A small but realistic mixed-input corpus: Python source files, a markdown paper with arXiv citations, and one image. Tests graphify's ability to handle different file types in a single run.

Corpus (5 files)

All input files are in raw/:

raw/
├── analyze.py          — graphify's graph analysis module (god_nodes, surprising_connections, etc.)
├── build.py            — graphify's graph builder (build_from_json, networkx wrapper)
├── cluster.py          — graphify's Leiden community detection (cluster, score_all)
├── attention_notes.md  — Transformer paper notes (Vaswani et al., 2017), with arXiv citation

Note: the original benchmark included attention_arabic.png (an Arabic-language figure from the Attention paper). PNG files are not stored in this repo. To reproduce with the image, save any diagram or figure from the Attention Is All You Need paper as raw/attention_arabic.png.

How to run

pip install graphifyy && graphify install
/graphify ./raw

Or from the CLI directly:

pip install graphifyy
graphify ./raw

What to expect

~20 nodes, ~19 edges from AST alone (3 Python modules)
3 communities: Graph Analysis, Clustering & Scoring, Graph Building
God nodes: analyze.py, cluster.py, build.py
attention_notes.md classified as paper (arXiv heuristic fires on 1706.03762)
If you include the image: 1 extra node describing the figure content via vision

Full eval with scores and analysis: review.md