ely-eye/bench

bench — the measurement harness

Everything here measures the product as it is. No arm may read a benchmark gold label.

run order

python -m bench.fetch_corpus      # 19 GB of third-party material -> bench/corpus + ~/ely_eye_bench_data
python -m bench.build_sets        # freeze the 6 query sets -> bench/sets/
python -m bench.validate_sets     # schema + gold + scope + modality checks (b-2 gate)
python -m bench.fetch_models      # dense + rerank models used by the ablation arms
python -m bench.ingest_baseline   # the unmodified product ingesting the whole corpus (16 -> 8 workers)
python -m bench.run               # five-arm ablation + latency/scale -> bench/reports/BASELINE.md
python -m pytest bench/tests -q   # metric definitions, hand-computed

bench/runs/ and bench/corpus/ are gitignored; bench/corpus/manifest.jsonl is tracked so the corpus is reproducible from the manifest alone.

the arms (b-3)

arm	what it is
bm25_only	the product's sparse signal alone
dense_only	the product's dense signal alone (product flag is off today)
graph_only	the product's graph signal alone — atom.relations is never populated, so it is a constant
no_rerank	the product's exact 0.55 / 0.35 / 0.10 fusion
fusion_rerank	the same fusion plus a cross-encoder rerank, the upper bound of the current design

python -m bench.run also drives the unmodified RetrievalService.search over the merged database and reports its latency plus how far its top-10 differs from the fast BM25 path.

known environment artifact

pi-lens' pyright reports Import "bench.sets_common" could not be resolved (and the same for bench.metrics, bench.systems, ely_eye.*, cv2, onnxruntime, pypdf.errors) even though every one of those imports works. It is not a repo defect:

• effective_config on any bench file returns 0 config file(s) with resolvedCwd at the repo root, so neither pyproject.toml [tool.pyright] nor pyrightconfig.json (both of which carry extraPaths: [".", "backend/src"]) is being read by that runner.
• Runtime proof, run from the repo root with .venv active:

.venv/bin/python -c "import bench.sets_common, bench.metrics, bench.systems, bench.run, bench.ingest_baseline, \
bench.build_mmlongbench, ely_eye.config, ely_eye.db, cv2, onnxruntime, pypdf.errors; print('runtime proof OK')"

Treat that command as the check of record for import health; treat pyright's type findings (it has caught real defects here: an AtomRunner typo, missing strict=True on zip, dict[str, object] over-narrowing) as separate and worth fixing.

corpus facts that constrain the numbers

• 50,789 sources / 5,308 queries (2,928 answerable, 2,380 unanswerable), all third-party.
• Video-MME ships mp4s for 129 of 900 videos; the set uses those 129 (387 questions).
• Video-MME has no temporal gold, so its gold region is the whole video — recorded in the set manifest.
• ScreenSpot-v2 boxes are [x, y, w, h], verified against 755 image headers (xywh in bounds 1271/1272).
• CodeSearchNet docstrings appear verbatim inside the code, so the query text is stripped before ingest or BM25 gets a free exact match.
• The product keys a source by {stem[:48]}_{sha256[:16]}, so identical bytes in two sets collapse to one product source; ingest_report.json reports shared_source_ids.