Video and audio files are now first-class corpus inputs.
Drop .mp4, .mp3, .wav, .mov, .webm, .m4a, .ogg, .mkv, .avi, or .m4v files into any folder and graphify transcribes them locally with faster-whisper before extraction. Transcripts are cached in graphify-out/transcripts/ so re-runs skip already-transcribed files.
You can also add YouTube links directly:
/graphify add https://www.youtube.com/watch?v=...
yt-dlp downloads audio-only (no video stream, much smaller) and the same Whisper pipeline picks it up from there.
The Whisper prompt is built from your corpus automatically. The coding agent reads the top god nodes from your non-video files and writes a one-sentence domain hint for Whisper itself. No separate API call, no configuration needed.
To use:
pip install 'graphifyy[video]'
This installs faster-whisper and yt-dlp. The default Whisper model is base. Pass --whisper-model medium or --whisper-model large-v3 for higher accuracy.
Audio never leaves your machine. Transcription runs fully locally.