File size: 2,633 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 | #!/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.embeddings import SMILESEmbedder
from spec_rag.faiss_index import build_hnsw_index, save_index
from spec_rag.io import ensure_dir, load_smiles, save_embeddings, save_smiles
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Build FAISS HNSW index from SMILES.")
parser.add_argument("--smiles-path", required=True)
parser.add_argument("--model-name", default="seyonec/ChemBERTa-zinc-base-v1")
parser.add_argument("--out-dir", required=True)
parser.add_argument("--batch-size", type=int, default=64)
parser.add_argument("--max-length", type=int, default=256)
parser.add_argument("--pooling", choices=["cls", "mean"], default="cls")
parser.add_argument("--no-normalize", action="store_true")
parser.add_argument("--metric", choices=["cosine", "l2"], default="cosine")
parser.add_argument("--m", type=int, default=32)
parser.add_argument("--ef-construction", type=int, default=200)
parser.add_argument("--ef-search", type=int, default=128)
parser.add_argument("--dedupe", action="store_true")
parser.add_argument("--device", default="cuda")
return parser.parse_args()
def main() -> None:
args = parse_args()
if args.device == "cuda":
try:
import torch
except Exception:
torch = None
if torch is None or not torch.cuda.is_available():
print("CUDA not available; falling back to CPU.")
args.device = "cpu"
out_dir = ensure_dir(args.out_dir)
smiles = load_smiles(args.smiles_path)
if args.dedupe:
seen = set()
smiles = [s for s in smiles if not (s in seen or seen.add(s))]
embedder = SMILESEmbedder(
model_name=args.model_name,
device=args.device,
pooling=args.pooling,
batch_size=args.batch_size,
max_length=args.max_length,
normalize=not args.no_normalize,
)
embeddings = embedder.encode(smiles)
save_embeddings(out_dir / "smiles_embeddings.npy", embeddings)
save_smiles(out_dir / "smiles.txt", smiles)
index = build_hnsw_index(
embeddings,
m=args.m,
ef_construction=args.ef_construction,
ef_search=args.ef_search,
metric=args.metric,
)
save_index(index, out_dir / "smiles.index")
print(f"Saved index and embeddings to {out_dir}")
if __name__ == "__main__":
main()
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