Text files sorted by stage 3 now flow onto a new work queue and worker
pool that enrich them with natural language (Languages.jl LanguageDetector:
name, ISO 639-3 code, confidence) and programming language (github-linguist),
writing a .meta.json sidecar to data/text_done/ like the stage-2 known-file
pipeline.
github-linguist reads the git blob of a path inside a repo, so untracked
data/ files are copied to /tmp (outside any repo, name preserved for
extension heuristics) before detection. Programming-language lookup is
best-effort (startup warning if missing, degraded/null on failure);
natural-language failure yields a degraded sidecar, not a quarantine.
Factored exiftool's timeout-kill into shared run_with_timeout and the
durable sidecar-first commit into commit_enriched!, both reused by stage 4.
Recovery re-drives data/text/; graceful drain closes the text queue after
its stage-3 producers finish.
Each uploaded file is scored by a fixed-structure neural net that labels it
known (resembling the training set) or unknown — novelty detection over the
first 16 + last 16 bytes (scaled to [0,1]), Dense(32->64->16->2), argmax.
- src/model.jl: shared architecture + byte->feature mapping (trainer + server)
- src/classify.jl: load committed artifact, classify a file at inference
- bin/train.jl: offline trainer, 1:1 blended negatives (random + grab-bag),
seeded 80/20 split, writes model/classifier.jld2
- worker: classify (annotate-only) and log classification=known|unknown
- config: FS_MODEL_PATH; server fails fast if the artifact is missing
- deps: Lux, JLD2, Optimisers, Zygote
Track the resolved dependency versions so deployments build byte-for-byte
identically. Un-ignore Manifest.toml (kept only data/ ignored).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
