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