scripts/staffing_500k/main.go: driver that reads workers_500k.csv,
embeds combined-text per worker via /v1/embed, adds to vectord index
"workers_500k", runs canonical staffing queries against the populated
index. Reproducible end-to-end test of the staffing co-pilot pipeline
at production scale.
Run results (2026-04-29 ~02:30):
500,000 vectors ingested in 35m 36s (~234/sec avg)
vectord peak RSS 4.5 GB (~9 KB/vector incl. HNSW graph)
Query latency: embed 40-59ms + search 1-3ms = ~50ms end-to-end
GPU avg ~65% (Ollama not the bottleneck — vectord Add is)
Semantic recall on canonical queries:
"electrician with industrial wiring": top 2 are literal Electricians (d=0.30)
"CNC operator with first article": Assembler / Quality Techs (adjacent, d=0.24)
"forklift driver OSHA-30": warehouse roles (d=0.33)
"warehouse picker night shift bilingual": Material Handlers (d=0.31)
"dental hygienist": Production Workers at d=0.49+ — correctly
LOW-similarity, signals "no dental hygienists in this manufacturing
dataset" rather than hallucinating a fake match.
Documented gaps:
- storaged's 256 MiB PUT cap blocks single-file LHV1 persistence
above ~150K vectors at d=768. Test ran with persistence disabled.
- vectord Add is RWMutex-serialized — with GPU at 65% util this is
the throughput cap. Concurrent Adds would be 2-3x faster but
require careful audit of coder/hnsw thread-safety (G1 scrum
documented two known quirks).
PHASE_G0_KICKOFF.md gains a "Staffing scale test" section with full
metrics + the gaps-surfaced list. The architectural payoff is real:
six binaries, one HTTP route, ~50ms from text query to top-K
semantically-relevant workers across 500K records.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
