SafiandClaude Opus 4.8
382b669481
Closes the non-crash tier of #1392 in the Claude-path skill fragments: - #6/#7: build_from_json/build_merge now take directed=IS_DIRECTED in Step 4, Step 5 rebuild, and the --update merge/diff, with prose telling the agent to substitute IS_DIRECTED like INPUT_PATH (a --directed run no longer silently rebuilds undirected and collapses reciprocal edges) - #10: semantic extraction only flattens document/paper/image, not code (AST already covers code) so subagents stop re-reading every source file - #12: .graphify_cached.json is deleted on a cache miss so Part C never merges a stale cache from a prior run - #11: --update now transcribes changed video files and moves transcripts to documents before the semantic pipeline - #4/#5/#23: transcribe writes via write_text (no shell redirect), uses GRAPHIFY_WHISPER_MODEL/PROMPT env, status to stderr - #2/#3: add-watch and exports use $(cat graphify-out/.graphify_python) explicitly; MCP Desktop config documents the absolute interpreter path - #21: extraction-spec example id namespaced (auth_session_validatetoken) - #22: query term split keeps tokens >= 3 chars aider/devin monoliths are pinned by the roundtrip invariant and excluded; their own Step 1 already instructs replacing python3 with the resolved interpreter. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
3.1 KiB
graphify reference: transcribe video and audio
Load this only when detect reported one or more video files. A corpus with no video never reads this.
Step 2.5 - Transcribe video / audio files (only if video files detected)
Skip this step entirely if detect returned zero video files.
Video and audio files cannot be read directly. Transcribe them to text first, then treat the transcripts as doc files in Step 3.
Strategy: Read the god nodes from graphify-out/.graphify_detect.json (or the analysis file if it exists from a previous run). You are already a language model — write a one-sentence domain hint yourself from those labels. Then pass it to Whisper as the initial prompt. No separate API call needed.
However, if the corpus has only video files and no other docs/code, use the generic fallback prompt: "Use proper punctuation and paragraph breaks."
Step 1 - Write the Whisper prompt yourself.
Read the top god node labels from detect output or analysis, then compose a short domain hint sentence, for example:
- Labels:
transformer, attention, encoder, decoder→"Machine learning research on transformer architectures and attention mechanisms. Use proper punctuation and paragraph breaks." - Labels:
kubernetes, deployment, pod, helm→"DevOps discussion about Kubernetes deployments and Helm charts. Use proper punctuation and paragraph breaks."
Export it as GRAPHIFY_WHISPER_PROMPT (the exact name the transcriber reads — and it must be exported so the child Python process sees it) for the next command.
Step 2 - Transcribe:
export GRAPHIFY_WHISPER_MODEL=base # or whatever --whisper-model the user passed (must be exported)
export GRAPHIFY_WHISPER_PROMPT="<the one-sentence domain hint you composed in Step 1>"
$(cat graphify-out/.graphify_python) -c "
import json, os, sys
from pathlib import Path
from graphify.transcribe import transcribe_all
detect = json.loads(Path('graphify-out/.graphify_detect.json').read_text(encoding=\"utf-8\"))
video_files = detect.get('files', {}).get('video', [])
prompt = os.environ.get('GRAPHIFY_WHISPER_PROMPT', 'Use proper punctuation and paragraph breaks.')
transcript_paths = transcribe_all(video_files, initial_prompt=prompt)
# Write the JSON from Python (NOT a shell '>' redirect): transcribe_all/Whisper
# print progress to stdout, which would otherwise corrupt the JSON file (#1392).
Path('graphify-out/.graphify_transcripts.json').write_text(json.dumps(transcript_paths, ensure_ascii=False), encoding=\"utf-8\")
print(f'Transcribed {len(transcript_paths)} file(s)', file=sys.stderr)
"
After transcription:
- Read the transcript paths from
graphify-out/.graphify_transcripts.json - Add them to the docs list before dispatching semantic subagents in Step 3B
- Print how many transcripts were created:
Transcribed N video file(s) -> treating as docs - If transcription fails for a file, print a warning and continue with the rest
Whisper model: Default is base. If the user passed --whisper-model <name>, export GRAPHIFY_WHISPER_MODEL=<name> (it must be exported, not just assigned) before running the command above.