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siemens_ragas/scenarios/online/siemens-pdf-question-bank-online.yaml

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Add Siemens CT document evaluation scenario (three-step pipeline) - scenarios/siemens_build/siemens-pdf-build.yaml: dataset build for all 17 Siemens medical-imaging PDFs (aliyun_docmind parser, 10 questions/doc, failure_mode=skip, ~170 question total) - scenarios/offline/siemens-pdf-offline-smoke.yaml: offline evaluation using source chunks as contexts and ground_truth as answer (up to 30 samples) - scenarios/online/siemens-pdf-question-bank-online.yaml: online evaluation calling siemens_pdf_qa adapter, batch_size=4, up to 50 samples - apps/siemens_pdf_qa/adapter.py: Siemens-specific adapter with bilingual (zh/en) system prompt and strict evidence-grounding for CT domain - scripts/build_siemens_offline_smoke.py: helper to derive offline smoke CSV from completed dataset build artifacts (run after dataset build step) - docs/superpowers/specs/2026-06-15-siemens-scenario-design.md: design spec All three scenarios are automatically discovered by the web console. Co-Authored-By: Claude Sonnet 4 <noreply@anthropic.com>
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scenario_name: siemens-pdf-question-bank-online
mode: online
dataset: ../../datasets/raw/generated/siemens-pdf-question-bank.csv
judge_model: deepseek-v4-flash
embedding_model: text-embedding-v3
更新
2026-06-16 18:12:33 +08:00
optimization_advisor: true
Add Siemens CT document evaluation scenario (three-step pipeline) - scenarios/siemens_build/siemens-pdf-build.yaml: dataset build for all 17 Siemens medical-imaging PDFs (aliyun_docmind parser, 10 questions/doc, failure_mode=skip, ~170 question total) - scenarios/offline/siemens-pdf-offline-smoke.yaml: offline evaluation using source chunks as contexts and ground_truth as answer (up to 30 samples) - scenarios/online/siemens-pdf-question-bank-online.yaml: online evaluation calling siemens_pdf_qa adapter, batch_size=4, up to 50 samples - apps/siemens_pdf_qa/adapter.py: Siemens-specific adapter with bilingual (zh/en) system prompt and strict evidence-grounding for CT domain - scripts/build_siemens_offline_smoke.py: helper to derive offline smoke CSV from completed dataset build artifacts (run after dataset build step) - docs/superpowers/specs/2026-06-15-siemens-scenario-design.md: design spec All three scenarios are automatically discovered by the web console. Co-Authored-By: Claude Sonnet 4 <noreply@anthropic.com>
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metrics:
更新
2026-06-16 18:12:33 +08:00
- faithfulness
- answer_relevancy
- context_recall
- context_precision
- noise_sensitivity
- factual_correctness
- semantic_similarity
Add Siemens CT document evaluation scenario (three-step pipeline) - scenarios/siemens_build/siemens-pdf-build.yaml: dataset build for all 17 Siemens medical-imaging PDFs (aliyun_docmind parser, 10 questions/doc, failure_mode=skip, ~170 question total) - scenarios/offline/siemens-pdf-offline-smoke.yaml: offline evaluation using source chunks as contexts and ground_truth as answer (up to 30 samples) - scenarios/online/siemens-pdf-question-bank-online.yaml: online evaluation calling siemens_pdf_qa adapter, batch_size=4, up to 50 samples - apps/siemens_pdf_qa/adapter.py: Siemens-specific adapter with bilingual (zh/en) system prompt and strict evidence-grounding for CT domain - scripts/build_siemens_offline_smoke.py: helper to derive offline smoke CSV from completed dataset build artifacts (run after dataset build step) - docs/superpowers/specs/2026-06-15-siemens-scenario-design.md: design spec All three scenarios are automatically discovered by the web console. Co-Authored-By: Claude Sonnet 4 <noreply@anthropic.com>
2026-06-15 17:00:52 +08:00
output_dir: ../../outputs/online/siemens-pdf-question-bank
runtime:
更新
2026-06-16 18:12:33 +08:00
batch_size: 3
app_concurrency: 3
metric_concurrency: 3
max_samples: 10
Add Siemens CT document evaluation scenario (three-step pipeline) - scenarios/siemens_build/siemens-pdf-build.yaml: dataset build for all 17 Siemens medical-imaging PDFs (aliyun_docmind parser, 10 questions/doc, failure_mode=skip, ~170 question total) - scenarios/offline/siemens-pdf-offline-smoke.yaml: offline evaluation using source chunks as contexts and ground_truth as answer (up to 30 samples) - scenarios/online/siemens-pdf-question-bank-online.yaml: online evaluation calling siemens_pdf_qa adapter, batch_size=4, up to 50 samples - apps/siemens_pdf_qa/adapter.py: Siemens-specific adapter with bilingual (zh/en) system prompt and strict evidence-grounding for CT domain - scripts/build_siemens_offline_smoke.py: helper to derive offline smoke CSV from completed dataset build artifacts (run after dataset build step) - docs/superpowers/specs/2026-06-15-siemens-scenario-design.md: design spec All three scenarios are automatically discovered by the web console. Co-Authored-By: Claude Sonnet 4 <noreply@anthropic.com>
2026-06-15 17:00:52 +08:00
app_adapter:
type: python
callable: apps.siemens_pdf_qa.adapter:run
static_kwargs:
source_chunks_path: ../../outputs/dataset-builds/siemens-pdf-question-bank/latest/source_chunks.jsonl
更新
2026-06-16 18:12:33 +08:00
model: glm-5
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