graphify-windows

You can turn a codebase into a navigable knowledge graph by running the graphify script on a project directory. It preserves origin data, clusters related files into communities, and generates an interactive HTML visualization for exploration.

Visualizing code dependencies is achieved by processing the directory to build a structured graph with auditable edges. The script outputs an interactive HTML visualization and a GraphJSON file representing the connections between code, docs, and images.

You need Python 3.10 or higher with the graphify and networkx libraries installed to generate the HTML visualization. Neo4j is optional and only required if you need advanced graph database visualization capabilities.

Yes, you can create an audit trail for code and research papers by processing the files into a persistent knowledge graph. The script generates a detailed GRAPH_REPORT.md specifically for human-friendly reviews and audits.

Yes, the code analysis supports clustering for large directories by grouping related nodes into communities using the networkx library. This helps organize complex codebases, documents, and media assets into manageable structures within the graph.

When building a knowledge graph from documents, the available export formats are an interactive HTML visualization, a GraphJSON file for data interchange, and a detailed GRAPH_REPORT.md for auditing persistent relationships and dependencies.