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        <title>Ollama - Tag - Daily Deep Think</title>
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    <title>PDF2Markdown: Complete Guide to Smart PDF Article Extraction</title>
    <link>https://blog.baifan.site/en/pdf2markdown-intelligent-pdf-extraction-tool/</link>
    <pubDate>Tue, 25 Nov 2025 14:00:00 &#43;0800</pubDate><author>
                    <name>ByF</name>
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    <a href="#pdf2markdown---an-intelligent-article-extraction-tool-for-large-pdf-documents" class="header-mark"></a>PDF2Markdown - An Intelligent Article Extraction Tool for Large PDF Documents</h1><p><a href="https://python.org" target="_blank" rel="noopener noreferrer"><img class="tw-inline" loading="lazy" src='/python-3.13+-blue_1423277123478464751.svg'   alt="Python Version"  ></a>
<a href="https://github.com/astral-sh/ruff" target="_blank" rel="noopener noreferrer"><img class="tw-inline" loading="lazy" src='/code%20style-ruff-green_7678052461911854896.svg'   alt="Code Style"  ></a>
<a href="https://mypy.readthedocs.io/" target="_blank" rel="noopener noreferrer"><img class="tw-inline" loading="lazy" src='/type%20checking-mypy-blue_13250137686053437545.svg'   alt="Type Checking"  ></a>
<a href="LICENSE" rel=""><img class="tw-inline" loading="lazy" src='/license-MIT-green_10730612941528799645.svg'   alt="License"  ></a></p>
<h2 id="project-overview" class="headerLink">
    <a href="#project-overview" class="header-mark"></a>Project Overview</h2><p>PDF2Markdown is an intelligent content extraction tool built specifically for large scanned PDF files. It combines traditional OCR technology with modern AI large language models to intelligently extract pure article content from documents, automatically filtering out non-article elements such as images and tables. Mixed Chinese-English documents are fully supported.</p>]]></description>
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    <title>RAG Complete Guide: Build a Local RAG System from Scratch</title>
    <link>https://blog.baifan.site/en/rag-system-complete-guide-langchain-ollama-pgvector/</link>
    <pubDate>Wed, 27 Aug 2025 14:00:00 &#43;0800</pubDate><author>
                    <name>ByF</name>
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  <p>This post is a directly actionable document. Follow it top to bottom and you will build a local RAG (retrieval-augmented generation) system from scratch and understand the key concepts and code. All core scripts come with comments to make learning and interview review easier.</p>]]></description>
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