<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:dc="http://purl.org/dc/elements/1.1/">
  <channel>
    <title>DEV Community: Jayjeo</title>
    <description>The latest articles on DEV Community by Jayjeo (@jayjeo).</description>
    <link>https://dev.to/jayjeo</link>
    <image>
      <url>https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F3209154%2F51740e16-ac50-466b-88ae-1d6e71e3d173.jpeg</url>
      <title>DEV Community: Jayjeo</title>
      <link>https://dev.to/jayjeo</link>
    </image>
    <atom:link rel="self" type="application/rss+xml" href="https://dev.to/feed/jayjeo"/>
    <language>en</language>
    <item>
      <title>Obsidian-Milvus-FastMCP</title>
      <dc:creator>Jayjeo</dc:creator>
      <pubDate>Mon, 26 May 2025 08:58:36 +0000</pubDate>
      <link>https://dev.to/jayjeo/obsidian-milvus-fastmcp-mi1</link>
      <guid>https://dev.to/jayjeo/obsidian-milvus-fastmcp-mi1</guid>
      <description>&lt;p&gt;&lt;a href="https://github.com/jayjeo/obsidian-milvus-FastMCP" rel="noopener noreferrer"&gt;https://github.com/jayjeo/obsidian-milvus-FastMCP&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;A powerful, production-ready system that connects your Obsidian vault to Claude Desktop via FastMCP, leveraging Milvus vector database for intelligent document search and retrieval.&lt;/p&gt;

&lt;p&gt;This program is useful for people who store extensive Markdown and PDF materials in Obsidian and need to extract comprehensive information from Obsidian for research, work, and study purposes.&lt;/p&gt;

&lt;p&gt;📝 Video Showcase&lt;br&gt;
&lt;a href="https://youtu.be/wPFiG9mC7e8?si=uF-TJrgG-guC33JG" rel="noopener noreferrer"&gt;https://youtu.be/wPFiG9mC7e8?si=uF-TJrgG-guC33JG&lt;/a&gt;&lt;br&gt;
🔍 Core Search Capabilities&lt;br&gt;
🟢 Hybrid Search Engine : Advanced vector similarity + keyword search fusion&lt;br&gt;
🟢 Intelligent Semantic Search : High-precision meaning-based document retrieval&lt;br&gt;
🟢 Adaptive Query Processing : Automatic parameter adjustment based on query complexity&lt;br&gt;
🟢 Multi-modal Search : Integrated text and attachment file search&lt;br&gt;
🟢 Contextual Expansion : Related document discovery and context-aware retrieval&lt;br&gt;
🧠 Advanced AI &amp;amp; RAG Features&lt;br&gt;
🟢 Hierarchical Retrieval : Document → Section → Chunk progressive search&lt;br&gt;
🟢 Multi-query Fusion : Intelligent combination of multiple search queries with weighted averaging, maximum value, and reciprocal rank fusion&lt;br&gt;
🟢 Adaptive Chunk Retrieval : Dynamic chunk size adjustment based on query complexity&lt;br&gt;
🟢 Knowledge Graph Exploration : Vector similarity-based connection discovery with BFS traversal and graph centrality ranking&lt;br&gt;
🟢 Temporal-aware Search : Balance between relevance and recency with time-weighted scoring&lt;br&gt;
🏷️ Advanced Metadata Filtering&lt;br&gt;
🟢 Complex Tag Logic : AND/OR/NOT combinations for sophisticated tag-based filtering&lt;br&gt;
🟢 Time Range Filtering : Precise temporal document filtering&lt;br&gt;
🟢 File Type &amp;amp; Quality Filtering : Content quality assessment and file type categorization&lt;br&gt;
🟢 Multi-dimensional Filtering : Simultaneous application of multiple filter criteria&lt;/p&gt;

</description>
      <category>productivity</category>
      <category>opensource</category>
      <category>machinelearning</category>
      <category>python</category>
    </item>
  </channel>
</rss>
