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>

scripts/build_siemens_offline_smoke.py

@@ -0,0 +1,72 @@
+"""Build the Siemens offline smoke dataset from a completed dataset_build run.
+
+Must be run AFTER `python main.py --dataset-build-config
+scenarios/siemens_build/siemens-pdf-build.yaml` has completed successfully.
+
+It uses the stable `latest/` alias so you don't need to know the run_id.
+
+Usage:
+    python scripts/build_siemens_offline_smoke.py
+
+Output:
+    datasets/normalized/siemens_pdf_offline_smoke.csv
+    (referenced by scenarios/offline/siemens-pdf-offline-smoke.yaml)
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+# ---------------------------------------------------------------------------
+# Paths — all relative to the siemens_ragas/ repository root
+# ---------------------------------------------------------------------------
+REPO_ROOT = Path(__file__).resolve().parents[1]
+
+DRAFT_DATASET_PATH = (
+    REPO_ROOT / "outputs" / "dataset-builds" / "siemens-pdf-question-bank"
+    / "latest" / "dataset_draft.csv"
+)
+SOURCE_CHUNKS_PATH = (
+    REPO_ROOT / "outputs" / "dataset-builds" / "siemens-pdf-question-bank"
+    / "latest" / "source_chunks.jsonl"
+)
+OUTPUT_PATH = REPO_ROOT / "datasets" / "normalized" / "siemens_pdf_offline_smoke.csv"
+
+
+def main() -> None:
+    """Convert the Siemens build artefacts into an offline-evaluable dataset."""
+    if not DRAFT_DATASET_PATH.exists():
+        raise FileNotFoundError(
+            f"Draft dataset not found: {DRAFT_DATASET_PATH}\n"
+            "Run the dataset build first:\n"
+            "  python main.py --dataset-build-config "
+            "scenarios/siemens_build/siemens-pdf-build.yaml"
+        )
+    if not SOURCE_CHUNKS_PATH.exists():
+        raise FileNotFoundError(
+            f"Source chunks not found: {SOURCE_CHUNKS_PATH}\n"
+            "Run the dataset build first."
+        )
+
+    # Import here so the script is importable even before rag_eval is fully set up.
+    from rag_eval.dataset_builder.offline_converter import build_offline_smoke_dataset
+
+    output = build_offline_smoke_dataset(
+        draft_dataset_path=DRAFT_DATASET_PATH,
+        source_chunks_path=SOURCE_CHUNKS_PATH,
+        output_path=OUTPUT_PATH,
+    )
+
+    import pandas as pd
+    frame = pd.read_csv(output)
+    print(f"Offline smoke dataset written to: {output}")
+    print(f"Total rows: {len(frame)}")
+    if len(frame) > 0:
+        lang_counts = frame["language"].value_counts().to_dict() if "language" in frame.columns else {}
+        diff_counts = frame["difficulty"].value_counts().to_dict() if "difficulty" in frame.columns else {}
+        print(f"Language distribution: {lang_counts}")
+        print(f"Difficulty distribution: {diff_counts}")
+
+
+if __name__ == "__main__":
+    main()