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21c7db9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 | #!/usr/bin/env python3
"""Build unified training corpus: HF + local + synthetic (+ optional web/DDI)."""
from __future__ import annotations
import argparse
import json
import os
from pathlib import Path
from typing import Any
import sys
ROOT = Path(__file__).resolve().parents[1]
if str(ROOT) not in sys.path:
sys.path.insert(0, str(ROOT))
from app.dataops.ddi_api import cache_ddi_records, fetch_ddi_api_records
from app.dataops.web_fallback import scrape_with_fallback
from app.env.env_core import PolyGuardEnv
from app.knowledge.drug_catalog import DRUG_CLASSES
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Build SFT/GRPO corpus from multiple data sources.")
parser.add_argument("--profile", choices=["small", "massive"], default="small")
parser.add_argument("--with-hf", action="store_true")
parser.add_argument("--with-local", action="store_true")
parser.add_argument("--with-synthetic", action="store_true")
parser.add_argument("--enable-ddi-api", action="store_true")
parser.add_argument("--enable-web-fallback", action="store_true")
return parser.parse_args()
def _load_local_sft(path: Path) -> list[dict[str, Any]]:
if not path.exists():
return []
payload = json.loads(path.read_text(encoding="utf-8"))
if isinstance(payload, list):
return [item for item in payload if isinstance(item, dict)]
return []
def _build_synthetic(count: int) -> list[dict[str, Any]]:
env = PolyGuardEnv()
rows: list[dict[str, Any]] = []
schedule = ["easy", "medium", "hard"]
for i in range(count):
env.reset(seed=8_000 + i, difficulty=schedule[i % len(schedule)])
obs = env._build_observation() # noqa: SLF001 - internal observation snapshot for synthetic corpus assembly.
candidates = [item.model_dump(mode="json") for item in obs.candidate_action_set]
target = candidates[0]["candidate_id"] if candidates else "cand_01"
rows.append(
{
"source": "synthetic",
"task": "planner_action_selection",
"prompt": {
"patient_summary": obs.patient_summary,
"medications": obs.medication_table,
"candidates": candidates,
"uncertainty": obs.abstention_indicators.get("uncertainty", 0.5),
"severe_pair_count": obs.graph_safety_summary.get("estimated_risk", 0.0),
},
"target_candidate_id": target,
}
)
return rows
def _load_hf(max_rows: int) -> list[dict[str, Any]]:
try:
from datasets import load_dataset
except Exception:
return []
records: list[dict[str, Any]] = []
try:
ds = load_dataset("tatsu-lab/alpaca", split="train")
for row in ds.select(range(min(max_rows, len(ds)))):
instruction = str(row.get("instruction", ""))
input_text = str(row.get("input", ""))
output_text = str(row.get("output", ""))
records.append(
{
"source": "hf_alpaca",
"task": "instruction_following",
"prompt": {
"instruction": instruction,
"input": input_text,
"candidates": [
{
"candidate_id": "cand_01",
"mode": "REVIEW",
"action_type": "REQUEST_SPECIALIST_REVIEW",
"estimated_safety_delta": 0.0,
"uncertainty_score": 0.5,
"legality_precheck": True,
}
],
},
"target_candidate_id": "cand_01",
"target_text": output_text,
}
)
except Exception:
return []
return records
def _write_jsonl(path: Path, rows: list[dict[str, Any]]) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
with path.open("w", encoding="utf-8") as f:
for row in rows:
f.write(json.dumps(row, ensure_ascii=True) + "\n")
def main() -> None:
args = parse_args()
root = Path(__file__).resolve().parents[1]
processed = root / "data" / "processed"
processed.mkdir(parents=True, exist_ok=True)
target_size = 80 if args.profile == "small" else 2000
rows: list[dict[str, Any]] = []
if args.with_local:
rows.extend(_load_local_sft(processed / "sft_examples.json"))
if args.with_synthetic:
synth_count = min(target_size, 60 if args.profile == "small" else 1200)
rows.extend(_build_synthetic(synth_count))
if args.with_hf:
hf_count = min(target_size, 40 if args.profile == "small" else 800)
rows.extend(_load_hf(hf_count))
if args.enable_ddi_api:
ddi_path = processed / "ddi_api_cache.json"
top_drugs = list(sorted(DRUG_CLASSES.keys()))[:20]
ddi_records = fetch_ddi_api_records(top_drugs)
cache_ddi_records(ddi_path, ddi_records)
if args.enable_web_fallback:
allow_domains = ["who.int", "nih.gov", "fda.gov", "ema.europa.eu"]
seeds = ["https://www.who.int", "https://www.nih.gov"]
crawled = [scrape_with_fallback(url, allow_domains) for url in seeds]
(processed / "web_fallback_records.json").write_text(
json.dumps(crawled, ensure_ascii=True, indent=2),
encoding="utf-8",
)
if not rows:
# last-resort generated seed rows
rows.extend(_build_synthetic(24))
rows = rows[:target_size] if args.profile == "small" else rows
(processed / "training_corpus_sft.json").write_text(json.dumps(rows, ensure_ascii=True, indent=2), encoding="utf-8")
_write_jsonl(processed / "training_corpus_sft.jsonl", rows)
grpo_prompts = [
{
"prompt": row.get("prompt", {}),
"task": row.get("task", "planner_action_selection"),
}
for row in rows
]
_write_jsonl(processed / "training_corpus_grpo_prompts.jsonl", grpo_prompts)
summary = {
"status": "ok",
"profile": args.profile,
"rows": len(rows),
"with_local": args.with_local,
"with_hf": args.with_hf,
"with_synthetic": args.with_synthetic,
"ddi_api": args.enable_ddi_api,
"web_fallback": args.enable_web_fallback,
}
(processed / "training_corpus_summary.json").write_text(json.dumps(summary, ensure_ascii=True, indent=2), encoding="utf-8")
print("training_corpus_done")
if __name__ == "__main__":
main()
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