graphify

To create a knowledge graph from a folder of code and documents, you can use a tool that extracts entities and relationships from files. This process converts your collection into a navigable graph with community detection to reveal connections.

Community detection in knowledge graph construction is the process of identifying clusters of related concepts within interconnected nodes. It helps reveal hidden connections and structure within a collection of code, documents, or images.

Yes, you need Python installed to extract entities and relationships from files. The process requires Python and specific libraries for parsing different file types and constructing the graph structure from your code, documents, and images.

Yes, you can generate an interactive HTML visualization from a directory of files. The graph construction process outputs interactive HTML, JSON data, and an audit report to help you navigate and understand the extracted relationships.

The best way to analyze a new codebase architecture is by converting the source files into a structured knowledge graph. This approach extracts entities and detects related clusters, giving you a clear visual map of the system's connections.

Yes, file analysis for graph construction works with images. The process is designed to extract entities and relationships from code, documents, and image files, turning them into interconnected nodes within the graph.