graphifylisted

# /graphify Turn any folder of files into a navigable knowledge graph with community detection, an honest audit trail, and three outputs: interactive HTML, GraphRAG-ready JSON, and a plain-language GRAPH_REPORT.md. > ⚡ **Before running on a corpus larger than ~50 files, READ [OPTIMIZATIONS.md](./OPTIMIZATIONS.md).** The wrapper scripts in `scripts/` (dedupe, regex preflight, word-balanced chunking, label canonicalization, cache integration) typically cut LLM token cost by 80–92% and produce a higher-quality graph. The single most important step is calling `graphify_canonicalize.py --cache` after merging — without it, the next `--update` run repays the entire cost. Skip these wrappers and a 1,500-file vault will burn ~10M LLM tokens for the same graph that costs ~1M with them. ## Usage ``` /graphify # full pipeline on current directory → Obsidian vault /graphify <path> # full pipeline on specific path /graphify <path> --mode deep # thorough extraction, richer INFERRED edges /graphify <path> --update # incremental - re-extract only new/changed files /graphify <path> --directed # build directed graph (preserves edge direction: source→target) /graphify <path> --whisper-model medium # use a larger Whisper model for better transcription accuracy (base|small|medium|large) /graphify <path> --cluster-only