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Introduces base, image-streams, and overlays for dev and prod environments to support backend-services deployment on OpenShift using kustomize. Includes ConfigMap, Deployment, Service, Route, PVC, NetworkPolicy, and environment-specific configuration for streamlined management and deployment.
Deleted all kustomize configuration files for backend-services and related image streams in the OpenShift deployments director. redundant now with overlays.
AI-489 QA Workflows Fix
alex-struk
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Jun 23, 2026
…op caller key (spans PR4/5/6) Workflow module hardening. This commit spans phases (relocate at split time): PR 5 (document sources / upload endpoint): - #8: sanitise file.originalname to a safe basename and run the key through validateBlobFilePath — closes the cross-tenant path-traversal write. - #9: add a static Multer fileSize ceiling so oversized uploads are rejected before buffering; keep the dynamic per-source limit. - #10: add the missing non-member -> Forbidden authorization test for upload. - #11: magic-byte content sniffing; reject when bytes contradict the declared MIME. - #12: store source uploads under a new generic OperationCategory.WORKFLOW, not OCR. PR 4 (try-in-place): - #16: versionRunCount cache is now a real bounded LRU (was an unbounded "LRU"). - #17: run-history first-page enrichment fans out fetchHistory with bounded concurrency. PR 6 (cross): temporal-client startGraphWorkflow drops the apiKey param (part of #4); upload-and-Try and /runs are symmetric (resolves #2). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
alex-struk
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Jul 2, 2026
The first SUMMARY framed the gap between iteration's 70.3% per-field
accuracy and the benchmark's 0.943 f1.median as "iteration was overly
strict, benchmark's fuzzy-0.85 was more forgiving."
Looking at the per-sample numbers, that framing is misleading:
- synth-full (1) iteration scorer (strict-with-normalisation):
52/74 = 70.3% match
- synth-full (1) benchmark scorer (fuzzy@0.85):
54/74 matched, recall 0.730, F1 0.844
Fuzzy gave 2 extra credits on close-but-not-exact OCR misreads. The
fuzzy-vs-strict gap is small. The actual driver is that synth-full (1)
is genuinely a hard sample for gpt-5.4 (F1 0.844 << run median 0.943);
the headline aggregate was pulled up by easier samples scoring 0.95+,
not by the scorer being lenient.
Also affirm explicitly that the schema-aware + fuzzy@0.85 + threshold-
0.8 evaluator config is identical across E01-E04 (wired in seed.ts:
2044-2062), so the cross-experiment comparison is apples-to-apples.
Updates:
- Iteration-section framing: scorer-difference is small; sample
selection is the dominant factor.
- Comparison-vs-E01-E03 section: explicit "identical evaluator" note
+ per-sample variance caveat.
- Retrospective lesson #1: rewritten — single-sample iteration is a
poor proxy for benchmark performance because per-sample variance
dominates.
- Retrospective lesson #12: rewritten — pick 2-3 samples spanning the
dataset rather than relying on fuzzy-vs-strict scoring difference
to bridge the gap.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
alex-struk
pushed a commit
that referenced
this pull request
Jul 2, 2026
The first SUMMARY framed the gap between iteration's 70.3% per-field
accuracy and the benchmark's 0.943 f1.median as "iteration was overly
strict, benchmark's fuzzy-0.85 was more forgiving."
Looking at the per-sample numbers, that framing is misleading:
- synth-full (1) iteration scorer (strict-with-normalisation):
52/74 = 70.3% match
- synth-full (1) benchmark scorer (fuzzy@0.85):
54/74 matched, recall 0.730, F1 0.844
Fuzzy gave 2 extra credits on close-but-not-exact OCR misreads. The
fuzzy-vs-strict gap is small. The actual driver is that synth-full (1)
is genuinely a hard sample for gpt-5.4 (F1 0.844 << run median 0.943);
the headline aggregate was pulled up by easier samples scoring 0.95+,
not by the scorer being lenient.
Also affirm explicitly that the schema-aware + fuzzy@0.85 + threshold-
0.8 evaluator config is identical across E01-E04 (wired in seed.ts:
2044-2062), so the cross-experiment comparison is apples-to-apples.
Updates:
- Iteration-section framing: scorer-difference is small; sample
selection is the dominant factor.
- Comparison-vs-E01-E03 section: explicit "identical evaluator" note
+ per-sample variance caveat.
- Retrospective lesson #1: rewritten — single-sample iteration is a
poor proxy for benchmark performance because per-sample variance
dominates.
- Retrospective lesson #12: rewritten — pick 2-3 samples spanning the
dataset rather than relying on fuzzy-vs-strict scoring difference
to bridge the gap.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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