<p>In the modern digital workflow, information is scattered everywhere—source code repositories, PDFs, screenshots, notes, research papers, tweets, diagrams, and documents. Most tools can search these files individually, but very few can reveal how they connect. <strong>Graphify</strong> is designed to solve that problem.</p>
<p>Graphify transforms any folder of mixed content into a <strong>living knowledge graph</strong>—an interactive system that maps relationships between concepts, files, functions, people, ideas, and documents. Instead of browsing isolated files one by one, users gain a navigable map of their knowledge ecosystem.</p>
<hr>
<h2>What Is Graphify?</h2>
<p>Graphify is a command-line workflow that converts raw data into structured intelligence through a multi-stage pipeline:</p>
<p><strong>Any input → Knowledge Graph → Clustered Communities → Interactive Outputs</strong></p>
<p>Whether you feed it codebases, notes, academic papers, images, or a personal “raw folder,” Graphify extracts entities and relationships, organizes them into communities, and generates outputs such as:</p>
<ul>
<li><strong>Interactive HTML graph visualizations</strong></li>
<li><strong>GraphRAG-ready JSON files</strong></li>
<li><strong>Plain-language audit reports</strong></li>
<li><strong>Obsidian knowledge vaults</strong></li>
<li><strong>Neo4j exports</strong></li>
<li><strong>GraphML / SVG visualizations</strong></li>
</ul>
<p>The result is not just searchable data—but explainable structure.</p>
<hr>
<h2>Why Graphify Matters</h2>
<p>Most AI assistants can summarize a document or answer a question. But they often lack persistence, transparency, and cross-document reasoning. Graphify fills those gaps in three major ways.</p>
<h3>1. Persistent Memory Through Graph Storage</h3>
<p>Graphify stores extracted relationships in a reusable graph file. That means your insights survive beyond a single session.</p>
<p>You can return weeks later and ask:</p>
<ul>
<li>What connects my authentication module to my database layer?</li>
<li>Which papers mention the same optimization strategy?</li>
<li>How is this new note related to prior research?</li>
</ul>
<p>No need to re-upload or re-read everything.</p>
<hr>
<h3>2. Honest Audit Trails</h3>
<p>One of Graphify’s strongest features is its transparency.</p>
<p>Every relationship in the graph is labeled as:</p>
<ul>
<li><strong>EXTRACTED</strong> – explicitly found in the source material</li>
<li><strong>INFERRED</strong> – logically derived from evidence</li>
<li><strong>AMBIGUOUS</strong> – uncertain and flagged for review</li>
</ul>
<p>This matters because many systems generate confident but unverifiable connections. Graphify shows users exactly what came from evidence and what came from interpretation.</p>
<hr>
<h3>3. Cross-Document Discovery</h3>
<p>Some of the most valuable insights are the ones you never thought to ask.</p>
<p>Graphify runs <strong>community detection</strong> across the graph to uncover hidden clusters of related concepts. This means it can surface surprising links such as:</p>
<ul>
<li>A function in code solving the same problem as a concept in a research paper</li>
<li>Notes from months ago connected to a new project idea</li>
<li>Similar error-handling patterns across different services</li>
<li>Repeated assumptions hidden across documents</li>
</ul>
<p>It helps users discover what they didn’t know was connected.</p>
<hr>
<h1>How Graphify Works</h1>
<p>Graphify follows a structured pipeline that balances deterministic analysis with semantic reasoning.</p>
<hr>
<h2>Step 1: Detect the Corpus</h2>
<p>It scans the target directory and classifies files such as:</p>
<ul>
<li>Code (.py, .ts, .go, etc.)</li>
<li>Documents (.md, .txt)</li>
<li>Papers (.pdf)</li>
<li>Images (.png, .jpg)</li>
</ul>
<p>Then it estimates scale:</p>
<ul>
<li>Total files</li>
<li>Approximate word count</li>
<li>Sensitive files skipped</li>
<li>Subdirectory distribution</li>
</ul>
<p>This prevents users from accidentally processing massive corpora blindly.</p>
<hr>
<h2>Step 2: Structural Extraction (Code Understanding)</h2>
<p>For codebases, Graphify uses AST-based parsing to extract:</p>
<ul>
<li>Functions</li>
<li>Classes</li>
<li>Imports</li>
<li>Modules</li>
<li>Structural relationships</li>
</ul>
<p>This is deterministic, fast, and reliable.</p>
<hr>
<h2>Step 3: Semantic Extraction</h2>
