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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>
2026-06-15 17:00:52 +08:00
scenario_name: siemens-pdf-question-bank-online
mode: online
dataset: ../../datasets/raw/generated/siemens-pdf-question-bank.csv
feat(advisor): add optimization advisor module - rag_eval/advisor/: new package with rules engine, LLM analyzer, writer - rules.py: 7-metric diagnostic rules (warning/critical thresholds, top-3 low samples) - llm_analyzer.py: Chinese optimization report via judge_model, graceful fallback - writer.py: writes optimization_advice.md + log summary - __init__.py: run_advisor() entry point (no-op when optimization_advisor=False) - Scenario.optimization_advisor: new bool field (default False) - ScenarioModel: same field added, loader.py透传 - RunArtifactPaths.advice_md: new path field - factory.py: build_models() now public; build_metric_pipeline() accepts pre-built llm/embeddings - runner.py: lifts llm, passes to pipeline and advisor; calls run_advisor() at end - siemens online YAML: optimization_advisor: true enabled - tests: 9 rules tests + 6 writer tests, all pass - docs: advisor section added to engine-flow.md and architecture.md Co-Authored-By: Claude <noreply@anthropic.com>
2026-06-16 17:06:19 +08:00
# judge_model: qwen3.5-flash
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
judge_model: deepseek-v4-flash
embedding_model: text-embedding-v3
feat(advisor): add optimization advisor module - rag_eval/advisor/: new package with rules engine, LLM analyzer, writer - rules.py: 7-metric diagnostic rules (warning/critical thresholds, top-3 low samples) - llm_analyzer.py: Chinese optimization report via judge_model, graceful fallback - writer.py: writes optimization_advice.md + log summary - __init__.py: run_advisor() entry point (no-op when optimization_advisor=False) - Scenario.optimization_advisor: new bool field (default False) - ScenarioModel: same field added, loader.py透传 - RunArtifactPaths.advice_md: new path field - factory.py: build_models() now public; build_metric_pipeline() accepts pre-built llm/embeddings - runner.py: lifts llm, passes to pipeline and advisor; calls run_advisor() at end - siemens online YAML: optimization_advisor: true enabled - tests: 9 rules tests + 6 writer tests, all pass - docs: advisor section added to engine-flow.md and architecture.md Co-Authored-By: Claude <noreply@anthropic.com>
2026-06-16 17:06:19 +08:00
optimization_advisor: true # 评测结束后自动生成优化建议报告
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
metrics:
- faithfulness
- answer_relevancy
- context_recall
- context_precision
feat(advisor): add optimization advisor module - rag_eval/advisor/: new package with rules engine, LLM analyzer, writer - rules.py: 7-metric diagnostic rules (warning/critical thresholds, top-3 low samples) - llm_analyzer.py: Chinese optimization report via judge_model, graceful fallback - writer.py: writes optimization_advice.md + log summary - __init__.py: run_advisor() entry point (no-op when optimization_advisor=False) - Scenario.optimization_advisor: new bool field (default False) - ScenarioModel: same field added, loader.py透传 - RunArtifactPaths.advice_md: new path field - factory.py: build_models() now public; build_metric_pipeline() accepts pre-built llm/embeddings - runner.py: lifts llm, passes to pipeline and advisor; calls run_advisor() at end - siemens online YAML: optimization_advisor: true enabled - tests: 9 rules tests + 6 writer tests, all pass - docs: advisor section added to engine-flow.md and architecture.md Co-Authored-By: Claude <noreply@anthropic.com>
2026-06-16 17:06:19 +08:00
# 已启用：鲁棒性 / 端到端指标（数据集已含 ground_truth）
- noise_sensitivity # 鲁棒性：对检索噪声的敏感度
- factual_correctness # 端到端：事实正确性（相对标准答案）
- semantic_similarity # 端到端：语义相似度（embedding，无 LLM 调用）
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:
batch_size: 4
app_concurrency: 4
metric_concurrency: 4
max_samples: 50
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
model: deepseek-v4-flash
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