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docs(deps): correct the dependency audit to match requirements.txt - #8
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VampiricCyborg wants to merge 4 commits into
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The audit still listed sentence-transformers as the embedding dependency. That stack was removed on purpose -- loading PyTorch exhausted memory on Railway's container and crashed the service on boot -- and replaced with onnxruntime plus tokenizers against a quantised INT8 model. Anyone reading the old table would have reinstated the exact dependency that caused the outage, so the rewrite says plainly that it must not come back and why. Four packages the code does import were missing entirely (onnxruntime, tokenizers, numpy, psutil), and the counts underneath them were wrong. The seven packages that are genuinely required but never imported by name -- drivers, the server, plugins -- now have their own table explaining why grepping for imports will not find them, instead of being scattered through footnotes. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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Setting state in an effect body trips react-hooks/set-state-in-effect, which makes `npm run lint` exit 1. Adjusting the count during render instead also removes the frame where the browser paints the previous line's length before snapping back to zero. Same change as cc718d0 on fix/auth-secret-guard-ci, applied byte-for-byte so the two branches merge without conflict. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
The README carried a hardcoded shields.io "build passing" badge that was never connected to anything and would have stayed green through any failure. Replace it with the GitHub Actions badge for a workflow that actually runs the suite: pytest on Python 3.13, then tsc, lint and build for the frontend. The workflow is a byte-identical copy of the one on fix/auth-secret-guard-ci so merging either branch is conflict-free. All four steps verified locally: 79 tests passed, tsc 0, lint 0, build 0. Also put the live URLs at the top, replacing a "Live demo" link that pointed at an in-page anchor showing a GIF rather than the running app. Both tiers were probed before linking them: docuqueryvc.vercel.app returns 200, and docuqueryprod.up.railway.app/health returns 200 with database: ok. The badge stays unresolved until this workflow reaches the default branch -- GitHub reads Actions badges from the default branch, not the current one. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
…arness
The project had no way to measure itself: no evaluation script, no benchmark,
no ground-truth data, and no document corpus. Nothing quantitative about
retrieval quality could be claimed or checked.
This adds a harness that drives the real application code -- the same parser,
chunker, embedding service and retrieval pipeline the API uses -- rather than
reimplementing any of it for measurement:
ingest_corpus.py per-stage ingestion timings, one synthetic user per
chunking config so the production per-user filter does
the isolation between configs
build_eval_set.py ground-truth queries generated from indexed passages,
anchored to (document, page) so one query set stays
valid across chunk sizes
run_retrieval_eval.py Recall@k, Precision@k, MRR, NDCG@k at document, page
and answer-span level
run_config_sweep.py chunk size, overlap, threshold and metadata filtering
compared on one fixed query set
run_perf_bench.py embedding throughput, retrieval latency percentiles,
LLM time-to-first-token
AUDIT.md records what is measurable today and, just as importantly, what is
not: retrieval quality cannot be computed until a document corpus exists, and
that is stated rather than approximated. Results in results/ are from a
four-file smoke corpus and exist to prove the code path, not to be quoted --
except the embedding batch-size sweep, which is hardware-bound and stands on
its own (batch 32 is 6.17x faster than unbatched, 3.4x faster than batch 64).
The audit also turned up four defects, recorded in AUDIT.md section 7.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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backend/DEPENDENCIES_VERIFIED.mdhad drifted far enough to be actively misleading.The important one: it still listed
sentence-transformersas the embedding dependency. That stack was removed deliberately — loading PyTorch exhausted memory on Railway's container and crashed the service on boot (2f46ff0,384e627) — and replaced withonnxruntime+tokenizersagainst a quantised INT8 model. Anyone following the old table to "fix" a missing dependency would have reinstalled the exact thing that caused the outage. The rewrite gives that its own section, says it must not come back, and says why.Also corrected:
onnxruntime,tokenizers,numpy,psutil.requirements.txthas 28 lines).import sentence_transformers, which now fails.uvicorn,python-multipart,asyncpg,psycopg2-binary,greenlet,python-dotenv,pytest-asyncio— now have their own table explaining why grepping for imports will not find them.The import list was derived from the tree rather than by hand, and every package now accounts for: 21 imported + 7 required-but-not-imported = the 28 lines in
requirements.txt.No code changes.
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