<p>For documents, papers, and images, Graphify performs deeper understanding to identify:</p>
<ul>
<li>Named entities</li>
<li>Concepts</li>
<li>Citations</li>
<li>Dependencies</li>
<li>Shared themes</li>
<li>Architectural patterns</li>
<li>Similar ideas across unrelated sources</li>
</ul>
<p>It can even interpret screenshots, charts, diagrams, and whiteboards.</p>
<hr>
<h2>Step 4: Build the Graph</h2>
<p>All extracted data is merged into a graph structure consisting of:</p>
<ul>
<li><strong>Nodes</strong> → concepts, modules, people, files, ideas</li>
<li><strong>Edges</strong> → calls, references, citations, similarities, dependencies</li>
</ul>
<p>Then clustering algorithms detect communities.</p>
<hr>
<h2>Step 5: Generate Outputs</h2>
<p>Graphify automatically produces multiple usable formats.</p>
<h3>Interactive HTML Graph</h3>
<p>Open in any browser and explore relationships visually.</p>
<h3>Obsidian Vault</h3>
<p>Every node becomes a note, communities become folders, and graph view is preserved.</p>
<h3>GraphRAG JSON</h3>
<p>Use the graph for retrieval-augmented AI systems.</p>
<h3>Audit Report</h3>
<p>A readable markdown report including:</p>
<ul>
<li>Key nodes (“God Nodes”)</li>
<li>Surprising connections</li>
<li>Suggested questions</li>
<li>Cohesion scores</li>
<li>Token costs</li>
</ul>
<hr>
<h1>Real Use Cases</h1>
<h2>Understanding a New Codebase</h2>
<p>Join a project and run Graphify before touching anything.</p>
<p>It reveals:</p>
<ul>
<li>Core modules</li>
<li>Bottlenecks</li>
<li>Hidden dependencies</li>
<li>Shared data flows</li>
</ul>
<hr>
<h2>Research Synthesis</h2>
<p>Drop in:</p>
<ul>
<li>Papers</li>
<li>Tweets</li>
<li>Notes</li>
<li>PDFs</li>
</ul>
<p>Graphify builds a unified concept graph that links ideas across sources.</p>
<hr>
<h2>Personal Knowledge Management</h2>
<p>Use it on a <code>/raw</code> folder containing everything you collect.</p>
<p>Instead of folders full of forgotten files, you get an evolving memory system.</p>
<hr>
<h2>Team Documentation</h2>
<p>Run Graphify continuously in watch mode or Git hooks so every code change updates architecture understanding automatically.</p>
<hr>
<h1>Advanced Features</h1>
<h2>Incremental Updates</h2>
<p>Only changed files are reprocessed.</p>
<p>This saves time and token cost.</p>
<hr>
<h2>Neo4j Integration</h2>
<p>Push graphs directly into Neo4j for enterprise-grade graph querying.</p>
<hr>
<h2>MCP Agent Access</h2>
<p>Run Graphify as an MCP server so other AI agents can query your knowledge graph live.</p>
<hr>
<h2>Query Modes</h2>
<p>Ask natural questions like:</p>
<ul>
<li>“What is AuthModule connected to?”</li>
<li>“Explain SwinTransformer”</li>
<li>“Shortest path between frontend and database”</li>
</ul>
<p>Graphify answers using only graph evidence.</p>
<hr>
<h1>Why Graphify Is Different</h1>
<p>Most productivity tools organize files.</p>
<p>Most AI tools summarize files.</p>
<p><strong>Graphify maps relationships between files, concepts, and systems.</strong></p>
<p>That makes it fundamentally different.</p>
<p>It turns passive storage into active intelligence.</p>
<hr>
<h1>The Future of Knowledge Work</h1>
<p>As people accumulate thousands of files, documents, screenshots, and code artifacts, linear search becomes insufficient.</p>
<p>The future belongs to systems that understand structure.</p>
<p>Graphify represents that shift:</p>
<p>From folders → graphs</p>
<p>From search → navigation</p>
<p>From notes → intelligence</p>
<p>From information → connected knowledge</p>
<hr>
<h1>Final Thoughts</h1>
<p>Graphify is more than a utility—it is a new interface for thinking.</p>
<p>If your data is fragmented, growing, or difficult to reason about, Graphify gives you something most tools cannot:</p>
<p><strong>A map of what you know—and what you didn’t know was connected.</strong></p>


Anubhav Bhatt

Graphify is a command-line tool that transforms mixed content folders (code, PDFs, notes, images, papers) into interactive knowledge graphs. It uses AST-based parsing for code and semantic extraction for documents to build a graph of entities and relationships, then exports to formats including interactive HTML, Obsidian vaults, GraphRAG JSON, and Neo4j. Key differentiators include labeled relationship provenance (EXTRACTED/INFERRED/AMBIGUOUS), community detection for cross-document discovery, incremental updates, and MCP server support for AI agent queries.

Graphify: Turning Chaos into Connected Knowledge