Obsidian Graph

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Discovers hidden connections and semantic relationships within markdown vaults using AI-powered vector embeddings and PostgreSQL+pgvector.

Obsidian Graph is a powerful semantic knowledge graph engine designed to uncover deeper connections within your markdown notes, particularly Obsidian vaults. It leverages AI-powered vector embeddings from Voyage Context-3 and a PostgreSQL+pgvector database to store and analyze your notes. This allows for advanced functionalities like semantic search, multi-hop graph traversal, and the identification of key conceptual hubs or isolated insights. Compatible with any folder of markdown files, it integrates seamlessly with AI applications via the Model Context Protocol (MCP) to transform your note-taking into a dynamic knowledge network.

주요 기능

01Orphan Detection: Find isolated insights that need integration
02Connection Discovery: Multi-hop BFS graph traversal to map note relationships
03Auto-Indexing: Automatic file watching with debounce for incremental updates
04Hub Analysis: Identify highly connected conceptual anchors (MOC candidates)
05Semantic Search: Find notes by meaning, not just keywords
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사용 사례

01Find notes by conceptual similarity using natural language queries
02Identify central conceptual notes (MOC candidates) and isolated insights needing integration
03Map complex knowledge networks and discover multi-hop relationships between ideas