graphify

To turn a folder into a knowledge graph, drop your code, documents, papers, and images into a directory and run the extraction pipeline. It generates a structured graph with persistent edges, clustering, and interactive HTML visualization.

An audit trail in a knowledge graph tracks edge origins by categorizing relationships as EXTRACTED, INFERRED, or AMBIGUOUS. This honest audit trail ensures transparency by distinguishing direct file evidence from algorithmic inference during the graph generation process.

Yes, you can export the generated knowledge graph to Neo4j, Obsidian, and HTML formats. The pipeline outputs interactive HTML, GraphML, and graph JSON files, enabling seamless integration with external graph databases and note-taking platforms.

Yes, graphify processes images and research papers alongside code and documents for data visualization. It applies end-to-end extraction and clustering to visualize concepts, dependencies, and cross-document connections within your research corpus.

The best way to visualize cross-document connections in a research corpus is by generating an interactive HTML graph. This approach maps concepts and dependencies, providing a navigable structure and a readable plain-language report of the relationships.

No dependencies are required to generate GraphML or JSON files from your files. The pipeline operates independently to extract relationships, apply clustering, and output the structured graph data along with an interactive HTML visualization.