ZacharyZhang-NY
c4a8c36416
Every number in CHECKLIST p2-1 and p0-3.7 came out of a scratch script: the
kind of artifact a reader cannot re-run, and the kind that quietly drifts
away from the code it describes. These reports are generated by committed
harnesses and each one stamps the commit that carries the code it measures.
DENSE.md 165,973 atoms / 153,857 vectors: build 1421.0s, search p50 75.6ms
p95 93.0ms, 0.998 agreement with a brute-force scan, one write into
a full index p50 518ms, 1,729 bytes per stored vector.
QUANT.md twelve encodings measured on the same vectors: int8 keeps 0.977 of
the ranking for a quarter of the bytes and no speed, bit is 14x
faster and keeps 0.728, so bit is a coarse filter and nothing more.
TERMS.md the stop-word bound swept, not guessed: 0.05 is the measured peak
(recall@10 0.803 at p50 69.5ms) and also the fastest of the rows
that keep recall.
SCALE.md the product's own search at 165,973 atoms: p50 71.7ms against the
frozen baseline's 20.16s.
LEGS.md what each retrieval leg is worth, per modality, with the page size
the product actually asks for in the table.
The acceptance bar the operator set is recall@10 >= 0.90 at n>=300. It is
not met. The best measured row is 0.858 (dense leg on, page widened to 30,
p50 414ms); the shipped configuration gives 0.848 at p50 72.5ms; the
in-repo counterpart at holder scope gives 0.866. The shortfall is
concentrated in picture golds (video 0.583-0.708, ui_screenshot 0.500-0.583,
image 0.750) while the word-shaped ones already pass (code 0.953, pdf 0.919).
Latency passes by three hundred fold; recall does not, and the gap is four
points of cross-modal retrieval, not index scale.
15 lines
820 B
Markdown
15 lines
820 B
Markdown
# SCALE: the product's own search at corpus size
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- commit: `dff5977ab638896cc8685c78ee41e5195fc0fbf2`
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- database: bench/runs/scale/data/ely_eye.sqlite
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- copied from the frozen baseline in 5.0s and migrated in 0.0s to schema 8; the baseline store itself is left untouched
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- atoms: 165973, probes: 300 (code, image, pdf, ui_screenshot, video)
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- dense embeddings: False
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- p50 0.0717s · p95 0.2177s · max 0.7805s over 300 queries
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- machine: Linux 6.18.40.1-microsoft-standard-WSL2, 32 cores, 4 threads per query, load [2.31, 3.72, 7.9]
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The baseline recorded p50 20.16s on this corpus because the old search pulled every
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atom into Python and rebuilt a BM25 index per query.
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_Latency is wall time per search call on an idle machine with the thread budget the benchmark pins; a run on a loaded machine is not comparable._
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