File size: 2,766 Bytes
db32e07 | 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 | #!/usr/bin/env python
from __future__ import annotations
import argparse
import sys
from pathlib import Path
ROOT = Path(__file__).resolve().parents[1]
if str(ROOT) not in sys.path:
sys.path.insert(0, str(ROOT))
from spec_rag.dataset import build_examples
from spec_rag.faiss_index import load_index
from spec_rag.io import load_embeddings, load_smiles, save_jsonl
from spec_rag.retrieval import search_index
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Build RAG dataset for MolT5/Llama.")
parser.add_argument("--index-path", required=True)
parser.add_argument("--smiles-path", required=True)
parser.add_argument("--spec-embeddings", required=True)
parser.add_argument("--ground-truth-smiles", default=None)
parser.add_argument("--ground-truth-mgf", default=None)
parser.add_argument("--out-jsonl", required=True)
parser.add_argument("--k", type=int, default=10)
parser.add_argument("--format", choices=["molt5", "chat"], default="molt5")
parser.add_argument("--spectrum-token", default="<Spectrum_Token>")
return parser.parse_args()
def _load_smiles_from_mgf(mgf_path: str) -> list[str]:
try:
from pyteomics import mgf # type: ignore
except Exception as e:
raise ImportError(f"pyteomics is required to read MGF: {e}")
smiles = []
with mgf.MGF(mgf_path) as reader:
for spec in reader:
params = spec.get("params", {})
smi = params.get("SMILES") or params.get("smiles") or ""
smiles.append(str(smi).strip())
return smiles
def main() -> None:
args = parse_args()
if bool(args.ground_truth_smiles) == bool(args.ground_truth_mgf):
raise ValueError("Provide exactly one of --ground-truth-smiles or --ground-truth-mgf.")
index = load_index(args.index_path)
smiles = load_smiles(args.smiles_path)
spec_embeddings = load_embeddings(args.spec_embeddings)
if args.ground_truth_mgf:
gt_smiles = _load_smiles_from_mgf(args.ground_truth_mgf)
else:
gt_smiles = load_smiles(args.ground_truth_smiles)
scores, indices = search_index(index, spec_embeddings, args.k)
contexts = []
for row in indices:
contexts.append([smiles[j] if j >= 0 else "" for j in row.tolist()])
examples = build_examples(
spectrum_ids=list(range(len(contexts))),
spectrum_token=args.spectrum_token,
contexts=contexts,
targets=gt_smiles,
)
if args.format == "chat":
rows = [ex.as_chat() for ex in examples]
else:
rows = [ex.as_molt5() for ex in examples]
save_jsonl(args.out_jsonl, rows)
print(f"Wrote {len(rows)} examples to {args.out_jsonl}")
if __name__ == "__main__":
main()
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