24 May 2026 11:50:05 GMT</pubDate>
    </item>    <item>
      <title>0.1.4</title>
      <link>https://pypi.org/project/agentic-rag-pdf/0.1.4/</link>
      <description>Agentic RAG over any PDF — grounded, cited answers from research reports, ESG/sustainability disclosures, contracts, manuals, and filings. Multi-agent LangGraph workflow with hybrid retrieval, RAPTOR summaries, NL→SQL over extracted tables, CRAG corrective loops, and a defense-in-depth hallucination stack.</description>
<author>bhagatkumar75@gmail.com</author>      <pubDate>Sun, 24 May 2026 11:37:14 GMT</pubDate>
    </item>    <item>
      <title>0.1.3</title>
      <link>https://pypi.org/project/agentic-rag-pdf/0.1.3/</link>
      <description>Agentic RAG over any PDF — grounded, cited answers from research reports, ESG/sustainability disclosures, contracts, manuals, and filings. Multi-agent LangGraph workflow with hybrid retrieval, RAPTOR summaries, NL→SQL over extracted tables, CRAG corrective loops, and a defense-in-depth hallucination stack.</description>
<author>bhagatkumar75@gmail.com</author>      <pubDate>Sun, 24 May 2026 11:29:21 GMT</pubDate>
    </item>    <item>
      <title>0.1.2</title>
      <link>https://pypi.org/project/agentic-rag-pdf/0.1.2/</link>
      <description>Agentic RAG over any PDF — grounded, cited answers from research reports, ESG/sustainability disclosures, contracts, manuals, and filings. Multi-agent LangGraph workflow with hybrid retrieval, RAPTOR summaries, NL→SQL over extracted tables, CRAG corrective loops, and a defense-in-depth hallucination stack.</description>
<author>bhagatkumar75@gmail.com</author>      <pubDate>Sun, 24 May 2026 11:05:56 GMT</pubDate>
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