"""Ingest Harvey-AI extractions from a single xlsx into per-country CSVs. `data/raw/harvey.xlsx` (Sheet1) holds Harvey's outputs for 23 jurisdictions with 4 columns per question (Value / Reasoning / Citations / Comments). We keep only the Value columns, clean them, match each row to a Goldenset case_id via the PDF filename, and write a CSV that looks exactly like the output of `legex.inference` — so `legex-evaluate`, `legex-analysis`, and `legex-plots` treat Harvey as just another model. """ import argparse import csv import logging import re import sys from collections import defaultdict from pathlib import Path from openpyxl import load_workbook from legex.config import settings from legex.inference import _output_columns from legex.utils import ( classified_csv_path, goldenset_path, goldenset_sheet, norm_case_id, ) log = logging.getLogger(__name__) HARVEY_FOLDER_TO_CC: dict[str, str] = { "Armenia": "am", "Australia": "au", "Belgium": "be", "Brazil": "br", "China": "cn", "Dominican_Republic": "do", "France": "fr", "Georgia": "ge", "Germany": "de", "Hong_Kong": "hk", "India": "in", "Nepal": "np", "New_Zealand": "nz", "Philippines": "ph", "Russia": "ru", "Schweiz_final": "ch", "Singapore": "sg", "South_Korea fixed": "kr", "Spain": "es", "Taiwan": "tw", "Ukraine": "ua", "United_Kingdom": "uk", "United_States": "us", } # Harvey Sheet1 value-column index → Goldenset header name. HARVEY_COL_TO_GOLD: dict[int, str] = { 7: "legal_subject_judgement", 11: "trial_start_date", 15: "trial_end_date", 19: "dispute_value_nominal", 23: "plaintiff_loosing_share", 27: "court_cost_awarded_nominal", 31: "party_compensation_awarded_nominal", 35: "plaintiffs_all_count", 39: "defendants_all_count", 43: "plaintiff_no1_ISIC1_industry_category", 47: "defendant_no1_ISIC1_industry_category", } _CITATION_RE = re.compile(r"\s*(?:\[\d+\])+\s*$") _EMPTY_LITERALS = {"", "—"} def _clean(value: object) -> str: if value is None: return "" s = str(value).strip() if s in _EMPTY_LITERALS: return "" s = _CITATION_RE.sub("", s).strip() if s.lower() == "nonpecuniary": return "nonpecuniary" return s def _gold_case_id_index(cc: str) -> dict[str, str] | None: """{ norm_case_id(gold) → gold case_id } for one country, or None if no Goldenset.""" gs = goldenset_path(cc) if not gs.exists(): return None wb = load_workbook(gs, read_only=True, data_only=True) ws = goldenset_sheet(wb) rows = ws.iter_rows(values_only=True) header = [str(c) if c is not None else "" for c in next(rows)] try: cid_idx = header.index("case_id") except ValueError as e: raise ValueError(f"{gs}: GOLDENSET sheet has no case_id column") from e index: dict[str, str] = {} for row in rows: if not any(row): continue raw = row[cid_idx] if raw is None: continue gold = str(raw).strip() if not gold: continue index.setdefault(norm_case_id(gold), gold) return index def ingest( xlsx: Path, prompt_version: str = "v3", source: str = "full_text", model: str = "harvey", ) -> None: columns = _output_columns() wb = load_workbook(xlsx, read_only=True, data_only=True) if "Sheet1" not in wb.sheetnames: raise ValueError(f"{xlsx} missing Sheet1 (found {wb.sheetnames})") ws = wb["Sheet1"] rows_iter = ws.iter_rows(values_only=True) next(rows_iter) # skip header by_folder: dict[str, list[tuple]] = defaultdict(list) for row in rows_iter: if not row or row[0] is None: continue folder = row[1] if folder is None: continue by_folder[str(folder)].append(row) for folder, rows in by_folder.items(): cc = HARVEY_FOLDER_TO_CC.get(folder) if cc is None: log.warning(f"unknown folder {folder!r}, skipping {len(rows)} row(s)") continue index = _gold_case_id_index(cc) if index is None: log.info(f"[{cc}] no Goldenset on disk, skipping {len(rows)} Harvey row(s)") continue out = classified_csv_path(cc, prompt_version, source, model) out.parent.mkdir(parents=True, exist_ok=True) matched = 0 unmatched = 0 with open(out, "w", encoding="utf-8", newline="") as f: writer = csv.DictWriter(f, fieldnames=columns, extrasaction="ignore") writer.writeheader() for row in rows: stem = Path(str(row[0])).stem gold = index.get(norm_case_id(stem)) if gold is None: unmatched += 1 log.info(f"[{cc}] no Goldenset match for {row[0]!r}") continue out_row = {col: "" for col in columns} out_row["case_id"] = gold out_row["model"] = model for harvey_idx, gold_col in HARVEY_COL_TO_GOLD.items(): if harvey_idx < len(row): out_row[gold_col] = _clean(row[harvey_idx]) writer.writerow(out_row) matched += 1 log.info( f"[{cc}] wrote {matched} Harvey row(s) → {out} " f"({unmatched} unmatched, {len(rows)} total)" ) def main() -> None: logging.basicConfig( level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s", handlers=[logging.StreamHandler(sys.stderr)], ) parser = argparse.ArgumentParser( prog="legex-harvey-ingest", description="Convert data/raw/harvey.xlsx into per-country Goldenset_*_harvey.csv files.", ) parser.add_argument( "--xlsx", type=Path, default=settings.raw_dir / "harvey.xlsx", help="Path to harvey.xlsx (default: data/raw/harvey.xlsx).", ) parser.add_argument("--prompt_version", default="v3") parser.add_argument( "--source", choices=("full_text", "pdf"), default="full_text", help="Source bucket label used in the CSV filename (default: full_text).", ) parser.add_argument("--model", default="harvey", help="Model slug for the CSV filename.") args = parser.parse_args() ingest( xlsx=args.xlsx, prompt_version=args.prompt_version, source=args.source, model=args.model, ) if __name__ == "__main__": main()